Machine learning classification and regional differentiation of neuropathologically-confirmed Alzheimer’s disease and comorbid Lewy body disease

Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) co-occur frequently, and growing evidence, including neuropathology, supports synergistic interplay between the diseases. We tested whether a single T1-weighted MRI scan may differentiate neuropathologically confirmed comorbid AD/DLB and AD controls using heterogeneously acquired neuroimaging. We obtained structural neuroimaging, on two groups, AD with and without DLB pathology. Convolutional neural networks are trained across dimensions. We introduce a triple-ensemble strategy consisting of majority voting schemes within a variety of plane permutations. In addition, we conduct voxel-wise statistical analyses. Here we show convolutional neural networks record a classification accuracy of 0.820 and an f1 score of 0.79 in identifying comorbid DLB/AD from AD patients. Prediction accuracy is higher proximal to date of death, while the trained model largely outperforms clinical baseline diagnosis. The slice-level performance varies depending on the sampled brain location, with sensitivity highest in the temporal lobe and specificity highest in the occipital lobe. In DLB/AD, gray matter is relatively preserved though atrophy is observed in the occipital lobe, suggesting that the comorbidity differentially affects brain loss and may accelerate it in the occipital lobe. This study demonstrates how machine learning approaches can address diverse neuroimaging data from clinical sources to differentiate neurodegenerative diseases using a true gold standard of neuropathological confirmation. The frameworks utilized here can be extended to other diseases that are frequently co-occurring and feasibly extend to single scan diagnostic clinical utility of scans already being acquired. Alzheimer’s disease (AD) and lewy body dementia (LBD) are both progressive neurodegenerative diseases that result in declines in memory and other cognitive functions. AD and LBD are known to occur together, however conventional methods that image the brain are unable to accurately show the effect of these diseases. Specialized computational methods may be able to more accurately diagnose these conditions than clinical evaluation. We used brain imaging data that is commonly obtained to see whether we could identify people with suspected AD who also had LBD. Our method identified the places in the brain whether both AD and LBD are likely to occur and was able to better diagnose in patients with confirmed disease at death that usual clinical diagnosis methods. Our method could be used to better identify people with AD and LBD and thus allow earlier appropriate treatment. Park et al. use autopsy-confirmed cases from two large multi-center research cohorts to train deep learning models on routine T1-weighted MRI to identify Lewy body comorbidity in Alzheimer’s disease. The models outperform clinical diagnosis and highlight regional patterns, including occipital changes in comorbid cases.