Predicting categorical and continuous outcomes of subjects on the Alzheimer's Disease spectrum using a single MRI without PET, cognitive or fluid biomarkers

Modern artificial intelligence (AI) and deep learning (DL) techniques have been remarkably successful in predicting Alzheimer's Disease diagnosis and conversion to dementia. However, more continuous outcome measures such as cognitive assessment are needed for richer diagnosis, prognosis, disease trajectory tracking and cohort enrichment in clinical trials. Currently, subjective, time-consuming and operator-sensitive neurocognitive batteries remain the only viable methods of assessing cognition. Very few successful DL studies exist for predicting continuous measures like cognition; those rare ones typically require multiple expensive molecular neuroimaging modalities, fluid biomarkers and multiple visits. Given that MRI is the most prevalent and clinically available imaging modality, its use for quantifying progression and predicting cognition is of great interest. Yet, MRI alone does not capture clinical heterogeneity effectively and has not proven useful in modern DL models of AD progression. Here we propose a novel multitask DL strategy leveraging both domain knowledge and large pretrained models. We experiment with transfer learning using pretrained models in imaging like ResNet50; and customizing domain knowledge-informed loss functions and image-derived latent representations. Our objective is to predict cognitive scores using only baseline MRI and demographics, bypassing the need for longitudinal or multimodal data, neurocognitive assessment or specialized neuroimaging analysis skills -- a more practical goal that can be accomplished in most community clinic settings. Specifically, we show that the latent representations of the patient's MRI tuned to the tissue segmentation task are powerful regularization attributes and excellent input features for the cognition prediction task. This knowledge-based multitask implementation of DL vastly outperforms all existing methods including straightforward transfer learning on large pre-trained models, producing high-quality segmentation maps, accurate diagnosis, and both current and future cognitive scores, using a single MRI and demographic data readily available at baseline. This study has deep implications in early diagnosis, prognosis, and clinical trial design.