Clustering functional connectivity alterations in Autism Spectrum Disorders

Descripción del proyecto

Desvelar los subtipos de autismo

El trastorno del espectro autista (TEA) es una discapacidad hereditaria del neurodesarrollo que afecta al funcionamiento social, comunicativo y conductual, a menudo acompañada de otras comorbilidades. La gran heterogeneidad de su presentación clínica, su desconocida fisiopatología subyacente, el origen y el significado de tal diversidad de sus patrones no permiten la selección de subtipos a grupos efectivos. En la actualidad, esto supone un reto importante para su diagnóstico y tratamiento tempranos y eficaces. El proyecto CANSAS, financiado con fondos europeos, propone una deconstrucción de la heterogeneidad conectiva y clínica del TEA. Su equipo utilizará mediciones de resonancia magnética funcional en estado de reposo (RMf-er) del encéfalo de ratones relevantes para la primera descomposición de los conjuntos de datos de RMf-er de los TEA humanos en subtipos homogéneos y recapitulará la información biológicamente validada proporcionada por la observación directa.

Objetivo

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.