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Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia
تغییرات پویا شبکه های مغزی در طی پردازش مربوط به بازخورد یادگیری تقویت در اسکیزوفرنی-2020 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 feedbackrelated
processing could be useful for understanding the pathophysiologic mechanisms underlying reinforcement
learning deficits in SZ patients. Keywords: Event-related network analysis | Support vector machine | Graph measures | Reinforcement learning | Schizophrenia |
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