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Validating dynamicity in resting state fMRI with activation-informed temporal segmentation
Human Brain Mapping ( IF 4.8 ) Pub Date : 2021-09-12 , DOI: 10.1002/hbm.25649
Marlena Duda 1 , Danai Koutra 2 , Chandra Sripada 3
Affiliation  

Confirming the presence (or absence) of dynamic functional connectivity (dFC) states during rest is an important open question in the field of cognitive neuroscience. The prevailing dFC framework aims to identify dynamics directly from connectivity estimates with a sliding window approach, however this method suffers from several drawbacks including sensitivity to window size and poor test–retest reliability. We hypothesize that time-varying changes in functional connectivity are mirrored by significant temporal changes in functional activation, and that this coupling can be leveraged to study dFC without the need for a predefined sliding window. Here, we introduce a data-driven dFC framework, which involves informed segmentation of fMRI time series at candidate FC state transition points estimated from changes in whole-brain functional activation, rather than a fixed-length sliding window. We show our approach reliably identifies true cognitive state change points when applied on block-design working memory task data and outperforms the standard sliding window approach in both accuracy and computational efficiency in this context. When applied to data from four resting state fMRI scanning sessions, our method consistently recovers five reliable FC states, and subject-specific features derived from these states show significant correlation with behavioral phenotypes of interest (cognitive ability, personality). Overall, these results suggest abrupt whole-brain changes in activation can be used as a marker for changes in connectivity states and provides new evidence for the existence of time-varying FC in rest.

中文翻译:

通过激活通知时间分割验证静息态 fMRI 的动态性

确认休息期间动态功能连接(dFC)状态的存在(或不存在)是认知神经科学领域的一个重要的悬而未决的问题。流行的 dFC 框架旨在通过滑动窗口方法直接从连通性估计中识别动态,但这种方法存在一些缺点,包括对窗口大小的敏感性和较差的重测可靠性。我们假设功能连接的时变变化反映了功能激活的显着时间变化,并且可以利用这种耦合来研究 dFC,而不需要预定义的滑动窗口。在这里,我们引入了一个数据驱动的 dFC 框架,该框架涉及根据全脑功能激活的变化估计的候选 FC 状态转换点的 fMRI 时间序列的知情分割,而不是固定长度的滑动窗口。我们表明,当应用于块设计工作记忆任务数据时,我们的方法可以可靠地识别真实的认知状态变化点,并且在这种情况下在准确性和计算效率方面都优于标准滑动窗口方法。当应用于来自四个静息状态 fMRI 扫描会话的数据时,我们的方法一致地恢复了五个可靠的 FC 状态,并且从这些状态得出的特定于主题的特征显示出与感兴趣的行为表型(认知能力、个性)显着相关。总的来说,这些结果表明全脑激活的突然变化可以作为连接状态变化的标志,并为休息时随时间变化的 FC 的存在提供新的证据。
更新日期:2021-11-01
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