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In this work, we propose a diffusion MRI protocol for mining Parkinson's disease diffusion MRI datasets and recover robust disease-specific biomarkers. Using advanced high angular resolution diffusion imaging (HARDI) crossing fiber modeling and tractography robust to partial volume effects, we automatically dissected 50 white matter (WM) fascicles. These fascicles connect deep nuclei (thalamus, putamen, pallidum) to different cortical functional areas (associative, motor, sensorimotor, limbic), basal forebrain and substantia nigra. Then, among these 50 candidate WM fascicles, only the ones that passed a test-retest reproducibility procedure qualified for further tractometry analysis. Leveraging the unique 2-timepoints test-retest Parkinson's Progression Markers Initiative (PPMI) dataset of over 600 subjects, we found statistically significant differences in tract profiles along the subcortico-cortical pathways between Parkinson's disease patients and healthy controls. In particular, significant increases in FA, apparent fiber density, tract-density and generalized FA were detected in some locations of the nigro-subthalamo-putaminal-thalamo-cortical pathway. This connection is one of the major motor circuits balancing the coordination of motor output. Detailed and quantifiable knowledge on WM fascicles in these areas is thus essential to improve the quality and outcome of Deep Brain Stimulation, and to target new WM locations for investigation.
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http://dx.doi.org/10.1016/j.nicl.2017.07.020 | DOI Listing |
J Educ Health Promot
October 2024
Adani Institute of Infrastructure Engineering, Ahmedabad, Gujarat, India.
Parkinson's disease (PD) is a neurodegenerative brain disorder that causes symptoms such as tremors, sleeplessness, behavioral problems, sensory abnormalities, and impaired mobility, according to the World Health Organization (WHO). Artificial intelligence, machine learning (ML), and deep learning (DL) have been used in recent studies (2015-2023) to improve PD diagnosis by categorizing patients and healthy controls based on similar clinical presentations. This study investigates several datasets, modalities, and data preprocessing techniques from the collected data.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Preclinical Department, Faculty of Medicine, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania.
This study investigates disruptions in functional brain networks in Parkinson's Disease (PD), using advanced modeling and machine learning. Functional networks were constructed using the Nonlinear Autoregressive Distributed Lag (NARDL) model, which captures nonlinear and asymmetric dependencies between regions of interest (ROIs). Key network metrics and information-theoretic measures were extracted to classify PD patients and healthy controls (HC), using deep learning models, with explainability methods employed to identify influential features.
View Article and Find Full Text PDFObjective: To investigate hippocampal volume changes in moderate to severe traumatic brain injury (TBI) patients compared to healthy controls and assess their association with post-traumatic epilepsy (PTE), focusing on age-related effects.
Methods: Imaging and demographic data for TBI patients were obtained from the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) database; healthy controls matched by age and sex were sourced from Alzheimer's Disease Neuroimaging Initiative (ADNI), the National Institute of Mental Health (NIMH) Intramural Healthy Volunteer Dataset, the Parkinson's Progression Markers Initiative (PPMI), and the Autism Brain Imaging Data Exchange (ABIDE). MRI images for TBI subjects were obtained within 14-32 days post-injury.
NPJ Parkinsons Dis
December 2024
Microsoft Research, Shanghai, China.
Parkinson's disease (PD) is a complex neurological disorder characterized by dopaminergic neuron degeneration, leading to diverse motor and non-motor impairments. This variability complicates accurate progression modelling and early-stage prediction. Traditional classification methods based on clinical symptoms are often limited by disease heterogeneity.
View Article and Find Full Text PDFSci Rep
November 2024
Department of Management Information Systems, Dong-A University, 225, Gudeok-ro, Seo-gu, Busan, 49236, Republic of Korea.
The application of deep learning techniques for the analysis of neuroimaging has been increasing recently. The 3D Convolutional Neural Network (CNN) technology, which is commonly adopted to encode volumetric information, requires a large number of datasets. However, due to the nature of the medical domain, there are limitations in the number of data available.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!