Neurocomputational models of stroke: a systematic review and perspectives for rehabilitation

 What a stupid word 'neurocomputational'!   You COMPLETELY FUCKING FAILED  at even trying for 100% recovery protocols! You also failed at trying to make this research sound important.

Neurocomputational models of stroke: a systematic review and perspectives for rehabilitation

• Anna Hoyle,
• Leen Jabban,
• Adrian R. Rivadulla,
• Balázs Nyírő,
• Eamonn O’Neill,
• Benjamin Metcalfe &
• Damien Coyle 

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Abstract

Background

Stroke recovery depends on complex, multi-scale neural mechanisms that remain poorly understood despite major advances in neuroimaging and rehabilitation science. Computational modelling has begun to bridge the gap between descriptive observations from neuroimaging and behavioural studies and mechanistic understanding by simulating how lesions disrupt neural dynamics and how the brain reorganises to restore function.

Objective

To systematically identify and analyse existing computational models of stroke across different scales; to examine which aspects of stroke-related impairment and recovery have been modelled, how different model types are used, and how stroke effects are incorporated; and to evaluate their current contributions, strengths, and limitations for understanding stroke and informing rehabilitation research.

Methods

A systematic search following PRISMA guidelines was conducted across PubMed, Embase, IEEE Xplore and Web of Science to identify peer-reviewed studies computationally modelling neural aspects of stroke. A structured scoring framework assessed each study across four dimensions: biological plausibility, empirical validation, data integration and personalisation. Studies were thematically grouped to identify methodological trends, limitations and opportunities for future model development.

Results

From 4749 studies screened, 36 met the inclusion criteria. The included models addressed a range of stroke-related processes, including cortical plasticity, lesion effects, functional recovery, pathoelectrophysiology and informing rehabilitation. Model types were used differently across these aims: neural network models were most common in studies of cortical plasticity, lesion effects, and functional recovery, whereas neural mass and whole-brain models were more often used for pathoelectrophysiology and rehabilitation-related questions. Stroke effects were incorporated in diverse ways, ranging from abstract lesioning to patient-specific neuroimaging-based approaches. Although some studies incorporated patient-specific brain imaging data (9/36) and empirical validation (23/36), many remained largely theoretical.

Conclusions

While current models capture different key aspects related to stroke, these phenomena are typically investigated in isolation, with little integration either functionally or across spatial scales. The review suggests that bringing together strengths that are currently distributed across different modelling approaches, such as multi-scale structure, patient-specific modelling, empirical validation and biologically plausible plasticity, could help advance the field.