Stress and Cognitive Electroimaging Laboratory, Department of Physiology, All India Institute of Medical Sciences, New Delhi, India.
Department of Psychiatry, All India Institute of Medical Sciences, New Delhi, India.
Schizophr Res. 2020 Aug;222:411-422. doi: 10.1016/j.schres.2020.03.066. Epub 2020 Jun 10.
Synchronized and coherent activity in resting-networks during normal brain functioning could be altered in disconnection syndrome like schizophrenia. Study of neural oscillations as assessed by EEG appears to be a promising proposition to understand the pathophysiology of schizophrenia in patients and their first-degree relatives, where disturbances in neural oscillations point towards genetic predisposition. Therefore, present study aims at establishing EEG based biomarkers for early detection and management strategies. Thirty-two patients with schizophrenia, 28 first-degree relatives and 31 healthy controls (HC) participated in the study. Resting brain activity was recorded using 128-channel electroencephalography. After pre-processing and independent component analysis (ICA), an equivalent current dipole was estimated for each IC. Total of 1551 independent and localizable EEG components across all groups were used in subsequent analysis. Power spectral density and source coherence between IC clusters were computed. Patients and first-degree relatives displayed significantly higher power spectral density (PSD) than HC for all frequency bands in left parahippocampal gyrus (PHG) (-7, -26, 8; BA 27). Another region within left deep PHG (-4, -28, 1), however, distinguished patients from first-degree relatives and HC in terms of significantly lower PSD in higher frequency bands. Functional connectivity (FC) was found to be lower in patients and higher in relatives compared to HC between different resting-state network areas. In patients, connectivity was lower compared to first-degree relatives. Altered activity within left PHG and FC of primarily this with other areas in resting-state network can serve as state and trait markers of schizophrenia.
在正常大脑功能中，静息网络中的同步和相干活动在精神分裂症等断开综合征中可能会发生改变。通过脑电图评估神经振荡的研究似乎是一种很有前途的方法，可以了解患者及其一级亲属的精神分裂症的病理生理学，其中神经振荡的紊乱指向遗传易感性。因此，本研究旨在建立基于脑电图的生物标志物，用于早期检测和管理策略。32 名精神分裂症患者、28 名一级亲属和 31 名健康对照者（HC）参与了这项研究。使用 128 通道脑电图记录静息脑活动。在预处理和独立成分分析（ICA）之后，为每个 IC 估计等效电流偶极子。在随后的分析中，使用了所有组别的总共 1551 个独立和可定位的 EEG 组件。计算了 IC 簇之间的功率谱密度和源相干性。与 HC 相比，患者和一级亲属在左海马旁回（PHG）的所有频带（-7、-26、8；BA 27）中显示出显着更高的功率谱密度（PSD）。然而，左深 PHG 内的另一个区域（-4、-28、1）以更高频带中的显着更低 PSD 区分了患者与一级亲属和 HC。与 HC 相比，患者和亲属的静息状态网络中不同区域之间的功能连接（FC）较低。与一级亲属相比，患者的连接性较低。左 PHG 内的活动改变以及与静息状态网络中其他区域的主要 FC 可以作为精神分裂症的状态和特征标志物。
