709: Predicting Analgesic Response to Motor Cortex Stimulation Based on Pre-Therapy Connectivity Measures

Topic: Experimental (Non-clinical) Human Studies
Subtopic: Non -specific to any diagnosis, Neuromodulation (e.g. rTMS, tDCS, FUS, DBS, biofeedback)

Background and Aims: Repetitive transcranial magnetic stimulation (rTMS) to primary motor cortex (M1) has been used to treat chronic neuropathic pain and fibromyalgia [1,2]. However, fewer than 50% of patients experience pain relief. Factors determining why a chronic pain patient respond to rTMS are not fully understood. Pre-treatment electroencephalographic combined with TMS (TMS-EEG) can be used to assess TMS-evoked oscillatory dynamics and cortical reactivity such as inter-trial coherence (ITC) and local mean field power (LMFP) [3]. In 90 chronic pain patients a baseline TMS-EEG assessment was used to investigate if such parameters predict the analgesic response to 12 sessions of 10Hz rTMS.

Methods: Patients undergo a baseline TMS–EEG assessment, followed by 12 rTMS (10Hz) sessions over 8 weeks. To date, 79 of 90 have been included, and data collection will be completed in April 2026. A logistic regression model with elastic-net regularization was trained to classify responders versus non-responders based on neurophysiological predictors at baseline ([3], LMFP from the M1 and posterior–occipital electrode clusters) and finally thresholded based on ITC in the alpha band. The target is to predict 50% of patients as responders (>30% pain from last 24h on last day of treatment compared to the week prior to baseline).

Results: Patients with nociceptive, neuropathic, and nociplastic pain lasting more than 3 months and between 3-9/10 on an NRS scale, are all included in the trial. The remaining 11 participants are expected to be included by end of February and the final rTMS session will thus be delivered end of April. Out of the 79 participants, 44% are predicted to become responders and the remaining (56%) are predicted non-responders based on their baseline assessment. When all treatment outcomes are known, the predictor effect will be tested against the actual response rate and the predictive values will be known.

Conclusions: This is a first step to investigate which TMS-evoked oscillatory dynamics and reactivity metrics can be used prospectively to determine which chronic pain patients will benefit from rTMS treatment to M1, thereby supporting patient-centred treatment strategies. If it is possible to predict responders to an 8-week rTMS intervention based on a feasible TMS-EEG baseline assessment, there will be clinical and societal impact due to time- and cost savings. Furthermore, being able to predict responders to M1 rTMS interventions opens up for the potential to predict responders to other brain regions, thereby supporting the development of precision medicine–based neuromodulation treatments.

References: [1] Attal et al., (2021). https://doi.org/10.1093/brain/awab208
[2] Mhalla et al., (2010). https://doi.org/10.1016/j.pain.2010.03.009
[3] De Martino, et al. (2025). https://doi.org/10.1101/2025.10.11.25337793

Presenting Author: Margit Midtgaard Bach
Submitting Author: Margit Bach

Senior Author: Daniel Ciampi de Andrade

IASP 2026 Global Year theme of Neuropathic Pain: Yes

• Margit Midtgaard Bach - Center for Neuroplasticity and Pain (CNAP), Department of Health and Science Technology, Faculty of Medicine, Aalborg University, Denmark
• Enrico De Martino - Center for Neuroplasticity and Pain (CNAP), Department of Health and Science Technology, Faculty of Medicine, Aalborg University, Denmark
• Nahian Chowdhury - Neuroscience Research Australia, University of New South Wales, New South Wales, Australia
• Bruno Nascimento Couto - Center for Neuroplasticity and Pain (CNAP), Department of Health and Science Technology, Faculty of Medicine, Aalborg University, Denmark
• Anne Jakobsen - Center for Neuroplasticity and Pain (CNAP), Department of Health and Science Technology, Faculty of Medicine, Aalborg University, Denmark
• Kristian Eskildsen - Center for Neuroplasticity and Pain (CNAP), Department of Health and Science Technology, Faculty of Medicine, Aalborg University, Denmark
• Teis Søgaard Kalsbøl - Center for Neuroplasticity and Pain (CNAP), Department of Health and Science Technology, Faculty of Medicine, Aalborg University, Denmark
• Thomas Graven-Nielsen - Center for Neuroplasticity and Pain (CNAP), Department of Health and Science Technology, Faculty of Medicine, Aalborg University, Denmark
• Daniel Ciampi de Andrade - Center for Neuroplasticity and Pain (CNAP), Department of Health and Science Technology, Faculty of Medicine, Aalborg University, Denmark