Connectionists: NEW RESEARCH Machine Learning and Mental Illness

[Stephen Jose Hanson]

Please find new Machine learning/Neuroimaging research.  Neuroimage:Clinical

Differences in atypical resting-state effective connectivity distinguish autism
from schizophrenia
Dana Mastrovito, Catherine Hanson, Stephen Jose Hanson

Abstract
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 can distinguish 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

https://www.sciencedirect.com/science/article/pii/S2213158218300147?via%3Dihub



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