Spatially Adaptive Morphometric Knowledge Transfer Across Neurodegenerative Diseases

We present a method to simultaneously learn several linear discriminative models with explicit information sharing. We use TV-Ll-regularized Logistic Regression in conjunction with a Tikhonov regularization term expressing shared information across disorders. The weighting of the crossdisorder term is spatially adapted based on the local mutual information between linear models. We apply the model to mesh-based morphometric features from 14 subcortical structures in Parkinson's and Alzheimer's disease datasets, PPMI and ADNI. To assess the overall improvement in model performance with cross-disorder information sharing over the baseline TV-LI model, we sample out-of-fold ROC AUC scores using a shuffle-split procedure. Beyond improved prediction, the procedure can be used to formally test for the presence of shared morphometric signatures across diseases in specific regions of interest. Significantly higher ROC AUC scores were found for Parkinson's prediction accuracy when regularized with the Alzheimer's model based on putamen and caudate morphometry.