Decreased subregional specificity of the putamen in Parkinson's Disease revealed by dynamic connectivity-derived parcellation

• Published in: NeuroImage clinical Vol. 20; pp. 1163 - 1175
• Main Authors: Liu, Aiping, Lin, Sue-Jin, Mi, Taomian, Chen, Xun, Chan, Piu, Wang, Z. Jane, McKeown, Martin J.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01-01-2018; Elsevier
• Subjects: Adult; Aged; Brain Mapping - methods; Cerebellum - physiopathology; Corpus Striatum - physiopathology; Dynamic connectivity; Dynamic parcellation; Female; fMRI; Frontal Lobe - physiopathology; Humans; Magnetic Resonance Imaging - methods; Male; Middle Aged; Neural Pathways - physiopathology; Parkinson Disease - physiopathology; Parkinson's Disease; Putamen; Putamen - physiopathology; Regular; Temporal homogeneous subunits; fMRI; Parkinson's Disease; Dynamic connectivity; Putamen; Temporal homogeneous subunits; Dynamic parcellation
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2018.10.022

Parkinson's Disease (PD) is associated with decreased ability to perform habitual tasks, relying instead on goal-directed behaviour subserved by different cortical/subcortical circuits, including parts of the putamen. We explored the functional subunits in the putamen in PD using novel dynamic connectivity features derived from resting state fMRI recorded from thirty PD subjects and twenty-eight age-matched healthy controls (HC). Dynamic functional segmentation of the putamina was obtained by determining the correlation between each voxel in each putamen along a moving window and applying a joint temporal clustering algorithm to establish cluster membership of each voxel at each window. Contiguous voxels that had consistent cluster membership across all windows were then considered to be part of a homogeneous functional subunit. As PD subjects robustly had two homogenous clusters in the putamina, we also segmented the putamina in HC into two dynamic clusters for a fair comparison. We then estimated the dynamic connectivity using sliding windowed correlation between the mean signal from the identified homogenous subunits and 56 other predefined cortical and subcortical ROIs. Specifically, the mean dynamic connectivity strength and connectivity deviation were then compared to evaluate subregional differences.
HC subjects had significant differences in mean dynamic connectivity and connectivity deviation between the two putaminal subunits. The posterior subunit connected strongly to sensorimotor areas, the cerebellum, as well as the middle frontal gyrus. The anterior subunit had strong mean dynamic connectivity to the nucleus accumbens, hippocampus, amygdala, caudate and cingulate. In contrast, PD subjects had fewer differences in mean dynamic connectivity between subunits, indicating a degradation of subregional specificity. Overall UPDRS III and MoCA scores could be predicted using mean dynamic connectivity strength and connectivity deviation. Side of onset of the disease was also jointly related with functional connectivity features.
Our results suggest a robust loss of specificity of mean dynamic connectivity and connectivity deviation in putaminal subunits in PD that is sensitive to disease severity. In addition, altered mean dynamic connectivity and connectivity deviation features in PD suggest that looking at connectivity dynamics offers an additional dimension for assessment of neurodegenerative disorders.
•Dynamic connectivity features are utilized for functional subunits parcellation by a joint temporal clustering method.•Temporal homogenous subregions in the putamen and their connectivity patterns are investigated in the HC and PD groups.•The decreased subregional specificity in the putamen is found in the PD group.•The subregional connectivity features predict the disease severity in PD.