With the acceleration of global population aging， the assessment and prediction of brain aging have become an important research direction in neuroscience. Brain age， as an indicator for measuring the biological age of the brain， can be used to assess individual cognitive function and predict the risk of neurodegenerative diseases. Neuroimaging techniques can reveal the structure and functional state of the brain， providing an important basis for brain age prediction. The hypoxic environment at high altitudes may accelerate brain aging， and its neuroimaging features provide a unique perspective for assessing brain aging. This article reviews the latest research advances in brain aging and brain age prediction under a high-altitude hypoxia environment， with a focus on the construction of brain age prediction models， the application of sMRI/DTI/fMRI， and the impact of high-altitude hypoxia on brain structure， function， and mechanisms， in order to provide a reference and directions for future research.
Neuroimaging

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

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

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*

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