Hierarchical cluster analysis of multimodal imaging data identifies brain atrophy and cognitive patterns in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a heterogeneous condition. Cluster analysis based on cortical thickness has been used to define distinct patterns of brain atrophy in PD. However, the potential of other neuroimaging modalities, such as white matter (WM) fractional anisotropy (FA), which has also been demonstrated to be altered in PD, has not been investigated. OBJECTIVE: We aim to characterize PD subtypes using a multimodal clustering approach based on cortical and subcortical gray matter (GM) volumes and FA measures. METHODS: We included T1-weighted and diffusion-weighted MRI data from 62 PD patients and 33 healthy controls. We extracted mean GM volumes from 48 cortical and 17 subcortical regions using FSL-VBM, and the mean FA from 20 WM tracts using Tract-Based Spatial Statistics (TBSS). Hierarchical cluster analysis was performed with the PD sample using Ward's linkage method. Whole-brain voxel-wise intergroup comparisons of VBM and TBSS data were also performed using FSL. Neuropsychological and demographic statistical analyses were conducted using IBM SPSS Statistics 25.0. RESULTS: We identified three PD subtypes, with prominent differences in GM patterns and little WM involvement. One group (n = 15) with widespread cortical and subcortical GM volume and WM FA reductions and pronounced cognitive deficits; a second group (n = 21) with only cortical atrophy limited to frontal and temporal regions and more specific neuropsychological impairment, and a third group (n = 26) without detectable atrophy or cognition impairment. CONCLUSION: Multimodal MRI data allows classifying PD patients into groups according to GM and WM patterns, which in turn are associated with the cognitive profile.

Cortical gray matter progression in idiopathic REM sleep behavior disorder and its relation to cognitive decline.

BACKGROUND: Idiopathic Rapid eye movement sleep behavior disorder (IRBD) is recognized as the prodromal stage of the alpha-Synucleinopathies. Although some studies have addressed the characterization of brain structure in IRBD, little is known about its progression. OBJECTIVE: The present work aims at further characterizing gray matter progression throughout IRBD relative to normal aging and investigating how these changes are associated with cognitive decline. METHODS: Fourteen patients with polysomnography-confirmed IRBD and 18 age-matched healthy controls (HC) underwent neuropsychological, olfactory, motor, and T1-weighted MRI evaluation at baseline and follow-up. We compared the evolution of cortical thickness (CTh), subcortical volumes, smell, motor and cognitive performance in IRBD and HC after a mean of 1.6 years. FreeSurfer was used for CTh and volumetry preprocessing and analyses. The symmetrized percent of change (SPC) of the CTh was correlated with the SPC of motor and neuropsychological performance. RESULTS: IRBD and HC differed significantly in the cortical thinning progression in regions encompassing bilateral superior parietal and precuneus, the right cuneus, the left occipital pole and lateral orbitofrontal gyri (FWE corrected, p < 0.05). The Visual form discrimination test showed worse progression in the IRBD relative to HC, that was associated with gray matter loss in the right superior parietal and the left precuneus. Increasing motor signs in IRBD were related to cortical thinning mainly involving frontal regions, and late-onset IRBD was associated with cortical thinning involving posterior areas (FWE corrected, p < 0.05). Despite finding olfactory identification deficits in IRBD, results did not show decline over the disease course. CONCLUSION: Progression in IRBD patients is characterized by parieto-occipital and orbitofrontal thinning and visuospatial loss. The cognitive decline in IRBD is associated with degeneration in parietal regions.