In recent years, remarkable progress has been made in brain–computer interface (BCI) technology. This review comprehensively explores its applications in multiple diseases, such as stroke, disorders of consciousness, and Parkinson's disease. However, this approach poses challenges in terms of signal processing accuracy, ethic, and individual differences. Future research is crucial to address these challenges and unlock the full potential of BCIs in clinical practice.

There is a linear negative or U-shaped relationship between early serum uric acid (SUA) exposure levels and the diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index. The DTI-ALPS index negatively mediated the positive relationship between early cumulative SUA exposure levels and MoCA scores. Moderately high SUA levels within the normal range and stable SUA levels were associated with a reduced risk of future cognitive decline, highlighting the clinical relevance of early SUA management.

This study investigates the neurobiological basis of gender identity using structural magnetic resonance imaging and behavioral data from a large non-clinical cohort. Cortical morphology was analyzed alongside psychological and socioeconomic factors to examine identity as a dimensional construct. These findings highlight gender identity as a multidimensional trait embedded in distributed neural circuits, advancing understanding beyond binary sex-based models.

Carbon doped black phosphorus artificial synaptic devices as a promising fire monitoring system, exhibit exceptional perceptual capabilities for rapid fire event detection, effectively overcoming the limitations of conventional fire detection systems by leveraging hardware sensitivity and system processing speed.

Brain-computer interfaces (BCIs) aim to create a connection pathway for information exchange between the brain and devices with computing capabilities. The complexities of non-invasive BCIs and the implantation risks associated with invasive BCIs have confined these technologies to laboratory settings. A lack of foundational technologies of 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 are challenges that need to be solved for BCI commercialization.