Explore the words cloud of the CANSAS project. It provides you a very rough idea of what is the project "CANSAS" about.
The following table provides information about the project.
FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA
|Coordinator Country||Italy [IT]|
|Total cost||269˙002 €|
|EC max contribution||269˙002 € (100%)|
1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
|Duration (year-month-day)||from 2020-04-01 to 2023-03-31|
Take a look of project's partnership.
|1||FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA||IT (GENOVA)||coordinator||269˙002.00|
|2||CHILD MIND INSTITUTE, INC||US (NEW YORK)||partner||0.00|
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.
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The information about "CANSAS" are provided by the European Opendata Portal: CORDIS opendata.
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