Baseline Brain Gray Matter Volume as a Predictor of Acupuncture Outcome in Treating Migraine

Abstract

Background: The present study aimed to investigate the use of imaging biomarkers to predict the outcome of acupuncture in patients with migraine without aura (MwoA). Methods: Forty-one patients with MwoA received 4 weeks of acupuncture treatment and two brain imaging sessions at the Beijing Traditional Chinese Medicine Hospital affiliated with Capital Medical University. Patients kept a headache diary for 4 weeks before treatment and during acupuncture treatment. Responders were defined as those with at least a 50% reduction in the number of migraine days. The machine learning method was used to distinguish responders from non-responders based on pre-treatment brain gray matter (GM) volume. Longitudinal changes in GM predictive regions were also analyzed. Results: After 4 weeks of acupuncture, 19 patients were classified as responders. Based on 10-fold cross-validation for the selection of GM features, the linear support vector machine produced a classification model with 73% sensitivity, 85% specificity, and 83% accuracy. The area under the receiver operating characteristic curve was 0.7871. This classification model included 10 GM areas that were mainly distributed in the frontal, temporal, parietal, precuneus, and cuneus gyri. The reduction in the number of migraine days was correlated with baseline GM volume in the cuneus, parietal, and frontal gyri in all patients. Moreover, the left cuneus showed a longitudinal increase in GM volume in responders. Conclusion: The results suggest that pre-treatment brain structure could be a novel predictor of the outcome of acupuncture in the treatment of MwoA. Imaging features could be a useful tool for the prediction of acupuncture efficacy, which would enable the development of a personalized medicine strategy.

The Spontaneous Activity Pattern of the Middle Occipital Gyrus Predicts the Clinical Efficacy of Acupuncture Treatment for Migraine Without Aura

“…A recent machine learning study demonstrated that the pre-treatment resting-state functional connectivity between the medial prefrontal cortex and specific subcortical regions could significantly predict the changes of symptom in patients with chronic low back pain receiving 4-week acupuncture treatment (16). The Multivariate Pattern Analysis (MVPA) study also suggested that the baseline white matter microstructure and gray matter volume could aid the identification of the migraine subjects who were sensitive to the placebo acupuncture stimulation (17,18). These studies indicated that the baseline neuroimaging properties might be the available markers to predict individual responses to acupuncture.…”

Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity

“…These ten studies were published from 2008 to 2020. Generally, for participant selection, these studies were performed on healthy subjects [ 45 – 47 , 49 , 54 ], patients with migraine [ 48 , 50 , 51 ], patients with chronic low back pain [ 53 ], and patients with functional dyspepsia [ 52 ], respectively. The sample size of these studies ranged from 12 to 94, and the average sample size of healthy subjects was 28.…”

“…This finding suggested that the properties of neuroplasticity that influenced the efficacy of acupuncture were multidimensional and complex. Moreover, another interesting finding was that both GMV and diffusion measures of white matter fiber could accurately discriminate between acupuncture-sensitive and acupuncture-insensitive migraine patients [ 50 , 51 ]. Does it mean that the prediction model achieves better performance to discriminate the acupuncture responders and acupuncture nonresponders if both gray matter and white matter features are applied as inputs?…”

“…For a comprehensive summary of the field, we systematically searched papers in PubMed (pubmed.ncbi.nlm.nih.gov), Web of Science (https://www.webofknowledge.com), EBSCO (search.ebscohost.com), and CNKI (https://www.cnki.net) databases. According to the established inclusion and exclusion criteria, a total of ten studies were finally included [45][46][47][48][49][50][51][52][53][54]. The details of data acquisition and literature selection process can be found in the supplementary materials (available here).…”

“…Except for one study that enrolled participants with a wide age span [54], these studies mainly included participants aged 20-45 years. For acupuncture intervention, nine studies [45][46][47][48][49][50][51][52][53] applied manual acupuncture, and one study [54] selected the electroacupuncture as the intervention method. For scan design, six studies [45][46][47][48][49]54] applied the on-off block design to detect the real-time effects of acupuncture on functional brain plasticity.…”