Methods for mapping brain regions associated with phenotypic features

Abstract

Provided herein are methods for determining brain areas or regions associated with phenotypic features, such as cognitive, functional, or behavioral features, involving voxelwise analysis of neuroimaging data with proper scaling.

Description

TITLEMETHODS FOR MAPPING BRAIN REGIONS ASSOCIATED WITH PHENOTYPIC FEATURESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of United States Provisional Patent Application No. 63 / 749,455, filed January 24, 2025, and United States Provisional Patent Application No. 63 / 771,528 filed March 13, 2025, the disclosures of each which are incorporated herein by reference in its entirety.BACKGROUND OF THE INVENTION

[0002] Neurodegenerative diseases, such as Alzheimer’s Disease (AD), and neuropsychiatric diseases, such as schizophrenia and major depressive disorder (MDD), continue to present major health problems. Mapping the associations between localized brain state as assessed by function, pathology, or neural stress indicators across the brain and phenotypic features, such as cognitive, functional or behavioral features, can enable deeper understanding of how specific brain structures contribute to various mental processes and behaviors, and contribute to further understanding of neurodegenerative and neuropsychiatric diseases.

[0003] Mapping associations between local brain state and phenotypic features employs neuroimaging techniques, such as various forms of Magnetic Resonance Imaging (MRI) including functional MRI, diffusion MRI, and anatomical MRI and Positron Emission Tomography (PET). Local brain state is derived from quantification of neuroimaging data to reflect localized function, connectivity, levels of pathology, neurodegeneration, or neural stress. For example, studies have identified key brain networks, such as the default mode network and the executive control network, and linked alterations in these regions to specific cognitive deficits or behavioral changes. Additionally, methodologies like lesion studies, where deficits are correlated with damage to specific brain areas, have further elucidated the functional roles of different regions. Understanding these associations not only enhances theoretical knowledge of brain function but may have clinical implications, potentially improving the diagnosis andtreatment of neuropsychiatric disorders by targeting specific brain networks associated with cognitive and behavioral impairments.

[0004] There is a need for methods for mapping associations between local brain state and phenotypic features, such as cognitive, behavioral, or functional features.SUMMARY OF INVENTION

[0005] Some of the main aspects of the present invention are summarized below. Additional aspects are described in the Detailed Description of the Invention, Examples, Drawings, and Claims sections of this disclosure. The description in each section of this disclosure is intended to be read in conjunction with the other sections. Furthermore, the various embodiments described in each section of this disclosure can be combined in various different ways, and all such combinations are intended to fall within the scope of the present invention.In one aspect, provided herein is a method for determining brain areas associated with phenotypic measures, the method comprising:a. obtaining neuro imaging data of a plurality of subjects, wherein the neuroimaging data comprises a set of volumetric voxels;b. obtaining multidimensional domain profiles of the subjects in step (a), wherein a domain profile comprises a plurality of phenotypic measurements or features; c. computing a voxel-wise correlation between the neuroimaging data from step (a) and each of the measurements of domains in step (b);d. transforming the voxel-wise correlation computed in step (c) into a ranking relative to the whole brain, wherein a higher ranking of a voxel indicates that the correlation at a voxel with a particular domain is higher as compared to the correlation at another voxel with the same domain; ande. determining a voxel’s preference for a particular domain by comparing the voxel’s ranking from step (d) across that from remaining domains in the profile, wherein ahigher preference for a domain indicates that the voxel’s ranking with respect to that domain is higher than its ranking with respect to another domain.In some embodiments, the method further comprises generating for each voxel a preference function, wherein the preference function comprises the voxel’s preference for each of the domains in the profile. In some other embodiments, the method further comprises assigning to each voxel a preferred domain by selecting the domain for which the voxel has the highest preference.In any one of the embodiments herein, the phenotypic measurement or feature is a cognitive, functional, or behavioral measurement or feature. In some embodiments, the cognitive, functional, or behavioral measurement or feature is a score from a clinical test assessing a cognitive, functional, or behavioral status or symptom of the subject. The clinical test can be any test, assessment, or scale known in the art for measuring a cognitive, functional, or behavioral status or symptom of the subject. For example, the clinical test for measuring cognitive status can be selected from: Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), Clinical Dementia Rating Scale (CDR), and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). As another example, the clinical test for measuring a neuropsychiatric status or symptom, (e.g., clinical scales for measuring depression, anhedonia, or schizophrenia), can be selected from: Patient Health Questionnaire-9 (PHQ-9), Hamilton Depression Rating Scale (HDRS), Montgomery- Asberg Depression Rating Scale (MADRS), Snaith-Hamilton Pleasure Scale (SHAPS), Dimensional Anhedonia Rating Scale (DARS), Positive and Negative Syndrome Scale (PANSS), Brief Psychiatric Rating Scale (BPRS), Schizophrenia Positive Symptoms Scale (SAPS), and Schizophrenia Negative Symptoms Scale (SANS).In some embodiments, the neuroimaging data is positron emission tomography (PET), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI) (e.g., diffusion weighted MRI, various forms of anatomical MRI including, but not limited to, T1 weighted and T2 weighted, and any combinations thereof), computed tomography (CT), or electroencephalography (EEG) data. In some other embodiments, the neuroimaging data can be any data from neuroimaging technique used to visualize structure and / or function of the brain.Neuroimaging techniques known in the art include, for example, structural imaging methods, such as magnetic resonance imaging (MRI) and computed tomography (CT), which provide detailed images of brain anatomy, aiding in the identification of tumors, lesions, and structural abnormalities. Other examples include functional imaging techniques like functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), which can assess brain activity by measuring blood flow and metabolic changes and help to map functional processes during cognitive tasks. Additionally, Electroencephalography (EEG) measures electrical activity in the brain, which can offer insights into temporal changes associated with various mental states.In some embodiments, the PET data is tau, amyloid beta, FDG, or S V2A PET data. In some other embodiments, at least one subject has or is diagnosed as having a neurodeg enerative or neuropsychiatric disease or disorder. In certain embodiments, the neurodegenerative disease or disorder is a tauopathy. In some embodiments, the tauopathy is selected from: Alzheimer’s disease, familial Alzheimer's disease, sporadic Alzheimer's disease, frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17), progressive supranuclear palsy, corticobasal degeneration, Pick's disease, progressive subcortical gliosis, tangle only dementia, diffuse neurofibrillary tangles with calcification, argyrophilic grain dementia, amyotrophic lateral sclerosis parkinsonism-dementia complex, Down syndrome, Gerstmann-Straussler-Scheinker disease, Hallervorden-Spatz disease, inclusion body myositis, Creutzfeld-Jakob disease, multiple system atrophy, Niemann-Pick disease type C, prion protein cerebral amyloid angiopathy, subacute sclerosing panencephalitis, myotonic dystrophy, non-Guamanian motor neuron disease with neurofibrillary tangles, postencephalitic parkinsonism, chronic traumatic encephalopathy, and dementia pugulistica (boxing disease).In some embodiments, the neuropsychiatric disease or disorder is major depressive disorder (MDD), anhedonia, or schizophrenia.In another aspect, provided herein is a non-transitory computer-readable storage medium storing a set of instructions which, when executed by a processor, causes the processor to perform the steps of any one of the aspects or embodiments described herein. The computing arrangement may comprise a processing arrangement that may be e.g., entirely or a part of, or include, but is not limited to, a computer / processor that can include, one or moremicroprocessors, and use instructions stored on a computer-accessible medium (e.g., memory storage device). The instructions may direct the computing arrangement to analyze image(s) of the brain of one or more subjects. The computer-accessible medium may, for example, be a non-transitory computer-accessible medium containing executable instructions therein. A storage arrangement may be provided separately from the computer-accessible medium, which may provide the instructions to the processing arrangement to configure the processing arrangement to execute certain exemplary procedures, processes and methods. In some embodiments, the computing arrangement may be connected to an output device to provide visual outputs (e.g., images) to a user. For example, the output device may be a monitor for providing visual outputs (e.g., images to the user). The computing arrangement may also be connected to an input device (e.g., keyboard, mouse, etc.} for receiving input(s) from the user. The computing arrangement may optionally comprise an input / output interface for communicating to the output device and receiving input from the input device. In certain embodiments the input device and the output device maybe a single input and output device having both input and output functionality (e.g., touchscreen, smart phone, tablet, etc. .

[0006] BRIEF DESCRIPTION OF THE FIGURES

[0007] FIG. 1 provides a schematic illustrating an approach for mapping preferred cognitive domains.

[0008] FIG. 2 shows a color-coded domain preferred map reflecting brain functional organization.DETAILED DESCRIPTION

[0009] While some of the main embodiments of the present invention are described in the Summary of the Invention, Examples and Claims sections of this patent disclosure, this Detailed Description section provides certain additional description relating to the invention and is intended to be read in conjunction with all other sections of the present patent application.

[0010] In order that the present invention can be more readily understood, certain terms are first defined. Additional definitions are set forth throughout the disclosure. Unless definedotherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention is related.

[0011] Any headings provided herein are not limitations of the various aspects or embodiments of the invention, which can be had by reference to the specification as a whole. Accordingly, the terms defined immediately below are more fully defined by reference to the specification in its entirety.

[0012] All references cited in this disclosure are hereby incorporated by reference in their entireties. In addition, any manufacturers’ instructions or catalogues for any products cited or mentioned herein are incorporated by reference. Documents incorporated by reference into this text, or any teachings therein, can be used in the practice of the present invention. Documents incorporated by reference into this text are not admitted to be prior art.Definitions

[0013] The phraseology or terminology in this disclosure is for the purpose of description and not of limitation, such that the terminology or phraseology of the present specification is to be interpreted by the skilled artisan in light of the teachings and guidance.

[0014] As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents, unless the context clearly dictates otherwise. The terms “a” (or “an”) as well as the terms “one or more” and “at least one” can be used interchangeably.

[0015] As used herein, the terms “comprising” and “including” can be used interchangeably. The terms “comprising” and “including” are to be interpreted as specifying the presence of the stated features or components as referred to, but does not preclude the presence or addition of one or more features, or components, or groups thereof. Additionally, the terms “comprising” and “including” are intended to include examples encompassed by the term “consisting of’. Consequently, the term “consisting of’ can be used in place of the terms “comprising” and “including” to provide for more specific embodiments.

[0016] As used herein, the term “or” is to be interpreted as an inclusive “or” meaning any one or any combination. Therefore, “A, B, or C” means any of the following: A; B; C; A and B; A and C; B and C; A, B, and C. An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive.

[0017] Furthermore, “and / or” is to be taken as specific disclosure of each of the two specified features or components with or without the other. Thus, the term “and / or” as used in a phrase such as “A and / or B” is intended to include A and B, A or B, A (alone), and B (alone). Likewise, the term “and / or” as used in a phrase such as “A, B, and / or C” is intended to include A, B, and C; A, B, or C; A or B; A or C; B or C; A and B; A and C; B and C; A (alone); B (alone); and C (alone).

[0018] Units, prefixes, and symbols are denoted in their Systeme International de Unites (SI) accepted form. Numeric ranges are inclusive of the numbers defining the range, and any individual value provided herein can serve as an endpoint for a range that includes other individual values provided herein. For example, a set of values such as 1, 2, 3, 8, 9, and 10 is also a disclosure of a range of numbers from 1-10, from 1-8, from 3-9, and so forth. Likewise, a disclosed range is a disclosure of each individual value (i.e., intermediate) encompassed by the range, including integers and fractions. For example, a stated range of 5-10 is also a disclosure of 5, 6, 7, 8, 9, and 10 individually, and of 5.2, 7.5, 8.7, and so forth.

[0019] Unless otherwise indicated, the terms “at least” or “about” preceding a series of elements is to be understood to refer to every element in the series. The term “about” preceding a numerical value includes ± 10% of the recited value. For example, a concentration of about 1 mg / mL includes 0.9 mg / mLto 1.1 mg / mL. Likewise, a concentration range of about 1% to 10% (w / v) includes 0.9% (w / v) to 11% (w / v).

[0020] By “subject” or “individual” or “animal” or “patient” or “mammal,” is meant any subject, particularly a mammalian subject, for whom diagnosis, prognosis, or therapy is desired. Mammalian subjects include humans, domestic animals, farm animals, sports animals, and zoo animals including, e.g., humans, non-human primates, dogs, cats, guinea pigs, rabbits, rats, mice, horses, cattle, bears, and so on. In some embodiments, the subject is a human.

[0021] As used herein, the term “tau” includes proteins comprising mutations, e.g., point mutations, fragments, insertions, deletions and splice variants of full-length wild type tau. The term “tau” also encompasses post-translational modifications of the tau amino acid sequence. Post-translational modifications include, but are not limited to, phosphorylation.

[0001] As used herein a “tauopathy” encompasses any neurodegenerative disease that involves the pathological aggregation of tau within the brain. In addition to familial and sporadic AD, other exemplary tauopathies are frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17), progressive supranuclear palsy, corticobasal degeneration, Pick’s disease, progressive subcortical gliosis, tangle only dementia, diffuse neurofibrillary tangles with calcification, argyrophilic grain dementia, amyotrophic lateral sclerosis parkinsonism-dementia complex, Down syndrome, Gerstmann-Straussler-Scheinker disease, Hallervorden-Spatz disease, inclusion body myositis, Creutzfeld-Jakob disease, multiple system atrophy, Niemann-Pick disease type C, prion protein cerebral amyloid angiopathy, subacute sclerosing panencephalitis, myotonic dystrophy, non-Guamanian motor neuron disease with neurofibrillary tangles, postencephalitic parkinsonism, and chronic traumatic encephalopathy, such as dementia pugulistica (boxing disease) (Morris et al., Neuron, 70:410-26, 2011).

[0022] Other terms are defined elsewhere in this patent disclosure, or else are used in accordance with their usual meaning in the art.

[0023] Embodiments of the present disclosure can be further described and understood by reference to the following non-limiting “Examples”. It will be apparent to those skilled in the art that many modifications to the specific description provided in the Examples can be practiced without undue experimentation and without departing from the scope of the present disclosure.EXAMPLESExample 1. Patterns of Cognitive Domain Associations with Tau PET Reflect Brain Functional Organization

[0024] An approach for mapping preferred cognitive domains is illustrated in FIG. 1, using Tau PET data and measurements of Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) cognitive domains. Tau PET data and corresponding measurements of each of the five cognitive domains of RBANS (immediate memory, visuospatial, language, attention, delayed memory) were obtained from subjects. In step 1 (top row of FIG. 1), voxel-wise correlations of Tau PET data with each of the five RBANS cognitive domains were computed. Next, in step 2 (middle row of FIG. 1), the voxel-wise correlations were then transformed to ranking (or Z-score) relative to the whole brain. The dark red portionsindicate highest relative correlation. For example, in the right-most image in step 2 showing voxel-wise correlations with the delayed memory domain, a voxel with the darkest red means that this voxel was the most correlated with the delayed memory score relative to all other voxels in the brain. It is noted that correlation and ranking qualifying terms such as highest, and most, may refer to the absolute strength of correlation or ranking as signs can vary depending on the scale, or phenotypical measures used.The rankings from step 2 were used to determine a voxel’s preference for a particular domain by comparing the voxel’s ranking across remaining domains. A higher preference for a domain indicates that the voxel’s ranking with respect to that domain is higher than its ranking with respect to another domain. In step 3 (bottom row of FIG. 1), a preferred cognitive domain is assigned to each voxel by selecting the cognitive domain (immediate memory, visuospatial, language, attention, delayed memory) for which the voxel has the highest preference.While a map of preferred cognitive domains such as the map show in the bottom row of FIG. 1 was generated by selecting the domain which the voxel had the highest preference for, analysis of a voxel’s preference function (a voxel’s preference for each of the domains) may also be useful. For example, information on a voxel’s next most-preferred domain or least preferred domain could be utilized for other purposes.The results of Example 1 were found to be consistent with published data across the disease spectrum, such as data described in Teng et al. 2019 and Tanner et al. 2021.It is shown herein that with proper scaling, a voxelwise analysis revealed intricate preferred domain associations that reflect brain functional organization. These results support the hypothesis that the presence of tangles affects cognitive domains depending on their location and that it may be better to model cross sectional and longitudinal cognition by using voxelwise domain maps rather than anatomical ROIsThis approach of mapping cognitive domains from ND measures readily generalizes to other imaging modalities. For instance, diffusion MRI based voxelwise ND measures could be used to find out which regions are preferably associated with domains of clinical scales and providing a biological basis for depression subtypes.Example 2. Assessment of Association of Tan PET Spatial Patterns with Cognitive Domains in Participants with Early Alzheimer's Disease in the Phase 2 Auronomy Trial

[0025] Background:

[0026] Posdinemab is an anti-phosphorylated tau monoclonal antibody currently under investigation in early Alzheimer’s disease (AD). The phase-2 Auronomy trial (NCT04619420) enrolled participants with mild cognitive impairment (MCI) or mild AD dementia (CDR GS 0.5 / memory box >0.5) with intermediate levels of tau, as determined by plasma p217+tau and tau PET. RBANS Indices representing five cognitive domains were analyzed for voxelwise associations with tau PET. A brain wide map that reveals associations between cognitive domains and tau pathology in specific brain regions was reconstructed.

[0027] Method:

[0028] NFT levels from 422 randomized participants were quantified using18F-MK-6240 SUVR (cerebellar gray reference region). SUVR volumes were non-linearly aligned to Montreal Neurological Institute space and smoothed within a gray matter mask to a Full Width at Half Max of 12 mm. Associations with each cognitive domain were computed using Spearman rank to minimize outlier effect. For each of the 5 correlation volumes, voxel values were transformed to their rank within the gray matter mask. Finally, a domain preference map was generated by assigning to each voxel the highest ranked of the 5 domains and its corresponding Spearman correlation to enable statistical thresholding.

[0029] Result:

[0030] Correlations were significant (p<10‘3, False Discovery Rate q<0.01) across most of the brain for all, except the visuospatial domain, for which significant correlations were largely in right occipital cortex. The following domain associations were observed (FIG. 2): Visuospatial to right occipital cortex, with trends of association seen in left occipital cortex; Language to left temporal, supramarginal, and inferior parietal; Delayed memory to inferior temporal, fusiform, anterior cingulate, insula, and hippocampus; Attention to left middle occipital, precuneus, and bilateral frontal; Immediate memory to bilateral middle and superior frontal, precuneus and middle temporal.

[0031] Conclusion:

[0032] In participants with early AD, who underwent screening tau PET in the Auronomy trial, it was demonstrated using a spatially unbiased data driven approach which cognitivedomains are relatively most impacted by tau pathology at specific brain regions. The ensuing maps recapitulate established brain functional organization. These findings demonstrate close association between presence / location of tangles and cognitive profile.

[0033] The invention is not to be limited in scope by the specific embodiments described herein. Indeed, various modifications of the invention in addition to those described will become apparent to those skilled in the art from the foregoing description and accompanying Figures. Such modifications are intended to fall within the scope of the appended claims.

[0034] All references cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes.REFERENCESTeng etal., Neurobiol Aging. 2019 Sep:81:138-145. doi: 10.1016 / j.neurobiolaging.2019.05.026. Tanner etal., J Nucl Med. 2021 May 10;62(5):612-613. doi: 10.2967 / jnumed.120.257824.

Claims

CLAIMS1. A method for determining brain areas associated with phenotypic measures, the method comprising:a. obtaining neuro imaging data of a plurality of subjects, wherein the neuroimaging data comprises a set of volumetric voxels;b. obtaining multidimensional domain profiles of the subjects in step (a), wherein a domain profile comprises a plurality of phenotypic measurements or features; c. computing a voxel-wise correlation between the neuroimaging data from step (a) and each of the measurements of domains in step (b);d. transforming the voxel-wise correlation computed in step (c) into a ranking relative to the whole brain, wherein a higher ranking of a voxel indicates that the correlation of the voxel with a particular domain is higher as compared to the correlation of another voxel with the same domain; ande. determining a voxel’s preference for a particular domain by comparing the voxel’s ranking from step (d) across that from remaining domains in the profile, wherein a higher preference for a domain indicates that the voxel’s ranking with respect to that domain is higher than its ranking with respect to another domain.

2. The method of claim 1 , further comprisingf. generating for each voxel a preference function, wherein the preference function comprises the voxel’s preference for each of the domains in the profile.

3. The method of claim 1 or 2, further comprisingg. assigning to each voxel a preferred domain by selecting the domain which the voxel has the highest preference for.

4. The method of any one of claims 1-3, wherein the phenotypic measurement or feature is a cognitive, functional, or behavioral measurement or feature.

5. The method of any one of claims 1-4, wherein the cognitive, functional, or behavioral measurement or feature is a score from a clinical test assessing a cognitive, functional, or behavioral status or symptom of the subject.

6. The method of claim 5, wherein the clinical test is selected from: Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), Clinical Dementia Rating Scale (CDR), and Repeatable Battery for the Assessment of NeuropsychologicalStatus (RBANS).

7. The method of claim 5, wherein the clinical test is selected from: Patient Health Questionnaire-9 (PHQ-9), Hamilton Depression Rating Scale (HDRS), Montgomery- Asberg Depression Rating Scale (MADRS), Snaith-Hamilton Pleasure Scale (SHAPS), Dimensional Anhedonia Rating Scale (DARS), Positive and Negative Syndrome Scale (PANSS), Brief Psychiatric Rating Scale (BPRS), Schizophrenia Positive Symptoms Scale (SAPS), and Schizophrenia Negative Symptoms Scale (SANS).

8. The method of any one of claims 1-7, wherein the neuroimaging data is positron emission tomography (PET), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), computed tomography (CT), or electroencephalography (EEG) data.

9. The method of claim 8, wherein the PET data is tau or amyloid beta PET data.

10. The method of any one of claims 1-9, wherein at least one subject has or is diagnosed as having a neurodegenerative or neuropsychiatric disease or disorder.

11. The method of claim 10, wherein the neurodegenerative disease or disorder is a tauopathy.

12. The method of claim 11, wherein the tauopathy is selected from: Alzheimer’s disease, familial Alzheimer's disease, sporadic Alzheimer's disease, frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17), progressive supranuclear palsy, corticobasal degeneration, Pick's disease, progressive subcortical gliosis, tangle onlydementia, diffuse neurofibrillary tangles with calcification, argyrophilic grain dementia, amyotrophic lateral sclerosis parkinsonism-dementia complex, Down syndrome, Gerstmann-Straussler-Scheinker disease, Hallervorden-Spatz disease, inclusion body myositis, Creutzfeld-Jakob disease, multiple system atrophy, Niemann-Pick disease type C, prion protein cerebral amyloid angiopathy, subacute sclerosing panencephalitis, myotonic dystrophy, non-Guamanian motor neuron disease with neurofibrillary tangles, postencephalitic parkinsonism, chronic traumatic encephalopathy, and dementia pugulistica (boxing disease).

13. The method of claim 10, wherein the neuropsychiatric disease or disorder is major depressive disorder (MDD), anhedonia, or schizophrenia.

14. A non-transitory computer-readable storage medium storing a set of instructions which, when executed by a processor, causes the processor to perform the steps of any one of claims 1-13.

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