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.

Functional connectivity in obesity during reward processing.

Obesity is a health problem that has become a major focus of attention in recent years. There is growing evidence of an association between obesity and differences in reward processing. However, it is not known at present whether these differences are linked exclusively to food, or whether they can be detected in other rewarding stimuli. We compared responses to food, rewarding non-food and neutral pictures in 18 young adults with obesity and 19 normal-weight subjects using independent component analysis. Both groups modulated task-related activity in a plausible way. However, in response to both food and non-food rewarding stimuli, participants with obesity showed weaker connectivity in a network involving activation of frontal and occipital areas and deactivation of the posterior part of the default mode network. In addition, obesity was related with weaker activation of the default mode network and deactivation of frontal and occipital areas while viewing neutral stimuli. Together, our findings suggest that obesity is related to a different allocation of cognitive resources in a fronto-occipital network and in the default mode network.

White matter integrity related to functional working memory networks in traumatic brain injury.

OBJECTIVE: This study explores the functional and structural patterns of connectivity underlying working memory impairment after severe traumatic axonal injury. METHODS: We performed an fMRI n-back task and acquired diffusion tensor images (DTI) in a group of 19 chronic-stage patients with severe traumatic brain injury (TBI) and evidence of traumatic axonal injury and 19 matched healthy controls. We performed image analyses with FSL software and fMRI data were analyzed using probabilistic independent component analysis. Fractional anisotropy (FA) maps from DTI images were analyzed with FMRIB's Diffusion Toolbox. RESULTS: We identified working memory and default mode networks. Global FA values correlated with both networks and FA whole-brain analysis revealed correlations in several tracts associated with the functional activation. Furthermore, working memory performance in the patient group correlated with the functional activation patterns and with the FA values of the associative fasciculi. CONCLUSION: Combining structural and functional neuroimaging data, we were able to describe structural white matter changes related to functional network alterations and to lower performance in working memory in chronic TBI.