Deep learning reveals pathology-confirmed neuroimaging signatures in Alzheimer’s, vascular and Lewy body dementias
Authors: Wang Di , Honnorat Nicolas , Toledo Jon B , Li Karl , Charisis Sokratis , Rashid Tanweer , Benet Nirmala Anoop , Brandigampala Sachintha Ransara , Mojtabai Mariam , Seshadri Sudha , Habes Mohamad 
Published: December 2024

Concurrent neurodegenerative and vascular pathologies pose a diagnostic challenge in the clinical setting, with histopathology remaining the definitive modality for dementia-type diagnosis. To address this clinical challenge, we introduce a neuropathology-based, data-driven, multi-label deep learning framework to identify and quantify in-vivo biomarkers for Alzheimer's disease (AD), vascular dementia (VD), and Lewy body dementia (LBD) using antemortem T1-weighted MRI scans of 423 demented and 361 control participants from NACC and ADNI datasets. Based on the best-performing deep learning model, explainable heatmaps are extracted to visualize disease patterns, and the novel Deep Signature of Pathology Atrophy REcognition (DeepSPARE) indices are developed, where a higher DeepSPARE score indicates more brain alterations associated with that specific pathology.

A substantial discrepancy in clinical and neuropathology diagnosis was observed in the demented patients: 71% of them had more than one pathology, but 67% of them were clinically diagnosed as AD only. Based on these neuropathology diagnoses and leveraging cross-validation principles, the deep learning model achieved the best performance with a balanced accuracy of 0.844, 0.839, and 0.623 for AD, VD, and LBD, respectively, and was used to generate the explainable deep-learning heatmaps and DeepSPARE indices. The explainable deep-learning heatmaps revealed distinct neuroimaging brain alteration patterns for each pathology: the AD heatmap highlighted bilateral hippocampal regions, the VD heatmap emphasized white matter regions, and the LBD heatmap exposed occipital alterations. The DeepSPARE indices were validated by examining their associations with cognitive testing, neuropathological, and neuroimaging measures using linear mixed-effects models. The DeepSPARE-AD index was associated with MMSE, Trail B, memory, PFDR-adjustedhippocampal volume, Braak stages, CERAD scores, and Thal phases (PFDR-adjusted &lt; 0.05). The DeepSPARE-VD index was associated with white matter hyperintensity volume and cerebral amyloid angiopathy (PFDR-adjusted &lt; 0.001). The DeepSPARE-LBD index was associated with Lewy body stages (PFDR-adjusted &lt; 0.05). The findings were replicated in an out-of-sample ADNI dataset by testing associations with cognitive, imaging, plasma, and CSF measures. CSF and plasma pTau181 were significantly associated with DeepSPARE-AD in the AD/MCIΑβ+ group (PFDR-adjusted &lt; 0.001), and CSF α-synuclein was associated solely with DeepSPARE-LBD (PFDR-adjusted = 0.036).

Overall, these findings demonstrate the advantages of our innovative deep-learning framework in detecting antemortem neuroimaging signatures linked to different pathologies. The newly deep learning-derived DeepSPARE indices are precise, pathology-sensitive, and single-valued noninvasive neuroimaging metrics, bridging the traditional widely available in-vivo T1 imaging with histopathology.