AUTHOR=Lu Mengxin , Du Zhongming , Zhao Jiping , Jiang Lan , Liu Ruoyi , Zhang Muzhao , Xu Tianjiao , Wei Jingpei , Wang Wei , Xu Lingling , Guo Haijiao , Chen Chen , Yu Xin , Tan Zhongjian , Fang Jiliang , Zou Yihuai 

TITLE=Neuroimaging mechanisms of acupuncture on functional reorganization for post-stroke motor improvement: a machine learning-based functional magnetic resonance imaging study

JOURNAL=Frontiers in Neuroscience

VOLUME=17

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1143239

DOI=10.3389/fnins.2023.1143239

ISSN=1662-453X

ABSTRACT=<sec><title>Objective</title><p>Motor recovery is crucial in stroke rehabilitation, and acupuncture can influence recovery. Neuroimaging and machine learning approaches provide new research directions to explore the brain functional reorganization and acupuncture mechanisms after stroke. We applied machine learning to predict the classification of the minimal clinically important differences (MCID) for motor improvement and identify the neuroimaging features, in order to explore brain functional reorganization and acupuncture mechanisms for motor recovery after stroke.</p></sec><sec><title>Methods</title><p>In this study, 49 patients with unilateral motor pathway injury (basal ganglia and/or corona radiata) after ischemic stroke were included and evaluated the motor function by Fugl–Meyer Assessment scores (FMA) at baseline and at 2-week follow-up sessions. Patients were divided by the difference between the twice FMA scores into one group showing minimal clinically important difference (MCID group, <italic>n</italic> = 28) and the other group with no minimal clinically important difference (N-MCID, <italic>n</italic> = 21). Machine learning was performed by PRoNTo software to predict the classification of the patients and identify the feature brain regions of interest (ROIs). In addition, a matched group of healthy controls (HC, <italic>n</italic> = 26) was enrolled. Patients and HC underwent magnetic resonance imaging examination in the resting state and in the acupuncture state (acupuncture at the Yanglingquan point on one side) to compare the differences in brain functional connectivity (FC) and acupuncture effects.</p></sec><sec><title>Results</title><p>Through machine learning, we obtained a balance accuracy rate of 75.51% and eight feature ROIs. Compared to HC, we found that the stroke patients with lower FC between these feature ROIs with other brain regions, while patients in the MCID group exhibited a wider range of lower FC. When acupuncture was applied to Yanglingquan (GB 34), the abnormal FC of patients was decreased, with different targets of effects in different groups.</p></sec><sec><title>Conclusion</title><p>Feature ROIs identified by machine learning can predict the classification of stroke patients with different motor improvements, and the FC between these ROIs with other brain regions is decreased. Acupuncture can modulate the bilateral cerebral hemispheres to restore abnormal FC <italic>via</italic> different targets, thereby promoting motor recovery after stroke.</p></sec><sec><title>Clinical trial registration</title><p><ext-link xlink:href="https://www.chictr.org.cn/showproj.html?proj=37359" ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.chictr.org.cn/showproj.html?proj=37359</ext-link>, ChiCTR1900022220.</p></sec>