Acupuncture for Migraine Without Aura and Connection-Based Efficacy Prediction: A Randomized Clinical Trial

Area of Science:

• Neuroscience
• Integrative Medicine
• Clinical Neurology

Background:

• Migraine without aura (MWOA) is a prevalent neurological disorder impacting quality of life.
• Acupuncture is a traditional therapy, but its efficacy and underlying neural mechanisms in MWOA require further investigation.
• Connectome-based predictive modeling (CPM) offers a data-driven approach to link brain connectivity to clinical outcomes.

Purpose of the Study:

• To assess the clinical effectiveness of real versus sham acupuncture for MWOA.
• To identify brain connectivity patterns associated with acupuncture response using CPM and fMRI.
• To explore potential biomarkers for personalized MWOA treatment.

Main Methods:

• A single-blinded randomized clinical trial involving 120 MWOA patients comparing real acupuncture (with deqi sensation) to sham acupuncture (without deqi).
• Functional magnetic resonance imaging (fMRI) was used for baseline brain scans.
• CPM analysis was applied to baseline fMRI data to predict treatment outcomes, including changes in monthly migraine days (MMDs) and pain scores (VAS).

Main Results:

• Real acupuncture significantly reduced MMDs, monthly headache days (MHDs), acute medication use, VAS pain scores, HIT-6 disability scores, and improved MSQ quality-of-life scores compared to sham acupuncture.
• CPM identified distinct neural signatures: default mode network and subcortical-cerebellum (DMN-SC) hypoconnectivity predicted reduced VAS scores (pain relief).
• Subcortical-cerebellum to motor cortex (SC-motor) hyperconnectivity predicted improved HIT-6 scores (reduced disability).

Conclusions:

• Acupuncture is clinically effective for improving pain and function in MWOA patients.
• CPM successfully identified specific brain connectivity patterns (DMN-SC hypoconnectivity and SC-motor hyperconnectivity) predictive of treatment response.
• These findings support a personalized treatment framework for MWOA based on neuroimaging biomarkers.