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Clustering functional connectivity alterations in Autism Spectrum Disorders

Objective

Autism spectrum disorders (ASD) are among the most heritable developmental disorders, associated with a large number of rare genetic alterations. A critical goal of current ASD research is to deconstruct its heterogeneity into clinically homogeneous sub-set of patients, characterized by distinct neurobiological or functional deficits, amenable to precise therapeutic targeting. Fostered by the advent of resting-state fMRI (rsfMRI), human brain mapping has revealed highly heterogeneous patterns of neural synchronization (i.e. “functional connectivity”) in ASD, with evidence of inconsistent, often contrasting, patterns of over- and under-connectivity across patient cohorts. However, the origin and significance of these highly heterogeneous findings remain unclear: does genetic heterogeneity account for the observed network divergences? And can we use functional connectivity fingerprints to cluster ASD into clinically relevant sub-types? The present project leverages translationally-relevant mouse brain rsfMRI measurements to propose a first-of-its-kind decomposition of human ASD rsfMRI datasets into homogeneous sub-types, recapitulating biologically-validated “ground truth” network features identified in the mouse. To this aim, I will use a set of etiologically-relevant rsfMRI fingerprints identified in a unique mouse datasets comprising 20 ASD-associated mutations to guide clustering of a large collection of human rsfMRI datasets. Socio-cognitive profiling will be employed to probe the clinical significance and homogeneity of the identified clusters. I will next combine advanced statistical modelling and gene ontologies to explore the biological underpinnings of each identified connectivity sub-type. These investigations will lead to a novel, etiologically-relevant deconstruction of the connectional and clinical heterogeneity of ASD that may improve patient stratification, guide the identification of dysfunctional pathways and help prediction of treatment response.

Call for proposal

H2020-MSCA-IF-2018
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Coordinator

FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA
Address
Via Morego 30
16163 Genova
Italy
Activity type
Research Organisations
EU contribution
€ 269 002,56

Partners (1)

CHILD MIND INSTITUTE, INC
United States
Address
101 E 56Th Street
10022 2606 New York
Activity type
Other