Connectionists: NEW paper Distinguishing ASD from Schziophernia using Resting State

[Stephen Jose Hanson]

Differences in atypical resting-state effective connectivity distinguish autism
from schizophrenia

Dana Mastrovito, Catherine Hanson, Stephen Jose' Hanson

Autism and schizophrenia share overlapping genetic etiology, common changes in brain
structure and common cognitive deficits. A number of studies using resting state fMRI
have shown that machine learning algorithms candistinguish between healthy controls and
individuals diagnosed with either autism spectrum disorder or schizophrenia.
However, it has not yet been determined whether machine learning algorithms can be used
to distinguish between the two disorders. Using a linear support vector machine, we
identify features that are most diagnostic for each disorder and successfully use them
to classify an independent cohort of subjects. We find both common and divergent
connectivity differences largely in the default mode network as well as in salience,
and motor networks. Using divergent connectivity differences, we are able to
distinguish autistic subjects from those with schizophrenia. Understanding the common
and divergent connectivity changes associated with these disorders
may provide a framework for understanding their shared cognitive deficits.



-- 
Stephen José Hanson
Professor & Director
Rutgers Brain Imaging Center (RUBIC)
Cognitive Science Center (NB)
Rutgers University

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