Abstract 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.

Abstract A steady‐state visually evoked potential (SSVEP) is a brain response to specific frequencies of visual stimuli, including their harmonic frequencies. However, this signal is susceptible to interference from spontaneous and rhythms in electroencephalography (EEG) signals because they overlap from 8 to 40 Hz. This can reduce the recognition accuracy of SSVEP brain–computer interfaces (BCIs). To address this problem, a variational mode decomposition–based filter bank canonical correlation analysis (VMD‐FBCCA) algorithm is proposed, which integrates the adaptive characteristics of VMD and the training‐free nature of the FBCCA algorithm. First, the EEG signal of each channel is transformed into intrinsic mode functions (IMFs) by the VMD algorithm, which extracts frequency components of the SSVEP from each IMF. Next, a particle swarm algorithm is employed to optimize the weights of the IMFs and reconstruct the EEG signals. This reconstruction selectively enhances the IMFs in the target SSVEP frequency band while suppressing interference from other bands. Finally, the reconstructed EEG is classified using FBCCA to decode the SSVEP‐BCI signal. To evaluate its effectiveness, the proposed algorithm is tested on datasets from the BCI Competition. The results demonstrate that VMD‐FBCCA outperforms FBCCA, showing improvements in both the average recognition accuracy and information transmission rate (8.91 bits/min). Moreover, the best recognition accuracy achieved for individual subjects is enhanced by .

Abstract In this article, we present the case for the adoption of a neurodiversity paradigm as an essential framework within the brain and behavioral sciences. We challenge the deficit‐focused medical model by advocating for the recognition of neurocognitive variances—including autism, ADHD, dyslexia, schizophrenia, and bipolar disorder—as natural representations of human diversity. We call for a shift in research and practice towards valuing neurodivergent individuals' unique strengths and contributions and promoting inclusivity and empathy. In critiquing the tendency to pathologize cognitive differences, we argue for a re‐evaluation of therapeutic goals to reflect a more nuanced understanding of neurodiversity. Highlighting the socio‐ethical implications of therapy‐focused research, we urge an appreciation of the potential for innovation and problem‐solving that neurodivergent individuals bring to society. The conclusion is a call to action for an integrated approach in research, policy, and societal attitudes that affirms neurodiversity, fostering an environment in which all forms of cognitive functioning are celebrated as part of human advancement.

Abstract This easy‐to‐follow handbook offers a straightforward guide to electroencephalogram (EEG) analysis using Python, aimed at all EEG researchers in cognitive neuroscience and related fields. It spans from single‐subject data preprocessing to advanced multisubject analyses. This handbook contains four chapters: Preprocessing Single‐Subject Data, Basic Python Data Operations, Multiple‐Subject Analysis, and Advanced EEG Analysis. The Preprocessing Single‐Subject Data chapter provides a standardized procedure for single‐subject EEG data preprocessing, primarily using the MNE‐Python package. The Basic Python Data Operations chapter introduces essential Python operations for EEG data handling, including data reading, storage, and statistical analysis. The Multiple‐Subject Analysis chapter guides readers on performing event‐related potential and time‐frequency analyses and visualizing outcomes through examples from a face perception task dataset. The Advanced EEG Analysis chapter explores three advanced analysis methodologies, Classification‐based decoding, Representational Similarity Analysis, and Inverted Encoding Model, through practical examples from a visual working memory task dataset using NeuroRA and other powerful packages. We designed our handbook for easy comprehension to be an essential tool for anyone delving into EEG data analysis with Python (GitHub website: https://github.com/ZitongLu1996/Python‐EEG‐Handbook; For Chinese version: https://github.com/ZitongLu1996/Python‐EEG‐Handbook‐CN).

Semantic reconstruction of continuous language from non-invasive brain recordings is an emerging research field that aims to decode the meaning of words, sentences,1 or even entire narratives from neural activity patterns recorded using non-invasive techniques like electroencephalography or magnetoencephalography.2 Semantic reconstruction of continuous language from non-invasive brain recordings can potentially to transform our understanding of how the brain processes language. Tang et al.3 presented a novel method for reconstructing continuous language from cortical semantic representations of functional magnetic resonance imaging (fMRI) recording of neural activity in the brains of three human participants while they listened to spoken stories. They decoded the fMRI signals using a neural network and reconstructed the auditory and semantic content of the stories. Their findings are crucial in developing brain–computer interfaces (BCIs) that can facilitate communication between humans and machines. Their research developed a BCI that can decode continuous language from non-invasive recordings to construct cortical semantic representations and reconstruct word sequences that recover the meaning of perceived speech, imagined speech, and even silent videos. Their study explored the viability of non-invasive language BCIs, which may provide advice or references for potential scientific and practical applications in the future. Tang et al.'s method introduces an innovative approach to explore language processing in the brain with fMRI. While their approach does not surmount fMRI's inherent low temporal resolution of fMRI, it employs a strategy that generates candidate word sequences, helping to gathering insights into the neural substrates and mechanisms associated with language processing. This method offers a nuanced perspective by leveraging some aspects of the fMRI data and grounding its analysis on certain assumptions about the statistical patterns in natural language processing. Conventional fMRI studies have grappled with challenges when delving into language processing due to the inherent lag in the blood oxygen level-dependent response. While not real-time, Tang et al.'s method, offers a direction that deviates from traditional static maps, like those presented by Huth et al.,4 and prompts considerations into a richer understanding of the brain's approach to language. BCIs have been instrumental in restoring communication capabilities to individuals who have lost the ability to speak. Previously, these technologies primarily relied on invasive methods, which were impractical for broader applications. The technological novelty of this BCI lies in its ability to decode continuous language from cortical semantic representations. Historically, fMRI's low temporal resolution posed a significant hurdle to achieving this feat. The authors tackled this challenge through an ingenious approach by generating candidate word sequences and scoring the likelihood of each candidate evoking the recorded brain responses. They accomplished this by employing an encoding model that predicts the subject's brain responses to natural language. Furthermore, the authors demonstrated the BCI's versatility by showing that it could decode language from multiple regions across the cortex. Another remarkable aspect is the emphasis on mental privacy, with the study reporting that successful decoding requires subject cooperation. As this technology becomes more advanced, its implementation of such technology also raises ethical considerations, particularly regarding mental privacy and the potential for misuse. Developing appropriate guidelines and regulations to protect individuals' privacy is vital. Another significant ethical concern is informed consent. Individuals who participate in studies involving non-invasive brain recordings should be fully informed of the risks and benefits of the study and should provide informed consent before participating. One of the key future directions of this field is developing more accurate and efficient decoding algorithms. While the current decoding algorithms have shown promising results, there is still room for improvement. Future research should focus on developing algorithms that are more robust to individual differences and can decode language in real-time.5 Another important future direction is exploring the neural mechanisms underlying language processing. While we have made significant progress in decoding language from non-invasive brain recordings, our understanding of the neural mechanisms underlying language processing remains limited. Future research should focus on elucidating these mechanisms to improve our ability to decode language from brain recordings. Another important future direction is translating this technology into clinical settings. Therefore, future research should focus on developing clinical applications of this technology and evaluating its efficacy in clinical settings. Overall, while semantic reconstruction of continuous language from non-invasive brain recordings is a promising technology with many potential applications, there are still significant technical and ethical challenges remain that must be addressed. By continuing to push the boundaries of this technology while adhering to ethical principles and ensuring regulatory oversight and transparency, we can maximize its benefits while minimizing its risks. Zhao Chen, Yanping Wang and Nannan Shi conceived and developed this commentary. Zhao Chen: Writing—original draft. Ning Liang, Haili Zhang, Huizhen Li, and Xiangwei Dai edited and approved the final version. We thank the anonymous reviewers for their valuable comments and improvement suggestions that enabled us to improve this commentary. We also thank the China Academy of Chinese Medical Sciences' Independent Selection Project (Z0830) and the Institute of Basic Research in Clinical Medicine's Independent Selection Project (Z0830-1) for supporting this work. All authors declare no conflicts of interest. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

A recent study, titled “A NPAS4-NuA4 Complex Couples Synaptic Activity to DNA Repair,” reveals an exciting new mechanism by which neurons maintain genomic stability in response to external stimuli. Published in Nature on February 23, 2023,1 this paper provides valuable insights into the molecular mechanisms underlying neurodegenerative diseases and brain aging that pave the way for the development of new therapeutic strategies. This commentary reviews the research findings and their potential for future applications. The brain is a highly dynamic and plastic organ that relies on neurons to modify their gene expression under a variety of pathophysiological conditions. Excessive or prolonged neuronal response to external stimuli can lead to DNA damage and genomic instability, thereby being harmful to the brain. Cumulative DNA damage in the neuronal genome is also a hallmark of neurodegeneration and brain aging. Until recently, the molecular mechanism by which neurons repair DNA and maintain genomic stability in response to external stimuli remained unclear. Neuronal PAS Domain Protein 4 (NPAS4) is an activity-induced transcription factor that is selectively expressed in neurons following membrane depolarization and subsequent calcium signaling. Through a series of biochemical and genomic experiments on mice, the researchers first determined that NPAS4 exists as part of a 21-protein complex called NPAS4-NuA4. They then measured DNA damage using γH2AX ChIP-seq, sBLISS-seq, END-seq and SAR-seq, which indicated that NPAS4 preferentially binds to active DNA breaking-induced sites in neurons. These advanced techniques can be used to analyze the binding sites of transcription factors,2 provide whole-genome maps with DNA double-strand breaks,3 monitor DNA terminal excision,4 and localize DNA repair synthesis in vivo and in various cell types.5 Thus, they could contribute greatly to future research in neuroscience and other biomedical fields. In addition, the NPAS4-NuA4 complex serves as a critical mediator in the mechanism underlying neurodegenerative diseases and aging in the brain. Activated in response to neuronal activity driven by changes in sensory experience, NPAS4-NuA4 binds to periodically damaged transcriptional regulatory elements and recruits DNA repair machinery to prevent age-dependent somatic mutation accumulation. Furthermore, impaired NPAS4-NuA4 leads to a range of cellular defects that result in a shortened lifespan, including dysregulation of neuronal activity-dependent transcriptional responses, loss of somatic inhibition of pyramidal neurons, impaired localization of protective repair machinery, and genomic instability. One of the most exciting aspects of this study is its discovery of a link between damage and neuronal activity-dependent regulatory elements and neuronal dysfunction in neurodegenerative diseases. Preventing mutations in the NPAS4-NuA4 complex during neurodevelopment and carefully balancing the proper ratio of excitatory and inhibitory neurons are key to sustaining neuronal survival. This conclusion suggests a new avenue for the development of drugs for Alzheimer's and Parkinson's diseases that target the NPAS4-NuA4 complex and its molecular pathways. The research also provides insight into the complex interplay between gene expression and DNA repair in aging. By targeting the DNA of hippocampal neurons in mice of different ages, researchers found that gene regulatory elements to which NPAS4–NuA4 binds are partially protected against age-dependent accumulation of somatic mutations. This finding suggests that promoting the protection of those recurrently broken sites can help maintain genome integrity and thus prevent mutation. It is worth considering whether all cell types in the body (i.e., beyond neurons) may regulate transcriptional activity, protect vulnerable DNA sites, and maintain genomic stability according to the type of stimulation they receive. There may be activity-dependent protective mechanisms for genome stability that remains unknown and thus need to be explored. Moreover, the unique repair mechanism proposed in this paper was based on mouse models and has not yet been verified in the human nervous system. If this could be explored in humans, the functions and upstream regulatory mechanisms of various protein components of the NPAS4-NuA4 complex could be further clarified to provide a better understanding of the occurrence and development of neurodegeneration. In conclusion, this study demonstrates the complex interplay between neuronal activity, gene expression, and DNA repair in the brain. Its findings broaden our knowledge of the pathogenesis of neurodegenerative diseases and aging in the brain and contribute to the field of neuroscience with important implications for the development of novel therapeutic approaches. Conglin Wang: Writing – original draft. Xintong Ge: Writing – original draft; Writing – review & editing. Wenqiang Xin: Writing – review & editing. Ping Lei: Writing – review & editing. This research was supported by Haihe Laboratory of Cell Ecosystem Innovation Fund (HH22KYZX0048), the National Natural Science Foundation of China (82271401, 82071394, 82072166), Science and Technology Foundation of Tianjin (21JCYBJC01180), and Science and Technology Project of Tianjin Municipal Health Commission (TJWJ2021QN005). The authors declare no conflicts of interest.

In recent years, the field of biosensors has seen significant advances in the development of fluorescent sensors, including quantum dots,1 upconversion nanoparticles,2 and fluorescent proteins,3 to monitor the generation of information in living systems. The fluorescence of these sensors can be observed by shining a laser at them. However, conventional fluorescent sensors are limited in their ability to image signals in deep tissues because most of the light is absorbed or scattered as it penetrates the tissue. To address this challenge, a team led by Jasanoff developed a novel sensor that converts light into a magnetic signal that is unaffected by absorption or scattering. This allows the response of the light detector to be visualized using magnetic resonance imaging (MRI; Figure 1).4 The development of this sensor has significant potential to improve our understanding of information processing in deep tissues. Schematic illustration of mapping light distribution in tissues using MRI-detectable photosensitive liposomes. To fabricate the photosensitive MRI probe, magnetic particles were encapsulated in light-responsive azobenzene-conjugated liposomes (called Light-LisNRs).5 By adjusting the composition and proportion of the lipid bilayer molecules, these liposome nanoparticles can switch from being permeable to being impenetrable, depending on the type of light exposure. This property allowed modulation of the MRI contrast of the Light-LisNRs and facilitated the optimization of the switchable longitudinal relaxation time (T1). Specifically, upon exposure to ultraviolet (UV) light, the Light-LisNRs became more permeable to water, resulting in a strong interaction between the magnetic particles and water and thereby producing strong MRI signals. Conversely, exposure to blue light caused the Light-LisNRs to become impermeable to water, resulting in the lack of a detectable MRI signal. The optimized Light-LisNRs could potentially be used to map light distribution in live animals. When these nanoparticles were injected into the living rat brain, they effectively diffused through the brain by convection, as evidenced by changes in the magnetic resonance signal. The probes exhibited exceptional light sensitivity, which could be demonstrated by changes in magnetic relaxation under blue and UV irradiation. Relative to the initial baseline, the probes showed significant differences in the mean MRI signals in response to UV and blue light, and the temporal characteristics of the light response observed during repeated photoperiods were consistent. The steady performance of Light-LisNRs in the rat brain suggests that they are suitable for the quantitative measurement of the light intensity distribution in tissues. In addition, the researchers used a hybrid model consisting of a beam spreading function combined with a homogeneous photon diffusion term to fit the experimental data and produced a quantitative map of the distribution of light emitted by the optical fiber implanted near the brain's striatum. These results highlight the potential of the optimized Light-LisNRs in mapping the distribution of light in living animals. In summary, this study describes the design of a novel sensor and its application elucidating light propagation in optically opaque environments. The sensor exploits the photomodulation of liposomal permeability to enhance the contrast produced by contrast agent molecules, leading to improved visualization in MRI. The results of this work demonstrate the potential of Light-LisNRs as a versatile tool for photon detection and highlight opportunities for further optimization through adjustments to action spectra, absorption cross sections, and contrast agent packaging parameters. The sensing approach outlined here holds promise for the future development of MRI probes capable of detecting stimuli beyond light, such as neurochemicals or other molecular species in the brain. In addition, the sensor may serve as a valuable tool for monitoring patients undergoing light-based therapies, including photodynamic therapy, which uses lasers to ablate cancer cells. Lei Luo: Conceptualization, Visualization, Writing - Original draft; Dandan Yang: Writing - Reviewing & Editing; Yu Yang: Conceptualization, Funding Acquisition, Writing - Reviewing & Editing. The authors declare no conflicts of interest.

No abstract is available for this article.