Resting State Dynamic Functional Connectivity in Neurodegenerative Conditions: A Review of Magnetic Resonance Imaging Findings

Abstract

In the last few decades, brain functional connectivity (FC) has been extensively assessed using resting-state functional magnetic resonance imaging (RS-fMRI), which is able to identify temporally correlated brain regions known as RS functional networks. Fundamental insights into the pathophysiology of several neurodegenerative conditions have been provided by studies in this field. However, most of these studies are based on the assumption of temporal stationarity of RS functional networks, despite recent evidence suggests that the spatial patterns of RS networks may change periodically over the time of an fMRI scan acquisition. For this reason, dynamic functional connectivity (dFC) analysis has been recently implemented and proposed in order to consider the temporal fluctuations of FC. These approaches hold promise to provide fundamental information for the identification of pathophysiological and diagnostic markers in the vast field of neurodegenerative diseases. This review summarizes the main currently available approaches for dFC analysis and reports their recent applications for the assessment of the most common neurodegenerative conditions, including Alzheimer's disease, Parkinson's disease, dementia with Lewy bodies, and frontotemporal dementia. Critical state-of-the-art findings, limitations, and future perspectives regarding the analysis of dFC in these diseases are provided from both a clinical and a technical point of view.

Keywords: Alzheimer’s disease; Lewy bodies; Parkinson’s disease; dementia; dynamic functional connectivity; fMRI; frontotemporal dementia; neurodegeneration.

FIGURE 1

Framework for construction of high-order functional connectivity (FC) network. (1) Partition of the RS-fMRI time series into multiple overlapping segments of subseries applying sliding-window technique; (2) collection of low-order FC matrices, one for each subseries; (3) stack of all matrices of all subjects together to obtain correlation time series for each element; (4) application of the clustering algorithm to group all the correlation time series; (5) construction of high-order FC network, considering the mean correlation time series for each cluster as vertex and the pairwise Pearson’s correlation coefficient between each pair of vertices as weight; (6) calculation of local clustering coefficients; (7) selection of a discriminative feature subset from the local clustering coefficients; (8) implementation of support vector machine (SVM) model for classification. RS-fMRI, resting-state functional magnetic resonance imaging; FC, functional connectivity (reproduced with permission from Chen et al., 2016).

FIGURE 2

Functional connectivity state results. (A) Group-specific cluster centroid for each state, averaged across subject-specific median cluster centroids of each group [percentage of total occurrences for stage I and II: 83.4% and 16.6% in the healthy controls (HC) and 70.8% and 29.2% in the Parkinson’s disease (PD) group, respectively]. (B) Functional connectivity in each state is shown for healthy controls and Parkinson’s disease groups, representing the 5% of the functional connectivity network with the strongest connections. BG, basal ganglia; AUD, auditory; SMN, sensorimotor; VIS, visual; CEN, cognitive executive; CB, cerebellar network (reproduced with permission from Kim et al., 2017).