AI-assisted MRI segmentation analysis of brain region volume alterations in Parkinson's disease

By employing deep learning-based automatic whole-brain region segmentation technology, we aim to investigate the cross-sectional associations between regional brain volumes and disease duration in patients with Parkinson's disease (PD).

A retrospective study design was implemented on 83 patients diagnosed with idiopathic PD who had complete clinical and imaging data. Cranial magnetic resonance images (MRI) were imported into the uAI platform for automated regional segmentation of brain tissue. Volumetric data from five major brain regions and 80 subregions were extracted to explore their potential associations with disease progression in PD patients. Statistical analysis was conducted using a multiple linear regression model within the framework of linear regression analysis, with statistical significance defined as p < 0.05.

Cross-sectional analysis revealed that in PD patients, volume ratios of multiple brain regions-including the bilateral precentral gyrus, right medial frontal gyrus, bilateral postcentral gyrus, bilateral superior and inferior parietal lobules, bilateral precuneus, right cuneus, right lingual gyrus, bilateral lateral occipital gyrus, and right globus pallidus-were negatively associated with disease duration (p < 0.05). In contrast, the right hippocampus, right inferior temporal gyrus, and left superior temporal gyrus showed positive correlations (p < 0.05). The combined volume ratios of these brain regions also decreased with longer disease duration (p < 0.05). Furthermore, absolute volume differences in the hippocampus, fusiform gyrus, isthmus of the cingulate gyrus, and cerebellar white matter increased as the disease progressed (p < 0.05).

In PD patients, volume ratios and absolute volume differences in specific brain subregions associated with lateralized intracranial changes may serve as potential biomarkers for assessing brain tissue alterations during disease progression.