Brain-X最新文献

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 au

Owing to their superior capabilities and advanced achievements, Transformers have gradually attracted attention with regard to understanding complex brain processing mechanisms. This study aims to comprehensively review and discuss the applications of Transformers in brain sciences. First, we present a brief introduction of the critical architecture of Transformers. Then, we overview and analyze their most relevant applications in brain sciences, including brain disease diagnosis, brain age prediction, brain anomaly detection, semantic segmentation, multi-modal registration, functional Magnetic Resonance Imaging (fMRI) modeling, Electroencephalogram (EEG) processing, and multi-task collaboration. We organize the model details and open sources for reference and replication. In addition, we discuss the quantitative assessments, model complexity, and optimization of Transformers, which are topics of great concern in the field. Finally, we explore possible future challenges and opportunities, exploiting some concrete and recent cases to provoke discussion and innovation. We hope that this review will stimulate interest in further research on Transformers in the context of brain sciences.

Most neuroimaging studies of the pathogenesis of early mild cognitive impairment (EMCI) rely on a node-centric network model, which only calculates correlations between brain regions. Considering the interaction of low-order correlations between pairs of brain regions, we use an edge-centric network model to study high-order functional network correlations. Here, we compute edge time series (eTS) to obtain overlapping communities and study the relationship between subnetworks and communities in space. Then, based on the overlapping communities, we calculate the normalized entropy to measure the diversity of each node. Next, we compute the high-amplitude co-fluctuation of the eTS to explore the pattern of brain activity with temporal precision. Our results show that the normal control and EMCI patients differ in brain regions, subnetworks, and the whole brain. In particular, entropy values show a gradual decrease, and brain network co-fluctuation increases with disease progression. Our study is the first to investigate the pathogenesis of EMCI from the perspective of spatiotemporal flexibility and cognitive diversity based on high-order edge connectivity, further characterizing brain dynamics and providing new insights into the search for biomarkers of EMCI.

Sensorineural hearing loss (SNHL) is a common otologic condition caused by damage to hair cells and spiral ganglion neurons that affects transmission pathways. Most of these cells cannot be regenerated, and there has been no breakthrough in regeneration techniques for inner ear cells. SNHL has a high incidence rate and can cause a variety of clinical symptoms, greatly impacting people's daily lives. With limited clinical treatments, the search for critical targets is urgent. Studies have shown that inflammation is prevalent in the pathogenesis of SNHL and plays a significant role in it. Inflammation is a normal body defense response, and a systemic anti-inflammatory approach is undesirable. It is crucial for us to identify potential targets of inflammation in SNHL and take measures specifically targeting those targets with minimal systemic impact. This paper firstly describes the role of inflammation in various types of SNHL and then provides an overview of the interactions between inflammation and cochlear immunity, cochlear microcirculation, vascular spasm, and glutamate metabolism and finally comprehensively examines the feasibility of targets in these interactions. This paper is expected to facilitate the development of targeted anti-inflammation for SNHL and provide strategies and approaches for treating clinical SNHL.

Spinal cord injuries (SCIs) often cause individuals to suffer from painful illnesses and debilitating disabilities. Excessive reactive oxygen species (ROS) generation in injured tissues hampers treatment effectiveness. Unfortunately, there is presently no established clinical remedy for addressing SCI, particularly the injuries related to ROS. However, the materials science and technology field has made remarkable progress, resulting in the development of a wide range of biomaterials with unique properties for regulating ROS. This review aims to summarize the latest advancements in ROS-targeted biomaterials designed specifically for the treatment of SCIs. Key scientific challenges in the evolution of ROS-targeted neuroprotection strategies are also discussed. We anticipate that this comprehensive summary will be valuable to new researchers and highlight specific future avenues of research, contributing to the further advancement of ROS-targeted biomaterials for SCI treatment.

Traditional computing architectures based on complementary metal-oxide semiconductor technology suffer from von Neumann computing bottleneck,¹ resulting in poor computing efficiency and a huge energy consumption. To surpass the limits of conventional computation, scientists have begun to imitate the computational behavior of the human brain.² With the advantages of highly parallel computing, high error tolerance and low power consumption, the human brain and its neural systems have inspired the rapid development of novel neuromorphic computing hardware.³ There are ∼86 billion neurons in the biological neural system. Neurons can govern the membrane potential for associative learning, memory, and information processing, with important roles in brain-inspired neuromorphic computing. Therefore, constructing artificial neuron via electronic devices is key to the realization of neuronal dynamics in the human brain.

Different types of memristive neurons have been reported recently, such as phase-change memory, Mott insulators, magnetic memory, diffusive memristors and ferroelectric memory. The integrate-and-fire neuron function and spiking neural networks could be simulated based on the integration characteristic of these artificial neurons. Besides the characteristic of integration, nonlinearity is another necessary characteristic in neuronal emulation, especially for integrating the datastream during neuromorphic computing. However, the realization of nonlinear integration of excitatory and inhibitory postsynaptic potentials has not been reported in above artificial neurons. It is in urgent need to develop a novel artificial neuron with both nonlinear and integrated capabilities for high-efficiency computing.

The research team of Harish Bhaskaran proposed an atomically thin optomemristive feedback neuron using a stack of MoS₂, WS₂, and graphene (Figure 1).⁴ The heterojunction of MoS₂/WS₂ acts as a neural membrane, and the graphene acts as neural soma. Different from traditional artificial neurons, the proposed two-dimensional (2D) neuron device could exhibit a rectified-type of nonlinearity in its output characteristics without the need for additional circuitry and software. The 2D optomemristive neuron shows great potential in winner-take-all learning (WTA) computational tasks and unsupervised learning, which provide guidance for atomic-scale rectified and nonlinear optoelectronic neurons.

The key performance of device is based on the combination and broadcast of electrical excitatory signals and optical inhibitory signals, which could be used for nonlinear and rectified integration of information in neuromorphic computing. Under light illumination, electron-hole pairs could be induced and separated by the intrinsic field in transition metal dichalcogenides. The electrons transit from the het

Approximately 20% of cancer patients experience brain metastases in the advanced stages as circulating tumor cells migrate to and colonize the brain microvasculature. Due to the challenges associated with biopsies, our understanding of the tumor microenvironment and heterogeneity in brain metastases remains limited, hindering the development of systemic approaches for detection and treatment. Emerging evidence suggests that specific brain metastases induce a substantial level of immune activation and infiltration, which provides an opportunity to design specific immunotherapies targeting brain metastases. This perspective aims to summarize recent advancements in molecular profiling of the immune repertoires of brain metastases using biopsy-based approaches, with an emphasis on tumor-reactive T cells. Additionally, we discuss the potential of alternative tissues and technologies that offer improved temporal resolution, throughput, and fidelity for tracking tumor dynamics.

A growing body of evidence suggests that patients who experience traumatic brain injuries (TBIs) exhibit significantly shorter healing periods compared to those with isolated fractures. However, the precise underlying mechanism behind this phenomenon remains unclear. Recent studies have shed light on the potential role of hormonal signals and neural circuits originating in the hypothalamus, which play vital roles in regulating the skeletal system. Despite these advances, there is a lack of comprehensive research summarizing the crucial role of bone healing in TBIs and the underlying mechanisms. This review aimed to explore the underlying mechanisms responsible for the accelerated bone healing observed in TBI patients, with a specific focus on the intricate crosstalk between TBI and bone remodeling. Additionally, we comprehensively discuss and summarize the beneficial effects of TBI on the skeletal system and examine the TBI-induced signaling pathways that result in accelerated fracture healing and bone remodeling. By dissecting these pathways, we aim to identify potential targets for intervention and bone repair promotion.

Disabilities of the central nervous system (CNS), including chronic degeneration, threaten human life. Cell-based therapy is one of the promising treatment strategies, but obtaining enough functional neurons and surgically transplanting them represent major obstacles in clinical neuroscience. In recent years, cell reprogramming technology has broken the traditional understanding of cell biology and advanced rapidly. Adult cells can be reprogrammed into induced pluripotent stem cells (iPSCs) and converted into somatic cells from different lineages, such as induced neurons (iNs).

Direct neuronal reprogramming (dNR) is an emerging biotechnology with significant biomedical potential to produce functional iNs.¹ Methods to obtain functional neurons for adult CNS therapy are limited and rely mainly on stem cell differentiation. iPSC reprogramming, firstly reported in 2006, opened the door to obtaining embryonic stem cell (ESC)-like cells. Since then, protocols for direct cell reprogramming (transdifferentiation or conversion) have been widely tested due to the risk and cost of iPSCs. These methods force cells to change lineages from one to another without passing through the pluripotent state and have inspired a new understanding of biology and ushered in a new era in cell technology.

dNR is mainly based on the overexpression of various transcription factors (TFs). Different TF formulas, such as Ascl1/Brn2/Myt1L (converting human fibroblasts into dopaminergic iNs) and Sox2/Ascl1 (converting human pericytes into iNs) have been proposed in the laboratories. TFs such as Sox2 alter not only the transcriptome profile but also the chromatin structure; thus, they are heavily influential in cell reprogramming. On the other hand, biochemists performed dNR using small molecules (SMs). Mechanism studies showed that sequential treatment with various SMs can trigger various signal pathways, resulting in a boost in the reprogramming efficiency or direct generation of iNs. However, understanding in SM-triggered dNR is insufficient, such as the underlying biological mechanism, partial electrophysiological functions and production of neuron transmitters.

Epigenetic modulations, using biochemical and biophysical methods, have been observed during dNR. Fluctuations in the epigenetic state can induce a certain degree of cell identity disorder; thus, dNR can be triggered (so-called epigenetic reprogramming). Additional modulation of the chromatin and metabolism of the starter cells can enhance the efficiency of dNR. Through epigenetic modulation, biophysical forces, such as cell squeezing² and substrate topography,³ have been reported to facilitate dNR and regulate the ratios of iN subtypes. Advantages of using biophysical forces are that these stimulators are well defined and do not enter the cells. They generate unique mechanotransduction signalings through cytoskeletal and cell n

The human brain is one of the most complex and mysterious systems known to science. Despite the significant advances in neuroscience over the past few decades, our understanding of how the brain works remains limited. One of the key challenges in understanding brain function is determining its relationship with brain structure. However, a recent article published in Nature titled “Geometric Constraints on Human Brain Function” presents an innovative approach to understanding the complex interplay between brain structure and function.¹

The article argues that the physical architecture of the brain imposes geometric constraints on its function. Specifically, the authors propose that the arrangement and structure of neural connections play a vital role in determining the brain's functional capabilities. The article describes how the brain can be viewed as a network of interconnected nodes and edges, with the nodes representing neurons and the edges representing the connections between neurons.

The authors present several examples supporting this concept. They demonstrate how certain brain regions have higher degrees of connectivity, while others exhibit more localization of function. For instance, regions of the brain that are responsible for motor control have higher connectivity, while those that mediate sensory processing are more specialized.

The article also discusses how changes in connectivity due to disease or injury can lead to functional impairment. For example, an injury in the parietal cortex, which is involved in spatial awareness, can affect an individual's ability to navigate their surroundings. Similarly, changes in connectivity in the amygdala, which is involved in processing emotions, can cause mood disorders and anxiety.

Another interesting concept presented in the article is how the geometry of neural connections may be optimized for specific functions, such as object recognition or language processing. The authors propose that this optimization may be achieved through the connectivity of subnetworks with different geometries within the brain.

One of the strengths of the article is the use of mathematical models and simulations to test the proposed hypotheses. The authors developed a set of models that demonstrated how the geometry of neural connections affected brain function in different scenarios, such as the execution of motor tasks or the recognition of objects. These simulations provided evidence supporting the hypothesis that the brain's functional capabilities are determined, to some extent, by its physical geometry.

However, some limitations of the article should also be noted. First, the article relied heavily on mathematical modeling and simulation, which may not accurately reflect the complexity of the brain. Second, the study's focus on the physical structure of the brain may ignore the roles of other factors, such as genetic and environmental infl