Wiafe Sir-Lord, Faghiri Ashkan, Fu Zening, Miller Robyn, Preda Adrian, Calhoun Vince D
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.
University of California, Irvine, Irvine, CA, United States.
Imaging Neurosci (Camb). 2024 Jun 3;2. doi: 10.1162/imag_a_00187. eCollection 2024.
In neuroimaging research, understanding the intricate dynamics of brain networks over time is paramount for unraveling the complexities of brain function. One approach commonly used to explore the dynamic nature of brain networks is functional connectivity analysis. However, while functional connectivity offers valuable insights, it fails to consider the diverse timescales of coupling between different brain regions. This gap in understanding leaves a significant aspect of brain dynamics unexplored in neuroimaging research. We propose an innovative approach that delves into the dynamic coupling/connectivity timescales of brain regions relative to one another, focusing on how brain region couplings stretch or shrink over time, rather than relying solely on functional connectivity measures. Our method introduces a novel metric called "warping elasticity," which utilizes dynamic time warping (DTW) to capture the temporal nuances of connectivity. Unlike traditional methods, our approach allows for (potentially nonlinear) dynamic compression and expansion of the time series, offering a more intricate understanding of how coupling between brain regions evolves. Through the adaptive windows employed by the DTW method, we can effectively capture transient couplings within varying connectivity timescales of brain network pairs. In extensive evaluations, our method exhibits high replicability across subjects and diverse datasets, showcasing robustness against noise. More importantly, it uncovers statistically significant distinctions between healthy control (HC) and schizophrenia (SZ) groups through the identification of warp elasticity states. These states are cluster centroids, representing the warp elasticity across subjects and time, offering a novel perspective on the dynamic nature of brain connectivity, distinct from conventional metrics focused solely on functional connectivity. For instance, controls spend more time in a warp elasticity state characterized by timescale stretching of the visual domain relative to other domains, suggesting disruptions in the visual cortex. Conversely, patients show increased time spent in a warp elasticity state with stretching timescales in higher cognitive areas relative to sensory regions, indicative of prolonged cognitive processing of sensory input. Overall, our approach presents a promising avenue for investigating the temporal dynamics of brain network interactions in functional magnetic resonance imaging (fMRI) data. By focusing on the elasticity of connectivity timescales, rather than adhering to functional connectivity metrics, we pave the way for a deeper understanding of neuropsychiatric disorders in neuroscience research.
在神经影像学研究中，了解大脑网络随时间的复杂动态对于揭示脑功能的复杂性至关重要。探索大脑网络动态性质常用的一种方法是功能连接分析。然而，尽管功能连接提供了有价值的见解，但它未能考虑不同脑区之间耦合的不同时间尺度。这种理解上的差距使得神经影像学研究中大脑动态的一个重要方面未被探索。我们提出了一种创新方法，该方法深入研究脑区之间相对于彼此的动态耦合/连接时间尺度，关注脑区耦合如何随时间伸展或收缩，而不是仅仅依赖于功能连接测量。我们的方法引入了一种名为“扭曲弹性”的新指标，它利用动态时间规整（DTW）来捕捉连接的时间细微差别。与传统方法不同，我们的方法允许（潜在地非线性）动态压缩和扩展时间序列，从而更深入地理解脑区之间的耦合如何演变。通过DTW方法采用的自适应窗口，我们可以有效地捕捉脑网络对不同连接时间尺度内的瞬态耦合。在广泛的评估中，我们的方法在不同受试者和多样数据集上表现出高可重复性，展示了对噪声的鲁棒性。更重要的是，通过识别扭曲弹性状态，它揭示了健康对照组（HC）和精神分裂症（SZ）组之间具有统计学意义的差异。这些状态是聚类中心，代表了跨受试者和时间的扭曲弹性，为大脑连接的动态性质提供了一个新视角，不同于仅关注功能连接的传统指标。例如，对照组在一种扭曲弹性状态下花费更多时间，该状态的特征是视觉区域相对于其他区域的时间尺度伸展，这表明视觉皮层存在干扰。相反，患者在一种扭曲弹性状态下花费的时间增加，该状态下较高认知区域相对于感觉区域的时间尺度伸展，这表明对感觉输入的认知处理延长。总体而言，我们的方法为研究功能磁共振成像（fMRI）数据中大脑网络相互作用的时间动态提供了一条有前景的途径。通过关注连接时间尺度的弹性，而不是坚持功能连接指标，我们为神经科学研究中更深入理解神经精神疾病铺平了道路。
