Background: Within the spectrum of Lewy body disorders (LBD), both Parkinson's disease (PD) and dementia with Lewy bodies (DLB) are characterized by gait and balance disturbances, which become more prominent under dual-task (DT) conditions. The brain substrates underlying DT gait variations, however, remain poorly understood in LBD.
Objective: To investigate the relationship between gray matter volume loss and DT gait variations in LBD.
Methods: Seventy-nine participants including cognitively unimpaired PD, PD with mild cognitive impairment, PD with dementia (PDD), or DLB and 20 cognitively unimpaired controls were examined across a multi-site study. PDD and DLB were grouped together for analyses. Differences in gait speed between single and DT conditions were quantified by dual task cost (DTC). Cortical, subcortical, ventricle, and cerebellum brain volumes were obtained using FreeSurfer. Linear regression models were used to examine the relationship between gray matter volumes and DTC.
Results: Smaller amygdala and total cortical volumes, and larger ventricle volumes were associated with a higher DTC across LBD and cognitively unimpaired controls. No statistically significant interaction between group and brain volumes were found. Adding cognitive and motor covariates or white matter hyperintensity volumes separately to the models did not affect brain volume and DTC associations.
Conclusion: Gray matter volume loss is associated with worse DT gait performance compared to single task gait, across cognitively unimpaired controls through and the LBD spectrum. Impairment in DT gait performance may be driven by age-related cortical neurodegeneration.
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http://dx.doi.org/10.1007/s00415-023-12052-y | DOI Listing |
F-Florbetaben (FBB) uptake in the supratentorial cortex is indicative of amyloid positivity. Due to PET's low spatial resolution, image noise, and spill-over of signal from adjacent white-matter into gray-matter, there are inconsistencies in ratings among trained readers. A set of 264 F-Florbetaben (amyloid) PET/MRI exams were reconstructed using conventional ordered subset expectation maximization (OSEM) method and MR-guided block sequential regularized expectation maximization (MRgBSREM) method.
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Department Radiology, Stanford University, Stanford, CA.
Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners.
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Advanced Neuroimaging Center, Institute for Quantum Medical Science National Institutes for Quantum Science and Technology Chiba Japan.
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Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, 85013, USA. Electronic address:
Parkinson's disease (PD) is a progressive neurodegenerative disorder that is characterized by motor symptoms such as tremors, rigidity, and bradykinesia. Magnetic resonance imaging (MRI) offers a non-invasive means to study PD and its progression. This study utilized the unilateral 6-hydroxydopamine (6-OHDA) rat model of parkinsonism to assess whether white matter microstructural integrity measured using advanced free-water diffusion tensor imaging metrics (fw-DTI) and gray matter density using voxel-based morphometry (VBM) can serve as imaging biomarkers of pathological changes following nigrostriatal denervation.
View Article and Find Full Text PDFNeuroimage
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Department of Medical Imaging, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China. Electronic address:
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