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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 |
مقاله انگلیسی |
2 |
Cognitive Control Errors in Nonhuman Primates Resembling Those in Schizophrenia Reflect Opposing Effects of NMDA Receptor Blockade on Causal Interactions Between Cells and Circuits in Prefrontal and Parietal Cortices
خطاهای کنترل شناختی در پستانداران غیر انسانی شباهت با کسانی که در اسکیزوفرنی وجود دارند ، تأثیرات مخالف محاصره گیرنده NMDA را بر تعاملات احتمالی بین سلولها و مدارهای موجود در قشر جلوی مغز و پاریتال منعکس می کنند-2020 The causal biology underlying schizophrenia is not well understood, but it is likely to involve a
malfunction in how neurons adjust synaptic connections in response to patterns of activity in networks. We examined
statistical dependencies between neural signals at the cell, local circuit, and distributed network levels in prefrontal
and parietal cortices of monkeys performing a variant of the AX continuous performance task paradigm. We then
quantified changes in the pattern of neural interactions across levels of scale following NMDA receptor (NMDAR)
blockade and related these changes to a pattern of cognitive control errors closely matching the performance of
patients with schizophrenia.
METHODS: We recorded the spiking activity of 1762 neurons along with local field potentials at multiple electrode
sites in prefrontal and parietal cortices concurrently, and we generated binary time series indicating the presence or
absence of spikes in single neurons or local field potential power above or below a threshold. We then applied causal
discovery analysis to the time series to detect statistical dependencies between the signals (causal interactions) and
compared the pattern of these interactions before and after NMDAR blockade.
RESULTS: Global blockade of NMDAR produced distinctive and frequently opposite changes in neural interactions at the
cell, local circuit, and network levels in prefrontal and parietal cortices. Cognitive control errors were associated with
decreased interactions at the cell level and with opposite changes at the network level in prefrontal and parietal cortices.
CONCLUSIONS: NMDAR synaptic deficits change causal interactions between neural signals at different levels of
scale that correlate with schizophrenia-like deficits in cognitive control. Keywords: AX-CPT | Causal modeling | Cognitive control | Neural dynamics | NMDA | Primate | Schizophrenia |
مقاله انگلیسی |
3 |
Prefrontal cortical alterations of glutamate and GABA neurotransmission in schizophrenia: Insights for rational biomarker development
تغییرات قشر جلوی مغزی گلوتامات و انتقال عصبی گابا در اسکیزوفرنی : بینش برای توسعه علامت تجاری زیستی منطقی-2020 Certain cognitive deficits in schizophrenia, such as impaired working memory, are thought to reflect alterations in
the neural circuitry of the dorsolateral prefrontal cortex (DLPFC). Gamma oscillations in the DLPFC appear to be a
neural corollary of working memory function, and the power of these oscillations during working memory tasks is
lower in individuals with schizophrenia. Thus, gamma oscillations represent a potentially useful biomarker to index
dysfunction in the DLPFC circuitry responsible for working memory in schizophrenia. Postmortem studies, by
identifying the cellular basis of DLPFC dysfunction, can help inform the utility of biomarker measures obtained in
vivo. Given that gamma oscillations reflect network activity of excitatory pyramidal neurons and inhibitory GABA
neurons, we review postmortem findings of alterations to both cell types in the DLPFC and discuss how these findings
might inform future biomarker development and use. Keywords: GABA | gamma oscillations | Glutamate | Cognition | Working memory | Schizophrenia |
مقاله انگلیسی |