fMRI 数据中动态时间扭曲的动态性:通过翘曲弹性捕捉网络间伸缩的方法

Sir-Lord Wiafe, A. Faghiri, Z. Fu, Robyn L. Miller, Adrian Preda, V. Calhoun
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摘要

摘要 在神经成像研究中,了解大脑网络随时间变化的复杂动态对于揭示大脑功能的复杂性至关重要。功能连通性分析是探索大脑网络动态性质的常用方法之一。然而,虽然功能连通性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。这种认识上的差距使得神经成像研究中大脑动态的一个重要方面尚未得到探索。我们提出了一种创新方法,深入研究脑区相对于彼此的动态耦合/连接时标,重点关注脑区耦合是如何随时间延伸或收缩的,而不是仅仅依赖于功能连接度量。我们的方法引入了一种名为 "翘曲弹性 "的新指标,它利用动态时间翘曲(DTW)来捕捉连通性在时间上的细微差别。与传统方法不同的是,我们的方法允许对时间序列进行(可能是非线性的)动态压缩和扩展,从而更深入地了解大脑区域之间的耦合是如何演变的。通过 DTW 方法采用的自适应窗口,我们可以有效捕捉大脑网络对不同连接时标内的瞬时耦合。在广泛的评估中,我们的方法在不同的研究对象和不同的数据集上都表现出很高的可重复性,展示了对噪声的鲁棒性。更重要的是,它通过识别翘曲弹性状态,发现了健康对照组(HC)和精神分裂症组(SZ)之间在统计学上的显著区别。这些状态是聚类中心点,代表了跨受试者和跨时间的翘曲弹性,为大脑连通性的动态性质提供了一个新的视角,有别于只关注功能连通性的传统指标。例如,对照组处于翘曲弹性状态的时间更长,这种状态的特点是视觉域相对于其他域的时间尺度拉伸,这表明视觉皮层受到了干扰。相反,患者在翘曲弹性状态下花费的时间更长,相对于感觉区域,高级认知区域的时间尺度被拉伸,这表明患者对感觉输入的认知处理时间延长。总之,我们的方法为研究功能性磁共振成像(fMRI)数据中大脑网络交互的时间动态提供了一种很有前景的途径。通过关注连接时间尺度的弹性,而不是拘泥于功能连接指标,我们为在神经科学研究中更深入地了解神经精神疾病铺平了道路。
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The dynamics of dynamic time warping in fMRI data: A method to capture inter-network stretching and shrinking via warp elasticity
Abstract 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.
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