Brain-X最新文献

{"title":"Development of a plasma biomarker diagnostic model as a screening strategy for Alzheimer's disease in older inpatients","authors":"Xiaoxia Fang,&nbsp;Zhengke Liu,&nbsp;Xiaojun Kuang,&nbsp;Xiushi Ni,&nbsp;Xu Han,&nbsp;Xuejun Wen,&nbsp;Hong Xu","doi":"10.1002/brx2.70029","DOIUrl":"https://doi.org/10.1002/brx2.70029","url":null,"abstract":"<p>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.</p>","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}

{"title":"From digitized whole-slide histology images to biomarker discovery: A protocol for handcrafted feature analysis in brain cancer pathology","authors":"Xuanjun Lu,&nbsp;Yawen Ying,&nbsp;Jing Chen,&nbsp;Zhiyang Chen,&nbsp;Yuxin Wu,&nbsp;Prateek Prasanna,&nbsp;Xin Chen,&nbsp;Mingli Jing,&nbsp;Zaiyi Liu,&nbsp;Cheng Lu","doi":"10.1002/brx2.70030","DOIUrl":"https://doi.org/10.1002/brx2.70030","url":null,"abstract":"<p>Hematoxylin and eosin (H&amp;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.</p>","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}

{"title":"Advancing brain tumor diagnosis: Deep siamese convolutional neural network as a superior model for MRI classification","authors":"Gowtham Murugesan,&nbsp;Pavithra Nagendran,&nbsp;Jeyakumar Natarajan","doi":"10.1002/brx2.70028","DOIUrl":"https://doi.org/10.1002/brx2.70028","url":null,"abstract":"<p>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.</p>","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}

{"title":"Defensin: The immune system regulatory factor against peripheral nerve disease","authors":"Tiantian Qi,&nbsp;Qi Yang,&nbsp;Haotian Qin,&nbsp;Yuanchao Zhu,&nbsp;Jinyuan Chen,&nbsp;Hongfa Zhou,&nbsp;Jian Weng,&nbsp;Hui Zeng,&nbsp;Fei Yu","doi":"10.1002/brx2.70022","DOIUrl":"https://doi.org/10.1002/brx2.70022","url":null,"abstract":"<p>Peripheral nerve disease is commonly encountered in orthopedics, neurology, and neurosurgery. Due to its large population, a substantial number of patients are affected by these conditions in China. Peripheral nerve disease has a high disability rate and current treatments show poor clinical efficacy, resulting in a heavy burden for patients and the country's healthcare system. Defensins are widespread proteins, commonly found in animals, plants, and fungi, with multiple subtypes able to kill a variety of pathogens. As regulatory factors of the immune system, defensins influence bodily function by participating in inflammatory processes, immune responses, and pathogen resistance; they can affect all stages of nerve conduction and play an important role in lesions of peripheral and effector nerves. This article provides a review of the possible roles and mechanisms of defensins in peripheral nerve disease.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741515","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}

{"title":"Brain–computer interfaces in 2023–2024","authors":"Shugeng Chen,&nbsp;Mingyi Chen,&nbsp;Xu Wang,&nbsp;Xiuyun Liu,&nbsp;Bing Liu,&nbsp;Dong Ming","doi":"10.1002/brx2.70024","DOIUrl":"https://doi.org/10.1002/brx2.70024","url":null,"abstract":"<p>Brain–computer interfaces (BCIs) have advanced at a rapid pace in recent years, particularly in the medical domain. This review provides a comprehensive summary of the progress made in medical BCIs during the 2023–2024 period, covering a wide range of topics from invasive to non-invasive techniques, and from fundamental mechanisms to clinical applications. The 2023–2024 period saw numerous research breakthroughs and clinical applications of BCI technology. As BCI hardware and software continue to evolve, and as the understanding of basic medical principles deepens, the expectation is that innovative BCI inventions will increasingly be introduced in clinical practice. Both invasive and non-invasive BCI technologies are paving the way for broader clinical applications. It is anticipated that BCI technologies will offer greater hope for disease treatment, provide additional methods of enhancing human bodily functions, and ultimately improve the quality of life.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741516","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}

{"title":"From uncertainty and entropy to coherence and consciousness","authors":"Majid Beshkar","doi":"10.1002/brx2.70027","DOIUrl":"https://doi.org/10.1002/brx2.70027","url":null,"abstract":"<p>Understanding the neural basis of consciousness remains a fundamental challenge in neuroscience. This study proposes a novel framework that conceptualizes consciousness through the lens of uncertainty reduction and negative entropy, emphasizing the role of coherence in its emergence. Sensory processing may operate as a Bayesian inference mechanism aimed at minimizing the brain's uncertainty regarding external stimuli, and conscious awareness emerges when uncertainty is reduced below a critical threshold. Computationally, this corresponds to minimizing informational uncertainty, while at a physical level it corresponds to reductions in thermodynamic entropy, thereby linking consciousness to negentropy. This study emphasizes the role of coherence in conscious perception and challenges existing models like Integrated Information Theory by exploring the potential contributions of quantum coherence and entanglement. Although direct empirical validation is currently lacking, we propose the hypothesis that consciousness acts as a cooling mechanism for the brain, as measured by the temperature of neuronal circuits. This perspective affords new insights into the physical and computational foundations of conscious experience and indicates a possible direction for future research in consciousness studies.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741519","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}

{"title":"Small-world network and neuroscience","authors":"Yan-Kun Han,&nbsp;Hai-Jun Zhang,&nbsp;Yu-Jing Chen,&nbsp;Chang Liu,&nbsp;Yu-He Zhang,&nbsp;Zhan-Jun Zhang,&nbsp;Run-Ting Jing,&nbsp;Li Guo,&nbsp;Da Li,&nbsp;Wen-Yue Chu,&nbsp;Wen-Jun Wu,&nbsp;Kan Zhang,&nbsp;Long-Biao Cui","doi":"10.1002/brx2.70025","DOIUrl":"https://doi.org/10.1002/brx2.70025","url":null,"abstract":"<p>Small-world networks are of great significance in the field of neuroscience. As the universal nature of the human brain network, their heterogeneous pattern of change in patients with different diseases may satisfy the need for auxiliary objective diagnostic tests. In recent years, combining non-invasive neuroimaging techniques (e.g., magnetic resonance imaging, electroencephalography, and magnetoencephalography) with graph-theory-based brain network topology analysis has provided a new direction for exploring neuroscience. In addition, researchers found more possible features for studying the diagnosis and treatment of neurological or psychiatric disorders based on the human brain's structural and functional connectivity patterns. Therefore, this review introduces the importance of small-world networks in neuroscience and the contribution of brain network topology analysis in treating and diagnosing mental and neurological disorders. It also summarizes the effects of lifestyle habits, the environment, and some novel therapeutic modalities on small-world brain networks. It concludes by discussing head-movement errors in the brain network topology analysis.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741518","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}

{"title":"Advances in brain computer interface for amyotrophic lateral sclerosis communication","authors":"Yuchun Wang,&nbsp;Yurui Tang,&nbsp;Qianfeng Wang,&nbsp;Minyan Ge,&nbsp;Jinling Wang,&nbsp;Xinyi Cui,&nbsp;Nianhong Wang,&nbsp;Zhijun Bao,&nbsp;Shugeng Chen,&nbsp;Jing Wang,&nbsp;Shumao Xu","doi":"10.1002/brx2.70023","DOIUrl":"https://doi.org/10.1002/brx2.70023","url":null,"abstract":"<p>Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that often results in the loss of speech, creating significant communication barriers. Brain–computer interfaces (BCIs) provide a transformative solution for restoring communication and enhancing the quality of life for ALS individuals. Recent advances in implantable electrocorticographic systems have demonstrated the feasibility of synthesizing intelligible speech directly from neural activity. By recording high-resolution neural signals from motor, premotor, and somatosensory cortices with decoding algorithms, these systems can transform neural patterns into acoustic features and intelligible speech, providing natural and intuitive communication pathways for ALS individuals. Non-invasive electroencephalography, while lacking the spatial resolution of electrocorticographic systems, offers a safer alternative with high temporal resolution for capturing speech-related neural dynamics. When combined with robust feature extraction techniques, such as common spatial pattern and time-frequency analyses, as well as multimodal integration with functional near-infrared spectroscopy or electromyography, it effectively enhances decoding accuracy and system robustness. Despite the progress, challenges remain, including user variability, BCI illiteracy, and the impact of fatigue on system performance. Personalized models, adaptive algorithms, and secure frameworks for brain data privacy are essential for addressing these limitations, enabling BCIs to enhance accessibility and reliability. Advancing these technologies and methodologies holds immense promise for restoring independence and bridging the communication gap for individuals with ALS. Future research could focus on long-term clinical studies to evaluate the stability and effectiveness of these systems, as well as the development of more natural and unobtrusive BCI paradigms.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735514","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}

{"title":"DeepSeek or ChatGPT: Can brain-computer interfaces/brain-inspired computing achieve leapfrog development with large AI models?","authors":"Long Bai,&nbsp;Shugeng Chen,&nbsp;Peng Wang,&nbsp;He Chen,&nbsp;Jiaqing Yan,&nbsp;Xiaojian Zhu,&nbsp;Enming Song,&nbsp;Bobo Tian,&nbsp;Jiacan Su,&nbsp;Xiaoli Li","doi":"10.1002/brx2.70021","DOIUrl":"https://doi.org/10.1002/brx2.70021","url":null,"abstract":"<p>Large language models, including DeepSeek and ChatGPT, have the potential to significantly advance brain-computer interfaces and brain-inspired computing by enhancing the accuracy of brain signal decoding and optimizing user interaction. In brain-computer interfaces, these models facilitate more precise and responsive communication, while in brain-inspired computing, they enable realistic simulation of neural networks and improved hardware energy efficiency. However, substantial challenges remain, particularly in healthcare applications and other broader fields.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622578","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}

{"title":"Cellular heterogeneity and inflammatory profiles in gliomas: Single-cell transcriptomic insights","authors":"Jiang Li,&nbsp;Liwei Qin,&nbsp;Kailong Xu,&nbsp;Jie Liu,&nbsp;Anren Xu,&nbsp;Yunlong Qu,&nbsp;XiaoLu Fu,&nbsp;Peng Wang,&nbsp;Yang Wang","doi":"10.1002/brx2.70013","DOIUrl":"https://doi.org/10.1002/brx2.70013","url":null,"abstract":"<p>This study investigates the transcriptional profiles of gliomas across different grades (WHO II-IV) and clinical states (primary vs. recurrent). Utilizing RNA-seq data from public databases (e.g., GEO), we analyzed low-grade gliomas and high-grade gliomas, including oligodendrogliomas, glioblastomas, and other glioma subtypes. Key analyses encompassed differential gene expression, glioma subpopulation characterization (e.g., glioma-associated microglia/macrophages), regulatory network construction (WGCNA and transcription factor activity), and cell state analysis comparing primary and recurrent gliomas. Our findings reveal distinct transcriptional signatures and identify potential biomarkers associated with glioma progression and recurrence.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481436","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}