Unveiling the Associations between EEG Indices and Cognitive Deficits in Schizophrenia-Spectrum Disorders: A Systematic Review

被引:15
|
作者
Perrottelli, Andrea [1 ]
Giordano, Giulia Maria [1 ]
Brando, Francesco [1 ]
Giuliani, Luigi [1 ]
Pezzella, Pasquale [1 ]
Mucci, Armida [1 ]
Galderisi, Silvana [1 ]
机构
[1] Univ Campania Luigi Vanvitelli, Dept Psychiat, Largo Madonna Delle Grazie 1, I-80138 Naples, Italy
关键词
electroencephalogram (EEG); cognition; schizophrenia; biomarkers; EVENT-RELATED POTENTIALS; EMOTION RECOGNITION DEFICITS; CONTEXT-RELATED FACTORS; VERBAL MEMORY DEFICITS; 1ST EPISODE PSYCHOSIS; ULTRA-HIGH RISK; MISMATCH NEGATIVITY; 1ST-EPISODE PSYCHOSIS; PROCESSING DEFICITS; EXECUTIVE FUNCTION;
D O I
10.3390/diagnostics12092193
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Cognitive dysfunctions represent a core feature of schizophrenia-spectrum disorders due to their presence throughout different illness stages and their impact on functioning. Abnormalities in electrophysiology (EEG) measures are highly related to these impairments, but the use of EEG indices in clinical practice is still limited. A systematic review of articles using Pubmed, Scopus and PsychINFO was undertaken in November 2021 to provide an overview of the relationships between EEG indices and cognitive impairment in schizophrenia-spectrum disorders. Out of 2433 screened records, 135 studies were included in a qualitative review. Although the results were heterogeneous, some significant correlations were identified. In particular, abnormalities in alpha, theta and gamma activity, as well as in MMN and P300, were associated with impairments in cognitive domains such as attention, working memory, visual and verbal learning and executive functioning during at-risk mental states, early and chronic stages of schizophrenia-spectrum disorders. The review suggests that machine learning approaches together with a careful selection of validated EEG and cognitive indices and characterization of clinical phenotypes might contribute to increase the use of EEG-based measures in clinical settings.
引用
收藏
页数:55
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