U.S. patent application number 17/651626 was filed with the patent office on 2022-06-02 for multimodal biomarkers predictive of transdiagnostic symptom severity.
The applicant listed for this patent is Blackthorn Therapeutics, Inc.. Invention is credited to Parvez Ahammad, Humberto Andres Gonzalez Cabezas, Matthew Kollada, Yuelu Liu, Monika Sharma Mellem.
Application Number | 20220172822 17/651626 |
Document ID | / |
Family ID | 1000006152673 |
Filed Date | 2022-06-02 |
United States Patent
Application |
20220172822 |
Kind Code |
A1 |
Mellem; Monika Sharma ; et
al. |
June 2, 2022 |
MULTIMODAL BIOMARKERS PREDICTIVE OF TRANSDIAGNOSTIC SYMPTOM
SEVERITY
Abstract
The method for evaluating mental health of a patient includes
displaying a series of inquiries from mental health questionnaires
on a display device. Each inquiry of the series of inquiries
includes text and a set of answers. A series of selections is
received from a user interface. Each selection of the series of
selections is representative of an answer of the set of answers for
each corresponding inquiry in the series of inquiries. Unprocessed
MRI data are received. The unprocessed MRI data correspond to a set
of MRI images of a biological structure associated with a patient.
Using a machine learning model, the series of selections and the
unprocessed MRI data are processed. The series of selections being
processed corresponds to the series of inquiries. A symptom
severity indicator for a mental health category of the patient is
outputted.
Inventors: |
Mellem; Monika Sharma; (San
Francisco, CA) ; Liu; Yuelu; (San Francisco, CA)
; Ahammad; Parvez; (San Francisco, CA) ; Gonzalez
Cabezas; Humberto Andres; (San Francisco, CA) ;
Kollada; Matthew; (San Francisco, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Blackthorn Therapeutics, Inc. |
San Francisco |
CA |
US |
|
|
Family ID: |
1000006152673 |
Appl. No.: |
17/651626 |
Filed: |
February 18, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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17270780 |
Feb 23, 2021 |
11289187 |
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PCT/US2019/048809 |
Aug 29, 2019 |
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17651626 |
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62726009 |
Aug 31, 2018 |
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62840178 |
Apr 29, 2019 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 20/70 20180101;
G16H 50/20 20180101; G06T 7/0012 20130101; G16H 10/20 20180101;
G06T 2207/20084 20130101; G06T 2207/30016 20130101; G06T 2207/20081
20130101; G06T 2207/10088 20130101 |
International
Class: |
G16H 20/70 20060101
G16H020/70; G16H 50/20 20060101 G16H050/20; G16H 10/20 20060101
G16H010/20; G06T 7/00 20060101 G06T007/00 |
Claims
1. A system for evaluating mental health of a patient, the system
comprising: a display device; a user interface; a memory containing
machine readable medium comprising machine executable code having
stored thereon instructions for performing a method; and a control
system coupled to the memory comprising one or more processors, the
control system configured to execute the machine executable code to
cause the control system to: display, on the display device, a
series of inquiries from mental health questionnaires, each inquiry
of the series of inquiries comprising text and a set of answers;
receive, from the user interface, a series of selections, each
selection of the series of selections being representative of an
answer of the set of answers for each corresponding inquiry in the
series of inquiries; receive, unprocessed MRI data corresponding to
a set of MRI images of a biological structure associated with the
patient; and process, using a machine learning model, the series of
selections corresponding to the series of inquiries and the
unprocessed MRI data to output a symptom severity indicator for a
mental health category of the patient.
2. The system of claim 1, wherein the unprocessed MRI data
corresponds to MRI data for a brain of the patient.
3. The system of claim 1, wherein the unprocessed MRI data
comprises fMRI data.
4. The system of claim 1, wherein the control system is further
configured to preprocess the unprocessed MRI data to identify a
plurality of features.
5. The system of claim 1, wherein the mental health category of the
patient comprises at least one of: depression, anxiety, and
anhedonia.
6. The system of claim 1, wherein the machine learning model is at
least one of: a generalized linear model, a regression model, a
supervised regression method, a logistical regression model, random
forest, lasso, and an elastic net.
7. A system for evaluating mental health of a patient, the system
comprising: a display device; a user interface; a memory containing
machine readable medium comprising machine executable code having
stored thereon instructions for performing a method; a control
system coupled to the memory comprising one or more processors, the
control system configured to execute the machine executable code to
cause the control system to: receive, from the user interface, a
selection of answers corresponding to each question in a series of
questions from mental health questionnaires; receive, unprocessed
MRI data corresponding to a set of MRI images of a biological
structure; process, using a machine learning model, multimodal
feature sets derived from (i) the selection of answers, and (ii)
the unprocessed MRI data to output a symptom severity indicator for
a mental health category of the patient.
8. The system of claim 7, wherein the unprocessed MRI data
corresponds to MRI data for a brain of the patient.
9. The system of claim 7, wherein the unprocessed MRI data
comprises fMRI data.
10. The system of claim 7, wherein the control system is further
configured to preprocess the unprocessed MRI data to identify a
plurality of features.
11. The system of claim 7, wherein the mental health category of
the patient comprises at least one of: depression, anxiety, and
anhedonia.
12. The system of claim 7, wherein the machine learning model is at
least one of: a generalized linear model, a regression model, a
supervised regression method, a logistical regression model, random
forest, lasso, and an elastic net.
13. A machine learning training system, comprising: at least one
nontransitory processor-readable storage medium that stores at
least one of processor-executable instructions or data; and at
least one processor communicatively coupled to the at least one
nontransitory processor-readable storage medium, in operation, the
at least one processor configured to: receive labeled training data
for a plurality of individuals indicating whether each of the
plurality of individuals has one or more mental health disorders,
the labeled training data comprising: MRI data recorded for each of
the plurality of individuals; and a selection of answers to a
series of questions for each of the plurality of individuals;
determine a plurality of features from the labeled training data;
train an initial machine learning model in a supervised manner,
based on the plurality of features; extract importance measures for
each of the plurality of features, based on the training of the
initial machine learning model; generate a plurality of subset
machine learning models based on the extracted importance measures
for the plurality of features; evaluate a classification
performance of the generated plurality of subset machine learning
models; select at least one of the subset machine learning models
as the machine learning model; and store the plurality of features
of the machine learning model in the at least one nontransitory
processor-readable storage medium for subsequent use as a screening
tool.
14. The machine learning system of claim 13, wherein each feature
in the plurality of features comprises an importance measure;
wherein each of the subset machine learning models includes a
sequentially lower number of features than a following subset
machine learning model; and wherein the features are selected for
each subset machine learning model based on a highest importance
measure.
15. The machine learning system of claim 13, wherein the selected
subset machine learning model includes a portion of the plurality
of features, the portion selected from features having an
importance measure above a threshold value.
16. The machine learning system of claim 13, wherein each of the
subset machine learning models includes (i) a different selection
of a portion of the plurality of features, or (ii) a different
combination of the plurality of features.
17. The machine learning system of claim 13, wherein training the
initial machine learning model further comprises using k-fold cross
validation with logistic regression.
18. The machine learning system of claim 13, wherein the labeled
training data further comprises at least one of functional
measurement data or physiological measurement data.
19. The machine learning system of claim 13, further comprising:
using the features of the machine learning model as a screening
tool to assess at least one of intermediate or end-point outcomes
in at least one clinical trial testing for treatment responses.
20. The machine learning system of claim 13, wherein the machine
learning model is trained on (i) clinical scales data corresponding
to the plurality of individuals; (ii) fMRI full connectivity data
corresponding to the plurality of individuals; (iii) sMRI data
corresponding to a plurality of individuals, the sMRI data
comprising cortical volume data, cortical thickness data, and
cortical surface area data; (iv) input data corresponding to the
plurality of individuals, wherein, for each individual, the input
data comprises clinical scales data and fMRI data; (v) input data
corresponding to the plurality of individuals, wherein, for each
individual, the input data comprises clinical scales data and sMRI
data; (vi) input data corresponding to the plurality of
individuals, wherein, for each individual, the input data comprises
fMRI data and sMRI data; or (vii) input data corresponding to the
plurality of individuals, wherein, for each individual, the input
data comprises fMRI data, clinical scales data, and sMRI data.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of U.S. patent
application Ser. No. 17/270,780 filed on Feb. 23, 2021, which is
the National Phase of International Application PCT/US2019/048809
filed on Aug. 29, 2019, which designated the United States, which
claims priority to and the benefit of U.S. Provisional Patent No.
62/726,009 filed on Aug. 31, 2018 and U.S. Provisional Patent No.
62/840,178 filed on Apr. 29, 2019, each of which is hereby
incorporated by reference herein in its entirety.
TECHNICAL FIELD
[0002] The present disclosure relates to biomarkers, and more
specifically, to the use of machine learning and multi-modal
biomarkers to predict symptom severity.
BACKGROUND
[0003] Conventional clinical psychiatric practice focuses on
diagnostic classification, relying on making diagnoses and
recommending treatment for disorders based solely on clinical
phenomenology. This approach hampers prognostic assessment,
treatment, and drug development because it does not take into
account the neurobiology of patients.
[0004] Biomarkers are biological characteristics that can serve as
indicators for normal or pathological processes or responses to
intervention. Biomarker development within psychiatry lags behind
other areas of medicine, partly because psychiatric syndromes have
a far more complex relationship between the biology and severity of
the symptoms than other fields of medicine. Conventional clinical
practice does not provide biological measures which are able to
robustly describe complex psychiatric syndromes. Additionally,
conventional diagnostic biomarker approaches do not fully account
for the heterogeneity of symptoms under the umbrella of a single
diagnosis or the shared symptoms between multiple diagnoses.
Clinical symptoms, such as depressed/elevated mood, anhedonia, and
anxiety, often span multiple diagnostic categories.
[0005] Conventional research suggests that derived symptom
dimensions are associated with resting-state functional magnetic
resonance imaging (rs-fMRI) connectivity transdiagnostically (i.e.,
where multiple diagnostically-distinct patient groups are modeled
together). Other research found links between task-based fMRI
activation or rs-fMRI connectivity and existing anhedonic,
depressive, and anxiety symptom dimensions transdiagnostically.
This symptom-to-neurophysiological-links in conventional research,
however, lacks a predictive framework, and any insights from
neuroimaging-based biomarker research have not translated into
clinical practice. Therefore, conventional clinical practice does
not provide transdiagnostic, multimodal predictive models of
symptom severity which include neurobiological characteristics.
[0006] In particular, conventional research does not identify
whether symptoms have a more circumscribed biological basis to few
brain networks as proposed in a recent taxonomy or to multiple
networks. Additionally, it is not known whether a single, broad
self-report clinical assessment (like the Temperament and Character
Inventory or the Hopkins Symptom Checklist, as known in the art) or
multiple, more specific instruments are better at assessing
multiple symptoms.
SUMMARY
[0007] Aspects of the present disclosure include a system for
evaluating mental health of a patient. The system comprises a
display device, a user interface, a memory, and a control system.
The memory contains machine readable medium, comprising machine
executable code. The machine executable code stores instructions
for performing a method. The control system is coupled to the
memory, and includes one or more processors. The control system is
configured to execute the machine executable code to cause the
control system to perform the method. The method includes
displaying a series of inquiries from mental health questionnaires
on the display device. Each inquiry of the series of inquiries
includes text and a set of answers. A series of selections is
received from the user interface. Each selection of the series of
selections is representative of an answer of the set of answers for
each corresponding inquiry in the series of inquiries. Unprocessed
MRI data are received. The unprocessed MRI data correspond to a set
of MRI images of a biological structure associated with the
patient. Using a machine learning model, the series of selections
and the unprocessed MRI data are processed. The series of
selections being processed corresponds to the series of inquiries.
A symptom severity indicator for a mental health category of the
patient is outputted.
[0008] In some aspects, the unprocessed MRI data corresponds to MRI
data for a brain of the patient. In some aspects, the unprocessed
MRI data includes fMRI data. In some aspects, the control system is
further configured to preprocess the unprocessed MRI data to
identify a plurality of features.
[0009] In some aspects, the mental health category of the patient
comprises at least one of: depression, anxiety, and anhedonia. In
some aspects, the symptom severity indicator for the mental health
category is quantitative.
[0010] In some aspects, the machine learning model is at least one
of: a generalized linear model, a regression model, a supervised
regression method, a logistical regression model, random forest,
lasso, and an elastic net.
[0011] In some aspects, the machine learning model is generated by
receiving labeled training data for a plurality of individuals. The
labeled training data is indicative of whether each of the
plurality of individuals has one or more mental health disorders
and a severity of symptoms corresponding to the one or more mental
health disorders. The labeled training data includes unprocessed
MRI data recorded for each of the plurality of individuals, and a
series of selections corresponding to the series of inquiries for
each of the plurality of individuals. The machine learning model is
further generated by determining a plurality of features based on
the received labeled training data. Based on the determined
plurality of features, an initial machine learning model is trained
in a supervised manner. Based on the training of the initial
machine learning model, importance measures for each of the
plurality of features are extracted. Based on the extracted
importance measures for the plurality of features, a plurality of
subset machine learning models is generated. A classification
performance of the generated plurality of subset machine learning
models is evaluated. At least one of the subset machine learning
models is selected as the machine learning model.
[0012] In some aspects, the machine learning model is trained on
clinical scales data corresponding to the plurality of individuals.
In some aspects, the machine learning model is trained on fMRI full
connectivity data corresponding to the plurality of
individuals.
[0013] In some aspects, the machine learning model is trained on
sMRI data corresponding to the plurality of individuals. The sMRI
data include cortical volume data, cortical thickness data, and
cortical surface area data.
[0014] In some aspects, the machine learning model is trained on
input data corresponding to the plurality of individuals. In an
exemplary aspect, for each individual, the input data include
clinical scales data and fMRI data. In another exemplary aspect,
for each individual, the input data include clinical scales data
and sMRI data. In yet another exemplary aspect, for each
individual, the input data include fMRI data and sMRI data. In a
further exemplary aspect, for each individual, the input data
include fMRI data, clinical scales data, and sMRI data.
[0015] Additional aspects of the present disclosure include a
system for evaluating mental health of a patient. The system
includes a display device, a user interface, a memory, and a
control system. The memory includes machine readable medium
comprising machine executable code. The machine executable code
stores instructions for performing a method. The control system is
coupled to the memory, and includes one or more processors. The
control system is configured to execute the machine executable code
to cause the control system to receive a selection of answers from
the user interface. The selection of answers corresponds to each
question in a series of questions from mental health
questionnaires. Unprocessed MRI data are received. The unprocessed
MRI data correspond to a set of MRI images of a biological
structure. Using a machine learning model, the selection of answers
and the unprocessed MRI data are processed. A symptom severity
indicator for a mental health category of the patient is
outputted.
[0016] Further aspects of the present disclosure include a machine
learning training system. The machine learning training system
includes at least one nontransitory processor-readable storage
medium, and at least one processor communicatively coupled to the
at least one nontransitory processor-readable storage medium. The
at least one nontransitory processor-readable storage medium stores
at least one of processor-executable instructions or data. In
operation, the at least one processor configured to receive labeled
training data for a plurality of individuals. The labeled training
data are indicative of whether each of the plurality of individuals
has one or more mental health disorders and a severity of symptoms
corresponding to the one or more mental health disorders. The
labeled training data include MRI data recorded for each of the
plurality of individuals, and a selection of answers to the series
of questions for each of the plurality of individuals. A plurality
of features is determined from the labeled training data. Based on
the plurality of features, an initial machine learning model is
trained in a supervised manner. Based on the training of the
initial machine learning model, importance measures for each of the
plurality of features are extracted. Based on the extracted
importance measures for the plurality of features, a plurality of
subset machine learning models is generated. A classification
performance of the generated plurality of subset machine learning
models is evaluated. At least one of the subset machine learning
models is selected as the machine learning model. The plurality of
features of the machine learning model is stored in the at least
one nontransitory processor-readable storage medium for subsequent
use as a screening tool.
[0017] In some aspects, the machine learning system further
includes using the features of the machine learning model as a
screening tool to assess at least one of intermediate or end-point
outcomes in at least one clinical trial testing for treatment
responses.
[0018] In some aspects, each feature in the plurality of features
comprises an importance measure. Each of the subset machine
learning models includes a sequentially lower number of features
than a following subset machine learning model. The features are
selected for each subset machine learning model based on a highest
importance measure.
[0019] In some aspects, the selected subset machine learning model
includes a portion of the plurality of features. The portion
selected from features includes an importance measure above a
threshold value. In some aspects, each of the subset machine
learning models includes a different selection of the portion of
the plurality of features. In some aspects, each of the subset
machine learning models includes a different combination of the
plurality of features. In some aspects, at least twenty features of
the plurality of features have an importance measure above the
threshold value. The portion includes at least ten features and
less than twenty features.
[0020] In some aspects, the machine learning model is configured to
output a symptom severity indicator identifying a severity of at
least one mental health symptom of a patient.
[0021] In some aspects, training the initial machine learning model
includes using k-fold cross validation with logistic
regression.
[0022] In some aspects, the labeled training data further comprises
at least one of functional measurement data or physiological
measurement data.
[0023] The above summary is not intended to represent each
embodiment or every aspect of the present disclosure. Rather, the
foregoing summary merely provides an example of some of the novel
aspects and features set forth herein. The above features and
advantages, and other features and advantages of the present
disclosure, will be readily apparent from the following detailed
description of representative embodiments and modes for carrying
out the present disclosure, when taken in connection with the
accompanying drawings and the appended claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] The foregoing and other advantages of the present disclosure
will become apparent upon reading the following detailed
description and upon reference to the drawings.
[0025] FIG. 1A illustrates X-Y plots of number of features versus
predicted outcome scores, according to some implementations of the
present disclosure;
[0026] FIG. 1B illustrates a comparison of measured outcome scores
and predicted outcome scores, according to some implementations of
the present disclosure;
[0027] FIGS. 2A-2B illustrate comparisons of measured values and
predicted values under dysregulated mood models, according to some
implementations of the present disclosure;
[0028] FIGS. 2C-2D illustrate comparisons of measured values and
predicted values under anhedonia models, according to some
implementations of the present disclosure;
[0029] FIG. 2E-2F illustrate comparisons of measured values and
predicted values under anxiety models, according to some
implementations of the present disclosure;
[0030] FIG. 3A illustrates exemplary proportions by feature types
in some of the disclosed models, according to some implementations
of the present disclosure;
[0031] FIG. 3B illustrates exemplary proportions by the top 25% of
the feature types in some of the disclosed models, according to
some implementations of the present disclosure;
[0032] FIGS. 4A-4B illustrate pie charts of exemplary proportions
of features from each scale under dysregulated mood models,
according to some implementations of the present disclosures
[0033] FIGS. 4C-4D illustrate pie charts of exemplary proportions
of features from each scale under anhedonia models, according to
some implementations of the present disclosure;
[0034] FIGS. 4E-4F illustrate pie charts of exemplary proportions
of features from each scale under anxiety models, according to some
implementations of the present disclosure;
[0035] FIGS. 5A-5B illustrate connectivity matrices and region of
interest (ROI) locations for fMRI connectivity features in some of
the disclosed models predicting mood outcome variables, according
to some implementations of the present disclosure;
[0036] FIGS. 5C-5D illustrate connectivity matrices and ROI
locations for fMRI connectivity features in some of the disclosed
models predicting anhedonia outcome variables, according to some
implementations of the present disclosure;
[0037] FIGS. 5E-5F illustrate connectivity matrices and ROI
locations for fMRI connectivity features in some of the disclosed
models predicting anxiety outcome variables, according to some
implementations of the present disclosure;
[0038] FIGS. 6A-6I illustrate distributions of in-scanner motion
measurements, having scales data, sMRI data, and fMRI data as
input, according to some implementations of the present
disclosure;
[0039] FIGS. 7A-7I illustrate comparisons of in-scanner motion
measurements, having scales data, sMRI data, and fMRI data as
input, and grouped by diagnoses, according to some implementations
of the present disclosure;
[0040] FIGS. 8A-8C illustrate distributions of outcome measures,
having scales data, sMRI data, and fMRI data as input, according to
some implementations of the present disclosure;
[0041] FIG. 9 illustrates a feature stability for the Elastic Net,
having scales data, sMRI data, and fMRI data as input, according to
some implementations of the present disclosure;
[0042] FIG. 10A illustrates binary heat maps for fMRI connectivity
features under dysregulated mood models, according to some
implementations of the present disclosure;
[0043] FIG. 10B illustrates binary heat maps for fMRI connectivity
features under anhedonia models, according to some implementations
of the present disclosure;
[0044] FIG. 10C illustrates binary heat maps for fMRI connectivity
features under anxiety models, according to some implementations of
the present disclosure;
[0045] FIGS. 11A-11B illustrate results of permutation tests under
dysregulated mood models, according to some implementations of the
present disclosure;
[0046] FIGS. 11C-11D illustrate results of permutation tests under
anhedonia models, according to some implementations of the present
disclosure;
[0047] FIGS. 11E-11F illustrate results of permutation tests under
anxiety models, according to some implementations of the present
disclosure;
[0048] FIG. 12A illustrates proportions of features from each scale
having clinical scales data only as input under dysregulated mood
models, according to some implementations of the present
disclosure;
[0049] FIG. 12B illustrates proportions of features from each scale
having clinical scales data only as input under anhedonia models,
according to some implementations of the present disclosure;
[0050] FIG. 12C illustrates proportions of features from each scale
having clinical scales data only as input under anxiety models,
according to some implementations of the present disclosure;
[0051] FIG. 13 illustrates current medication usage status, grouped
by medication class, for groups of participants, according to some
implementations of the present disclosure;
[0052] FIG. 14 illustrates an exemplary system for implementing
various methodologies disclosed herein, according to some
implementations of the present disclosure;
[0053] FIG. 15 illustrates an exemplary methodology for determining
a symptom severity indicator for a patient, according to some
implementations of the present disclosure;
[0054] FIG. 16 illustrates an exemplary methodology for using a
machine learning model to analyze input and output a symptom
severity indicator, according to some implementations of the
present disclosure;
[0055] FIGS. 17A-17B illustrate a block diagram of an MRI system
used to acquire NMR data, according to some implementations of the
present disclosure; and
[0056] FIG. 18 illustrates a block diagram of a transceiver which
forms part of the MRI system of FIG. 17A.
[0057] While the present disclosure is susceptible to various
modifications and alternative forms, specific embodiments have been
shown by way of example in the drawings and will be described in
detail herein. It should be understood, however, that the present
disclosure is not intended to be limited to the particular forms
disclosed. Rather, the present disclosure is to cover all
modifications, equivalents, and alternatives falling within the
spirit and scope of the present disclosure as defined by the
appended claims.
DETAILED DESCRIPTION
[0058] The present disclosure is described with reference to the
attached figures, where like reference numerals are used throughout
the figures to designate similar or equivalent elements. The
figures are not drawn to scale, and are provided merely to
illustrate the instant disclosure. Several aspects of the
disclosure are described below with reference to example
applications for illustration. It should be understood that
numerous specific details, relationships, and methods are set forth
to provide a full understanding of the disclosure. One having
ordinary skill in the relevant art, however, will readily recognize
that the disclosure can be practiced without one or more of the
specific details, or with other methods. In other instances,
well-known structures or operations are not shown in detail to
avoid obscuring the disclosure. The present disclosure is not
limited by the illustrated ordering of acts or events, as some acts
may occur in different orders and/or concurrently with other acts
or events. Furthermore, not all illustrated acts or events are
required to implement a methodology in accordance with the present
disclosure.
[0059] Aspects of the present disclosure can be implemented using
one or more suitable processing device, such as general-purpose
computer systems, microprocessors, digital signal processors,
micro-controllers, application-specific integrated circuits (ASIC),
programmable logic devices (PLD), field-programmable logic devices
(FPLD), field-programmable gate arrays (FPGA), mobile devices such
as a mobile telephone or personal digital assistants (PDA), a local
server, a remote server, wearable computers, tablet computers, or
the like.
[0060] Memory storage devices of the one or more processing devices
can include a machine-readable medium on which is stored one or
more sets of instructions (e.g., software) embodying any one or
more of the methodologies or functions described herein. The
instructions can further be transmitted or received over a network
via a network transmitter receiver. While the machine-readable
medium can be a single medium, the term "machine-readable medium"
should be taken to include a single medium or multiple media (e.g.,
a centralized or distributed database, and/or associated caches and
servers) that store the one or more sets of instructions. The term
"machine-readable medium" can also be taken to include any medium
that is capable of storing, encoding, or carrying a set of
instructions for execution by the machine and that cause the
machine to perform any one or more of the methodologies of the
various embodiments, or that is capable of storing, encoding, or
carrying data structures utilized by or associated with such a set
of instructions. The term "machine-readable medium" can accordingly
be taken to include, but not be limited to, solid-state memories,
optical media, and magnetic media. A variety of different types of
memory storage devices, such as a random access memory (RAM) or a
read-only memory (ROM) in the system or a floppy disk, hard disk,
CD ROM, DVD ROM, flash, or other computer-readable medium that is
read from and/or written to by a magnetic, optical, or other
reading and/or writing system that is coupled to the processing
device, can be used for the memory or memories.
Overview
[0061] The present disclosure provides predictive models for three
common symptoms in psychiatric disorders (dysregulated mood,
anhedonia, and anxiety). In some examples, the predictive models
successfully utilize data from the Consortium for Neuropsychiatric
Phenomics (CNP) dataset, which includes clinical scale assessments,
resting-state functional MRI (rs-fMRI), and structural-MRI (sMRI)
imaging measures. The data is provided from healthy control
participants and patients with schizophrenia, bipolar disorder, and
attention deficit and hyperactivity disorder (ADHD). In addition,
examining symptoms in transdiagnostic groups can be highly
informative. The CNP dataset further includes three patient groups
with shared genetic risk and includes MRI and clinical scale data
for each patient.
[0062] According to some implementations of the present disclosure,
the disclosed predictive models, using the disclosed feature
selection approach discussed herein, were able to explain 65-90% of
variance across the three symptom domains. In some examples, the
predictive models explain 22% of variance without using the feature
selection approach. For feature selection, the present disclosure
provides a data-driven feature selection approach from the field of
machine learning, relying on importance-weighted forward selection,
to search through a high-dimensional space and optimize model
performance and interpretability. This importance-ranked, forward
selection modeling approach searches for the most predictive input
features from a set of clinical scale measures, sMRI measures, and
rs-fMRI measures. Notably, this data-driven way of selecting
feature subsets led to multimodal neurobehavioral models with
consistently high predictability across multiple symptom domains
and to high interpretability enabled by importance scores for
individual features. Thus, the present disclosure demonstrates that
a shorter, broadly-applicable five-minute rs-fMRI scan and a small
set of clinical scale assessments can be used to predict a panel of
core symptoms commonly found in various psychiatric disorders.
[0063] Exemplary methods involve combining rs-fMRI with select
questions from clinical scales; this enables high levels of
prediction of symptom severity across diagnostically distinct
patient groups. In some examples, connectivity measures beyond a
few intrinsic RSNs may carry relevant information for symptom
severity.
[0064] Overall, Elastic Net regression models with all three input
feature types explained the most variance. However, features from
the different modalities were not equally represented in the models
when evaluating feature importance. The individual, edge-level fMRI
connectivity measures between specific network nodes dominated
across symptom models, with self-report clinical scales being also
highly predictive. These models maximize predictability (in terms
of variance) for models whose regression coefficients can be used
for interpretation, including features that can be assessed for
various clinical and scientific insights.
[0065] The transdiagnostic symptom-based approach of the present
disclosure provides more options for predicting longitudinal and
treatment outcomes beyond those afforded by conventional clinical
diagnosis alone. The disclosed approach allows clinicians to
estimate symptom severity (and can be used as practical tools in
other clinical applications) in broader populations, where the
patient might not have an initial diagnosis. Also, using a
biological marker of a symptom to track and predict eventual
treatment response can introduce clinical efficiencies during a
medical treatment. If the biomarker detects treatment-related
changes sooner than behavioral/symptomatic changes, it can indicate
if a patient is responding to an intervention earlier than
conventional treatments; this can be the basis for an earlier
continue/switch/end treatment decision by a clinician. In some
examples, predicting symptom variation itself and symptom severity
could predict treatment response altogether.
[0066] Therefore, the present disclosure seeks to integrate
functional neuroimaging with other data modalities. Combining
biological and clinical variables has led to improved
predictability in cancer models but is yet underexplored in
psychiatry, outside of the present disclosure. The predictive
framework is especially powerful beyond associative frameworks
(such as correlation analyses), as it not only allows multivariate
modeling to deal with high-dimensional, multimodal data but also
provides testing of predictive value and generalizability of those
models on an independent sample.
[0067] The disclosed models retain a high level of
interpretability, enabling several clinical and scientific
insights, including: (1) structural features do not substantially
contribute to the predictive strength of the models, (2) the
Temperament and Character Inventory scale is a valuable predictor
of symptom variation across diagnoses, and (3) predictive rs-fMRI
connectivity features are widely distributed across many intrinsic
resting-state networks (RSN). This disclosed models also clarify
the biological basis of symptoms or the utility of different
clinical scales for prediction.
Exemplary Systems and Methodologies
[0068] The present disclosure contemplates that a variety of
systems can be used to perform various embodiments of the present
disclosure. Referring now to FIG. 14, an exemplary system 1400 is
shown, which can be configured to perform various methods of the
present disclosure, including methods 1500 and 1600 of FIGS. 15 and
16, respectively. In particular, system 1400 includes a display
1402, a user interface 1404, a control system 1406, and a memory
1408. In some examples, the system 1400 further includes one or
more servers 1410.
[0069] The user interface 1404 is configured to receive input from
a user. For example, the user interface 1404 can be a keyboard, a
touchscreen, a mobile device, or any other device for receiving
input, as known in the art. The user enters data on the user
interface 1404 in response to prompts on the display 1402. For
example, the display 1402 outputs a series of mental health
questions, and the user inputs an answer to each question on the
user interface 1404. In some examples, the user interface 1404
directly displays the input on display 1402 and relays the data to
the control system 1406. In some examples, the data is then stored
in the memory 1408.
[0070] The display 1402 is configured to receive data from the
control system 1406 and the user interface 1404. For example, the
display 1402 displays input received from the user interface 1404;
in some examples, the data is first sent to the control system
1406, which then processes the data and instructs the display 1402
according to the processed data. In other examples, the display
1402 displays data received from the control system 1406. Exemplary
data from the control system 1406 includes questions from a mental
health questionnaire, answer boxes, answer options, answer data, or
a symptom severity indicator related data. In some examples, the
display 1402 is on a smart phone.
[0071] The present disclosure also contemplates that more than one
display 1402 can be used in system 1400, as would be readily
contemplated by a person skilled in the art. For example, one
display can be viewable by a patient, while additional displays are
visible to researchers and not to the patient. The multiple
displays can output identical or different information, according
to instructions by the control system 1406.
[0072] The control system 1406 can be communicatively coupled to
the display 1402, the user interface 1404, and the memory 1408.
Further, the control system 1406 can be communicatively coupled to
the server 1410. For example, the communication can be wired or
wireless. The control system 1406 is configured to perform any
methods as contemplated according to FIGS. 15-16 (discussed further
below). The control system 1406 can process and/or store input from
the display 1402, the user interface 1404, and the memory 1408. In
some examples, the methodologies disclosed herein can be
implemented, via the control system 1406, on the server 1410. It is
also contemplated that the server 1410 includes a plurality of
servers, and can be remote or local. Optionally, the control system
and/or the memory 1408 may be incorporated into the server
1410.
[0073] In some examples, system 1400 can be a unitary device, for
example, a smart phone, which includes a display 1402, a user
interface 1404, a control system 1406, and a memory 1408.
[0074] Turning now to FIG. 15, an exemplary methodology 1500 is
discussed for determining a symptom severity indicator for a
patient. Additional details and alternate steps for methodology
1500 are discussed further with regards to FIGS. 1A-13 and the
corresponding description.
[0075] Methodology 1500 begins at step 1510 which provides for
displaying a series of questions. An exemplary series of questions
includes questions from mental health questionnaires, and includes
both text and answers for each question.
[0076] Methodology 1500 then provides for, at step 1520, receiving
answers for each of the series of questions (the questions provided
for in step 1510). In some examples, the answers are received at a
user interface (e.g., user interface 1404 of FIG. 14). In some
examples, the answers include selection of a multiple choice
question, a textual response, or any other user input as
contemplated by one skilled in the art. In some examples, the
answers are retrieved from a record entry corresponding to one
patient in a database of patient records. This database can be
stored in memory 1408 of FIG. 14, for example. In some examples,
the database can be stored in the sever 1410 of FIG. 14. In some
examples, methodology 1500 begins directly at step 1520.
[0077] Step 1530 provides for receiving unprocessed MRI data. The
unprocessed MRI data corresponds to a set of MRI images of a
biological structure. In some examples, the MRI data corresponds to
MRI data for a patient's brain (i.e., the same patient who provided
answers at step 1520). The MRI data can include task-based fMRI
data, rs-fMRI data, and/or sMRI data. In additional examples of
step 1530, methodology 1500 can provide for receiving clinical
scales data. In some examples of step 1530, methodology 1500
provides for receiving processed MRI data.
[0078] Step 1540 then provides for processing, using a machine
learning model, the selection of answers from step 1520 and the
data received at step 1530. In some examples of methodology 1500,
the data received at step 1530 is preprocessed to identify a
plurality of features.
[0079] At step 1550, methodology 1500 provides for outputting a
symptom severity indicator for a mental health category of a user.
In some examples of the present disclosure, step 1550 performs
processing of the answers and the received data as discussed
further below with respect to method 1600 of FIG. 16. The mental
health category can include any of (1) depression, (2) anxiety, and
(3) anhedonia. In some aspects, the symptom severity indicator for
the mental health category is quantitative. For example, the
symptom severity indicator includes a numerical scale (such as 1 to
5, 1 to 10, etc.), a color scale (green to yellow to red), an emoji
scale, or the like, or in any combination thereof.
[0080] In some examples, the symptom severity indicators are scores
across a scale. For example, the score can range from zero to forty
(0-40); zero indicates no evidence of a symptom, and forty
indicates that the patient is severely symptomatic. In some
examples, each questionnaire to measure symptom severity can have a
different scale. Any other symptom severity scale can be used as
well, as would be readily apparent to one skilled in the art.
[0081] Referring now to methodology 1600 of FIG. 16, an exemplary
methodology is shown for using a machine learning model to analyze
input and output a symptom severity indicator, according to various
embodiments of the present disclosure. In some examples, the
machine learning model is any of: a generalized linear model, a
regression model, a supervised regression method, random forest
model, LASSO model, and an elastic net model. In some examples, the
machine learning model is any of the models and algorithms
discussed further below. In one embodiment of method 1600, the
present disclosure provides two regularized general linear model
regression algorithms, LASSO and Elastic Net, and one non-linear
regression model algorithm, Random Forest. Elastic Net in
particular can be used when the number of predictor variables is
much greater than the number of samples.
[0082] In step 1610, methodology 1600 provides for receiving
labeled training data regarding mental health disorder status for a
plurality of individuals. In some examples, the labeled training
data identifies whether each of the individuals has one or more
mental health disorders and the severity of their symptoms. The
labeled training data includes, for each individual, a selection of
answers to mental health questionnaires and includes MRI data. The
MRI data can be task-based fMRI data, sMRI data, and/or rs-fMRI
data. In some examples, the labeled training data includes, for
each individual, an indication of any of: whether the individual is
healthy, whether the individual has a general mental health issue,
whether the individual has one or more specific mental health
disorders, whether the individual is at risk of developing a
general mental health issue, or whether the individual is at risk
of developing one or more specific mental health disorders. In some
examples, the labeled training data includes another functional
and/or physiological measurement dataset, as known in the art.
[0083] In step 1620, methodology 1600 provides for determining
features from the labeled training data of step 1610. The features
are determined according to any methods, as known in the art. In
other examples, features will not be determined from the labeled
training data and they will be input directly into the
algorithm.
[0084] In step 1630, methodology 1600 provides for training an
initial machine learning model in a supervised manner, based on the
features determined in step 1620. In some examples, training this
initial machine learning model includes using k-fold
cross-validation with LASSO and Elastic Net regression.
[0085] In some examples, training this initial machine learning
model in step 1630 includes training the model on clinical scales
data corresponding to the plurality of individuals.
[0086] In some examples, training this initial machine learning
model in step 1630 includes training the model on fMRI full
connectivity data corresponding to the plurality of
individuals.
[0087] In some examples, training this initial machine learning
model in step 1630 includes training the model on sMRI data
corresponding to a plurality of individuals, the sMRI data
including cortical volume data, cortical thickness data, and
cortical surface area data.
[0088] In some examples, training this initial machine learning
model in step 1630 includes training the model on input data
corresponding to the plurality of individuals, wherein, for each
individual, the input data includes clinical scales data and fMRI
data.
[0089] In some examples, training this initial machine learning
model in step 1630 includes training the model on input data
corresponding to the plurality of individuals, wherein, for each
individual, the input data includes clinical scales data and sMRI
data.
[0090] In some examples, training this initial machine learning
model in step 1630 includes training the model on input data
corresponding to the plurality of individuals, wherein, for each
individual, the input data comprises fMRI data and sMRI data.
[0091] In some examples, training this initial machine learning
model in step 1630 includes training the model on input data
corresponding to the plurality of individuals, wherein, for each
individual, the input data comprises fMRI data, clinical scales
data, and sMRI data. This particular combination of input data
provides a high r.sup.2 metric (calculated on an untouched
evaluation set data to avoid biasing and overfitting our models)
when using Elastic Net across the different outcome variables.
[0092] In step 1640, methodology 1600 provides for extracting
importance measures for each of the features. These importance
measures are selected based on the trained initial machine learning
model.
[0093] In step 1650, methodology 1600 provides for generating a
plurality of subset machine learning models, based on the extracted
importance measures of step 1640. In step 1660, methodology 1600
provides for evaluating a regression performance of the generated
subset machine learning models from step 1650. In some examples,
each of the subset machine learning models includes a different
selection of features. In some examples, the subset machine
learning models include only features with an importance measure
above a threshold value. In some examples, the features are ranked
based on the importance measure. In some examples, each of the
subset machine learning models includes a sequentially lower number
of features than a following subset machine learning model, wherein
the features are selected for each subset machine learning model
based on a highest importance measure.
[0094] In step 1670, methodology 1600 provides for selecting one of
the subset machine learning models as a generalized linear learning
model. The selection is based on the classification performances as
evaluated in step 1660. The selected subset machine learning model
includes a portion of the plurality of features determined from
step 1620. The portion of features is selected from features with
an importance measure (as determined in step 744) above a threshold
value. In some examples, more than one subset machine learning
model is selected.
[0095] In some examples of step 1670, the threshold value is set so
that at least twenty features of the plurality of features
determined in step 1620 have an importance measure above the
threshold value. In some examples, the threshold value is set to
select a portion of between ten and twenty features.
[0096] In some examples of step 1670, the features of the machine
learning model are stored in a non-transitory processor-readable
storage medium (e.g., memory 1408 of FIG. 14). The features can
then be later used as a screening tool. In some examples, the
screening tool can output a symptom severity indicator of a mental
health condition. In some examples, the screening tool assesses
intermediate and/or end-point outcomes in clinical trial testing
for treatment responses.
[0097] Therefore, the selected machine learning model can then be
used to process any of the input data as provided for in the
present disclosure.
[0098] In other examples of steps 1660 and 1670, one hundred
twenty-six (126) sets of models can be built to examine all
permutations of seven feature set inputs, three modeling
algorithms, and six outcome variables. The seven feature set inputs
include: (1) clinical scales data; (2) fMRI full connectivity data;
(2) sMRI cortical volume, cortical area, and cortical thickness
data; (3) clinical scales data and fMRI data; (4) clinical scales
data and sMRI data; (5) sMRI data and fMRI data; and (7) clinical
scales data, fMRI data, and sMRI data. The three modeling
algorithms include anxiety, depression, and anhedonia. The six
outcome variables can be symptom severity scores, as discussed
herein.
[0099] As discussed herein, conventional diagnostic biomarker
approaches do not fully account for the heterogeneity of symptoms
under the umbrella of a single diagnosis or the shared symptoms
between multiple diagnoses. It must be noted that conventional
clinical practice does not provide transdiagnostic, multimodal
predictive models of symptom severity. Thus, based on the seven
feature set input, such as the examples disclosed herein with
regard to steps 1660 and 1670, various combinations of feature
types are evaluated as inputs. For example, instead of only
analyzing one type of biomarkers, the various combinations of input
data include single and multimodal feature sets. The experimental
data herein provides that the multimodal models perform better than
those of single feature sets. Therefore, the models disclosed
herein can be highly predictive based at least in part on their
transdiagnostic and/or multimodal data input.
Example Application of the Disclosed Models
[0100] FIGS. 1A-1B show evaluations using the disclosed models (the
models are discussed further with regard to FIGS. 14-16 and
corresponding description) using candidate subsets according to a
prespecified criterion to find an optimal model. FIG. 1A
illustrates X-Y plots of number of features versus predicted
outcome scores. FIG. 1A shows exemplary data predicting a total
Mood_Bipolar score using Elastic Net and clinical scales data, sMRI
data, and fMRI data as input. The median MSE and median r.sup.2 are
shown to vary with each feature subset (standard deviation bars are
also shown). FIG. 1B compares measured outcome scores against
predicted outcome scores; the data demonstrates how closely the
model predictions are to actual outcome scores for individuals. The
model predictions are trained on a portion of a dataset, while the
measured outcome scores are reserved in a held-out sample, from the
same dataset. Additional details of the methodology are discussed
further with regards to the experimental methodology.
[0101] FIGS. 2A-2F compare measured and predicted values for best
models for mood, anhedonia, and anxiety. For example: FIGS. 2A-2B
illustrate comparisons of measured values and predicted values
under dysregulated mood models; FIGS. 2C-2D illustrate comparisons
of measured values and predicted values under anhedonia models; and
FIG. 2E-2F illustrate comparisons of measured values and predicted
values under anxiety models. Each dot in the scatter plot, marked
by diagnosis (shown as dots with different hatching), represents a
single participant from the held-out evaluation set. Their measured
symptom severity score is along the x-axis, and their predicted
symptom severity score is along the y-axis. The dashed diagonal
line represents a perfect one-to-one linear relationship between
measured and predicted values. FIGS. 2A-3C show how closely the
model predictions are to actual outcome scores for individuals in
the held-out samples for this set of models. In this example, there
is no particular diagnostic group that is further from the
measured/predicted line across all six models; this suggests that
the models generalize across the multiple diagnoses. FIGS. 2A-2C
also demonstrate that healthy control subjects are generally in the
lower half of the symptom score ranges.
[0102] FIGS. 3A-3B show exemplary proportions of feature types in
some of the disclosed models. FIG. 3A shows a proportion of all
features returned by the model. The densest hatching represents the
proportion of features from scales, the medium density hatching
represents proportion from fMRI connectivity measures, and the
least dense hatching represents proportion from sMRI measures. FIG.
3B shows a proportion of feature types in the top 25% of features
returned by the model; this indicates that most of the disclosed
models have equal or greater proportion of scale features than
among all the non-zero features.
[0103] FIGS. 4A-4F show exemplary data for a proportion of features
from each scale for the best model predicting mood, anhedonia, and
anxiety. For example, FIGS. 4A-4B illustrate pie charts of
exemplary proportions of features from each scale under
dysregulated mood models; FIGS. 4C-4D illustrate pie charts of
exemplary proportions of features from each scale under anhedonia
models; and FIGS. 4E-4F illustrate pie charts of exemplary
proportions of features from each scale under anxiety models. Of
the features returned by the best model that were scale items, each
pie chart shows the proportion of those items that were from the
corresponding scales for the model for each outcome variable. For
example, for the Mood/Dep_Hopkins model, 31% of the scale items
were from the TCI scale, 6% from the Chaphyp scale, etc. This
representation of features does not show the sign of the regression
coefficient and whether predictive features indicate increasing or
decreasing symptom severity.
[0104] FIGS. 5A-5F show connectivity matrices and ROI locations for
fMRI connectivity features of best models predicting mood (FIGS.
5A-5B), anhedonia (FIGS. 5C-5D), and anxiety (FIGS. 5E-5F) outcome
variables. For all non-zero fMRI connectivity features returned by
the respective model, the number of individual edges between two
nodes is plotted in the connectivity matrix (shown in the left
plots of each of FIGS. 5A-5F) for that model. Each row and column
represent a single resting-state network (RSN) from the Power
atlas. Darker squares represent more features within or between the
given networks with actual feature number superimposed numerically
on each square.
[0105] Connectivity matrices have the same RSNs listed on both
axes, so upper and lower triangles show redundant information.
Cortical surface plots (shown in the right plots of each of FIGS.
5A-5F) show the ROI locations marked by RSN membership for each
model to display the breadth of networks with informative features
for each model. Because only cortical surfaces are shown, no
cerebellar nodes were plotted in the brain plots. Network labels
are AUD: Auditory, CER: Cerebellar, COTC: Cingulo-opercular Task
Control, DM: Default Mode, DA: Dorsal Attention, FPTC:
Fronto-parietal Task Control, MEM: Memory Retrieval, SAL: Salience,
SSM-H: Sensory/somatomotor Hand, SSM-M: Sensory/somatomotor Mouth,
SUB: Subcortical, UNC: Uncertain (i.e., miscellaneous regions not
assigned to a specific RSN), VA: Ventral Attention, VIS:
Visual.
[0106] The models with the least complexity are scales-only models
shown in FIGS. 6A-6I. FIGS. 6A-6I shows distributions of the
scales+sMRI+fMRI cohort's in-scanner motion measurements. FIG. 6A
shows percentage of frames that exceeded 0.5 mm. FIG. 6B shows mean
framewise displacement (FD). FIG. 6C mean sharp head motion. FIGS.
6D-6I show mean of motion for each of six motion
parameters--x-direction, y-direction, z-direction, pitch, roll, and
yaw. Each plot displays the histogram with a Gaussian kernel
density estimated distribution superimposed.
[0107] FIGS. 7A-7I shows comparisons of in-scanner motion
measurements for the scales data, sMRI data, and fMRI data as
input, grouped by diagnosis, according to an exemplary methodology
of the present disclosure. FIG. 7A shows box plots of percentage of
frames that exceeded 0.5 mm. FIG. 7B shows mean framewise
displacement (FD). FIG. 7C shows mean sharp head motion. FIGS.
7D-7I show mean of motion for each of six motion
parameters--x-direction, y-direction, z-direction, pitch, roll, and
yaw. For those group comparisons that yielded significant
differences on Kruskal Wallis tests (see Supplementary text), post
hoc pairwise Wilcoxon rank-sum tests were performed and indicated
with stars. Significantly different comparisons are indicated with
* for p<0.05, ** for p<0.01, and *** for p<0.001.
[0108] FIGS. 8A-8C show distributions of outcome measures for
scales data, sMRI data, and fMRI data as input. FIG. 8A shows
dysregulated mood models, Hopkins_depression and Bipolar_mood
scores. FIG. 8B shows anhedonia models, Anhedonia_Chapphy and
Anhedonia_Chapsoc scores. FIG. 8C shows anxiety models,
Hopkins_anxiety and Bipolar_anxiety scores. Each plot displays the
histogram with a Gaussian kernel density estimated distribution
superimposed.
[0109] Referring now to FIGS. 10A-10C, binary heat maps for fMRI
connectivity features of a best model, are shown. For example, FIG.
10A illustrates binary heat maps for fMRI connectivity features
under dysregulated mood models; FIG. 10B illustrates binary heat
maps for fMRI connectivity features under anhedonia models; and
FIG. 10C illustrates binary heat maps for fMRI connectivity
features under anxiety models. For all non-zero fMRI connectivity
features returned by the respective model, the regression
coefficients for each individual edge between two nodes is plotted
in the connectivity matrix for that model. Each row and column
represents a single ROI from the Power atlas, ordered consistently
in both directions. Coefficients have been binarized (positive
plotted as stars, negative as dots) for easier viewing of sparse
matrices. Upper and lower triangles show redundant information, so
only upper triangles are plotted. Lines delineate intrinsic resting
state networks for easier visualization of network category for
each feature. Network labels are AUD: Auditory, CER: Cerebellar,
COTC: Cingulo-opercular Task Control, DM: Default Mode, DA: Dorsal
Attention, FPTC: Fronto-parietal Task Control, MEM: Memory
Retrieval, SAL: Salience, SSM-H: Sensory/somatomotor Hand, SSM-M:
Sensory/somatomotor Mouth, SUB: Subcortical, UNC: Uncertain (i.e.,
miscellaneous regions not assigned to a specific RSN), VA: Ventral
Attention, VIS: Visual.
[0110] FIGS. 11A-11F show results of permutation tests of the
alternative hypothesis that the best model results were
significantly greater than the baseline model results (where the
outcome variable scores were permuted across subjects). For
example, FIGS. 11A-11B illustrate results of permutation tests
under dysregulated mood models; FIGS. 11C-11D illustrate results of
permutation tests under anhedonia models; and FIGS. 11E-11F
illustrate results of permutation tests under anxiety models. One
hundred (100) permuted models were used to generate the empirical
distribution of r.sup.2 values, and the r.sup.2 value of the best
model is shown with the star. Distributions extended below negative
two (-2) r.sup.2 in some models, but all models are shown on the
same x- and y-scales for ease of comparison. All six models had
p<0.01.
[0111] FIGS. 12A-12C show the proportion of features from each
scale for the clinical scales data only as input, according to an
exemplary methodology of the present disclosure. For example, FIG.
12A illustrates proportions of features from each scale having
clinical scales data only as input under dysregulated mood models;
FIG. 12B illustrates proportions of features from each scale having
clinical scales data only as input under anhedonia models; and FIG.
12C illustrates proportions of features from each scale having
clinical scales data only as input under anxiety models. The model
displayed in FIGS. 12A-12C used Elastic Net with the median r.sup.2
value.
[0112] FIG. 13 shows current medication usage status, grouped by
medication class, for groups of participants, according to an
exemplary methodology of the present disclosure. Each bar
represents the percent of a diagnostic group that was using a
stable medication of that particular class. Some diagnostic groups
did not have any subjects using medications from a particular
class. No healthy control (HC) subjects were on psychiatric
medications per enrollment criteria.
Contributions of Scale Assessments and fMRI Connectivity Features
to Models
[0113] In some examples of the present disclosure, rs-fMRI features
can be grouped according to intrinsic RSN membership. These
networks partially overlap with a proposed taxonomy of
symptom-related networks in a focused set of brain regions as known
in the prior art. By contrast to conventional research, the systems
and methods of the present disclosure identify that these
highly-predictive features are distributed across elements of many
networks. The wide set of RSNs reflects the relatively wide nature
of scale-based symptom constructs in contrast to the targeting
allowed by finely-tuned behavioral tasks. It may also reflect a
compilation of different mechanisms across theoretical subgroups of
patients with differing brain dysregulation. Ultimately, the
present disclosure provides systems and methods indicating that
whole-brain connectivity between individual nodes is useful when
creating models; this whole-brain connectivity is different from
relying solely upon summary metrics of networks such as graph
theory metrics, independent components, or more circumscribed ROI
approaches to connectivity (these approaches are commonly used in
the prior art). Specifically, anhedonia models of the present
disclosure found not only elements of a reward circuit but also
multiple nodes in the DM, Salience (SAL), Cingulo-Opercular Task
Control (COTC), Fronto-Parietal Task Control (FPTC), and Visual
(VIS) networks among others.
[0114] The disclosed anxiety models also retained features across a
widespread set of networks including high representation in the DM
network and sparser representation across executive networks (FPTC,
COTC, Dorsal Attention (DA)), SAL network, and sensory networks.
Though conventional research links anxiety to a set of networks
including a threat circuit, the SAL, DM, and Attention networks,
the models of the present disclosure indicate that the dysfunction
is related to the DM, SAL, and Sensory/Somatomotor networks, the
FPTC network, in addition to COTC and Visual Attention (VA)
networks. The disclosed models indicate that the underlying
elements of anxiety--difficulties regulating emotion in fearful
situations, detecting and controlling conflict, increased attention
to emotional stimuli--have relationships to this set of
networks.
[0115] The disclosed depression and mood models predicted outcome
variables that were not as narrowly focused on a single symptom.
The Mood/Dep_Hopkins subscore contained depressed mood questions
but also ones about guilt, suicide, loss of interest, and somatic
concerns, while Mood_Bipolar contained questions about both
depressed and manic moods, states which the brain may reflect
differently. Both models relied on a broad set of networks beyond
the negative affective circuit (ACC, mPFC, insula, and amygdala)
previously proposed in the prior art. Both anterior and posterior
nodes of the DM network were informative to the disclosed model as
well as FPTC, COTC, Attention, SAL, and Sensory networks. Cognitive
Control networks, Salience, and Attention, and Affective networks
were involved in depressed mood while a central node, the subgenual
cingulate, is involved in mood and connected within the DM
network.
[0116] The clinical scale feature categories, as used in the
present disclosure, include items from the TCI, Hopkins Symptom
Checklist, and the Chapman scales across nearly all six symptom
models. The TCI is a consistently predictive scale, as used in the
present disclosure, as assessed by a number of questions
contributed for all six models of mood, anhedonia, and anxiety.
This scale measures temperaments such as harm avoidance and novelty
seeking, which have previously been associated with depression and
anxiety. In addition, the disclosed models picked out questions
from TCI that pertained to social situations as predictive of the
social anhedonia severity. Therefore, the consistent representation
of TCI across the models suggests its potential utility when
screening patients for multiple symptom domains.
[0117] The relative contributions of the different feature types as
provided for by method 1600 above indicate that both scale items
and fMRI connectivity are highly important to model predictability.
While scale features tended to be more highly represented in the
top 25% of features. Thus, their relative importance may be higher
than fMRI features, though the multimodal models performed better
than scales-only models, suggesting that both scale and fMRI
components contain unique information. Such a comparison of
different feature types in transdiagnostic or community-based
psychiatric symptom severity biomarker studies is not common in the
prior art. Therefore, the present disclosure provides a valuable
step when multiple data types are available for creating predictive
models; each data type has benefits and drawbacks in ease of
collection, measurement stability, resources required for
processing, etc. Different data types can be used according to
these benefits and drawbacks.
[0118] Conventional research suggests that (1) sMRI regularly
underperforms at Major Depressive Disorder (MDD) diagnostic
classification in comparison to fMRI for MDD patients and (2) the
lack of studies reporting sMRI abnormalities in SZ, BD, and ADHD
reflects the lack of predictability or need for larger sample sizes
in detecting effects in this modality.
[0119] The present disclosure further examines the categorical
origins of the fMRI features and clinical scale features for the
disclosed models. Specifically, anhedonia models found not only
elements of a reward circuit but also multiple nodes in the DM,
Salience (SAL), Cingulo-Opercular Task Control (COTC),
Fronto-Parietal Task Control (FPTC), and Visual (VIS) networks
among others. Connectivity changes tied to rewarding contexts in
this wider set of networks have been observed in conventional
research, while a meta-analysis of task-based reward processing in
MDD demonstrated dysfunctional activation in a broad set of regions
including frontal, striatal, cerebellar, visual, and inferior
temporal cortex. As nodes within the DM network are activated both
during self-referential processing and social and emotional
processing, symptoms that decrease socially pleasurable experiences
could have bases in this network. Moreover, coordination between
several of these networks are necessary for healthy function, but
patients with disruptions to the Salience network may have trouble
switching between DM and executive control networks, which may
underlie rumination or impaired reward processing. Indeed,
subcortical nodes of the Salience network are located in
mesocorticolimbic emotional and reward processing centers of the
brain, so disruption of these functions may propagate to cortical
salience regions and beyond.
[0120] The Mood_Bipolar outcome score contained questions about
both depressed and manic moods, states which the brain may reflect
differently. Conventional research found (1) increased
amygdala-sensory connectivity and abnormal prefrontal-parietal
connectivity during manic states, and extensive orbitofrontal to
subcortical and cortical connectivity in depressed states and (2)
the ratio of DM to sensory-motor network activity was greater in a
depressed state of BD and less in manic states in BD. The results
of the disclosed models provide that a wide set of regions and
networks is linked to depressed and elevated mood; additionally,
there may be some dissociation between the two with more DM in
depressed mood and sensory involvement in elevated mood. The
Mood_Bipolar model includes multiple nodes from both sets of RSNs
as important features.
[0121] Regarding the MRI features that may be indexing
neurobiological mechanisms, the disclosed transdiagnostic
regression approach is agnostic to the question of same/different
mechanisms underlying these symptoms. It is likely that multiple
mechanisms exist for each of these symptoms, but the modeled
symptom constructs are based on sum scores that likely cannot
differentiate different mechanisms (like anticipatory v.
consummatory anhedonia). In theory, differing mechanisms may even
span diagnoses rather than differ between diagnoses. Regularized
regression modeling identifies all predictive features from a
sample, and thus multiple possible mechanistic features, features
reported as important for each model might not be related to a
single underlying mechanism; rather, the reported features may be
related to multiple underlying mechanisms. Models that incorporate
multiple mechanisms can be applicable to a wider population.
Experimental Method and Additional Details
[0122] An experimental methodology is disclosed further herein
which provides additional examples of methodologies 1500 and 1600,
as would be readily apparent to one skilled in the art. The
experimental methodology includes experimental results which verify
additional aspects of the disclosed systems and methods; the
experimental results further verify additional benefits of the
present disclosure as compared against conventional systems and
methods.
[0123] Four groups of participants were included in the sample
data, the participants drawn from adults aged 21-50 years: healthy
controls (HC, n=130), individuals with schizophrenia (SZ, n=50),
Bipolar Disorder (BD, n=49), or Attention Deficit and Hyperactivity
Disorder (ADHD, n=43). This full set of participants is outlined
below in Table 1.
TABLE-US-00001 TABLE 1 Demographic information for full set of
participants HC SCZ BD ADHD No. of participants 130 50 49 43 Age
Mean age 31.26 36.46 35.15 33.09 SD age 8.74 8.88 9.07 10.76 Range
age 21-50 22-49 21-50 21-50 Gender No. of female participants 62 12
21 22 Percent female participants 47.69% 24.00% 42.86% 51.16% Race
American Indian or 19.23% 22.00% 6.25% 0% Alaskan Native Asian
15.38% 2.00% 0% 2.33% Black/African American 0.77% 4.00% 2.08%
2.33% White 78.46% 66.00% 77.08% 88.37% More than one race 0% 2.00%
14.58% 6.98% Education No high school 1.54% 18.00% 2.08% 0% High
school 12.31% 44.00% 29.17% 23.26% Some college 20.77% 18.00%
25.00% 30.23% Associate's degree 7.69% 4.00% 6.25% 6.98% Bachelor's
degree 50.00% 10.00% 29.17% 32.56% Graduate degree 6.92% 0% 4.17%
2.33% Other 0.77% 4.00% 4.17% 4.65% MRI Scanner No. of participants
on 106 25 26 23 scanner 1 No. of participants on 24 25 23 20
scanner 2
[0124] Comorbid diagnoses were allowed and identified for 81% of
patients. For the three patient groups, stable medications were
permitted. Diagnoses were based on the Structured Clinical
Interview for DSM-IV (SCID) and supplemented with the Adult ADHD
Interview. After examining subjects for missing data and performing
quality control on the data (as detailed herein), the subject pool
was reduced. Referring momentarily to FIG. 13, current medication
usage status, grouped by medication class, is identified for each
group. Each bar represents the percent of a diagnostic group that
was using a stable medication of that particular class. Some
diagnostic groups did not have any subjects using medications from
a particular class. No HC subjects were on psychiatric medications
per enrollment criteria.
CNP Dataset
[0125] The CNP dataset (release 1.0.5), retrieved from the
OpenNeuro platform, contains demographic, behavioral, clinical, and
imaging data (no genetic data is included). Of the extensive
behavioral testing that participants underwent, the present
disclosure provides analysis from tests of participant
self-reported symptoms and traits (clinician-administered
instruments were only given to subsets of participants). The
self-reported scales used in our analysis include the Chapman
social anhedonia scale (denoted Chapsoc), Chapman physical
anhedonia scale (Chapphy), Chapman perceptual aberrations scale
(Chapper), Chapman hypomanic personality scale, Hopkins symptom
checklist (Hopkins), Temperament and Character Inventory (TCI),
adult ADHD self-report scale v1.1 screener (ASRS), Barratt
Impulsiveness Scale (Barratt), Dickman functional and dysfunctional
impulsivity scale (Dickman), multidimensional personality
questionnaire--control sub scale (MPQ), Eysenck's impulsiveness,
venturesomeness, and empathy scale (Eysenck), scale for traits that
increase risk for bipolar II disorder (Bipolar_ii), and Golden and
Meehl's Seven MMPI items selected by taxonomic method (Golden).
MRI Data Acquisition
[0126] The MRI data acquired according to the experiments of the
present disclosure were provided on 3T Siemens Trio scanners.
Exemplary sMRI data was T1-weighted and acquired using a
magnetization-prepared rapid gradient-echo (MPRAGE) sequence with
the following acquisition parameters: TR=1.9 s, TE=2.26 ms, FOV=250
mm, matrix=256.times.256, 176 1-mm thick slices oriented along the
sagittal plane. The resting-state fMRI scan was a single run
lasting 304 s. The scan was acquired using a T2*-weighted
echoplanar imaging (EPI) sequence using the following parameters:
34 oblique slices, slice thickness=4 mm, TR=2 s, TE=30 ms, flip
angle=90.degree., matrix size 64.times.64, FOV=192 mm. During the
resting-state scan, subjects remained still and relaxed inside the
scanner and kept their eyes open. No specific stimulus or task was
presented to them.
Preprocessing Data into Features
[0127] Experiments conducted according to the present disclosure
used responses to individual questions from the thirteen (13)
self-report scales as input features for a total of five hundred
seventy-eight (578) questions. One participant had incomplete
clinical scale data and was not included in subsequent analyses.
Outcome variables for modeling dysregulated mood, anhedonia, and
anxiety were also selected from the clinical scales. Mood
dysregulations included both depressed mood and mania.
[0128] Preprocessing of sMRI was performed using the recon-all
processing pipeline from the FreeSurfer software package. The
T1-weighted structural image from each participant was
intensity-normalized and skull-stripped. The subcortical
structures, white matter, and ventricles were segmented and
labeled. The pial and white matter surfaces were then extracted and
tessellated, and cortical parcellation was obtained on the surfaces
according to a gyral-based anatomical atlas which partitions each
hemisphere into thirty-four (34) regions. The structural features
from bilateral aparc.stats and aseg.stats files were extracted via
the aparcstats2table and asegstats2table functions in FreeSurfer,
and this included cortical and subcortical regional volumes,
cortical surface area, and cortical thickness estimates. Ten
subjects had missing sMRI scans and were not included in subsequent
analyses.
[0129] Preprocessing of rs-fMRI was performed using the AFNI
software package. Preprocessing of each participant's echo planar
image (EPI) data included: removal of the first three volumes
(before the scanner reached equilibrium magnetization), de-spiking,
registration of all volumes to the now first volume, spatial
smoothing with a 6 mm full-width half-maximum Gaussian filter, and
normalization of all EPI volumes by the mean signal to represent
data as percent signal change. Anatomical data also underwent
several steps: deobliquing of the T1 data, uniformization of the T1
to remove shading artifacts, skull-stripping of the T1, spatial
alignment of the T1 and FreeSurfer-segmented and -parceled anatomy
to the first volume of the EPI data, and resampling of the
FreeSurfer anatomy to the resolution of the EPI data. Subsequently,
the ANATICOR procedure was used for nuisance tissue regression.
White matter and ventricle masks were created and used to extract
the blood-oxygen-level-dependent (BOLD) signals (before
spatially-smoothing the BOLD signal). A 25 mm-radius sphere at each
voxel of the white matter mask was used to get averaged local white
matter signal estimates while the average ventricle signal was
calculated from the whole ventricle mask. Time series for the
motion estimates, and the BOLD signals in the ventricles and white
matter were detrended with a fourth-order polynomial. To clean the
BOLD signal, the experimental methodology provided for regressing
out the nuisance tissue regressors and the six motion estimate
parameters. Cleaned data residuals were used for all subsequent
analysis. Both the preprocessed T1 scan and the cleaned residuals
of the EPI scan were warped to MNI space and resampled to 2 mm
isotropic voxels. The time series of the cleaned residual data was
extracted from each of two hundred sixty-four (264) ROIs as
delineated by the Power atlas. At each ROI, the signals from the
voxels within a 5 mm radius sphere were averaged. Pearson's
correlations were then calculated between the averaged time series
from all ROIs yielding 34,716 unique edges in the functional
connectivity graph (upper triangle of the full correlation matrix).
Ten additional subjects (beyond the ten with missing sMRI data) did
not have fMRI scans and were thus excluded from subsequent
analysis.
[0130] Quality control (QC) for MRI preprocessing was performed
individually on the whole dataset by two authors (MM, YL) who had
85% and 89% agreement between them regarding rejection decisions
for each participant's sMRI and rs-fMRI data, respectively.
Specifically, participants were excluded if they had
misregistration between fMRI and sMRI scans, >3 mm head motion
in the fMRI scan (to correspond with edge length of a voxel in
functional scan), headphone artifacts that overlapped with brain
tissue in the sMRI scan, incorrect FreeSurfer-automated grey/white
segmentation and anatomical parcellation in the sMRI scan, and
aliasing or field of view artifacts in either scan. Discrepancies
were resolved between the two authors in order to create a final
rejection list of participants. The disclosed methodology used two
hundred seventy (270) sMRI features from FreeSurfer-calculated
cortical and subcortical regional volumes, cortical surface area,
and cortical thickness estimates, and 34,716 AFNI-processed fMRI
connectivity features calculated from pairwise Pearson's
correlations between two hundred sixty-four (264) ROIs of the Power
atlas. Subsets of these input features were used as predictor
variables in subsequent modeling as explained below.
[0131] Output variables that were modeled included those which
indexed depression, anxiety, anhedonia, or other negative symptoms.
A mix of total scores, sub-scale sum or average scores, and
individual question scores were predicted as each has their
advantages. These scores include the twenty-eight (28)-question
versions of the total HAMD score (`hamd`), the HAMD subscore for
questions 1, 7, and 8 (`hamd178`, indexes a melancholic-type of
symptom), the HAMD item score for question 7 (`hamd7`, indexes lack
of interest or anhedonia), the Chapman Social Anhedonia total score
(`chapsoc`), the Chapman Physical Anhedonia total score
(`chapphy`), BPRS negative sub score (`bprs_negative`, the average
of negative symptom questions 13, 16, 17, and 18), BPRS
depression-anxiety subscore (`bprs_depanx`, the average of
depression and anxiety symptom questions 2, 3, 4, and 5), Hopkins
anxiety score (`hopkins_anxiety`, the average of anxiety symptom
questions 2, 17, 23, 33, 39, and 50), Hopkins depression score
(`hopkins_depression`, the average of depression symptom questions
5, 15, 19, 20, 22, 26, 29, 30, 31, 32, and 54), Bipolar ii mood
score (`bipolarii_mood`, the sum of mood questions 1-9), Bipolar ii
anxiety score (`bipolar_anxiety`, the sum of anxiety questions
24-31), SANS anhedonia factor score (`sans_factor_anhedonia`, the
average of anhedonia questions 17, 18, 19, and 20), SANS anhedonia
global score (`sans_global_anhedonia`, questions 21 which is the
clinician's overall anhedonia assessment score), SANS avolition
factor score (`sans_factor_avolition`, the average of avolition
items 12, 13, 14, and 15), SANS avolition global score
(`sans_globals_avolition`, question 16 which is the clinician's
overall avolition assessment score), SANS blunt affect factor score
(`sans_factor_bluntaffect`, the average of affective flattening
items 1, 2, 3, 4, 5, and 6), SANS blunt affect global score
(`sans_global_bluntaffect`, question 7 which is the clinician's
overall blunt affect assessment score), SANS alogia factor score
(`sans_factor_alogia`, the average of alogia items 8, 9, and 10),
SANS alogia global score (`sans global alogia`, question 11 which
is the clinician's overall alogia assessment score), SANS attention
factor score (`sans_factor attention`, the average of attention
items 22 and 23), and SANS attention global score (`sans global
attention`, question 24 which is the clinician's overall attention
assessment score).
[0132] Sum scores are commonly accepted by the FDA regarding
positive efficacy results, but using only sum scores may obfuscate
brain-behavior relationships at more fine-grained levels of
symptoms. Subjects with missing values ("n/a") for any input or
output variables or who did not pass MRI QC were removed from the
input set. As different input feature sets were used, different
models had different sample sizes. The availability of clinical
scores for particular clinical scales taken only by certain subsets
of patients also affected the final sample size for each model. See
the samples sizes resulting from these factors in Table 2.
TABLE-US-00002 TABLE 2 Sample Sizes Resulting from Select Factors
Scales_ Scales_ sMRI_ Scales_ sMRI_ Predicted Scores Scales sMRI
fMRI sMRI fMRI fMRI fMRI Chapman Social Anhedonia 271 206 147 205
117 146 116 Chapman Physical 271 206 147 205 117 146 116 Anhedonia
HAMD, total score 141 108 82 107 63 81 62 HAMD, q1, 7, 8 sum score
140 108 82 107 63 81 62 HAMD, q7 140 108 82 107 63 81 62 BPRS,
negative score 141 108 82 107 63 81 62 BPRS, depression-anxiety 141
108 82 107 63 81 62 score Hopkins, anxiety score 271 206 147 205
117 146 116 Hopkins, depression score 271 206 147 205 117 146 116
Bipolar II, depression score 271 206 147 205 117 146 116 Bipolar
II, anxiety score 271 206 147 205 117 146 116 SANS, anhedonia
factor 99 75 54 74 40 53 39 SANS, avolition factor 99 75 54 74 40
53 39 score SANS, blunt affect factor 99 75 54 74 40 53 39 score
SANS, alogia factor score 99 75 54 74 40 53 39 SANS, attention
factor 99 75 54 74 40 53 39 score SANS, anhedonia global 87 74 83
73 39 52 38 score SANS, avolition global 99 75 54 74 40 53 39 score
SANS, blunt affect global 99 75 54 74 40 53 39 score SANS, alogia
global score 99 75 54 74 40 53 39 SANS, attention global 99 75 54
74 40 53 39 score
Regression Modeling
[0133] All regression modeling was performed with a combination of
Python language code and the Python language toolbox scikit-learn
(http://scikit-learn.org/stable/index.html). The disclosed
experiment modeled six different symptom severity scores across the
clinical scales, two each for mood, anhedonia, and anxiety. All
outcome measures were precalculated in the CNP dataset. For mood,
the average of the Hopkins scale depression symptom questions was
used (further referenced as Mood/Dep_Hopkins) and the sum of mood
questions from the Bipolar_ii inventory (Mood_Bipolar). The two
anhedonia variables were derived from total scores on the Chapman
Social Anhedonia scale (Anhedonia_Chapsoc) and the Chapman Physical
Anhedonia scale (Anhedonia_Chapphy). Anxiety was indexed from the
sum of Bipolar_ii anxiety questions (Anxiety_Bipolar) and average
of anxiety symptom questions (Anxiety_Hopkins).
[0134] The experimental methodology built one hundred twenty-six
(126)--(6 outcome variables.times.7 predictor variable sets.times.3
model algorithms)--sets of models. For each of these sets of
models, hyperparameters were tuned using 5-fold cross-validated
grid-search on a training set of data (80% of data), and the model
using the selected hyperparameters was tested on a separate
evaluation set of data (20% held-out sample). This train/evaluation
split was performed twenty-five (25) times for a version of nested
cross-validation where the outer loop was repeated random
sub-sampling validation. Critically, this nested cross-validation
approach means that models are trained on a training set that is
completely separate from an evaluation set used to generate
evaluation metrics reported as final results. For each of the 126
sets of models, the experimental methodology took an
importance-weighted, forward selection approach to regression
modeling, involving three main steps: first, an initial
rank-ordering step for ordering features by importance; second, a
forward-selection search step for building a series of models
utilizing growing subsets of ordered features (i.e., the best
features) selected from the first step; and third, an evaluation
step to choose the best model and subset of features according to a
prespecified criterion to find the optimal model.
[0135] The twenty-five (25) iterations of training/evaluation set
splits for modeling and validation as explained above allowed
generation of descriptive statistics for each feature subset to
calculate median and standard deviation metric scores. The metrics
chosen for the final step of evaluation were mean squared error
(MSE) and r.sup.2 calculated on the held-out evaluation sets. The
median r.sup.2 and standard deviation of r.sup.2 were found for
each subset. And the "best model" overall was selected by finding
the maximum median r.sup.2 value over all feature subsets and
selecting the model that corresponded to that max median r.sup.2
value (FIGS. 1A-1B). To find which input feature set and which
model type led to the best biomarkers, subsequent comparisons were
also made based on the r.sup.2 of the best models.
[0136] Thus, further examination of features focused on this set of
models. An initial comparison between models that used the full
feature sets (up to 35,564 features) and those that used the
optimal set of truncated features (ordered subsets of the full
feature sets identified through the forward modeling approach)
demonstrated vastly different performances between the modeling
approaches. The modeling results for Elastic Net using the full
feature sets on average explained 22% of the variance while
truncated sets explained an average of 78% for the clinical scales
data, sMRI data and fMRI data input models (metrics for full
features sets are presented in below in Table 3).
TABLE-US-00003 TABLE 3 Comparison of all Elastic Net Models using
Full Feature Sets. Full Features Sets Feature Set Scales Outcome
Scales_ sMRI_ Scales_ sMRI_ Variables Metric Scales sMRI fMRI sMRI
fMRI fMIRI _fMRI Mood/Dep_ median MSE 0.196 0.310 0.276 0.208 0.349
0.224 0.103 Hopkins median r.sup.2 0.373 0.019 -0.595 0.229 -0.157
0.290 0.238 Mood_ median MSE 2.172 11.538 7.683 1.809 6.379 2.426
2.099 Bipolar median r.sup.2 0.670 -0.282 -0.140 0.676 -0.255 0.579
0.658 Anhedonia_ median MSE 48.835 69.992 64.141 36.425 65.951
25.679 59.355 Chapphy median r.sup.2 0.249 -1.046 -0.156 0.052
-0.537 0.435 0.247 Anhedonia_ median MSE 23.597 118.983 60.536
21.494 39.454 25.938 25.641 Chapsoc median r.sup.2 0.539 -0.941
-0.255 0.597 -0.443 0.565 0.293 Anxiety_ median MSE 0.105 0.328
0.343 0.130 0.261 0.253 0.213 Hopkins median r.sup.2 0.324 -0.028
-0.110 0.322 0.031 -0.097 -0.510 Anxiety_ median MSE 1.831 3.432
3.634 1.815 2.762 1.200 1.108 Bipolar median r.sup.2 0.183 -0.115
0.003 0.624 -0.072 0.473 0.387
[0137] For the six models using Elastic Net with the clinical
scales data, sMRI data, and fMRI data input feature set, model
performance was evaluated on the held-out evaluation set with
measured v. predicted plots (FIGS. 2A-2C) and r.sup.2 values across
models for different outcome variables (see Table 4 below, last
column). All six models were highly predictive with the variance
explained ranging from 65-90% and number of non-zero features p
ranging from 28-106 (Mood/Dep_Hopkins r.sup.2=0.72, p=28;
Mood_Bipolar r.sup.2=0.90, p=93; Anhedonia_Chapphy r.sup.2=0.65,
p=32; Anhedonia_Chapsoc r.sup.2=0.80, p=106; Anxiety_Hopkins
r.sup.2=0.75, p=47; Anxiety_Bipolar r.sup.2=0.85 p=31).
TABLE-US-00004 TABLE 4 Comparison of all Elastic Net Models using
Truncated Feature Sets Returned by Forward Selection Approach
Truncated Features Sets Feature Set Scales_ Outcome Scales_ sMRI_
Scales_ sMRI_ Variables Metric Scales sMRI fMRI sMRI fMRI fMRI fMRI
Mood/Dep_ median MSE 0.148 0.299 0.159 0.106 0.138 0.110 0.076
Hopkins median r.sup.2 0.530 0.019 0.450 0.677 0.415 0.610 0.721 p
50 3 29 102 16 26 28 Mood_Bipolar median MSE 1.183 6.408 2.530
1.021 1.867 0.814 0.614 median r.sup.2 0.836 0.123 0.625 0.864
0.719 0.874 0.904 p 112 22 255 114 241 236 93 Anhedonia_ median MSE
19.670 42.835 21.406 15.178 14.419 9.648 15.814 Chapphy median
r.sup.2 0.642 0.158 0.656 0.690 0.740 0.841 0.652 p 240 61 358 123
211 211 32 Anhedonia_ median MSE 12.510 43.893 13.246 10.847 14.682
8.627 9.886 Chapsoc median r.sup.2 0.782 0.065 0.732 0.796 0.677
0.829 0.804 p 126 32 345 63 559 31 106 Anxiety_ median MSE 0.134
0.260 0.110 0.145 0.098 0.095 0.077 Hopkins median r.sup.2 0.525
0.042 0.653 0.471 0.650 0.704 0.751 p 54 6 85 49 29 127 47 Anxiety_
median MSE 1.252 2.865 0.988 0.798 0.703 0.589 0.535 Bipolar median
r.sup.2 0.616 0.121 0.644 0.735 0.789 0.825 0.847 p 62 16 153 58
161 32 31
[0138] Next, the proportions of features derived from clinical
scale data, fMRI data, and sMRI data feature sets were compared for
the best model for each outcome variable both among the whole
feature set and the top 25% of features (FIGS. 3A-3B). The best
models for Mood/Dep_Hopkins, Anhedonia_Chapphy, and Anxiety_Bipolar
had a roughly equal number of clinical scale and fMRI features
while Anxiety_Hopkins, Anhedonia_Chapsoc, and Mood_Bipolar models
had a bias towards fMRI features (FIG. 3A). FIG. 3B shows, however,
that for many outcome variables there was a disproportionate number
of scale features in the top features. Notably, there was a paucity
of sMRI features in both these models as only Anhedonia_Chapphy had
any sMRI features selected by the models.
[0139] The disclosed experimental methodology modeled six different
symptom severity scores across the clinical scales, two each for
mood, anhedonia, and anxiety. First, the methodology provides for
predicting a mix of total scores and sub-scale sum or average
scores from scales that were given to all three patient groups and
HCs to retain the largest number of participants possible in the
models. Each of these scores was already calculated and included in
the CNP dataset. For mood, the average of depression symptom
questions 5, 15, 19, 20, 22, 26, 29, 30, 31, 32, and 54 from the
Hopkins inventory (precalculated "Hopkins_depression" score,
further referenced as Mood/Dep_Hopkins in this study) and the sum
of mood questions 1-9 from the Bipolar_ii inventory (precalculated
"Bipolar_mood" score, further referenced as Mood_Bipolar in this
study) was used. The two anhedonia variables were derived from
total scores on the Chapman Social Anhedonia scale (precalculated
"Chapsoc" score, further referenced as Anhedonia_Chapsoc in this
study) and the Chapman Physical Anhedonia scale (precalculated
"Chapphy" score, further referenced as Anhedonia_Chapphy in this
study). And anxiety was indexed from the sum of Bipolar_ii anxiety
questions 24-31 (precalculated "Bipolar_anxiety" score, further
referenced as Anxiety_Bipolar in this study) and average of anxiety
symptom questions 2, 17, 23, 33, 39, and 50 from the Hopkins
anxiety score (precalculated "Hopkins_anxiety" score, further
referenced as Anxiety_Hopkins in this study).
[0140] For each of the six models (Mood/Dep_Hopkins, Mood_Bipolar,
Anhedonia_Chapsoc, Anhedonia_Chapphy, Anxiety_Bipolar,
Anxiety_Hopkins), seven combinations of feature types were used as
inputs to be able to evaluate the performance of single and
multimodal feature sets. These included (1) clinical scales data
only, (2) sMRI data only, (3) fMRI data only, (4) clinical scales
data and sMRI data, (5) clinical scales data and fMRI data, (6)
sMRI data and fMRI data, and (7) clinical scales data, sMRI data,
and fMRI data. As different input feature sets were used, different
models had different sample sizes. The samples sizes resulting from
this factor were n=271 for the clinical scales only models, n=206
for sMRI only models, n=147 for fMRI only models, n=205 for
clinical scales data and sMRI data models, n=117 for sMRI data and
fMRI models, n=146 for clinical scales data and fMRI data models,
and n=116 for clinical scales data, sMRI data, and fMRI data models
(see Table 5 below. As input features varied in their mean values
and regularized models require normally distributed data, each
input feature was scaled separately to have zero mean and unit
variance. This approach of using clinical scales, sMRI, and fMRI
features as inputs reflect multiple goals of the disclosed
methodology: (1) finding the feature set that maximizes
predictability, and (2) exploring ways to reduce the dimensionality
of feature sets. For example, in the case of using clinical
self-report measures to predict clinical scale measures, reducing
the dimensionality is useful in removing redundancy and finding a
compact, optimized set of questions which could reduce time and/or
cost of administration and which could potentially better map onto
neural circuitry.
TABLE-US-00005 TABLE 5 Sample size for each model Number of
Subjects Scales_ Outcome Scales_ sMRI_ Scales_ sMRI_ Variables
Scales sMRI fMRI sMRI fMRI fMRI fMRI Mood/Dep_ 271 206 147 205 117
146 116 Hopkins Mood_Bipolar 271 206 147 205 117 146 116 Anhedonia_
271 206 147 205 117 146 116 Chapphy Anhedonia_ 271 206 147 205 117
146 116 Chapsoc Anxiety_ 271 206 147 205 117 146 116 Hopkins
Anxiety_ 271 206 147 205 117 146 116 Bipolar
[0141] In order to probe performance with a variety of modeling
algorithms, for each scale output and feature set input, two
regularized general linear model regression algorithms, LASSO and
Elastic Net, and one non-linear regression model algorithm, Random
Forest, were used for the modeling. These methods improved
prediction accuracy and interpretability over regular regression
methods using ordinary least squares. LASSO uses regularization by
imposing an L1-penalty parameter to force some coefficients to
zero; this step introduces model parsimony that benefits
interpretability and predictive performance while guarding against
overfitting. If predictor variables are correlated, however, the
LASSO approach arbitrarily forces only a subset of the variables to
zero, which makes interpretation of specific features more
difficult. The Elastic Net algorithm uses both L1- and L2-penalty
parameters to better retain groups of correlated predictor
variables; this improves interpretability as highly predictive
features will not randomly be set to zero (thereby diminishing
their importance to the model). It is also better suited in cases
when the number of predictor variables is much greater than the
number of samples (p>>n). The non-linear regression algorithm
Random Forest was also chosen for comparison purposes.
[0142] In the one hundred twenty-six (126) sets of models built
according to the present disclosure, hyperparameters were tuned
using 5-fold cross-validated grid-search on a training set of data
(80% of data), and selected hyperparameters were used on a separate
evaluation set of data (20% held-out sample). This train/evaluation
split was performed twenty-five times (25.times.) for a version of
nested cross-validation (inner loop is 5-fold for hyperparameter
optimization and model fitting, and outer loop is repeated random
sub-sampling validation twenty-five times (25.times.) for model
evaluation). This nested cross-validation approach means that
models are trained on a training set that is completely separate
from an evaluation set used to generate evaluation metrics reported
as final results. The approaches of nested cross-validation and of
splitting data between training and evaluation data is one way to
minimize overfitting in addition with permutation testing (which
can also be performed). The hyperparameter range for LASSO was
alpha equal to 0.01, 0.03, and 0.1 (three samples through the log
space between 0.01 and 0.1) which is the coefficient of the L1
term. Hyperparameter ranges for Elastic Net were alpha equal to
0.01, 0.03, and 0.1, and l1_ratio equal to 0.1, 0.5, and 0.9 which
is the mixing parameter used to calculate both L1 and L2 terms.
Hyperparameter ranges for Random Forest included the number of
estimators equal to 10 or 100 and the minimum samples at a leaf
equal to 1, 5, and 10. The best hyperparameters were chosen from
the model that maximized an interim r.sup.2 score (coefficient of
determination) across the 5-fold cross-validation procedure in the
training set and applied to the model of the never-seen evaluation
set.
[0143] For each of the one hundred twenty-six (126) sets of models,
an importance-weighted, forward selection approach to regression
modeling (a variation of forward-stepwise selection) was applied as
a data-driven way to identify the optimal feature subset to include
in regression modeling. Finding an optimal subset helps in
high-dimensional cases where the number of features is greater than
the number of samples to avoid overfitting of the models. It also
reduces nuisances from uninformative input variables without
requiring the modeler to decide a priori whether a variable is
signal or noise. This approach involves three main steps: (1) an
initial rank-ordering step for ordering features by importance; (2)
a forward-selection search step for building a series of models
utilizing growing subsets of ordered features (i.e., the best
features) selected from the first step; and (3) an evaluation step
to choose the best model and subset of features according to a
prespecified criterion to find the optimal model. This approach
thus integrates feature selection into modeling using a
multivariate embedded method that can take variable interactions
into account to potentially construct more accurate models. Within
each step, each new model utilized the training/evaluation set
split and grid-search procedure to optimize hyperparameters as
explained above. First, the feature rank-ordering step uses the
full feature set (either clinical scales data only, sMRI data only,
etc.) as the input to the model algorithms which returns not only
predicted values for the evaluation dataset but also the importance
of each feature for the resulting model. Feature importance was
assessed from the regression coefficients of LASSO and Elastic Net
models with ordering (most important to least important) based on
the absolute value of the coefficient. Ordering by absolute value
reflects that features with the largest magnitude influence the
symptom severity scores the most. Feature ordering for the Random
Forest algorithm (typical regression coefficients are not
available) was done using the "gini importance" or mean decrease in
impurity as implemented in the scikit-learn library.
[0144] Second, the forward-selection search step systematically
searches through subsets of the rank-ordered features (truncated
feature sets) for the subset that leads to the best model. Since
having more features than samples (p>>n) both increases the
risk of overfitting and decreases the performance due to
uninformative features adding nuisances, this data-driven way of
searching the ordered feature space for an optimal subset of
features was used. A series of regressions on subsets of the
ordered features was run with subsets chosen in powers of two
(i.e., inputting the top feature only, the top two features only,
the top four features only, etc.) up to two hundred fifteen (215)
features. The outer loop of nested cross-validation (the
twenty-five (25) iterations of training/evaluation set splits for
modeling and validation as explained above) also allowed generation
of descriptive statistics for each feature subset to get median and
standard deviation metric scores. The metrics chosen for the final
step of evaluation were mean squared error (MSE) and r.sup.2
calculated on the held-out evaluation sets. The median r.sup.2 and
standard deviation of r.sup.2 were found for each subset. And the
"best model" overall was selected by finding the maximum median
r.sup.2 value over all feature subsets and selecting the model that
corresponded to that max median r.sup.2 value (FIGS. 1A-1B). All
subsequent follow-up is on the one hundred twenty-six (126) best
models for each combination of input/model type/output.
[0145] To find which input feature set (clinical scales data only,
sMRI data only, fMRI data only, clinical scales data and sMRI data,
clinical scales data and fMRI data, sMRI data and fMRI data, and
clinical scales data, sMRI, and fMRI) and which model type (LASSO,
Elastic Net, Random Forest) led to the best biomarkers, subsequent
comparisons were also made based on the r.sup.2 of the best models.
The r.sup.2 is a standardized measurement of explained variance
(with a maximum value of 1 but an unbounded minimum) while the MSE
values are not standardized across the different models making it
less appropriate to use MSE for comparison.
[0146] Several control scenarios were implemented to test
alternative hypotheses that modeling may have been impacted by
overfitting or variables of no interest. Model performance for the
best models (chosen by the methods above) was compared with (1)
models with permuted outcome variables (to test for overfitting)
and (2) models that included variables of no interest in addition
to the features of interest. In the first case, the null hypothesis
is that the features and severity scores are independent; however,
an overfit model could misidentify dependence. But if the high
performance of the disclosed models is due to identification of
real structure in the data rather than overfitting, the best models
will perform significantly better than models built from the
permuted data and the null hypothesis can be rejected. After the
original ordering of features and selection of the 2n subset that
led to the best model, severity scores were permuted across
participants for a given outcome variable one hundred (100) times,
and one hundred (100) models were built based on the permuted
scores.
[0147] Predictability (assessed with r.sup.2) was calculated from
these one hundred (100)-permuted models which allowed generation of
an empirically-derived distribution of r.sup.2 values for
calculating a test statistic (p-value) compared to the median
r.sup.2 of the chosen best model. In the second control case,
models built with predictor variables of no interest allowed
assessment of the predictability of these variables in relation to
predictors of interest (scales data, sMRI data, fMRI data) to see
if possible confounding variables drive the results. Variables of
no interest included age; gender; years of schooling; in-scanner
mean framewise displacement (FD) which was calculated as an L2
norm; the six in-scanner motion measures for x, y, z directions and
pitch, roll, yaw; sharp head motion (output of AFNI's @ 1dDiffMag);
number of frames that would have been censored at a threshold of
FD>0.5 mm; the current use of medication categorized by
medication class; and scanner number (since two scanners were
used). Six sets of models were generated for the six outcome
variables also using the importance-weighted forward-selection
approach. The r.sup.2 score distribution from the best of the
nuisance models (p that optimized median r.sup.2) was compared to
the best models without nuisance variables included with the
non-parametric Wilcoxon rank-sum tests to assess if nuisance
variables change the predictive ability of the models.
Feature Stability
[0148] Feature stability (a measure of the consistency of feature
weightings) was calculated using a correlation approach for the
best models. The Pearson correlation coefficient between predictor
variable coefficients (i.e., the importance measurements) retains
more information over feature rankings or include/exclude subsets.
From the 25 subsamplings of subjects into training and evaluation
sets for each subset of features (p=1, 2, 4, 8, . . . , 32768
during the forward-selection search step), feature stability was
calculated across these twenty-five (25) iterations for both the
32768 set of features (as it is nearly the full set of 35564
features) and the best models (the ones with the optimal p features
as found from the best median r.sup.2). Thus, given the vector of
predictor coefficients for each of the twenty-five (25) model
iterations, the pairwise correlations can be calculated between the
twenty-five (25) coefficient vectors (25*(25-1)/2=300 combinations)
which gives three hundred (300) pairwise correlations for each
feature subset. The mean of these three hundred (300) correlation
coefficients can be taken for each feature subset to find these
correlation coefficient means.
[0149] FIG. 9 shows the feature stability for the Elastic Net
scales+sMRI+fMRI models. For the set of 32768 features, the
correlation coefficient means were in a moderate range of 0.4 to
0.61 for Anxiety_Hopkins and Mood/Dep_Hopkins, respectively (shown
in FIG. 9). In contrast, the feature stability of the models with
optimal p features (as found from the best median r.sup.2) range
from 0.8 to 0.93 for Anhedonia_Chapsoc and Mood/Dep_Hopkins,
respectively. So feature stability is moderate when using the
nearly full set of features but improves when using an optimal
subset selected with the forward modeling approach.
Clinical Features Associated with Symptom Severity
[0150] The disclosed methodology further examined groupings of the
scale-based features sorted by the proportion of the scales from
which they are derived. For each model, the scale features for the
best model were proportionately selected from the scales shown in
FIGS. 4A-4C. The TCI scale, in particular, was highly represented
compared to the other scales in all six models (note that Hopkins,
Bipolar, Chapphy, and Chapsoc items were not be included in all
models). TCI contained a number of questions on temperament and
character traits that could be related to a variety of symptoms,
and the disclosed results suggested that it contained questions
that are predictive of mood, anhedonia, and anxiety (shown in Table
6 below). Regression coefficients (ordered by magnitude) are either
positive or negative, indicating that a "True" answer for the
respective question increased or decreased the outcome variable
score, respectively.
TABLE-US-00006 TABLE 6 Predictive Temperament and Character
Inventory (TCI) questions for models of mood, anhedonia, and
anxiety. CNP Regression Question Outcome Variable Coefficient Label
True/False Question Mood/Dep_Hopkins 0.06 tci149t I often stop what
I am doing because I get worried, even when my friends tell me
everything will go well. -0.05 tci76p I am more hard-working than
most people. 0.04 tci92t I need much extra rest, support, or
reassurance to recover from minor illnesses or stress. 0.02 tci22t
I have less energy and get tired more quickly than most people.
-0.01 tci210t People find it easy to come to me for help, sympathy,
and warm understanding. Mood_Bipolar 0.17 tci140p I often give up
on a job if it takes much longer than I thought it would. 0.07
tcil2t I often feel tense and worried in unfamiliar situations,
even when others feel there is little to worry about. 0.06 tci8lt
Usually I am more worried than most people that something might go
wrong in the future. 0.05 tci217t I usually feel tense and worried
when I have to do something new and unfamiliar 0.04 tci53t I lose
my temper more quickly than most people. Anhedonia_Chapphy 0.77
tci217t I usually feel tense and worried when I have to do
something new and unfamiliar -0.52 tci5p I like a challenge better
than easy jobs. 0.52 tci156t I don't go out of my way to please
other people. 0.47 tci120t I find sad songs and movies pretty
boring. 0.25 tci83t I feel it is more important to be sympathetic
and understanding of other people than to be practical and
tough-minded. Anhedonia_Chapsoc -1.19 tcil 17t I would like to have
warm and close friends with me most of the time. 1.18 tci231t I
usually stay away from social situations where I would have to meet
strangers, even if I am assured that they will be friendly. -0.79
tci2lt I like to discuss my experiences and feelings openly with
friends instead of keeping them to myself. 0.57 tci44t It wouldn't
bother me to be alone all the time. 0.55 tci46t I don't care very
much whether other people like me or the way I do things. -0.48
tci210t People find it easy to come to me for help, sympathy, and
warm understanding. 0.37 tci201t Even when I am with friends, I
prefer not to \"open up\" very much. 0.34 tci180t I usually like to
stay cool and detached from other people. 0.14 tci70t I like to
stay at home better than to travel or explore new places.
Anxiety_Hopkins 0.05 tcil4lt Even when most people feel it is not
important, I often insist on things being done in a strict and
orderly way. 0.05 tci27t I often avoid meeting strangers because I
lack confidence with people I do not know. 0.04 tci180t I usually
like to stay cool and detached from other people. Anxiety_Bipolar
-0.25 tci157t I am not shy with strangers at all. 0.25 tci54t When
I have to meet a group of strangers, I am more shy than most
people. 0.23 tci8lt Usually I am more worried than most people that
something might go wrong in the future. 0.16 tci129t I often feel
tense and worried in unfamiliar situations, even when others feel
there is no danger at all. -0.14 tci3t I am often moved deeply by a
fine speech or poetry. 0.10 tci2lit I am slower than most people to
get excited about new ideas and activities.
[0151] TCI was a scale with important predictive features for all
six models. It contained a number of questions on temperament and
character traits that could be related to a variety of symptoms,
and the results suggested that it contained questions that are
predictive of mood, anhedonia, and anxiety, as shown above in Table
5. For example, 43% of the questions predictive of Anxiety_Bipolar
were from TCI, with the most predictive question being, "I am not
shy with strangers at all." Positive responses to this question
predicted a lower Anxiety_Bipolar score since the regression
coefficient was negative in this model. Though not uniformly so,
some of the other questions also assessed shyness or worry. The
Anhedonia_Chapsoc model also had a very high percentage of TCI
questions, with the most predictive question being, "I would like
to have warm and close friends with me most of the time." Here
positive responses indicated decreased social anhedonia severity as
the regression coefficient was also negative. While not all
questions in the TCI pertain to people and social situations, all
but one of the remaining questions that were predictive of the
Anhedonia_Chapsoc score did include mention of these situations.
The predictive questions for the Anhedonia_Chapphy,
Mood/Dep_Hopkins, Mood_Bipolar, and Anxiety_Hopkins scores were
more mixed overall, though. Additionally, FIGS. 4A-4C show that
Chaphyp questions were also predictive in all models (but only
contributed 1-2 items in five of the six scales). The most numerous
questions (6/31) from Chaphyp were for Mood_Bipolar which may be
expected as the Chapman hypomanic scale, and the Mood_Bipolar
subscore of this scale both included an assessment of mania (as
opposed to the Mood/Dep_Hopkins score which is more related to
depressed mood and depressive symptoms).
Neurobiological Characteristics of Dysregulated Mood, Anhedonia,
and Anxiety
[0152] The fMRI connectivity features were composed of the
strengths of network edges (connections between nodes) but can also
be grouped by suggested intrinsic resting-state networks from the
Power atlas. As the number of fMRI connectivity features selected
by the models were a small subset of all possible fMRI connectivity
features, full connectivity matrices are quite sparse (the full
connectivity matrices are shown in FIGS. 10A-10C). Therefore, the
number of edges within and between each intrinsic resting-state
network (RSN) is shown in the connectivity matrices of FIGS. 5A-5F
for each outcome variable. The predictive fMRI connectivity
features appeared mostly distributed across multiple networks
rather than selective to a few particular networks (FIGS. 5A-5F).
Connectivity features implicate nodes in ten RSNs for
Mood/Dep_Hopkins, 12 RSNs for Mood_Bipolar, 10 RSNs for
Anhedonia_Chapphy, 12 RSNs for Anhedonia_Chapsoc, 13 RSNs for
Anxiety_Hopkins, and 10 RSNs for Anxiety_Bipolar models (out of
fourteen (14) RSNs from this atlas). While Anhedonia_Chapsoc
connectivity measures were also distributed, there was a higher
concentration of connectivity features between the Default Mode
(DM) network and other networks. In particular, the predictive
edges between the DM and other networks mostly originate from the
anterior cingulate and/or the medial orbitofrontal lobe. The
Anhedonia_Chapsoc model also contained nodes in the top five
features that were located within a reward circuit including the
putamen and orbitofrontal cortex (OFC). Edges either within the DM
network or between the DM and other networks consistently were the
most numerous features relative to all other within- and
between-network features across all models. All the features in
each model, including sMRI, are available upon request from the
authors.
[0153] Control models, including nuisance variables, found no
predictive advantage of motion, age, gender, years of schooling,
scanner number, or medication usage with the exception of SNRI
antidepressants on Anhedonia_Chapphy models.
Controls and Additional Models
[0154] To evaluate whether the disclosed models are influenced by
confounding demographic or other variables, for each of the six
outcome variables, six Elastic Net models were built with the
Scales+sMRI+fMRI+nuisance features; the distribution of 25 r.sup.2
measures was selected for the best nuisance models (p that
optimized r.sup.2) to compare with the r.sup.2 measures of the best
Scales+sMRI+fMRI non-nuisance models. Two comparisons returned no
difference (Mood/Dep_Hopkins: Scales+sMRI+fMRI median r.sup.2=0.72,
Scales+sMRI+fMRI+nuisance median r.sup.2=0.69, Wilcoxon Rank Sum U
statistic=296, p=0.38; Mood_Bipolar: Scales+sMRI+fMRI median
r.sup.2=0.90, Scales+sMRI+fMRI+nuisance median r.sup.2=0.90, U
statistic=305, p=0.45). Two comparisons show significantly improved
performance of the Scales+sMRI+fMRI models (Anxiety_Bipolar:
Scales+sMRI+fMRI median r.sup.2=0.85, Scales+sMRI+fMRI+nuisance
median r.sup.2=0.76, U statistic=135, p=0.0003; Anxiety_Hopkins:
Scales+sMRI+fMRI median r.sup.2=0.75, Scales+sMRI+fMRI+nuisance
median r.sup.2=0.59, U statistic=128, p=0.0002). And two
comparisons show significantly improved performance of the
Scales+sMRI+fMRI+nuisance models (Anhedonia_Chapsoc:
Scales+sMRI+fMRI median r.sup.2=0.80, Scales+sMRI+fMRI+nuisance
median r.sup.2=0.83, U statistic=221, p=0.038; Anhedonia_Chapphy:
Scales+sMRI+fMRI median r.sup.2=0.65, Scales+sMRI+fMRI+nuisance
median r.sup.2=0.83, U statistic=94, p<0.0001). Critically,
though Scales+sMRI+fMRI+nuisance models for Anhedonia_Chapsoc and
Anhedonia_Chapphy outcomes performed better, none of the nuisance
variables were actually selected in models (they all had
coefficients equal to zero) except for "Antidepressant-SNRI" in the
Anhedonia_Chapphy model (it was ranked 22 out of 207 non-zero
features). Thus, current usage of SNRI antidepressants may affect
physical anhedonia severity, but none of the other measured
confounding variables affect the disclosed models.
[0155] Additionally, another set of Elastic Net models with the
Scales+sMRI+fMRI feature set but with scrambled severity scores (a
permutation testing approach) was built and used to test for
overfitting, but no evidence of overfitting was found using this
approach as demonstrated by the empirical null distributions (shown
in FIGS. 11A-11C). For all six models, the median r.sup.2 of the
best models was statistically significant (p<0.01).
Methodology Using Resting-State fMRI Data
[0156] Uncovering the biological basis of patient heterogeneity is
a key to creating clinically relevant biomarkers. Non-invasive
imaging enables the visualization of the brain-to-symptom links
underlying neurobehavioral disorders. Conventional technology is
often too narrowly applied by only examining one aspect of biology
(e.g., anatomical or functional MRI measures) or a single
diagnostic group. Since symptoms such as depression span multiple
neurobehavioral disorders, a more robust symptom biomarker can be
better captured by examining transdiagnostic patient cohorts and
utilizing multiple neuroimaging modalities.
[0157] Therefore, embodiments of the present disclosure provide a
transdiagnostic multimodal MRI model that successfully identified
biomarkers that can reliably predict clinician-rated depression
severity across multiple neurobehavioral disorders. This model can
use the Consortium for Neuropsychiatric Phenomics dataset, which
includes resting-state functional MRI (rs-fMRI) and structural-MRI
(sMRI) imaging measures from patients with schizophrenia, bipolar
disorder, and attention deficit and hyperactivity disorder (n=142
total). Input features included preprocessed sMRI volume, surface,
and thickness measures (270 features) and preprocessed rs-fMRI
connectivity measures (34,716 features). The model provides an
outcome measure of depression in the clinician-rated 28-item total
score from the Hamilton Rating Scale for Depression (HAMD). The
disclosed model also used an importance-ranked forward selection
procedure with Elastic Net regression and cross-validation for an
efficient, data-driven feature selection approach to identify the
most predictive features from these high-dimensional data. This
data-driven approach yielded a highly predictive transdiagnostic
model that explained 61% of variance of the HAMD total score.
Moreover, the feature selection step of this machine learning
procedure returned a subset of features that were predictive and
highly interpretable. Of the rs-fMRI connectivity features, the
Default Mode Network was the primary source, while other predictive
features were widely distributed across various resting-state
networks including the Fronto-parietal Task Control, Salience,
Somatosensory/motor, Subcortical, Attention, and Sensory networks.
Structural features did not contribute much to the predictive
strength of this model, representing only about 1% of features
found to be predictive.
[0158] Altogether, the disclosed model provides an algorithm to
predict depression across multiple neurobehavioral disorders. The
features important to this algorithm suggest that functional
connectivity, rather than anatomy, provides a "depressive brain
signature," which could be targeted for intervention.
Additional Experimental Data Regarding the Disclosed Models
[0159] Table 7, below, shows metrics of the median mean square
error (MSE), r.sup.2 or variance explained, and p or features with
non-zero regression coefficients for the three different model
algorithms using the input feature set of individual scale
items.
TABLE-US-00007 TABLE 7 Models with Scales as Input Feature Set
Scales Only Input Features Model Algorithm Outcome Variables Metric
Lasso ElasticNet RandomForest Mood/Dep_Hopkins median MSE 0.120
0.148 0.160 median r.sup.2 0.550 0.530 0.510 p 30 50 n/a
Mood_Bipolar median MSE 1.486 1.183 1.685 median r.sup.2 0.791
0.836 0.745 p 53 112 n/a Anhedonia_Chapphy median MSE 22.145 19.670
30.152 median r.sup.2 0.627 0.642 0.469 p 30 240 n/a
Anhedonia_Chapsoc median MSE 11.127 12.510 20.134 median r.sup.2
0.796 0.782 0.651 p 60 126 n/a Anxiety_Hopkins median MSE 0.123
0.134 0.183 median r.sup.2 0.561 0.525 0.471 p 16 54 n/a
Anxiety_Bipolar median MSE 1.144 1.252 1.569 median r.sup.2 0.608
0.616 0.523 p 55 62 n/a
[0160] Table 8, below, shows metrics of median square error (MSE),
r.sup.2 or variance explained, and p or features with non-zero
regression coefficients for the three different model algorithms
using the input feature set of sMRI measures (subcortical volume,
cortical volume, etc.).
TABLE-US-00008 TABLE 8 Models with sMRI as Input Feature Set. sMRI
Only Input Features Model Algorithm Outcome Variables Metric Lasso
ElasticNet RandomForest Mood/Dep_Hopkins median MSE 0.282 0.299
0.283 median r.sup.2 0.026 0.019 0.073 p 4 3 n/a Mood_Bipolar
median MSE 6.604 6.408 5.957 median r.sup.2 0.041 0.123 0.135 p 15
22 n/a Anhedonia_Chapphy median MSE 45.608 42.835 51.740 median
r.sup.2 0.083 0.158 0.019 p 29 61 n/a Anhedonia_Chapsoc median MSE
46.644 43.893 48.993 median r.sup.2 0.131 0.065 0.072 p 32 32 n/a
Anxiety_Hopkins median MSE 0.262 0.260 0.306 median r.sup.2 0.035
0.042 0.042 p 3 6 n/a Anxiety_Bipolar median MSE 2.966 2.865 3.166
median r.sup.2 0.055 0.121 0.034 p 15 16 n/a
[0161] Table 9, below, shows metrics of median square error (MSE),
r.sup.2 or variance explained, and p or features with non-zero
regression coefficients for the three different model algorithms
using the input feature set of fMRI connectivity features.
TABLE-US-00009 TABLE 9 Models with fMRI as Input Feature Set.
Predicted Scores Metric Lasso ElasticNet RandomForest Chapman
Social Anhedonia median MSE 24.1366711 13.2464801 35.74676667
median r.sup.2 0.5908661 0.7319229 0.258061818 p 75 345 n/a Chapman
Physical Anhedonia median MSE 30.901329 21.4057029 39.74679883
median r.sup.2 0.61525222 0.65566807 0.247477881 p 76 358 n/a HAMD,
total score median MSE 33.2005788 38.9725415 71.34669412 median
r.sup.2 0.66777371 0.59569346 0.207388274 p 16 500 n/a HAMD, q1, 7,
8 sum score median MSE 0.95578396 0.91593487 2.650023529 median
r.sup.2 0.79179955 0.74061902 0.332485876 p 28 191 n/a HAMD, q7
median MSE 0.31201916 0.37790437 0.808070588 median r.sup.2
0.73200199 0.69881084 0.356340961 p 38 54 n/a BPRS, negative score
median MSE 0.18757938 0.1506109 0.241262868 median r.sup.2
0.58051551 0.57369558 0.279867098 p 15 131 n/a BPRS,
depression-anxiety score median MSE 0.48898993 0.38228361
0.69174958 median r.sup.2 0.51218225 0.54291803 0.343333691 p 23 36
n/a Hopkins, anxiety score median MSE 0.16332994 0.10991548
0.250750368 median r.sup.2 0.4518252 0.65341477 0.307182751 p 24 85
n/a Hopkins, depression score median MSE 0.14661675 0.1591489
0.206618425 median r.sup.2 0.48845674 0.45016098 0.280274072 p 31
29 n/a Bipolar II, depression score median MSE 2.2093303 2.53026198
4.058693333 median r.sup.2 0.64341678 0.62534857 0.323775594 p 50
255 n/a Bipolar II, anxiety score median MSE 1.74502231 0.98772213
2.523746667 median r.sup.2 0.48106 0.6442545 0.304516018 p 43 153
n/a SANS, anhedonia factor score median MSE 0.57249993 0.44668826
0.671989773 median r.sup.2 0.54341834 0.72603119 0.476297105 p 16
66 n/a SANS, avolition factor score median MSE 0.38427335
0.41349887 0.523188636 median r.sup.2 0.60922858 0.68171714
0.393902033 p 20 63 n/a SANS, blunt affect factor score median MSE
0.35097663 0.13853868 0.319771716 median r.sup.2 0.36340364
0.81112876 0.421756359 p 22 29 n/a SANS, alogia factor score median
MSE 0.1804207 0.0918634 0.208887871 median r.sup.2 0.57666364
0.78317095 0.406578967 p 15 37 n/a SANS, attention factor score
median MSE 0.5114133 0.5450171 0.863740909 median r.sup.2
0.58044727 0.55803846 0.351176332 p 11 16 n/a SANS, anhedonia
global score median MSE 0.6479366 0.653431 0.703936364 median
r.sup.2 0.57849286 0.5910022 0.567246117 p 13 22 n/a SANS,
avolition global score median MSE 0.65626457 0.45879379 1.112490909
median r.sup.2 0.65501818 0.7356483 0.41697963 p 8 126 n/a SANS,
blunt affect global score median MSE 0.76806032 0.40498193
0.725527273 median r.sup.2 0.5075324 0.67717999 0.438660494 p 20 18
n/a SANS, alogia global score median MSE 0.18058636 0.25612571
0.360454545 median r.sup.2 0.71239653 0.44974554 0.20472973 p 12 21
n/a SANS, attention global score median MSE 0.82370349 0.84473842
1.018854545 median r.sup.2 0.53408014 0.55862342 0.381446429 p 8 70
n/a
[0162] Table 10, below, shows metrics of median square error (MSE),
r.sup.2 or variance explained, and p or features with non-zero
regression coefficients for the three different model algorithms
using the input feature set of sMRI and fMRI items.
TABLE-US-00010 TABLE 10 Models with sMRI + NRI as Input Feature
Set. Predicted Scores Metric Lasso ElasticNet RandomForest Chapman
Social Anhedonia median MSE 20.3800964 14.682152 34.56825833 median
r.sup.2 0.61287335 0.67729341 0.271666677 p 30 559 n/a Chapman
Physical Anhedonia median MSE 29.5944321 14.4191528 39.64058054
median r.sup.2 0.47730737 0.73988389 0.270972364 p 72 211 n/a HAMD,
total score median MSE 40.444502 27.0655892 65.45605995 median
r.sup.2 0.46827069 0.61111933 0.317391988 p 13 448 n/a HAMD, q1, 7,
8 sum score median MSE 2.36930173 1.3053619 2.42418007 median
r.sup.2 0.57469992 0.78027275 0.415661799 p 21 78 n/a HAMD, q7
median MSE 0.48427727 0.44843705 0.733507692 median r.sup.2
0.51760748 0.62850068 0.374694444 p 4 95 n/a BPRS, negative score
median MSE 0.10894905 0.20808541 0.228798077 median r.sup.2
0.58309527 0.48416688 0.339701299 p 10 12 n/a BPRS,
depression-anxiety score median MSE 0.74672192 0.4527803
0.745567681 median r.sup.2 0.34811518 0.59421236 0.305609826 p 15
119 n/a Hopkins, anxiety score median MSE 0.12090694 0.09822079
0.189571301 median r.sup.2 0.55139531 0.65036656 0.303938076 p 8 29
n/a Hopkins, depression score median MSE 0.17540687 0.1383836
0.199493796 median r.sup.2 0.38018872 0.4151432 0.199243267 p 21 16
n/a Bipolar II, depression score median MSE 3.58417568 1.86722182
4.104375843 median r.sup.2 0.45476109 0.71921058 0.297745091 p 48
241 n/a Bipolar II, anxiety score median MSE 1.28026365 0.70325067
2.257191667 median r.sup.2 0.60209066 0.78935698 0.32304625 p 25
161 n/a SANS, anhedonia factor score median MSE 0.40512803
0.35700521 0.63381875 median r.sup.2 0.71570239 0.77152059
0.552279155 p 8 48 n/a SANS, avolition factor score median MSE
0.18738998 0.11302804 0.45203125 median r.sup.2 0.67281 0.84257768
0.519285076 p 7 41 n/a SANS, blunt affect factor score median MSE
0.1055558 0.16598636 0.406562166 median r.sup.2 0.7535557
0.76532171 0.476367098 p 24 65 n/a SANS, alogia factor score median
MSE 0.40859079 0.11637806 0.236031927 median r.sup.2 0.29255013
0.7725277 0.559877681 p 7 77 n/a SANS, attention factor score
median MSE 0.57872686 0.43584679 0.648390625 median r.sup.2
0.6007934 0.60740377 0.31332093 p 13 93 n/a SANS, anhedonia global
score median MSE 0.88823187 0.57576394 1.095494073 median r.sup.2
0.40161221 0.63872035 0.319304654 p 17 107 n/a SANS, avolition
global score median MSE 0.55331606 0.21081588 0.7125 median r.sup.2
0.56105366 0.82726123 0.5096 p 6 67 n/a SANS, blunt affect global
score median MSE 0.76193885 0.26705401 0.75875 median r.sup.2
0.5411395 0.84012485 0.526436782 p 11 41 n/a SANS, alogia global
score median MSE 0.52887025 0.25762776 0.3487375 median r.sup.2
0.31534639 0.65101623 0.320888889 p 8 26 n/a SANS, attention global
score median MSE 0.35172813 0.28886716 0.5875 median r.sup.2
0.70380789 0.75674345 0.5975 p 8 13 n/a
[0163] Table 11, below, shows metrics of median square error (MSE),
r.sup.2 or variance explained, and p or features with non-zero
regression coefficients for the three different model algorithms
using the input feature set of individual scale items and sMRI
features.
TABLE-US-00011 TABLE 11 Models with Scales + sMRI as Input Feature
Set. Predicted Scores Metric Lasso ElasticNet RandomForest Chapman
Social Anhedonia median MSE 10.1872913 10.8473571 23.01353415
median r.sup.2 0.8189687 0.79591289 0.599748178 p 59 63 n/a Chapman
Physical Anhedonia median MSE 15.1648738 15.1775051 31.00075366
median r.sup.2 0.6745091 0.69034822 0.429974564 p 92 123 n/a HAMD,
total score median MSE 44.8743169 21.4111889 51.17416788 median
r.sup.2 0.62386495 0.80822534 0.600713723 p 31 123 n/a HAMD, q1,
7,8 sum score median MSE 2.04269051 1.25720156 2.447818182 median
r.sup.2 0.59849127 0.76841313 0.474861019 p 38 110 n/a HAMD, q7
median MSE 0.5660152 0.37695234 0.771495455 median r.sup.2
0.60961318 0.73435387 0.407349419 p 28 58 n/a BPRS, negative score
median MSE 0.22947823 0.11052677 0.223487784 median r.sup.2
0.3567901 0.68800207 0.426280069 p 12 54 n/a BPRS,
depression-anxiety score median MSE 0.47774909 0.37200915
0.716768024 median r.sup.2 0.6784626 0.76758706 0.520999457 p 39 58
n/a Hopkins, anxiety score median MSE 0.15278469 0.14505891
0.147720373 median r.sup.2 0.46629576 0.47103414 0.475010058 p 14
49 n/a Hopkins, depression score median MSE 0.13015768 0.10560835
0.148762648 median r.sup.2 0.58405287 0.67747487 0.516387069 p 15
102 n/a Bipolar II, depression score median MSE 1.11473489
1.02057729 1.722802026 median r.sup.2 0.84118256 0.86421896
0.745336441 p 32 114 n/a Bipolar II, anxiety score median MSE
0.81608355 0.79832926 1.565412195 median r.sup.2 0.7409768
0.73479011 0.523792469 p 30 58 n/a SANS, anhedonia factor score
median MSE 0.80659301 0.8876104 1.02542 median r.sup.2 0.4847042
0.43714204 0.251376657 p 24 13 n/a SANS, avolition factor score
median MSE 0.35767455 0.29545236 0.76713875 median r.sup.2
0.64133778 0.69348871 0.252067852 p 30 54 n/a SANS, blunt affect
factor score median MSE 0.29046712 0.35398284 0.555033335 median
r.sup.2 0.65317772 0.57650195 0.346361453 p 15 29 n/a SANS, alogia
factor score median MSE 0.27355305 0.22486147 0.370562213 median
r.sup.2 0.43984006 0.45944781 0.260900092 p 16 22 n/a SANS,
attention factor score median MSE 0.61391724 0.52919515 0.840366667
median r.sup.2 0.42634181 0.56616671 0.26694625 p 15 90 n/a SANS,
anhedonia global score median MSE 0.82696728 0.91638191 1.31 median
r.sup.2 0.52922904 0.52124193 0.349258197 p 16 57 n/a SANS,
avolition global score median MSE 1.0735851 0.96201314 1.69874
median r.sup.2 0.48944093 0.4703767 0.186773404 p 15 92 n/a SANS,
blunt affect global score median MSE 0.47919468 0.64615998
0.941919557 median r.sup.2 0.56575973 0.5970739 0.403780513 p 16 89
n/a SANS, alogia global score median MSE 0.42319702 0.51674167
0.709333333 median r.sup.2 0.41947137 0.43807514 0.181628238 p 15
16 n/a SANS, attention global score median MSE 0.5766073 0.87984786
1.165269856 median r.sup.2 0.53935525 0.37445084 0.141182351 p 23
56 n/a
[0164] Table 12, below, shows metrics of median square error (MSE),
r.sup.2 or variance explained, and p or features with non-zero
regression coefficients for the three different model algorithms
using the input feature set of individual scale items and fMRI
features.
TABLE-US-00012 TABLE 12 Models with Scales + fMRI as Input Feature
Set. Scales + fMRI Input Features Model Algorithm Outcome Variables
Metric Lasso ElasticNet RandomForest Mood/Dep_Hopkins median MSE
0.077 0.110 0.205 median r.sup.2 0.709 0.610 0.360 p 16 26 n/a
Mood_Bipolar median MSE 0.861 0.814 3.003 median r.sup.2 0.869
0.874 0.600 p 50 236 n/a Anhedonia_Chapphy median MSE 11.439 9.648
35.942 median r.sup.2 0.805 0.841 0.349 p 46 211 n/a
Anhedonia_Chapsoc median MSE 7.549 8.627 23.482 median r.sup.2
0.857 0.829 0.486 p 47 31 n/a Anxiety_Hopkins median MSE 0.150
0.095 0.205 median r.sup.2 0.597 0.704 0.312 p 27 127 n/a
Anxiety_Bipolar median MSE 0.928 0.589 1.782 median r.sup.2 0.729
0.825 0.469 p 31 32 n/a
[0165] Table 13, below, shows metrics of median square error (MSE),
r.sup.2 or variance explained, and p or features with non-zero
regression coefficients for the three different model algorithms
using the input feature set of individual scale items, sMRI, and
fMRI features.
TABLE-US-00013 TABLE 13 Models with Scales + sMRI + fMRI as Input
Feature Set. Scales + sMRI + fMRI Input Features Model Algorithm
Outcome Variables Metric Lasso ElasticNet RandomForest
Mood/Dep_Hopkins median MSE 0.132 0.076 0.129 median r.sup.2 0.514
0.721 0.470 p 8 28 n/a Mood_Bipolar median MSE 0.724 0.614 2.163
median r.sup.2 0.874 0.904 0.658 p 31 93 n/a Anhedonia_Chapphy
median MSE 23.104 15.814 27.952 median r.sup.2 0.620 0.652 0.259 p
48 32 n/a Anhedonia_Chapsoc median MSE 7.027 9.886 25.091 median
r.sup.2 0.822 0.804 0.438 p 30 106 n/a Anxiety_Hopkins median MSE
0.086 0.077 0.149 median r.sup.2 0.684 0.751 0.420 p 27 47 n/a
Anxiety_Bipolar median MSE 0.521 0.535 1.745 median r.sup.2 0.838
0.847 0.435 p 72 31 n/a
[0166] Table 14, below, shows Wilcoxon rank-sum U statistics and
p-values for a post hoc group-comparison statistic of significantly
different motion measures shown in FIGS. 7A-7I. Significant
p-values can be identified from the table.
TABLE-US-00014 TABLE 14 Post-hoc group comparison statistic of
significantly different motion measures shown in FIGS. 7A-7I.
Motion Measures Group Comparison Mean FD Sharp Motion Y-axis Motion
Z-axis Motion HC v. SZ U stat = 162 U stat = 151 U stat = 167 U
stat = 174 p = 0.0005 p = 0.0003 p = 0.0007 p = 0.0010 HC v. BD U
stat = 458 U stat = 435 U stat = 438 U stat = 434 p = 0.0112 p =
0.0058 p = 0.0063 p = 0.0056 HC v. ADHD U stat = 581 U stat = 600 U
stat = 628 U stat = 481 p = 0.2358 p = 0.3035 p = 0.4164 p = 0.0357
SZ v. BD U stat = 132 U stat = 139 U stat = 140 U stat = 140 p =
0.1067 p = 0.1493 p = 0.1562 p = 0.1562 SZ v. ADHD U stat = 91 U
stat = 90 U stat = 77 U stat = 112 p = 0.0103 p = 0.0095 p = 0.0031
p = 0.0465 BD v. ADHD U stat = 232 U stat = 219 U stat = 205 U stat
= 251 p = 0.0885 p = 0.0537 p = 0.0294 p = 0.1660
Additional Embodiments
[0167] Additional aspects of the present disclosure include the
following method: Clinical scale data, resting-state functional-MRI
data, and structural-MRI scans are received for multiple patients
with schizophrenia, bipolar disorder, attention deficit and
hyperactivity disorder ("ADHD"), or healthy controls. The received
data are preprocessed. At least one predictive model of symptom
expression is generated based on the preprocessed data. Subsets of
features in the received data are identified from the at least one
predictive model to predict transdiagnostic symptoms related to
depression, anxiety, anhedonia, and other negative symptoms.
[0168] Further aspects of the present disclosure include the
following computer system: A computing system includes at least one
database, a memory, and a processor. The database stores clinical
scale data, resting-state functional-MRI data, and structural-MRI
scans for multiple patients with schizophrenia, bipolar disorder,
ADHD, or healthy controls. The memory stores computer instructions.
The processor that is configured to execute the computer
instructions to preprocess the data stored in the at least one
database. At least one predictive model of symptom expression is
generated based on the preprocessed data. Subsets of features in
the received data are identified from the at least one predictive
model to predict transdiagnostic symptoms related to depression,
anxiety, anhedonia, and other negative symptoms.
[0169] Although the present disclosure provides for models trained
on the CNP database, the present disclosure contemplates that any
database comprising clinical scales data and MRI data can be used
to produce models, as would be readily contemplated by one skilled
in the art.
[0170] The disclosed models selected as informative the features
which trend in the same direction for all participants. The present
disclosure contemplates that brain activity can be examined which
diverges between patient groups; such an approach can yield other
features.
[0171] Although the present disclosure discusses input primarily in
terms of fMRI data and sMRI data, other embodiments can provide for
receiving rs-fMRI.
[0172] Altogether, the present disclosure provides a data-driven
way to improve biomarker development for predicting symptom
severity transdiagnostically and can be used in a personalized
medicine approach in diagnosing and treating behavioral
disorders.
Machine Learning Implementation
[0173] Various aspects of the present disclosure can be performed
by a machine-learning algorithm, as readily understood by a person
skilled in the art. In some examples, step 1540 of FIG. 15 and
methodology 1600 of FIG. 16 can be performed by a supervised or
unsupervised algorithm. For instance, the system may utilize more
basic machine learning tools including 1) decision trees ("DT"),
(2) Bayesian networks ("BN"), (3) artificial neural network
("ANN"), or (4) support vector machines ("SVM"). In other examples,
deep learning algorithms or other more sophisticated machine
learning algorithms, e.g., convolutional neural networks ("CNN"),
or capsule networks ("CapsNet") may be used.
[0174] DT are classification graphs that match input data to
questions asked at each consecutive step in a decision tree. The DT
program moves down the "branches" of the tree based on the answers
to the questions (e.g., First branch: Did the clinical scales data
include certain input? yes or no. Branch two: Did the MRI data
include certain features? yes or no, etc.).
[0175] Bayesian networks ("BN") are based on likelihood something
is true based on given independent variables and are modeled based
on probabilistic relationships. BN are based purely on
probabilistic relationships that determine the likelihood of one
variable based on another or others. For example, BN can model the
relationships between MRI data, clinical scales data, and any other
information as contemplated by the present disclosure.
Particularly, if a question type and particular features of the
patient's MRI data are known, a BN can be used to compute a symptom
severity indicator. Thus, using an efficient BN algorithm, an
inference can be made based on the input data.
[0176] Artificial neural networks ("ANN") are computational models
inspired by an animal's central nervous system. They map inputs to
outputs through a network of nodes. However, unlike BN, in ANN the
nodes do not necessarily represent any actual variable.
Accordingly, ANN may have a hidden layer of nodes that are not
represented by a known variable to an observer. ANNs are capable of
pattern recognition. Their computing methods make it easier to
understand a complex and unclear process that might go on during
determining a symptom severity indicator based on a variety of
input data.
[0177] Support vector machines ("SVM") came about from a framework
utilizing of machine learning statistics and vector spaces (linear
algebra concept that signifies the number of dimensions in linear
space) equipped with some kind of limit-related structure. In some
cases, they may determine a new coordinate system that easily
separates inputs into two classifications. For example, a SVM could
identify a line that separates two sets of points originating from
different classifications of events.
[0178] Deep neural networks (DNN) have developed recently and are
capable of modeling very complex relationships that have a lot of
variation. Various architectures of DNN have been proposed to
tackle the problems associated with algorithms such as ANN by many
researchers during the last few decades. These types of DNN are CNN
(Convolutional Neural Network), RBM (Restricted Boltzmann Machine),
LSTM (Long Short Term Memory) etc. They are all based on the theory
of ANN. They demonstrate a better performance by overcoming the
back-propagation error diminishing problem associated with ANN.
[0179] Machine learning models require training data to identify
the features of interest that they are designed to detect. For
instance, various methods may be utilized to form the machine
learning models, including applying randomly assigned initial
weights for the network and applying gradient descent using back
propagation for deep learning algorithms. In other examples, a
neural network with one or two hidden layers can be used without
training using this technique.
[0180] In some examples, the machine learning model can be trained
using labeled data, or data that represents certain user input. In
other examples, the data will only be labeled with the outcome and
the various relevant data may be input to train the machine
learning algorithm.
[0181] For instance, to determine whether particular mental health
disorder fits the input data, various machine learning models may
be utilized that input various data disclosed herein. In some
examples, the input data will be labeled by having an expert in the
field label the relevant regulations according to the particular
situation. Accordingly, the input to the machine learning algorithm
for training data identifies various data as from a healthy control
or from a patient.
Exemplary NMR System
[0182] Referring now to FIGS. 17A-18, the methods and embodiments
of the present disclosure can be performed on an exemplary nuclear
magnetic resonance ("NMR system"). As a person of ordinary skill in
the art understands, NMR commonly refers to the hardware used to
generate different types of scans, including MRI scans. Referring
now to FIGS. 17A-18, there is shown the major components of an NMR
system which can be used to carry out the methods of the various
embodiments. FIG. 18 shows the components of an exemplary
transceiver for the NMR system of FIGS. 17A-17B. It should be noted
that the methods of the various embodiments can also be carried out
using other NMR systems.
[0183] The operation of the system of FIGS. 17A-18 is controlled
from an operator console 100 which includes a console processor 101
that scans a keyboard 102 and receives inputs from a human operator
through a control panel 103 and a plasma display/touch screen 104.
The console processor 101 communicates through a communications
link 116 with an applications interface module 117 in a separate
computer system 107. Through the keyboard 102 and controls 103, an
operator controls the production and display of images by an image
processor 106 in the computer system 107, which connects directly
to a video display 118 on the console 100 through a video cable
105.
[0184] The computer system 107 is formed about a backplane bus
which conforms with the VME standards, and it includes a number of
modules which communicate with each other through this backplane.
In addition to the application interface 117 and the image
processor 106, these include a CPU module 108 that controls the VME
backplane, and an SCSI interface module 109 that connects the
computer system 107 through a bus 110 to a set of peripheral
devices, including disk storage 111 and tape drive 112. The
computer system 107 also includes a memory module 113, known in the
art as a frame buffer for storing image data arrays, and a serial
interface module 114 that links the computer system 107 through a
high speed serial link 115 to a system interface module 120 located
in a separate system control cabinet 122.
[0185] The system control 122 includes a series of modules which
are connected together by a common backplane 118. The backplane 118
is comprised of a number of bus structures, including a bus
structure which is controlled by a CPU module 119. The serial
interface module 120 connects this backplane 118 to the high speed
serial link 115, and pulse generator module 121 connects the
backplane 118 to the operator console 100 through a serial link
125. It is through this link 125 that the system control 122
receives commands from the operator which indicate the scan
sequence that is to be performed.
[0186] The pulse generator module 121 operates the system
components to carry out the desired scan sequence. It produces data
which indicates the timing, strength and shape of the RF pulses
which are to be produced, and the timing of and length of the data
acquisition window. The pulse generator module 121 also connects
through serial link 126 to a set of gradient amplifiers 127, and it
conveys data thereto which indicates the timing and shape of the
gradient pulses that are to be produced during the scan. The pulse
generator module 121 also receives patient data through a serial
link 128 from a physiological acquisition controller 129. The
physiological acquisition control 129 can receive a signal from a
number of different sensors connected to the patient. For example,
it may receive ECG signals from electrodes or respiratory signals
from a bellows and produce pulses for the pulse generator module
121 that synchronizes the scan with the patient's cardiac cycle or
respiratory cycle. And finally, the pulse generator module 121
connects through a serial link 132 to scan room interface circuit
133 which receives signals at inputs 135 from various sensors
associated with the position and condition of the patient and the
magnet system. It is also through the scan room interface circuit
133 that a patient positioning system 134 receives commands which
move the patient cradle and transport the patient to the desired
position for the scan.
[0187] The gradient waveforms produced by the pulse generator
module 121 are applied to a gradient amplifier system 127 comprised
of Gx, Gy, and Gz amplifiers 136, 137 and 138, respectively. Each
amplifier 136, 137, and 138 is utilized to excite a corresponding
gradient coil in an assembly generally designated 139. The gradient
coil assembly 139 forms part of a magnet assembly 155 which
includes a polarizing magnet 140 that produces a 1.5 Tesla
polarizing field that extends horizontally through a bore. The
gradient coils 139 encircle the bore, and when energized, they
generate magnetic fields in the same direction as the main
polarizing magnetic field, but with gradients Gx, Gy and Gz
directed in the orthogonal x-, y- and z-axis directions of a
Cartesian coordinate system. That is, if the magnetic field
generated by the main magnet 140 is directed in the z direction and
is termed BO, and the total magnetic field in the z direction is
referred to as Bz, then Gx.differential.Bz/.differential.x,
Gy=.differential.Bz/.differential.y and
Gz=.differential.Bz/.differential.z, and the magnetic field at any
point (x,y,z) in the bore of the magnet assembly 141 is given by
B(x,y,z)=Bo+Gxx+GyyGzz. The gradient magnetic fields are utilized
to encode spatial information into the NMR signals emanating from
the patient being scanned. Because the gradient fields are switched
at a very high speed when an EPI sequence is used to practice the
preferred embodiment of the invention, local gradient coils are
employed in place of the whole-body gradient coils 139. These local
gradient coils are designed for the head and are in close proximity
thereto. This enables the inductance of the local gradient coils to
be reduced and the gradient switching rates increased as required
for the EPI pulse sequence. For a description of these local
gradient coils which is incorporated herein by reference, see U.S.
Pat. No. 5,372,137 issued on Dec. 13, 1994, and entitled "NMR Local
Coil For Brain Imaging".
[0188] Located within the bore 142 is a circular cylindrical
whole-body RF coil 152. This coil 152 produces a circularly
polarized RF field in response to RF pulses provided by a
transceiver module 150 in the system control cabinet 122. These
pulses are amplified by an RF amplifier 151 and coupled to the RF
coil 152 by a transmit/receive switch 154 which forms an integral
part of the RF coil assembly. Waveforms and control signals are
provided by the pulse generator module 121 and utilized by the
transceiver module 150 for RF carrier modulation and mode control.
The resulting NMR signals radiated by the excited nuclei in the
patient may be sensed by the same RF coil 152 and coupled through
the transmit/receive switch 154 to a preamplifier 153. The
amplified NMR signals are demodulated, filtered, and digitized in
the receiver section of the transceiver 150.
[0189] The transmit/receive switch 154 is controlled by a signal
from the pulse generator module 121 to electrically connect the RF
amplifier 151 to the coil 152 during the transmit mode and to
connect the preamplifier 153 during the receive mode. The
transmit/receive switch 154 also enables a separate local RF head
coil to be used in the transmit and receive mode to improve the
signal-to-noise ratio of the received NMR signals. With currently
available NMR systems such a local RF coil is preferred in order to
detect small variations in NMR signal. Reference is made to the
above cited U.S. Pat. No. 5,372,137 for a description of the
preferred local RF coil.
[0190] In addition to supporting the polarizing magnet 140 and the
gradient coils 139 and RF coil 152, the main magnet assembly 141
also supports a set of shim coils 156 associated with the main
magnet 140 and used to correct inhomogeneities in the polarizing
magnet field. The main power supply 157 is utilized to bring the
polarizing field produced by the superconductive main magnet 140 to
the proper operating strength and is then removed.
[0191] The NMR signals picked up by the RF coil are digitized by
the transceiver module 150 and transferred to a memory module 160
which is also part of the system control 122. When the scan is
completed and an entire array of data has been acquired in the
memory modules 160, an array processor 161 operates to Fourier
transform the data into an array of image data. This image data is
conveyed through the serial link 115 to the computer system 107
where it is stored in the disk memory 111. In response to commands
received from the operator console 100, this image data may be
archived on the tape drive 112, or it may be further processed by
the image processor 106 and conveyed to the operator console 100
and presented on the video display 118 as will be described in more
detail hereinafter.
[0192] Referring particularly to FIG. 18, the transceiver 150
includes components which produce the RF excitation field B1
through power amplifier 151 at a coil 152A and components which
receive the resulting NMR signal induced in a coil 152B. As
indicated above, the coils 152A and B may be a single whole-body
coil, but the best results are achieved with a single local RF coil
specially designed for the head. The base or carrier frequency of
the RF excitation field is produced under control of a frequency
synthesizer 200 which receives a set of digital signals (CF)
through the backplane 118 from the CPU module 119 and pulse
generator module 121. These digital signals indicate the frequency
and phase of the RF carrier signal, which is produced at an output
201. The commanded RF carrier is applied to a modulator and up
converter 202 where its amplitude is modulated in response to a
signal R(t) also received through the backplane 118 from the pulse
generator module 121. The signal R(t) defines the envelope, and
therefore the bandwidth, of the RF excitation pulse to be produced.
It is produced in the module 121 by sequentially reading out a
series of stored digital values that represent the; desired
envelope. These stored digital values may, in turn, be changed from
the operator console 100 to enable any desired RF pulse envelope to
be produced. The modulator and up converter 202 produces an RF
pulse at the desired Larmor frequency at an output 205. The
magnitude of the RF excitation pulse output through line 205 is
attenuated by an exciter attenuator circuit 206 which receives a
digital command, TA, from the backplane 118. The attenuated RF
excitation pulses are applied to the power amplifier 151 that
drives the RF coil 152A. For a more detailed description of this
portion of the transceiver 122, reference is made to U.S. Pat. No.
4,952,877, which is incorporated herein by reference.
[0193] Referring still to FIGS. 17A-18, the NMR signal produced by
the subject is picked up by the receiver coil 152B and applied
through the preamplifier 153 to the input of a receiver attenuator
207. The receiver attenuator 207 further amplifies the NMR signal,
and this is attenuated by an amount determined by a digital
attenuation signal (RA) received from the backplane 118. The
receive attenuator 207 is also turned on and off by a signal from
the pulse generator module 121 such that it is not overloaded
during RF excitation. The received NMR signal is at or around the
Larmor frequency, which in the preferred embodiment is around 63.86
MHz for 1.5 Tesla. This high-frequency signal is down-converted in
a two-step process by a down converter 208 which first mixes the
NMR signal with the carrier signal on line 201 and then mixes the
resulting difference signal with the 2.5 MHz reference signal on
line 204. The resulting down-converted NMR signal on line 212 has a
maximum bandwidth of 125 kHz, and it is centered at a frequency of
187.5 kHz. The down-converted NMR signal is applied to the input of
an analog-to-digital (A/D) converter 209, which samples and
digitizes the analog signal at a rate of 250 kHz. The output of the
A/D converter 209 is applied to a digital detector, and signal
processor 210 which produce 16-bit in-phase (I) values and 16-bit
quadrature (Q) values corresponding to the received digital signal.
The resulting stream of digitized I and Q values of the received
NMR signal is output through backplane 118 to the memory module 160
where they are employed to reconstruct an image.
[0194] To preserve the phase information contained in the received
NMR signal, both the modulator and up converter 202 in the exciter
section and the down converter 208 in the receiver section are
operated with common signals. More particularly, the carrier signal
at the output 201 of the frequency synthesizer 200 and the 2.5 MHz
reference signal at the output 204 of the reference frequency
generator 203 are employed in both frequency conversion processes.
Phase consistency is thus maintained, and phase changes in the
detected NMR signal accurately indicate phase changes produced by
the excited spins. The 2.5 MHz reference signal as well as 5, 10
and 60 MHz reference signals are produced by the reference
frequency generator 203 from a common 20 MHz master clock signal.
The latter three reference signals are employed by the frequency
synthesizer 200 to produce the carrier signal on output 201. For a
more detailed description of the receiver, reference is made to
U.S. Pat. No. 4,992,736, which is incorporated herein by
reference.
Computer & Hardware Implementation of Disclosure
[0195] It should initially be understood that the disclosure herein
may be implemented with any type of hardware and/or software, and
may be a pre-programmed general purpose computing device. For
example, the system may be implemented using a server, a personal
computer, a portable computer, a thin client, or any suitable
device or devices. The disclosure and/or components thereof may be
a single device at a single location, or multiple devices at a
single, or multiple, locations that are connected together using
any appropriate communication protocols over any communication
medium such as electric cable, fiber optic cable, or in a wireless
manner.
[0196] It should also be noted that the disclosure is illustrated
and discussed herein as having a plurality of modules which perform
particular functions. It should be understood that these modules
are merely schematically illustrated based on their function for
clarity purposes only, and do not necessary represent specific
hardware or software. In this regard, these modules may be hardware
and/or software implemented to substantially perform the particular
functions discussed. Moreover, the modules may be combined together
within the disclosure, or divided into additional modules based on
the particular function desired. Thus, the disclosure should not be
construed to limit the present invention, but merely be understood
to illustrate one example implementation thereof.
[0197] The computing system can include clients and servers. A
client and server are generally remote from each other and
typically interact through a communication network. The
relationship of client and server arises by virtue of computer
programs running on the respective computers and having a
client-server relationship to each other. In some implementations,
a server transmits data (e.g., an HTML page) to a client device
(e.g., for purposes of displaying data to and receiving user input
from a user interacting with the client device). Data generated at
the client device (e.g., a result of the user interaction) can be
received from the client device at the server.
[0198] Implementations of the subject matter described in this
specification can be implemented in a computing system that
includes a back-end component (e.g., as a data server) or a
middleware component (e.g., an application server) or a front-end
component (e.g., a client computer having a graphical user
interface or a Web browser through which a user can interact with
an implementation of the subject matter described in this
specification) or any combination of one or more such back-end,
middleware, or front-end components. The components of the system
can be interconnected by any form or medium of digital data
communication (e.g., a communication network). Examples of
communication networks include a local area network ("LAN") and a
wide area network ("WAN"), an inter-network (e.g., the Internet),
and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).
[0199] Implementations of the subject matter and the operations
described in this specification can be implemented in digital
electronic circuitry, or in computer software, firmware, or
hardware, including the structures disclosed in this specification
and their structural equivalents, or in combinations of one or more
of them. Implementations of the subject matter described in this
specification can be implemented as one or more computer programs
(i.e., one or more modules of computer program instructions)
encoded on computer storage medium for execution by, or to control
the operation of, data processing apparatus. Alternatively or in
addition, the program instructions can be encoded on an
artificially-generated propagated signal (e.g., a machine-generated
electrical, optical, or electromagnetic signal) that is generated
to encode information for transmission to suitable receiver
apparatus for execution by a data processing apparatus. A computer
storage medium can be, or be included in, a computer-readable
storage device, a computer-readable storage substrate, a random or
serial access memory array or device, or a combination of one or
more of them. Moreover, while a computer storage medium is not a
propagated signal, a computer storage medium can be a source or
destination of computer program instructions encoded in an
artificially-generated propagated signal. The computer storage
medium can also be, or be included in, one or more separate
physical components or media (e.g., multiple CDs, disks, or other
storage devices).
[0200] The operations described in this specification can be
implemented as operations performed by a "data processing
apparatus" on data stored on one or more computer-readable storage
devices or received from other sources.
[0201] The term "data processing apparatus" encompasses all kinds
of apparatus, devices, and machines for processing data, including
by way of example a programmable processor, a computer, a system on
a chip, or multiple ones, or combinations, of the foregoing The
apparatus can include special purpose logic circuitry (e.g., an
FPGA (field-programmable gate array) or an ASIC
(application-specific integrated circuit)). The apparatus can also
include, in addition to hardware, code that creates an execution
environment for the computer program in question (e.g., code that
constitutes processor firmware, a protocol stack, a database
management system, an operating system, a cross-platform runtime
environment, a virtual machine, or a combination of one or more of
them). The apparatus and execution environment can realize various
different computing model infrastructures, such as web services,
distributed computing, and grid computing infrastructures.
[0202] A computer program (also known as a program, software,
software application, script, or code) can be written in any form
of programming language, including compiled or interpreted
languages, declarative or procedural languages, and it can be
deployed in any form, including as a stand-alone program or as a
module, component, subroutine, object, or other unit suitable for
use in a computing environment. A computer program may, but need
not, correspond to a file in a file system. A program can be stored
in a portion of a file that holds other programs or data (e.g., one
or more scripts stored in a markup language document), in a single
file dedicated to the program in question, or in multiple
coordinated files (e.g., files that store one or more modules,
sub-programs, or portions of code). A computer program can be
deployed to be executed on one computer or on multiple computers
that are located at one site or distributed across multiple sites
and interconnected by a communication network.
[0203] The processes and logic flows described in this
specification can be performed by one or more programmable
processors executing one or more computer programs to perform
actions by operating on input data and generating output. The
processes and logic flows can also be performed by, and apparatus
can also be implemented as, special purpose logic circuitry (e.g.,
an FPGA (field-programmable gate array) or an ASIC
(application-specific integrated circuit)).
[0204] Processors suitable for the execution of a computer program
include, by way of example, both general and special purpose
microprocessors, and any one or more processors of any kind of
digital computer. Generally, a processor will receive instructions
and data from a read-only memory or a random access memory or both.
The essential elements of a computer are a processor for performing
actions in accordance with instructions and one or more memory
devices for storing instructions and data. Generally, a computer
will also include, or be operatively coupled to receive data from
or transfer data to, or both, one or more mass storage devices for
storing data (e.g., magnetic, magneto-optical disks, or optical
disks). However, a computer need not have such devices. Moreover, a
computer can be embedded in another device (e.g., a mobile
telephone, a personal digital assistant (PDA), a mobile audio or
video player, a game console, a Global Positioning System (GPS)
receiver, or a portable storage device (e.g., a universal serial
bus (USB) flash drive), to name just a few). Devices suitable for
storing computer program instructions and data include all forms of
non-volatile memory, media and memory devices, including by way of
example semiconductor memory devices (e.g., EPROM, EEPROM, and
flash memory devices; magnetic disks, e.g., internal hard disks or
removable disks; magneto-optical disks; and CD-ROM and DVD-ROM
disks). The processor and the memory can be supplemented by, or
incorporated in, special purpose logic circuitry.
CONCLUSION
[0205] The various methods and techniques described above provide a
number of ways to carry out the invention. Of course, it is to be
understood that not necessarily all objectives or advantages
described can be achieved in accordance with any particular
embodiment described herein. Thus, for example, those skilled in
the art will recognize that the methods can be performed in a
manner that achieves or optimizes one advantage or group of
advantages as taught herein without necessarily achieving other
objectives or advantages as taught or suggested herein. A variety
of alternatives are mentioned herein. It is to be understood that
some embodiments specifically include one, another, or several
features, while others specifically exclude one, another, or
several features, while still others mitigate a particular feature
by inclusion of one, another, or several advantageous features.
[0206] Furthermore, the skilled artisan will recognize the
applicability of various features from different embodiments.
Similarly, the various elements, features, and steps discussed
above, as well as other known equivalents for each such element,
feature or step, can be employed in various combinations by one of
ordinary skill in this art to perform methods in accordance with
the principles described herein. Among the various elements,
features, and steps, some will be specifically included and others
specifically excluded in diverse embodiments.
[0207] Although the application has been disclosed in the context
of certain embodiments and examples, it will be understood by those
skilled in the art that the embodiments of the application extend
beyond the specifically disclosed embodiments to other alternative
embodiments and/or uses and modifications and equivalents
thereof.
[0208] In some embodiments, the terms "a" and "an" and "the" and
similar references used in the context of describing a particular
embodiment of the application (especially in the context of certain
of the following claims) can be construed to cover both the
singular and the plural. The recitation of ranges of values herein
is merely intended to serve as a shorthand method of referring
individually to each separate value falling within the range.
Unless otherwise indicated herein, each individual value is
incorporated into the specification as if it were individually
recited herein. All methods described herein can be performed in
any suitable order unless otherwise indicated herein or otherwise
clearly contradicted by context. The use of any and all examples,
or exemplary language (for example, "such as") provided with
respect to certain embodiments herein is intended merely to better
illuminate the application and does not pose a limitation on the
scope of the application otherwise claimed. No language in the
specification should be construed as indicating any non-claimed
element essential to the practice of the application.
[0209] Certain embodiments of this application are described
herein. Variations on those embodiments will become apparent to
those of ordinary skill in the art upon reading the foregoing
description. It is contemplated that skilled artisans can employ
such variations as appropriate, and the application can be
practiced otherwise than specifically described herein.
Accordingly, many embodiments of this application include all
modifications and equivalents of the subject matter recited in the
claims appended hereto as permitted by applicable law. Moreover,
any combination of the above-described elements in all possible
variations thereof is encompassed by the application unless
otherwise indicated herein or otherwise clearly contradicted by
context.
[0210] Particular implementations of the subject matter have been
described. Other implementations are within the scope of the
following claims. In some cases, the actions recited in the claims
can be performed in a different order and still achieve desirable
results. In addition, the processes depicted in the accompanying
figures do not necessarily require the particular order shown, or
sequential order, to achieve desirable results.
[0211] All patents, patent applications, publications of patent
applications, and other material, such as articles, books,
specifications, publications, documents, things, and/or the like,
referenced herein are hereby incorporated herein by this reference
in their entirety for all purposes, excepting any prosecution file
history associated with same, any of same that is inconsistent with
or in conflict with the present document, or any of same that may
have a limiting affect as to the broadest scope of the claims now
or later associated with the present document. By way of example,
should there be any inconsistency or conflict between the
description, definition, and/or the use of a term associated with
any of the incorporated material and that associated with the
present document, the description, definition, and/or the use of
the term in the present document shall prevail.
[0212] In closing, it is to be understood that the embodiments of
the application disclosed herein are illustrative of the principles
of the embodiments of the application. Other modifications that can
be employed can be within the scope of the application. Thus, by
way of example, but not of limitation, alternative configurations
of the embodiments of the application can be utilized in accordance
with the teachings herein. Accordingly, embodiments of the present
application are not limited to that precisely as shown and
described.
[0213] While various examples of the present disclosure have been
described above, it should be understood that they have been
presented by way of example only, and not limitation. Numerous
changes to the disclosed examples can be made in accordance with
the disclosure herein without departing from the spirit or scope of
the disclosure. Thus, the breadth and scope of the present
disclosure should not be limited by any of the above described
examples. Rather, the scope of the disclosure should be defined in
accordance with the following claims and their equivalents.
[0214] Although the disclosure has been illustrated and described
with respect to one or more implementations, equivalent alterations
and modifications will occur to others skilled in the art upon the
reading and understanding of this specification and the annexed
drawings. In addition, while a particular feature of the disclosure
may have been disclosed with respect to only one of several
implementations, such feature may be combined with one or more
other features of the other implementations as may be desired and
advantageous for any given or particular application.
[0215] The terminology used herein is for the purpose of describing
particular examples only and is not intended to be limiting of the
disclosure. As used herein, the singular forms "a," "an," and "the"
are intended to include the plural forms as well, unless the
context clearly indicates otherwise. Furthermore, to the extent
that the terms "including," "includes," "having," "has," "with," or
variants thereof, are used in either the detailed description
and/or the claims, such terms are intended to be inclusive in a
manner similar to the term "comprising."
[0216] Unless otherwise defined, all terms (including technical and
scientific terms) used herein have the same meaning as commonly
understood by one of ordinary skill in the art to which this
disclosure belongs. Furthermore, terms, such as those defined in
commonly used dictionaries, should be interpreted as having a
meaning that is consistent with their meaning in the context of the
relevant art, and will not be interpreted in an idealized or overly
formal sense unless expressly so defined herein.
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