静止状态fMRI的动态模式分解揭示了精神分裂症中异常的大脑区域特征
Yaning Wang1, Yihong Wang1,2, Xuying Xu1,2
1Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China.
Frontiers in computational neuroscience
|January 30, 2026
概括
动态模式分解 (DMD) 能够有效地识别精神分裂症患者的异常大脑区域,使用功能磁共振成像 (fMRI) 数据,帮助诊断和理解功能偏差.
科学领域:
- 神经成像是一种神经成像.
- 计算神经科学是一种神经科学.
- 精神疾病 精神疾病
背景情况:
- 对精神分裂症的准确诊断依赖于识别异常的大脑区域.
- 现有的方法在脑成像数据中难以捕捉时空动态.
- 动态模式分解 (DMD) 为分析时间序列信号 (如fMRI) 提供了一个有希望的方法.
研究的目的:
- 应用DMD从精神分裂症患者的静止状态fMRI数据中提取时空特征.
- 识别与精神分裂症相关的异常大脑区域和功能异常.
- 使用机器学习评估DMD衍生特征的诊断实用性.
主要方法:
- 利用68名健康受试者和68名精神分裂症患者的静止状态fMRI数据.
- 采用动态模式分解 (DMD) 来提取动态模式的平均幅度作为特征.
- 应用最小绝对收缩和选择运算符 (LASSO) 回归用于特征选择.
- 使用支持矢量机 (SVM) 分类验证的特征预测能力.
主要成果:
- DMD成功地从fMRI数据中提取了平均振幅特征,捕获了依赖频率的模式.
- 拉索 (Lasso) 确定了大脑区域,在平均幅度上有显著的群体间差异,表明异常.
- 结合DMD,LASSO和SVM,准确地识别了精神分裂症患者,并突出了功能性偏差.
- 提取的特征与区域激活强度和功能连接相关.
结论:
- DMD有效地从fMRI数据中提取时间空间特征,用于精神分裂症研究.
- 这种方法有助于识别精神分裂症中异常的大脑区域和功能异常.
- 该方法提供了增强的功能解释性,信号重建和预测功能.
- 这项研究为开发精神疾病潜在生物标志物提供了新的见解.
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