Regional brain morphology predicts pain relief in trigeminal neuralgia

• Main Authors: Peter Shih-Ping Hung, Alborz Noorani, Jia Y. Zhang, Sarasa Tohyama, Normand Laperriere, Karen D. Davis, David J. Mikulis, Frank Rudzicz, Mojgan Hodaie
• Format: Article
• Language: English
• Published: Elsevier 2021-01-01
• Series: NeuroImage: Clinical
• Subjects: Trigeminal neuralgia; Chronic neuropathic pain; Machine learning; Pain relief; Brain morphology
• Online Access: http://www.sciencedirect.com/science/article/pii/S2213158221001509

Background: Trigeminal neuralgia, a severe chronic neuropathic pain disorder, is widely believed to be amenable to surgical treatments. Nearly 20% of patients, however, do not have adequate pain relief after surgery. Objective tools for personalized pre-treatment prognostication of pain relief following surgical interventions can minimize unnecessary surgeries and thus are of substantial benefit for patients and clinicians. Purpose: To determine if pre-treatment regional brain morphology-based machine learning models can prognosticate 1 year response to Gamma Knife radiosurgery for trigeminal neuralgia. Methods: We used a data-driven approach that combined retrospective structural neuroimaging data and support vector machine-based machine learning to produce robust multivariate prediction models of pain relief following Gamma Knife radiosurgery for trigeminal neuralgia. Surgical response was defined as ≥ 75% pain relief 1 year post-treatment. We created two prediction models using pre-treatment regional brain gray matter morphology (cortical thickness or surface area) to distinguish responders from non-responders to radiosurgery. Feature selection was performed through sequential backwards selection algorithm. Model out-of-sample generalizability was estimated via stratified 10-fold cross-validation procedure and permutation testing. Results: In 51 trigeminal neuralgia patients (35 responders, 16 non-responders), machine learning models based on pre-treatment regional brain gray matter morphology (14 regional surface areas or 13 regional cortical thicknesses) provided robust a priori prediction of surgical response. Cross-validation revealed the regional surface area model was 96.7% accurate, 100.0% sensitive, and 89.1% specific while the regional cortical thickness model was 90.5% accurate, 93.5% sensitive, and 83.7% specific. Permutation testing revealed that both models performed beyond pure chance (p < 0.001). The best predictor for regional surface area model and regional cortical thickness model was contralateral superior frontal gyrus and contralateral isthmus cingulate gyrus, respectively. Conclusions: Our findings support the use of machine learning techniques in subsequent investigations of chronic neuropathic pain. Furthermore, our multivariate framework provides foundation for future development of generalizable, artificial intelligence-driven tools for chronic neuropathic pain treatments.