Validating dynamicity in resting state fMRI with activation‐informed temporal segmentation

Abstract

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

Individual functional parcellation revealed compensation of dynamic limbic network organization in healthy ageing

“…Recent methodological breakthroughs, in which individual‐level parcellation has been mapped using iterative clustering analysis on group‐level parcellations (M. L. Li et al, 2019 ; D. H. Wang et al, 2015 ), offer the opportunity to characterize dynamic functional network organization changes with age at the individual level. In addition, researchers have noticed the drawback and limitation of fixed‐length sliding window analysis and proposed data‐driven segmentation of sliding windows (Choe et al, 2017 ), such as the hidden Markov model (G. M. Zhang et al, 2020 ), the dynamic conditional correlation model (Lindquist et al, 2014 ), and activation‐informed temporal segmentation (Duda et al, 2021 ). Considering differences in interindividual parcellation, internetwork organization and interindividual sliding window activities with increasing age, we used individual participant parcellation and individual network dynamic analysis on individual sliding window lengths to advance the understanding of dynamic network organization.…”

Precision functional magnetic resonance imaging reveals individual brain functional network organization

“…, 而 传统 fMRI 研究或是未采用此方法对头动噪声进 行进一步的控制, 或是虽然运用了此方法但采用 了更加宽松的阈值(一般为删除 FD > 0.5 mm 的图像) (Duda et al, 2021;Fan et al, 2021;Power et al, 2012;Sripada et al, 2020;Tarchi et al, 2022) (Bergmann et al, 2020;Dworetsky, Seitzman, Adeyemo, Smith, et al, 2021;Kong et al, 2019;Seitzman et al, 2019;Yang et al, 2022) 的 (Glasser et al, 2013;Miller et al, 2016)…”