Brain-X最新文献 - Book学术

Heterogeneity in brain morphology and psychological, cognitive, and contextual factors of gender identity 性别认同的脑形态、心理、认知和背景因素的异质性

Brain-X Pub Date : 2025-10-06 DOI: 10.1002/brx2.70037

Hiuying Yip, Yifei He, Yoonmi Hong, Jiaolong Qin, Fan Zhang, Ye Wu

{"title":"Heterogeneity in brain morphology and psychological, cognitive, and contextual factors of gender identity","authors":"Hiuying Yip, Yifei He, Yoonmi Hong, Jiaolong Qin, Fan Zhang, Ye Wu","doi":"10.1002/brx2.70037","DOIUrl":"https://doi.org/10.1002/brx2.70037","url":null,"abstract":"Once understood in binary terms, gender identity is increasingly recognized as a multidimensional and continuous construct shaped by both sociocultural and neurobiological factors. Although prior studies have reported associations between gender identity and brain structure, few have adopted an integrative approach to examine how gender identity emerges. Drawing on a large, non-clinical sample of young adults from the Amsterdam Open Magnetic Resonance Imaging Collection (n = 544), this study integrated psychological assessments, socioeconomic indicators, and structural MRI to investigate the relationship between gender identity and brain morphology. For participants assigned female at birth, a feminine identity was linked to reduced cortical thickness in several brain regions, including the parahippocampal, fusiform, lingual, and pericalcarine cortices. Among these regions, two distinct pathways related to the fusiform cortex were identified: a self-referential pathway (through the parahippocampal cortex) and a visual-perceptual pathway (through the pericalcarine and lingual cortices). Besides, an additional pathway related to the fusiform cortex was also identified, which connected higher socioeconomic status to crystallized intelligence. For participants assigned male at birth, a feminine identity was associated with increased anxiety and reduced cortical thickness in visual-emotional regions. In contrast, masculine identity was linked to a larger cortical area in the supramarginal gyrus and insula. Altogether, these findings suggest that gender identity is embedded in distributed neural systems that support self-representation, and that its structural correlates emerge through distinct psychological and cognitive-contextual mechanisms. By moving beyond binary classification, this study may offer a more nuanced neurobiological model of gendered self-concept in the general population.","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust carbon-doped black phosphorus multi-perception memristor for a hazardous information detection system 用于危险信息检测系统的鲁棒掺碳黑磷多感知忆阻器

Brain-X Pub Date : 2025-10-04 DOI: 10.1002/brx2.70026

Shuai Yuan, Zhe Feng, Guodong Wei, Liyan Dong, Pan Wang, Yong Niu, Ying Su, Peifen Zhu, Bingshe Xu, Bocang Qiu, Zuheng Wu

{"title":"Robust carbon-doped black phosphorus multi-perception memristor for a hazardous information detection system","authors":"Shuai Yuan, Zhe Feng, Guodong Wei, Liyan Dong, Pan Wang, Yong Niu, Ying Su, Peifen Zhu, Bingshe Xu, Bocang Qiu, Zuheng Wu","doi":"10.1002/brx2.70026","DOIUrl":"https://doi.org/10.1002/brx2.70026","url":null,"abstract":"The perception of hazardous information is a crucial factor in ensuring safety in production. In recent years, multi-mode sensing has been proven to be an effective approach for developing efficient perception systems. However, these systems still rely on various combinations of single-function sensors within traditional von Neumann architecture, which increases the system's overall complexity. In this study, carbon-doped black phosphorus (C–BP)-based multi-perception memristors were successfully developed for hazardous information perception. The C–BP multi-perception memristor exhibits remarkable stability and high surface activity due to the coupling and synergistic effects of C doping. Its high surface activity enables the reliable perception of hazardous visual (ultraviolet light) and olfactory (ethanol, acetone, and human expirations) information in an open environment. Consequently, a hazardous detection system based on the C–BP multi-perception memristor was simulated. The results indicate that the developed system outperforms traditional detection systems with an enhanced performance rate (97.6% vs. 90.5%) in perceiving hazardous information. This work may provide new insights into developing enhanced-performance hazardous information perception systems.","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The application and challenges of brain-computer interfaces in the medical industry 脑机接口在医疗工业中的应用与挑战

Brain-X Pub Date : 2025-07-01 DOI: 10.1002/brx2.70036

Qi Chen, Sha Zhao, Wei Wei, Tianyu Zhao, Rui He, Sishu Zhou, Zhenhang Yu

{"title":"The application and challenges of brain-computer interfaces in the medical industry","authors":"Qi Chen, Sha Zhao, Wei Wei, Tianyu Zhao, Rui He, Sishu Zhou, Zhenhang Yu","doi":"10.1002/brx2.70036","DOIUrl":"https://doi.org/10.1002/brx2.70036","url":null,"abstract":"Brain-computer interface (BCI) technology aims to create a connection pathway for exchanging information between the brain and devices with computing capabilities. This technology has become a global research focus, and many countries and regions are working to establish a BCI industry. BCIs have many potential applications, especially in the medical field. However, the complexities of non-invasive BCIs and the implantation risks associated with invasive BCIs have limited these technologies to laboratory settings. The main challenges for the practical implementation of BCIs include the lack of foundational technologies for non-invasive and invasive BCIs, the signal processing challenges associated with BCIs, the key components of BCIs, and the compatibility of BCI software and hardware. These shortcomings should be addressed to enhance the competitiveness of BCI products and promote the application of BCIs in medicine. In the future, if novel methods for acquiring or decoding neural signals are developed that enable non-invasive BCIs to achieve signal quality comparable to that of invasive techniques, it will propel BCI technology to leapfrog in development. Technological breakthroughs will enable BCIs to enhance medical technology and improve people's quality of life.","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synergizing DeepSeek's artificial intelligence innovations with brain–computer interfaces 将DeepSeek的人工智能创新与脑机接口协同起来

Brain-X Pub Date : 2025-06-28 DOI: 10.1002/brx2.70035

Canbiao Wu, Nayu Chen, Tuo Sun, Ping Tan, Peng Wang, Guangli Li

引用次数: 0

Licochalcone A selectively modulates mTORC1-TFEB to enhance autophagy and demonstrates neuroprotective effects in a mouse model of Parkinson's disease 在帕金森病小鼠模型中，甘草查尔酮A选择性调节mTORC1-TFEB增强自噬并显示神经保护作用

Brain-X Pub Date : 2025-06-27 DOI: 10.1002/brx2.70031

Sisi Wang, Ziyang Ding, Zhou Zhu, Xiaoru Zhong, Ashok Iyaswamy, Yaping Niu, Wei Zhang, Jichao Sun, Yulin Feng, Chuanbin Yang, Jigang Wang

{"title":"Licochalcone A selectively modulates mTORC1-TFEB to enhance autophagy and demonstrates neuroprotective effects in a mouse model of Parkinson's disease","authors":"Sisi Wang, Ziyang Ding, Zhou Zhu, Xiaoru Zhong, Ashok Iyaswamy, Yaping Niu, Wei Zhang, Jichao Sun, Yulin Feng, Chuanbin Yang, Jigang Wang","doi":"10.1002/brx2.70031","DOIUrl":"https://doi.org/10.1002/brx2.70031","url":null,"abstract":"Activation of transcription factor EB (TFEB), a key regulator of autophagy induction and lysosomal biogenesis, is considered a promising therapeutic strategy for treating the currently incurable Parkinson's disease (PD). However, most TFEB activators also inhibit mTORC1, which regulates several other cellular pathways. Therefore, small molecules that selectively modulate the mTORC1-TFEB pathway represent a novel and promising approach for treating PD. This study reveals that licochalcone A (LA), a flavonoid derived from the widely used Chinese herbal medicine licorice, selectively activates TFEB-mediated autophagy and exerts neuroprotective effects in a mouse model of PD. Specifically, we found that LA promoted the displacement of TFEB to the nucleus and enhanced autophagic flux. Knockout of the TFEB gene effectively inhibited LA-induced autophagy, suggesting that LA induced autophagy through TFEB activation. Mechanistic investigations revealed that LA activates TFEB through the Rag C-mediated non-canonical mTORC1 pathway, rather than through the canonical mTOR signaling or the PPP3/calcineurin pathway. Moreover, in a mouse model of MPTP-induced PD, oral administration of LA reduced the depletion of dopaminergic cells in the striatum and substantia nigra and alleviated motor symptoms. In conclusion, LA selectively modulates the mTORC1-TFEB pathway to induce autophagy, and reduces dopaminergic neuron loss and alleviates motor dysfunction in a mouse model of PD. These findings suggest that LA could serve as a novel TFEB activator and a potential therapeutic agent for treating PD.","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-human primate models of Parkinson's disease: Decoding pathogenesis and advancing therapies 帕金森病的非人类灵长类动物模型：解码发病机制和推进治疗

Brain-X Pub Date : 2025-06-24 DOI: 10.1002/brx2.70032

Sihui Zhang, Lin Yuan, Zihan Wu, Xuguang Du, Jacek Z. Kubiak, Feng Yue, Xuejing Yan, Gaolin Jiang, Yongye Huang

{"title":"Non-human primate models of Parkinson's disease: Decoding pathogenesis and advancing therapies","authors":"Sihui Zhang, Lin Yuan, Zihan Wu, Xuguang Du, Jacek Z. Kubiak, Feng Yue, Xuejing Yan, Gaolin Jiang, Yongye Huang","doi":"10.1002/brx2.70032","DOIUrl":"https://doi.org/10.1002/brx2.70032","url":null,"abstract":"Parkinson's disease (PD) is a neurodegenerative disorder in which the clinical manifestations include resting tremor, bradykinesia, akinesia, rigidity, and postural instability. The disease can be accompanied by non-motor symptoms such as depression and insomnia. The leading factors in the initiation of this disease include genetic alteration, exposure to toxins, and age. However, the exact mechanisms underlying the pathogenesis of PD remain elusive. Animal models play a critical role in the research on the pathogenesis and treatment of PD. Non-human primates share similar characteristics with humans, particularly in motor and cognitive abilities and the complexity of the neural structure. Non-human primate models for PD can be roughly classified into spontaneous, neurotoxin-based, and gene-editing models. Although having several current limitations, non-human primate models can play an increasingly important role in the research on PD, especially given the rapid development of novel methods in neuroscience.","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of a plasma biomarker diagnostic model as a screening strategy for Alzheimer's disease in older inpatients 血浆生物标志物诊断模型在老年住院患者阿尔茨海默病筛查中的应用

Brain-X Pub Date : 2025-05-28 DOI: 10.1002/brx2.70029

Xiaoxia Fang, Zhengke Liu, Xiaojun Kuang, Xiushi Ni, Xu Han, Xuejun Wen, Hong Xu

{"title":"Development of a plasma biomarker diagnostic model as a screening strategy for Alzheimer's disease in older inpatients","authors":"Xiaoxia Fang, Zhengke Liu, Xiaojun Kuang, Xiushi Ni, Xu Han, Xuejun Wen, Hong Xu","doi":"10.1002/brx2.70029","DOIUrl":"https://doi.org/10.1002/brx2.70029","url":null,"abstract":"Neural proteins in the bloodstream have emerged as promising biomarkers for diagnosing Alzheimer's disease (AD). However, their applicability in older individuals and those with multiple co-existing health conditions remains under-investigated. This study evaluated the diagnostic potential of blood-based neuro-markers in participants over 75 years old using an ultra-sensitive single molecule array. We recruited 108 Chinese inpatients with an average age of 92 years, including 30 diagnosed with AD, 46 diagnosed with dementia not caused by AD, and 32 without dementia. Plasma concentrations of amyloid β-40 (Aβ40), amyloid β-42 (Aβ42), tau phosphorylated at threonine 181 (p-tau181), neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) in plasma were quantified along with the Aβ42/Aβ40 ratio. Associations between these biomarkers and clinical characteristics (comorbidities and physiological indicators) were examined. Diagnostic models were developed using binary logistic regression based on these neuro-markers. Among the six neuro-markers, p-tau181 exhibited the highest discriminatory power for AD identification, with an area under the curve (AUC) of 0.7731 (95% CI: 0.6493–0.8969). A model combining p-tau181, GFAP, and age achieved an AUC of 0.8654 (95% CI: 0.7762–0.9546), with 75.9% sensitivity and 80.6% specificity in distinguishing AD from individuals without dementia. These findings suggest that plasma biomarkers of neurodegeneration, particularly p-tau181, may hold significant promise as diagnostic tools for AD, even among older patients. The simplified diagnostic model based on plasma neuro-markers offers a feasible approach for AD screening in both clinical and community settings.","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From digitized whole-slide histology images to biomarker discovery: A protocol for handcrafted feature analysis in brain cancer pathology 从数字化全切片组织学图像到生物标志物发现：脑癌病理中手工特征分析的协议

Brain-X Pub Date : 2025-05-28 DOI: 10.1002/brx2.70030

Xuanjun Lu, Yawen Ying, Jing Chen, Zhiyang Chen, Yuxin Wu, Prateek Prasanna, Xin Chen, Mingli Jing, Zaiyi Liu, Cheng Lu

{"title":"From digitized whole-slide histology images to biomarker discovery: A protocol for handcrafted feature analysis in brain cancer pathology","authors":"Xuanjun Lu, Yawen Ying, Jing Chen, Zhiyang Chen, Yuxin Wu, Prateek Prasanna, Xin Chen, Mingli Jing, Zaiyi Liu, Cheng Lu","doi":"10.1002/brx2.70030","DOIUrl":"https://doi.org/10.1002/brx2.70030","url":null,"abstract":"Hematoxylin and eosin (H&E)-stained histopathological slides contain abundant information about cellular and tissue morphology and have been the cornerstone of tumor diagnosis for decades. In recent years, advancements in digital pathology have made whole-slide images (WSIs) widely applicable for diagnosis, prognosis, and prediction in brain cancer. However, there remains a lack of systematic tools and standardized protocols for using handcrafted features in brain cancer histological analysis. In this study, we present a protocol for handcrafted feature analysis in brain cancer pathology (PHBCP) to systematically extract, analyze, model, and visualize handcrafted features from WSIs. The protocol enabled the discovery of biomarkers from WSIs through a series of well-defined steps. The PHBCP comprises seven main steps: (1) problem definition, (2) data quality control, (3) image preprocessing, (4) feature extraction, (5) feature filtering, (6) modeling, and (7) performance analysis. As an exemplary application, we collected pathological data of 589 patients from two cohorts and applied the PHBCP to predict the 2-year survival of glioblastoma multiforme (GBM) patients. Among the 72 models combining nine feature selection methods and eight machine learning classifiers, the optimal model combination achieved discriminative performance with an average area under the curve (AUC) of 0.615 over 100 iterations under five-fold cross-validation. In the external validation cohort, the optimal model combination achieved a generalization performance with an AUC of 0.594. We provide an open-source code repository (GitHub website: https://github.com/XuanjunLu/PHBCP) to facilitate effective collaboration between medical and technical experts, thereby advancing the field of computational pathology in brain cancer.","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing brain tumor diagnosis: Deep siamese convolutional neural network as a superior model for MRI classification 推进脑肿瘤诊断：深连体卷积神经网络作为MRI分类的优越模型

Brain-X Pub Date : 2025-04-25 DOI: 10.1002/brx2.70028

Gowtham Murugesan, Pavithra Nagendran, Jeyakumar Natarajan

{"title":"Advancing brain tumor diagnosis: Deep siamese convolutional neural network as a superior model for MRI classification","authors":"Gowtham Murugesan, Pavithra Nagendran, Jeyakumar Natarajan","doi":"10.1002/brx2.70028","DOIUrl":"https://doi.org/10.1002/brx2.70028","url":null,"abstract":"The timely detection and precise classification of brain tumors using techniques such as magnetic resonance imaging (MRI) are imperative for optimizing treatment strategies and improving patient outcomes. This study evaluated five state-of-the-art classification models to determine the optimal model for brain tumor classification and diagnosis using MRI. We utilized 3064 T1-weighted contrast-enhanced brain MRI images that included gliomas, pituitary tumors, and meningiomas. Our analysis employed five advanced classification model categories: machine learning classifiers, deep learning-based pre-trained models, convolutional neural networks (CNNs), hyperparameter-tuned deep CNNs, and deep siamese CNNs (DeepSCNNs). The performance of these models was assessed using several metrics, such as accuracy, precision, sensitivity, recall, and F1-score, to ensure a comprehensive evaluation of their classification capabilities. DeepSCNN exhibited remarkable classification performance, attaining exceptional precision and recall values, with an overall F1-score of 0.96. DeepSCNN consistently outperformed the other models in terms of F1-score and robustness, setting a new standard for brain tumor classification. The superior accuracy of DeepSCNN across all classification tasks underscores its potential as a tool for precise and efficient brain tumor classification. This advance may significantly contribute to improved patient outcomes in neuro-oncology diagnostics, offering insight and guidance for future studies.","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Defensin: The immune system regulatory factor against peripheral nerve disease 防御素：免疫系统对周围神经疾病的调节因子

Brain-X Pub Date : 2025-03-31 DOI: 10.1002/brx2.70022

Tiantian Qi, Qi Yang, Haotian Qin, Yuanchao Zhu, Jinyuan Chen, Hongfa Zhou, Jian Weng, Hui Zeng, Fei Yu

引用次数: 0