A mega-analysis framework for delineating autism neurosubtypes

Active Project Grant Awarded Last action

Award ID
R01MH133334
Recipient
Awarding agency
Obligated
$5,093,499

Award facts

Assistance listing
93.242 Mental Health Research Grants
Completion date 74% complete
Place of performance
New York, NY 10022, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

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.

Transactions

Action Obligated Date
Amendment $709,383
Amendment $618,109
Amendment $75,405
Amendment $75,405
Amendment $678,645
Amendment $678,645
Amendment $678,645
Amendment $789,631
Original award $789,631

Subawards

Subrecipient Amount Awarded
University Of California, LOS Angeles Dr. Lord will curate and provide de-identified datasets from four datasets that we have accumulated over the years with deep phenotyping data to be used as… $101,109
National University Of Singapore Dr. Yeo will provide support for the application of preprocessing and analysis tools (developed by Dr. Yeo's team) to this study. Dr. Yeo will also provide… $21,275
The Regents Of The University Of California. San Francisco Dr. Bishop will work closely with Drs. Di Martino, Lord and Salum, as well as the overall team of investigators, on the curation and harmonization of the… $17,586
The Trustees Of The University Of Pennsylvania Dr. Shinohara will oversee Ms. Liu’s work on the MRI data harmonization effort in regular meetings. Over the course of the proposed project, he will assist with… $51,256
The Regents Of The University Of California. San Francisco The UCSF PI, Dr. Bishop, will work closely with Drs. Di Martino, Lord and Salum, as well as the overall team of investigators, on the curation and harmonization… $22,767
The Trustees Of The University Of Pennsylvania The team at the University of Pennsylvania will lead the imaging harmonization in the proposal. Leveraging ComBat and adaptations thereof, they will assess both… $59,175
National University Of Singapore The subaward Principal Investigator, Dr. Yeo, develops machine learning algorithms for the analysis of large-scale datasets comprising many thousands of… $24,300

Quick answers

Who received award R01MH133334?

R01MH133334 was awarded to Child Mind Institute Inc. by National Institute of Mental Health (National Institutes of Health) on April 1, 2023.

How much is award R01MH133334 worth?

$5,093,499 has been obligated on R01MH133334.

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