4.2 Article

Predicting responses to electroconvulsive therapy in schizophrenia patients undergoing antipsychotic treatment: Baseline functional connectivity among regions with strong electric field distributions

期刊

PSYCHIATRY RESEARCH-NEUROIMAGING
卷 299, 期 -, 页码 -

出版社

ELSEVIER IRELAND LTD
DOI: 10.1016/j.pscychresns.2020.111059

关键词

Schizophrenia; ECT; Electric field simulation; Neuroimage; Prediction; Longitudinal study

资金

  1. National Basic Research Program of China [2015CB856403, 2014CB543203]
  2. Science and Technology Projects of Xi'an, China [201809170CX11JC12]
  3. National Natural Science Foundation of China [81471811, 81471738]

向作者/读者索取更多资源

This study explored imaging predictors of electroconvulsive therapy (ECT) outcome in schizophrenia patients based on pre-treatment functional connectivity (FC) within regions with strong ECT electric fields distribution. Forty-seven patients received standard antipsychotic drugs combined with ECT as well as two brain imaging sessions. Regions of interest (ROI) with strong electric field distribution were determined by ECT simulation. Using baseline functional connectivity between ROIs, a model was constructed to predict the percentage reduction of Positive and Negative Syndrome Scale (PANSS) scores. The strong electric fields were distributed in the orbital prefrontal lobe, medial temporal lobe, and other parts of the temporal lobe. Ten functional connectivity features within the electric field distribution areas showed a predictive ability for ECT outcome. The correlation coefficient between the predictive and real values of cross-validation was 0.7165. Among the predictive features, ECT induced a significant decrease in functional connectivity between the right amygdala and the left hippocampus. These results suggest that pretreatment functional connectivity patterns in brain regions with strong electric field distributions during ECT could be potential predictors of the efficacy of ECT augmentation in schizophrenia. These findings may help to improve individualized clinical treatment in the future.

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