Data-driven techniques for translational neuroscience and personalized neuro-health

Authors:Vishal Subedi, Shashipraba N. K. Rajakaruna, Pratyusha Sarkar, Subhankar Chattoraj, Anjali Khasa, Siddhartha Nandy, Hamza Farooq, Animikh Biswas, Sanjay Chaudhuri, Asim K. Dey, Karuna Joshi, Christophe Lenglet, Ansu Chatterjee

Abstract:Neurodegenexrative diseases such as Alzheimer's disease and Parkinson's disease are diagnosed most reliably only after substantial, often irreversible, neuronal loss has already occurred, creating an urgent need for quantitative tools that can detect subtle, early, and individual-specific brain changes from neuroimaging data. This review surveys a broad and rapidly evolving toolkit of data-driven techniques for translational neuroscience and personalized neuro-health, organized around four complementary methodological pillars. Throughout, we emphasize how these methodologically diverse approaches converge on a common translational goal: personalized, mechanistically grounded, and clinically actionable models of individual brain health, and we close by discussing the principal open statistical, computational, and clinical challenges that remain.

Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Applications (stat.AP); Machine Learning (stat.ML)

arXiv:2608.13749 [q-bio.NC]

(or arXiv:2608.13749v1 [q-bio.NC] for this version)

https://doi.org/10.48550/arXiv.2608.13749 arXiv-issued DOI via DataCite (pending registration)