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Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia.

Abstract：

:Previous studies have reported that schizophrenia (SZ) patients showed selective reinforcement learning deficits and abnormal feedback-related event-related potential (ERP) components. However, how the brain networks and their topological properties evolve over time during transient feedback-related cognition processing in SZ patients has not been investigated so far. In this paper, using publicly available feedback-related ERP data which were recorded from SZ patients and healthy controls (HC) when they performed a reinforcement learning task, we carried out an event-related network analysis where topology of brain functional networks was characterized with some graph measures including clustering coefficient (C), global efficiency (Eglobal) and local efficiency (Elocal) on a millisecond timescale. Our results showed that the brain functional networks displayed rapid rearrangements of topological properties during transient feedback-related cognition process for both two groups. More importantly, we found that SZ patients exhibited significantly reduced theta-band (time window of 170-350 ms after stimuli onset) brain functional connectivity strength, Eglobal, Elocal and C in response to negative feedback stimuli compared to HC group. The network based statistic (NBS) analysis detected one significantly decreased theta-band subnetwork in SZ patients mainly involving in frontal-occipital and temporal-occipital connections compared to HC group. In addition, clozapine treatment seemed to greatly reduce theta-band power and topological measures of brain networks in SZ patients. Finally, the theta-band power, graph measures and functional connectivity were extracted to train a support vector machine classifier for classification of HC from SZ, or Cloz + SZ or Cloz- SZ, and a relatively good classification accuracy of 84.48%, 89.47% and 78.26% was obtained, respectively. The above results suggested a less optimal organization of theta-band brain network in SZ patients, and studying the topological parameters of brain networks evolve over time during transient feedback-related processing could be useful for understanding the pathophysiologic mechanisms underlying reinforcement learning deficits in SZ patients.

journal_name

Brain Res

journal_title

Brain research

authors

Zhao Z,Wang C,Yuan Q,Zhao J,Ren Q,Xu Y,Li J,Yu Y

doi

10.1016/j.brainres.2020.146979

subject

Has Abstract

pub_date

2020-11-01 00:00:00

pages

146979

eissn

0006-8993

issn

1872-6240

pii

S0006-8993(20)30335-8

journal_volume

1746

pub_type

杂志文章

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Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia.