Neuroimage Signature in Post-Stroke Pain: A Systematic Review

Central Post-Stroke Pain (CPSP) is a debilitating condition with a significant prevalence in stroke survivors. Set apart by its refractory to treatment neuropathic pain, it appears to arise from lesions in the spino-thalamo-cortical pathways, particularly in the thalamus. Despite advances in neuroimaging techniques, the pathophysiology of CPSP remains poorly understood, with limited diagnostic criteria and therapeutic approaches.

This systematic review aims to identify neuroimaging markers associated with CPSP, establish correlations between brain lesions and CPSP development, and explore the utility of neuroimaging techniques for diagnosis and prognosis. Secondary objectives involve establishing a link between CPSP and secondary brain area lesions and what type of pain patients with CPSP are more likely to experience.

A systematic review was conducted following PRISMA guidelines. Relevant articles were sourced from PubMed, Embase, and Web of Science, using a predefined search strategy targeting CPSP and neuroimaging studies (MRI, PET, SPECT). Studies involving adult CPSP patients with post-stroke neuroimaging were included. Data from 14 studies was analyzed, focusing on lesion locations, neuroimaging findings, and functional connectivity. Risk of bias was assessed using the QUADAS-2 tool.

Key findings highlighted the association of CPSP with lesions in the ventral posterolateral and the ventral medial nuclei of the thalamus, spinothalamic pathway, and cortical areas like the insula and operculum. Functional connectivity studies highlighted the role of disrupted neural networks in pain perception. Limitations in the reviewed studies included small sample sizes, selection bias, and heterogeneity in study designs.

Neuroimaging findings confirm the significance of thalamic and spinothalamic pathway lesions in CPSP pathophysiology. Despite these insights, gaps in research underline the need for larger, multicenter trials to identify reliable biomarkers for diagnosis and therapeutic targets. Advanced neuroimaging combined with machine learning could be the key to understanding and managing CPSP.