Brain-X最新文献

The integration of artificial intelligence (AI) and brain–computer interfaces (BCIs) represents a significant advancement in neurotechnology, with broad potential applications in healthcare, communication, and human augmentation. This study examines the synergy between DeepSeek, a leader in efficient, open-source AI models, and next-generation BCI technologies. We analyze DeepSeek's contributions to model training efficiency, adaptive reasoning, and open-source accessibility, and propose a framework for BCI development that incorporates these innovations. Additionally, we explore how AI-driven neural signal processing, hardware optimization, and ethical AI–BCI systems can address the critical limitations of current BCI technologies, including signal fidelity, scalability, and real-world applicability. Finally, we offer recommendations for interdisciplinary collaboration, regulatory improvements, and equitable technology dissemination to foster the sustainable development of AI–BCI technology.

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.

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.

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.

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.

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.

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.

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.

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.

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.