Brain-X最新文献

Serum uric acid (SUA) demonstrates dual roles as both an antioxidant and a pro-oxidant; however, its long-term effects on glymphatic function and cognitive performance remain unclear. This study included 944 participants in a longitudinal cohort to investigate the associations between the temporal progression of SUA levels and diffusion tensor imaging (DTI) analysis along the perivascular space (DTI-ALPS) with cognition. The results revealed that there were negative linear correlations between early average SUA levels and DTI-ALPS index for the left hemisphere (beta = −0.02, 95% confidence interval [CI] −0.04 to 0.00, p = 0.03), and cumulative SUA (cumSUA) levels and DTI-ALPS index for the left hemisphere (beta = −0.02, 95% CI −0.04 to −0.01, p = 0.01) and the whole brain (beta = −0.02, 95% CI −0.04 to 0.00, p = 0.03). A U-shaped relationship was observed between coefficient of variation of early SUA (CVSUA) levels and DTI-ALPS index in the left hemisphere (p = 0.01) and whole brain (p = 0.03). Higher early CVSUA levels, lower early cumSUA levels and reduced DTI-ALPS index were associated with lower MoCA scores. These findings suggest that moderately elevated SUA levels within the normal ranges, as well as stable SUA concentrations, may help mitigate future cognitive decline.

With the establishment of interdisciplinary platforms in medical engineering, the twenty-first century has witnessed rapid advancements in brain‒computer interface (BCI) technology, demonstrating significant potential in clinical applications. The fundamental framework of BCIs encompasses signal acquisition, preprocessing, feature extraction, signal translation, and control devices. This article explores the applications of BCIs across various disorders, including post-stroke motor recovery, consciousness disorders, mental health, and neurological diseases. Through technological innovations, BCI technology has assisted patients in overcoming communication and motor impairments resulting from neurological damage. By bypassing damaged neural pathways and enabling bidirectional interactions with brain signals, BCIs facilitate the gradual rehabilitation of motor functions in post-stroke patients and show promise in treating psychiatric disorders such as depression. Compared with traditional treatment methods, BCI technology has several unique advantages. Despite challenges in signal processing accuracy, hardware stability, and real-time data processing technology, technological innovation and interdisciplinary collaboration facilitate greater breakthroughs in the near future for BCIs. In summary, the clinical application of BCI technology presents unprecedented opportunities and challenges in modern healthcare, underscoring the need for continued research and development in this area.

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.

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.

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.

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.