Efficacy prediction of acupuncture treatment for migraine without aura based on multimodal MRI: A study protocol

Introduction: Acupuncture is an effective and safe therapy for patients with migraine without aura (MwoA), but only 41-59% of patients show improvement with this treatment. Screening positive responders to acupuncture treatment for MwoA can ensure that healthcare resources can be appropriately targeted to specific patients who would most benefit. The objective of this study is to determine whether the structure and functional activity in certain brain regions can predict analgesia response in patients with MwoA who receive acupuncture treatment. Methods and analysis: A total of 72 patients with MwoA and 72 healthy controls (HCs) will be enrolled in this study. Resting-state structural and functional magnetic resonance imaging (MRI) data will be collected from each participant at baseline. Patients with MwoA will undergo 12 sessions of acupuncture treatment for 8 weeks, twice per week in the first 4 weeks and once per week for the last 4 weeks. The follow-up will be 12 weeks. The number of days with migraine, frequency of migraine attacks, and average visual analog scale scores will be recorded in detail at weeks 0, 4, 8, 12, and 16 and at the end of follow-up (week 20). The positive response rate will be calculated as the proportion of patients with >= 50% reduction in the number of migraine days during follow-up compared with baseline. Machine learning methods will be applied to classify patients with MwoA and HCs and predict patients with response or non-response to acupuncture treatment based on multimodal MRI parameters, such as gray matter volume, regional homogeneity, amplitude of low-frequency fluctuation, fractional anisotropy, and mean diffusivity. Discussion: This study aims to establish brain structural and functional characteristics that can identify patients with MwoA who will derive analgesia benefits from acupuncture treatment.

Automated summary

This study aims to predict analgesia response in patients with MwoA receiving acupuncture treatment based on brain structural and functional characteristics. Machine learning methods will be used to classify patients and predict response or non-response to acupuncture treatment using multimodal MRI parameters.