U.S. patent application number 16/980761 was filed with the patent office on 2021-01-14 for systems and methods for generating biomarkers based on multivariate mri and multimodality classifiers for disorder diagnosis.
The applicant listed for this patent is Emory University. Invention is credited to Daniel Huddleston, Babak Mahmoudi.
Application Number | 20210007603 16/980761 |
Document ID | / |
Family ID | 1000005161478 |
Filed Date | 2021-01-14 |
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United States Patent
Application |
20210007603 |
Kind Code |
A1 |
Huddleston; Daniel ; et
al. |
January 14, 2021 |
Systems and Methods for Generating Biomarkers Based on Multivariate
MRI and Multimodality Classifiers for Disorder Diagnosis
Abstract
In some embodiments, the systems and methods of the disclosure
can efficiently and accurately classify neurodegenerative
disorder(s) and/or movement disorder(s) of a subject (e.g., a
patient) using at least quantitative features associated with one
or more regions of interest determined from one or more sets of
image data of the subjects brain. The method may include processing
one or more sets of MRI image data of the subjects brain to extract
one or more quantitative features for one or more regions. The one
or more quantitative features may include a first quantitative and
a second quantitative feature. The method may further include
classifying at least the one or more quantitative features into one
or more classes associated with neurodegenerative dementia
disorder, neurodegenerative movement disorder,
non-neurodegenerative movement disorder and/or heathy control. The
method may include generating a report including a classification
of at least the one or more quantitative features.
Inventors: |
Huddleston; Daniel;
(Marietta, GA) ; Mahmoudi; Babak; (Alpharetta,
GA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Emory University |
Atlanta |
GA |
US |
|
|
Family ID: |
1000005161478 |
Appl. No.: |
16/980761 |
Filed: |
March 14, 2019 |
PCT Filed: |
March 14, 2019 |
PCT NO: |
PCT/US2019/022229 |
371 Date: |
September 14, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62642782 |
Mar 14, 2018 |
|
|
|
62737471 |
Sep 27, 2018 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/055 20130101;
A61B 5/7267 20130101; G16H 30/40 20180101; G06T 7/0012 20130101;
G06K 9/628 20130101; G16H 10/20 20180101; G06K 9/46 20130101; G06K
9/3233 20130101; A61B 2576/026 20130101; A61B 5/0042 20130101; G16H
50/20 20180101; G06T 2207/10088 20130101; G06T 2207/30016 20130101;
A61B 5/4082 20130101; G16H 70/60 20180101; A61B 5/7264 20130101;
G01R 33/5608 20130101; G16H 15/00 20180101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/055 20060101 A61B005/055; G16H 30/40 20060101
G16H030/40; G16H 15/00 20060101 G16H015/00; G16H 50/20 20060101
G16H050/20; G16H 70/60 20060101 G16H070/60; G16H 10/20 20060101
G16H010/20; G06T 7/00 20060101 G06T007/00; G06K 9/46 20060101
G06K009/46; G06K 9/62 20060101 G06K009/62; G06K 9/32 20060101
G06K009/32; G01R 33/56 20060101 G01R033/56 |
Claims
1. A computer-implemented method for classifying neurodegenerative
disorder(s) and/or movement disorder(s) of a subject, the method
comprising: receiving subject data of a subject, the subject data
including one or more sets of MRI image data of a brain of the
subject; processing one or more sets of MRI image data to extract
one or more quantitative features for one or more regions, the one
or more quantitative features for the one or more regions includes
a first quantitative feature for the one or more regions and a
second quantitative feature for the one or more regions;
classifying at least the one or more quantitative features for the
one or more regions into one or more classes associated with
neurodegenerative dementia disorder, neurodegenerative movement
disorder, non-neurodegenerative movement disorder, and/or heathy
control; and generating a report including a classification of at
least the one or more quantitative features.
2. The method according to claim 1, wherein the one or more classes
associated with the neurodegenerative dementia disorder includes a
parkinsonian class and a non-parkinsonian class, and/or the one or
more classes associated with the neurodegenerative movement
disorder include a parkinsonian class.
3. The method according to claim 2, wherein: the parkinsonian class
for the neurodegenerative dementia disorder includes one or more
parkinsonian neurodegenerative dementia subclasses; and the one or
more parkinsonian neurodegenerative dementia subclasses includes
Parkinson's disease dementia (PDD), dementia with Lewy bodies
(DLB), and/or one or more other atypical parkinsonism dementia
disorder subclasses.
4. The method according to claim 3, wherein the other atypical
parkinsonism dementia disorder subclass includes multiple system
atrophy (MSA), progressive supranuclear palsy (PSP), and/or
corticobasal degeneration (CBD).
5. The method according to claim 1, wherein: the one or more
classes for the non-neurodegenerative movement disorder includes
one or more non-neurodegenerative movement disorder subclasses; and
the one or more non-neurodegenerative movement disorder subclasses
includes psychogenic, essential tremor, and drug-induced.
6. The method according to claim 2, wherein: the parkinsonian class
for the neurogenerative movement disorder includes one or more
parkinsonian movement disorder subclasses; and the one or more
parkinsonian movement disorder subclasses includes Parkinson's
Disease (PD) and/or one or more other atypical parkinsonism
movement disorder subclasses.
7. The method according to claim 6, wherein the one or more other
atypical parkinsonism movement disorder subclasses includes MSA,
PSP, and/or CBD.
8. The method according to claim 1, wherein the MRI image data is
acquired by one or more stored protocols.
9. The method according to claim 1, wherein the one or more
quantitative features include NM-MRI feature(s), R2* feature(s),
QSM feature(s), diffusion MRI feature(s), and/or other sequence
feature(s).
10. The method according to claim 9, wherein the one or more
regions includes one or more of the following: substantia nigra
pars compacta (SNc), locus coeruleus (LC), subthalamic nucleus, red
nucleus, globus pallidus (total, pars interna and/or pars externa),
putamen (lateral, medial, and/or total), caudate, cerebellar
dentate nucleus, substantia nigra pars reticulata, middle
cerebellar peduncle, superior cerebellar peduncle, hippocampus (one
or more individual subfields and/or total), entorhinal cortex,
occipital cortex (primary visual cortext, visual association
cortext, and/or total), parietal cortex, cingulate gyms,
parahippocampal gyms, and/or frontal cortext (M1, premotor,
supplementary motor area, Broca's area, prefrontal, orbitofrontal,
inferolateral frontal, and/or total).
11. The method according to claim 1, wherein the first quantitative
feature and the second quantitative feature are based on different
imaging protocols.
12. The method according to claim 1, wherein first quantitative
feature and the second quantitative feature are determined for
different regions of the brain.
13. The method according to claim 1, wherein: the subject data
includes additional subject data that is different from the one or
more sets of medical image data; and the classifying is also based
on one or more features extracted from the additional subject
data.
14. The method according to claim 1, wherein: the subject data
includes clinical data; the processing including processing the
clinical data to determine one or more clinical features; and the
classifying includes classifying the one or more clinical features
and the one or more quantitative features into the one or more
classes.
15. A system for classifying neurodegenerative disorder(s) and/or
movement disorder(s) of a subject, the system comprising: at least
one processor; and a memory, wherein the processor is configured to
cause: processing one or more sets of MRI image data of a brain of
the subject to extract one or more quantitative features for one or
more regions, the one or more quantitative features for the one or
more regions includes a first quantitative feature for the one or
more regions and a second quantitative feature for the one or more
regions; classifying at least the one or more quantitative features
for the one or more regions into one or more classes associated
with neurodegenerative dementia disorder, neurodegenerative
movement disorder, non-neurogenerative movement disorder, and/or
heathy control; and generating a report including a classification
of at least the one or more quantitative features.
16. The system according to claim 15, wherein the one or more
quantitative features include NM-MRI feature(s), R2* feature(s),
QSM feature(s), diffusion MRI feature(s), and/or other sequence
feature(s).
17. The system according to claim 15, wherein the one or more
classes associated with the neurodegenerative dementia disorder
includes a parkinsonian class and a non-parkinsonian class, and/or
the one or more classes associated with the neurodegenerative
movement disorder include a parkinsonian class.
18. The system according to claim 17, wherein: the parkinsonian
class for the neurodegenerative dementia disorder includes one or
more parkinsonian neurodegenerative dementia subclasses; and the
one or more parkinsonian neurodegenerative dementia subclasses
includes Parkinson's disease dementia (PDD), dementia with Lewy
bodies (DLB), and/or one or more other atypical parkinsonism
dementia disorder subclasses.
19. The system according to claim 18, wherein the one or more other
atypical parkinsonism dementia disorder subclasses includes
multiple system atrophy (MSA), progressive supranuclear palsy
(PSP), and/or corticobasal degeneration (CBD).
20. The system according to claim 16, wherein the one or more
regions includes one or more of the following: substantia nigra
pars compacta (SNc), locus coeruleus (LC), subthalamic nucleus, red
nucleus, globus pallidus (total, pars interna and/or pars externa),
putamen (lateral, medial, and/or total), caudate, cerebellar
dentate nucleus, substantia nigra pars reticulata, middle
cerebellar peduncle, superior cerebellar peduncle, hippocampus (one
or more individual subfields and/or total), entorhinal cortex,
occipital cortex (primary visual cortext, visual association
cortext, and/or total), parietal cortex, cingulate gyms,
parahippocampal gyms, and/or frontal cortext (M1, premotor,
supplementary motor area, Broca's area, prefrontal, orbitofrontal,
inferolateral frontal, and/or total).
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application No. 62/642,782 filed Mar. 14, 2018 and U.S. Provisional
Application No. 62/737,471 filed Sep. 27, 2018. The entirety of
each of these applications is hereby incorporated by reference for
all purposes.
BACKGROUND
[0002] Neurodegenerative diseases can inflict differential effects
across a variety of brain structures and circuits and can cause
different kinds of molecular and microstructural changes within
neuroanatomic systems. Diagnosis is currently made on clinical
grounds but can be challenging. Generally, misdiagnosis of
Parkinson's disease (PD) is a problem in patient care and in
clinical trials. Accurate, objective biomarkers are lacking, and
they are urgently needed to assist diagnosis and clinical trial
design. Most attempts at developing biomarkers for
neurodegenerative disorders have been univariable and therefore
generally do not capture the complex and distinct multi-system
biologies of neurodegenerative diseases. Further, biomarker studies
can be expensive and time consuming to conduct.
SUMMARY
[0003] Thus, there is a need for more efficient biomarkers and
related classifiers that can more efficiently and accurately
classify neurodegenerative disorders in a subject.
[0004] The disclosure relates to systems and methods that can
automatically classify neurodegenerative disorder(s) and/or
movement disorder(s) of a subject (e.g., a subject) using at least
quantitative features associated with one or more regions of
interest determined from one or more sets of image data of the
subject's brain. This can improve diagnosis and thus can improve
patient care and costs. Additionally, biomarkers that can be
automatically and be reproducibly determined can also improve the
cost- and time-efficiency of therapeutic clinical trials, as they
can be used as objective tools for subject selection and outcome
measurement. This can increase the odds of success in such
trials.
[0005] In some embodiments, the methods may include a
computer-implemented method for classifying neurodegenerative
disorder(s) and/or movement disorder(s) of a subject. In some
embodiments, the method may include receiving subject data of a
subject. The subject data may include one or more sets of MRI image
data of a brain of the subject. The method may include processing
one or more sets of MRI image data to extract one or more
quantitative features for one or more regions. The one or more
quantitative features for one or more regions may include a first
quantitative feature for the one or more regions and a second
quantitative feature for the one or more regions. The method may
include classifying at least the one or more quantitative features
for the one or more regions into one or more classes associated
with neurodegenerative dementia disorder, non-neurodegenerative
movement disorder, neurodegenerative movement disorder, and/or
heathy control. In some embodiments, the one or more classes may
include one or more stages one or more of the disorders. The method
may further include generating a report including a classification
of at least the one or more quantitative features. The one or more
quantitative features may result in a classification of a
disease/condition, as well as a differentiation between
diseases/conditions.
[0006] In some embodiments, the systems may include a system for
classifying neurodegenerative disorder(s) and/or movement
disorder(s) of a subject. The system may include at least one
processor and a memory. The processor may be configured to cause
processing one or more sets of MRI image data of a brain of the
subject to extract one or more quantitative features for one or
more regions. The one or more quantitative features for one or more
regions may include a first quantitative feature for the one or
more regions and a second quantitative feature for the one or more
regions. The processor may be further configured to cause
classifying at least the one or more quantitative features for the
one or more regions into one or more classes associated with
neurodegenerative dementia disorder, non-neurodegenerative movement
disorder, neurodegenerative movement disorder, and/or heathy
control. In some embodiments, the one or more classes may include
one or more stages one or more of the disorders. The processor may
be further configured to cause generating a report including a
classification of at least the one or more quantitative features.
The one or more quantitative features may result in a
classification of a disease/condition, as well as a differentiation
between diseases/conditions.
[0007] In some embodiments, the disclosure also relates to a
computer readable media configured to classify neurodegenerative
disorder(s) and/or movement disorder(s) of a subject (e.g., a
subject) using at least quantitative features associated with one
or more regions of interest determined from one or more sets of
image data of the subject's brain. In some embodiments, the
computer readable media may include a non-transitory computer
readable medium storing instructions, executable by a processor,
for classifying neurodegenerative disorder(s) and/or movement
disorder(s) of a subject. The instructions may include processing
one or more sets of MRI image data of a brain of the subject to
extract one or more quantitative features for one or more regions.
The one or more quantitative features for one or more regions may
include a first quantitative feature for the one or more regions
and a second quantitative feature for the one or more regions. The
instructions may include classifying at least the one or more
quantitative features for the one or more regions into one or more
classes associated with neurodegenerative dementia disorder,
non-neurodegenerative movement disorder, neurodegenerative movement
disorder, or heathy control. In some embodiments, the one or more
classes may include one or more stages associated of one or more of
the disorders. The instructions may include generating a report
including a classification of at least the one or more quantitative
features. The one or more quantitative features may result in a
classification of a disease/condition, as well as a differentiation
between diseases/conditions.
[0008] In some embodiments, the one or more classes associated with
neurodegenerative dementia disorder may include parkinsonian and
non-parkinsonian, and/or the one or more classes associated with
neurodegenerative movement disorder may include parkinsonian.
[0009] In some embodiments, the parkinsonian class for
neurodegenerative dementia disorder may include one or more
parkinsonian neurodegenerative dementia subclasses. The one or more
parkinsonian neurodegenerative dementia subclasses may include
Parkinson's disease dementia (PDD), dementia with Lewy bodies
(DLB), and/or other atypical parkinsonism dementia disorder
subclass. In some embodiments, the atypical parkinsonism dementia
disorder subclass may include multiple system atrophy (MSA),
progressive supranuclear palsy (PSP), and/or corticobasal
degeneration (CBD).
[0010] In some embodiments, the non-neurodegenerative class for
non-neurodegenerative movement disorder may include one or more
non-neurodegenerative movement disorder subclasses. The one or more
non-neurodegenerative movement disorder subclasses may include
psychogenic, essential tremor, and/or drug-induced.
[0011] In some embodiments, the parkinsonian movement disorder
class may include one or more parkinsonian movement disorder
classes. The one or more parkinsonian movement disorder classes may
include PD and/or other atypical parkinsonism. In some embodiments,
the atypical parkinsonism movement disorder subclass may include
MSA, PSP, and/or CBD.
[0012] In some embodiments, the MRI image data may be acquired
using one or more stored protocols.
[0013] In some embodiments, the one or more quantitative features
may include NM-MRI feature(s), R2* feature(s), QSM feature(s),
diffusion MRI feature(s), MR spectroscopy feature(s),
hyperpolarized MRI feature(s), functional MRI feature(s). and/or
other sequence feature(s).
[0014] In some embodiments, the one or more regions may include one
or more of the following: substantia nigra pars compacta (SNc),
locus coeruleus (LC), subthalamic nucleus, red nucleus, globus
pallidus (total, pars interna and/or pars externa), putamen
(lateral, medial, and/or total), caudate, cerebellar dentate
nucleus, substantia nigra pars reticulata, middle cerebellar
peduncle, superior cerebellar peduncle, hippocampus (individual
subfields and/or total), entorhinal cortex, parahippocampal gyms,
occipital cortex (primary visual cortext, visual association
cortext, and/or total), parietal cortex, cingulate gyms, and/or
frontal cortext (M1, premotor, supplementary motor area, Broca's
area, prefrontal, orbitofrontal, inferolateral frontal, and/or
total).
[0015] In some embodiments, the first quantitative feature and the
second quantitative feature may be based on different imaging
protocols.
[0016] In some embodiments, the first quantitative feature and the
second quantitative feature may be determined for different regions
of the brain.
[0017] In some embodiments, the subject data may include additional
subject data that is different from the one or more sets of medical
image data. The classifying may also be based on one or more
features extracted from the additional subject data.
[0018] Additional advantages of the disclosure will be set forth in
part in the description which follows, and in part will be obvious
from the description, or may be learned by practice of the
disclosure. The advantages of the disclosure will be realized and
attained by means of the elements and combinations particularly
pointed out in the appended claims. It is to be understood that
both the foregoing general description and the following detailed
description are exemplary and explanatory only and are not
restrictive of the disclosure, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The disclosure can be better understood with the reference
to the following drawings and description. The components in the
figures are not necessarily to scale, emphasis being placed upon
illustrating the principles of the disclosure.
[0020] FIG. 1 shows an example of classifying a neurodegenerative
and/or movement disorder using at least one or more quantitative
features according to embodiments;
[0021] FIG. 2 shows a method of classifying a neurodegenerative
and/or movement disorder using at least one or more quantitative
features according to embodiments;
[0022] FIG. 3 shows a method of determining one or more
quantitative features for one or more regions according to
embodiments;
[0023] FIG. 4 shows an example of a method of generating one or
more classifiers to identify biomarkers according to
embodiments;
[0024] FIG. 5 shows a block diagram illustrating an example of a
computing system;
[0025] FIG. 6 shows example of a multivariate classification of
Parkinson's disease using more than one quantitative measure
according to embodiments;
[0026] FIG. 7A and 7B show an example of a multivariate
classification of Parkinson's disease using more than one clinical
feature; FIG. 7A shows a ROC curve for the classification and FIG.
7 B shows as a box plot for the classification; and
[0027] FIGS. 8A and 8B show another example of a multivariate
classification of Parkinson's disease using more than one clinical
feature and quantitative measure according to embodiments; FIG. 8A
shows a ROC curve for the classification and FIG. 8B shows as a box
plot for the classification.
DESCRIPTION OF THE EMBODIMENTS
[0028] In the following description, numerous specific details are
set forth such as examples of specific components, devices,
methods, etc., in order to provide a thorough understanding of
embodiments of the disclosure. It will be apparent, however, to one
skilled in the art that these specific details need not be employed
to practice embodiments of the disclosure. In other instances,
well-known materials or methods have not been described in detail
in order to avoid unnecessarily obscuring embodiments of the
disclosure. While the disclosure is susceptible to various
modifications and alternative forms, specific embodiments thereof
are shown by way of example in the drawings and will herein be
described in detail. It should be understood, however, that there
is no intent to limit the disclosure to the particular forms
disclosed, but on the contrary, the disclosure is to cover all
modifications, equivalents, and alternatives falling within the
spirit and scope of the disclosure.
[0029] In some embodiments, the systems and methods of the
disclosure can efficiently and accurately classify
neurodegenerative disorder(s) and/or movement disorder(s) of a
subject (e.g., a subject), and/or more stages associated with the
neurodegenerative disorder(s) and/or the movement disorder(s),
using at least quantitative features, such as quantitative MRI
feature(s) or measure(s), associated with one or more regions of
interest determined from one or more sets of image data of the
subject's brain. In some embodiments, one or more features from
other subject data, such as other image data, physiological data,
clinical data, demographic data, epigenetic data, omics data, among
others, or any combination thereof, may also be used. In some
embodiments, the systems and method can generate biomarker profiles
of one or more features (biomarkers), e.g., tremor or cognitive
deficits, associated with neurodegenerative disorders and/or
movement disorders.
[0030] The classification results may be used to assist clinical
diagnosis of Parkinson's disease, and to aid clinicians in
differentiating between parkinsonian and non-parkinsonian
disorders. For example, the classifier may be configured for (i)
the differential diagnosis of parkinsonian disorder vs. other
(non-neurodegenerative) movement disorders or (ii) the differential
diagnosis of parkinsonian vs. non-parkinsonian neurodegenerative
dementia disorders.
[0031] In another example, the classifier may be configured for (i)
the detection of a prodromal neurodegenerative disease or (ii) the
differential diagnosis of prodromal neurodegenerative disease
(e.g., prodromal Parkinson's disease) vs. non-prodromal
neurodegenerative disease.
[0032] The classification results may include a baseline prediction
of rate of future conversion to symptomatic neurodegenerative
disease.
[0033] In some embodiments, the classification results may be used
for detecting and quantitative monitoring of disease progression.
The classification results can also be used as companion
diagnostics to guide targeted therapy (e.g., i.e., personalized
medicine).
[0034] The classification results may also be used for example, for
research applications, including subject selection for clinical
trials and outcome measurement in clinical trials.
[0035] Classification results may also be used to select disease
subtypes based on a desired biomarker profile. This may enable
biomarker directed clinical trial designs or ultimately biomarker
targeted selection of therapeutics for individual patients (i.e.,
subjects).
[0036] In some embodiments, the disorder(s) may include a
neurodegenerative (dementia) disorder, a movement disorder (e.g.,
non-neurodegenerative, neurodegenerative, etc.), a sub-type of the
neurodegenerative disorder or movement disorder, a stage of the
neurodegenerative or movement disorder, among others, or any
combination thereof.
[0037] By way of example, the neurodegenerative dementia disorders
can include but are not limited to Parkinson's disease (PD),
Alzheimer's disease, frontotemperal Dementia (FTD), vascular
related dementia, dementia with Lewy bodies, progressive
supranuclear palsy (PSP), among others, or any combination thereof.
The non-neurodegenerative motor disorder sub-types may include but
are not limited to healthy, psychogenic, essential tremor,
drug-induced, among others, or any combination thereof. In some
embodiments, the neurodegenerative motor disorder may include but
is not limited to a parkinsonian disorder. The sub-types of a
parkinsonian movement disorder may include parkinsonian and
atypical parkinsonian movement disorder (e.g., multiple system
atrophy (MSA) (e.g., parkinsonian type (MSA-P), cerebellar type
(MSA-C), coriticobasal degeneration (CBD), progressive supranuclear
palsy (PSP), etc.) among others, or any combination thereof). In
some embodiments, the parkinsonian dementia sub-types may include
but are not limited to Parkinson's disease dementia (PDD), dementia
with lewy bodies (DLB), other atypical parkinsonian disorders,
among others, or any combination thereof. For example, the other
atypical parkinsonian disorders may include but are not limited to
multiple system atrophy (MSA) (e.g., parkinsonian type (MSA-P),
cerebellar type (MSA-C), etc.), coriticobasal degeneration (CBD),
progressive supranuclear palsy (PSP), among others, or any
combination thereof.
[0038] The one or more stages may include a prodromal group that
may convert to symptomatic synucleinopathy, such as PD, DLB, MSA.
By way of example, a prodromal group may include one or more or of
idiopathic rapid-eye-movement sleep behavior disorder (iRBD), other
prodromal group(s) (e.g., LRRK2 mutation carriers, GBA mutation
carriers, individuals with olfactory loss, combinations of these
features with iRBD, etc.), among others, or any combination
thereof.
[0039] In some embodiments, the one or more sets of image data may
include image data that can provide a measurable indicator of a
neurological state or condition. For example, the image data can
include but is not limited to image data of the brain of the
subject acquired by the system using one or more stored protocols.
In some embodiments, the protocols may relate to protocols for MR
imaging system to acquire one or more sets of image data. For
example, the protocols include but are not limited to protocols for
pulse sequences for neuromelanin-sensitive MRI (e.g., explicit or
incidental magnetization transfer contrast-based); iron-sensitive
MRI sequences (e.g., T2 weighted imaging, R2* imaging,
susceptibility weighted imaging, quantitative susceptibility
mapping (QSM), etc.); diffusion MRI; resting and task-based
functional MRI chemical shift imaging; proton density imaging;
spin-lattice MRI; hyperpolarized C.sup.13 MRI (or other
hyperpolarizable nuclei); intravenous contrast enhanced MRI (e.g.,
gadolinium, iron based contrast, other ferromagnetic contrasts,
contrasts relating to non-H.sup.1 based MRI such as phosphorus or
fluorine, etc.); among others; or any combination thereof.
[0040] In some embodiments, the methods and systems may process the
MRI pulse sequence data to determine one or more quantitative
features associated with one or more regions of interest. The one
more quantitative features (e.g., quantitative MRI feature(s) (can
also be referred to as quantitative MRI measure(s)) may be
associated with one or more regions of interest of the brain.
[0041] The one or more regions of interest may include but are not
limited to one or more subregions and/or the region of substantia
nigra pars compacta (SNc), locus coeruleus (LC), subthalamic
nucleus, red nucleus, globus pallidus (total, pars interna and/or
pars externa), putamen (lateral, medial, and/or total), caudate,
cerebellar dentate nucleus, substantia nigra pars reticulata,
middle cerebellar peduncle, superior cerebellar peduncle,
ahippocampus (individual subfields and/or total), entorhinal
cortex, occipital cortex (primary visual cortext, visual
association cortext, and/or total), parietal cortex, cingulate
gyms, parahippocampal gyms, frontal cortext (M1, premotor,
supplementary motor area, Broca's area, prefrontal, orbitofrontal,
inferolateral frontal, and/or total), among others, or any
combination thereof. By way of example, the one or more
quantitative features may include one or more measurements
determined from the one or more sets of image data related to but
are not limited to NM-MRI feature(s), R2* feature(s), QSM
feature(s), diffusion MRI feature(s), MR spectroscopy feature(s),
hyperpolarized MRI feature(s), functional MRI feature(s), other
sequence feature(s), among others, or any combination thereof. By
way of example, the one or more quantitative features may include
but are not limited to one or more of the following: NM-MRI volume
of SNc and LC; NM-MRI, R2*, and/or QSM contrast of entire SNc and
lateral-ventral SNc; NM-MRI, R2* and/or QSM contrast of the
T2-weighted MRI-defined substantia nigra pars reticulate; R2*
and/or QSM contrast in the cerebellar dentate nucleus; among
others; or any combination thereof.
[0042] "Physiological data" can include data obtained by measuring
one or more aspects of a subject's physiology. Examples of
physiological data can include but are not limited to measurements
of a subject's blood chemistry, including measurements of cortisol
or other hormones; measurements of a subject's skin conductance
response; respiratory data; and electrophysiological data, which
can include electrocardiography ("ECG"), electroencephalography
("EEG"), magnetoencephalography ("MEG"), and electromyography
("EMG") measurements; other vital sign measurements (e.g., blood
pressure, orthostatic vital signs, etc.), among others, or any
combination thereof.
[0043] In some embodiments, the physiological data can include data
acquired using personal fitness trackers or other mobile devices
(e.g., smart phones, tablets, etc.) that incorporate one or more
sensors including heart rate sensors, pedometers, accelerometers,
temperature sensors, among others, or any combination thereof.
[0044] "Clinical data" can include about the subject that is
obtained from a clinician, a subject, a computer, a mobile device
(e.g., smart phones, tablets, etc.), fitness tracker, among others,
including data associated with a clinical assessment, a clinical
characterization, among other, or any combination thereof. In some
embodiments, the clinical data may include medical history data,
one or more clinical ratings, scores or scales, for example,
derived from one more clinical assessment instruments, among
others, or any combination thereof.
[0045] In some embodiments, the one or more clinical assessment
instruments may be one or more assessments of neurocognitive
assessments (e.g., that can be used to assess parkinsonian motor
symptoms and/or parkinsonian non-motor symptoms). The one or more
clinical assessment instruments may include one or more of
questionnaires, structured neurological examinations, among others,
or any combination thereof. The one or more clinical assessment
instruments may be administered by and/or recorded by a clinician
(or other trained individual), the patient (e.g., a
self-administered questionnaire), a computer, a mobile device,
among others, or any combination thereof. In some embodiments, the
one or more clinical assessment instruments may use the
physiological data and/or other sensor data collected during the
administration (e.g., using sensors of the mobile device) of the
clinical assessment instrument.
[0046] By way of example, the one or more questionnaires may
include assessments of single domain areas, multi-domain areas,
functioning, activities of daily living, among others, or any
combination thereof. By way of example, one or more assessments of
single domain areas (i.e., separate questionnaires) may include but
are not limited to: depression (e.g., Beck Depression Index II),
fatigue (e.g., Fatigue Questionnaire), autonomic dysfunction (e.g.
SCOPA-AUT), sleep (e.g., REM Sleep Behavior Disorder Questionnaire
(RBDQ), Epworth Sleepiness Scale, etc.), gait symptoms (e.g.,
Freezing of Gait Questionnaire (FOGQ)), among others, or any
combination thereof. By way of example, one or more assessments of
multiple domain areas (i.e., a single questionnaire screening
multiple categories of symptoms) may include but are not limited to
the Non-motor Symptoms Questionnaire (NMSQ), among others, or any
combination thereof. By way of example, one or more assessments of
functioning and/or daily activities may include but are not limited
to the Movement Disorders Society Unified Parkinson's Disease
Rating Scale Part II (MDS-UPDRS II), among others, or any
combination thereof.
[0047] By way of example, the one or more structured neurological
examinations performed by a clinician or other trained individuals
may include but are not limited to: one or more examination
components of the MDS-UPDRS Part III motor examination, the
Progressive Supranuclear Palsy Rating Scale, the Unified Multiple
System Atrophy Rating Scale, Montreal Cognitive Assessment, among
others, or any combination thereof. In each of these cases, either
individual items (e.g., single questions or tasks) may be included
as separate features in analytic models, or summaries (e.g., a sum
of the scores from all items in a questionnaire, such as the sum of
the scores of all items in the MDS-UPDRS Part II questionnaire) may
be included as separate features in analytic models.
[0048] By way of example, the one or more clinical assessment
instruments that can be deployed or administered by a computer or a
mobile device may include online cognitive tests. By way of another
example, the one or more clinical assessment instruments that can
be deployed as a software application may include one or more
augmented reality applications that assess cognition or motor
skills, applications that test motor function through app-based
tasks, such as alternating taps, voice recording, and various
measurements obtained using any combination of instructions to the
patient and sensors (e.g., accelerometer and/or GPS device)(e.g.,
timed task of standing and walking a distance).
[0049] In some embodiments, the clinical data may include
behavioral data that generally indicates a behavior of the subject.
For example, the behavioral data that can be obtained from
behavioral tracking of the subject. For example, the behavioral
data may include data obtained from eye tracking, facial feature
analysis, among others, or any combination thereof.
[0050] "Genetic data" can include data associated with genetic
influences on a subject's gene expression. For example, the genetic
data can include allelic variants or single nucleotide
polymorphisms that identify imaging endophenotypes associated with
clinical features.
[0051] "Epigenetic data" can include data associated with heritable
phenotype information that do not involve alternations in the DNA
sequence. For example, the epigenetic data may include data related
methylation sites, histone proteins, suprastructural aspects of DNA
conformations, among others, or any combination thereof.
[0052] "Omics data" can include data associated with profiles a
biological organism via detailed analysis of particular biological
structures or systems. For example, the omics data may include
data, such as those obtained with metabolomics, proteomics,
lipidomics, genomics, transcriptomics, immunomics, metals
profiling, among others, or any combination thereof.
[0053] "Other image data" may include structural or anatomical
images or data acquired with MRI or other medical imaging
modalities, such as positron emission tomography ("PET") or single
photon emission computed tomography ("SPECT").
[0054] As used herein, "biomarker" may relate to a measurable
indicator of a neurological or movement condition, whether of a
normal or healthy neurological state or condition, or of a state or
condition related to a neurodegenerative dementia disorder and/or
movement disorder. The biomarkers described here can generally be
based one or more quantitative features associated with one or more
regions of interest using the image data but can also be based on
features from other subject data, such as other image data,
clinical data, physiological data, genetic data, epigenetic data,
omics data, among others, or any combination thereof.
[0055] FIG. 1 shows a system 100 that can diagnosis a
neurodegenerative disorder and/or movement disorder by classifying
at least one or more quantitative features for one or more regions
of interest determined from one or more sets of the image data of
the brain according to embodiments. In some embodiments, the system
may include a disorder classifying device 120 configured to use one
or more classifiers to diagnosis a neurodegenerative disorder
and/or movement disorder by classifying at least the one or more
quantitative features or measures.
[0056] The disorder classifying device 120 may be configured to
determine one or more quantitative features for one or more regions
of interest and use one or more classifiers to perform multi-class
classification of movement and/or neurodegenerative disorder using
at least the one or more quantitative features for the one or more
regions of interest. For example, the disorder classifying device
120 may be configured to use one or more classifiers to classify
the features and diagnose the subject as 1) having movement
disorder and/or 2) having neurodegenerative dementia; or 3) normal
(e.g., healthy) with respect to the movement and/or
neurodegenerative disorder. In some embodiments, the disorder
classifying device 120 may be configured to classify the features
and diagnose the subject as having a stage of a movement or
neurodegenerative dementia (e.g., prodromal).
[0057] For example, with regards to the movement disorder, the
classifying device 120 may be configured to use one or more
classifiers to classify the feature(s) to diagnosis a subject as i)
having a non-neurodegenerative movement disorder; ii) having a
parkinsonian movement disorder; or 3) normal (e.g. healthy). In
some embodiments, the classifying device 120 may be further
configured to implement one or more classifiers to classify within
these classes (subclasses). For example, for non-neurodegenerative
movement disorder, the device 120 may be configured to determine
whether the subject corresponds to the one or more of the following
classes: psychogenic, essential tremor, drug-induced, among others,
or any combination thereof. For parkinsonian movement disorder, the
device 120 may be configured to determine whether the parkinsonian
disorder can be PD or atypical parkinsonism. In some embodiments,
the device 120 may be further configured to further classify the
feature(s) associated with atypical parkinsonism into one or more
sub-classes. For example, the device may be configured to classify
the atypical parkinsonism movement disorder as: MSA, PSP, or CBD.
In some embodiments, the device 120 may be further configured to
classify MSA as either MSA-P or MSA-C.
[0058] By way of another example, with regards to the
neurodegenerative dementia, the device 120 may be configured to use
one or more classifiers to classify a subject as i) having a
non-parkinsonian disorder; ii) having a parkinsonian disorder; or
iii) normal (i.e., healthy). In some embodiments, the classifying
device 120 may be configured to further classify these classes
(subclasses). For example, for non-neurodegenerative disorder, the
device 120 may be configured to determine whether the subject
corresponds to the one or more of the following classes: AD,
[0059] FTD, Vascular, among others, or any combination thereof. For
parkinsonian dementia disorder, the device 120 may be configured to
determine whether the parkinsonian disorder can be PD, DLB, or one
or more other atypical parkinsonism subclasses. In some
embodiments, the device 120 may be further configured to further
classify the one or more other atypical parkinsonism subclasses.
For example, the device may be configured to classify the other
atypical parkinsonism disorder as either MSA, PSP, or CBD. In some
embodiments, the device 120 may be further configured to classify
MSA as either MSA-P or MSA-C.
[0060] In some embodiments, the system 100 may include one or more
medical imaging devices 110 configured to acquire at least one or
more sets of image data of a brain of the subject using one or more
stored protocols 112.
[0061] In some embodiments, the medical imaging device(s) 110 may
include one or more
[0062] MRI systems, such as a 3T MRI scanner system. The imaging
devices 110 may be configured to acquire NM-MRI related data using
NM-MRI protocols, for example, the stored protocols 112.
[0063] In some embodiments, the stored protocols 112 may include: a
neuromelanin-sensitive MRI pulse sequence with a reduced flip angle
explicit magnetization transfer preparation pulse; a multi-echo
gradient echo MRI pulse sequence suitable for acquisition of R2*
and quantitative susceptibility mapping data; a diffusion protocol;
among others; or any combination thereof. In some embodiments, the
device 120 may determine the one or more protocols for the image
acquired by the one or more medical imaging device(s) 110.
[0064] In some embodiments, the system 100 may include one or more
other subject collection and/or storage devices 130 configured to
collect and/or store the image data acquired by the one or more
medical imaging device(s), (additional) subject data (such as
physiological data, clinical data, epigenetic data, omics data,
among others, or any combination thereof), among others, or any
combination thereof. In some embodiments, the image data and/or
(additional) subject data may be annotated with other data, such as
clinical/phenotypic data.
[0065] In some embodiments, the subject data, including the one or
more sets of image data as well as the other subject data, may be
stored on a healthcare record system (e.g., electronic medical
record system, radiological image storage (e.g., Picture Archiving
and Communication System (PACS), etc.). The system 100 may be
additionally and/or alternatively configured to retrieve the
relevant subject data from the healthcare record system.
[0066] In some embodiments, the device 120 may be configured to
process the raw MRI data to determine one or more regions of
interest and related quantitative feature(s). For example, the one
or more regions of interest may include but are not limited to one
or more subregions and/or the region of SNc, LC, subthalamic
nucleus, red nucleus, globus pallidus (total, pars interna and/or
pars externa), putamen (lateral, medial, and/or total), caudate,
cerebellar dentate nucleus, substantia nigra pars reticulata,
middle cerebellar peduncle, superior cerebellar peduncle,
hippocampus (individual subfields and/or total), entorhinal cortex,
occipital cortex (primary visual cortext, visual association
cortext, and/or total), parietal cortex, cingulate gyms,
parahippocampal gyms, frontal cortext (M1, premotor, supplementary
motor area, Broca's area, prefrontal, orbitofrontal, inferolateral
frontal, and/or total), among others, or any combination
thereof.
[0067] By way of example, the one or more quantitative features for
one or more region of interest may include but are not limited to
NM-MRI feature(s), R2* feature(s), QSM feature(s), diffusion MRI
feature(s), MR spectroscopy feature(s), hyperpolarized MRI
feature(s), and functional MRI feature(s), other sequence
feature(s), among others, or any combination thereof. For example,
the one or more quantitative features for one or more regions of
interest may include but are not limited to one or more of the
following: NM-MRI volume SNc and/or LC; NM-MRI, R2*, and/or QSM
contrast of entire SNc and/or lateral-ventral SNc; NM-MRI, R2*
and/or QSM contrast of the T2-weighted MRI-defined substantia nigra
pars reticulate; R2* and/or QSM contrast in the cerebellar dentate
nucleus; among others; or any combination thereof.
[0068] In some embodiments, the device 120 may be configured to
classify a subject as having a movement and/or neurodegenerative
disorder based on at least one or more quantitative features (and
optionally the other subject feature(s)) using one or more
classifiers. In some embodiments, the device may store one or more
classifiers. Each classifier may be a machine-learning trained
multivariate classifier. In some embodiments, one or more
classifiers may be a binary classifier incorporating at least the
one or more quantitative features. The classifier(s) may also
incorporate one or more features extracted from the (other) subject
data (e.g., physiological data, clinical data, epigenetic data,
other image data, etc.). In some embodiments, the classifier(s) may
be trained using (i) one or more machine-learning algorithms and
(ii) at least the quantitative features of subjects having the
movement and/or neurodegenerative disorder and subjects without the
disorder(s).
[0069] In some embodiments, each classifier may be configured to
classify between, within (e.g., subclasses), and/or stage(s) (e.g.,
prodromal) the disorders (e.g., movement and/or neurodegenerative)
and normal. By way of example, at least one classifier (e.g.,
generated using an iRBD dataset and a control dataset) can be
configured to differentiate the MRI/multimodal signature of
prodromal synucleinopathy from controls; at least one classifier
(e.g., generated using a PSP dataset and a PD dataset) can be
configured to differentiate the MRI/multimodal signature of PSP
from PD; and at least one classifier (e.g., generated using a DLB
dataset and an AD dataset) can be configured to differentiate the
MRI/multimodal signature of DLB from AD. The device 120 may store
additional classifiers configured to classify between, within
(e.g., subclasses), and/or stage(s) (e.g., prodromal) the disorders
(e.g., movement and/or neurodegenerative) and normal.
[0070] In some embodiments, the device 120 may select the one or
more classifiers based on inputted data, user input, stored
pathways (e.g., decision tree), or any combination thereof.
[0071] In some embodiments, the one or more quantitative features
may include any number of features for any number of regions of
interest. In some embodiments, the one or more quantitative
features may include one or more NM-MRI feature associated with one
or more regions and one or more R2* features associated with one or
more features. In some embodiments, the one or more quantitative
features may include additional quantitative feature(s), region(s)
of interest, among others, or any combination thereof.
[0072] For example, each machine learning classifier may be trained
using one or more machine learning algorithms. The one or more
algorithms may include but are not limited to logistic regression
with elastic net, support vector machines, artificial neural
networks (such as convolutional neural networks, Bayesian
classifiers, and ensemble methods, etc.), other available machine
learning algorithms, among others, or any combination thereof.
[0073] In some embodiments, the one or more classifiers may use a
decision tree approach for differential diagnosis. For example, the
classifier(s) may be configured to sequentially differentiate
groups using multiple classifiers in sequence, moving from broad
category diagnosis (e.g., parkinsonism vs. no parkinsonism), to
narrower category (e.g., within parkinsonism, PD vs.
[0074] atypical parkinsonism), to a more specific diagnosis (e.g.,
within atypical parkinsonism, multiple system atrophy (MSA) vs.
progressive supranuclear palsy (PSP), etc.). As additional datasets
are acquired (e.g. from other neurodegenerative conditions, such as
Alzheimer's disease), additional nodes can be added to this
decision tree to provide additional diagnostic information.
[0075] In some embodiments, the classifier device 120 may use the
classification results to evaluate treatment response and/or
monitor patients over time. For example, the classification can be
repeated to track progression of disease (e.g., before and/or after
treatment is initiated), by the device 120 repeating the
classification, using one or more classifiers, at different time
intervals using the same type of data and/or different data. In
some embodiments, the device 120 may be configured to
quantitatively monitor the disease progression. By way of example,
the device may track the classification using at least one or more
NM-MRI features (e.g., lateral-ventral SNc magnetization transfer
contrast) over time to monitor the progression of disease in that
patient. This can help determine whether a treatment therapy has
been helping to prevent neurodegeneration.
[0076] In some embodiments, the device 120 may be configured to
generate the one or more classifiers, for example, using the one or
more quantitative features, the medical image data and/or
(additional) subject data retrieved and/or stored on the storage
devices 130, among others, or any combination thereof. In some
embodiments, the one or more classifiers may be generated using
data from a range of specific subject/patient groups to apply to
specific clinical and translational applications. By way of
example, classifiers can be generated from MRI and multimodality
datasets collected from subject-groups of interest. MRI datasets
can include but are not limited to neuromelanin-sensitive MRI and
iron-sensitive MRI (e.g., R2*, QSM, SWI, etc.) acquisitions and
feature extractions to quantify neurodegeneration in multiple
different diseases and at different stages of disease, or
longitudinally over time. In some embodiments, the one or more
classifiers may be trained using datasets from the diseases and
stages of interest, which can be cross-sectional or longitudinal.
These datasets may also be used by the device 120 to test and
validate the one or more classifiers.
[0077] In some embodiments, the one or more other collection and/or
storage devices 130 and/or the classifying device 120 may be
cloud-based. By way of example, the one or more imaging devices 110
may acquire the image data of a patient (e.g., with an MRI system)
and cause the image data to upload to a cloud-based database, e.g.,
one or more other subject collection and/or storage devices 130. In
this example, the one or more other subject collection and/or
storage devices 130 can interface with the cloud-based classifying
device 120 that includes software which executes image processing
and feature extraction followed by execution of one or more
classifiers to generate a report providing the (diagnostic)
classification information and quantitative measures of
neurodegenerative pathology. The report can inform diagnostic
assessments.
[0078] In some embodiments, the devices 120 and/or 130 may include
one or modules, such as containers. By way of example, the device
132 may include one or more containers for each classifier. The
end-user can select from a wide range of applications (e.g.,
diagnosis), and modules for different classifiers can be then
automatically selected as different options such that the
appropriate image processing, feature selection and classifier(s)
can be applied to perform the desired analysis relevant to the
application of interest. Containerized modules for each of these
elements and sub-processes within each element can be maintained in
a software structure that is manipulated easily in a cloud-based
platform. This way manipulations can occur instantly at a scale
that can accommodate high volume clinical and research scan
workloads.
[0079] In some embodiments, the devices 110, 120, and/or 130, as
well as the stored protocols 112, may be disposed within the same
device or otherwise have connectivity via a communication network.
By way of example, the communication network of system 100 can
include one or more networks such as a data network, a wireless
network, a telephony network, or any combination thereof. The data
network may be any local area network (LAN), metropolitan area
network (MAN), wide area network (WAN), a public data network
(e.g., the Internet), short range wireless network, or any other
suitable packet-switched network, such as a commercially owned,
proprietary packet-switched network, e.g., a proprietary cable or
fiber-optic network, and the like, NFC/RFID, RF memory tags,
touch-distance radios, or any combination thereof. In addition, the
wireless network may be, for example, a cellular network and may
employ various technologies including enhanced data rates for
global evolution (EDGE), general packet radio service (GPRS),
global system for mobile communications (GSM), Internet protocol
multimedia subsystem (IMS), universal mobile telecommunications
system (UMTS), etc., as well as any other suitable wireless medium,
e.g., worldwide interoperability for microwave access (WiMAX), Long
Term Evolution (LTE) networks, code division multiple access
(CDMA), wideband code division multiple access (WCDMA), wireless
fidelity (WiFi), wireless LAN (WLAN), Bluetooth.RTM., Internet
Protocol (IP) data casting, satellite, mobile ad-hoc network
(MANET), and the like, or any combination thereof.
[0080] Although the systems/devices of the system 100 are shown as
being directly connected, the systems/devices may be indirectly
connected to one or more of the other systems/devices of the system
100. In some embodiments, a system/device may be only directly
connected to one or more of the other systems/devices of the system
100.
[0081] It is also to be understood that the system 100 may omit any
of the devices illustrated and/or may include additional systems
and/or devices not shown. It is also to be understood that more
than one device and/or system may be part of the system 100
although one of each device and/or system is illustrated in the
system 100. It is further to be understood that each of the
plurality of devices and/or systems may be different or may be the
same. For example, one or more of the devices of the devices may be
hosted at any of the other devices.
[0082] In some embodiments, any of the devices of the system 100,
for example, the device 120, may include a non-transitory
computer-readable medium storing program instructions thereon that
is operable on a user device. A user device may be any type of
mobile terminal, fixed terminal, or portable terminal including a
mobile handset, station, unit, device, multimedia computer,
multimedia tablet, Internet node, communicator, desktop computer,
laptop computer, notebook computer, netbook computer, tablet
computer, personal communication system (PCS) device, wearable
computer (e.g., smart watch), or any combination thereof, including
the accessories and peripherals of these devices, or any
combination thereof. FIG. 4 shows an example of a user device.
[0083] FIGS. 2 and 3 show methods of classifying a subject using at
least one or more quantitative features for one or more regions of
brain of the subject using one or more sets of medical image data
according to embodiments and FIG. 4 shows a method of generating a
classifier according to embodiments. Unless stated otherwise as
apparent from the following discussion, it will be appreciated that
terms such as "encoding," "generating," "determining,"
"displaying," "obtaining," "applying," "processing," "computing,"
"selecting," "receiving," "detecting," "classifying,"
"calculating," "quantifying," "outputting," "acquiring,"
"analyzing," "retrieving," "inputting," "assessing," "performing,"
or the like may refer to the actions and processes of a computer
system, or similar electronic computing device, that manipulates
and transforms data represented as physical (e.g., electronic)
quantities within the computer system's registers and memories into
other data similarly represented as physical quantities within the
computer system memories or registers or other such information
storage, transmission or display devices. The system for carrying
out the embodiments of the methods disclosed herein is not limited
to the systems shown in FIGS. 1 and 5. Other systems may also be
used.
[0084] The methods of the disclosure are not limited to the steps
described herein. The steps may be individually modified or
omitted, as well as additional steps may be added. It will be also
understood that at least some of the steps may be performed in
parallel.
[0085] FIG. 2 illustrates a method 200 for determining a
neurodegenerative and/or motion disorder based on at least one more
quantitative features associated with one or more regions of the
brain determined from acquired one or more sets of medical imaging
of the brain. For example, the method 200 may result in a diagnosis
in one of the classes and/or sub-classes described above.
[0086] In some embodiments, the method 200 may include a screening
step (not shown). For example, one or more features of the clinical
data may be used to screen a subject. In some embodiments, one or
more features of the clinical data may be inputted into a
classifier to determine whether an individual is at risk for
Parkinson's disease or a related disorder (neurodegenerative
disorder and/or movement disorder). For example, in practice, if
the subject is determined to be at risk, the subject may need
further evaluation (e.g., MRI and additional assessment) to obtain
additional data for diagnostic classification shown in FIG. 2.
[0087] In some embodiments, the method 200 may include a step 210
of receiving medical image data of the subject. The medical image
data may include one or more sets of MRI image data of the brain
acquired using one or more stored protocols. For example, the one
or more stored protocols may relate to NM-MRI, R2*, QSM, among
others, or any combination thereof.
[0088] The method 200 may further include a step 220 of extracting
one or more quantitative features associated with one or more
regions of interest from the one or more sets of MRI image data. In
some embodiments, the one or more quantitative features may include
but are not limited to: NM-MRI volume; NM-MRI, R2*, and/or QSM
contrast; among others; among others; or any combination thereof.
In some embodiments, the one or more quantitative features may be
associated with one or more of the following regions of interest:
one or more subregions of and/or the region of SNc, LC, subthalamic
nucleus, red nucleus, globus pallidus (total, pars interna and/or
pars externa), putamen (lateral, medial, and/or total), caudate,
cerebellar dentate nucleus, sub stantia nigra pars reticulata,
middle cerebellar peduncle, superior cerebellar peduncle,
hippocampus (individual subfields and/or total), entorhinal cortex,
parahippocampal gyms, occipital cortex (primary visual cortext,
visual association cortext, and/or total), parietal cortex,
cingulate gyms, frontal cortext (M1, premotor, supplementary motor
area, Broca's area, prefrontal, orbitofrontal, inferolateral
frontal, and/or total), among others, or any combination
thereof.
[0089] In some embodiments, the method 200 may optionally include a
step 230 of receiving other or additional subject data, such other
image data, physiological data, clinical data, demographic data,
epigenetic data, among others, or any combination thereof. If the
method 200 includes this step, the method may further include a
step 240 of extracting one or more features from the other subject
data. For example, one more features for clinical data (e.g.,
clinical data feature(s)) may include individual items (e.g.,
answers to single questions), summaries or scores for an
assessment, among others, or any combination thereof.
[0090] In some embodiments, the method 200 may include a step 250
of classifying the subject based on at least the one or more
quantitative features associated with one or more regions of
interest using one or more classifiers. In some embodiments, the
classifying 250 may also use one or more features associated with
the other subject data.
[0091] In some embodiments, the one or more classifiers may be
selected from a plurality of stored classifiers based on user input
(e.g., those corresponding to desired analysis).
[0092] In some embodiments, each classifier may be a
machine-learning trained multivariate classifier. In some
embodiments, the classifier(s) may be a binary classifier
incorporating at least the one or more quantitative features. The
classifier(s) may incorporate one or more other subject features.
In some embodiments, the classifier(s) may be trained using (i) one
or more machine-learning algorithms and (ii) at least the
quantitative features of subjects having the movement and/or
neurodegenerative disorder and subjects without the
disorder(s).
[0093] For example, the machine learning classifier(s) may be
trained using one or more machine learning algorithms. The one or
more algorithms may include but are not limited to logistic
regression with elastic net, support vector machines, artificial
neural networks (such as convolutional neural networks, Bayesian
classifiers, and ensemble methods, etc.), other available machine
learning algorithms, among others, or any combination thereof.
[0094] In some embodiments, the classifier(s) may use a decision
tree approach for differential diagnosis. For example, the
classifier(s) may be configured to sequentially classify a subject
with one or more classes using multiple classifiers in sequence,
moving from broad category or class diagnosis (e.g., parkinsonism
vs. no parkinsonism), to narrower category or subclass (e.g.,
within parkinsonism, PD vs. atypical parkinsonism), to a more
specific diagnosis or subclass (e.g., within atypical parkinsonism,
multiple system atrophy (MSA) vs. progressive supranuclear palsy
(PSP)).
[0095] In some embodiments, the method 200 may include a step 260
of outputting the results of the one or more classifiers and/or
extracted features (e.g., one or more quantitative measures) for
display, storage, among others, or any combination thereof. The
results may include a classification of a subject with regards to
the movement and/or neurodegenerative disorder (i.e., whether the
individual subject has brain changes consistent with a diagnosis of
Parkinson's disease). In some embodiments, the results may include
a probability that the classification is correct (e.g.,
confidence). For example, the probability may relate to which a
subject's profile (e.g., extracted features) matches the typical
class (e.g., PD profile) of brain changes, based on the
machine-learning trained classifier(s). The output may be presented
in a customizable result report with optional graphical display of
results from individual brain areas.
[0096] In some embodiments, the step 260 may include outputting the
results of the classifier(s) of a specific patient for different
times. This way, the results may be used to (e.g., quantitatively)
monitor the progression of the disease and/or the effectiveness of
medical treatment.
[0097] In some embodiments, the step 260 may include generating a
visualization of the one or more quantitative measures, the
classification, the medical image(s) of the subject and/or
normative population control, among others, or any combination
thereof. By way of example, the volumes of the SNc and the LC
(e.g., generated from NM-MRI data) of the patient/subject side by
side (or overlaid) with an image representing a normative
population control set of SNc and LC volumes; SNc iron-sensitive
contrast (e.g., R2*, QSM, SWI, and/or other T2-weighted images) of
the patient/subject side by side (or overlaid) with an image
representing a normative population control set of SNc
iron-sensitive contrast images; iron sensitive contrast in a
variety of regions implicated in atypical parkinsonian disorders,
including globus pallidus, dentate nucleus of the cerebellum,
putamen, caudate, subthalamic nucleus, red nucleus substantia nigra
and relevant subregions within these structures, e.g. SNc,
substantia nigra pars reticulata, lateral-ventral SNc side by side
(or overlaid) with an image representing a normative population
control set of images; diffusion MRI images of the superior
cerebellar peduncle and middle cerebellar peduncles side by side
(or overlaid) with an image representing a normative population
control set of superior cerebellar peduncle and middle cerebellar
peduncles, among others, or any combination thereof.
[0098] In some embodiments, the method 200 may include a step 224
of updating and/or generating the one or more classifiers, for
example, using the extracted feature(s) (e.g., the one or more
quantitative measure(s) or other subject data (e.g., longitudinal
data)). As new data is acquired, which can be deidentified data,
and is stored, in addition to providing desired classifications,
the new data can be used to create new, larger datasets to train,
test and validate the classifiers. As the datasets to train, test
and validate the classifiers become larger, one or more algorithms
including one or more machine-learning algorithms (such as
artificial neural networks using deep learning algorithms) may be
used to generate updated classifier(s). This can enable prospective
improvement of the performance of the classifiers.
[0099] For example, the extracted feature(s) may be used to update
the classifier(s), using a machine learning classifier device
(e.g., the device 120) to develop multivariate progression marker
profiles to generate improved classifier(s). These progression
marker profiles may be used as outcome measures for clinical
trials. The classifier may also be updated with additional MRI data
from other diseases that can be difficult to differentiate from PD,
in order to assist with differential diagnosis of PD. The
classifier may also be updated with new diagnostic data, such as
autopsy diagnosis data, to improve accuracy of the classifier.
[0100] In some embodiments, the results (e.g., the extracted
feature(s) (e.g., the one or more quantitative measures(s) or
subject data)) may be used to generate one or more classifiers
using the machine learning classifier device (e.g., the device
120), for example, by training the classifier with the results
and/or retrospective data.
[0101] FIG. 3 shows a method 300 of processing the medical image
data to determine one or more quantitative features associated with
one or more regions of interest. In some embodiments, the one or
more quantitative features associated with one or more regions of
interest can relate to the pulse sequence protocol. For example,
the one more quantitative features associated with one more regions
of interest may include: NM-MRI feature(s), R2* features, QSM
features, Diffusion MRI features, MR spectroscopy features,
hyperpolarized MRI features, functional MRI feature, among other
sequence features, or any combination thereof.
[0102] In some embodiments, the method 300 may include a step 310
of receiving medical image data of the brain of the subject. In
some embodiments, the medical image data may include one or more
sets of MRI data acquired using one or more stored MRI protocols
related to NM-MRI feature(s), R2* features, QSM features, Diffusion
MRI features, among other sequence features, or any combination
thereof. For example, the MRI protocol to acquire a set of MRI data
associated with NM-MRI features, may include a
neuromelanin-sensitive MRI pulse sequence with a reduced flip angle
explicit magnetization transfer preparation pulse. See, for
example, Chen X, Huddleston D E, Langley J, Ahn S, Barnum C J,
Factor S A, et al. Simultaneous imaging of locus coeruleus and
substantia nigra with a quantitative neuromelanin MRI approach.
Magnetic resonance imaging. 2014. For example, the MRI protocol to
acquire MRI data associated with R2* features, R2* and quantitative
susceptibility mapping data may be a multi-echo gradient echo MRI
pulse sequence suitable for acquisition of R2* and quantitative
susceptibility mapping data may be a multi-echo gradient echo MRI
pulse sequence. See, for example, Langkammer C, Pirpamer L, Seiler
S, Deistung A, Schweser F, Franthal S, et al. Quantitative
Susceptibility Mapping in Parkinson's Disease. PloS one. 2016;
11(9):e0162460.
[0103] In some embodiments, the method 300 may include a step 320
of determining one or more regions of interest using one or more
sets of image data of a brain of a subject acquired using the
respective protocol. For example, the step 320 may include
separately processing one or more sets of image data associated
with NM-MRI feature(s), R2* feature(s), QSM feature(s), diffusion
MRI feature(s), and/or other sequence feature(s), to determine the
one or more regions of interest.
[0104] In some embodiments, the step 320 may include segmenting one
or more sets of image data into one or more regions of interest
using a respective mask and/or atlas within one or more sets of
image data. The masks and/or atlases may be associated with one or
more regions of a brain of the subject: SNc (e.g., entire,
lateral-ventral SNc, and/or medial SNc (e.g., defined as the medial
50% of the NM-MRI defined SNc determined by measurement along the
long axis of the NM-MRI determined SNc in the axial plane), etc.),
LC, subthalamic nucleus, red nucleus, globus pallidus (total, pars
interna and/or pars externa), putamen (lateral, medial, and/or
total), caudate, cerebellar dentate nucleus, substantia nigra pars
reticulata, middle cerebellar peduncle, superior cerebellar
peduncle, hippocampus (individual subfields and/or total),
entorhinal cortex, occipital cortex (primary visual cortext, visual
association cortext, and/or total), parietal cortex, cingulate
gyms, parahippocampal gyms, frontal cortext (M1, premotor,
supplementary motor area, Broca's area, prefrontal, orbitofrontal,
inferolateral frontal, and/or total), among others, or any
combination thereof. In some embodiments, one or more image
contrasts may be used to define these masks so as to determine the
location of the structure based on tissue characteristics
measurable with MRI. For example, the contrasts may include but are
on limited to: Iron-sensitive image datasets (R2*, QSM,
susceptibility weighted imaging (SWI), or other T2-weighted
contrasts) may be used for ROI masks for iron-rich structures and
its subregions (e.g. caudate, putamen, globus pallidus, substantia
nigra pars reticulata, subthalamic nucleus, red nucleus);
neuromelanin-sensitive image datasets may be used for ROI masks for
SNc and its subregions, LC, and the ventral tegmental area;
diffusion MRI datasets may be used for masks for white matter
tracts and structures (e.g., the superior cerebellar peduncle,
middle cerebellar peduncle, brainstem oculomotor tracts, the
nigrostriatal tract, and/or the dentato-rubro-thalamic tract);
among others; or any combination thereof.
[0105] By way of example, to determine the SNc region and the
associated NM-MRI features, a set of NM-MRI data with coverage of
SNc and a set of T1-weighted structural image data may be
processed. Using a region of interest (ROI) mask for SNc, the
subject image data may be transformed from MNI-152 standard space
to individual T1 space. For example, the T1-weighted structural
image may automatically be extracted and then aligned with the MNI
extracted image using an affine transformation with the FSL linear
registration tool (FLIRT). Next the FSL nonlinear registration tool
(FNIRT) may be used to carry out a nonlinear transformation between
individual subject T1 space and common space. Then this
transformation can be inverted and the SNc population ROI can be
transformed back to subject T1-space. The individual's processed
NM-MRI image data may be registered to the brain extracted
T1-weighted image and transformed using FLIRT into T1-space. The
transformed SNc population mask may be then used to select the SNc
ROI.
[0106] For example, for the lateral-ventral SNc, the method
described in Huddleston D E, Langley J, Sedlacik J, Boelmans K,
Factor S A, Hu X P. In vivo detection of lateral-ventral tier
nigral degeneration in Parkinson's disease. Human brain mapping.
2017; 38(5):2627-3, may be used. By way of another example, a
substantia nigra pars reticulata (SNr), a mask similar to the one
described in Langley J, Huddleston D E, Merritt M, Chen X, McMurray
R, Silver M, et al. Diffusion tensor imaging of the substantia
nigra in Parkinson's disease revisited. Human brain mapping. 2016;
37(7):2547-56, may be used. In another example, for the cerebellar
dentate nucleus mask, a mask similar to the one described in He N,
Langley J, Huddleston D E, Ling H, Xu H, Liu C, et al. Improved
Neuroimaging Atlas of the Dentate Nucleus. Cerebellum. 2017;
16(5-6):951-6, may be used.
[0107] For example, in some embodiments, one or more QSM defined
group masks for subthalamic nucleus, red nucleus, globus pallidus
(total, pars interna and/or pars externa), putamen, and/or caudate
may be used. For the lateral putamen, a QSM defined atlas may be
used, for example, that is defined as the lateral 50% of the QSM
putamen group mask along its long axis in the axial plane.
[0108] After a region of interest is segmented from the set of
image data, the method 300 may include a step 330 of determining or
extracting one or more quantitative features associated with one or
more regions of interest of a brain of a subject. In the example
above, from the SNc region of interest determined using the NM-MRI
data with coverage of SNc and a set of T1-weighted structural image
data, the mean MTC may be determined. Additionally, SNc volume can
also be determined. By way of example, after the LC is determined,
the LC volume may be determined. For example, the volume may be
determined using methods described in Chen X, Huddleston D E,
Langley J, Ahn S, Barnum C J, Factor S A, et al. Simultaneous
imaging of locus coeruleus and sub stantia nigra with a
quantitative neuromelanin MRI approach. Magnetic resonance imaging.
2014, and Langley J, Huddleston D E, Liu C J, Hu X. Reproducibility
of locus coeruleus and substantia nigra imaging with neuromelanin
sensitive MRI. MAGMA. 2017; 30(2):121-5.
[0109] For example, the nigrostriatal tract volume may be
determined with tractography using the NM-MRI defined SNc and QSM
defined lateral putamen as seed regions, and diffusion MRI measures
in the nigrostriatal tract. Subtract volumes may also be also
determined using the lateral-ventral SNc ROI atlas and the medial
SNc ROI atlas as seed regions.
[0110] In some embodiments, other methods may be used to determine
one or more regions and/or quantitative feature(s) associated with
NM-MRI feature(s), R2* feature(s), QSM feature(s), diffusion MRI
feature(s), and/or other sequence feature(s). For example, other
methods may be used to determine the one or more regions and/or one
or more quantitative features. In some embodiments, additional,
alternative, different, less and/or more feature(s) and/or
region(s) may be determined. FIG. 4 shows an example of a method
400 of generating one or more classifiers to identify biomarkers,
for example, using a machine learning classifying device (e.g., the
device 120). By way of example, labels 410 and features 420 can be
inputted into a classifier 430. For example, the classifier may use
logistic regression with elastic net regularization (ENR) and
5-fold cross-validation to classify between subject groups. After
classifying the subjects (step 430), the receiver operating
characteristic (ROC) and the area under the curve (AUC) may be
calculated in step 440. In some embodiments, the step 440 may
include generating a confusion matrix, calculating the sensitivity,
specificity, and/or accuracy of the classification among others, or
any combination thereof. In some embodiments, the step 430 of
classifying may further include a feature selection step 450. By
way of example, the classifier step 430 may determine and output
ENR coefficients for each feature to determine feature importance.
For example, those features with higher mean ENR coefficients were
considered to have greater importance than those features with
lower mean ENR coefficients.
[0111] One or more of the devices and/or systems of the system 100
may be and/or include a computer system and/or device. FIG. 5 is a
block diagram showing an example of a computer system 400. The
modules of the computer system 500 may be included in at least some
of the systems and/or modules, as well as other devices and/or
systems of the system 100.
[0112] The system for carrying out the embodiments of the methods
disclosed herein is not limited to the systems shown in FIGS. 1 and
5. Other systems may also be used. It is also to be understood that
the system 500 may omit any of the modules illustrated and/or may
include additional modules not shown.
[0113] The system 500 shown in FIG. 5 may include any number of
modules that communicate with each other through electrical or data
connections (not shown). In some embodiments, the modules may be
connected via any network (e.g., wired network, wireless network,
or any combination thereof).
[0114] The system 500 may be a computing system, such as a
workstation, computer, or the like. The system 500 may include one
or more processors 512. The processor(s) 512 may include one or
more processing units, which may be any known processor or a
microprocessor. For example, the processor(s) may include any known
central processing unit (CPU), graphical processing unit (GPU)
(e.g., capable of efficient arithmetic on large matrices
encountered in deep learning models), among others, or any
combination thereof. The processor(s) 512 may be coupled directly
or indirectly to one or more computer-readable storage media (e.g.,
memory) 514. The memory 514 may include random access memory (RAM),
read only memory (ROM), disk drive, tape drive, etc., or any
combinations thereof. The memory 514 may be configured to store
programs and data, including data structures. In some embodiments,
the memory 514 may also include a frame buffer for storing data
arrays.
[0115] In some embodiments, another computer system may assume the
data analysis, image processing, or other functions of the
processor(s) 512. In response to commands received from an input
device, the programs or data stored in the memory 514 may be
archived in long term storage or may be further processed by the
processor and presented on a display.
[0116] In some embodiments, the system 500 may include a
communication interface 516 configured to conduct receiving and
transmitting of data between other modules on the system and/or
network. The communication interface 516 may be a wired and/or
wireless interface, a switched circuit wireless interface, a
network of data processing devices, such as LAN, WAN, the internet,
or any combination thereof. The communication interface may be
configured to execute various communication protocols, such as
Bluetooth, wireless, and Ethernet, in order to establish and
maintain communication with at least another module on the
network.
[0117] In some embodiments, the system 510 may include an
input/output interface 518 configured for receiving information
from one or more input devices 520 (e.g., a keyboard, a mouse, and
the like) and/or conveying information to one or more output
devices 520 (e.g., a printer, a CD writer, a DVD writer, portable
flash memory, etc.). In some embodiments, the one or more input
devices 520 may be configured to control, for example, the
generation of the management plan and/or prompt, the display of the
management plan and/or prompt on a display, the printing of the
management plan and/or prompt by a printer interface, the
transmission of a management plan and/or prompt, among other
things.
[0118] In some embodiments, the disclosed methods (e.g., FIGS. 2-4)
may be implemented using software applications that are stored in a
memory and executed by the one or more processors (e.g., CPU and/or
GPU) provided on the system 100. In some embodiments, the disclosed
methods may be implemented using software applications that are
stored in memories and executed by the one or more processors
distributed across the system.
[0119] As such, any of the systems and/or modules of the system 100
may be a general purpose computer system, such as system 500, that
becomes a specific purpose computer system when executing the
routines and methods of the disclosure. The systems and/or modules
of the system 100 may also include an operating system and micro
instruction code. The various processes and functions described
herein may either be part of the micro instruction code or part of
the application program or routine (or any combination thereof)
that is executed via the operating system.
[0120] If written in a programming language conforming to a
recognized standard, sequences of instructions designed to
implement the methods may be compiled for execution on a variety of
hardware systems and for interface to a variety of operating
systems. In addition, embodiments are not described with reference
to any particular programming language. It will be appreciated that
a variety of programming languages may be used to implement
embodiments of the disclosure. An example of hardware for
performing the described functions is shown in FIGS. 1 and 5. It is
to be further understood that, because some of the constituent
system components and method steps depicted in the accompanying
figures can be implemented in software, the actual connections
between the systems components (or the process steps) may differ
depending upon the manner in which the disclosure is programmed.
Given the teachings of the disclosure provided herein, one of
ordinary skill in the related art will be able to contemplate these
and similar implementations or configurations of the
disclosure.
EXAMPLES
[0121] FIG. 6 shows an example of multivariate classification of
Parkinson's disease using quantitative measures of neuromelanin and
iron pathology. This example is based on a study.
[0122] Aim of the Study
[0123] In previous work, an automated and highly reproducible
neuromelanin-sensitive MRI (NM-MRI) approach was developed (Langley
et al, 2017, PMID: 27687624) that detects Parkinson's disease (PD)
effects in substantia nigra pars compacta (SNc) and locus coeruleus
in vivo. The objective of the study was to combine this NM-MRI
approach with R2* imaging, which is sensitive to iron accumulation,
in order to accurately classify PD. To attempt to develop an
accurate classifier, NM-MRI determined SNc volume, SNc R2*, age and
gender in multivariate models was included in the study.
[0124] Method
[0125] Research was conducted under an Emory Institutional Review
Board approved protocol. 35 patients with PD (UK Brain Bank
Criteria) and 32 control participants were scanned using a 3T MRI
scanner (Prisma, Siemens Medical Solutions, Malvern, PD, USA) with
a 64 channel receive-only head coil. NM-MRI data and R2* data were
acquired and processed using published methods (Langley et al,
2017, PMID: 27687624; Huddleston et al, 2017, PMID: 28240402;
Barbosa et al, 2015, PMID: 25721997). Using these methods, the
NM-MRI SNc volume was determined and R2* was measured in the NM-MRI
defined SNc ROI. A logistic regression model with five-fold cross
validation was used for multivariate classification of PD and
control subjects. The area under the receiver operating
characteristic curve (AUC) was used to quantify the performance of
the classifier. To test the effect of different input features on
the classification accuracy, logistic regression models were
trained using different combinations of feature sets as the input
and model performance was compared using the corresponding
AUCs.
Results
[0126] FIG. 6 shows receiver operating characteristic curves and
AUCs for each classification model. C1=age, C2=gender, C3=SNc
volume, C41=SNc R2*.
Conclusion
[0127] The model including 1) NM-MRI determined SNc volume, 2) R2*
in the NM-MRI defined SNc ROI and 3) age performed best for
classification of PD and control individuals, with an AUC of 0.81.
This multivariate profile warrants further investigation as a
candidate PD biomarker.
[0128] FIGS. 7A and 7B show an example of results of a classifier
based only on clinical data features. For example, the classifier
may be used as a screening classifier to determine risk of a
neurocognitive or neurodegenerative condition (e.g., Parkinson's
disease). In this example, the classifier consisted of 20 clinical
features, of which the 9 most informative features were 1) heart
rate lying flat, 2) heart rate standing up, 3) systolic blood
pressure, 4) Questionnaire item (RBDQ item 2): Aggressive,
action-packed dreams? yes/no, 5) Questionnaire item (NMSQ item 1):
dribbling of saliva during daytime? yes/no, 6) Questionnaire item
(NMSQ item 2): loss or change in taste or smell? yes/no, 7)
Questionnaire item (NMSQ item 5): constipation or strain to pass
stool? yes/no, 8) Questionnaire item (NMSQ item 20): feeling
light-headed, dizzy or weak standing from sitting or lying? yes/no,
9) Questionnaire item (FOGQ item 1): During your worst state do you
walk: (0) normally, (1) almost normally-somewhat slow, (2) slow but
fully independent, (3) need assistance or walking aid, (4) unable
to walk. This clinical classifier performed with 90.8% accuracy.
FIG. 7A shows the ROC curve for the classifier used in this
example. FIG. 7B shows a boxplot of the relative importance of each
of the features to the classification
[0129] FIGS. 8A and 8B show an example of results of a classifier
using the method according to the disclosure (e.g., shown in FIGS.
2-4). In this example, a dataset composed of 12 clinical,
demographic, and MRI-based features was used. In this example, the
classifier combined 6 MRI features and 6 clinical features. The MRI
features were collected using a Siemens Prisma-Fit 3T scanner. The
MRI features (i.e., quantitative features) included
neuromelanin-sensitive MRI (NM-MRI), R2* (iron-sensitive MRI), and
magnetization transfer contrast (MTC) of the substantia nigra pars
compacta (SNc) and profound locus coeruleus (LC). Specifically, the
MRI features are 1) SNc volume, 2) LC volume, 3) SNc R2*, 4) SNc
magnetization transfer contrast (MTC--the NM-MRI contrast), 5)
lateral-ventral SNc MTC, and 6) lateral-ventral SNc R2*. The
classifier also used features from data from several clinical
questionnaires that included the Movement Disorders Society Unified
Parkinson's Disease Rating Scale (MDS-UPDRS) Parts I and II, the
REM Sleep Behavior Disorder Questionnaire (RBDQ), and the
Non-Motors Symptoms Questionnaire (NMSQ); and demographic data that
included age and sex. Specifically, the clinical/demographic
features are 1) gender, 2) age, 3) MDS-UPDRS Part 1 questionnaire
total score (Non-motor aspects of experiences of daily living), 4)
MDS-UPDRS Part 2, 5) RBD-Q item 2, and 6) NMSQ total score.
[0130] FIG. 8A shows the ROC curve for the classifier used in this
example. FIG. 8B shows a boxplot of the relative importance of each
of the features to the classification. The accuracy of this
classifier is 94.2%. As shown in FIG. 8B, the five most important
features were (1) mds_updrs_2tot, (2) SNcR2str, (3) SNcVol, (4)
rbd_2, and (5) sex. Aside from the top three or four features, most
of the other features seemed to have comparable importance.
[0131] This diagnostic accuracy of the classifier enhances
diagnostic confidence (as compared to a clinical assessment alone).
The classifier can confirm and quantify neurodegenerative changes
in neuromelanin and iron on MRI enhances diagnostic confidence. The
incorporation of MRI features in the classifier can also better
enable clinicians to convey the meaning of the test results, i.e.
this can provide direct observation of the brain changes that
underlie the diagnosis.
[0132] The disclosure of the references cited in the Description is
hereby incorporated herein by reference in its entirety.
[0133] While the disclosure has been described in detail with
reference to exemplary embodiments, those skilled in the art will
appreciate that various modifications and substitutions may be made
thereto without departing from the spirit and scope of the
disclosure as set forth in the appended claims. For example,
elements and/or features of different exemplary embodiments may be
combined with each other and/or substituted for each other within
the scope of this disclosure and appended claims.
* * * * *