Wang Ze
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States.
Imaging Neurosci (Camb). 2025 May 30;3. doi: 10.1162/IMAG.a.15. eCollection 2025.
Long-range temporal coherence (LRTC) is a fundamental characteristic of self-organized dynamic systems and plays a crucial role in their function. In the brain, LRTC has been shown to be essential for cognition. Assessing LRTC may provide critical insights into the underlying mechanisms of brain organization, function, and cognition. To facilitate this overarching goal, I present a method called temporal coherence mapping (TCM) to explicitly quantify brain LRTC and validate it using resting-state fMRI in this paper. TCM is based on correlation analysis of the transit states of the phase space reconstructed by temporal embedding. Several TCM properties were derived to measure LRTC, including the averaged correlation, anti-correlation, the balance between correlation and anticorrelation, the mean coherent and incoherent duration, and the balance between the coherent and incoherent time. TCM was first evaluated with simulations and then applied to the large-scale Human Connectome Project data. The results showed that TCM metrics can successfully differentiate signals with different temporal coherence regardless of the parameters used to reconstruct the phase space. In the human brain, all TCM metrics showed high test-retest reproducibility; TCM metrics were associated with age, sex, and total cognitive scores. In summary, TCM provides a first-of-its-kind tool to assess LRTC and the balance between coherence and incoherence. The physiological and cognitive relevance of TCM properties highlights their potential for advancing our understanding of brain dynamics.
长程时间相干性(LRTC)是自组织动态系统的一个基本特征,在其功能中起着关键作用。在大脑中,LRTC已被证明对认知至关重要。评估LRTC可能为脑组织、功能和认知的潜在机制提供关键见解。为了推动这一总体目标,我在本文中提出了一种称为时间相干性映射(TCM)的方法,以明确量化大脑LRTC,并使用静息态功能磁共振成像对其进行验证。TCM基于对通过时间嵌入重建的相空间的过渡状态进行相关分析。推导了几个TCM属性来测量LRTC,包括平均相关性、反相关性、相关性和反相关性之间的平衡、平均相干和非相干持续时间,以及相干和非相干时间之间的平衡。首先通过模拟对TCM进行评估,然后将其应用于大规模人类连接组计划数据。结果表明,无论用于重建相空间的参数如何,TCM指标都能成功区分具有不同时间相干性的信号。在人类大脑中,所有TCM指标都显示出高重测信度;TCM指标与年龄、性别和总认知分数相关。总之,TCM提供了一种首创的工具来评估LRTC以及相干性和非相干性之间的平衡。TCM属性的生理和认知相关性突出了它们在推进我们对大脑动力学理解方面的潜力。