Abstract
The clinicopathological correlations between aspects of cognition, disease severity and imaging in Parkinson's Disease (PD) have been unclear. We studied cognitive profiles, demographics, and functional connectivity patterns derived from resting-state fMRI data (rsFC) in 31 PD subjects from the Parkinson's Progression Markers Initiative (PPMI) database. We also examined rsFC from 19 healthy subjects (HS) from the Pacific Parkinson's Research Centre. Graph theoretical measures were used to summarize the rsFC patterns. Canonical correlation analysis (CCA) was used to relate separate cognitive profiles in PD that were associated with disease severity and demographic measures as well as rsFC network measures. The CCA model relating cognition to demographics suggested female gender and education supported cognitive function in PD, age and depression scores were anti-correlated with overall cognition, and UPDRS had little influence on cognition. Alone, rsFC global network measures did not significantly differ between PD and controls, yet some nodal network measures, such as network segregation, were distinguishable between PD and HS in cortical “hub” regions. The CCA model relating cognition to rsFC global network values, which was not related to the other CCA model relating cognition to demographic information, suggested modularity, rich club coefficient, and transitivity was also broadly related to cognition in PD. Our results suggest that education, aging, comorbidity, and gender impact cognition more than overall disease severity in PD. Cortical “hub” regions are vulnerable in PD, and impairments of processing speed, attention, scanning abilities, and executive skills are related to enhanced functional segregation seen in PD.
Highlights
Parkinson’s disease (PD) is a neurodegenerative movement disorder resulting in motor symptoms of tremor, rigidity, bradykinesia, and postural instability
We aimed to investigate [1] the cognitive profile associated with demographics in PD, [2] the functional connectivity differences between PD and healthy subjects measured by graph theory analysis, and [3] the cognitive profiles associated with altered graphical measures from resting-state functional connectivity (rsFC) in PD
Canonical correlation analysis (CCA) revealed that demographics/clinical data were intercorrelated with cognitive scores (Figure 2)
Summary
Parkinson’s disease (PD) is a neurodegenerative movement disorder resulting in motor symptoms of tremor, rigidity, bradykinesia, and postural instability. In addition to motor symptoms, nonmotor deficits, especially cognitive impairments, have a major impact on quality of life in patients with PD. Patients with PD show cognitive deficits in several common domains such as attention, memory, visuospatial, and executive functions [1, 2]. Non-tremor subtype, and higher Unified Parkinson’s Disease Rating Scale (UPDRS) scores are risk factors for the rapid overall cognitive decline [3], and information retrieval and visuospatial abilities can predict global cognitive impairments in PD [3]. Performance on different cognitive tests often correlate with each other, and it is likely that novel analyses such as machine-learning approaches will be more suitable in establishing cognitive patterns in subjects with neurological disorders
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
