PMID- 35992927 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20220823 IS - 1662-4548 (Print) IS - 1662-453X (Electronic) IS - 1662-453X (Linking) VI - 16 DP - 2022 TI - Predicting response to tVNS in patients with migraine using functional MRI: A voxels-based machine learning analysis. PG - 937453 LID - 10.3389/fnins.2022.937453 [doi] LID - 937453 AB - BACKGROUND: Migraine is a common disorder, affecting many patients. However, for one thing, lacking objective biomarkers, misdiagnosis, and missed diagnosis happen occasionally. For another, though transcutaneous vagus nerve stimulation (tVNS) could alleviate migraine symptoms, the individual difference of tVNS efficacy in migraineurs hamper the clinical application of tVNS. Therefore, it is necessary to identify biomarkers to discriminate migraineurs as well as select patients suitable for tVNS treatment. METHODS: A total of 70 patients diagnosed with migraine without aura (MWoA) and 70 matched healthy controls were recruited to complete fMRI scanning. In study 1, the fractional amplitude of low-frequency fluctuation (fALFF) of each voxel was calculated, and the differences between healthy controls and MWoA were compared. Meaningful voxels were extracted as features for discriminating model construction by a support vector machine. The performance of the discriminating model was assessed by accuracy, sensitivity, and specificity. In addition, a mask of these significant brain regions was generated for further analysis. Then, in study 2, 33 of the 70 patients with MWoA in study 1 receiving real tVNS were included to construct the predicting model in the generated mask. Discriminative features of the discriminating model in study 1 were used to predict the reduction of attack frequency after a 4-week tVNS treatment by support vector regression. A correlation coefficient between predicted value and actual value of the reduction of migraine attack frequency was conducted in 33 patients to assess the performance of predicting model after tVNS treatment. We vislized the distribution of the predictive voxels as well as investigated the association between fALFF change (post-per treatment) of predict weight brain regions and clinical outcomes (frequency of migraine attack) in the real group. RESULTS: A biomarker containing 3,650 features was identified with an accuracy of 79.3%, sensitivity of 78.6%, and specificity of 80.0% (p < 0.002). The discriminative features were found in the trigeminal cervical complex/rostral ventromedial medulla (TCC/RVM), thalamus, medial prefrontal cortex (mPFC), and temporal gyrus. Then, 70 of 3,650 discriminative features were identified to predict the reduction of attack frequency after tVNS treatment with a correlation coefficient of 0.36 (p = 0.03). The 70 predictive features were involved in TCC/RVM, mPFC, temporal gyrus, middle cingulate cortex (MCC), and insula. The reduction of migraine attack frequency had a positive correlation with right TCC/RVM (r = 0.433, p = 0.021), left MCC (r = 0.451, p = 0.016), and bilateral mPFC (r = 0.416, p = 0.028), and negative with left insula (r = -0.473, p = 0.011) and right superior temporal gyrus/middle temporal gyrus (r = -0.684, p < 0.001), respectively. CONCLUSIONS: By machine learning, the study proposed two potential biomarkers that could discriminate patients with MWoA and predict the efficacy of tVNS in reducing migraine attack frequency. The pivotal features were mainly located in the TCC/RVM, thalamus, mPFC, and temporal gyrus. CI - Copyright (c) 2022 Fu, Zhang, Ye, Hou, Wen, Yan, Luo, Feng and Liu. FAU - Fu, Chengwei AU - Fu C AD - Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. AD - The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China. FAU - Zhang, Yue AU - Zhang Y AD - Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. FAU - Ye, Yongsong AU - Ye Y AD - Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. FAU - Hou, Xiaoyan AU - Hou X AD - Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. FAU - Wen, Zeying AU - Wen Z AD - Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. AD - The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China. AD - Department of Radiology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China. FAU - Yan, Zhaoxian AU - Yan Z AD - Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. FAU - Luo, Wenting AU - Luo W AD - Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. AD - The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China. FAU - Feng, Menghan AU - Feng M AD - Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. AD - The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China. FAU - Liu, Bo AU - Liu B AD - Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. LA - eng PT - Journal Article DEP - 20220805 PL - Switzerland TA - Front Neurosci JT - Frontiers in neuroscience JID - 101478481 PMC - PMC9388938 OTO - NOTNLM OT - functional magnetic resonance imaging OT - machine learning OT - migraine OT - support vector machine (SVM) OT - transcutaneous vagus nerve stimulation OT - vagus nerve COIS- The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. EDAT- 2022/08/23 06:00 MHDA- 2022/08/23 06:01 PMCR- 2022/01/01 CRDT- 2022/08/22 04:35 PHST- 2022/05/06 00:00 [received] PHST- 2022/07/13 00:00 [accepted] PHST- 2022/08/22 04:35 [entrez] PHST- 2022/08/23 06:00 [pubmed] PHST- 2022/08/23 06:01 [medline] PHST- 2022/01/01 00:00 [pmc-release] AID - 10.3389/fnins.2022.937453 [doi] PST - epublish SO - Front Neurosci. 2022 Aug 5;16:937453. doi: 10.3389/fnins.2022.937453. eCollection 2022.