精神分裂症(面向对象编程)
心理学
脑磁图
重性抑郁障碍
精神病
面部表情
临床心理学
情绪识别
抑郁症状
精神分裂症谱
神经科学
认知
认知心理学
签名(拓扑)
萧条(经济学)
发展心理学
精神科
作者
Mauro Scala,Juergen Dukart,Giuseppe De Simone,Giuseppe Fanelli,Rocío Arroyo Iturra,S. Bordignon,Chiara Fabbri,Alessandro Serretti
摘要
INTRODUCTION: Social withdrawal is a frequent marker of functional decline in schizophrenia, Alzheimer's disease (AD), and major depressive disorder (MDD), and is associated with deficits in facial-emotion recognition (FER). Magnetoencephalography (MEG) captures neuronal activity at millisecond resolution, enabling the assessment of fast oscillatory dynamics and functional connectivity during FER. This systematic review compares MEG responses during implicit and explicit FER tasks in adults with schizophrenia, AD, and MDD to identify possible transdiagnostic and disorder-specific alterations. However, no eligible studies were identified for AD. METHODS: Following PRISMA guidelines, PubMed, Web of Science, and CINAHL were systematically searched from inception to February 2025 for MEG studies comparing adults with schizophrenia, AD, or MDD with healthy controls during implicit (i.e., passive viewing) or explicit (i.e., labeling) FER tasks. RESULTS: Eighteen studies met inclusion criteria (four schizophrenia, 14 MDD, none in AD). Schizophrenia and MDD showed transdiagnostic MEG patterns across disorder-specific studies, including early temporal-cortical hyperactivation (50-150 ms) during implicit FER and early amygdala hyper-reactivity (within 100 ms) followed by prefrontal hypo-recruitment (100-500 ms) during explicit FER, with disrupted cortico-limbic connectivity. Schizophrenia was associated with specific increases in theta-gamma coupling and unidirectional visual-limbic-prefrontal connectivity during explicit FER. MDD showed a shift from early gamma hyper-synchrony (50-150 ms) to late beta/gamma hypo-synchrony (250-500 ms), with reduced fronto-limbic and fusiform-amygdala coupling. CONCLUSIONS: MEG reveals a transdiagnostic signature of FER dysfunction that may be linked to social withdrawal and disorder-specific oscillatory patterns that may inform targeted neuromodulation.
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