Volume Alterations in Thalamic Subnuclei in Parkinson's Disease Dementia and Machine Learning-Based Prediction of Diagnosis and Severity.
Authors
Affiliations (3)
Affiliations (3)
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Neurostimulation, Beijing, China.
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Abstract
Cognitive decline in Parkinson's disease (PD) is associated with pathological alterations within the thalamus. Nevertheless, volumetric changes in the specific subnuclei of the thalamus in PD patients with dementia (PD-D) remain inadequately characterized. Furthermore, the clinical challenges of diagnosing PD-D at an individual level and forecasting the trajectory of cognitive decline persist. This study acquired structural magnetic resonance imaging (MRI) data from 60 healthy normal controls (NC), 63 PD patients without dementia (PD-nD), and 57 PD-D patients. The volumes of 25 thalamic subnuclei were quantified using FreeSurfer and a novel thalamic segmentation algorithm. Subsequently, individual PD-D diagnosis and severity prediction of cognitive impairment were performed using support vector machines (SVMs). Our findings demonstrated atrophy in seven out of 25 left and two out of 25 right thalamic subnuclei in PD-D patients relative to PD-nD patients. When compared to NC subjects, the PD-D group exhibited volume reductions in two left and one right subnuclei, alongside enlargement in several others. Within the PD cohort, the volumes of four left thalamic subnuclei showed a negative correlation with cognitive impairment severity. Machine learning models achieved high accuracy in differentiating PD-nD from NC (89.19%), PD-D from NC (94.29%), and PD-D from PD-nD (83.33%). Moreover, the prediction of Mini-Mental State Examination (MMSE) scores yielded a Pearson correlation coefficient of 0.7568. Specific thalamic subnuclei undergo atrophy in PD-D, and these morphological changes are linked to cognitive deficit severity. Leveraging these features with machine learning enables accurate individual diagnosis and severity prediction.