Altered Cerebro-Cerebellar Dynamic Functional Connectivity in Alcohol Use Disorder: a Resting-State fMRI Study

Abstract

Alcohol use disorder (AUD) is widely associated with cerebellar dysfunction and altered cerebro-cerebellar functional connectivity (FC) that lead to cognitive impairments. Evidence for this association comes from resting-state functional magnetic resonance imaging (rsfMRI) studies that assess time-averaged measures of FC across the duration of a typical scan. This approach, however, precludes the assessment of potentially FC dynamics happening at faster timescales. In this study, using rsfMRI data, we aim at exploring cerebro-cerebellar FC dynamics in AUD patients (N = 18) and age- and sex-matched controls (N = 18). In particular, we quantified group-level differences in the temporal variability of FC between the posterior cerebellum and large-scale cognitive systems, and we investigated the role of the cerebellum in large-scale brain dynamics in terms of the temporal flexibility and integration of its regions. We found that, relative to controls, the AUD group exhibited significantly greater FC variability between the cerebellum and both the frontoparietal executive control (F_1,31 = 7.01, p(FDR) = 0.028) and ventral attention (F_1,31 = 7.35, p(FDR) = 0.028) networks. Moreover, the AUD group exhibited significantly less flexibility (F_1,31 = 8.61, p(FDR) = 0.028) and greater integration (F_1,31 = 9.11, p(FDR) = 0.028) in the cerebellum. Finally, in an exploratory analysis, we found distributed changes in the dynamics of canonical large-scale networks in AUD. Overall, this study brings evidence of AUD-related alterations in dynamic FC within major cerebro-cerebellar networks. This pattern has implications for explaining the development and maintenance of this disorder and improving our understating of the cerebellum's involvement in addiction.

Keywords: Alcohol use disorder; Cerebellum; Cerebro-cerebellar networks; Dynamic functional connectivity; Resting-state fMRI.

Fig. 1

Schematic overview of the main methods used in this study. a Whole-brain parcellation using functionally defined regions of interest with an improved representation of the cerebellum and the subcortex. b BOLD signals from all ROIs were segmented into overlapping windows using the tapered sliding window analysis, and a whole-brain FC matrix was constructed by computing pairwise Pearson’s correlation in each window. c An example FC matrix in an arbitrary time window w_s. The FC profile between distinct cerebral cognitive networks and the cerebellum is a sub-matrix (shown as boxes marked by black lines) containing the FC weights between its constituent nodes and every node in the cognitive cerebellum. Then, temporal variability of FC was calculated as the extent of variation of cerebro-cerebellar FC profiles across time windows. d Maximizing the multilayer modularity quality function Q(γ,ω) helps detecting the optimal community assignment for each node in each window. Using the optimal community structure, we calculated flexibility as the proportion of time a node changes its community assignment in time. Also, we calculated integration as the average probability for a node to be assigned to the same community with nodes outside its native system. Brain networks were visualized with the BrainNet Viewer toolbox http://www.nitrc.org/projects/bnv/ [60]

Fig. 2

(Left) Violin plots of temporal variability of FC between the cerebellum and seven large-scale cognitive networks for the AUD group and controls. Asterisk indicates p < 0.05 (FDR corrected) for group differences. (Right) Brain plot of the frontoparietal and ventral attention networks

Fig. 3

Violin plots of cerebellar flexibility and integration (right) for the AUD group and controls. Asterisk indicates p < 0.05 (FDR corrected) for group differences

Fig. 4

(Left) Violin plots of network flexibility for the AUD group and controls. Asterisk indicates p < 0.05 (uncorrected) for group differences. (Right) Brain plots of the salience and cingulo-opercular networks

Fig. 5

Violin plots of network integration for the AUD group and controls. Asterisk indicates p < 0.05 (uncorrected) for group differences