  Early and accurate diagnosis of Parkinson's Disease (PD) remains challenging.
This study compares deep learning architectures for MRI-based PD
classification, introducing the first three-dimensional (3D) implementation of
Convolutional Kolmogorov-Arnold Networks (ConvKANs), a new approach that
combines convolution layers with adaptive, spline-based activations. We
evaluated Convolutional Neural Networks (CNNs), ConvKANs, and Graph
Convolutional Networks (GCNs) using three open-source datasets; a total of 142
participants (75 with PD and 67 age-matched healthy controls). For 2D analysis,
we extracted 100 axial slices centred on the midbrain from each T1-weighted
scan. For 3D analysis, we used the entire volumetric scans. ConvKANs integrate
learnable B-spline functions with convolutional layers. GCNs represent MRI data
as graphs, theoretically capturing structural relationships that may be
overlooked by traditional approaches. Interpretability visualizations,
including the first ConvKAN spline activation maps, and projections of graph
node embeddings, were depicted. ConvKANs demonstrated high performance across
datasets and dimensionalities, achieving the highest 2D AUROC (0.98) in one
dataset and matching CNN peak 3D performance (1.00). CNN models performed well,
while GCN models improved in 3D analyses, reaching up to 0.97 AUROC. 3D
implementations yielded higher AUROC values compared to 2D counterparts across
all models. ConvKAN implementation shows promise for MRI analysis in PD
classification, particularly in the context of early diagnosis. The improvement
in 3D analyses highlights the value of volumetric data in capturing subtle
PD-related changes. While MRI is not currently used for PD diagnosis, these
findings suggest its potential as a component of a multimodal diagnostic
approach, especially for early detection.
