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首发精神分裂症患者行为控制与冲动性的神经关联:基于脑磁图的β振荡分析

Neural correlates of behavioral control and impulsivity in first-episode schizophrenia: A MEG-Based beta oscillation analysis.

作者信息

Han Yinglin, Hua Linglin, Xia Yi, Sun Hao, Sheng JunLing, Dai Zhongpeng, Yao Zhijian, Lu Qing

机构信息

Department of Psychiatry, Nanjing Medical University Affiliated Brain Hospital, No.264 Guangzhou Road, Nanjing, 210029, China.

Medical School of Nanjing University, Nanjing Brain Hospital, No.22 Hankou Road, Nanjing, 210093, China.

出版信息

J Psychiatr Res. 2025 Sep;189:104-115. doi: 10.1016/j.jpsychires.2025.05.079. Epub 2025 Jun 4.

Abstract

BACKGROUND

Impaired behavioral control and heightened impulsivity are core neurocogntive deficits in schizophrenia. However, the underlying neural mechanisms remain poorly understood. Studying individuals at inherited risk for schizophrenia may reveal potential endophenotypes, aiding early biomarker identification. Magnetoencephalography (MEG) provides high temporal to investigate inhibitory control deficits at the neurophysiological level. This study hypothesized that patients with first-episode schizophrenia (FES) and their first-degree relatives (FDR) would exhibit altered beta-band oscillatory activity and discrupted functional connectivity during behavioral control tasks, reflecting distinct neural signatures of impaired inhibitory control impairment and impulsivity.

METHODS

Our study comprised 20 patients with FES, 20 FDR, and 22 matched healthy controls (HCs) to perform a Go/NoGo task during MEG scanning. Beta-band oscillatory activity and functional connectivity (FC) were analyzed in key behavioral control regions, particularly the pre-supplementary motor area (pre-SMA) and left motor cortex (lM1). A machine learning classifier was applied to assess the discriminative power of these neurophysiological features.

RESULTS

Compared to HCs, FES participants exhibited significant reduced beta power in both pre-SMA and lM1 (P < 0.005), along with increased beta-band connectivity between these regions during the late-stage of inhibition (P = 0.013). FDR showed intermediate beta power reductions and FC increases, suggesting a potential inherited liability. BIS-11 impulsivity scores were significantly correlated with beta power in both regions (P < 0.01). A classification model integrating neural and behavioral features achieved an original accuracy of 88.7 % and a cross-validated accuracy of 72.6 %, with the highest classification performance observed in the FES group (95 %).

CONCLUSIONS

These findings highlight beta-band oscillations and pre-SMA-lM1 connectivity as potential neurophysiological markers of behavioral control deficits in schizophrenia. These results provide novel insights into the neural mechanisms underlying impulsivity in schizophrenia and highlight the potential utility of beta-band dynamics as biomarkers for early detection and intervention.

摘要

背景

行为控制受损和冲动性增强是精神分裂症的核心神经认知缺陷。然而,其潜在的神经机制仍知之甚少。对有精神分裂症遗传风险的个体进行研究可能会揭示潜在的内表型,有助于早期生物标志物的识别。脑磁图(MEG)能够在神经生理学水平上提供高时间分辨率来研究抑制控制缺陷。本研究假设,首发精神分裂症(FES)患者及其一级亲属(FDR)在行为控制任务期间会表现出β波段振荡活动改变和功能连接中断,反映出抑制控制受损和冲动性的独特神经特征。

方法

我们的研究包括20名FES患者、20名FDR和22名匹配的健康对照(HC),在MEG扫描期间执行Go/NoGo任务。分析关键行为控制区域,特别是前辅助运动区(pre-SMA)和左运动皮层(lM1)的β波段振荡活动和功能连接(FC)。应用机器学习分类器来评估这些神经生理特征的判别能力。

结果

与HC相比,FES参与者在pre-SMA和lM1中的β功率均显著降低(P < 0.005),并且在抑制后期这些区域之间的β波段连接性增加(P = 0.013)。FDR表现出中等程度的β功率降低和FC增加,表明存在潜在的遗传易感性。BIS-11冲动性评分与两个区域的β功率均显著相关(P < 0.01)。整合神经和行为特征的分类模型的原始准确率为88.7%,交叉验证准确率为72.6%,在FES组中观察到最高的分类性能(95%)。

结论

这些发现突出了β波段振荡和pre-SMA-lM1连接性作为精神分裂症行为控制缺陷的潜在神经生理标志物。这些结果为精神分裂症冲动性的潜在神经机制提供了新的见解,并突出了β波段动力学作为早期检测和干预生物标志物的潜在效用。

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