瞬时频率：动态脑因果网络的一种新型功能生物标志物。
Instantaneous Frequency: A New Functional Biomarker for Dynamic Brain Causal Networks.
作者信息
Tang Haoteng, Dai Siyuan, Guo Lei, Gu Pengfei, Liu Guodong, Leow Alex D, Thompson Paul M, Huang Heng, Zhan Liang
机构信息
Department of Computer Science, University of Texas Rio Grande Valley, Edinburg, Texas, USA.
Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
出版信息
AI Neurosci. 2025 Jun 6. doi: 10.1089/ains.2025.0005.
BACKGROUND
This study introduces instantaneous frequency (IF) analysis as a novel method for characterizing dynamic brain causal networks from functional magnetic resonance imaging blood-oxygen-level-dependent signals.
METHODS
Effective connectivity, estimated using dynamic causal modeling, is analyzed to derive IF sequences, with the average IF across brain regions serving as a potential biomarker for global network oscillatory behavior.
RESULTS
Analysis of data from the Alzheimer's Disease (AD) Neuroimaging Initiative, Open Access Series of Imaging Studies, and Human Connectome Project demonstrates the method's efficacy in distinguishing between clinical and demographic groups, such as cognitive decline stages (e.g., normal control, early mild cognitive impairment [MCI], late MCI, and AD), sex differences, and sleep quality levels.
CONCLUSION
Statistical analyses reveal significant group differences in IF metrics, highlighting its potential as a sensitive indicator for early diagnosis and monitoring of neurodegenerative and cognitive conditions.
背景
本研究引入瞬时频率（IF）分析，作为一种从功能磁共振成像血氧水平依赖信号中表征动态脑因果网络的新方法。
方法
使用动态因果模型估计有效连通性，以推导IF序列，将脑区的平均IF作为全局网络振荡行为的潜在生物标志物。
结果
对来自阿尔茨海默病（AD）神经影像倡议、开放获取影像研究系列和人类连接体项目的数据进行分析，证明了该方法在区分临床和人口统计学组方面的有效性，如认知衰退阶段（例如，正常对照、早期轻度认知障碍[MCI]、晚期MCI和AD）、性别差异和睡眠质量水平。
结论
统计分析揭示了IF指标在组间存在显著差异，突出了其作为神经退行性和认知疾病早期诊断及监测的敏感指标的潜力。
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