Tracking the Main States of Dynamic Functional Connectivity in Resting State

Abstract

Dynamical changes have recently been tracked in functional connectivity (FC) calculated from resting-state functional magnetic resonance imaging (R-fMRI), when a person is conscious but not carrying out a directed task during scanning. Diverse dynamical FC states (dFC) are believed to represent different internal states of the brain, in terms of brain-regional interactions. In this paper, we propose a novel protocol, the signed community clustering with the optimized modularity by two-step procedures, to track dynamical whole brain functional connectivity (dWFC) states. This protocol is assumption free without a priori threshold for the number of clusters. By applying our method on sliding window based dWFC’s with automated anatomical labeling 2 (AAL2), three main dWFC states were extracted from R-fMRI datasets in Human Connectome Project, that are independent on window size. Through extracting the FC features of these states, we found the functional links in state 1 (WFC-C 1 ) mainly involved visual, somatomotor, attention and cerebellar (posterior lobe) modules. State 2 (WFC-C 2 ) was similar to WFC-C 1 , but more FC’s linking limbic, default mode, and frontoparietal modules and less linking the cerebellum, sensory and attention modules. State 3 had more FC’s linking default mode, limbic, and cerebellum, compared to WFC-C 1 and WFC-C 2 . With tests of robustness and stability, our work provides a solid, hypothesis-free tool to detect dWFC states for the possibility of tracking rapid dynamical change in FCs among large data sets.

Altered dynamic effective connectivity of the default mode network in newly diagnosed drug-naïve juvenile myoclonic epilepsy

“…Although the underlying cognitive processes remains largely unknown, these diverse FC states are believed to reflect distinct internal states of the brain (e.g. a state of alertness or drowsiness), in terms of functional interactions ( Calhoun and Adali, 2016 , Lim et al, 2018 , Zhou et al, 2019 ). This can be supported by the evidence that dynamic FC states derived from rs-fMRI are linked to discrete mental states observed in EEG measurements ( Allen et al, 2018 ).…”

A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

“…Other studies have applied k-means clustering to identify the functional connectivity patterns that reoccur over time and across subjects [292]- [294]. However, the method requires the setting of initial values and the number of states to achieve a good performance [295].…”

Temporal dynamic changes of intrinsic brain activity in Alzheimer’s disease and mild cognitive impairment patients: a resting-state functional magnetic resonance imaging study

“…Recent studies have reported that the regional dynamic characteristics of brain activity can be captured more effectively using the dynamic sliding window method throughout the scanning procedure (17). Compared to the static measurement tool, abnormal brain function can be examined more easily through dynamic analysis (18)(19)(20)(21). The dynamic ALFF (dALFF) and dynamic fALFF (dfALFF) methods are recommended for measuring the variance of ALFF and fALFF over time.…”