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抑郁症中人类皮质微电路抑制减少的脑电生物标记物的计算研究。

In-silico EEG biomarkers of reduced inhibition in human cortical microcircuits in depression.

机构信息

Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada.

Department of Physiology, University of Toronto, Canada.

出版信息

PLoS Comput Biol. 2023 Apr 10;19(4):e1010986. doi: 10.1371/journal.pcbi.1010986. eCollection 2023 Apr.

Abstract

Reduced cortical inhibition by somatostatin-expressing (SST) interneurons has been strongly associated with treatment-resistant depression. However, due to technical limitations it is impossible to establish experimentally in humans whether the effects of reduced SST interneuron inhibition on microcircuit activity have signatures detectable in clinically-relevant brain signals such as electroencephalography (EEG). To overcome these limitations, we simulated resting-state activity and EEG using detailed models of human cortical microcircuits with normal (healthy) or reduced SST interneuron inhibition (depression), and found that depression microcircuits exhibited increased theta, alpha and low beta power (4-16 Hz). The changes in depression involved a combination of an aperiodic broadband and periodic theta components. We then demonstrated the specificity of the EEG signatures of reduced SST interneuron inhibition by showing they were distinct from those corresponding to reduced parvalbumin-expressing (PV) interneuron inhibition. Our study thus links SST interneuron inhibition level to distinct features in EEG simulated from detailed human microcircuits, which can serve to better identify mechanistic subtypes of depression using EEG, and non-invasively monitor modulation of cortical inhibition.

摘要

生长抑素表达(SST)中间神经元的皮质抑制减少与治疗抵抗性抑郁症强烈相关。然而,由于技术限制,无法在人类中通过实验确定 SST 中间神经元抑制减少对微电路活动的影响是否具有可在临床相关脑信号(如脑电图(EEG))中检测到的特征。为了克服这些限制,我们使用具有正常(健康)或减少 SST 中间神经元抑制(抑郁)的人类皮质微电路的详细模型模拟静息状态活动和 EEG,并发现抑郁微电路表现出增加的θ、α和低β功率(4-16 Hz)。抑郁的变化涉及非周期性宽带和周期性θ分量的组合。然后,我们通过证明它们与对应于减少表达 parvalbumin(PV)中间神经元抑制的 EEG 特征不同,证明了 EEG 中 SST 中间神经元抑制减少的特征的特异性。因此,我们的研究将 SST 中间神经元抑制水平与从详细的人类微电路模拟的 EEG 中的独特特征联系起来,这可以用于使用 EEG 更好地识别抑郁症的机制亚型,并非侵入性地监测皮质抑制的调节。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f46/10085061/46058ed47fec/pcbi.1010986.g001.jpg

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