Child Mind Institute Inc

NEURONAL CORRELATES OF AUTISTIC TRAITS IN ADHD AND AUTISM

NEURAL SIGNATURES OF OUTCOME IN PRESCHOOLERS WITH AUTISM

A MEGA-ANALYSIS FRAMEWORK FOR DELINEATING AUTISM NEUROSUBTYPES - ABSTRACT THIS APPLICATION PROPOSES TO LAY THE GROUNDWORK FOR PRECISION MEDICINE APPROACHES TO AUTISM SPECTRUM DISORDER (ASD) BY IDENTIFYING REPRODUCIBLE CLINICALLY RELEVANT BRAIN-CONNECTOME-BASED SUBTYPES. THE PROPOSAL ADDRESSES THE CLINICAL AND BIOLOGICAL HETEROGENEITY OF ASD BY FOCUSING ON THE INTERMEDIATE LEVEL OF ANALYSIS OF SYSTEMS NEUROSCIENCE, FOLLOWING CLUES THAT ASD IS ASSOCIATED WITH ABNORMALITIES IN THE BRAIN FUNCTIONAL CONNECTOME. THUS, WE AIM TO IDENTIFY NEUROSUBTYPES (NS), I.E., SUBGROUPS OF INDIVIDUALS WITH HOMOGENEOUS ATYPICAL FEATURES, BASED ON MEASURES OF INTRINSIC FUNCTIONAL CONNECTIVITY (IFC). PRIMARY AIMS ARE TO: 1) GENERATE A LARGE, RETROSPECTIVELY HARMONIZED DATA RESOURCE WITH COMPREHENSIVE ASSESSMENT OF IFC AND CLINICAL PHENOTYPES; 2) IDENTIFY IFC-BASED NEUROSUBTYPES AND ESTABLISH THEIR ASSOCIATIONS WITH CLINICALLY RELEVANT PHENOTYPES; 3) TEST THE REPLICABILITY OF NEUROSUBTYPES AND THEIR ASSOCIATIONS WITH PHENOTYPIC MEASURES IN AN INDEPENDENT SAMPLE . TO THIS END, WE PROPOSE TO LEVERAGE EXISTING LARGE-SCALE ASD NEUROIMAGING DATA COLLECTIONS FROM THE AUTISM BRAIN IMAGING DATA EXCHANGE, THE NATIONAL DATABASE FOR AUTISM RESEARCH, AND THE HEALTHY BRAIN NETWORK. SAMPLE: AGE/SEX: BOYS AND GIRLS, 6-18 YEARS OLD. DIAGNOSIS: ASD AND NEUROTYPICAL (NT) INDIVIDUALS. SIZE: TO DATE, THE ABOVE NEUROIMAGING RESOURCES CONTAIN A TOTAL N=3528; ASD N=2136, NT N=1392. METHODS: FOLLOWING SYSTEMATIC AND EXTENSIVE DATA ORGANIZATION, RIGOROUS QUALITY ASSURANCE, AND PREPROCESSING WE WILL PROCEED WITH QUANTITATIVE DATA HARMONIZATION USING STATE-OF-THE-ART METHODS. COVBAT, THE MOST ADVANCED VERSION OF THE BAYESIAN FRAMEWORK, COMBAT, WILL BE APPLIED TO HARMONIZE MRI DATA. IT HAS BEEN DEVELOPED BY CO-I SHINOHARA TO CONTROL FOR INTER-SCANNER DIFFERENCES IN MRI-BASED MEASURES, AS WELL AS FOR ERRORS ARISING FROM SUBJECT DIFFERENCES IN MEASUREMENT COVARIANCE. RECENT ADVANCES IN ITEM RESPONSE THEORY WILL BE USED TO HARMONIZE PHENOTYPIC DATA, INFORMED BY PRELIMINARY CLINICAL WORK. TO FURTHER ENHANCE OUR CLINICAL DATA HARMONIZATION EFFORTS, THE NEUROIMAGING DATA WILL BE AGGREGATED WITH PHENOTYPIC-ONLY COLLECTIONS FROM CO-IS LORD AND BISHOP (ASD N=1513). CONNECTOPATHY FEATURES: TO SCOPE THE ENTIRE SPECTRUM OF ASD CONNECTOPATHY, MULTIPLE FEATURES WILL BE ASSESSED SIMULTANEOUSLY FOR THE FIRST TIME. NEUROSUBTYPES: BUILDING ON OUR FEASIBILITY WORK WITH CO-I YEO, HOMOGENEOUS NEURAL ASD SUBGROUPS WILL BE IDENTIFIED THROUGH NOVEL BAYESIAN LATENT FACTOR MODELING. IT ALLOWS FOR SUBJECTS TO BELONG TO SUBTYPES IN VARYING DEGREES, IDENTIFYING HYBRID, CATEGORICAL AND DIMENSIONAL, NEUROSUBTYPES. OTHER KEY QUESTIONS INCLUDE THE RELEVANCE OF MRI FEATURES STUDIED, THE DIAGNOSTIC SPECIFICITY OF NEUROSUBTYPES, AND CROSS-SUBTYPING METHOD VALIDITY. THE NEUROSUBTYPES IDENTIFIED AND METHODS FOR HARMONIZATION, ALONG WITH ALL DATA GENERATED FOR MEGA-ANALYSES WILL BE REGULARLY SHARED, STARTING AT THE END OF YEAR TWO. FINDINGS WILL ADDRESS CRITICAL KNOWLEDGE GAPS AND THE NOVEL RESOURCE WILL OFFER THE SCIENTIFIC COMMUNITY OPPORTUNITIES TO PURSUE INDEPENDENT INQUIRIES TRANSFORMING BIOLOGICAL RESEARCH AND KNOWLEDGE OF ASD.

C-PAC: A CONFIGURABLE, COMPUTE-OPTIMIZED, CLOUD-ENABLED NEUROIMAGING ANALYSIS SOFTWARE FOR REPRODUCIBLE TRANSLATIONAL AND COMPARATIVE

IMPROVING THE ROBUSTNESS OF NEUROIMAGING THROUGH EXPLOITATION OF VARIABILITY IN PROCESSING PIPELINES - ABSTRACT REPRODUCIBLE FINDINGS ARE ESSENTIAL TO SCIENTIFIC ADVANCEMENT. UNFORTUNATELY, WHEN FIELDS LACK CONSENSUS STANDARDS FOR METHODS, OR THEIR IMPLEMENTATIONS, REPRODUCIBILITY TENDS TO BE MORE OF AN IDEAL THAN A REALITY. SUCH IS THE CASE FOR FUNCTIONAL NEUROIMAGING ANALYSIS, WHERE THERE IS A SPRAWLING AND HETEROGENEOUS ANALYTIC SPACE FROM WHICH SCIENTISTS CAN SELECT TOOLS, CONSTRUCT PROCESSING PIPELINES, AND DRAW INTERPRETATIONS FROM THEIR RESULTS. RECENT DEMONSTRATIONS OF DISAPPOINTING LEVELS OF REPRODUCIBILITY FOR FINDINGS ACROSS LABS, EVEN WHEN USING THE SAME DATASETS, HAVE MADE THE URGENT NEED TO OVERCOME ANALYTIC HETEROGENEITY CLEAR. DIFFERENCES IN PROCESSING STEPS, PARAMETERS, AND THEIR SOFTWARE IMPLEMENTATION HAVE ALL BEEN SHOWN TO BIAS RESULTS, LIMITING THEIR COMPARABILITY WITH ONE ANOTHER. ONE SOLUTION THAT HAS EMERGED IN THE LITERATURE IS THE ADOPTION OF HIGHLY PRESCRIBED PIPELINES, SUCH AS THE FMRIPREP AND HCP PIPELINES. WHILE SUCCESSFUL IN RESTRICTING VARIABILITY, THE LACK OF GROUND TRUTHS OR CONSENSUS PROCESSING COMPONENTS AND PARAMETERS PREVENTS SUCH EFFORTS FROM BEING A DESIRABLE LONG-TERM SOLUTION. AN ALTERNATIVE STRATEGY, WHICH OUR TEAM HAS SUCCESSFULLY DEPLOYED TO ACHIEVE ROBUST RESULTS IN THE FACE OF NUMERICAL INSTABILITIES, IS TO DEVELOP TOOLS THAT ENSEMBLE RESULTS ACROSS A SPACE OF PIPELINE CONFIGURATIONS (I.E., A RANGE OF COMPONENTS AND PARAMETERS). BASED ON OUR PRIOR WORK, WE PREDICT THAT SUCH A STRATEGY WOULD NOT ONLY IMPROVE THE ROBUSTNESS OF FINDINGS, BUT MINIMIZE BIASES ARISING FROM SINGLE PIPELINE SELECTIONS THAT COMPROMISE THE SUCCESS OF BIOMARKER DISCOVERY EFFORTS. WE ADDRESS THIS CHALLENGE BY PROPOSING A FRAMEWORK FOR CHARACTERIZING, SUMMARIZING, AND MINIMIZING ANALYTIC BIASES IN EXPERIMENTAL FINDINGS. BUILDING ON PRIOR WORK IMPLEMENTING INDEPENDENTLY DEVELOPED PIPELINES (E.G., ABCD-HCP, CCS, FMRIPREP) WITHIN A COMMON PLATFORM (I.E., THE CONFIGURABLE PIPELINE FOR THE ANALYSIS OF CONNECTOMES; C-PAC), WE WILL SYSTEMATICALLY VARY THEIR COMPONENTS TO GENERATE A BROAD SPACE OF PIPELINES (N=192). WE WILL QUANTIFY THE VARIABILITY IN FULL-BRAIN FUNCTIONAL CONNECTIVITY MATRICES GENERATED ACROSS CONFIGURATIONS, AND IDENTIFY BOTH THE CONTRIBUTION OF INDIVIDUAL COMPONENTS (E.G., SEGMENTATION, SPATIAL NORMALIZATION) AND THE RELATIONSHIPS BETWEEN PIPELINES (AIM 1). WE WILL CONSTRUCT ROBUST ESTIMATES OF FUNCTIONAL CONNECTIVITY BY SAMPLING THE VARIABILITY OBSERVED ACROSS PIPELINES (AIM 2), AND IMPROVE THE GENERALIZABILITY OF BRAIN-PHENOTYPE RELATIONSHIPS THROUGH THE EXTENSION OF MACHINE LEARNING ENSEMBLING TECHNIQUES (AIM 3). WE WILL INCREASE THE ACCESSIBILITY OF OUR APPROACH BY SAMPLING THE PIPELINE CONFIGURATION SPACE TO IDENTIFY A MINIMAL SET OF REPRESENTATIVE PIPELINES. THE STRENGTH OF THESE TECHNIQUES WILL BE DEMONSTRATED BY IDENTIFYING GENERALIZABLE BRAIN-BASED BIOMARKERS OF COGNITIVE AND PSYCHIATRIC WELLNESS USING THE NIH ABCD STUDY DATASET. THIS PROJECT WILL LEAD A SHIFT IN NEUROIMAGING TOWARDS THE CAPTURE AND INCLUSION OF DOMINANT SOURCES OF VARIABILITY IN FUNCTIONAL NEUROIMAGING, AND IN DOING SO, HELP TO CARRY FUNCTIONAL NEUROIMAGING OUT OF THE REPRODUCIBILITY CRISIS INTO AN ERA OF ROBUSTNESS. CONSISTENT WITH THE VALUES OF OPEN SCIENCE, ALL CONTRIBUTIONS WILL BE MADE PUBLICLY AND FREELY AVAILABLE.

AN ALIGNMENT FRAMEWORK FOR MAPPING BRAIN DYNAMICS AND SUBSTRATES OF HUMAN COGNITION ACROSS SPECIES - ABSTRACT THE NON-HUMAN PRIMATE (NHP) MODEL IS CRITICAL TO THE ADVANCEMENT OF TRANSLATIONAL NEUROSCIENCE, AS IT ALLOWS RESEARCHERS TO LINK OBSERVATIONS REGARDING MACROSCALE BRAIN DYNAMICS AND COGNITION IN THE HUMAN TO UNDERLYING MESO- AND MICROSCALE PHENOMENA THAT CANNOT BE FULLY INVESTIGATED IN HUMANS. IMPORTANTLY, THE ULTIMATE VALUE OF FINDINGS FROM THE NHP FOR INFORMING HUMAN MODELS RELIES ON THE ADEQUACY OF METHODS FOR CROSS-SPECIES ANATOMICAL AND FUNCTIONAL ALIGNMENT. IN THIS REGARD, ANATOMICAL LANDMARK-BASED METHODS FOR INTERSPECIES BRAIN ALIGNMENT HAVE EXCELLED IN LOWER ORDER SENSORY AND MOTOR AREAS, BUT FACED LIMITATION IN REGISTERING HETEROMODAL ASSOCIATION AREAS, WHICH HAVE A PAUCITY OF LANDMARKS. IN RESPONSE, WE HAVE DEVELOPED AN FMRI-BASED CROSS- SPECIES ALIGNMENT FRAMEWORK THAT LEVERAGES RECENT ADVANCES IN REPRESENTATION OF NETWORK ORGANIZATION TO GENERATE A COMMON COORDINATE SPACE (REFERRED TO AS “JOINT-EMBEDDING”). OUR INITIAL APPLICATION OF THIS FUNCTION- BASED METHOD FOR CORTICAL ALIGNMENT ALLOWED US TO QUANTIFY HOMOLOGIES BETWEEN HUMAN AND MACAQUE IN HIGH DETAIL AT ALL LEVELS OF THE CORTICAL HIERARCHY. IN THIS PROPOSAL, WE WILL EXTEND THE JOINT-EMBEDDING ALIGNMENT APPROACH TO ALIGN BRAIN BETWEEN HUMAN AND NON-HUMAN PRIMATES USING MULTIMODAL MRI DATA (AIM 1). WE WILL MAKE USE OF THE PUBLICLY AVAILABILITY OF LARGE SAMPLE MULTIMODAL MRI DATASETS (HUMAN CONNECTOME PROJECT [HCP], CONSORTIUM FOR RELIABILITY AND REPRODUCIBILITY [CORR]), AS WELL AS RECENT OPENLY SHARED NON-HUMAN PRIMATE DATA (PRIMATE DATA EXCHANGE) TO INCORPORATE OF WITHIN- AND BETWEEN SPECIES VARIATIONS. WE WILL ASSESS THE ALIGNMENT PERFORMANCE BY COMPARING TO TRADITIONAL LANDMARK-BASED REGISTRATION AND UNIMODAL JOINT- EMBEDDING ALIGNMENTS. USING THE HIGHEST PERFORMING ALIGNMENT TO TRANSFORM BRAIN MAPS BETWEEN SPECIES, WE WILL QUANTIFY SPATIOTEMPORAL SIMILARITIES AND DIVERGENCE OF BRAIN NETWORK BETWEEN HUMAN AND MACAQUE MONKEY BASED ON AUTOREGRESSIVE, QUASI-PERIODIC PATTERN, AND COACTIVATION PATTERN ANALYSIS (AIM 2). ADDITIONALLY, WE WILL TRANSFORM THE HUMAN COGNITIVE ONTOLOGY MAPS TO MACAQUE SPACE AND ASSESS THE SIMILARITIES OF CORRESPONDING BRAIN NETWORKS FOR EACH COGNITIVE COMPONENT BETWEEN HUMAN AND MACAQUE. WE WILL ALSO BUILD AN INTERACTIVE WEBPAGE VIEWER TO SHARE THE TRANSLATION HUMAN COGNITION ONTOLOGY IN MACAQUE (AIM 3). ALL DATA, DATA PRODUCTS (E.G. CROSS-SPECIES TRANSLATION) AND CODE GENERATED WILL BE OPENLY SHARED THROUGH THE OPEN SCIENCE RESOURCE - PRIME-DE.

TOOLS FOR INTEGRATING BRAIN MICROSTRUCTURE WITH FUNCTIONAL DYNAMICS IN HUMAN AND NON-HUMAN PRIMATES - PROJECT SUMMARY UNDERSTANDING HOW MACROSCOPIC BRAIN FUNCTION EMERGES FROM MICROSTRUCTURE IS ESSENTIAL FOR UNDERSTANDING DISEASE MECHANISMS AND NEUROPHARMACOLOGICAL INTERVENTIONS. DESPITE PROGRESS IN COLLECTING MESOSCALE CYTO-, CHEMO-, AND MYELOARCHITECTURE DATA UNDER THE NIH BRAIN INITIATIVE, THE LACK OF TOOLS TO RECONSTRUCT AND LINK THESE MICROSTRUCTURE MAPS TO BRAIN DYNAMICS HINDERS ADVANCEMENT. THIS PROPOSAL AIMS TO DEVELOP NOVEL TOOLS TO ENABLE RESEARCHERS TO TEST HYPOTHESES LINKING BRAIN STRUCTURE AND FUNCTION IN SILICO. WE HAVE DEVELOPED A TOOL, CALLED BRAINBUILDER, TO RECONSTRUCT 3D MESOSCALE MICROSTRUCTURAL ATLASES OF THE HUMAN AND MACAQUE CORTEX. IN THE PROPOSED WORK (AIM 1) WE WILL EXTEND THIS TOOL TO RECONSTRUCT THE SUBCORTEX AND THEREBY CREATE COMPLETE CHEMO-, MYELO- AND CYTOARCHITECTURAL MESOSCALE ATLASES THAT SPAN THE ENTIRE CEREBRUM FOR THE MACAQUE AND HUMAN. TO MAKE THESE AND OTHER BRAIN IMAGING DATA EASILY ACCESSIBLE WE WILL (AIM 2) ENHANCE A SOFTWARE TOOLBOX CALLED NEUROMAPS FOR MULTI-SCALE, MULTI-MODAL AND CROSS-SPECIES BRAIN IMAGING ANALYSIS. NEUROMAPS FACILITATES REPRESENTATION AND TRANSFORMATION OF BRAIN ANNOTATIONS BETWEEN STANDARD REFERENCE SPACES. WE WILL INTEGRATE EXISTING MACAQUE BRAIN IMAGING DATA INTO NEUROMAPS, STREAMLINE INTERSPECIES BRAIN MAPPING, AND ADD MOLECULARLY-ENRICHED FUNCTIONAL NETWORK ANALYSIS. THIS WILL ALLOW RESEARCHERS TO LINK MICROSTRUCTURAL FEATURES TO BOLD SIGNALS AND UNDERSTAND MOLECULAR CONTRIBUTIONS TO BRAIN DYNAMICS. FINALLY, WE WILL (AIM 3) EXTEND MECHANISTIC BRAIN SIMULATION TOOLS TO INCORPORATE MICROSTRUCTURAL FEATURE MAPS. WE WILL ENHANCE AN OPEN TOOLBOX FOR NEURAL MASS MODELING (NMM) SIMULATIONS, BY INTEGRATING USER-PROVIDED MICROSTRUCTURAL MAPS, SUCH AS THE ONES WE HAVE CREATED. THIS WILL ENABLE THE SIMULATION OF NEURONAL DYNAMICS WITH MORE ACCURATE REPRESENTATIONS OF BRAIN STRUCTURE. WE WILL VALIDATE THESE MODELS AGAINST EMPIRICAL DATA, FOCUSING ON THEIR ABILITY TO REPRODUCE FUNCTIONAL DYNAMICS BASED ON CHEMOARCHITECTONIC MAPS. THIS PROJECT WILL THEREFORE GREATLY EASE THE INTEGRATION OF MICROSTRUCTURAL DATA INTO MODELS OF WHOLE-BRAIN FUNCTION. THESE ADVANCEMENTS WILL FACILITATE THE EXPLORATION OF THE BIOLOGICAL PARAMETERS AND MECHANISMS UNDERPINNING BRAIN FUNCTION. THEY WILL ENABLE RESEARCHERS TO LINK MICROSTRUCTURAL INFORMATION WITH BRAIN DYNAMICS, PROMOTE REPRODUCIBLE SCIENCE, AND ACCELERATE THE TRANSLATION OF BASIC AND CLINICAL RESEARCH FROM MODEL ORGANISMS TO HUMANS.

DEVELOPMENTAL VARIATIONS IN CORTICOSTRIATAL THALAMOCORTICAL CIRCUITS AND THEIR RELATIONSHIP TO PSYCHOPATHOLOGY

CRCNS: LINKING RECEPTORARCHITECTURE AND FUNCTIONAL BRAIN NETWORKS ACROSS SPECIES - PROJECT SUMMARY (SEE INSTRUCTIONS): NEUROTRANSMITTER RECEPTORS, VARYING IN DISTRIBUTION ACROSS THE NEOCORTEX, PLAY A CRUCIAL ROLE IN MODULATING NEURAL EXCITABILITY AND NEURAL NETWORK COMMUNICATION. THE RELATIONSHIP BETWEEN RECEPTOR ARCHITECTURE AND BRAIN FUNCTION, PARTICULARLY CONSIDERING THE ORIGIN AND EVOLUTION OF THE COMPLEX STRUCTURE-FUNCTION INTERPLAY ACROSS SPECIES, REMAINS LARGELY UNEXPLORED. THE OVERARCHING GOAL OF THE PROPOSED PROJECT IS TO BRIDGE THIS GAP THROUGH INTEGRATION OF 3D NEUROTRANSMITTER RECEPTOR AUTORADIOGRAPHY WITH MULTIMODAL MRI ACROSS FOUR SPECIES (HUMANS, MACAQUES, MARMOSETS, RATS). TOWARDS THIS GOAL, WE WILL RECONSTRUCT HIGH-RESOLUTION RECEPTOR MAPS IN RATS AND MARMOSETS USING A TRANSFER-LEARNING NEURAL NETWORK (HUMAN AND MACAQUE DATA WERE COMPLETED). IN AIM 1, WE WILL INNOVATE MULTIMODAL CROSS-SPECIES BRAIN ALIGNMENT BY USING GRAPHMATCHING AND JOINT-EMBEDDING ALGORITHMS TO EXTRACT HOMOLOGOUS MULTIMODAL FEATURES (I.E. RECEPTOR ARCHITECTURE, FMRI FUNCTIONAL CONNECTIVITY) AND IMPROVE INTERSPECIES BRAIN ALIGNMENT ACROSS FOUR SPECIES. SUCH ALIGNMENT WILL PROVIDE A CROSS-SPECIES TRANSFORMATION FOR FUTURE TRANSLATION STUDIES USING ANIMAL MODELS. FOLLOWING OPTIMAL BRAIN ALIGNMENT, AIM 2 WILL FOCUS ON STRUCTURE-FUNCTION COUPLING BY COMPARING RECEPTOR AND FUNCTION CONNECTIVITY GRADIENTS ACROSS NEOCORTEX, AS WELL AS THE REGIONAL SIMILARITY BETWEEN RECEPTOR LAMINATION COVARIANCE AND FUNCTIONAL CONNECTIVITY WITHIN AND ACROSS SPECIES. THIS WILL UNCOVER THE COMMON AND SPECIES-SPECIFIC RELATIONSHIP BETWEEN MICROSCALE RECEPTOR ARCHITECTURE AND MACROSCALE FUNCTIONAL CONNECTIVITY. FINALLY, AIM 3 AIMS TO LINK BRAIN SPATIOTEMPORAL DYNAMIC SIGNATURES (I.E., COACTIVATION PATTERNS, TRAVELING WAVES) WITH RECEPTOR ARCHITECTURE WITHIN AND ACROSS SPECIES - PROVIDING AN EVOLUTIONARY PERSPECTIVE OF THE NEUROCHEMICAL BASIS FOR BRAIN DYNAMICS. THE OUTCOMES, INCLUDING FMRI DERIVATIVES, 3D RECEPTOR ATLASES, AND CROSS-SPECIES TRANSFORMATION WILL BE SHARED VIA THE PRIMATE DATA RESOURCE EXCHANGE AND THE EBRAI NS PLATFORM TO FOSTER FURTHER BASIC AND TRANSLATIONAL RESEARCH IN THE NEUROSCIENCE COMMUNITY.