Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity.

Abstract:

:Identifying biomarkers in schizophrenia during the first episode without the confounding effects of treatment has been challenging. Leveraging these biomarkers to establish diagnosis and make individualized predictions of future treatment responses to antipsychotics would be of great value, but there has been limited progress. In this study, by using machine learning algorithms and the functional connections of the superior temporal cortex, we successfully identified the first-episode drug-naive (FEDN) schizophrenia patients (accuracy 78.6%) and predict their responses to antipsychotic treatment (accuracy 82.5%) at an individual level. The functional connections (FC) were derived using the mutual information and the correlations, between the blood-oxygen-level dependent signals of the superior temporal cortex and other cortical regions acquired with the resting-state functional magnetic resonance imaging. We also found that the mutual information and correlation FC was informative in identifying individual FEDN schizophrenia and prediction of treatment response, respectively. The methods and findings in this paper could provide a critical step toward individualized identification and treatment response prediction in first-episode drug-naive schizophrenia, which could complement other biomarkers in the development of precision medicine approaches for this severe mental disorder.

journal_name

Mol Psychiatry

journal_title

Molecular psychiatry

authors

Cao B,Cho RY,Chen D,Xiu M,Wang L,Soares JC,Zhang XY

doi

10.1038/s41380-018-0106-5

subject

Has Abstract

pub_date

2020-04-01 00:00:00

pages

906-913

issue

4

eissn

1359-4184

issn

1476-5578

pii

10.1038/s41380-018-0106-5

journal_volume

25

pub_type

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