2022 SFARI Human Cognitive and Behavioral Science awardees announced

Young teenage girl and child therapist during EEG neurofeedback session. Electroencephalography concept.


The Simons Foundation Autism Research Initiative (SFARI) is pleased to announce that it intends to award ten grants in response to the 2022 Human Cognitive and Behavioral Science request for applications (RFA).

Grants awarded through this RFA are intended to produce foundational knowledge about the neurobehavioral differences associated with autism spectrum disorder (ASD). To enhance support of projects all along the continuum of translation, SFARI offered two tracks within this RFA solicitation: Explorer and Expansion.

The Explorer track is intended to support early-stage projects, where establishing feasibility and proof-of-concept are the most relevant outcomes of the grant period.

The Expansion track is intended to support more mature projects with evidence of feasibility and preliminary validity, for which goals such as scalability, generalizability and/or ecological validity are now the most relevant translational outcomes.

The projects selected for funding span topics including identifying early biomarkers of ASD in naturalistic motor behavior, decoding neuroimaging data to identify changes in excitatory/inhibitory balance and validating online behavioral assessment across large ASD cohorts. A variety of experimental approaches will be used, including online eye-tracking assessments, computational models of behavior and electroretinograms. Importantly, many of the projects will use phenotypic and genetic data from SPARK, a landmark research project that has so far recruited more than 100,000 individuals with ASD and their families.

SFARI intends to provide approximately $6.2 million in funding over the next three years to 16 investigators as part of this RFA.

“SFARI is pleased to be supporting these ten new projects,” says Kelsey Martin, director of SFARI and the Simons Foundation Neuroscience Collaborations. “Not only are findings from these studies likely to expand our knowledge about cognitive and behavioral changes associated with autism but the outcomes may also inform the development and refinement of tools needed for translational efforts.”

The projects that SFARI intends to fund are:

Explorer track

Ralph Adolphs, Ph.D. (California Institute of Technology)
Dissecting social attention in autism using large-sample eye tracking over the Internet

Gillian Forrester, Ph.D. (University of Sussex)
Identifying early markers of autism in naturalistic motor behavior using high-frequency sampling

Randi Hagerman, M.D. (University of California, Davis) and Paul Hagerman, M.D., Ph.D. (University of California, Davis)
The electroretinogram and FMRP: Correlate biomarkers for autism

John D. Murray, Ph.D. (Yale University) and Suma Jacob, M.D., Ph.D. (University of Minnesota)
Computational phenotyping of individual variation in latent-state learning, generalization and attention across the autism spectrum

Rachel D. Reetzke, Ph.D. (Kennedy Krieger Institute; Johns Hopkins Medicine) and Rebecca Landa, Ph.D. (Kennedy Krieger Institute)
Validation of a digital movement-based measure of early social communication

Gabriela Rosenblau, Ph.D. (George Washington University)
Leveraging computational modeling and genomics to constrain the heterogeneity of autism phenotypes

Rujuta Wilson, M.D. (University of California, Los Angeles)
Quantitative motor phenotyping in nonsyndromic autism and chromatin modifying disorders

Expansion track

Thomas Frazier, Ph.D. (John Carroll University)
Validation of an online neurobehavioral evaluation tool across large autism cohorts

Emily Jones, Ph.D. (Birkbeck College, University of London) and Sarah Lippé, Ph.D. (CHU Sainte-Justine Research Center)
Genetics and artificial intelligence for individualized neural stratification

Michael Lombardo, Ph.D. (Italian Institute of Technology), Alessandro Gozzi, Ph.D. (Italian Institute of Technology) and Stefano Panzeri, Ph.D. (University Medical Center Hamburg-Eppendorf)
Decoding excitation-inhibition imbalance from neuroimaging data in autism

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