U.S. patent application number 16/149903 was filed with the patent office on 2019-04-04 for methods and tools for detecting, diagnosing, predicting, prognosticating, or treating a neurobehavioral phenotype in a subject.
The applicant listed for this patent is BlackThorn Therapeutics, Inc., Yale University. Invention is credited to Alan Anticevic, William J. Martin, John D. Murray.
Application Number | 20190102511 16/149903 |
Document ID | / |
Family ID | 63963540 |
Filed Date | 2019-04-04 |
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United States Patent
Application |
20190102511 |
Kind Code |
A1 |
Murray; John D. ; et
al. |
April 4, 2019 |
METHODS AND TOOLS FOR DETECTING, DIAGNOSING, PREDICTING,
PROGNOSTICATING, OR TREATING A NEUROBEHAVIORAL PHENOTYPE IN A
SUBJECT
Abstract
The present tools and methods for detecting, diagnosing,
predicting, prognosticating, or treating a neurobehavioral
phenotype in a subject. These tools and methods relates to a
genotype and neurophenotype topography-based approach for analyzing
brain neuroimaging and gene expression maps to identify drug
targets associated with neurobehavioral phenotypes and, conversely,
neurobehavioral phenotypes associated with potential drug targets,
to develop rational design and application of pharmacological
therapeutics for brain disorders, and to provide methods and tools
for treatment of subjects in need of neurological therapy.
Inventors: |
Murray; John D.; (New Haven,
CT) ; Anticevic; Alan; (New Haven, CT) ;
Martin; William J.; (San Francisco, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
BlackThorn Therapeutics, Inc.
Yale University |
San Francisco
New Haven |
CA
CT |
US
US |
|
|
Family ID: |
63963540 |
Appl. No.: |
16/149903 |
Filed: |
October 2, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62567087 |
Oct 2, 2017 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16B 25/10 20190201;
G16B 20/00 20190201; G16B 40/00 20190201; G16H 20/10 20180101; G16H
30/20 20180101; G16H 50/20 20180101 |
International
Class: |
G06F 19/18 20060101
G06F019/18 |
Claims
1. A method of detecting a neurobehavioral phenotype in a subject,
said method comprising: a. obtaining or having obtained a sample of
neurobehavioral phenotype mapping data from the subject; b.
defining a genotype topography of a first brain area for a gene
based on gene expression mapping data; c. defining a neurophenotype
topography of a second brain area for the neurobehavioral phenotype
based on neurobehavioral phenotype mapping data; d. contacting the
genotype topography of the first brain area and the neurophenotype
topography of the second brain area to establish an alignment; e.
detecting whether the neurobehavioral phenotype is present in the
sample by contacting the sample with the aligned genotype
topography and neurophenotype topography.
2. The method of claim 1, wherein the neurobehavioral phenotype is
at least one of: an affective disorder, a personality disorder, an
attention deficit hyperactivity disorder, a neurodegenerative
disease, a neurodevelopmental disorder, a cognitive change
associated with chemotherapy; a psychiatric symptom associated with
neurodegenerative diseases, a sex difference in brain function in
health and disease, a traumatic brain injury, and a measurable
neural feature.
3. A method of diagnosing, predicting, prognosticating, or treating
a neurobehavioral phenotype in a subject, said method comprising:
a. obtaining or having obtained a sample of neurobehavioral
phenotype mapping data from the subject; b. defining a genotype
topography of a first brain area for a gene based on gene
expression mapping data; c. defining a neurophenotype topography of
a second brain area for the neurobehavioral phenotype based on
neurobehavioral phenotype mapping data; d. contacting the genotype
topography of the first brain area and the neurophenotype
topography of the second brain area to establish an alignment; e.
detecting whether the neurobehavioral phenotype is present in the
sample by contacting the sample with the aligned genotype
topography and neurophenotype topography; and f. diagnosing,
predicting, prognosticating, or treating the subject when the
neurobehavioral phenotype is detected.
4. The method of claim 3, further comprising administering a
therapeutic agent to the subject.
5. The method of claim 4, further comprising identifying one or
more therapeutic agents suitable for treatment of the detected
neurobehavioral phenotype.
6. The method of claim 5, wherein the one or more therapeutic
agents are selected based on a gene associated with the detected
neurobehavioral phenotype.
7. The method of claim 6, wherein the gene is one of more of PDYN,
OXTR, OPRK1, PNOC, OXT, AVP, OPRL1, APOE, GRIN2C, GABRA2, HTR2A,
HTR3A, HRTR2C, HTR6, MAOA, CHRM1, CHRM3, CCR5, CXCR4, CXCR7, HRH3,
ADRB2, DRD2, SNCA, GBA, GPR88, GPR139, and LRRK2.
8. The method of claim 5, further comprising identifying gene
expression targets associated with the detected neurobehavioral
phenotype.
9. The method of claim 5, further comprising combining one or more
therapeutic agents indicated to be suitable for treatment of the
detected neurobehavioral phenotype.
10. The method of claim 5, further comprising dosing of one or more
therapeutic agents in amounts indicated to be effective for
treatment of the detected neurobehavioral phenotype.
11. The method of claim 5, further comprising selecting a
therapeutic agent indicated to be most suitable for treatment of
the detected neurobehavioral phenotype.
12. The method of claim 5, further comprising not administering one
or more therapeutic agents to the subject indicated to not be
suitable for treatment of the detected neurobehavioral
phenotype.
13. The method of claim 12, wherein the one or more therapeutic
agents is shown to have activity in a brain area outside the
alignment of the first brain area and the second brain area.
14. The method of claim 4, wherein the one or more of steps of
claim 3 are repeated after the subject has been diagnosed,
prognosticated to be at risk for, or treated for the detected
neurobehavioral phenotype.
15. The method of claim 14, further comprising altering a
therapeutic regimen for the subject based on changes in the
detected neurobehavioral phenotype.
16. The method of claim 3, further comprising selecting the subject
for inclusion in a clinical study.
17. The method of claim 16, further comprising forming a patient
population suitable for inclusion in the clinical study.
18. The method of claim 3, wherein the neurobehavioral phenotype is
one of: a. an affective disorder such as obsessive compulsive
disorder, bipolar disorder, unipolar depression, dysthymia and
cyclothymia, generalized anxiety disorder, panic disorder, phobias,
and post-traumatic stress disorder; b. a personality disorder such
as schizophrenia, paranoid personality disorder; schizoid
personality disorder; schizotypal personality disorder; antisocial
personality disorder; borderline personality disorder; histrionic
personality disorder; narcissistic personality disorder; avoidant
(or anxious) personality disorder; dependent personality disorder;
and obsessive compulsive personality disorder; c. an attention
deficit hyperactivity disorder such as inattentive type,
hyperactive-impulsive type, and combination type; d. a
neurodegenerative diseases such as Alzheimer's disease, Parkinson's
disease; amyotrophic lateral sclerosis; Friedreich's ataxia;
Huntington's disease; Lewy body disease; and spinal muscular
atrophy; e. a neurodevelopmental disorders such as autism spectrum
disorder, attention-deficit/hyperactivity disorder (ADHD) and
learning disorders; cognitive changes associated with chemotherapy;
f. a psychiatric symptom associated with neurodegenerative diseases
such as feeling sad or down, confused thinking or reduced ability
to concentrate, excessive fears or worries, or extreme feelings of
guilt, extreme mood changes of highs and lows, withdrawal from
friends and activities, significant tiredness, low energy or
problems sleeping, detachment from reality (delusions), paranoia or
hallucinations, inability to cope with daily problems or stress,
trouble understanding and relating to situations and to people,
alcohol or drug abuse, major changes in eating habits, sex drive
changes, excessive anger, hostility or violence, and suicidal
thinking; g. a sex differences in brain function in health and
disease; h. a traumatic brain injury; and i. any measurable neural
feature.
19. The method of claim 3, wherein the subject does not undergo
invasive pharmacoimaging.
20. A method for treating a subject with a neurobehavioral
phenotype, the method comprising the steps of: determining whether
the subject has neurobehavioral phenotype mapping data indicative
of the neurobehavioral phenotype by: obtaining or having obtained a
sample of neurobehavioral phenotype mapping data from the subject;
defining a genotype topography of a first brain area for a gene
based on gene expression mapping data; defining a neurophenotype
topography of a second brain area for the neurobehavioral phenotype
based on neurobehavioral phenotype mapping data; contacting the
genotype topography of the first brain area and the neurophenotype
topography of the second brain area to establish an alignment;
performing or having performed a comparison of the sample with the
aligned genotype topography and neurophenotype topography to
determine if the subject has the neurobehavioral phenotype; and if
the subject has the neurobehavioral phenotype as determined by
comparison of the sample with the aligned genotype topography and
neurophenotype topography, then administering a therapeutic agent
targeted to one or more genes associated with the aligned genotype
topography and neurophenotype topography, or if the subject has the
neurobehavioral phenotype as determined by comparison of the sample
with the aligned genotype topography and neurophenotype topography,
then administering a therapeutic agent targeted to one or more
neurobehavioral phenotypes associated with the aligned genotype
topography and neurophenotype topography.
21. The method of claim 20, further comprising increasing the
likelihood that the treatment for the subject will be effective for
treatment of the neurobehavioral phenotype.
22. A method of detecting a neurobehavioral phenotype in subjects
of a patient population, said method comprising: a. obtaining or
having obtained a sample of neurobehavioral phenotype mapping data
from each subject in the patient population; b. defining a
neurophenotype topography of a second brain area for the
neurobehavioral phenotype based on neurobehavioral phenotype
mapping data; c. contacting the genotype topography of the first
brain area and the neurophenotype topography of the second brain
area to establish an alignment; d. detecting whether the
neurobehavioral phenotype is present in the sample by contacting
the sample with the aligned genotype topography and neurophenotype
topography.
Description
BACKGROUND
Technical Field
[0001] The tools and methods described herein relate to a genotype
and neurophenotype topography-based approach for analyzing brain
neuroimaging and gene expression maps to identify drug targets
associated with neurobehavioral phenotypes and, conversely,
neurobehavioral phenotypes associated with potential drug targets,
to develop rational design and application of pharmacological
therapeutics for brain disorders. These tools and methods also
provide for treatment of subjects in need of neurological therapy.
Described herein is the selection, optimization, and ultimately
targeting of therapeutics to specific neural circuits based on the
bi-directional alignment of the neurobehavioral phenotypes and gene
expression maps. This approach produces an actionable set of
practical steps to aid therapeutic design and decision making based
on the alignment or comparison of neuro-behavioral and
transcriptomic data and the definition, and exploitation of, new
neurophenotype topographies and genotype topographies.
[0002] Among other things, this approach may facilitate clinical
trial design, for example, by providing for screening of individual
subjects for inclusion or exclusion in a trial based on
neuroimaging or behavioral measurements, and helps determine for
which measurements efficacy should be assessed.
[0003] Also, described herein is a set of specific computational
procedures, including definition of unique neurophenotype
topographies and genotype topographies and the ability to score
alignment or comparison of neurobehavioral phenotype information
and transcriptomic information using new neurophenotype
topographies and genotype topographies to yield the desired
results. Exemplary functional block diagrams of the computation
workflow are provided and described herein.
Description of the Related Art
[0004] Development of new central nervous system (CNS) drugs is
hindered by, among other things, a poor understanding of CNS
disease biology. For example, choosing suitable targets and knowing
when to intervene and how to move the biology effectively is
difficult. This is particularly the case as some diseases such as
schizophrenia and Parkinson's disease (PD) develop over may years,
which makes target identification challenging. Moreover, this
challenge is made greater by the massive variation across groups of
patients suffering from neuropsychiatric disorders; picking the
correct treatment for the correct patient based on their specific
central nervous system alterations is currently out of reach. Also,
while targets may be validated by animal models, genetics,
pathophysiology, or human pharmacology, assessing validation study
results is generally based on judgement that varies among
individuals and companies about the strength and productivity of
the data.
[0005] The many limitations of animal models used for CNS drug
development are recognized; thus, alternative validation methods
are becoming increasingly important. Also, there is a paucity of
predictive animal models for CNS disorders. Bain et al.,
Therapeutic Development in the Absence of Predictive Animal Models
of Nervous System Disorders: Proceedings of a Workshop, THE
NATIONAL ACADEMIES PRESS (2017), available at: http://nap.edu/24672
("Bain"). And even while animal models may be used to link
well-described, distinct biological phenomena to symptoms of a
complex disease such as schizophrenia, the explanatory power of
such models comes from working out the mechanistic basis for a
phenotype and application of great discipline to prevent
overgeneralization of results. Generally, testing CNS behavioral
paradigms in animals to measure neurobehavioral phenotype in the
animal may only loosely relate to the human neurobehavioral
phenotype of interest for clinical application.
[0006] Also, even as many animal models are based on an increased
understanding of human genetics, it is understood that individual
genes and variants may have only small effects and not be fully
penetrant; meanwhile, large-effect variants often cause
constellations of symptoms which further complicate interpretation.
Also, large-effect risk factors may not be shared across species
and an animal's genetic background can complicate phenotype
interpretation. For some CNS disorders, existing animal models do
not produce the key pathologic features or symptoms of the disease,
and as a result may not be able to demonstrate whether a drug is
going to be effective (e.g., Parkinson's disease animal models do
not show Lewy bodies composed of aggregated alpha-synuclein and
highly heterogeneous diseases such as schizophrenia would require
several models for specific disease aspects or subtypes). Finally,
there are simply aspects of the human nervous system that are not
represented in virtually any other animal such that attempts to
recapitulate human CNS disease in animal models may be
fundamentally flawed.
[0007] Translational gaps also exist between identifying and
validating a target and developing a clinical measure or biomarker
that can predict a response and a disease. Moreover, even if a
target is identified and validated, it may be inaccessible or
difficult to move the biology in a way that will be therapeutic.
These problems are especially severe for CNS disorders.
[0008] Another factor complicating further CNS drug development is
that current CNS therapeutics are screened for broad symptom
indications rather than specific neurobehavioral phenotypes and,
ultimately, specific people. Thus, patient populations are defined
at a group level to minimize adverse events while maintaining
effects with respect to broad symptoms. This generalized `group
average` approach overlooks specific neurobehavioral phenotype
complexities and may not best address patient needs.
[0009] The above-noted and other difficulties facing CNS drug
development account for the fact that the success rates for
development of CNS drugs are among the lowest of all therapeutic
areas. TCSDD. 2014. CNS drugs take longer to develop, have lower
success rates, than other drugs. IMPACT REPORT Volume 16, No. 6,
Tufts University. Further, because many of the approved drugs are
merely iterative, apparent gains in approved drug numbers can lead
to a false sense of success. Thus, to serve patients well and to
increase the flow of drugs needed to treat the hundreds of millions
of people with CNS disorders (such as depression, schizophrenia,
and Alzheimer's disease (AD)) and other problematic CNS symptoms
and cognitive processes, more efficient discovery and development
methods are needed. To allow practical and actionable difference
and impact relative to existing approaches, such methods need to be
grounded in human neurobiology.
[0010] Importantly, brain function has been conventionally
described as involving neural circuits, or a collection of brain
regions that are connected to carry out a particular function. That
is, it is understood that biological systems achieve their
cognitive capabilities solely through brain mechanisms: the
physiological operation of anatomical circuitries. Brain circuits
are important because neurons do not work in isolation and can
constitute various sizes ranging from small (micro) scale to large
(macro) scale. The brain circuits concept is built on the principle
that what allows our brain to process information is the fact that
one neuron sends information to the next and so on. Thus, it is the
connection between the neurons that matters. Brain circuits, which
can be observed and mapped with neuroimaging and related mapping
data, reflect the fact that a number of different neurons in
different regions may connect with each other to work together and
to treat or process information jointly. Growing knowledge in
neuroscience and related fields is revealing the data crucial for
characterizing the layout and properties of these circuits, yet
much remains to be learned and the characterization of various
circuits is not totally or imperfectly defined.
[0011] It is generally believed that the human brain consists of
evolutionarily recent forebrain circuit designs (telencephalic
circuits) layered on top of preserved ancient (e.g., reptilian)
circuits, with the new designs accounting for more than 90% of the
volume of the human brain. There are four primary divisions of
telencephalic forebrain (cortex, striatal complex, hippocampal
formation, amygdala nuclei), and many subdivisions (e.g., anterior
vs posterior cortex, five cortical layers, local circuits, striatal
components, hippocam pal fields CA1, CA3, dentate gyrus, subiculum,
etc.), each with its own cell types and local circuit design
layouts, thus presumably each conferring unique computational
properties. R. Granger, Essential circuits of cognition: The
brain's basic operations, architecture, and representations (2006).
Nonetheless, understanding of brain circuitry continues to develop
as new circuits are discovered and previously described circuits
are redefined or better characterized.
[0012] Currently, efforts are underway to building a human
"connectome," or a comprehensive map of the brain's circuits. This
is an enormously challenging endeavor, for the brain consists of
billions of cells, and each cell contacts thousands of others. It
is believed that an improved understanding of brain circuits will
bring scientists one step closer to understanding how the brain
functions when healthy and how it fails to function when injured or
diseased, and how to best return the brain to health.
[0013] Coincidentally, there is also a growing recognition that
redefining mental disorders as disorders of brain circuits is vital
for the rational design of pharmaceutical treatments for CNS
disorders. Insel et al., Next-generation treatments for mental
disorders, SCI. TRANSL. MED., 4:155ps19 (2012). Yet a great
challenge remains in how to harness emerging findings of circuit
definition and characterization for neurobehavioral processes and
pathologies, such as specificity of effects at the level of brain
regions as revealed by noninvasive neuroimaging for the rational
design of pharmaceutical treatments for CNS disorders. This problem
can be posed bi-directionally. That is, for a given drug, which
neurobehavioral pathology might it be well suited to treat?
Conversely, for a given pathology, which drug targets (e.g.,
synaptic receptors) or drugs might be well suited for its
treatment?
[0014] Noninvasive neuroimaging methods, such as functional
magnetic resonance imaging (fMRI), have enabled great progress in
elucidating circuits involved in diverse neurobehavioral
phenotypes, including disorders (e.g., schizophrenia), symptom
dimensions (e.g. cognitive deficits), and processes (e.g., working
memory). Moreover, these methods are being applied to discover
neural biomarkers, which can potentially inform patient-specific
treatments. See, e.g., Drysdale et al., Resting-state connectivity
biomarkers define neurophysiological subtypes of depression, NAT.
MED. (2016), Epub Ahead of Print available at:
http://000ev39.myregisteredwp.com/wp-content/uploads/sites/3661/2017/01/R-
esting-state-connectivity-biomarkers-define-neurophysiological-subtypes-of-
-depression.pdf; Drysdale et al. Resting-state connectivity
biomarkers define neurophysiological subtypes of depression, NAT.
MED., January; 23(1):28-38 (2017) (collectively, "Drysdale").
Neuroimaging research reveals structure and variation of
phenotype-related effects across different brain regions, which
highlights the need for the circuit-based perspective so as to
better include all regions of a particular circuit. This variation
can be expressed as a brain map. In one example, a brain map may
use an assignment of a numerical value to each brain region
reflecting the magnitude of a particular feature which may relate
to phenotype-related variation within or across subjects.
[0015] Meanwhile, to the extent that genetic information has been
used to make circuit-based maps, these were based on post-mortem
analyses without a reference functional map derived from within or
between subject imaging data. See Tebbenkamp et al., The
developmental transcriptome of the human brain: implications for
neurodevelopmental disorders, www.co-neurology.com, vol. 27, no. 00
(2014); Akbarian et al., The PsychENCODE project, NATURE
NEUROSCIENCE, Vol. 18, No. 12, December (2015); and Gandal et al.,
Shared molecular neuropathology across major psychiatric disorders
parallels polygenic overlap, SCIENCE, 359, 693-697, 9 February
(2018).
[0016] As described further below, conventionally understood neural
circuits are readily distinguishable from the neurophenotype
topographies and genotype topographies described herein. Here,
neural circuit-based findings raise several questions, including
the question of how administration of a pharmaceutical drug, which
is systemic, can be tailored to preferentially target a specific
brain circuit or subset of brain circuits. In rational drug design
and real world patient treatment, an important consideration is
minimization of "off-target" molecule effects. And a brain
circuit-based approach may also consider the potential effects of
systemic drug administration on "off-target" brain regions, or
brain regions that fall outside of a brain circuit or subset of
brain circuits.
[0017] Innovative modeling systems, such as cellular and
computational models, may mitigate the current lack of predictive
animal models. It has been suggested that data from human clinical
studies and experimental medicine approaches should be better used
to advance a fundamental understanding of human diseases. Also,
significantly, the scientific community has gained open access to
neuroimaging databases and spatially comprehensive maps of brain
gene expression. And the amount of publicly available neuroimaging
and gene expression data continues to increase. This data opens up
exciting ways to use gene expression data and neuroimaging data to
understand brain organization, with major benefits for both basic
and clinical science. Yet these new opportunities also present
numerous technical and theoretical challenges. Such challenges
include, for example: (1) the absence of multimodal data analytic
pipelines to scalaby, reproducibly and efficiently ingest and
analyze neuroimaging data from open sources; (2) the difficulty of
projecting gene expression data into cortical surface and brain
volumes within which neuroimaging results are interpreted; and (3)
the use of categorical descriptions of patients populations without
resolution into the underlying behavioral or symptom structures
that characterize these patients.
[0018] Historically, the conventional approach to using
neuroimaging to guide drug discovery or development has focused on
identifying if a candidate drug binds (e.g. PET-based imaging) or
changes the activity (e.g. fMRI-based imaging) in a brain region.
Gunn et al., Imagine in CNS Drug Discovery, SEMINARS IN NUCLEAR
MEDICINE, UPDATES IN MOLECULAR BRAIN IMAGING, vol. 47, issue 1,
January (2017); Wong et al., The Role of Imaging in Proof of
Concept for CNS Drug Discovery and Development,
NEUROPSYCHOPHARMACOLOGY REVIEWS, 34, 187-203 (2009). Each method
relies on a Region of Interest (ROI) approach. By contrast, the
approach proposed here incorporates surface-based topography and
cortical parcellation to relate genes, and potential drug targets,
to global brain activity associated with a phenotype of interest.
The omission of cortical surface topography from ROI-based methods
provides an inherent limitation to the conventional uses of
neuroimaging for CNS drug discovery and development.
BRIEF SUMMARY
[0019] The tools and methods described herein relate to new
genotype and neurophenotype topography-based methods and tools for
analyzing brain neuroimaging and gene expression maps, or genotype
topographies, to identify drug targets associated with
neurobehavioral phenotypes and, conversely, neurobehavioral
phenotypes associated with potential drug targets. In one
embodiment, these tools and methods can be used to facilitate or
develop rational design and application of pharmacological
therapeutics for brain disorders. In another embodiment, the
present tools and methods also provide topography-based methods and
tools for treatment of subjects in need of neurological
therapy.
[0020] These tools and methods may include a computational
neuroinformatics software and computer platform. This platform
integrates derived brain neuroimaging maps, which provide a
numerical value to each brain region reflecting the magnitude of a
particular feature which may relate to phenotype-related variation
within or across subjects, with gene expression maps or genotype
topographies, which provide a numerical value reflecting the
expression levels of genes across brain regions obtain from one or
more subjects, and leverages advances in large-scale brain mapping
neuroinformatics to derive a score that reflects the alignment of
the derived maps. By pooling, selecting, assessing, adjusting,
weighting, masking, comparing, and quantifying the alignment of
gene expression maps with neuroimaging maps, and using a
topography-based approach to characterize those brain areas or
regions, or circuits, associated with a particular neurophenotype,
these tools and methods provide predictive capabilities for
association of therapeutic targets with neurobehavioral phenotypes
(e.g., disorders, symptoms, cognitive processes, etc.). The present
tools and methods may also provide enhanced capabilities for
defining and assessing genotype and neurophenotype topography-based
methods of treatment relating to CNS disorders. Thus, the present
tools and methods open a new route to efficient rational design and
refinement and application of genotype and neurophenotype
topography-based therapeutics for modulating neurobehavioral
phenotypes (i.e., for both treating dysfunction and augmentation of
function).
[0021] The present tools and methods are needed to untangle,
re-order, prioritize, layer, compare, interpret, integrate, and
apply available brain mapping information (e.g., neuroimaging maps
and gene expression maps) with respect to targets of therapeutic
interest, and do so using a genotype and neurophenotype
topography-based approach, i.e., an approach that is not
necessarily confined by conventionally understood brain circuit
characterizations.
[0022] The present tools and methods newly characterize neural
circuits by taking into account neurobehavioral phenotype
information and transcriptomic information. This approach includes
methods designed to include or be informed or guided by data
derived from individual or group behavioral or symptom phenotypes.
In this aspect, the present approach differs from other approaches
relying on ontological associations of transcriptomic profiles to
implicate genes or drugs in particular genes, or descriptions of
resting-state functional connectivity as a potential biomarker for
psychiatric disorders without reference to particular genes or drug
targets. Hawrylcz et al., Canonical genetic signatures of the adult
human brain, NATURE NEUROSCIENCE, vol. 18, no. 12, pp. 1832-1842
and online methods (December 2015); Yamada et al., Resting-State
Functional Connectivity-Based Biomarkers and Functional MRI-Based
Neurofeedback for Psychiatric Disorders: A Challenge for Developing
Theranostic Biomarkers, INTL. J. OF NEUROPSYCHOPHARMACOLOGY,
20(10): 769-781 (2017).
[0023] The present tools and methods address, among other things,
certain gaps in the field. For example, many investigations focused
on identifying gene transcripts that were differentially regulated
between control and patient populations; accordingly, such studies
defined patient populations at the "spectrum" level, i.e. without
reference to underlying biology that accounts for particular
symptom profiles. See e.g., Liu et al., DAWN: a framework to
identify autism genes and subnetworks using gene expression and
genetics, MOLECULAR AUTISM, 5:22 (2014); Zhao et al.,
Connectome-scale group-wise consistent resting-state network
analysis in autism spectrum disorder, NEUROIMAGE: CLINICAL 12;
23-33 (2016). Here, the present tools and methods bridge such gaps
by including reference to the underlying biology that accounts for
neurobehavioral phenotypes.
[0024] Here, problems affecting rational CNS drug design and
treatment of CNS disorders are addressed using a genotype and
neurophenotype topography-based approach that incorporates gene
expression data and neuroimaging data for the rational design of
pharmaceutical treatments for CNS disorders. The present approach
improves, builds on, and refines, and redefines, circuit-derived
knowledge of how the biophysical properties of neural circuits and
the drug target densities vary across brain regions for a
particular neural phenotype, and integrates two types of brain
mapping--neurobehavioral phenotype mapping and gene expression
mapping--to provide the new genotype and neurophenotype
topography-based approach detailed below.
[0025] Inquiries addressed by the present tools and methods may be,
for example, directed to identification of drug targets associated
with neurobehavioral phenotypes and, conversely, neurobehavioral
phenotypes associated with potential drug targets. Accordingly, the
present genotype and neurophenotype topography-based approach
provides for the development of rational design and application of
pharmacological therapeutics for brain disorders.
[0026] The present tools and methods address several problems,
including providing greater specificity for discerning,
identifying, comparing, determining, or mapping links between
neurobehavioral phenotypes and therapeutics. In this instance, the
conventional circuit-based approach is replaced by a genotype and
neurophenotype topography-based approach that takes into
consideration both neuroimaging maps and gene expression maps to
define or characterize areas or regions of potential or actual
therapeutic activity, and may also identify potential areas or
regions of off-target delivery.
[0027] A problem addressed by the present tools and methods is the
provision of more precise targeting which is needed to address
variations existing within a broad neurobehavioral phenotype.
[0028] Another problem addressed by the present tools and methods
is the provision of more precise targeting of therapeutics to
specific brain areas needed to preferentially modulate more
critical areas or regions and to minimize effects on off-target
areas or regions by providing a genotype and neurophenotype
topography-based approach.
[0029] Yet another problem addressed by the present tools and
methods is the provision of formalism needed to identify potential
therapeutics to more precisely target critical areas or regions
involved with a particular neurobehavioral phenotype of interest.
For example, the present tools and methods may be used to identify
drugs which can selectively target the brain areas or regions
involved in a neurobehavioral phenotype of interest.
[0030] Another problem addressed by the present tools and methods
is the provision of the formalism needed to identify
neurobehavioral phenotypes as candidates for treatment, which can
be identified by phenotypes whose characteristic brain maps are
aligned with the gene expression maps associated with a particular
drug of interest.
[0031] Another problem addressed by the present tools and methods
is the provision of the formalism needed to generate insight across
species based on relating gene expression maps.
[0032] Another problem addressed by the present tools and methods
is the provision of the brain genotype and neurophenotype
topography-based formalism needed to rationally develop
combinations of multiple therapeutics to precisely target key brain
areas or regions. At present, no formalism exists for maximizing
effects of polypharmacy to areas that express genes coding for drug
targets.
[0033] The present tools and methods may also provide for
individualized treatment selection. The present platform provides
tools and methods to inform putative treatment response at the
individual patient level based on either neural or behavioral data
obtained from the patient.
[0034] The present tools and methods may also provide for
identification of a drug target based on similarity to a gene
implicated. For example, the present tools and methods may be sued
to identify a drug target based on similarity to the APOE gene
which is linked to Alzheimer's, and which is not directly
drugable.
[0035] The present tools and methods may also provide for
identification of drug targets based on one or more genes'
similarity to a neural circuit implicated.
[0036] The present tools and methods may also provide for selection
of a suitable patient population subset, or purification of patient
population, to test efficacy of application (i.e. clinical trial
optimization), by examining drug targets associated with
neurobehavioral phenotypes or, conversely, neurobehavioral
phenotypes associated with potential drug targets.
[0037] The present tools and methods may also provide for selection
of drugs for a clinical trial or for animal testing. The present
approach provides a method to inform putative target engagement
based on alignment of potential drug targets to a neuroimaging
map.
[0038] The present tools and methods may also provide for animal
applications of phenotype-transcriptome mapping. The present
approach provides a method to produce a high-throughput screen via
a disease animal model (e.g. knockout). Given a neurophenotype map
in the animal, the present approach provides a method to sweep
across genes that maximally align with such map. This provides a
method of use for improved or more accurate therapeutic design.
[0039] The present tools and methods may also provide for
diagnostic decisions for specific people based on implicated neural
circuits, or based on behavioral variation for which there are
quantitative links to relevant neurophenotypes.
[0040] The present tools and methods may also provide for
prognosticating the effect of an administered therapy based on gene
transcriptome alignment. The present tools and methods may also
provide for prognosticating the putative treatment response prior
to full blown illness (i.e. risk) for neural circuit alteration
based on gene transcriptome alignment.
[0041] The present tools and methods may also provide for bypassing
invasive pharmacoimaging. Specifically, the present approach can
provide a way to identify a neurophenotype if there is a known
clinical pharmacological response in a group of individuals with
known symptom responses. Here, if the neural-behavioral mapping is
unknown then this application would pinpoint a given circuit based
on known response in relation to gene transcriptome for that
drug.
[0042] The present tools and methods may also provide for
polypharmacy.
[0043] The present tools and methods may also inform
neurobehavioral mapping in clinical response to a given drug via
transcriptome profile, or gene mapping, for the receptor targeted
by a given drug. For example, here "transcriptome profile" may
refer to gene-gene mapping, i.e., because we know what a drug that
targets a particular gene (gene #1) does based on clinical
evidence, we can infer a similar clinical response based on the
similarity of distribution of a drug that targets a novel gene
(gene #2). And "gene mapping" may refer to the ability to infer
effect of a therapeutic based solely on the pattern of expression
of the gene it targets within functional circuits (i.e. collection
of brain regions that together to carry out a particular
function).
[0044] Specifically, if two drugs induce differential symptom
response in a clinical trial, then the known alignment of their
receptor targeting with a given transcriptome map implicates a
neural circuit in that symptom change.
[0045] For instance, while conventional neural circuit boundaries
are established by invasive or non-invasive neural recording or
neuroimaging techniques, the present alignment between the
neurophenotype topography and the gene expression maps, or genotype
topography, can point to a circuit that would be invisible to the
conventional circuit mapping techniques. Put differently, using the
conjunction of the gene expression and neural or neurophenotype
maps allows the definition of novel putative circuits that are
maximally co-aligned.
[0046] Therefore, the neural circuit boundaries established using
the present gene-neurophenotype alignment topographic approach may
deviate from conventional neural circuit boundaries. One example of
this deviation may be that the neurophenotypic variation map
associated with a given disease exhibits maximal alignment with
more than a single gene map, thus yielding an alignment across a
circuit that would traditionally not be identifiable without such
multi-gene alignment.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0047] The patent or application file contains at least one drawing
executed in color. Copies of this patent or patent application
publication with color drawing(s) will be provided by the Office
upon request and payment of the necessary fee.
[0048] FIGS. 1A and 1B provide computational frameworks relating
the scoring of pairs of neurophenotype maps and gene expression
maps.
[0049] FIGS. 2-4 are block diagrams of a process for performing the
computation framework relating to correlating phenotype maps and
gene expression map between neurobehavioral phenotypes to drug
targets.
[0050] FIGS. 5A-5C illustrates the bi-directionality of innovation,
showing gene to neuroimaging map and, conversely, neuroimaging map
to drug target. FIG. 5A illustrates the gene-to-phenotype
direction. FIG. 5B illustrates the phenotype-to-gene direction.
FIG. 5C illustrates the gene-to-gene direction, which identifies
genes based on the statistical association of their topographies
with the topography of a selected gene of interest.
[0051] FIGS. 6A, 6B, and 6C provide an example of cortical and
subcortical gene group-averaged expression maps for four genes,
OPRK1, PDYN, OXTR, and PNOC. FIG. 6A illustrates parcellated maps
of cortical (left) and subcortical (right) expression topographies.
FIG. 6B illustrates the mean expression values for the gene PDYN,
at the resolution of brain structures (vertical axis) partitioned
by functional networks (horizontal axis). FIG. 6C illustrates dense
cortical maps.
[0052] FIGS. 7A, 7B, 7C, 7D, and 7E provide opposing correlations
with the T1w/T2w (myelin) map for two GABA receptor subunit genes,
GABRA1 and GABRA5.
[0053] FIG. 8 provides a correlation between gene expression and
the T1/T2w (myelin) map for seven (7) genes, PDYN, OXTR, OPRK1,
PNOC, OXT, AVP, and OPRL1.
[0054] FIG. 9 provides a proof-of-principle demonstration showing
the bi-directionality of the platform using HCP task activation
maps. FIG. 9 shows a gene-to-phenotype approach. FIG. 9A depicts a
gene expression map for OPRK1 correlated with a set of
neurobehavioral phenotype maps. FIG. 9B depicts a gene expression
map for OPRL1 correlated with a set of neurobehavioral phenotype
maps.
[0055] FIG. 10 provides another proof-of-principle demonstration
showing the bi-directionality of the platform using HCP task
activation maps. FIG. 10 shows a phenotype-to-gene approach. FIG.
10A depicts story-math tasks correlated with a set of gene
expression maps. FIG. 10B depicts fearful-neutral face stimuli
correlated with a set of gene expression maps.
[0056] FIGS. 11A and 11B illustrate a gene-to-gene approach. FIG.
11A shows the cortical gene similarity scores for four NMDA
receptor subunits (GRIN2A, GRIN2B, GRIN2C, and GRIN2D). FIG. 11B
shows the cortical gene similarity scores for four GABA.sub.A
receptor subunits (GABRA1, GABRA2, GABRA3, GABRA4, and GABRA5).
[0057] FIGS. 12A, 12B, 12C, and 12D. FIG. 12 shows that the
platform can link from gene expression patterns to the neural
effects of a drug. FIG. 12A shows the fMRI-derived cortical map
showing the change in mean functional connectivity (Global Brain
Connectivity, GBC), which exhibits a large increase in occipital
visual cortex. FIG. 12B shows gene expression maps for three
serotonin receptor genes, including HTR2A. FIG. 12C shows the
gene-map correlation between the LSD-related neurophenotype map and
six candidate genes which code for serotonin and dopamine
receptors. FIG. 12D shows these correlation values in relation to
the gray background distribution histograms showing the
distribution of scores across all available genes in the AHBA
dataset, showing that HTR2A is in the top 5% of all genes in its
alignment with the LSD-related neurophenotype map.
[0058] FIGS. 13A and 13B (left) shows the behavioral symptom
profile and neural GBC map for two latent dimensions of individual
variation and FIGS. 13A and 13B (right) also shows the gene-map
correlation scores for specific genes of interest.
[0059] FIGS. 14A, 14B, and 14C provide images for a gene to
phenotype example, wherein a negative results is explained and a
drug is repurposed for a different neurobehavioral phenotype. FIG.
14A provides brain mapping images for the gene HRH3. FIG. 14B
provides brain mapping images for the phenotype map BSNIP Symptom
Correlations/GBC N436 BACS Comp Correlation. FIG. 14C provides
alignment brain mapping images for the brain mapping images
provided as FIG. 14A and FIG. 14B.
[0060] FIGS. 15A, 15B, 15C, 15D, 15E, and 15F provide images for a
phenotype to gene example, wherein patient screening risks and
novel therapeutic intervention are taken into account. FIG. 15A
provides a screen shot of the phenotypic gene distribution relating
to Achenback Adult Self-Report Questionnaire Syndrome Scale. FIG.
15B provides an image showing the gene-map correlation for six (6)
genes (HTR6, CHRM3, CHRM1, MAOA, HTR2A, and HTR2C). FIG. 15C
provides a phenotype map HCP Cognitive Behavioral/HCP N338 GBC ASR
SS Correlation. FIG. 15D provides another screen shot of the
phenotypic gene distribution relating to Achenback Adult
Self-Report Questionnaire Syndrome Scale. FIG. 15E provides another
image showing the gene-map correlation for six (6) genes (HTR6,
CHRM3, CHRM1, MAOA, HTR2A, and HTR2C). FIG. 15F provides a screen
shot of the phenotypic gene distribution relating to Achenback
Adult Self-Report Questionnaire Syndrome Scale. FIG. 15G provides
another image showing the gene-map correlation for three (3) genes
(CCR5, CXCR7, and CXCR4).
DETAILED DESCRIPTION
[0061] The present tools and methods integrate neurobehavioral
phenotype mapping and gene expression mapping information for
targeted genotype and neurophenotype topography delivery and
comprise a computational neuroinformatics platform. This platform
integrates neuroimaging maps with maps of gene expression in the
human brain, leveraging advances in large-scale brain-mapping
neuroinformatics. By quantifying the alignment of gene expression
maps with neuroimaging maps and defining brain areas and regions of
interest using a genotype and neurophenotype topography-based
approach, this platform provides a method to associate drug targets
with neurobehavioral phenotypes (e.g., disorders, symptoms,
cognitive processes, etc.) and opens a route to efficient rational
design of pharmacological therapeutics for brain disorders.
[0062] Generally, the tools and methods comprise two primary data
inputs, neurobehavioral phenotype mapping and gene expression
mapping, which is combined and processed to produce a numerical
score for a given map-gene pair. The numerical score reflects the
alignment of a given phenotype and gene expression mapping and
includes a measure of statistical significance or confidence for
this relationship based on a particular genotype and neurophenotype
topography. The numerical score may also reflect the correlation of
map values across brain locations, and may relate to one or more
map-gene pairs, maps, genes, or neurobehavioral phenotypes.
[0063] The neuroimaging maps and gene expression maps may be from
distinct sources, and may comprise heterogeneous source materials.
The neuroimaging maps and gene expression maps may be pre-processed
to sort or to exclude certain information or averaged prior to or
during processing by a computational neuroinformatics platform. The
neuroimaging maps and gene expression maps may be pre-processed or
averaged in view of, or in keeping with, a particular genotype and
neurophenotype topography prior to or during processing by a
computational neuroinformatics platform. Optionally, the
neurophenotype mapping information may be weighted or explicitly
restricted to select brain locations. Optionally, the gene mapping
information may be weighted or explicitly restricted to select
brain locations.
[0064] The platform outputs comprise neuroimaging data files of all
computed map data. These outputs include maps characterizing
aligned and misaligned brain locations of phenotypic and gene
expression mapping. Such outputs may relate to "off target" brain
locations/regions. Output maps may be visualized using publically
available neuroimaging software. Platform outputs may be provided
in a format that reflects a particular genotype and neurophenotype
topography as determined by the present tools and methods.
DEFINITION OF TERMS
[0065] Unless defined otherwise, all technical and scientific terms
used herein have the same meaning as is commonly understood by one
of skill in the art to which these tools and methods belongs.
Additional definitions are set forth throughout this
disclosure.
[0066] As used herein, the term "neurophenotype topography" refers
to the spatial pattern of values from a given neuroimaging measure
associated with a neurophenotype. This is in contrast with a more
conventional circuit-based approach because such an approach would
provide a location-specific readout of some measure. Here the tools
and methods consider distributed whole-brain or neural systems for
spatial mapping of on-target versus off-target relationships of
gene expression with a neurophenotype. In turn, this method moves
well beyond a circuit-based approach based on a neuroimaging maps
alone because it permits a spatial quantification of putative
therapeutic effect beyond a punctate pre-defined circuit. As used
herein, the terms neuroimaging map, neurobehavioral phenotype map,
and neurophenotype topography are synonymous with one another.
[0067] As used herein, the term "gene expression mapping" and
"genotype topography" may be used interchangeably.
[0068] As used herein, the term "neurobehavioral phenotype" refers
to a behavioral or neural measurable feature depicted or provided,
for example, as neuroimaging mapping data. Examples of
neurobehavioral phenotype include, but are not limited to: broad
psychiatric or neurological disorders or spectrums (e.g.,
schizophrenia); symptom dimensions (e.g., executive function);
mental processes (e.g., working memory); functional features (e.g.,
resting-state functional connectivity derived from BOLD fMRI);
structural features (e.g., DWI-derived probabilistic tractography,
myelin, cortical curvature, cortical thickness, subcortical volume,
fractional anisotropy); metabolic features (e.g., PET tracer map);
electrophysiological features (e.g., EEG map); latent measures
derived from a feature (e.g., latent measure of network topology);
and features reflecting effect of pharmacological manipulations
(e.g., effect of antipsychotic medication of PET metabolism and/or
BOLD functional connectivity). As used herein, a neurobehavioral
phenotype may be synonymous with a neurophenotype.
[0069] As used herein, the term "brain map" refers to an assignment
of a numerical value to each brain location/region from a given
analysis.
[0070] As used herein, the term "neuroimaging maps" refers to a
numerical value for each brain region reflecting the magnitude of a
particular feature which may relate to phenotype-related variation
within or across subjects.
[0071] As used herein, the term "gene expression map" refers to a
numerical value reflecting the expression levels of a specific gene
across brain regions obtained from one or more subjects.
[0072] As used herein in neurophenotype map generation, the term
"location" refers to a specific point, the term "region" or "area"
refers to some broader areal extent, and the terms "system" or
"network" refers group of regions that are functionally
organized.
[0073] As used herein, the term "pre-processing" data refers to any
cleanup strategy on the data leading to an neurophenotype map. For
instance, in the case of BOLD data, these steps may involve but are
not limited to motion correction, alignment across frames, phase
unwrapping, removal of nuisance signal that may be artefactual,
data-driven removal of spatially specific or pervasive artifact,
registration to the group atlas, etc.
[0074] As used herein, the term "contacting" may be used with
respect to data from a first source communicating, touching, coming
into proximity with, aligning, or interacting with data from a
second source, wherein said contacting allows for data from a first
source to be one or more of analyzed, compared, assessed for
similarity or contrast, likened, correlated, associated with,
linked, or related to data from a second source. "Contacting" may
occur in any physical or electronic medium that stores and allows
distribution, processing, or other use of data.
[0075] As used herein, the term "normalizing" data refers to the
procedure of quantitatively scaling the data to value relative to a
common reference.
[0076] As used herein, the term "weighting" data refers to
procedure of quantitatively scaling the values of data according to
a relative priority.
[0077] As used herein, the term "masking" data refers to the
procedure of excluding or including portions of the data from
further analyses.
Neurobehavioral Phenotypes and Mapping
[0078] Neurobehavioral phenotypes refer to disorders, symptoms,
cognitive processes, etc. (and may be collectively referred to
herein as "disorders"). Examples of such disorders include, but are
not limited to, the following disorders: schizophrenia, including
psychosis; anxiety disorders, including panic disorder,
post-traumatic stress disorder, and anxiety; mood and other
affective disorders, including major depression, geriatric
depression, and bipolar disorder; mood disorders in epilepsy;
personality disorders, such as borderline personality disorder,
obsessive-compulsive disorder; cognitive changes associated with
chemotherapy; attention deficit hyperactivity disorder (ADHD); sex
differences in brain function in health and disease (e.g.,
premenstrual dysphoric disorder); and traumatic brain injury.
[0079] Main classes of mental illness include, for example, the
following. Neurodevelopmental disorders refer to a mental illness
class that covers a wide range of problems that usually begin in
infancy or childhood, often before the child begins grade school.
Examples include autism spectrum disorder,
attention-deficit/hyperactivity disorder (ADHD) and learning
disorders. Schizophrenia spectrum and other psychotic disorders
refer to a class of psychotic disorders that cause detachment from
reality, such as delusions, hallucinations, and disorganized
thinking and speech. The most notable example is schizophrenia,
although other classes of disorders can be associated with
detachment from reality at times. Bipolar and related disorders
refer to a class that includes disorders with alternating episodes
of mania, periods of excessive activity, energy and excitement, and
depression. Depressive disorders refers to a class that include
disorders that affect how you feel emotionally, such as the level
of sadness and happiness, and they can disrupt your ability to
function. Examples include major depressive disorder and
premenstrual dysphoric disorder. Anxiety disorders relate to
feelings of anxiety, an emotion characterized by the anticipation
of future danger or misfortune, along with excessive worrying.
Anxiety disorders can include behavior aimed at avoiding situations
that cause anxiety. This class includes generalized anxiety
disorder, panic disorder and phobias. Obsessive-compulsive and
related disorders include disorders that involve preoccupations or
obsessions and repetitive thoughts and actions. Examples include
obsessive-compulsive disorder, hoarding disorder and hair-pulling
disorder (trichotillomania). Trauma- and stressor-related disorders
include adjustment disorders in which a person has trouble coping
during or after a stressful life event. Examples include
post-traumatic stress disorder (PTSD) and acute stress disorder.
Dissociative disorders include disorders in which your sense of
self is disrupted, such as with dissociative identity disorder and
dissociative amnesia. Somatic symptom and related disorders may be
found in person that may have physical symptoms with no clear
medical cause, but the disorders are associated with significant
distress and impairment. The disorders include somatic symptom
disorder (previously known as hypochondriasis) and factitious
disorder. Feeding and eating disorders may include disturbances
related to eating, such as anorexia nervosa and binge-eating
disorder. Elimination disorders may relate to the inappropriate
elimination of urine or stool by accident or on purpose. Bedwetting
(enuresis) is an example. Sleep-wake disorders may include
disorders of sleep severe enough to require clinical attention,
such as insomnia, sleep apnea and restless legs syndrome. Sexual
dysfunctions may include disorders of sexual response, such as
premature ejaculation and female orgasmic disorder. Gender
dysphoria may refer to the distress that accompanies a person's
stated desire to be another gender. Disruptive, impulse-control and
conduct disorders may include problems with emotional and
behavioral self-control, such as kleptomania or intermittent
explosive disorder. Substance-related and addictive disorders may
include problems associated with the excessive use of alcohol,
caffeine, tobacco and drugs. This class also includes gambling
disorder. Neurocognitive disorders may affect a person's ability to
think and reason. These acquired (rather than developmental)
cognitive problems include delirium, as well as neurocognitive
disorders due to conditions or diseases such as traumatic brain
injury or Alzheimer's disease. Personality disorders may involve a
lasting pattern of emotional instability and unhealthy behavior
that causes problems in your life and relationships. Examples
include borderline, antisocial and narcissistic personality
disorders. Paraphilic disorders may include sexual interest that
causes personal distress or impairment or causes potential or
actual harm to another person. Examples are sexual sadism disorder,
voyeuristic disorder and pedophilic disorder. Other mental
disorders may include mental disorders that are due to other
medical conditions or that don't meet the full criteria for one of
the above disorders.
[0080] The defining symptoms for each mental illness are detailed
in the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5), published by the American Psychiatric Association. This
manual is used by mental health providers to diagnose mental
conditions and by insurance companies to reimburse for
treatment.
[0081] Conventional diagnosis of a mental illness may include a
physical exam to try to rule out physical problems that could cause
your symptoms, lab tests including, for example, a check of your
thyroid function or a screening for alcohol and drugs, and a
psychological evaluation. During a psychological evaluation a
doctor or mental health provider may talk to a person about his or
her symptoms, thoughts, feelings and behavior patterns, and a
person may be asked to fill out a questionnaire to help answer
these questions.
[0082] Psychiatrists tend to use a system of diagnosis which
identifies 10 types of personality disorder: paranoid personality
disorder; schizoid personality disorder; schizotypal personality
disorder; antisocial personality disorder; borderline personality
disorder; histrionic personality disorder; narcissistic personality
disorder; avoidant (or anxious) personality disorder; dependent
personality disorder; and obsessive compulsive personality
disorder. The types are grouped into three categories: (1)
Suspicious--paranoid, schizoid, schizotypal and antisocial; (2)
Emotional and impulsive--borderline, histrionic and narcissistic;
and (3) Anxious--avoidant, dependent and obsessive compulsive.
[0083] Attention deficit hyperactivity disorder may be divided into
three different types: inattentive type; hyperactive-impulsive
type; and combination type.
[0084] Neurodegenerative diseases may include, for example,
Alzheimer's disease, Parkinson's disease; amyotrophic lateral
sclerosis; Friedreich's ataxia; Huntington's disease; Lewy body
disease; and spinal muscular atrophy.
[0085] Signs and symptoms of mental illness can vary, depending on
the disorder, circumstances and other factors. Mental illness
symptoms can affect emotions, thoughts and behaviors. Examples of
signs and symptoms may include, for example: feeling sad or down,
confused thinking or reduced ability to concentrate, excessive
fears or worries, or extreme feelings of guilt, extreme mood
changes of highs and lows, withdrawal from friends and activities,
significant tiredness, low energy or problems sleeping, detachment
from reality (delusions), paranoia or hallucinations, inability to
cope with daily problems or stress, trouble understanding and
relating to situations and to people, alcohol or drug abuse, major
changes in eating habits, sex drive changes, excessive anger,
hostility or violence, and suicidal thinking. Sometimes symptoms of
a mental health disorder appear as physical problems, such as
stomach pain, back pain, headache, or other unexplained aches and
pains.
[0086] Symptoms of major depression include feelings of sadness,
loss of interest in normally pleasurable activities (anhedonia),
changes in appetite and sleep, loss of energy, and problems with
concentration and decision-making. Episodes of dysthymia resemble
depression but are milder and often last longer. Bipolar disorder
is characterized by alternating cycles of depression and mania.
Symptoms of mania include elevated or expansive mood, inflated
sense of self-esteem or self-importance, decreased need for sleep,
racing thoughts, and impulsive behavior. Episodes of hypomania are
typically shorter in length and less severe than mania. Cyclothymia
is marked by cycles of low-level depression and hypomania.
[0087] Affective disorders may include Unipolar Depression and its
variants including: postpartum depression, atypical depression,
seasonal affective disorder; bipolar disorder; dysthymia and
cyclothymia; generalized anxiety disorder; panic disorder; phobias
including agoraphobia; obsessive compulsive disorder (OCD); and
post-traumatic stress disorder (PTSD). There are several types of
mood disorders: major depression, bipolar disorder (also known as
manic depression), dysthymia, and cyclothymia.
[0088] Mental illnesses, in general, are thought to be caused by a
variety of genetic and environmental factors. These factors may
include inherited traits, environmental exposures before birth, and
brain chemistry. For example, mental illness is more common in
people whose blood relatives also have a mental illness. Certain
genes may increase your risk of developing a mental illness, and
your life situation may trigger it. Also, exposure to environmental
stressors, inflammatory conditions, toxins, alcohol or drugs while
in the womb can sometimes be linked to mental illness.
Additionally, neurotransmitters are naturally occurring brain
chemicals that carry signals to other parts of your brain and body.
When the neural networks involving these chemicals are impaired,
the function of nerve receptors and nerve systems change, leading
to depression.
[0089] Certain factors may increase a person's risk of developing
mental health problems, including: having a blood relative, such as
a parent or sibling, with a mental illness; stressful life
situations, such as financial problems, a loved one's death or a
divorce; an ongoing (chronic) medical condition, such as diabetes;
brain damage as a result of a serious injury (traumatic brain
injury), such as a violent blow to the head; traumatic experiences,
such as military combat or being assaulted; use of alcohol or
recreational drugs; being abused or neglected as a child; having
few friends or few healthy relationships; and a previous mental
illness.
[0090] Mental illness is common. About one in five adults has a
mental illness in any given year. Mental illness can begin at any
age, from childhood through later adult years, but most begin
earlier in life. The effects of mental illness can be temporary or
long lasting. A person also can have more than one mental health
disorder at the same time. For example, a person may have
depression and a substance use disorder.
[0091] Mental illness is a leading cause of disability. Untreated
mental illness can cause severe emotional, behavioral and physical
health problems. Complications sometimes linked to mental illness
include: unhappiness and decreased enjoyment of life; family
conflicts; relationship difficulties; social isolation; problems
with tobacco, alcohol and other drugs; missed work or school, or
other problems related to work or school; legal and financial
problems; poverty and homelessness; self-harm and harm to others,
including suicide or homicide; weakened immune system, so your body
has a hard time resisting infections; heart disease and other
medical conditions.
[0092] Such neurobehavioral phenotypes, including associated neural
areas, may be elucidated using, for example, noninvasive
neuroimaging methods.
[0093] A range of neuroimaging types is available, such as,
structural magnetic resonance imaging (MRI), resting-state or
task-based functional MRI (fMRI), diffusion weighted imaging (DWI),
positron emission tomography (PET), electroencephalography (EEG),
magnetoencephalography (MEG), electrocorticography (ECoG), etc.,
from nonpublic and public databases.
[0094] These neuroimaging techniques can produce brain maps, i.e.,
an assignment of a numerical value to each location in the brain
reflecting the magnitude of a feature, which can be associated with
a neurobehavioral phenotype. Examples of features assessed or
quantified by neuroimaging techniques include, but are not limited
to, MR-based (e.g. activation in response to a cognitive paradigm,
geometry of the brain, diffusivity properties of tissue such as
diffusion-weighted imaging, relationships between signals across
time such as functional connectivity analyses, individual
difference maps between any imaging measure and behavioral
measures, etc.), non-MR-based (e.g. electrophysiological recordings
via EEG, MEG, ECoG, changes in spectra properties of power,
oscillatory signatures, etc.), stimulation-based brain changes in
any of the aforementioned techniques such as transcranial magnetic
stimulation (TMS), pharmacological manipulations of aforementioned
MR-based and non-MR-based signals, etc.
[0095] Data sources include neuroimaging maps from public and
private databases or future studies. Examples include, but are not
limited to, The Human Connectome Project Database, The National
Institute of Mental Health Data Archive, and the Neuroimaging
Informatics Tools and Resources Clearinghouse, which are further
described below.
[0096] The Human Connectome Project Database. The Human Connectome
Project (HCP) has tackled key aspects of this challenge by charting
the neural pathways that underlie brain function and behavior,
including high-quality neuroimaging data in over 1100 healthy young
adults. Using greatly improved methods for data acquisition,
analysis, and sharing, the HCP has provided the scientific
community with data and discoveries that greatly enhance our
understanding of human brain structure, function, and connectivity
and their relationships to behavior. The `ICP-style` neuroimaging
approach is generalizable and is being applied to other projects as
well.
[0097] The National Institute of Mental Health Data Archive (NDA).
NDA makes available human subjects data collected from hundreds of
research projects across many scientific domains. The NDA provides
infrastructure for sharing research data, tools, methods, and
analyses enabling collaborative science and discovery.
De-identified human subjects data, harmonized to a common standard,
are available to qualified researchers. Summary data is available
to all.
[0098] Neuroimaging Informatics Tools and Resources Clearinghouse
(NITRC). NITRC is a free one-stop-shop collaboratory for science
researchers that need resources such as neuroimaging analysis
software, publicly available data sets, or computing power. Since
its debut in 2007, NITRC has helped the neuroscience community to
use software and data produced from research that, before NITRC,
was routinely lost or disregarded, to make further discoveries.
[0099] Here the inventors leveraged neuroimaging phenotype maps
derived from the publically available Human Connectome Project
(HCP) database. Maps from this dataset relate fMRI activity to
neurobehavioral phenotypes. It is contemplated that the utility of
the present platform will increase upon increasing interface with a
database of phenotype maps.
[0100] The HCP dataset includes resting-state and task-based fMRI
data and a range of demographic, behavioral measures from a large
number of healthy subjects. Barch D M et al., Function in the Human
Connectome: Task-fMRI and Individual Differences in Behavior,
NEUROIMAGE, 80: 169-189, Oct. 15 (2013). HCP-derived maps used here
provide group-level activation (N=334) across, for example, the
following cognitive tasks: (i) Motor Strip Mapping Task (Right
versus left toe movements or finger movements; tongue movements).
See Bizzi A. et al., Presurgical functional MR imaging of language
and motor functions: validation with intraoperative electrocortical
mapping, RADIOLOGY, 248:579-589 (2008); Morioka T. et al.,
Comparison of magnetoencephalography, functional MRI, and motor
evoked potentials in the localization of the sensory-motor cortex,
NEUROLOGICAL RESEARCH, 17:361-367 (1995); (ii) Language Processing
Task. ((a) Auditory sentence presentation with detection of
semantic, syntactic and pragmatic violations; versus (b) auditory
story presentation with comprehension questions versus math
problems.) See Binder J R et al., Mapping anterior temporal lobe
language areas with fMRI: a multicenter normative study,
NEUROIMAGE, 54:1465-1475 (2011); Ditman T. et al., An investigation
of concurrent ERP and self-paced reading methodologies,
PSYCHOPHYSIOLOGY, 44:927-935 (2007); and Kuperberg G R et al.,
Neuroanatomical distinctions within the semantic system during
sentence comprehension: evidence from functional magnetic resonance
imaging, NEUROIMAGE, 40:367-388 (2008); (iii) Working Memory &
Cognitive Control Task. (Alternating blocks of 0-back and 2-back
working memory; faces, non-living man-made objects, animals, body
parts, houses, or words. N-back Task (2-back versus 0-back)
embedded in Category Specific Representation Task). See
Drobyshevsky A. et al., A rapid fMRI task battery for mapping of
visual, motor, cognitive, and emotional function, NEUROIMAGE,
31:732-744 (2006) ("Drobyshevsky"); and Caceres A. et al.,
Measuring fMRI reliability with the intra-class correlation
coefficient, NEUROIMAGE, 45:758-768 (2009); and Emotion Processing.
((i) Valence Judgments (negative and neutral pictures from IAPS)
versus (ii) Hariri Hammer Task). See Drobyshevsky; Phan K L et.
al., Real-time fMRI of cortico-limbic brain activity during
emotional processing, NEUROREPORT, 15:527-532 (2004); Manuck S B et
al., Temporal stability of individual differences in amygdala
reactivity, AM. J. PSYCHIATRY, 164:1613-1614 (2007a); Hariri A R et
al., The amygdala response to emotional stimuli: a comparison of
faces and scenes, NEUROIMAGE, 17:317-323 (2002).
[0101] Additional sources of maps could be derived from
meta-analytic sources, such as the Neurosynth online database.
Yarkoni et al., Large-scale automated synthesis of human functional
neuroimaging data, NAT. METHODS 8:665-70 (2011) ("Yarkoni").
Neurosynth generates statistical maps from automated meta-analysis
of published fMRI studies. One can download from the Neurosynth
site a map whose values are the statistical strength of modulation
related to a given term, such as "working memory," derived from
synthesis of hundreds of fMRI studies labeled with that term. There
are two main caveats with using Neurosynth data. First, they are
thresholded maps, and therefore lacking values for large portions
of the brain. Unthresholded statistical maps, which have full
coverage, would be better suited for gene-map correlations. Second,
these maps are given in the volumetric Neuroimaging Informatics
Technology Initiative (NIfTI) format. The present inventors found
that conversion of these maps to the Connectivity Informatics
Technology Initiative (CIFTI) format is possible, but the spatial
resolution may be coarse because such maps are not inherently
CIFTI-optimized. Nonetheless, it is contemplated that maps related
to terms of interest may be selected for use with the present tools
and methods. For instance, maps related to the following terms:
working memory, cognitive control, motivation, decision-making, and
emotional processing may be extracted.
[0102] Collections of current neuroimaging maps are heterogeneous.
As one illustrative example of regional neural specificity in
clinical neuroimaging, the present inventors studied the pattern of
cortical dysconnectivity in schizophrenia with fMRI. The present
inventors found that patients with schizophrenia exhibited an
overall increase in the covariance of resting-state BOLD signals.
Yang et al., Functional hierarchy underlies preferential
connectivity disturbances in schizophrenia, PROC. NATL. ACAD. SCI.
USA 113:E219-28 (2016) ("Yang"). Strikingly, this
neuroimaging-derived map of increased covariance was not uniform
across cortex, but preferentially elevated in association cortex
relative to sensory cortex, which are consistent with other
findings revealing preferential alterations to higher-order
association regions. Whitfield-Gabrieli et al, Hyperactivity and
hyperconnectivity of the default network in schizophrenia and in
first-degree relatives of persons with schizophrenia, PROC. NATL.
ACAD. SCI. USA 106:1279-84 (2009) ("Whitfield-Gabrieli"); Baker et
al., Disruption of cortical association networks in schizophrenia
and psychotic bipolar disorder, JAMA PSYCHIATRY 71:109-18 (2014)
("Baker"). This provides an example that a neuroimaging map of
clinical relevance (here, resting-state dysconnectivity in
schizophrenia) shows potentially important regional variation
across cortex. Currently, no neuro-informatics platform links,
extrapolates, associates, construes, or derives from, these
patterns with variation in biophysical properties such as gene
expression. Targeted drug discovery for neurobehavioral phenotypes
could be better informed by neuroimaging maps related to:
particular functions (e.g. activation during working memory, or
reward processing), symptom dimensions (e.g. negative symptoms in
schizophrenia), or data-driven "biotypes" within a categorical
disorder. Drysdale.
Gene Expression and Mapping
[0103] Genes code for proteins, e.g., receptor subunits, which may
be targets of drugs or otherwise involved in effects of
therapeutics. Gene expression is remarkably heterogeneous across
different brain locations, across the lifespan, across different
disease stages, different treatment stages. Also, some genetic
traits are fully penetrant (i.e. all individuals that carry a
mutation present with the phenotype) versus not fully penetrant
(i.e. proportion of individuals carrying a particular variant (or
allele) of a gene (the genotype) that also express an associated
trait (the phenotype) is not 100%). This distinction matters
because in the case of a fully penetrant mutation that gene may be
a high candidate target. That said a further distinction needs to
be drawn between genes that are associated with risk of developing
a given phenotype and genes that code for potential therapeutic
targets. It is contemplated that maps related to fully penetrant,
not fully penetrant, or downstream therapeutic target genes of
interest may be selected for use with the present tools and
methods.
[0104] Gene expression can be measured through techniques including
DNA microarray, in situ hybridization and RNA sequencing. Gene
expression in brain structures, e.g. cortex, can be measured at
multiple levels of spatial resolution, including bulk tissue,
specific cortical layers, and individual cells.
[0105] Data sources for gene expression across brain locations,
across humans and other species, include the Allen Human Brain
Atlas (AHBA) (gene expression across the whole adult human brain);
the Allen Mouse Brain Atlas (gene expression across the whole adult
mouse brain); the Allen Developing Mouse Brain Atlas (gene
expression across the mouse brain at multiple stages of
development); the BrainSpan Atlas of the Developing Human Brain
(transcriptome of the human brain at multiple stages of
development); the NIH Blueprint Non-Human Primate (NHP) Atlas (gene
expression data and neuroanatomical data from the developing rhesus
macaque brain); the Aging, Dementia and Traumatic Brain Injury
(TBI) Study (neuropathologic, molecular and transcriptomic
characterization of brains of control and TBI exposure cases); the
Allen Cell Types Database (single-cell level gene expression from
neuronal cell types); and the BrainCloud database (transcriptome in
human prefrontal cortex across the lifespan).
[0106] Collections of current gene expression maps are also
heterogeneous. To achieve regional specificity of pharmaceutical
effects, regional variation in expression of drug target across
brain areas is needed. These patterns can be revealed by analysis
of the expression of genes coding proteins involved in the drug
targets.
[0107] The AHBA is a publicly available database of gene expression
from around 30,000 genes represented by about 60,000 microarray
probes, sampled from hundreds of brain locations (cortical and
subcortical) from six subjects. Hawrylycz et al., Canonical genetic
signatures of the adult human brain, NAT. NEUROSCI. 18:1832-44
(2015) ("Hawrylycz 2015"). The AHBA database provides a unique
opportunity to characterize the regional variation in drug targets.
Indeed, gene expression is remarkably heterogeneous across
different brain regions. For instance, there is strong variation in
the expression of dopamine signaling pathway genes across cortical
and subcortical brain regions. See e.g., Hawrylycz et al., An
anatomically comprehensive atlas of the adult human brain
transcriptome, NATURE 489:391-9, FIG. 2 (2012) ("Hawrylycz 2012").
Even within neocortex, gradients of gene expression reveal the
coordinated specialization of microcircuitry, such as from primary
sensory to association cortex. Burt et al., Hierarchy of
transcriptomic specialization across human cortex captured by
structural neuroimaging topography, NATURE NEUROSCIENCE 21:1251-9
(2018) ("Burt"). Prior studies using the AHBA data had already
demonstrated the feasibility of integrating gene expression and
neuroimaging maps. Cortical regions with similar gene expression
profiles are more likely to be structurally interconnected and more
likely to have high functional connectivity (as characterized by
resting-state BOLD signals). (Hawrylycz 2015; Richiardi et al.,
Correlated gene expression supports synchronous activity in brain
networks, IMAGEN consortium, SCIENCE 348:1241-4 (2015)
("Richiardi"). Studies have also found that risk genes for
schizophrenia are expressed in meaningful patterns related to
neurodevelopment, and to schizophrenia-related alterations of
diffusion-MRI-derived structural connectivity. Whitaker et al.,
Adolescence is associated with genomically patterned consolidation
of the hubs of the human brain connectome, NSPN Consortium, PROC.
NATL. ACAD. SCI. USA 113:9105-10 (2016) ("Whitaker"); Romme et al.,
Connectome disconnectivity and cortical gene expression in patients
with schizophrenia, BIOL. PSYCHIATRY (2016) ("Romme"). These prior
studies, as well as the inventors' analyses, support the validity
of the AHBA dataset as a high-quality source of meaningful gene
expression variation across the human brain. The present tools and
methods go beyond these prior studies to bi-directionally identify
genes and neurobehavioral phenotypes based on quantitative
alignment of their spatial maps.
[0108] Here, the inventors used the AHBA dataset. The AHBA dataset
contains gene expression levels across the human brain, for about
30,000 genes represented by about 60,000 microarray probes, sampled
from hundreds of regions in the left hemisphere (cortical and
subcortical), from six subjects. Hawrylycz 2012; Hawrylycz 2015. In
the terminology used by the AHBA, a "sample" is a gene expression
measurement from a specific location in the brain. For a gene of
interest, a microarray probe (specific DNA sequence) is selected
for which expression values are measured.
[0109] The present tools and methods may include three levels for
analysis of cortical gene expression data: (1) sparse samples or
specific locations; (2) interpolated dense map or across an entire
continuous map at its native (i.e., dense) resolution; and (3) a
map parcellated into discrete regions or areas. Map coordinates for
locations of expression can be transformed into different
neuroimaging brain atlases (e.g., the Montreal Neurological
Institute (MNI) atlas). The level of sparseness used can be
selected based on resolution of neurobehavioral phenotype mapping
data or area of interest for which one seeks to quantify the gene
expression profile.
[0110] The AHBA dataset provides MNI coordinates for each sample.
For samples in cortex, the method maps the coordinates to the
nearest grayordinate in CIFTI cortical surface. There are two ways
to achieve this. A courser way would involve mapping of the AHBA
provided MNI coordinates for each location of gene expression onto
a common atlas. The second more precise method would involve
computing a complete segmentation of all gray and white matter for
each individual subject for whom gene expression data exists. Then
these segmentations would be used to compute cortical surface
boundaries for each subject via automated tools such as FreeSurfer
(See e.g., Dale A M et al., Cortical surface-based analysis I.
Segmentation and surface reconstruction, NEUROIMAGE, 9 (2),
179-194, 4931 (1999); Fischl B. et al, Whole brain segmentation:
Automated labeling of neuroanatomical structures in the human
brain, NEURON, 33 (3), 341-355, 3776 (2002); and Fischl B. et al,
Cortical surface-based analysis. II: Inflation, flattening, and a
surface-based coordinate system, NEUROIMAGE, 9 (2), 195-207
(1999)). In turn, the subject-specific `native space` locations of
gene expression would be mapped onto that subject's cortical
surface mesh. In turn, the values on the mesh would then be
transformed into a common atlas based on state-of-the-art
surface-based registration methods (See above, and Anticevic A. et
al., Comparing surface-based and volume-based analyses of
functional neuroimaging data in patients with schizophrenia,
NEUROIMAGE, 41(3):835-48, July 1 (2008); Glasser M F et al., A
multi-modal parcellation of human cerebral cortex, NATURE, 536,
171-178 (11 Aug. 2016)).
[0111] For samples in sub-cortex, the AHBA-assigned label for the
brain region may be used. Similarly to the second method described
for cortex, an alternative method would involve computing a
complete segmentation of all gray and white matter for each
individual subject for whom gene expression data exists. Then these
segmentations would be used to compute subcortical volume
boundaries for each subject via automated tools such as FreeSurfer
(Dale A M et al., Cortical surface-based analysis I. Segmentation
and surface reconstruction, NEUROIMAGE, 9 (2), 179-194, 4931
(1999); Fischl B. et al, Whole brain segmentation: Automated
labeling of neuroanatomical structures in the human brain, NEURON,
33 (3), 341-355, 3776 (2002) Fischl B. et al, Cortical
surface-based analysis. II: Inflation, flattening, and a
surface-based coordinate system, NEUROIMAGE, 9 (2), 195-207
(1999)). In turn, the subject-specific `native space` locations of
gene expression would be mapped onto that subject's subcortical
volumes and transformed into a common atlas based on
state-of-the-art registration methods (Anticevic A. et al.,
Comparing surface-based and volume-based analyses of functional
neuroimaging data in patients with schizophrenia, NEUROIMAGE,
41(3):835-48, July 1 (2008); Glasser M F et al., A multi-modal
parcellation of human cerebral cortex, NATURE, 536, 171-178 (11
Aug. 2016)).
[0112] Analyses can proceed at the sample level, using the sparse
grayordinates to which AHBA samples are mapped.
[0113] The dense and parcellated maps require interpolation of gene
expression values to all grayordinates in cortex. To perform this
interpolation, multiple methods may be used. Our current method is
to construct a Voronoi diagram, assigning each grayordinate to its
nearest AHBA sample location; that sample's gene expression values
are then given to those grayordinate. Other methods may be used,
including weighted averaging based on exponential decay with
increasing geodesic distance from the sample along the cortical
surface (e.g. with characteristic length constant determined by the
gene expression spatial autocorrelation structure). This provides
the dense map. To produce a parcellated map, the dense map may be
parcellated with a CIFTI-defined parcellation using, for instance,
the Connectome Workbench software or any other matrix manipulation
software that can read the CIFTI format (e.g. Matlab, R statistical
computing environment, Octave, Python, etc.). For example, the new
cortical parcellation from the HCP team may be used. Glasser et
al., A multi-modal parcellation of human cerebral cortex, NATURE
536:171-8 (2016) ("Glasser"). For parcels that contain gene
expression, one method is to assign the parcel value as an average
of the samples within that parcel, which can be a weighted average
(e.g., based on the samples' relative Voronoi diagram coverages
within the parcel).
[0114] As noted, additional coordinates can be assigned to
measurement sites by explicitly computing the cortical and
subcortical segmentation of each individual subject contributing to
the AHBA based on their high-resolution structural post-mortem
scans. In turn, such segmentations can be leveraged to compute a
cortical mesh and subcortical anatomical nucleus assignment. In
turn, the mesh forms a surface along with proximity that can then
be calculated for each individual subject, yielding a set of
subject-specific coordinates. In turn, such a cortical surface mesh
can be aligned across subjects to the group atlas using
surface-based features. This also applies to subcortical locations
of expression, which can be defined at the subject level based on
their anatomy and in turn aligned to a given group atlas. Following
this spatial transformation, each subjects' individual coordinates
are brought into alignment. Next, analyses can proceed at the
specific location level, using specific coordinates for gene
expression at that location. Importantly, neuroimaging maps would
also capture the relevant cortical and subcortical locations.
[0115] Conversely, the continuous "dense" and discrete
"parcellated" maps require assigning a gene expression value to a
given cortical location either the native resolution of a given
dense map or into a given discrete parcel/area. Example methods for
this assignment include: (1) assignment of value to a given map
location based its proximity to the locations at which gene
expression was measured, i.e., the gene expression measurement
sites. This can be done via, for example, nearest neighbor
assignment; e.g., through construction of a Voronoi diagram; (2)
assignment by a weighted sum to a map location based on the
proximity to the locations at which gene expression was measured;
e.g., weighted by distance along cortical surface from the gene
expression measurement sites; and (3) assignment by a weighted sum
across gene expression measurement sites which are within parcel
boundaries according to a given parcellation.
[0116] Subcortical gene expression data can be used to assign
values to subcortical locations or regions. Methods for this
assignment include labeling by neuroanatomical evaluation, weighted
sum within a parcellation of subcortical regions, or other forms of
anatomical or functional location assignment. Further processing
steps can be applied to gene expression data to remove extraneous
biases and improve signal-to-noise before combination with the
neurobehavioral phenotype mapping data. These steps can improve the
reproducibility of the maps, which can be quantified by a stability
metric across subjects. Example steps include: (1) expression
values can be normalized within each subject (i.e., brain) before
combining across subjects (e.g., via mean or median); (2) gene
expression measurement sites can be filtered out on the basis of
their exhibiting exceptionally low similarity with other
measurement sites in their expression levels across genes; and (3)
signal-to-noise of the spatial expression pattern can be improved
through data processing techniques such as dimensionality reduction
via principal component analysis (PCA).
[0117] The present inventors encountered a variety of complications
in using the raw AHBA dataset, necessitating further preprocessing
for use with the platform of the present tools and methods. These
problems necessitate the development a number of additional
pre-processing steps to remove extraneous biases and improve
signal-to-noise. For instance, the present inventors found biases
in the mean expression levels across AHBA samples which should be
corrected. Best ways to combine data across the six subjects are
also assessed, which requires de-meaning and normalizing data.
[0118] It is also noted that selection of probes for a given gene
of interest is non-trivial. For many genes there are multiple
probes, which can be selected based on their expression levels and
overall coverage across samples. The present tools and methods test
whether a probe or gene is suitable for analyses by characterizing
it differential stability across subjects (i.e., the average
between-subject correlation of expression values). Hawrylycz 2015.
Also, the present tools and methods may use differential stability
to select subsets of subjects with stable across-subject expression
maps for further analysis. Careful characterization of these steps
is expected to greatly improve the ability to get meaningful
results from the AHBA dataset. For instance, the present inventors
filtered out probes whose coverage across cortical parcels
(defining coverage scores) were below a threshold percentage and
therefore not well suited to interpolation to form dense or
parcellated gene expression maps. If two probes were available for
a gene, each with acceptable coverage and differential stability
scores, the selected probe was set as the one with maximum gene
expression variance. If three or more acceptable probes were
available, the selected probe is the one with the highest
similarity to the other probes, as it is most highly representative
among available gene probes.
[0119] For gene expression datasets derived from DNA microarray
measurements, selection of the microarray probe for a gene of
interest is important. Probes are selected on the basis of multiple
factors, including their coverage across brain regions, and their
consistency of expression patterns across subjects (e.g.,
post-mortem human or animal brains). Example DNA microarray probes
in the AHBA (made by Agilent) are: A_23_P40262 (for PDYN),
A_23_P132619 (for OXTR), A_23_P169061 (for OPRK1), A_24_P382579
(for OXT), A_23_P9883 (for AVP), and A_23_P345564 (for OPRL1].
Multiple selected probes can be combined in a weighted sum to
improve signal-to-noise, e.g., by using the first principal
component from PCA, by using the mean or median value across
probes, or by using the most representative probe through some
central tendency measure. The probe information can be obtained
through, for example, publically available databases or optimized
through future experiments,
[0120] Computational Framework
[0121] The overall computational framework for the present platform
is shown schematically in FIG. 1. Briefly, embodiments described
herein relate to correlating neurobehavioral phenotypes (e.g., a
disorder, symptom, cognitive process, etc.) and genes (or their
associated drugs or drug targets) by pre-processing brain mapping
data and gene expression data, and computing similarities between a
brain map related to a neurobehavioral phenotype (as can be
produced by human neuroimaging) and a brain map of expression
values for a gene.
[0122] In general, the platform involves two paths, which are
represented as path 110 and path 120 in FIG. 1.
[0123] Path 110 of the schematic platform depiction begins with a
list of one or more neurobehavioral phenotypes 112. As described in
more detail herein, the process may begin with a selected
neurobehavioral phenotype to identify or predict a gene or drug
target, or it may begin with a selected gene or drug target to
identify or predict a neurobehavioral phenotype. A set of
neurobehavioral phenotype maps 114 (i.e., neurophenotype
topographies) are generated from neurobehavioral phenotype mapping
data from one or a plurality of neural images for one or more
people. These neurobehavioral phenotype maps 114 reflect
characteristics of disorders, symptoms, and cognitive processes at
the level of whole-brain measurement or across select locations or
brain regions. Such neurobehavioral phenotype maps 114 can be
derived for use with the genotype topography and neurophenotype
topography-based methods described herein from a range of
neuroimaging modalities: task-based fMRI, resting-state fMRI, DWI,
structural, EEG, MEG, PET maps, etc. These neurobehavioral
phenotype maps are labeled by their associated neurobehavioral
phenotypes. The neurobehavioral phenotype mapping datasets used to
generate the neurobehavioral phenotype maps 114 can therefore come
from a variety of sources as well as from publicly available
databases. Therefore, the system can interface with a database
relating an ontology of neurobehavioral phenotypes with
neuroimaging maps.
[0124] In various embodiments, the system utilizes neuroimaging
maps that reflect neurobehavioral phenotype characteristics either
at a whole-brain level or across select locations or brain regions
as the neurobehavioral phenotype maps. The neurobehavioral
phenotype maps can be derived by precomputing and/or gathering
information from prior sources or can be empirically generated in
new observational and experimental work across animal and human
studies.
[0125] In some embodiments, optional weighting and masking 116 of
neurobehavioral phenotype maps 114 may be employed. That is, for
optional weighting, a weight value may be assigned for each brain
location or region. Such weighting allows for prioritization of
particular locations or brain regions, penalization of expressions
in certain locations or brain regions, etc. For example,
prioritizing particular brain regions may include assigning those
regions with a weight above a threshold, and assigning other brain
regions with a weight below the threshold. Also, optional masking
may be accomplished by weighting that is used to mask or remove
information from specific locations or brain regions, such as by
assigning weights to zero or below another, lower threshold.
Masking allows flexibility of assessing alignment of
neurobehavioral phenotype maps with gene expression maps
prioritization of particular brain structures or only within
certain brain structures, rather than at the whole-brain level,
e.g., only within cortex (masking out subcortical structures).
[0126] For example, the flexibility of the neurobehavioral
phenotype maps 114 can be extended by combining it with an optional
weight map, in which a weight value is defined for each brain
region as part of the present genotype topography and
neurophenotype topography-based methods. The weight map then can be
used in calculation of the alignment measure, e.g., via the
weighted Pearson correlation coefficient. This allows flexible
implementation of operations such as masking out certain brain
regions, giving priority to some regions over others, penalizing
expression in certain regions, etc. The neurobehavioral phenotype
maps 114 and weight maps are then contacted with and used for
comparison to the gene expression maps 128.
[0127] Path 120 of the diagram, aligns therapeutic action related
to the molecular targets of select therapeutics (e.g., drugs
targeting the specific neurotransmitter receptors and their
subunits), which are encoded by specific genes 126. The gene
expression maps 128 (i.e., genotype topographies) characterize the
differential expression of specific genes 126 across the brain.
These gene expression maps 128 may be computed from the AHBA
dataset and may result for pre-processed gene mapping information.
The proteins encoded by these genes 126, and the biochemical
pathways in which they are involved, can be linked with specific
drug targets 124, and in turn with specific drugs 122 or
therapeutics. Thus, the present platform may create new gene
expression maps (i.e., genotype topographies) showing linkage with
specific genes, associated drug targets or specific drugs. The
system may interface with a database relating drugs 122 and drug
targets 124 with genes 116.
[0128] In some embodiments, these two paths are used as input and
contacted and correlated to define one or more phenotype-gene pair
topographies for a given neurobehavioral phenotype (i.e., one or
more phenotype-gene pairs for a same phenotype with different
genes). A numerical score 130 is generated for each phenotype-gene
pair topography for each phenotype-gene pair based on the
contacting of data and alignment of the corresponding
neurobehavioral phenotype map with the respective gene expression
map.
[0129] For example, weighted neurobehavioral phenotype maps (i.e.,
neurophenotype topographies) and gene expression maps (genotype
topographies) may be contacted and compared to define a
corresponding phenotype-gene pair topography for a phenotype-gene
pair, and a score reflecting the level of association is calculated
for such maps. The numerical score for a given phenotype-gene pair
may be based on the contact and alignment of the weighted
neurobehavioral phenotype map with the gene expression map, and can
be computed as the correlation of the map values across regions.
Definition and characterization of the brain region or regions
contacted, correlated, or aligned between the neurobehavioral
phenotype map data and the gene expression map data results in a
phenotype-gene pair topography for that phenotype-gene pair. This
score can be derived from a measure of statistical association
(e.g., correlation calculation or other measures of shared
variance) with stronger associations ranked higher. Higher
associations indicate stronger relationships between
neurobehavioral phenotype maps and gene expression maps, suggesting
a stronger possible link between associated therapeutic effects and
neurobehavioral phenotypes. To assess this score, a measure of
statistical significance or confidence intervals is also generated
and provided.
[0130] In other embodiments, these two paths are used as input and
contacted and correlated to define one or more gene-phenotype pair
topographies for a given gene (i.e., one or more gene-phenotype
pairs for a same gene with different phenotypes). A numerical score
130 is generated for each gene-phenotype pair topography for each
gene-phenotype pair based on the contacting of data and alignment
of the corresponding gene expression map with the respective
neurobehavioral phenotype map. Similar to above, weighted
neurobehavioral phenotype maps (i.e., neurophenotype topographies)
and gene expression maps (genotype topographies) may be contacted
and compared to define a corresponding gene-phenotype pair
topography for a gene-phenotype pair, and a score reflecting the
level of association is calculated for such maps.
[0131] In other embodiments, and not illustrated in FIG. 1, path
120 may be used as input and contacted and correlated to define one
or more gene-gene pair topographies for a given gene (i.e., one or
more gene-gene pairs for a same gene with different other genes). A
numerical score 130 is generated for each gene-gene pair topography
for each gene-gene pair based on the contacting of data and
alignment of the corresponding gene expression maps with each
other. Similarly, gene expression maps (genotype topographies)
(which may be weighted or masked) may be contacted and compared to
define a corresponding gene-gene pair topography for a gene-gene
pair, and a score reflecting the level of association is calculated
for such maps.
[0132] In various embodiments, the outputs of the platform comprise
neuroimaging data files of all computed maps or other information
and data in tangible, audible, or other formats. This includes maps
characterizing in which regions the neurobehavioral phenotype map
and gene expression map are contacted and aligned (i.e., a pair
topography) and contacted and misaligned. Misaligned
neurobehavioral phenotype map and gene expression maps can provides
insight into potential "off-target" circuit effects. For
visualization, maps data files may be compatible with Human
Connectome Project (HCP) Connectome Workbench software. The outputs
may also include identification of genes (e.g., when scoring
phenotype-gene pairs or gene-gene pairs) or neurobehavioral
phenotypes (e.g., when scoring gene-phenotype pairs). In some
embodiments, the output may be a highest scoring pair or those
pairs with a score above a threshold value.
[0133] In one embodiment, all brain maps (neuroimaging and gene
expression), and the present inventive platform may use the new
CIFTI file format for neuroimaging data utilized by the HCP.
Glasser. In contrast to the purely volumetric NIfTI format, CIFTI
represents cortex as a geometrically faithful two-dimensional mesh,
and subcortical samples as volumes, collectively comprising about
95,000 grayordinates. The present inventors integrated legacy NIfTI
data with CIFTI-based analyses to allow integration of the present
inventive platform with existing data, such as large neuroimaging
databases, as well as emerging CIFTI-compliant datasets.
[0134] CIFTI-based analyses have several advantages, including
superior management and alignment of cortical folding using
surface-based analysis, which minimizes signal bleed across sulci.
Anticevic et al., Comparing surface-based and volume-based analyses
of functional neuroimaging data in patients with schizophrenia,
NEUROIMAGE 41:835-48 (2008) ("Anticevic"); Glasser. CIFTI-style
formats are highly flexible and able to represent `matrix-level`
information under parcellation. As described herein, the CIFTI
format is advantageous for working with gene expression data, as it
allows surface-based interpolation from discrete samples onto a
dense cortical mantle. Furthermore, CIFTI is compatible with
visualization and analysis in the HCP Connectome Workbench
software, which the present inventive platform may use for map
visualization.
[0135] The present inventive platform requires improvements in
statistical analysis. As described above, proper analysis of AHBA
gene expression data will require substantial pre-processing to
support interpretable results. For instance, characterizing
differential stability will allow us to distinguish whether a low
gene-map correlation value is due to dissimilar maps or just due to
poor differential stability. For a given gene, selecting subsets of
subjects with high differential stability may improve the
signal-to-noise relative to combining all subjects.
[0136] Another important issue involves assessing the probability
observed correlations could occur by chance, i.e., their
statistical significance. A simple correlation (e.g., Pearson or
Spearman) provides an associated parametric p-value. However, this
p-value is derived under the assumption of statistical independence
across data points (here, brain regions); this independence
assumption may be violated in different brain maps because the
measures are spatially autocorrelated across brain regions. The
present inventors may use statistical tests for spatial
autocorrelation (e.g., Moran's I, Mantel's test) to evaluate the
impact of autocorrelation on inferences of statistical significance
for correlations scores. To correct for autocorrelation-induced
biases in model inference, the present inventors can calculate
statistical significance with a Spatial Autoregression (SAR) model.
These statistical and data analytic advances may further improve
the inferential power of the platform.
[0137] Turning to FIG. 2, the processing steps involve the
generation of three types of maps: neurobehavioral phenotype maps
(i.e., neurobehavioral phenotype topographies), weight maps
(optionally), and gene expression maps (i.e., genotype
topographies). These maps are contacted and used to define one or
more pair topographies for phenotype-gene pairs, gene-phenotype
pairs, or gene-gene pairs. The maps are also used to calculate
scores quantifying a weighted measure of alignment between
neurobehavioral phenotype maps and gene expression maps for
corresponding pairs. Processing begins with the generation of
behavioral neurophenotype maps (box 210) and gene expression maps
(box 230). The generation of the behavioral neurophenotype maps is
discussed in more detail above in the "Neurobehavioral phenotypes
and mapping" sub-section. The generation of the gene expression
maps is discussed in more detail above in the "Gene expression and
mapping" sub-section and in more detail below in conjunction with
FIGS. 3 and 4.
[0138] In some embodiments, weight or masking maps may be
optionally generated (box 220), which is described in more detail
above with respect to optional weighting and masking 116 in FIG.
1.
[0139] Generation of gene expression maps (230) from the AHBA
dataset involves multiple steps (FIG. 3). For each subject, brain
maps are generated for gene expression probes (310), which involves
multiple stages of data processing (shown in more detail below in
conjunction with FIG. 4).
[0140] As denoted in Box 320 (FIG. 3), for each gene of interest,
one or more representative probes is selected for each subject.
Probe-gene associations can be obtained through, for example,
publically available databases or optimized through experimental
trials. For many genes there are multiple associated probes, which
can be selected based on their expression levels and overall
coverage across samples. The present tools and methods test whether
a probe or gene is suitable for analyses by characterizing its
differential stability across multiple subjects (i.e., the average
between-subject correlation of expression values). Hawrylycz 2015.
Also, the present tools and methods may use differential stability
to select subsets of subjects with stable across-subject expression
maps for further analysis.
[0141] Careful characterization of these steps is expected to
greatly improve the ability to get meaningful results from the AHBA
dataset. For instance, in some embodiments, probes whose coverage
across cortical parcels (defining coverage scores) are below a
threshold percentage and therefore not well suited to interpolation
to form dense or parcellated gene expression maps may be filtered
out. If two probes are available for a gene, each with acceptable
coverage and differential stability scores, the selected probe can
be set as the one with maximum gene expression variance. If three
or more acceptable probes are available, the selected probe is the
one with the highest similarity to the other probes, as it is most
highly representative among available gene probes.
[0142] For gene expression datasets derived from DNA microarray
measurements, the microarray probe for a gene of interest may be
selected on the basis of multiple factors, including their coverage
across brain regions, and their consistency of expression patterns
across subjects (e.g., post-mortem human or animal brains). Example
DNA microarray probes in the AHBA (made by Agilent) are:
A_23_P40262 (for PDYN), A_23_P132619 (for OXTR), A_23_P169061 (for
OPRK1), A_24_P382579 (for OXT), A_23_P9883 (for AVP), and
A_23_P345564 (for OPRL1]. Multiple selected probes can be combined
in a weighted sum to improve signal-to-noise, e.g., by using the
first principal component from PCA, by using the mean or median
value across probes, or by using the most representative probe
through some central tendency measure. The probe information can be
obtained through, for example, publically available databases or
optimized through future experiments.
[0143] As denoted in Box 330 (FIG. 3), a group-level gene
expression map for a gene of interest can be computed by contacting
and combining the individual-level gene expression maps across
subjects. This step can be performed by averaging, and improved
through additional processing steps. For instance, each
subject-level gene expression profile can be z-scored before
computing group-level expression profiles, which are obtained by
computing the mean across subjects which are assigned a probe for
that gene. Subjects may be excluded from inclusion if too few of
their samples contained values for probes associated with that
gene, as determined by a threshold number. Finally, group-level
expression profiles may be z-scored across all areas for each gene.
Other optional steps in computing group-level maps may include
preferential weighting across subjects, for each parcel, based on
whether the parcel contained a sample for each subject.
[0144] Turning to FIG. 4, gene probes are filtered, so that they
correspond to known genes, as denoted in Box 410. For instance,
probes without a valid Entrez Gene ID can be excluded.
[0145] In general, embodiments include three levels for analysis of
cortical or subcortical gene expression data: (1) sparse samples or
specific locations; (2) interpolated dense map or across an entire
continuous map at its native (i.e., dense) resolution; and (3) a
map parcellated into discrete regions or areas. Map coordinates for
locations of expression can be transformed into different
neuroimaging brain atlases (e.g., the Montreal Neurological
Institute (MNI) atlas). The level of sparseness used can be
selected based on resolution of neurobehavioral phenotype mapping
data or area of interest for which one seeks to quantify the gene
expression profile.
[0146] As denoted in Box 420 (FIG. 4), gene expression samples are
mapped to locations in brain structures from their volumetric
imaging space. The AHBA dataset provides MNI coordinates for each
sample.
[0147] In some embodiments, for samples in cortex, there are two
ways to map the coordinates to the nearest grayordinate in CIFTI
cortical surface. A courser way may involve mapping of the AHBA
provided MNI coordinates for each location of gene expression onto
a common atlas. A second, more precise method, may involve
computing a complete segmentation of all gray and white matter for
each individual subject for whom gene expression data exists.
[0148] For example, a sample from cortex can be mapped to a
CIFTI-format surface grayordinate by selecting the grayordinate
with minimum Euclidian distance between the stereotaxic MNI
coordinates for that sample and the coordinates of grayordinate
vertices in each subject's native cortical surface mesh.
[0149] Single-subject surface registration for each of the six
subjects in the AHBA can be performed following a procedure adapted
from the HCP's minimal preprocessing pipelines. Briefly, the T1w
image can be first rigidly aligned to the MNI coordinate axes to
produce a native space volume, which can be then nonlinearly
registered to the standard MNI template using FSL's FLIRT and
FNIRT. Cortical surface boundaries for each subject can be computed
via automated tools such as FreeSurfer (See e.g., Dale A M et al.,
Cortical surface-based analysis I. Segmentation and surface
reconstruction, NEUROIMAGE, 9 (2), 179-194, 4931 (1999); Fischl B.
et al, Whole brain segmentation: Automated labeling of
neuroanatomical structures in the human brain, NEURON, 33 (3),
341-355, 3776 (2002); and Fischl B. et al, Cortical surface-based
analysis. II: Inflation, flattening, and a surface-based coordinate
system, NEUROIMAGE, 9 (2), 195-207 (1999)). Here, the native space
image can be run through FreeSurfer's recon-all pipeline, which
performs automated segmentation of brain structures to reconstruct
the white matter and pial surfaces. The FreeSurfer output surface
is then converted to standard GIFTI format to produce each
subject's native surface mesh. Finally, subjects' native surface
meshes may be registered to the standard HCP surface mesh.
[0150] A sample from subcortical structure is mapped to a
volumetric voxel, in contrast to a surface grayordinate.
Subcortical samples in the AHBA are annotated by the structure from
which they are taken (e.g., thalamus, or striatum). A sample can be
mapped to a voxel in a similar procedure as for cortex, in which it
is mapped to the voxel with minimum Euclidean distance for voxels
labeled with that Freesurfer structure (e.g. thalamus, striatum)
segmented in each subject's native space. This method involves
computing a complete segmentation of all gray and white matter for
each individual subject for whom gene expression data exists. Then
these segmentations can be used to compute subcortical volume
boundaries for each subject via automated tools such as FreeSurfer
(Dale A M et al., Cortical surface-based analysis I. Segmentation
and surface reconstruction, NEUROIMAGE, 9 (2), 179-194, 4931
(1999); Fischl B. et al, Whole brain segmentation: Automated
labeling of neuroanatomical structures in the human brain, NEURON,
33 (3), 341-355, 3776 (2002) Fischl B. et al, Cortical
surface-based analysis. II: Inflation, flattening, and a
surface-based coordinate system, NEUROIMAGE, 9 (2), 195-207
(1999)). In turn, the subject-specific `native space` locations of
gene expression can be mapped onto that subject's subcortical
volumes and transformed into a common atlas based on
state-of-the-art registration methods (Anticevic A. et al.,
Comparing surface-based and volume-based analyses of functional
neuroimaging data in patients with schizophrenia, NEUROIMAGE,
41(3):835-48, July 1 (2008); Glasser M F et al., A multi-modal
parcellation of human cerebral cortex, NATURE, 536, 171-178 (11
Aug. 2016)).
[0151] As denoted in Box 430 (FIG. 4), samples are filtered for
quality according to various criteria. For instance, samples whose
measured expression level is not well above background, as provided
in the AHBA dataset, can be excluded. Samples surviving this step
(i) belonged to a probe whose mean signal is significantly
different from the corresponding background, and (ii) had a
background-subtracted signal which is at minimum 2.6 times greater
than the standard deviation of the background. Furthermore, samples
whose Euclidean distance to the nearest surface grayordinate is
more than 2 standard deviations above the mean distance computed
across all samples can be excluded.
[0152] As denoted in Box 440 (FIG. 4), imputation can be performed
on samples which are missing values. For a given gene probe, not
all AHBA samples contain values for that probe. These missing
values can be estimated via multiple algorithmic approaches. For
instance, missing values can be imputed via a Singular Value
Decomposition (SVD) approach. This utilizes the property that
although a sample is missing a value for some probes, it contains
values for many other probes which are shared across samples.
SVD-based imputation uses the similarity of samples, with respect
to the shared probes, to estimate the expression value for a sample
missing a probe. Other imputation approaches can include methods
based on Principal Component Analysis (PCA), and spatial
proximity.
[0153] As denoted in Box 450 (FIG. 4), various steps of data
quality clean-up can be performed, such as to remove extraneous
biases and improve signal-to-noise before combination with the
neurobehavioral phenotype mapping data. These steps can improve the
reproducibility of the maps, which can be quantified by a stability
metric across subjects (differential stability). Example steps
include: (1) expression values can be normalized within each
subject (i.e., brain) before combining across subjects (e.g., via
mean or median); (2) gene expression measurement sites can be
filtered out on the basis of their exhibiting exceptionally low
similarity with other measurement sites in their expression levels
across genes; and (3) signal-to-noise of the spatial expression
pattern can be improved through data processing techniques such as
dimensionality reduction via principal component analysis
(PCA).
[0154] For instance, expression levels for samples mapped onto the
same surface vertex can be averaged. Using the raw AHBA dataset,
however, can present additional challenges that can be addressed
with further preprocessing. For instance, In some situations biases
may be in the mean expression levels across AHBA samples, which
should be corrected. Therefore, expression levels within each
remaining sample can be de-meaned and normalized by z-scoring
across all gene probes, to correct for variation across samples in
the overall mean of data values, which may be driven by
experimental artifacts.
[0155] As denoted in Box 460 (FIG. 4), generation brain-wide maps
entails interpolation from the sparse samples to other brain
regions which are not directly sampled, based on spatial proximity
within a brain structure (e.g., cortex, or thalamus). These maps
can be calculated at `dense` or `parcellated` levels.
[0156] Multiple methods can be used for interpolation. For
instance, the method of `Burt` to generate parcellated cortical
maps is the following. Using cortical samples mapped onto subjects'
native surface meshes, expression profiles for each of the 180
unilateral parcels in the HCP's MMP1.0 cortical parcellation can be
computed in one of the two following ways. (i) For parcels that had
at least one sample mapped directly onto one of their constituent
surface vertices, parcellated expression values can be computed by
averaging expression levels across all samples mapped directly onto
the parcel. (ii) For parcels that had no samples mapped onto any of
their constituent vertices, first a densely interpolated expression
maps is created, in which each vertex in the native surface mesh is
assigned the expression level associated with the most proximal
surface vertex onto which a sample had been directly mapped,
determined using surface-based geodesic distance along each
subject's cortical surface mesh (i.e., a Voronoi diagram approach);
the average of expression levels across parcels' constituent
vertices is then computed to obtain parcellated expression values,
effectively equivalent to performing a weighted average.
[0157] A dense cortical map could be generated directly from a
Voronoi tessalation of the cortical surface. Other methods may be
used, including weighted averaging based on exponential decay with
increasing geodesic distance from the sample along the cortical
surface (e.g. with characteristic length constant determined by the
gene expression spatial autocorrelation structure).
[0158] Gene expression maps for subcortical structures can be
computed at the parcellated or dense level. This follows a similar
procedure as for cortex, described above, except that parcellations
are defined as sets of 3-dimensional voxels, and distance is taken
as Euclidean distance rather than geodesic distance along a
surface.
[0159] The dense and parcellated maps include interpolation of gene
expression values to all grayordinates in cortex. To perform this
interpolation, multiple methods may be used. For example, a Voronoi
diagram is constructed, assigning each grayordinate to its nearest
AHBA sample location; that sample's gene expression values are then
given to those grayordinate. Other methods may be used, including
weighted averaging based on exponential decay with increasing
geodesic distance from the sample along the cortical surface (e.g.,
with characteristic length constant determined by the gene
expression spatial autocorrelation structure). This provides the
dense map. To produce a parcellated map, the dense map may be
parcellated with a CIFTI-defined parcellation using, for instance,
the Connectome Workbench software or any other matrix manipulation
software that can read the CIFTI format (e.g. Matlab, R statistical
computing environment, Octave, Python, etc.). For example, the new
cortical parcellation from the HCP team may be used. Glasser et
al., A multi-modal parcellation of human cerebral cortex, NATURE
536:171-8 (2016) ("Glasser"). For parcels that contain gene
expression, one method is to assign the parcel value as an average
of the samples within that parcel, which can be a weighted average
(e.g., based on the samples' relative Voronoi diagram coverages
within the parcel).
[0160] As noted, additional coordinates can be assigned to
measurement sites by explicitly computing the cortical and
subcortical segmentation of each individual subject contributing to
the AHBA based on their high-resolution structural post-mortem
scans. In turn, such segmentations can be leveraged to compute a
cortical mesh and subcortical anatomical nucleus assignment. In
turn, the mesh forms a surface along with proximity that can then
be calculated for each individual subject, yielding a set of
subject-specific coordinates. In turn, such a cortical surface mesh
can be aligned across subjects to the group atlas using
surface-based features. This also applies to subcortical locations
of expression, which can be defined at the subject level based on
their anatomy and in turn aligned to a given group atlas. Following
this spatial transformation, each subjects' individual coordinates
are brought into alignment. Next, analyses can proceed at the
specific location level, using specific coordinates for gene
expression at that location. Neuroimaging maps can also capture the
relevant cortical and subcortical locations.
[0161] Conversely, the continuous "dense" and discrete
"parcellated" maps include assigning a gene expression value to a
given cortical location either the native resolution of a given
dense map or into a given discrete parcel/area. Example methods for
this assignment include: (1) assignment of value to a given map
location based its proximity to the locations at which gene
expression is measured, i.e., the gene expression measurement
sites. This can be done via, for example, nearest neighbor
assignment; e.g., through construction of a Voronoi diagram; (2)
assignment by a weighted sum to a map location based on the
proximity to the locations at which gene expression is measured;
e.g., weighted by distance along cortical surface from the gene
expression measurement sites; and (3) assignment by a weighted sum
across gene expression measurement sites which are within parcel
boundaries according to a given parcellation.
[0162] Subcortical gene expression data can be used to assign
values to subcortical locations or regions. Methods for this
assignment include labeling by neuroanatomical evaluation, weighted
sum within a parcellation of subcortical regions, or other forms of
anatomical or functional location assignment.
[0163] The present platform can function bidirectionally. In the
gene (or drug target)-to-phenotype direction (FIG. 5A) or the
phenotype-to-gene (or drug target) direction (FIG. 5B), or the
gene-to-gene direction (FIG. 5C). With respect to FIG. 5A the
platform can identify one or more neurobehavioral phenotypes whose
characteristic brain maps (neurobehavioral phenotype mapping data)
are aligned with the gene expression map for a given drug target of
interest. This direction will be increasingly powerful with a
larger database of neuroimaging maps linked with phenotypes. The
goal is to go from a gene or drug target and identify a gene
expression map, which in turn is used to identify one or more
neurobehavioral phenotypes that statistically aligns with that gene
expression map. This can in turn yield neurobehavioral phenotypes
that are identified from gene or drug targets.
[0164] Specifically, a gene is identified (box 502), which may
include selecting the gene based on an association with a selected
drug or drug target. Gene expression mapping data for the
identified gene and neurobehavioral phenotype mapping for one or
more phenotypes are obtained (box 504). Scores are generated for
each respective gene-phenotype pair by contacting and correlating
the gene expression mapping data for the identified gene with the
neurobehavioral phenotype mapping data for the respective phenotype
of the respective pair (box 506). The gene-phenotype pairs are
ranked based on their corresponding scores (box 508). And a highest
score pair is identified for the selected gene (or drug or drug
target) (box 510).
[0165] Conversely, in the phenotype-to-gene (or drug target)
direction (FIG. 5B), the platform can identify genes or drug
targets whose associated gene expression maps are contacted and
aligned with the brain map (neurobehavioral phenotype mapping data)
associated with a given neurobehavioral phenotype of interest. The
goal is to go from a specific neurobehavioral phenotype and
identify one or more gene expression maps that statistically aligns
with that neurobehavioral phenotype, which in turn is used to
identify which drug target aligns with those identified gene
expression maps. This can in turn yield drug targets that are
identified from neurobehavioral phenotypes.
[0166] Specifically, a neurobehavioral phenotype is selected (box
512). Neurobehavioral phenotype mapping for the selected phenotype
and gene expression mapping data for one or more genes are obtained
(box 514). Scores are generated for each respective phenotype-gene
pair by contacting and correlating the neurobehavioral mapping data
for the selected neurobehavioral phenotype with the gene expression
mapping data for the respective gene of the respective pair (box
516). The phenotype-gene pairs are ranked based on their
corresponding scores (box 518). And genes (or drug target)
associated with a highest score pair is identified for the selected
neurobehavioral phenotype (box 520).
[0167] In some embodiments, in the gene-to-gene direction (FIG.
5C), the platform can identify genes or drug targets whose
associated gene expression maps are contacted and aligned with the
gene expression maps of other genes or drug targets. The goal is to
go from a specific gene and identify one or more gene expression
maps for other genes that statistically aligns with that specific
gene, which in turn is used to identify which drug target aligns
with those identified gene expression maps. This can in turn yield
drug targets that are identified from other genes.
[0168] Specifically, a gene is selected (box 522). Gene expression
mapping for the selected gene and gene expression mapping data for
one or more other genes are obtained (box 524). Scores are
generated for each respective gene-gene pair by contacting and
correlating the gene expression mapping data for the selected gene
with the gene expression mapping data for the respective other gene
of the respective pair (box 526). The gene-gene pairs are ranked
based on their corresponding scores (box 528). And genes (or drug
target) associated with a highest score pair is identified for the
selected gene (box 530).
[0169] In some embodiments, previously generated phenotype-gene
pair topographies may be utilized to identify a gene or drug target
from a plurality of genes or drug targets for a specific
individual. For example, an individual subject's neuroimaging may
be obtained and the neurophenotype topography generated. This
neurophenotype topography is then compared to a plurality of
previously generated phenotype-gene pair topographies (when
generated as described herein). A target phenotype-gene pair
topography that most closely aligns with the individual's
neurobehavioral phenotype topography is then selected. The
corresponding genotype topography that was used to generate the
target phenotype-gene pair topography is identified and its
corresponding gene selected. From this gene selection, a drug
target associated with the selected gene is then selected as a
specific drug target for that individual.
[0170] In other embodiments, previously generated gene-phenotype
pair topographies may be utilized to identify individuals for a
specific drug or drug target. For example, genotype topography for
a gene associated with a selected drug target may be generated.
This genotype topography is then compared to a plurality of
previously generated gene-phenotype pair topographies (when
generated as described herein). A target gene-phenotype pair
topography that most closely aligns with the genotype topography is
then selected. Neurobehavioral phenotype mapping data of
individuals is then compared to the target gene-phenotype pair
topography, and those individuals whose neurobehavioral phenotype
mapping data aligns with the target gene-phenotype pair topography
(within a threshold level) are selected as being candidates that
can benefit from the selected drug target.
[0171] In this way individual subject's neuroimaging and/or gene
expression data can be contacted/aligned with a previously
generated topography pair for detecting, diagnosing, predicting,
prognosticating, or treating a neurobehavioral phenotype in a
subject.
[0172] Implementation of embodiments described herein may be
performed by one or more computing devices or systems. One or more
special-purpose computing systems may be used to implement such
embodiments described herein. Accordingly, various embodiments
described herein may be implemented in software, hardware,
firmware, or in some combination thereof. Such a computing system
includes memory or other computer-readable media, one or more
processors, a display device, a network interface, other
input/output (I/O) interfaces, and other components.
[0173] The one or more processors include processing device(s) that
execute computer instructions to perform actions, including at
least some embodiments described herein. In various embodiments,
the processor may include one or more central processing units
(CPUs), programmable logic, or other processing circuitry.
[0174] The memory may include one or more various types of
non-volatile and/or volatile storage technologies. Examples of such
memory include, but are not limited to, flash memory, hard disk
drives, optical drives, solid-state drives, various types of random
access memory (RAM), various types of read-only memory (ROM), other
computer-readable storage media (also referred to as
processor-readable storage media), or other memory technologies, or
any combination thereof. The memory may be utilized to store
information, including computer-readable instructions that are
utilized by the one or more processors to perform actions,
including at least some embodiments described herein. The memory
may also store other programs and other content, such as operating
systems, user applications, other computer programs, the
neurobehavioral phenotype mapping data, the gene expression mapping
data, the generated neurophenotype topographies and
scores/rankings, or other data. The computing system may include
other computer-readable media that may include other types of
stationary or removable computer-readable media, such as removable
flash drives, external hard drives, or the like.
[0175] The display device is any display device capable of
rendering content to a user, such as the neurophenotype
topographies, scores, drug target or neurobehavioral phenotype
selections, etc. Examples of such a display device may include a
liquid crystal display, light emitting diode, or other type of
display device, and may include a touch sensitive screen capable of
receiving inputs from a user's hand, stylus, or other object.
[0176] The network interfaces are configured to communicate with
other computing devices, via a wired or wireless communication
network. Such network interfaces include transmitters and receivers
to send and receive data, such as, but not limited to, gene
expression mapping data or neurobehavioral phenotype mapping data.
The other I/O interfaces may include interfaces for various other
input or output devices, such as audio interfaces, other video
interfaces, USB interfaces, physical buttons, keyboards, or the
like.
[0177] In some embodiments, the present platform includes a
computing device, comprising: a memory that stores computer
instructions; a processor that, when executing the computer
instructions, performs actions to: generate a neurophenotype
topography for a selected neurobehavioral phenotype based on
neurobehavioral phenotype mapping data for the selected
neurobehavioral phenotype; generate a genotype topography for each
respective gene of a plurality of genes based on gene expression
mapping data for the respective gene; define a plurality of
phenotype-gene pair topographies between the selected
neurobehavioral phenotype and the plurality of genes, each
phenotype-gene pair topography for each respective phenotype-gene
pair being defined based on the neurophenotype topography of the
selected neurobehavioral phenotype and the genotype topography of
the respective gene for the respective phenotype-gene pair;
determine a quantitative score for each of the plurality of
phenotype-gene pair topographies based on a correlation between the
neurophenotype topography of the selected neurobehavioral phenotype
and the genotype topography of the respective gene for the
respective phenotype-gene pair; select one or more of the plurality
of phenotype-gene pair topographies having a respective score above
a selected threshold; and display the respective genes of the
selected one or more phenotype-gene pair topographies to a user. In
an embodiment, the processor, when executing the computer
instructions, further performs actions to identify one or more
respective neural drug targets associated with the respective genes
of the selected one or more phenotype-gene pair topographies. In an
embodiment, the processor generates the neurophenotype topography
by executing further computer instructions to generate the
neurophenotype topography from the neurobehavioral phenotype
mapping data for each of a plurality of people having the selected
neurobehavioral phenotype. In an embodiment, the processor
determines the score for each of the plurality of phenotype-gene
pair topographies by executing further computer instructions to
determine a statistical significance for each phenotype-gene pair
topography based on an alignment between the gene expression
mapping data for the respective gene with the neurobehavioral
phenotype mapping data. In an embodiment, the processor selects the
one or more phenotype-gene pair topographies by executing further
computer instructions to select a target phenotype-gene pair
topography having a highest determined measure of association
between the neurophenotype topography of the selected
neurobehavioral phenotype and the genotype topography of the
respective gene for the target phenotype-gene pair topography. In
an embodiment, the gene expression mapping data for each of the
plurality of genes includes gene expression mapping data for a
plurality of gene expressions from a plurality of people without
the selected neurobehavioral phenotype. In an embodiment, the
processor generates the genotype topography for each respective
gene by executing further computer instructions to select a
representative probe for each of the plurality of genes across the
plurality of gene expressions for the plurality of people. In an
embodiment, the processor generates the genotype topography for
each respective gene by executing further computer instructions to
map gene expression mapping samples to locations in brain
structures. In an embodiment, the processor generates the genotype
topography for each respective gene by executing further computer
instructions to filter gene expression mapping samples by excluding
samples with measured expression levels below a threshold level
above background signals. In an embodiment, the processor generates
the genotype topography for each respective gene by executing
further computer instructions to impute probe values in gene
expression mapping samples that are missing probe values. In an
embodiment, the processor generates the genotype topography for
each respective gene by executing further computer instructions to
remove extraneous biases from the gene expression mapping data. In
an embodiment, the processor removes the extraneous biases by
executing further computer instructions to de-mean and normalize
z-scores across gene probes used to capture the gene expression
mapping data. In an embodiment, the processor generates the
genotype topography for each respective gene by executing further
computer instructions to increase a signal-to-noise ratio in the
gene expression mapping data. In an embodiment, the processor
increases the signal-to-noise ratio by executing further computer
instructions to average expression levels of the gene expression
mapping data for samples mapped onto a same surface vertex. In an
embodiment, the processor generates the genotype topography for
each respective gene by executing further computer instructions to
interpolate sparse gene expression samples from sampled brain
regions to other non-sampled brain regions. In an embodiment, the
processor interpolates the sparse gene expression samples by
executing further computer instructions to generate at least one of
parcellated cortical or subcortical maps or a dense cortical or
subcortical map. In an embodiment, the processor generates the
genotype topography for each respective gene by executing further
computer instructions to assign a weight value for each of a
plurality of brain regions in the gene expression mapping data. In
an embodiment, the processor generates the neurobehavioral
topography by executing further computer instructions to assign a
weight value for each of a plurality of brain regions in the
neurobehavioral phenotype mapping data. In an embodiment, the
processor assigns the weight value for each of the plurality of
brain regions by executing further computer instructions to: assign
a first set of weight values above a threshold value for a first
set of brain regions of the plurality of brain regions in the
neurobehavioral phenotype mapping data; and assign a second set of
weight values below the threshold value for a second set of brain
regions of the plurality of brain regions in the neurobehavioral
phenotype mapping data. In an embodiment, the processor assigns the
weight value for each of the plurality of brain regions by
executing further computer instructions to assign a masking weight
value to a target brain region of the plurality of brain regions to
remove information associated with the target brain region from the
neurobehavioral phenotype mapping data. In an embodiment, the
processor defines the plurality of phenotype-gene pair topographies
by executing further computer instructions to define at least one
combination phenotype-gene pair topography between the
neurobehavioral phenotype topography and a combination of genotype
topographies for a combination of genes. In an embodiment, the
processor, when executing the computer instructions, further
performs actions to: select the at least one combination
phenotype-gene pair topography as the one or more of the plurality
of phenotype-gene pair topographies having the respective score
above the selected threshold; and display the combination of genes
to the user. In an embodiment, the processor, when executing the
computer instructions, further performs actions to: identify
combinations of genes or neural drug targets by combining gene
expression mapping data that exhibits improved alignment with the
neurobehavioral phenotype mapping data relative to the alignment of
gene expression mapping data and neurobehavioral phenotype mapping
data for each separate gene or neural drug target. In an
embodiment, the neurobehavioral phenotype mapping data is for one
of a brain disorder, a symptom, or a cognitive process.
[0178] In some embodiments, the present platform includes a method,
comprising: obtaining, by a computing device, neuro phenotype
mapping data for a selected neurophenotype; obtaining, by the
computing device, gene expression mapping data for one or more
genes; determining, by the computing device, a quantitative score
for each respective phenotype-gene pair between the selected
neurobehavioral phenotype and a respective gene of the one or more
genes based on a correlation between the neurobehavioral phenotype
mapping data for the selected neurobehavioral phenotype and the
gene expression mapping data for the respective gene of the
respective phonotype-gene pair; and presenting, by the computing
device, the determined score for each phenotype-gene pair to a
user.
[0179] In some embodiments, the present platform includes a
computing device, comprising: a memory that stores computer
instructions; a processor that, when executing the computer
instructions, performs actions to: generate, by the computing
device, a genotype topography for a selected gene based on gene
expression mapping data for the selected gene; generate, by a
computing device, a neurophenotype topography for each respective
neurobehavioral phenotype of a plurality of neurobehavioral
phenotypes based on neurobehavioral phenotype mapping data for the
respective neurobehavioral phenotype; define, by the computing
device, a plurality of gene-phenotype pair topographies between the
selected gene and the plurality of neurobehavioral phenotypes, each
gene-phenotype pair topography for each respective gene-phenotype
pair being defined based on the genotype topography of the selected
gene and the neurophenotype topography of the respective
neurobehavioral phenotype for the respective gene-phenotype pair;
determine, by the computing device, a quantitative score for each
of the plurality of gene-phenotype pair topographies based on a
correlation between the genotype topography of the selected gene
and the neurophenotype topography of the respective neurobehavioral
phenotype for the respective gene-phenotype pair; select one or
more of the plurality of gene-phenotype pair topographies having a
respective score above a selected threshold; and display the
respective neurobehavioral phenotypes of the selected one or more
gene-phenotype pair topographies to a user. In an embodiment, the
processor, when executing the computer instructions, further
performs actions to select the selected gene based on a user
selected neural drug target associated with the selected gene. In
an embodiment, the processor generates the neurophenotype
topography by executing further computer instructions to generate
the neurophenotype topography from the neurobehavioral phenotype
mapping data for each of a plurality of people having the selected
neurobehavioral phenotype. In an embodiment, the processor
determines the score for each of the plurality of gene-phenotype
pair topographies by executing further computer instructions to
determine a statistical significance for each gene-phenotype pair
topography based on an alignment between the neurobehavioral
phenotype mapping data for the respective neurobehavioral phenotype
with the gene expression mapping data. In an embodiment, the
processor selects the one or more gene-phenotype pair topographies
by executing further computer instructions to select a target
gene-phenotype pair topography having a highest determined measure
of association between the genotype topography of the selected gene
and the neurophenotype topography of the respective neurobehavioral
phenotype for the target gene-phenotype pair topography. In an
embodiment, the gene expression mapping data for the selected gene
includes gene expression mapping data for a plurality of gene
expressions from a plurality of people without one of the plurality
of neurobehavioral phenotypes. In an embodiment, the processor
generates the genotype topography for the selected gene by
executing further computer instructions to select a representative
probe for the selected gene across the plurality of gene
expressions for the plurality of people. In an embodiment, the
processor generates the genotype topography for the selected gene
by executing further computer instructions to map gene expression
mapping samples to locations in brain structures. In an embodiment,
the processor generates the genotype topography for the selected
gene by executing further computer instructions to filter gene
expression mapping samples by excluding samples with measured
expression levels below a threshold level above background signals.
In an embodiment, the processor generates the genotype topography
for the selected gene by executing further computer instructions to
impute probe values in gene expression mapping samples that are
missing probe values. In an embodiment, the processor generates the
genotype topography for the selected gene by executing further
computer instructions to remove extraneous biases from the gene
expression mapping data. In an embodiment, the processor removes
the extraneous biases by executing further computer instructions to
de-mean and normalize z-scores across gene probes used to capture
the gene expression mapping data. In an embodiment, the processor
generates the genotype topography for the selected gene by
executing further computer instructions to increase a
signal-to-noise ratio in the gene expression mapping data. In an
embodiment, the processor increases the signal-to-noise ratio by
executing further computer instructions to average expression
levels of the gene expression mapping data for samples mapped onto
a same surface vertex. In an embodiment, the processor generates
the genotype topography for the selected gene by executing further
computer instructions to interpolate sparse gene expression samples
from sampled brain regions to other non-sampled brain regions. In
an embodiment, the processor interpolates the sparse gene
expression samples by executing further computer instructions to
generate at least one of parcellated cortical or subcortical maps
or a dense cortical or subcortical map. In an embodiment, the
processor generates the genotype topography for the selected gene
by executing further computer instructions to assign a weight value
for each of a plurality of brain regions in the gene expression
mapping data. In an embodiment, the processor generates the
neurobehavioral topography for each respective neurobehavioral
phenotype by executing further computer instructions to assign a
weight value for each of a plurality of brain regions in the
neurobehavioral phenotype mapping data. In an embodiment, the
processor assigns the weight value for each of the plurality of
brain regions by executing further computer instructions to: assign
a first set of weight values above a threshold value for a first
set of brain regions of the plurality of brain regions in the
neurobehavioral phenotype mapping data; and assign a second set of
weight values below the threshold value for a second set of brain
regions of the plurality of brain regions in the neurobehavioral
phenotype mapping data. In an embodiment, the processor assigns the
weight value for each of the plurality of brain regions by
executing further computer instructions to assign a masking weight
value to a target brain region of the plurality of brain regions to
remove information associated with the target brain region from the
neurobehavioral phenotype mapping data. In an embodiment, the
processor defines the plurality of gene-phenotype pair topographies
by executing further computer instructions to define at least one
combination gene-phenotype pair topography between the genotype
topography and a combination of neurophenotype topographies for a
combination of neurobehavioral phenotypes. In an embodiment, the
processor, when executing the computer instructions, further
performs actions to: select the at least one combination
gene-phenotype pair topography as the one or more of the plurality
of gene-phenotype pair topographies having the respective score
above the selected threshold; and display the combination of
neurobehavioral phenotype to the user. In an embodiment, the
processor, when executing the computer instructions, further
performs actions to: identify combinations of neurobehavioral
phenotypes by combining neurophenotype mapping data that exhibits
improved alignment with the gene expression mapping data relative
to the alignment of neurophenotype mapping data and gene expression
mapping data for each separate neurobehavioral phenotype. In an
embodiment, the neurobehavioral phenotype mapping data is for one
of a brain disorder, a symptom, or a cognitive process.
[0180] In some embodiments, the present platform includes a method,
comprising: obtaining, by the computing device, gene expression
mapping data for one or more genes; obtaining, by a computing
device, neurophenotype mapping data for a selected neurophenotype;
determining, by the computing device, a quantitative score for each
respective gene-phenotype pair between the selected gene and a
respective neurophenotype of the one or more neurobehavioral
phenotypes based on a correlation between the gene expression
mapping data for the selected gene and the neurophenotype mapping
data for the respective neurobehavioral phenotype of the respective
gene-phonotype pair; and presenting, by the computing device, the
determined score for each gene-phenotype pair to a user.
[0181] In some embodiments, the present platform includes a
computing device, comprising: a memory that stores computer
instructions; a processor that, when executing the computer
instructions, performs actions to: generate a plurality of genotype
topographies for a plurality of genes based on respective gene
expression mapping data for each respective gene; select a first
genotype typography from the plurality of genotype topographies for
a first gene from the plurality of genes; select a plurality of
second genotype topographies from the plurality of genotype
topographies for a plurality of second genes from the plurality of
genes; define a plurality of gene-gene pair topographies between
the first gene and the plurality of second genes, each gene-gene
pair topography for each respective gene-gene pair being defined
based on the first genotype topography of the selected gene and a
respective second genotype topography of the respective second gene
for the respective gene-gene pair; determine a quantitative score
for each of the plurality of gene-gene pair topographies based on a
correlation between the first genotype topography of the first gene
and the second genotype topography of the respective second gene
for the respective gene-gene pair; select one or more of the
plurality of gene-gene pair topographies having a respective score
above a selected threshold; and display the respective second genes
of the selected one or more gene-gene pair topographies to a user.
In an embodiment, the processor, when executing the computer
instructions, further performs actions to select the first gene
based on a user selected neural drug target associated with the
first gene. In an embodiment, the processor, when executing the
computer instructions, further performs actions to identify one or
more respective neural drug targets associated with the respective
second genes of the selected one or more gene-gene pair
topographies. In an embodiment, the processor determines the score
for each of the plurality of gene-gene pair topographies by
executing further computer instructions to determine a statistical
significance for each gene-gene pair topography based on an
alignment between the respective gene expression mapping data for
the respective second gene with the respective gene expression
mapping data for the first gene. In an embodiment, the processor
selects the one or more gene-gene pair topographies by executing
further computer instructions to select a target gene-gene pair
topography having a highest determined measure of association
between the first genotype topography of the first gene and the
respective second genotype topography of the respective second gene
for the target gene-gene pair topography. In an embodiment, the
gene expression mapping data includes gene expression mapping data
for a plurality of gene expressions from a plurality of people. In
an embodiment, the processor generates the plurality of genotype
topographies by executing further computer instructions to select a
representative probe for a respective gene across the plurality of
gene expressions for the plurality of people. In an embodiment, the
processor generates the plurality of genotype topographies by
executing further computer instructions to map gene expression
mapping samples to locations in brain structures. In an embodiment,
the processor generates the plurality of genotype topographies by
executing further computer instructions to filter gene expression
mapping samples by excluding samples with measured expression
levels below a threshold level above background signals. In an
embodiment, the processor generates the plurality of genotype
topographies by executing further computer instructions to impute
probe values in gene expression mapping samples that are missing
probe values. In an embodiment, processor generates the plurality
of genotype topographies by executing further computer instructions
to remove extraneous biases from the gene expression mapping data.
In an embodiment, the processor removes the extraneous biases by
executing further computer instructions to de-mean and normalize
z-scores across gene probes used to capture the gene expression
mapping data. In an embodiment, the processor generates the
plurality of genotype topographies by executing further computer
instructions to increase a signal-to-noise ratio in the gene
expression mapping data. In an embodiment, the processor increases
the signal-to-noise ratio by executing further computer
instructions to average expression levels of the gene expression
mapping data for samples mapped onto a same surface vertex. In an
embodiment, the processor generates the plurality of genotype
topographies by executing further computer instructions to
interpolate sparse gene expression samples from sampled brain
regions to other non-sampled brain regions. In an embodiment, the
processor interpolates the sparse gene expression samples by
executing further computer instructions to generate at least one of
parcellated cortical or subcortical maps or a dense cortical or
subcortical map. In an embodiment, the processor generates the
plurality of genotype topographies by executing further computer
instructions to assign a weight value for each of a plurality of
brain regions in the gene expression mapping data. In an
embodiment, the processor assigns the weight value for each of the
plurality of brain regions by executing further computer
instructions to: assign a first set of weight values above a
threshold value for a first set of brain regions of the plurality
of brain regions in the gene expression mapping data; and assign a
second set of weight values below the threshold value for a second
set of brain regions of the plurality of brain regions in the gene
expression mapping data. In an embodiment, the processor assigns
the weight values for each of the plurality of brain regions by
executing further computer instructions to assign a masking weight
value to a target brain region of the plurality of brain regions to
remove information associated with the target brain region from the
gene expression mapping data. In an embodiment, the processor
defines the plurality of gene-gene pair topographies by executing
further computer instructions to define at least one combination
gene-gene pair topography between the first genotype topography and
a combination of second genotype topographies for a combination of
second genes. In an embodiment, the processor, when executing the
computer instructions, further performs actions to: select the at
least one combination gene-gene pair topography as the one or more
of the plurality of gene-gene pair topographies having the
respective score above the selected threshold; and display the
combination of second genes to the user.
[0182] In some embodiments, the present platform includes a method,
comprising: obtaining, by the computing device, gene expression
mapping data for a plurality of genes; determining, by the
computing device, a quantitative score for each respective
gene-gene pair between a selected gene and one or more other genes
based on a correlation between the gene expression mapping data for
the selected gene and the gene expression mapping data for the one
or more other genes of the respective gene-gene pair; and
presenting, by the computing device, the determined score for each
gene-gene pair to a user.
[0183] In some embodiments, the present platform includes a method
for identifying a neural drug target comprising: selecting a
neurobehavioral phenotype; processing gene expression mapping data
and neurobehavioral phenotype mapping data; defining a relevant
neurophenotype topography; and predicting the likelihood of
association between gene expression for the neural drug target and
the neurobehavioral phenotype, wherein at least one method step is
performed using one of a computer-implemented method or a
computer-readable medium. In an embodiment, this method further
comprises pre-processing the neurobehavioral phenotype mapping
data. In an embodiment, this method further comprises one of
weighting or masking the neurobehavioral phenotype mapping data. In
an embodiment, this method further comprises at least one of
removing extraneous biases from the gene expression mapping data or
improving gene expression mapping data signal-to-noise ratio. In an
embodiment, this method includes a step of defining the relevant
neurophenotype topography that includes pre-processing the gene
expression mapping data associated with at least one brain location
or region. In an embodiment, this method includes gene expression
mapping data that occurs at one of a sparse sample level, an
interpolated dense map level, or a discrete parcellated brain map
level. In an embodiment, this method further comprises assigning
one or more gene expression values to continuous dense locations in
cortex or to discrete locations in cortex. In an embodiment, this
method includes neurobehavioral phenotype mapping data that is for
one of a brain disorder, a symptom, or a cognitive process. In an
embodiment, this method further comprises predicting the likelihood
of a neural drug target therapy to affect off-target brain regions.
In an embodiment, this method further comprises identifying
combinations of neural drug targets by combining gene expression
mapping data, wherein said combined gene expression mapping data
exhibits improved alignment with the neurobehavioral phenotype
mapping data relative to the alignment of gene expression mapping
data and neurobehavioral phenotype mapping data for each separate
neural drug target.
[0184] In some embodiments, the present platform includes a method
for identifying neurobehavioral phenotypes comprising: aligning
pre-processed gene expression mapping data with neurobehavioral
phenotype mapping data; and defining a relevant neural
neurophenotype topography. In an embodiment, this method further
comprises pre-processing the gene expression mapping data. In an
embodiment, this method further comprises one of weighting or
masking the gene expression mapping data. In an embodiment, this
method further comprises pre-processing the gene expression mapping
data either to remove extraneous biases or to improve
signal-to-noise ratio. In an embodiment, this method includes a
step of defining the relevant neurophenotype topography that
includes pre-processing the gene expression mapping data associated
with at least one brain location or region. In an embodiment, this
method includes gene expression mapping data that occurs at one of
a sparse sample level, an interpolated dense map level, or a
discrete parcellated brain map level. In an embodiment, this method
further comprises assigning one or more gene expression values to
continuous dense locations in cortex or to discrete locations in
cortex. In an embodiment, this method includes neurobehavioral
phenotype mapping data that is for one of a brain disorder, a
symptom, or a cognitive process. In an embodiment, this method
further comprises predicting the likelihood of a neural drug target
therapy to affect off-target brain regions.
[0185] In some embodiments, the present platform includes a
non-transitory computer-readable medium having instructions stored
thereon that, upon execution by a computing device, cause the
computing device to perform operations for identifying a
therapeutic target comprising: quantifying alignment of gene
expression mapping data with neurobehavioral phenotype mapping data
and defining a relevant neural neurophenotype topography.
[0186] In some embodiments, the present platform includes a
non-transitory computer-readable medium having instructions stored
thereon that, upon execution by a computing device, cause the
computing device to perform operations for identification of a
neurobehavioral phenotype comprising: quantifying alignment of gene
expression mapping data with neurobehavioral phenotype mapping data
and defining a relevant neural neurophenotype topography.
[0187] In some embodiments, the present platform includes a
computer-implemented system for analyzing alignment of gene
expression mapping data with neurobehavioral phenotype mapping
data, comprising: a memory; and one or more processors coupled to
the memory, wherein the one or more processors are configured to
quantify alignment of gene expression mapping data with
neurobehavioral phenotype mapping data.
[0188] Methods of Use
[0189] Individualized treatment selection. A common problem when
making treatment choices for central nervous system (CNS) disorders
and neuropsychiatric disorders is optimally tailoring treatment for
a given individual. At present this problem remains unaddressed and
the way the medical field makes these decisions is at the group
level based on group categorical assignment made via clinician
behavioral observation and/or patients' self-report.
[0190] The present tools and methods provide for optimization of a
putative treatment response at the individual patient level.
Specifically, one can take a neurobehavioral phenotype for a given
patient, which can be measured either neurally or behaviorally.
That is, the neurophenotype information can be derived from the
neurophenotype map directly or by leveraging a set of behavioral
scores that are associated with a neurophenotype map sensitive to
variation in this neurobehavioral phenotype. Once the
neurobehavioral phenotype map is derived then one would compute the
maximal alignment with a gene expression map, or genotype
topography, to determine a suitable neurophenotype topography. In
one scenario, for example, five (5) drugs that target somewhat
distinct mechanisms but are all indicated for a range of
neuropsychiatric diagnoses may be examined relative to a
neurophenotype topography. Thus, this method would allow a
quantitative ranked ordering of the five (5) drugs based on the
relative similarity or linkage between gene expression and the
neuro-phenotype map for a specific patient as determined using this
genotype and neurophenotype topography approach. This method may be
used to prioritize treatment decisions for a patient.
[0191] The present tools and methods also provide for
identification of drug targets based on similarity to a gene
implicated. At present if a molecule is implicated in a given
disease but that target is not directly drugable then a way is
needed to pharmacologically target the neural circuits involved in
the disease. To achieve this, alternative drugs are needed that can
be screened based on their similarity to the implicated target
which is not drugable. The present approach enables this by
starting with a gene implicated in a given disease. Because such a
gene and its associated proteins may be difficult to modulate
directly via pharmacological treatments, an alternative strategy is
needed whereby one can modulate another drug target whose
brain-wide gene expression pattern is aligned with that of a
disrupted target. The present tools and methods can identify such
genes by computing similarity scores for genes that show expression
topographies highly similar to disrupted genes and therefore would
exhibit high gene-gene map similarity scores. This gene-to-gene
alignment suggests that drugs which target the receptor proteins
associated with the derived genes are well-distributed to
preferentially modulate the same regions that strongly express the
disrupted mechanism that may not be directly modulated.
[0192] The present tools and methods also provide for
identification of drug targets based on a gene similarity to a
neural circuit implicated. A major knowledge gap in treating
neuropsychiatric conditions is the ability to identify drug targets
for a specific neural alteration. Put differently, if one is able
to identify a neural circuit alteration that is associated with a
neuropsychiatric symptom then the challenge is mapping that neural
circuit to a drug target. Here the present tools and methods
provide a method for quantifying the obtained neurobehavioral map
in relation to a gene expression profile. As noted, the
neurobehavioral phenotype information can be derived from the
neurobehavioral phenotype map directly or by leveraging a set of
behavioral scores that are associated with a neurobehavioral
phenotype map sensitive to group variation in this behavior. Once
the group neurobehavioral phenotype map is derived, one would
compute or compare the maximal alignment of the neurobehavioral
phenotype map with gene expression maps. This would yield a
quantitative score for the genes that are maximally aligned with
the disrupted circuit, which in turn would allow development of
molecules for such circuits.
[0193] The present tools and methods also provide for selection of
a suitable patent population subset, or purification of patient
population, to test efficacy of application (i.e. clinical trial
optimization), either via brain or behavior. For example, this
means that one could select patients based on their brain map,
which the tool has previously mapped to a behavior or symptom
profile or could select patients using responses to a question or
performance of a behavioral task which the tool has mapped
previously to a brain map.
[0194] A key challenge in therapeutic development is identification
of the optimal cohort of patients for which the new treatment may
be optimal. At present, these decisions are made based on broad
indication at the categorical level (e.g. depression versus
psychosis). Ultimately, this broad approach does not allow for a
quantitatively-driven selection or purification of the patient
population that may be best aligned with a given drug that is used
to investigate clinical efficacy. The present tools and methods
provides a quantitative method for deriving a gene expression map
for a given molecular target (i.e. gene map). In turn, the present
tools and methods would screen patients based on a neurobehavioral
phenotype mapping that produces maximal alignment with the given
gene expression map. In doing so, the present tools and methods
provide guidance or direction for the inclusion or exclusion of
patients in a given study or trial based on alignment of their
neurobehavioral phenotype mapping or profiles and the gene
expression mapping of interest.
[0195] The present tools and methods also provide for selection of
putative molecules for a human clinical trial. A major challenge in
design of new molecules for a given human clinical population
involves the selection of molecular targets that may be relevant
for such a population based on the pattern of disrupted
brain-behavior relationships. The present approach provides a
method to inform putative target engagement based on alignment to a
neurobehavioral phenotype map of interest with a given gene
expression map. In doing so, the present tools and methods may
directly inform a choice of which existing molecule to use in a
clinical trial by selecting the molecule that exhibits the maximal
alignment with the clinical neurobehavioral phenotype of
interest.
[0196] The present tools and methods also provide for preclinical
or animal applications of neurobehavioral phenotype mapping and
transcriptome or gene expression mapping for drug molecule
selection. A fundamental challenge for design of new molecules
involves selection of the right molecules for a given neural
target. The present approach provides a method to produce a
high-throughput screen via a disease animal model (e.g. knockout).
Specifically, if one obtains a neurobehavioral phenotype map in the
animal (e.g. via animal neuroimaging), then this approach provides
a method to quantitatively screen across genes that maximally align
with such a neurobehavioral phenotype map. This method allows
application of the present tools and methods to therapeutic design
by screening for potential molecular targets.
[0197] The present tools and methods also provide for diagnostic
decisions for specific people based on implicated neural circuits.
A major need in the field of neuropsychiatry is the ability to
derive diagnostically relevant decisions based on implicated neural
circuits. At present, the field fundamentally lacks a framework to
achieve this goal. The present tools and methods provide a method
for quantifying the level of alignment between an existing
neurobehavioral phenotype for a given person and a given gene
expression profile. To the extent that the two maps deviate from
each other (i.e. reflect a dis-similarity), this information also
can be used to reach a diagnostic decision for a given
individual.
[0198] The present tools and methods also provide for diagnostic
decisions for specific people based on behavioral variation for
which there are quantitative links to relevant neurobehavioral
phenotypes. A related major need in the field of neuropsychiatry is
the ability to inform diagnostically relevant variation in a neural
circuit that is linked to an altered neurobehavioral phenotype
profile (e.g. psychosis). At present there is no method to quantify
if such a neural circuit is exhibiting variation that is similar or
dissimilar from a normative gene expression profile. The present
tools and methods provide a method for establishing a genotype and
neurophenotype topography by quantifying the level of alignment
between a neurobehavioral phenotype for a given person and a given
gene expression profile. To the extent that the two maps deviate
from each other (i.e. reflect a dis-similarity), this information
also can be used to reach a diagnostic decision for a given
individual.
[0199] The present tools and methods also provide for
prognosticating the effect of an administered therapy based on gene
transcriptome or gene expression mapping alignment. A critical goal
in treatment decisions for neuropsychiatric disorders involves the
ability to make clinically meaningful predictions over time. One
method of use available using the present tools and methods would
be to quantify the level of similarity between a neurobehavioral
phenotype for a given person and a given gene expression mapping
profile for that same person over time, for instance before and
after treatment. Specifically, the present tools and methods would
provide a genotype and neurophenotype topography quantitative score
reflecting whether the neurobehavioral phenotype is, or is not,
more closely aligned with the gene expression map after
treatment.
[0200] The present tools and methods also provide for
prognosticating the putative treatment response prior to full blown
illness (i.e. risk) for neural circuit alteration based on gene
transcriptome alignment with a neurobehavioral phenotype. Another
key goal in treatment decisions for neuropsychiatric disorders
involves the ability to make clinically meaningful predictions
prior to the onset of full-blow illness. In other words, often
times it is vital to identify people `at risk` for severe
neuropsychiatric illness prior to the onset of the full range of
neurobehavioral phenotype symptoms. One method of use of the
present tools and methods would be to quantify the level of
similarity between a neurobehavioral phenotype for a given person
and a given gene expression profile or gene expression mapping in
individuals at elevated genetic or clinical risk for a given
neuropsychiatric condition. In this context, a "gene expression
profile" may refer to a next-level analysis of the gene expression
within canonical functional networks (i.e. specific collections of
brain regions that we know are involved in a specific function);
whereas "gene expression mapping" may refer to expression pattern
across all brain regions sampled. For instance, a specific
actionable method of use would be to derive neurobehavioral
phenotype mapping for individuals at risk for psychosis and then
quantify the level of neurobehavioral phenotype mapping similarity
to a gene expression profile or gene expression mapping that would
reflect variation in the neurobehavioral phenotype mapping of
interest. Specifically, the present tools and methods would provide
a quantitative score reflecting the level of `risk` for psychosis
conversion based on the quantitative similarity to or deviation
from a given gene expression map.
[0201] The present tools and methods also provide for practical
application of bypassing invasive pharmacoimaging. A frequent
bottleneck in rational drug design in human clinical trials is the
verification of target engagement, typically via invasive
pharmaconeuroimaging (e.g. fMRI or PET). In this context
specifically, this approach can provide a way to identify a
neurobehavioral phenotype if there is a known clinical
pharmacological response in a group of individuals with known
symptom response. Here, if there is no prior evidence for target
engagement based on the drug of interest, then the present tools
and methods provide a method of use that would pinpoint a given
neural circuit that is responsive to the drug molecule by alignment
of such a neural circuit with a gene transcriptome or gene
expression map for that drug to establish a relevant genotype and
neurophenotype topography. Put differently, the present tools and
methods can derive a neurophenotype topography for a given molecule
based on the transcriptome pattern or gene expression mapping of
that the gene involved in a given mechanism, pharmacological
response. In turn, this neurophenotype topography can be used to
select neural circuits that would be maximally aligned with the
mechanism of interest, effectively bypassing the need for target
engagement pharmacoimaging.
[0202] The present tools and methods also provide for optimization
of polypharmacy. It is often the case that many patients respond
best to more than a single drug. The process of `fine tuning` the
selection of such a polypharmacy treatment regimen is at present
not driven by quantitative or neurobiologically principled methods
but rather a clinican's qualitative assessment of the patient or
the patient's self-report. Consequently, this process of
polypharmacy administration is often difficult to precisely
optimize. Furthermore, prior to initiating any treatment it is at
present impossible to arrive at a quantitatively-grounded choice
for which combination of drugs may be efficacious for a given
person, symptom or set of symptoms. Therefore, the present tools
and methods provide for a method of use to select and optimize
polypharmacy for a specific person or set of symptoms.
[0203] The present tools and methods also provide for informing
therapeutic dosing decisions. It is often the case that many
patients do not respond best to the initially prescribed dose of
one or more drugs. The process of `fine tuning` the selection of
the optimal dose range at present is not driven by quantitative or
neurobiologically principled methods but rather a clinican's
qualitative assessment of the patient or the patient's self-report.
Consequently, the process of fine tuning dosing decisions often
difficult to precisely optimize. Therefore, the present tools and
methods provide for a method of use to select and optimize dose
ranges for a specific person or set of symptoms based on similarity
of a derived neurobehavioral phenotype map to the gene
transcriptome profile or gene expression map as a function of
different doses. Relatedly, the present tools and methods provide a
method of use whereby the initial pre-treatment neurobehavioral
phenotype mapping alignment with a given gene expression map
provides a guide to potentially optimize a dose level.
[0204] The present tools and methods also provide for informing
exclusion of drug targets. It is often the case that many patients
do not respond at all or respond poorly to a given treatment of
choice that may be indicated for the broad range of symptoms the
person is experiencing. At present, there is no quantitative or
neurobiologically principled methods to decide prior to treatment
if a given drug may be a poor candidate for a given neural circuit.
Therefore, there is high risk of no response or poor response to a
given drug or dose. The present tools and methods provide a method
of use to inform which drugs or dose ranges may be exclusionary for
a specific person or set of symptoms based on dissimilarity of a
derived neurobehavioral phenotype map to the gene transcriptome
profile or gene expression map.
[0205] The present tools and methods also provide for informing
differential neurobehavioral phenotype clinical response to a given
treatment. At present it is difficult to make decisions in humans
which of the two or more drugs may be optimal for a given neural
circuit based on behavioral efficacy. Specifically, if two drugs
induce differential symptom response in a clinical trial then the
known alignment of their receptor targeting with a given
transcriptome map or gene expression map implicates a neural
circuit in that symptom change. This method of use provides
guidance in the context of clinical trial design concerning which
drug may be optimal for a given pipeline of development and
targeting of specific circuits.
[0206] In some embodiments, the present tools and methods provide a
method of detecting a neurobehavioral phenotype in a subject, said
method comprising: obtaining or having obtained a sample of
neurobehavioral phenotype mapping data from the subject; defining a
genotype topography of a first brain area for a gene based on gene
expression mapping data; defining a neurophenotype topography of a
second brain area for the neurobehavioral phenotype based on
neurobehavioral phenotype mapping data; contacting the genotype
topography of the first brain area and the neurophenotype
topography of the second brain area to establish an alignment;
detecting whether the neurobehavioral phenotype is present in the
sample by contacting the sample with the aligned genotype
topography and neurophenotype topography. In an embodiment of this
method, the neurobehavioral phenotype is at least one of: an
affective disorder, a personality disorder, an attention deficit
hyperactivity disorder, a neurodegenerative disease, a
neurodevelopmental disorder, a cognitive change associated with
chemotherapy; a psychiatric symptom associated with
neurodegenerative diseases, a sex difference in brain function in
health and disease, a traumatic brain injury, and a measurable
neural feature.
[0207] In some embodiments, the present tools and methods provide a
method of diagnosing, predicting, prognosticating, or treating a
neurobehavioral phenotype in a subject, said method comprising:
obtaining or having obtained a sample of neurobehavioral phenotype
mapping data from the subject; defining a genotype topography of a
first brain area for a gene based on gene expression mapping data;
defining a neurophenotype topography of a second brain area for the
neurobehavioral phenotype based on neurobehavioral phenotype
mapping data; contacting the genotype topography of the first brain
area and the neurophenotype topography of the second brain area to
establish an alignment; detecting whether the neurobehavioral
phenotype is present in the sample by contacting the sample with
the aligned genotype topography and neurophenotype topography; and
diagnosing, predicting, prognosticating, or treating the subject
when the neurobehavioral phenotype is detected. In an embodiment,
this method further comprises administering a therapeutic agent to
the subject. In an embodiment, this method further comprises
identifying one or more therapeutic agents suitable for treatment
of the detected neurobehavioral phenotype. In an embodiment, this
method includes one or more therapeutic agents are selected based
on a gene associated with the detected neurobehavioral phenotype.
In an embodiment, this method includes one of more of the PDYN,
OXTR, OPRK1, PNOC, OXT, AVP, OPRL1, APOE, GRIN2C, GABRA2, HTR2A,
HTR3A, HRTR2C, HTR6, MAOA, CHRM1, CHRM3, CCR5, CXCR4, CXCR7, HRH3,
ADRB2, DRD2, SNCA, GBA, GPR88, GPR139, and LRRK2 genes. In an
embodiment, this method further comprises identifying gene
expression targets associated with the detected neurobehavioral
phenotype. In an embodiment, this method further comprises
combining one or more therapeutic agents indicated to be suitable
for treatment of the detected neurobehavioral phenotype. In an
embodiment, this method further comprises dosing of one or more
therapeutic agents in amounts indicated to be effective for
treatment of the detected neurobehavioral phenotype. In an
embodiment, this method further comprises selecting a therapeutic
agent indicated to be most suitable for treatment of the detected
neurobehavioral phenotype. In an embodiment, this method further
comprises not administering one or more therapeutic agents to the
subject indicated to not be suitable for treatment of the detected
neurobehavioral phenotype. In an embodiment, this method includes
one or more therapeutic agents that is shown to have activity in a
brain area outside the alignment of the first brain area and the
second brain area. In an embodiment, this method includes repeating
one or more steps of the method after the subject has been
diagnosed, prognosticated to be at risk for, or treated for the
detected neurobehavioral phenotype. In an embodiment, this method
further comprises altering a therapeutic regimen for the subject
based on changes in the detected neurobehavioral phenotype. In an
embodiment, this method further comprises selecting the subject for
inclusion in a clinical study. In an embodiment, this method
further comprises forming a patient population suitable for
inclusion in the clinical study. In an embodiment, this method
includes a neurobehavioral phenotype that is one of: an affective
disorder such as obsessive compulsive disorder, bipolar disorder,
unipolar depression, dysthymia and cyclothymia, generalized anxiety
disorder, panic disorder, phobias, and post-traumatic stress
disorder; a personality disorder such as schizophrenia, paranoid
personality disorder; schizoid personality disorder; schizotypal
personality disorder; antisocial personality disorder; borderline
personality disorder; histrionic personality disorder; narcissistic
personality disorder; avoidant (or anxious) personality disorder;
dependent personality disorder; and obsessive compulsive
personality disorder; an attention deficit hyperactivity disorder
such as inattentive type, hyperactive-impulsive type, and
combination type; a neurodegenerative diseases such as Alzheimer's
disease, Parkinson's disease; amyotrophic lateral sclerosis;
Friedreich's ataxia; Huntington's disease; Lewy body disease; and
spinal muscular atrophy; a neurodevelopmental disorders such as
autism spectrum disorder, attention-deficit/hyperactivity disorder
(ADHD) and learning disorders; cognitive changes associated with
chemotherapy; a psychiatric symptom associated with
neurodegenerative diseases such as feeling sad or down, confused
thinking or reduced ability to concentrate, excessive fears or
worries, or extreme feelings of guilt, extreme mood changes of
highs and lows, withdrawal from friends and activities, significant
tiredness, low energy or problems sleeping, detachment from reality
(delusions), paranoia or hallucinations, inability to cope with
daily problems or stress, trouble understanding and relating to
situations and to people, alcohol or drug abuse, major changes in
eating habits, sex drive changes, excessive anger, hostility or
violence, and suicidal thinking; a sex differences in brain
function in health and disease; a traumatic brain injury; and any
measurable neural feature. In an embodiment, this method includes a
subject that does not undergo invasive pharmacoimaging.
[0208] In some embodiments, the present tools and methods provide a
method for treating a subject with a neurobehavioral phenotype, the
method comprising the steps of: determining whether the subject has
neurobehavioral phenotype mapping data indicative of the
neurobehavioral phenotype by: obtaining or having obtained a sample
of neurobehavioral phenotype mapping data from the subject;
defining a genotype topography of a first brain area for a gene
based on gene expression mapping data; defining a neurophenotype
topography of a second brain area for the neurobehavioral phenotype
based on neurobehavioral phenotype mapping data; contacting the
genotype topography of the first brain area and the neurophenotype
topography of the second brain area to establish an alignment;
performing or having performed a comparison of the sample with the
aligned genotype topography and neurophenotype topography to
determine if the subject has the neurobehavioral phenotype; and (i)
if the subject has the neurobehavioral phenotype as determined by
comparison of the sample with the aligned genotype topography and
neurophenotype topography, then administering a therapeutic agent
targeted to one or more genes associated with the aligned genotype
topography and neurophenotype topography, or (ii) if the subject
has the neurobehavioral phenotype as determined by comparison of
the sample with the aligned genotype topography and neurophenotype
topography, then administering a therapeutic agent targeted to one
or more neurobehavioral phenotypes associated with the aligned
genotype topography and neurophenotype topography. In an
embodiment, this method further comprises increasing the likelihood
that the treatment for the subject will be effective for treatment
of the neurobehavioral phenotype.
[0209] In some embodiments, the present tools and methods provide a
method of detecting a neurobehavioral phenotype in subjects of a
patient population, said method comprising: obtaining or having
obtained a sample of neurobehavioral phenotype mapping data from
each subject in the patient population; defining a neurophenotype
topography of a second brain area for the neurobehavioral phenotype
based on neurobehavioral phenotype mapping data; contacting the
genotype topography of the first brain area and the neurophenotype
topography of the second brain area to establish an alignment;
detecting whether the neurobehavioral phenotype is present in the
sample by contacting the sample with the aligned genotype
topography and neurophenotype topography.
EXAMPLES
Example 1: Gene Expression Maps for Genes of Interest and Map
Validation
[0210] The present inventors developed algorithms to produce
group-averaged parcellated gene expression maps from the AHBA
dataset.
[0211] FIG. 6A shows these parcellated group-averaged expression
maps for four genes of interest (OPRK1, PDYN, OXTR, and PNOC) in
cortex (left) and subcortex (right). These maps reveal substantial
yet systematic variation and structure in the expression patterns
for these genes across cortex and subcortical structures. For
instance, within cortex, PDYN shows high expression in
anterior/medial temporal and medial prefrontal regions, but low
expression in lateral prefrontal regions. Gene expression patterns
can also be analyzed and visualized by their mean values across
gross brain structures (e.g., cortex, caudate, thalamus,
cerebellum), and across different functionally defined brain
networks (e.g., auditory (AUD), somatomotor (SOM), visual (VIS)).
FIG. 6B shows this structure-by-network analysis for the gene PDYN,
which shows that PDYN has high expression in structures of the
striatum (caudate, putamen, accumbens).
[0212] The validity of gene expression maps for serotonin receptors
was assessed through correspondence with PET maps because
biological validity of these gene expression maps is crucial to the
ability to interpret their meaning and apply them to inform
therapeutic targets. Validity and interpretability can be supported
through convergent evidence from another experimental methodology,
such as PET imaging. For instance, validity is supported by
observation of a high similarity between the PET-derived map for
the density of a given binding target (which may be closer to
`ground truth`) and the expression map for the gene coding for that
binding target. Shown are juxtaposed PET and gene expression maps
for multiple serotonin receptor subunits, using the PET maps from
the following article, Beliveau et al., A high-resolution in vivo
atlas of the human brain's serotonin system, J. NEUROSCI. (2016)
("Beliveau").
[0213] A strong overall correspondence was found between PET and
gene expression maps. For instance, in both maps, the 5-HT1AR
subunit (encoded by the gene HTR1A) has low levels in primary
visual cortex and high levels in anterior temporal cortex, whereas
the 5-HT2AR subunit (encoded by the gene HTR2A) has high levels in
primary visual cortex. This correspondence between measures
provides support for the biological validity and interpretability
of the gene expression maps.
[0214] FIG. 6C also provides images of the dense (in contrast to
parcellated) cortical maps of gene expression for OPRK1, PDYN,
OXTR, and PNOC.
Example 2: Opposing Correlations with T1w/T2w (Myelin) Map for Two
GABA Receptor Subunit Genes: GABRA1 and GABRA5
[0215] A crucial step in the present platform is measuring the
similarity between a gene expression map and a neuroimaging map. As
a test case for a neurophenotype map, the present inventors used
the map of T1w/T2w ratio which is derived from structural MRI
(i.e., ratio of T1-weighted and T2-weighted MRI images). Glasser et
al., Trends and properties of human cerebral cortex: correlations
with cortical myelin content, NEUROIMAGE 93 Pt 2:165-75 (2014). The
T1w/T2w map functions as an interpretable neurophenotype map
because it captures microstructural specialization of cortical
areas related to the hierarchical organization of cortex. Burt. The
T1w/T2w map has high values in sensory cortex and low values in
association cortex. Therefore if a cortical gene expression pattern
exhibits a positive correlation with the T1w/T2w map, it is well
distributed to preferentially modulate sensory cortex; conversely,
if a cortical gene expression pattern exhibits a negative
correlation with the T1w/T2w map, it is well distributed to
preferentially modulate association cortex.
[0216] FIG. 7 shows the relationship between the T1w/T2w map and
expression maps for two genes coding for subunits of the GABAA
receptor: GABRA1 and GABRA5, which encode the .alpha.1 and .alpha.5
subunit, respectively. The .alpha.1 and .alpha.5 GABAA subunits
have different biophysical properties, cellular distributions, and
developmental trajectories. Gonzalez-Burgos et. .alpha.1, GABA
neurons and the mechanisms of network oscillations: implications
for understanding cortical dysfunction in schizophrenia, SCHIZOPHR.
BULL. 34:944-961 (2008); Datta et al., Developmental expression
patterns of gabaa receptor subunits in layer 3 and 5 pyramidal
cells of monkey prefrontal cortex, CEREB. CORTEX 25:2295-305
(2015).
[0217] In pyramidal neurons, the .alpha.1 subunit is in
intra-synaptic receptors that are preferentially distributed in the
peri-somatic region and activated by parvalbumin-expressing
interneurons, and has fast kinetics. In contrast, the .alpha.5
subunit is in extra-synaptic receptors that are preferentially
distributed in the distal dendritic regions and activated by
somatostatin-expressing interneurons, and has slow kinetics. They
are also differentially sensitive to some drugs; for instance,
.alpha.5 PAMs have been investigated for cognitive symptoms in
schizophrenia. Gill et al., The role of .alpha.5 gabaa receptor
agonists in the treatment of cognitive deficits in schizophrenia,
CURR. PHARM. DES. 20:5069-76 (2014). Here, the inventors found
opposing trends in their inter-areal distributions, in relation to
the T1w/T2w map.
[0218] FIG. 7A shows the neurophenotype topography of the cortical
T1 w/T2w map, as an example neurophenotype map. FIG. 7B and FIG. 7C
show the cortical gene expression maps, or genotype topographies,
(top) for the genes GABRA1 and GABRA5, respectively, and their
relationship with the neurophenotype map (bottom). GABRA1
expression exhibits a strong positive correlation with T1w/T2w
(Spearman rank correlation, r.sub.s=0.52), whereas GABRA5 exhibits
a negative correlation (r.sub.s=-0.61).
[0219] The platform also allows the user to sweep across and
compare genes within a given set of genes, returning the gene-map
alignment scores. FIG. 7D shows such results for the T1w/T2w map
comparing across a set of GABAA receptor subunit genes (GABRA1,
GABRA2, GABRA3, GABRA4, and GABRA5). This analysis shows that
GABRA1 exhibits a strong positive correlation with the
neurophenotype map, which is statistically significant; GABRA2,
GABRA3, and GABRA5 exhibit strong negative correlations, which are
statistically significant; GABRA4 exhibits a weak correlation that
is not statistically significant. These findings demonstrate the
feasibility of the present platform, demonstrating that it can
reveal significant structured relationships between gene expression
maps and neurophenotype maps.
[0220] These findings derived from the platform can inform
actionable decisions in development and application of
therapeutics, with multiple methods of use. For example, one can
examine a case in which the goal were to treat disinhibition
preferentially in higher association areas (low T1w/T2w values)
relative to primary sensory areas (high T1w/T2w values). This is
plausible because multiple neuropsychiatric and neurological
disorders may involve preferential alteration in association
cortical areas, relative to sensory cortical areas. Informing this
example goal, these specific findings provide evidence that an
.alpha.5 PAM may be more effective than an .alpha.1 PAM at
maximizing effects on prioritized target areas while minimizing
effects on off-target areas. This evidence could be used to inform
design of clinical trials, to better align a patient population
(e.g., for a disorder exhibiting with association vs. sensory
cortical alterations) with a pharmacological drug (e.g., ones
preferentially modulating association vs. sensory cortical
regions). The correlation between these maps' values can serve as
the quantitative score of similarity for the gene-map pair. These
results demonstrate meaningful variation of gene expression
patterns even for two subunits of the same receptor, which can be
related to neuroimaging maps.
[0221] FIG. 7E shows images from another embodiment of the present
platform. The results indicated here differ quantitatively, but not
qualitatively, from those provided in FIG. 7B and FIG. 7C, for
reasons that include, but are not necessarily limited to,
methodological differences in surface based mapping and
interpolation method (parcellated vs. dense) used to generate the
figures.
Example 3: Gene-Map Correlations for Genes of Interest
[0222] FIG. 8 shows scores, here the correlation with the T1w/T2w
(myelin) map, for seven genes of interest (PDYN, OXTR, OPRK1, PNOC,
OXT, AVP, and OPRL1). The inventors found that four of the seven
genes had highly significant negative correlations with T1w/T2w
(myelin) map values (PDYN, OXTR, OPRK1, and PNOC), only one gene
had a significant positive correlation (OXT), and two genes did not
have a significant correlation (AVP, OPRL1).
[0223] These findings demonstrate the feasibility of the present
platform, demonstrating that it can reveal significant structured
relationships between gene expression maps and neuroimaging
maps.
Example 4: Proof-of-Principle Demonstrations of Platform
Bi-Directionality
[0224] FIGS. 9A, 9B, 10A, and 10B provide proof-of-principle
demonstrations of the bi-directional platform, using HCP task
activation maps.
[0225] FIGS. 9A and 9B depict a gene-to-phenotype approach. Here,
the proof-of-principle implementation flows in the direction from a
gene as therapeutic target to neurobehavioral phenotypes,
corresponding to direction (A) in FIG. 5. The set of example
neurophenotype maps was calculated from fMRI-derived task
activation maps for cortex, for specific tasks from the Human
Connectome Project. For (A) the gene expression map is that of
OPRK1 and for (B) is that of OPRL1. Plotted is the correlation
between the cortical gene expression map and each of a set of
neurobehavioral phenotype maps. The gene-phenotype score (here the
spearman rank correlation) varies markedly across neurobehavioral
phenotypes, differently for the two example genes.
[0226] FIGS. 10A and 10B depict a phenotype-to-gene approach. Here,
the proof-of-principle implementation flows in the direction from a
neurobehavioral phenotype to genes as therapeutic targets,
corresponding to direction (B) in FIG. 5. Each of the two example
neurophenotype maps was calculated as the contrast between two
fMRI-derived task activation maps for cortex, for specific tasks
from the Human Connectome Project. For (A) the phenotype map is the
contrast between story vs. math tasks (to isolate language
processing), and for (B) it is the contrast between presentation of
fearful vs. neutral face stimuli (to isolate fear processing).
Plotted is the correlation between the neurobehavioral phenotype
map and each of a set of gene expression maps, for various genes
which may encode for drug targets. The gene-phenotype score (here
the spearman rank correlation) varies markedly across genes,
differently for the two example neurobehavioral phenotypes.
[0227] Such bi-directional sweeps, across phenotypes for a given
gene of interest and across genes for a given phenotype, can inform
actionable decisions for multiple methods of use, such as:
selecting tasks or behavioral measures to evaluate efficacy of
given a drug in a clinical trial (in the gene-to-phenotype
direction), or identifying and selecting candidate drug targets for
a given behavioral or cognitive deficit (in the phenotype-to-gene
direction).
Example 5: Gene-to-Gene Alignment for the Gene APOE
[0228] One method of use is identification of drug targets based on
similarity to a gene implicated in a given disorder or process,
corresponding to direction (C) in FIG. 5. For instance, the gene
APOE is important in Alzheimer's disease. Because APOE and its
associated protein have proven difficult to modulate
pharmacologically, a therapeutic strategy may be modulate another
drug target whose brain-wide gene expression pattern is aligned
with that of APOE. The platform can identify such genes based on
sweeping across genes and quantifying gene-to-gene alignment of
expression patterns. FIGS. 11A and 11B illustrates a gene-to-gene
approach. FIG. 11A shows the cortical gene similarity scores for
four NMDA receptor subunits (GRIN2A, GRIN2B, GRIN2C, and GRIN2D).
FIG. 11B shows the cortical gene similarity scores for four
GABA.sub.A receptor subunits (GABRA1, GABRA2, GABRA3, GABRA4, and
GABRA5). The background distribution histogram shows the
distribution of scores across all available genes. These analyses
show that among these gene sets, GRIN2C and GABRA2 have cortical
expression topographies highly similar to APOE, and are in the top
1% of all available genes. This gene-to-gene alignment provides
evidence that drugs which target the receptor proteins associated
with GRIN2C and GABRA2 are well-distributed to preferentially
modulate the same cortical regions that strongly express APOE.
These results could inform identification and selection of genes
with high alignment to APOE (e.g., GRIN2C and GABRA2) as potential
therapeutic targets for Alzheimer's disease.
Example 6: Gene Expression Topography Relates to Brain-Wide Pattern
of Pharmacological Effects of LSD
[0229] Multiple methods of use evaluate alignment of a gene's
expression map with a neuroimaging map related to a phenotype, to
inform decision making about pharmacological therapeutics. The
utility and feasibility of this approach, to make predictions for
pharmacological therapeutics, can be supported by demonstrating
that the brain wide effects of a drug on neuroimaging measures can
be related to the gene expression topographies of the receptors
modulated by that particular drug.
[0230] FIG. 12 shows that the platform can link from gene
expression patterns to the neural effects of a drug. In this study,
resting-state fMRI was used to measure the change in functional
connectivity induced by acute administration of lysergic acid
diethylamide (LSD) in healthy human subjects. Preller et al.,
Changes in global and thalamic brain connectivity in LSD-induced
altered states are attributable to the 5-HT2A receptor. ELIFE. (In
Press) ("Preller"). FIG. 12A shows the fMRI-derived cortical map
showing the change in mean functional connectivity (Global Brain
Connectivity, GBC), which exhibits a large increase in occipital
visual cortex. Importantly, this neural change, as well as
behavioral effects of LSD, were found to be blocked by
pre-administration with ketanserin, a selective antagonist of the
5-HT2A serotonin receptor. Preller. This finding strongly
implicates the gene HTR2A, which codes for the 5-HT2A receptor, in
the neural and behavioral effects of LSD. FIG. 12B shows gene
expression maps for three serotonin receptor genes, including
HTR2A. FIG. 12C shows the gene-map correlation between the
LSD-related neurophenotype map and six candidate genes which code
for serotonin and dopamine receptors. Among these six candidate
genes, HTR2A exhibits the greatest alignment (i.e., highest
positive correlation) with the LSD-related neurophenotype map. FIG.
12D shows these correlation values in relation to the gray
background distribution histograms showing the distribution of
scores across all available genes in the AHBA dataset, showing that
HTR2A is in the top 5% of all genes in its alignment with the
LSD-related neurophenotype map. Preller. This example illustrates
the potential for the platform to predict the neural effects of
pharmacology based on the topography of gene expression.
Example 7: Bi-Directional Identification of Drug Targets and
Phenotypes in the BSNIP Dataset
[0231] FIG. 13 shows application of platform to show bi-directional
identification of drug targets and phenotypes in the BSNIP dataset.
The BSNIP (Bipolar-Schizophrenia Network for Intermediate
Phenotypes) dataset includes resting-state fMRI data and symptom
scores from a large number of subjects along a
schizophrenia--bipolar continuum. Tamminga et al., Bipolar and
Schizophrenia Network for Intermediate Phenotypes: Outcomes Across
the Psychosis Continuum. SCHIZOPHR. BULL. 40:S131-S137 (2014)
("Tamminga"). Combined analysis of resting-state fMRI and
behavioral symptom scores yielded multiple latent neuro-behavioral
dimensions of individual variation, each of which characterizes
both a behavioral symptom profile and a related brain map of
individual differences in GBC. An individual with high GBC in the
positive (light-colored) regions and low GBC in the negative
(dark-colored) regions would score highly on symptoms associated
with that latent dimension. FIG. 13A and FIG. 13B (top) shows the
behavioral symptom profile and neural GBC map for two latent
dimensions of individual variation. An individual patient may
exhibit a neuro-behavioral phenotype similar to one specific latent
dimension and not the other, or exhibit a mixture of the
phenotypes.
[0232] The platform to these cortical phenotype maps. For each
neurophenotype map, the gene-map correlation score was computed
across all genes in the AHBA dataset, yielding a background
distribution histogram shown in FIG. 13A and FIG. 13B (bottom).
FIG. 13A and FIG. 13B (bottom) also shows the gene-map correlation
scores for specific genes of interest. For the latent dimension
shown in FIG. 13A (top), "Neurophenotype A," the score for the gene
OPRK1 is near zero, indicating that the cortical expression
topography of OPRK1 is uncorrelated with the neural map associated
with that phenotype. In contrast, for the latent dimension shown in
FIG. 13B (top), "Neurophenotype B," OPRK1 exhibits a strong
negative correlation in the extreme 1% of all genes.
[0233] These results provide evidence that OPRK1 is a promising
therapeutic target for the behavioral symptom profile provided by
Neurophenotype B, due to overlap in the cortical topography. The
Neurophenotype B symptom profile could therefore be used for
patient segmentation in the design of a clinical trial for a
kappa-opioid pharmaceutical. Pharmacological neuroimaging could
provide further useful evidence by characterizing the impact on GBC
by kappa-opioid modulation. This example demonstrates how operation
of the platform can inform decision making in the context of the
development and application of therapeutics.
Example 8: Gene to Phenotype Example Demonstrating Explanation of
Negative Result and Repurposing of Therapeutic Agent for Different
Phenotype
[0234] The following example addresses the question of whether an
H3 antagonist should be tested in CIAS. Here, the answer is "no"
(r=0.04). Another follow-up question then is, for what phenotype
would H3 inverse agonist be useful?
[0235] To support potential drug repurposing, one can examine the
relationship between a gene and a phenotype. Here a gene map for
gene HRH3 is provided in FIG. 14A, and a phenotype map for BSNIP
Symptom Correlation GBCS Comp Correlation rZ is provided in FIG.
14B. The similarity score between a gene and a phenotype computes
the correlation and associated p-value between two maps. Here, FIG.
14C shows the HRH3 gene and the phenotype map for BSNIP Symptom
Correlation GBCS Comp Correlation rZ, wherein the cortex only was
masked, as mapped for alignment. A correlation measure of
0.039666395207 was found.
[0236] Here, the expression pattern of the HRH3 gene, which encodes
for the human histamine H3 receptor, was compared to the phenotype
map associated derived from Global Brain Connectivity measures
associated with the Brief Assessment of Cognition in Schizophrenia
(BACS) Battery. There was very poor alignment between these two
maps at the level of the cortex (Pearson's r=0.04). This result
suggests that pharmacological intervention targeting the H3
receptor would not be expected to improve cognitive impairment
associated with schizophrenia.
[0237] Indeed, Egan and colleagues showed that promoting histamine
release with MK-0249 failed to improve cognitive deficits in
patients with schizophrenia. Egan et al., Randomized crossover
study of the histamine H3 inverse agonist MK-0249 for the treatment
of cognitive impairment in patients with schizophrenia, SCHIZOPHR
RES., 146(1-3): 224-30 May (2013); doi:
10.1016/j.schres.2013.02.030 (2013). However, H3 receptor
expression was significantly correlated with whole-brain
connectivity changes associated with questions that comprise the
General subscale of the PANSS instrument (r=0.21; P<0.0001).
This result suggests a potential benefit of H3 receptor modulation
in patients with schizophrenia who present with symptoms indexed by
the PANSS-General scale such as anxiety, depression, or poor
attention.
Example 9: Gene to Gene Example (De Novo Therapeutic and Patient
Selection)
[0238] The following example addresses the question of how to
pursue disease modification within Parkinson's disease.
[0239] To support novel therapeutic intervention and patient
selection in CNS disease, one can examine the relationship between
a gene implicated in the disease and another gene that has not yet
been implicated. The similarity score between two genes computes
the correlation and associated p-value between two expression maps.
Recent genetic findings in patients with Parkinson's disease (PD)
led to the possibility of developing therapies against specific
genotypes by targeting alpha-synuclein (SNCA), glucocerebrosidase
(GBA), and leucine-rich repeat kinase (LRRK2). In addition to
directly targeting the proteins encoded by these genes, evidence
suggests other proteins can indirectly modulate these proteins to
modify symptoms or disease progression in patients with PD.
[0240] For example, .beta.2-adrenoreceptor (encoded by ADRB2)
agonists may regulate alpha-synuclein. Mittal et al.,
.beta.2-Adrenoreceptor is a regulator of the .alpha.-synuclein gene
driving risk of Parkinson's disease, SCIENCE, 357(6354):891-898
(2017). And use of dopamine agonists acting via the D2 receptors
(encoded by DRD2) may be beneficial in PD patients with LRRK2
mutations. Tozzi et al., Dopamine D2 receptor activation potently
inhibits striatal glutamatergic transmission in a G2019S LRRK2
genetic model of Parkinson's disease, NEUROBIOL DIS, 118: 1-8
(2018). The similarity scores between ADRB2 and SNCA (r=-0.16;
P<0.0001) as well as DRD2 and LRRK1 (r=0.2; P<0.0001) are
consistent with the published literature.
[0241] These observations may be extended using the tools and
methods described herein to identify non-obvious genes that could
alter symptoms and/or disease progression in PD patients. By
comparing whole brain maps for the PNOC gene (which encodes the
peptide N/OFQ) with maps for genes implicated in PD, one can
predict the involvement of N/OFQ signaling in patients with SNCA
(r=0.51; P<0.0001), LRRK2 (r=0.62; P<0.0001) and GBA (r=0.71;
P<0.0001) mutations. This hypothesis can be tested preclinically
by examining the effect of blocking N/OFQ signaling, via NOP
receptors (NOPR) in alpha-synuclein-based models of PD and by
testing NOPR antagonists in PD patients with these mutations.
Moreover, this approach can be applied to identify novel drug
targets that might regulated GBA activity such as those that
modulate dipeptidyl-peptidase-like proteins (DPP10--GBA
correlation: r=0.85; 99.3% similarity).
Example 10: Phenotype to Gene Example (Patient Screening Risks and
Novel Therapeutic Intervention
[0242] The following example addresses the question of which
non-disease phenotypes can be associated with genes.
[0243] To identify patients who could be placed at higher risk with
a therapeutic intervention or to guide the identification of novel
therapeutics, we can examine the relationship between a particular
phenotype and a gene or set of genes associated with the symptoms
that comprise it.
[0244] For example, the antiretroviral drug efavirenz, which is
effective in suppressing HIV-1, is known to increase the risk of
neuropsychiatric symptoms. These neuropsychiatric adverse events
have been attributed to the drug's interactions with multiple drug
targets. Dalwadi et al., Molecular mechanisms of serotonergic
action of the HIV-1 antiretroviral efavirenz, PHARMACOL RES.,
110:10-24 (2016).
[0245] The Adult Self-Report (ASR) Syndrome Scale (SS) contains
symptom-based scales that allows individuals to report on
psychiatric symptoms such as depression, mood, anxiety, ADHD and
psychotic behavior. By comparing responses on the ASR with
resting-state brain connectivity measures, one can assess the
relationship between behavioral variations along this scale with
global brain connectivity (GBC). As shown in FIG. 15C, a phenotype
map (HCP Cognitive Behavioral GBC ASR SS Correlation) the "hot
spots" in red correspond to hyperconnected regions in individuals
with high ASR scores.
[0246] FIG. 15D shows a phenotype gene distribution chart. FIG. 15E
shows a gene-map correlation for six (6) genes (HTR6, CHRM3, CHRM1,
MAOA, HTR2A, and HTR2C).
[0247] Next, we build on reported observations to make new
predictions about different drugs.
[0248] Next, one can examine the relationship between the ASR-SS
GBC map and the molecular targets with which efavirenz interacts.
The finding that HTR6 gene exhibits a high correlation with the
ASR-SS GBC map (r=0.39, 99.7% similarity) is consistent with the
published literature that suggests at least part of the psychiatric
side effects associated with efavirenz can be attributed to the
inverse agonist activity of the drug at 5HT6 receptors and
antagonist activity at the muscarinic M3 (CHRM3) receptor.
[0249] We extend this observation to assess whether individuals
receiving different medicines to treat cancer or HIV infection
could be at risk for psychiatric symptoms. FIG. 15F shows a
phenotype gene distribution chart.
[0250] Two such drugs are plerixafor and maraviroc which target
chemokine receptors, CXCR4 and CXCR (plerixafor) and CCR5
(maraviroc). An ASR-SS GBC phenotype by gene comparison revealed
that these genes have statistically significant correlations with
the psychiatric phenotype map (CCR5, r=0.24; CXCR7, r=0.25; CXCR4,
r=0.28 with 94.1% similarity). These results are shown in FIG. 15G.
These results suggest that individuals receiving plerixafor or
maraviroc should be screened for psychiatric symptoms using the
ASR-SS form.
[0251] The various embodiments described above can be combined to
provide further embodiments. These and other changes can be made to
the embodiments in light of the above-detailed description. In
general, in the following claims, the terms used should not be
construed to limit the claims to the specific embodiments disclosed
in the specification and the claims, but should be construed to
include all possible embodiments along with the full scope of
equivalents to which such claims are entitled. Accordingly, the
claims are not limited by the disclosure.
* * * * *
References