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 role of white matter of brain has always been neglected by scholars.With the development of neuroimaging technology,the role of white matter has attracted increasing attention.Perioperative neurocognitive disorders have been a hot issue in the research on anesthesia,and recent studies have suggested that white matter may be involved in the effects of general anesthetics on cognitive function.This paper reviews the progress in the relationship between white matter,general anesthesia,and cognitive function from clinical practice and research,aiming to provide new ideas for the research on the mechanism.
White Matter ; Cognition ; Brain ; Neuroimaging ; Anesthesia, General

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer's disease.
Humans ; Alzheimer Disease/diagnostic imaging* ; Neurodegenerative Diseases ; Magnetic Resonance Imaging/methods* ; Neural Networks, Computer ; Neuroimaging/methods* ; Cognitive Dysfunction/diagnosis*

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

Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.
Humans ; Obsessive-Compulsive Disorder/epidemiology* ; Brain/pathology* ; Neuroimaging/methods* ; Machine Learning ; Comorbidity ; Magnetic Resonance Imaging/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

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

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that damages patients' memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.
Humans ; Alzheimer Disease/diagnostic imaging* ; Neural Networks, Computer ; Magnetic Resonance Imaging/methods* ; Neuroimaging/methods* ; Diagnosis, Computer-Assisted ; Brain ; Cognitive Dysfunction

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*