Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data.

ABSTRACT: Parkinson's disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age

SUBMITTER: Camacho M 

PROVIDER: S-EPMC10897162 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature