Mechanism-Based Prediction of Chronic Post-Stroke Pain Using Advanced Neuroimaging Techniques

Central post-stroke pain (CPSP) is a chronic condition which develops in 8% to 18% of stroke patients (Andersen, et al., 1995; Liampass, et al., 2020). CPSP is heterogenous in onset, presentation, and intensity, and is, unfortunately, difficult to treat. Pharmacological trials have found mixed results: antidepressants, anticonvulsants, and anaesthetics have shown mild to moderate improvements but are limited by small effect sizes and poor reproducibility (Oh & Seo, 2015). Pharmacological treatments also have troublesome side-effects which negatively impact on patient quality of life. Nonpharmacological therapies such as Deep Brain Stimulation and repetitive Transcranial Magnetic Stimulation (rTMS) to primary motor cortex (M1) can improve CPSP but there is large inter-individual variability in pain relief. Some patients experience no benefit but others attain almost complete pain relief. The cause of this variability is unknown but may relate to generic targeting of M1. Treatments targeting brain regions that are mechanistically relevant to pain generation may represent a better option. However, this will require a detailed understanding of CPSP's pathophysiology.
This PhD builds on the novel finding of primary somatosensory cortex's cytoarchitectonic areas 3A, 3B and 1 involvement in pain processing, and aims to identify novel targets to effectively treat CPSP (Whitsel, et al., 2019). 3A is hypothesised to contribute to abnormal pain processing, leading to chronic pain, when it is overactive. In CPSP this could happen when projections to 3A become hyperexcitable; when 3B/1 areas are damaged and no longer inhibit 3A through intracortical connections or because of stroke-induced alterations in glia and microglia cell function that cause abnormal network level excitability. As the location of 3A is directly adjacent to M1, rTMS stimulation of M1 could inadvertently target 3A, which could explain some therapeutic responses to rTMS. Interventions which more effectively inhibit 3A could lead to long-term chronic pain reduction in CPSP.
There is currently a gap in literature examining the role of 3A in the development of CPSP. This project aims to determine whether disruption of brain circuits involving 3A are important in causing CPSP and also whether 3A represents a valid target for treating and preventing CPSP. The specific aims are to: (1) identify dysfunctional brain circuits that cause CPSP, (2) use lesion mapping and functionnectonomic analysis to find biomarkers for predicting the transition to CPSP and treatment response to rTMS, which will contribute to precision medicine approach to treatment, and (3) build a machine learning prediction algorithm to predict CPSP development and treatment response.
This project and studentship is organised in stages: (1) training in R Project, Python and MatLab to undertake data-driven analyses of MRI and fMRI scans, as well as learning to build a machine learning algorithm to predict stroke patient outcomes in relation to CPSP, (2) conducting a systematic analysis of current CPSP literature, (3) data collection of CPSP patients, non-CPSP stroke patients, and healthy controls, (4) data-driven analysis of lesion mapping as it relates to development and outcomes of CPSP, (5) development of machine learning based biomarkers predicting development and outcomes of CPSP, (6) hypothesis-driven investigation of 3A afferents role in CPSP, (7) experimental rTMS targeting of 3A area to alleviate pain for stroke patients.
Participants will be recruited through the local NHS Trusts (Aintree University Hospital and Royal Liverpool University Hospital) and potentially through other Trusts that are able to participate. As MRI or CT scanning is standard practice in stroke patients, some data will be used from patients' records.