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

Diffuse infiltrating retinoblastoma is an extremely rare form of retinoblastoma which is characterized by its atypical growth pattern. This unusual presentation adds complexity to the diagnostic process. The purpose of this paper is to report a rare presentation of diffuse infiltrating retinoblastoma presenting after an ocular trauma. We described a 7-year-old Filipino boy presenting with total hyphema following an ocular trauma. Comprehensive ophthalmologic clinical and diagnostic evaluations were performed including visual acuity, slitlamp biomicroscopy, ocular ultrasound, neuroimaging, and histopathology post enucleation to determine diagnosis. The misleading, atypical presentation of diffuse infiltrating retinoblastoma may delay diagnosis. While this dilemma is expected in these scenarios, it should be remembered that timing of diagnosis in retinoblastoma is crucial, as this also equates to optimal management. One should remain vigilant for these uncommon presentations especially in the setting of any intraocular inflammation in children.

Human ; Male ; Child: 6-12 Yrs Old ; Wounds And Injuries ; Visual Acuity ; Retinoblastoma ; Research Report ; Neuroimaging ; Inflammation ; Hyphema ; Contusions

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

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

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 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*