Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer's disease

Abstract

We studied the dynamic functional connectivity profile of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) compared to controls, how it differs between the two dementia subtypes, and a possible relation between dynamic connectivity alterations and temporally transient clinical symptoms in DLB. Resting state fMRI data from 31 DLB, 29 AD, and 31 healthy control participants were analyzed using dual regression to determine between-network functional connectivity. Subsequently, we used a sliding window approach followed by k-means clustering and dynamic network analyses to study dynamic functional connectivity. Dynamic connectivity measures that showed significant group differences were tested for correlations with clinical symptom severity. Our results show that AD and DLB patients spent more time than controls in sparse connectivity configurations with absence of strong positive and negative connections and a relative isolation of motor networks from other networks. Additionally, DLB patients spent less time in a more strongly connected state and the variability of global brain network efficiency was reduced in DLB compared to controls. There were no significant correlations between dynamic connectivity measures and clinical symptom severity. An inability to switch out of states of low inter-network connectivity into more highly and specifically connected network configurations might be related to the presence of dementia in general as it was observed in both AD and DLB. In contrast, the loss of global efficiency variability in DLB might indicate the presence of an abnormally rigid brain network and the lack of economical dynamics, factors which could contribute to cognitive slowing and an inability to respond appropriately to situational demands.

Keywords: Cognitive fluctuations; Dual regression; Dynamic network analysis; Neurodegeneration; Resting state fMRI; Sliding window.

Fig. 1

Resting state networks. Spatial maps of the 27 RSNs obtained from the independent healthy control group. RSN maps are thresholded at 3 < z < 12. Images are shown in radiological convention, i.e. the left side of the image corresponds to the right hemisphere.

Fig. 2

Sliding window approach and k-means analysis. A) Data from all healthy control subjects from the independent cohort is concatenated in time and subjected to group ICA to identify RSN spatial maps. Subject-specific time courses of each RSN are estimated using dual regression. B) Static functional connectivity (FC) analysis by calculating correlation between each pair of RSNs using the whole time course (see (Schumacher et al., 2018)). C) Sliding window approach and estimation of standard deviation (SD) of connectivity over time. D) K-means clustering.

Fig. 3

Results from dynamic functional connectivity analysis. Matrices representing mean standard deviation over time for all HC, AD, and DLB participants and boxplot showing a group comparison of mean standard deviation across all connections.

Fig. 4

Results from k-means analysis. A) Centroids resulting from clustering on all windows with the overall percentage of windows assigned to the respective cluster (shown above each matrix). B) Cluster medians in the healthy control (HC) group and the number of HC patients expressing a state displayed above the respective matrix. C) Cluster medians in the Alzheimer's disease (AD) group and the number of AD patients expressing a state displayed above the respective matrix. D) Cluster medians in the DLB group and the number of DLB patients expressing a state displayed above the respective matrix. E) Network representation of cluster centroids showing only the 5% strongest positive (red) and negative (blue) connections. F) Comparison of frequency of occurrence between the three groups for each state, solid lines represent the means per group, shaded areas represent error bars of the standard error. G) Comparison of mean dwell time in each state between the three groups. FDR-corrected p-values<0.05 (from post-hoc tests) are marked with an asterisk. LSMN, lateral sensorimotor network; MSMN, medial sensorimotor network; SMAN, supplementary motor network; LMN/RMN, left/right motor network; BGN, basal ganglia network; THN, thalamic network; CBN, cerebellar network; MVN, medial visual network; LVN, lateral visual network; SVN, superior visual network; TN, temporal network; TPN, temporal pole network; ISN, insular network; ACN, anterior cingulate network; DMN, default mode network; SPGN, supramarginal gyrus network; RFPN/LFPN, right/left fronto-parietal network; DAN, dorsal attention network; VAN, ventral attention network.

Fig. 5

Results from the dynamic network analysis. Comparison of the variability of A) global (p-values FDR-corrected) and B) local efficiency between groups. C) Variability of global efficiency at different network edge densities.