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IntroductionParkinson's disease (PD) is a systems-level disorder, implicating basal ganglia-thalamo-cortical (BTC) and cerebello-thalamo-cortical (CTC) networks. While regional atrophy has been reported, network-wide volumetric profiles and their relevance for subtype classification and symptom association remain underexplored.MethodsWe acquired T1-weighted MRI and quantitative susceptibility mapping (QSM) from 40 PD patients and 21 healthy control participants (HC). Volumes were extracted from 19 regions of interest (ROI) within the BTC and CTC networks using a multimodal pipeline. We assessed asymmetry, group differences, and symptom associations using regression models, and applied ridge regression models for PD vs. HC and motor subtype classification.ResultsNetwork-level ROI volumes successfully classified PD vs. HC and PD motor subtypes, with the highest optimistic AUC of 0.88 for PD vs. HC (mean AUC of 0.63) and 0.95 for PD-TD vs. PD-PIGD (mean AUC reached 0.68). The thalamic nuclei and cerebellar lobules I-V, VIIIa, X were identified as key features. Atrophy in the dentate nucleus (DN), substantia nigra-subthalamic complex (SN-STN), and M1 predicted PD. Tremor severity correlated with the ventral lateral posterior thalamus (VLp), VIIb, and SN-STN volumes; bradykinesia severity with the thalamus; and postural instability and gait disturbance (PIGD) with lobule IV. No significant group-level differences for single volumes were found.ConclusionMultiregional volumetric analysis within the BTC and CTC motor networks uncovered group differences between PD and HC that were not apparent when examining single ROIs alone. These findings highlight that PD-related alterations manifest as distributed volumetric patterns across interconnected motor circuits, supporting their role as imaging biomarkers.
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