Structural and Statistical Knowledge-Enhanced Attention Network for early Parkinson's disease diagnosis

Early diagnosis of Parkinson's disease (PD) is crucial for prompt treatment and improved clinical outcomes, but accurate early diagnosis remains challenging. Deep learning (DL) methods have demonstrated significant potential for diagnosing neurological diseases using magnetic resonance imaging (MRI). However, most existing models use generic pattern recognition approaches without incorporating the distinctive characteristics of neuroimaging data and domain-specific knowledge. These limitations restrict both diagnostic accuracy (ACC) and clinical interpretability. This diagnostic ACC study aims to develop a specialized DL framework that effectively integrates neuroimaging domain knowledge to enhance both diagnostic performance and clinical interpretability for early PD detection.

In this study, we propose a Structural and Statistical Knowledge-Enhanced Attention Network (SSKEA-Net) for early PD diagnosis, consisting of two innovative cascaded modules: the Gray-White Interactive Modulation (GWIM) module, which utilizes a structurally specific gray-white matter separation mechanism to modulate channel-wise attention and further enhances tissue-specific features; and the Statistical Prior-Guided Attention (SPGA) module, which incorporates voxel-level statistically significant maps as spatial attention weights to guide feature extraction toward disease-related brain regions. By incorporating the domain knowledge-guided explicit feature enhancement strategy, SSKEA-Net effectively reduces feature interference between gray and white matter, significantly enhancing both model performance and interpretability. We evaluated the diagnostic performance of model using diffusion tensor imaging as input on a rigorously matched early-stage PD dataset with strictly controlled age and gender distributions, and employed activation heatmaps to visualize the interpretability of model.

SSKEA-Net achieved superior diagnostic performance in five-fold cross-validation, with an ACC of 0.8798±0.0158, positive predictive value of 0.9185±0.0138, true positive rate of 0.8067±0.0267, specificity of 0.9406±0.0091, and an area under the curve of 0.9301±0.0146, outperforming both classic three-dimensional DL models and the current top-performing neuroimaging model, Simple Fully Convolutional Network. Compared to the baseline model, the cumulative activation heatmaps demonstrated that SSKEA-Net achieved precise anatomical localization, with activation specifically concentrated on the substantia nigra, putamen, midbrain, and corpus callosum, which correspond to clinically relevant brain structures associated with early PD, confirming the enhanced interpretability and clinical relevance.

The proposed SSKEA-Net effectively combines domain-specific structural and statistical knowledge with DL, achieving both high diagnostic ACC and clinical interpretability for early PD detection. By incorporating neuroimaging priors into the network architecture, this approach provides a valuable framework for developing more reliable and interpretable artificial intelligence systems in clinical neuroimaging applications.