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Published on: April 12, 2018
ERP Decoding Analysis of Visual Working Memory Processes in Schizophrenia
Zhongsi Wang1, Chunlei Liu1, Yuyan Jing1
1School of Psychology, Qufu Normal University, 273165 Qufu, Shandong, China.
Background:
Deficits in visual working memory (vWM) represent a core cognitive impairment in schizophrenia; however, the dynamic spatiotemporal characterization of their underlying neural mechanisms remains unclear. The present study employed multivariate pattern classification (MVPC) and searchlight analysis to investigate neural signaling differences between patients with schizophrenia (PSZ) and healthy control subjects (HCS) during a vWM task.
Methods:
A total of 46 participants (22 PSZ, 24 HCS) completed a change detection task comprising three conditions: two targets, zero distractors (2T0D); two targets, two distractors (2T2D); and four targets, zero distractors (4T0D). Contralateral delay activity (CDA) was extracted through event-related potential (ERP) analysis. MVPC was applied in the temporal dimension, while a searchlight approach was employed in the spatial dimension to decode memory load (2T0D/2T2D/4T0D) and memory side (left/right) information.
Results:
CDA amplitude was significantly reduced in the PSZ group, particularly in the 2T2D condition (p = 0.01), indicating that the scope and control of attention elicited comparable CDA amplitudes. MVPC analysis revealed that decoding accuracy in the PSZ group was significantly lower than in the HCS group during the time window of 93-652 ms (p corrected < 0.05), suggesting diminished efficiency of neural information encoding during the delay period. The searchlight analysis identified the most pronounced decrease in decoding accuracy within the left parietal region in the PSZ group, consistent with the hypothesis of abnormal functional connectivity in the inferior parietal gyrus (IPG).
Conclusions:
This study reveals the spatiotemporal dynamics of vWM deficits in schizophrenia, characterized by ERP decoding technology. It offers a novel target for the development of neuromarker-based cognitive interventions.
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