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Abstract In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identifywhereandhowcomputational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation. 

ABSTRACT From navigating a crowded hallway to skiing down a treacherous hill, humans are constantly making decisions while moving. Insightful past work has provided a glimpse of decision deliberation at the moment of movement onset. Yet it is unknown whether ongoing deliberation can be expressed during movement, following movement onset and prior to any decision. Here we tested the idea that an ongoing deliberation continually influences motor processes—prior to a decision—directing online movements. Over three experiments, we manipulated evidence to influence deliberation during movement. The deliberation process was manipulated by having participants observe evidence in the form of tokens that moved into a left or right target. Supporting our hypothesis we found that lateral hand movements reflected deliberation, prior to a decision. We also found that a deliberation urgency signal, which more heavily weighs later evidence, was fundamental to predicting decisions and explains past movement behaviour in a new light. Our paradigm promotes the expression of ongoing deliberation through movement, providing a powerful new window into understanding the interplay between decision and action.