Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson’s Disease

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.

Directed functional brain connectivity is altered in sub-threshold amyloid-β accumulators

“…As the current study employs functional MRI scans that have been acquired with longer repetition times, we evaluate the between-group differences at small delays up to lag of 5, which corresponds to the same temporal scale used in ref. 15. The between-group differences in the clustering coefficient and global efficiency at lag of 3 and are shown in Figure 1B and 1C respectively, whereas the corresponding results for lags 2 and 4 are plotted in Supplementary Fig.…”

“…properties of the network. We have previously shown that the changes in directed functional connectivity due to neurodegenerative processes can be captured by small temporal lags, up to the lag of 7 15 . As the current study employs functional MRI scans that have been acquired with longer repetition times, we evaluate the between-group differences at small delays up to lag of 5, which corresponds to the same temporal scale used in ref.…”

“…Anti-symmetric correlations can be used for quantifying directed connectivity between brain regions by harvesting the information contained in the temporal lags between the activation time series of different regions 15 . In this study, we used this approach to demonstrate the impact of Aβ deposition on directed functional connectivity in a group of healthy, cognitively normal adults.…”

“…Therefore, to achieve a coherent characterization of the functional connectivity, it is crucial to capture the information contained in this complicated temporal lag structure 21,22 . To this purpose, we have developed an anti-symmetric correlation-based approach for determining the directed connections between pairs of brain areas, allowing for the computation of whole-brain directed functional connectivity networks 15 . In this approach, if one brain area’s activation time series has similar properties to the time-shifted version of the activation pattern in another brain region, then the first region is considered to have a directed interaction with the second region.…”

“…We use the anti-symmetric matrix to approximate the whole-brain directed functional connectivity; this method summarizes the directed connection between regions I and j with a single entry which captures its direction and the magnitude. More details about the method are presented elsewhere 15 .…”