Electroencephalography (EEG) and magnetic resonance imaging (MRI), as neuroimaging technologies, provided objective and visualized technical tools for analyzing the brain effect mechanisms of acupuncture and moxibustion from the perspectives of brain structure, function, metabolism, and hemodynamics. The advancement of artificial intelligence (AI) algorithms can compensate for issues such as the large and scattered nature of neuroimaging data, inconsistent quality, and high heterogeneity of image information. The integration of AI with neuroimaging can facilitate individualized, intelligent, and precise prediction of acupuncture and moxibustion effects, enable intelligent classification of differential acupuncture responses, and identify brain activation patterns. This paper focuses on EEG and MRI, analyzing how machine learning and deep learning optimize multimodal neuroimaging data and their applications in the study of acupuncture and moxibustion brain effects mechanisms. Furthermore, it highlights current research gaps and limitations to provide insights for future studies on acupuncture brain effects mechanisms.
Humans ; Acupuncture Therapy ; Brain/physiology* ; Moxibustion ; Neuroimaging/methods* ; Artificial Intelligence ; Magnetic Resonance Imaging ; Electroencephalography

Alzheimer's disease (AD) classification models usually segment the entire brain image into voxel blocks and assign them labels consistent with the entire image, but not every voxel block is closely related to the disease. To this end, an AD auxiliary diagnosis framework based on weakly supervised multi-instance learning (MIL) and multi-scale feature fusion is proposed, and the framework is designed from three aspects: within the voxel block, between voxel blocks, and high-confidence voxel blocks. First, a three-dimensional convolutional neural network was used to extract deep features within the voxel block; then the spatial correlation information between voxel blocks was captured through position encoding and attention mechanism; finally, high-confidence voxel blocks were selected and combined with multi-scale information fusion strategy to integrate key features for classification decision. The performance of the model was evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets. Experimental results showed that the proposed framework improved ACC and AUC by 3% and 4% on average compared with other mainstream frameworks in the two tasks of AD classification and mild cognitive impairment conversion classification, and could find the key voxel blocks that trigger the disease, providing an effective basis for AD auxiliary diagnosis.
Alzheimer Disease/diagnosis* ; Humans ; Neuroimaging/methods* ; Neural Networks, Computer ; Brain/diagnostic imaging* ; Magnetic Resonance Imaging ; Deep Learning ; Machine Learning

Magnetic resonance imaging (MRI), as a non-invasive neuroimaging technique, has been widely employed to investigate changes in functional brain regions. In recent years, the application of MRI in the study of lifelong premature ejaculation (LPE) has progressively uncovered the pathological mechanisms underlying abnormalities in LPE-associated brain regions. These mechanisms involve brain areas associated with higher-order cognitive and decision-making regulation, sensory and perceptual processing, as well as emotional regulation and reward systems. The application and findings of MRI in the study of LPE mechanisms will be introduced in this review, with the goal of deepening our understanding of the neuroimaging-based mechanisms of LPE.
Humans ; Premature Ejaculation/physiopathology* ; Male ; Magnetic Resonance Imaging ; Neuroimaging ; Brain

Objective:To exploring the value of MR neuroimaging for quantitative assessment of the facial nerve and peripheral lymph nodes in patients with acute peripheral facial paralysis. Methods:Based on a prospective experimental design, 32 patients with idiopathic peripheral facial palsy were enrolled in the experiment. Based on MR neuroimaging technology, MR high-resolution thin-layer images of bilateral facial nerves were acquired. The diameters of different segments of the bilateral facial nerve were measured, including the labyrinthine segment, the geniculate ganglion, the horizontal segment, the vertical segment, the stem-mammary foramen segment, the trunk of the parotid segment, the temporal trunk, and the cervical trunk, as well as the quantitative indicators of peri-auricular and parotid lymph nodes（number, length and diameter of the largest lymph nodes）. Differences in quantitative indices of nerve diameter and peripheral lymph nodes between the paraplegic and healthy sides were compared using the paired t-test and Wilcoxon signed rank test. Results:The diameter of geniculate ganglion, mastoid foramen stem, parotid main trunk, temporal facial trunk, and cervical facial trunk were notably increased on the facial paralysis side compared to the contralateral side（P<0.05）. However, no significant differences were observed in the diameter of labyrinthine segment, horizontal segment, or vertical segment compared to the contralateral side. There were significantly more periauricular lymph nodes on the facial paralysis side than the contralateral side（P=0.001）. Conclusion:MR neuroimaging enables the quantitative assessment of structural changes in the facial nerve of patients with acute peripheral facial paralysis, demonstrating nerve enlargement in the geniculate ganglion, stylomastoid foramen segment, main trunk of the parotid segment, temporal facial trunk, and cervical facial trunk. Additionally, an increased number of periauricular lymph nodes is observed on the affected side. These findings may aid clinicians in assessing the efficacy of treatments and predict the prognosis of these patients.
Humans ; Facial Nerve/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Prospective Studies ; Female ; Male ; Neuroimaging/methods* ; Lymph Nodes/diagnostic imaging* ; Facial Paralysis/diagnostic imaging* ; Adult ; Middle Aged

OBJECTIVE: To identify topics attracting growing research attention as well as frontier trends of acupuncture-neuroimaging research over the past two decades. METHODS: This paper reviewed data in the published literature on acupuncture neuroimaging from 2000 to 2020, which was retrieved from the Web of Science database. CiteSpace was used to analyze the publication years, countries, institutions, authors, keywords, co-citation of authors, journals, and references. RESULTS: A total of 981 publications were included in the final review. The number of publications has increased in the recent 20 years accompanied by some fluctuations. Notably, the most productive country was China, while Harvard University ranked first among institutions in this field. The most productive author was Tian J with the highest number of articles (50), whereas the most co-cited author was Hui KKS (325). Evidence-Based Complementary and Alternative Medicine (92) was the most prolific journal, while Neuroimage was the most co-cited journal (538). An article written by Hui KKS (2005) exhibited the highest co-citation number (112). The keywords "acupuncture" (475) and "electroacupuncture" (0.10) had the highest frequency and centrality, respectively. Functional magnetic resonance imaging (fMRI) ranked first with the highest citation burst (6.76). CONCLUSION The most active research topics in the field of acupuncture-neuroimaging over the past two decades included research type, acupoint specificity, neuroimaging methods, brain regions, acupuncture modality, acupoint specificity, diseases and symptoms treated, and research type. Whilst research frontier topics were "nerve regeneration", "functional connectivity", "neural regeneration", "brain network", "fMRI" and "manual acupuncture".
Humans ; Acupuncture ; Acupuncture Therapy ; Bibliometrics ; Magnetic Resonance Imaging ; Neuroimaging

Major advances have been made over the past few decades in identifying and managing disorders of consciousness (DOC) in patients with acquired brain injury (ABI), bringing the transformation from a conceptualized definition to a complex clinical scenario worthy of scientific exploration. Given the continuously-evolving framework of precision medicine that integrates valuable behavioral assessment tools, sophisticated neuroimaging, and electrophysiological techniques, a considerably higher diagnostic accuracy rate of DOC may now be reached. During the treatment of patients with DOC, a variety of intervention methods are available, including amantadine and transcranial direct current stimulation, which have both provided class II evidence, zolpidem, which is also of high quality, and non-invasive stimulation, which appears to be more encouraging than pharmacological therapy. However, heterogeneity is profoundly ingrained in study designs, and only rare schemes have been recommended by authoritative institutions. There is still a lack of an effective clinical protocol for managing patients with DOC following ABI. To advance future clinical studies on DOC, we present a comprehensive review of the progress in clinical identification and management as well as some challenges in the pathophysiology of DOC. We propose a preliminary clinical decision protocol, which could serve as an ideal reference tool for many medical institutions.
Humans ; Transcranial Direct Current Stimulation/methods* ; Consciousness Disorders/etiology* ; Brain Injuries/complications* ; Consciousness ; Neuroimaging

Primary dysmenorrhea (PDM), cyclic menstrual pain in the absence of pelvic anomalies, is characterized by acute and chronic gynecological pain disorders in childbearing age women. PDM strongly affects the quality of life of patients and leads to economic losses. PDM generally do not receive radical treatment and often develop into other chronic pain disorders later in life. The clinical treatment status of PDM, the epidemiology of PDM and chronic pain comorbidities, and the abnormal physiological and psychological characteristics of patients with PDM suggest that PDM not only is related to the inflammation around the uterus, but also may be related to the abnormal pain processing and regulation function of patients' central system. Therefore, exploring the brain neural mechanism of PDM is indispensable and important to understand the pathological mechanism of PDM, and is also a hotspot of brain science research in recent years, which will bring new inspiration to explore the target of PDM intervention. Based on the progress of the neural mechanism of PDM, this paper systematically summarizes the evidence from neuroimaging and animal model studies.
Animals ; Humans ; Female ; Dysmenorrhea ; Brain Mapping ; Chronic Pain ; Quality of Life ; Neuroimaging ; Models, Animal

The extraction of neuroimaging features of migraine patients and the design of identification models are of great significance for the auxiliary diagnosis of related diseases. Compared with the commonly used image features, this study directly uses time-series signals to characterize the functional state of the brain in migraine patients and healthy controls, which can effectively utilize the temporal information and reduce the computational effort of classification model training. Firstly, Group Independent Component Analysis and Dictionary Learning were used to segment different brain areas for small-sample groups and then the regional average time-series signals were extracted. Next, the extracted time series were divided equally into multiple subseries to expand the model input sample. Finally, the time series were modeled using a bi-directional long-short term memory network to learn the pre-and-post temporal information within each time series to characterize the periodic brain state changes to improve the diagnostic accuracy of migraine. The results showed that the classification accuracy of migraine patients and healthy controls was 96.94%, the area under the curve was 0.98, and the computation time was relatively shorter. The experiments indicate that the method in this paper has strong applicability, and the combination of time-series feature extraction and bi-directional long-short term memory network model can be better used for the classification and diagnosis of migraine. This work provides a new idea for the lightweight diagnostic model based on small-sample neuroimaging data, and contributes to the exploration of the neural discrimination mechanism of related diseases.
Humans ; Time Factors ; Migraine Disorders/diagnostic imaging* ; Magnetic Resonance Imaging ; Brain/diagnostic imaging* ; Neuroimaging

Chronic Painful Temporomandibular Disorders (TMD) are challenging to diagnose and manage due to their complexity and lack of understanding of brain mechanism. In the past few decades' neural mechanisms of pain regulation and perception have been clarified by neuroimaging research. Advances in the neuroimaging have bridged the gap between brain activity and the subjective experience of pain. Neuroimaging has also made strides toward separating the neural mechanisms underlying the chronic painful TMD. Recently, Artificial Intelligence (AI) is transforming various sectors by automating tasks that previously required humans' intelligence to complete. AI has started to contribute to the recognition, assessment, and understanding of painful TMD. The application of AI and neuroimaging in understanding the pathophysiology and diagnosis of chronic painful TMD are still in its early stages. The objective of the present review is to identify the contemporary neuroimaging approaches such as structural, functional, and molecular techniques that have been used to investigate the brain of chronic painful TMD individuals. Furthermore, this review guides practitioners on relevant aspects of AI and how AI and neuroimaging methods can revolutionize our understanding on the mechanisms of painful TMD and aid in both diagnosis and management to enhance patient outcomes.
Humans ; Facial Pain/diagnostic imaging* ; Artificial Intelligence ; Temporomandibular Joint Disorders/diagnostic imaging* ; Neuroimaging/methods* ; Pain Measurement/methods*

Acupuncture-moxibustion is remarkably effective on encephalopathy, but its mechanism is unclear. With the continuous development of imaging technology, the in vivo brain imaging technology has been used increasingly in life science research and it also becomes a more effective tool for the basic research of acupuncture-moxibustion in treatment of encephalopathy. The paper summarizes the application of its technology in the basic research of acupuncture-moxibustion for encephalopathy and the characteristics of imaging, as well as the advantages and shortcomings. It is anticipated that the references may be provided for the basic research of acupuncture-moxibustion in treatment of encephalopathy and be conductive to the modernization of acupuncture-moxibustion.
Humans ; Moxibustion ; Acupuncture Therapy ; Acupuncture ; Brain Diseases/therapy* ; Neuroimaging