在认知衰退,抑郁症和精神分裂症的神经精神疾病中异常的大脑网络重新配置
Yan He1, Zhiqiang Yan2, Yuan Liang3
1School of Tourism, School of Artificial Intelligence, Xi'an International Studies University, Xi'an, China.
PloS one
|December 4, 2025
概括
这项研究引入了一种新的基于EEG的框架,用于分析神经精神疾病中大脑网络动态. 这些发现揭示了大脑网络凝聚力的改变,作为改善诊断的潜在生物标志物.
科学领域:
- 神经科学是一个神经科学.
- 计算精神病学是一种计算精神病学.
- 生物医学工程 生物医学工程
背景情况:
- 神经精神疾病在诊断和治疗方面存在复杂的挑战,原因是人们对病原性机制的理解不足.
- 现有的诊断方法往往缺乏特异性,导致患者和社会承担重大负担.
研究的目的:
- 开发和验证一种用于量化和描述神经精神疾病中大脑网络动态的新型框架.
- 确定潜在的生物标志物,以提高诊断准确度和有针对性的治疗策略.
主要方法:
- 从236名受试者获得脑电图 (EEG) 数据,包括患有阿尔茨海默病 (AD),轻度认知障碍 (MCI),严重抑郁症 (MDD),精神分裂症和健康对照 (HC) 的患者.
- 使用相锁值 (PLV) 分析构建了时间变化的功能性大脑网络.
- 使用动态社区检测和网络重新配置指标量化节点行为.
主要成果:
- 与健康对照人群相比,在所有神经精神疾病中发现了网络重新配置指标的显著差异 (p < 0.001).
- 在所有频段和记录地点的患者中,下脑网络凝聚力的强度始终被观察到.
- 在这些指标上训练的机器学习模型实现了超过80%的分类准确度,随机森林分类器在MCI/MDD上达到0.88.
结论:
- 拟议的框架有效地捕捉了大脑网络动态的共享和疾病特异性变化.
- 动态社区结构分析为神经精神疾病的病理机制提供了新的见解.
- 这些发现可能有助于开发更有针对性,更有效的治疗干预措施.
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