Shared imaging biomarkers across Alzheimer’s and Parkinson’s disease

Abstract

AbstractBackgroundAlzheimer's disease (AD) and Parkinson's disease (PD) are the two of the most common neurodegenerative diseases [1]. In previous studies, AD and PD demonstrate overlap in clinical syndromes, including presence of cognitive impairment, dementia in PD, and post‐mortem studies with regional atrophy and amyloid and tau deposition [2][3]. Here, we propose a novel morphometric‐feature based machine learning model to explore shared degenerative patterns in AD and PD on neuroimaging.MethodWe used T1‐weighted (T1w) MRI data from (PPMI) 166 controls/356 de novo PD patients, and (ADNI‐1) 242 controls/199 AD patients. As a baseline model we trained sparse spatially regularized (TV‐L1) logistic regression [6][7] using subcortical shape and cortical thickness [4][5] from T1w MRI. To train the two models jointly, we constrain the optimization with a cross‐disorder (CD) agreement term: the L2 model difference, weighted by the models’ local normalized mutual information (MI). The CD‐MI term allows the models to guide each other’s optimization, dynamically amplifying information sharing where degenerative patterns are most similar and minimizing sharing where they are dissimilar. We evaluate all models’ ROC area‐under‐the‐curve (ROC AUC) using nested cross‐validation and a shuffle‐split bootstrap test to assess model stability. For subcortical regions, we trained models using all regions, as well as individual models for each region (L/R thalamus, putamen, caudate, pallidum, hippocampus, amygdala, accumbens).ResultInformation sharing significantly improved predictive power for cortical thickness‐based, L amygdala, L caudate, and bilateral putamen models (Table 1, Figure 4). Weight maps of AD and PD models (Figures 1a,b,2) show areas of similar degenerative patterns in frontal and parietal association areas, thalamus caudate and putamen, confirmed by the mutual information maps (Figures 1c,3).ConclusionWe designed a novel machine learning model to explore joint imaging biomarkers between Alzheimer's and Parkinson’s disease. Our findings on neuroimaging suggest shared topography of frontal, parietal, temporal, and cingulate changes for AD and PD. This may signify overlap of underlying pathologies with co‐existent amyloid and tau burden in both AD and PD.