A longitudinal evaluation of personalized intrinsic network topography and cognitive decline in Parkinson’s disease

Abstract

Resting state functional MRI (R-fMRI) offers insight into how synchrony within and between brain networks is altered in disease states. Individual and disease-related variability in intrinsic connectivity networks may influence our interpretation of R-fMRI data. We used a personalized approach designed to account for individual variation in the spatial location of correlation maxima to evaluate R-fMRI differences between Parkinson's disease (PD) patients who showed cognitive decline, those who remained cognitively stable, and cognitively stable controls. We compared fMRI data from these participant groups, studied at baseline and 18 months later, using both Network-based Statistics (NBS) and calculations of mean inter- and intra-network connectivity within pre-defined functional networks. The NBS analysis showed that PD participants who remained cognitively stable showed exclusively (at baseline) or predominantly (at follow-up) increased intra-network connectivity, whereas decliners showed exclusively reduced intra-network and inter- (ventral attention and default mode) connectivity, in comparison to the control group. Evaluation of mean connectivity between all ROIs within a priori networks showed that decliners had consistently reduced inter-network connectivity for ventral attention, somatomotor, visual, and striatal networks, and reduced intra-network connectivity for ventral attention network to striatum and cerebellum. These findings suggest that specific functional connectivity covariance patterns differentiate PD cognitive subtypes and may predict cognitive decline. Further, increased intra and internetwork synchrony may support cognitive function in the face of PD-related network disruptions.