In:
npj Schizophrenia, Springer Science and Business Media LLC, Vol. 6, No. 1 ( 2020-11-06)
Abstract:
Recently, we developed a machine-learning algorithm “EMPaSchiz” that learns, from a training set of schizophrenia patients and healthy individuals, a model that predicts if a novel individual has schizophrenia, based on features extracted from his/her resting-state functional magnetic resonance imaging. In this study, we apply this learned model to first-degree relatives of schizophrenia patients, who were found to not have active psychosis or schizophrenia. We observe that the participants that this model classified as schizophrenia patients had significantly higher “schizotypal personality scores” than those who were not. Further, the “EMPaSchiz probability score” for schizophrenia status was significantly correlated with schizotypal personality score. This demonstrates the potential of machine-learned diagnostic models to predict state-independent vulnerability, even when symptoms do not meet the full criteria for clinical diagnosis.
Type of Medium:
Online Resource
ISSN:
2334-265X
DOI:
10.1038/s41537-020-00119-y
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2020
detail.hit.zdb_id:
2813844-2
detail.hit.zdb_id:
3133210-9
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