دانلود مقاله انگلیسی رایگان:تغییرات پویا شبکه های مغزی در طی پردازش مربوط به بازخورد یادگیری تقویت در اسکیزوفرنی - 2020
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  • Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia
    Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

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


    ترجمه فارسی عنوان مقاله:

    تغییرات پویا شبکه های مغزی در طی پردازش مربوط به بازخورد یادگیری تقویت در اسکیزوفرنی


    منبع:

    Sciencedirect - Elsevier - Brain Research, 1746 (2020) 146979. doi:10.1016/j.brainres.2020.146979


    نویسنده:

    Zongya Zhaoa,b,c,⁎, Chang Wanga,b,c, Qingli Yuana, Junqiang Zhaoa,b,c, Qiongqiong Rena,b,c, Yongtao Xua,b,c, Jie Lid, Yi Yua,


    چکیده انگلیسی:

    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


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 17
    حجم فایل: 5907 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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