Multimodal Brain Data and Deep Learning for Clinical Prediction in Neurology

Abstract

Neurological disorders unfold across distributed structural, functional, physiological, cognitive and behavioral systems, making single-modality prediction attractive yet biologically incomplete. Against this backdrop, deep learning has enabled models that preserve modality-specific information while learning relationships among anatomy, metabolism, connectivity, electrophysiology, clinical features and genetics. Evidence has accumulated most coherently in Alzheimer’s disease, where multimodal MRI, PET, cognition and demographic data improve discrimination, staging and prognostic estimation across heterogeneous clinical states. Attention-based and hierarchical architectures suggest that these gains arise from biologically meaningful fusion rather than from simply adding variables. Related advances extend the approach to autism, multiple sclerosis, depression, epilepsy, stroke, glioma and sleep disorders, while wearable physiological signals broaden prediction beyond hospital-based imaging. Interpretable probability maps, tract-specific representations and latent markers of apparently normal tissue further shift prediction from opaque classification toward patient-level evidence synthesis with potential relevance for risk stratification and treatment-response assessment. Yet clinical value remains conditional rather than guaranteed. Much of the literature relies on small, internally tested cohorts, inconsistent metrics and limited external or prospective validation, while scanner differences, missing modalities, domain shift and systematic bias can undermine apparently strong performance. Robustness therefore requires biologically grounded fusion, calibrated uncertainty, fairness assessment, explicit correction of biases such as brain-age effects and validation across institutions and workflows. The next step is not merely larger models, but prospective implementation studies that establish when multimodal predictions change care, how clinicians can interpret them and whether they improve outcomes.