Brain-X最新文献

Hybrid neurophysiological signals, such as the combination of electroencephalography (EEG) and electromyography (EMG), can be used to reduce road traffic accidents by obtaining the driver's intentions in advance and accordingly applying appropriate auxiliary controls. However, whether they can be used in combination and can achieve better results in situations of detecting emergency braking from normal driving and soft braking has not been explored. This study used one feature-level (hybrid BCI-FL) and three classifier-level (hybrid BCIs-CLs) hybrid strategies, the spectral band, and spectral point features to construct recognition models. Offline and pseudo-online experiments were conducted. The recognition performance with the spectral point features showed a better result than that with spectral band features. In all experiments, the two proposed hybrid BCI strategies could achieve a detection accuracy close to or above 95%, while the detection advanced time is less than 300 ms. In particular, for the developed hybrid BCI recognition models, the hybrid BCI-FL and hybrid BCI-CL2 recognition models with spectral point features achieved 4.25% (p < 0.015) and 4.69% (p < 0.006) higher system accuracies, respectively, than that of the current better single EMG-based recognition model. This research promotes the application of hybrid EEG and EMG signals in intelligent driving assistance systems.

Illustration of brain-inspired AI-driven scientific research: predicting new information, discovering novel therapies, and designing new materials.

Network analysis, an interdisciplinary method rooted in graph theory and complex systems, is a promising approach for advancing our understanding of the brain's complex architecture and its implications for behavior, cognition, and mental health. Network analysis transcends the traditional psychiatric diagnostic model, which oversimplifies mental disorders by treating them as distinct physical illnesses, often creating an “epistemic prison” that fails to account for the nuanced interplay between neurological, biological, psychosocial, and cultural influences shaped by patients' life experiences.¹ By mapping and examining the intricate network of neuronal connections and larger brain region interactions, network analysis offers deep insights into brain communication pathways, their role in cognitive function, and how their disruption may lead to neurological disorders. Despite the potential of this method, the application of network analysis in brain science is underutilized, highlighting the need for increased awareness and the development of network-based studies to fully realize its transformative potential for behavior and brain research. Therefore, we introduce an insightful behavioral exemplar to increase awareness of the potential application of network analysis in brain science.

In their landmark study, Hu et al. not only challenged the compartmentalization of psychiatric diagnoses but also provided a novel lens through which we can view mental disorders from a neurobiological perspective.² By employing network analysis, they illustrated that psychiatric symptoms occur in isolation but as a part of a complex network at the behavioral level, significantly resonating with a variety of human brain functions and structures. This approach underscores the centrality of the motivation and pleasure factor, which is potentially linked to the brain's reward system, and its significant impact on broader cognitive and social functioning across different psychiatric conditions. The study integrated the transdiagnostic model with sophisticated statistical methods, such as the least absolute shrinkage and selection operator, further elucidating ways to examine potential intricate brain–behavior relationships in the future.³ Such neuroscientific insights pave the way for a more nuanced understanding of psychopathology; additionally, they can inform targeted interventions that can modulate specific neural circuits implicated in multiple psychiatric disorders.

Although network analysis was employed behaviorally in this study, it offers methodological breakthroughs for prospective neurological studies, allowing for a unified representation of complex brain functions and statistically significant control over variables of interest. It illuminates how alterations in one node can reverberate throughout the entire network, providing a level of insight traditional models have f

Neurodegenerative diseases (NDs) stand for a group of disorders characterized by the progressive loss of neurons in the brain and peripheral organs, resulting in motor and cognitive dysfunction. The global prevalence of NDs, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis, is on the rise globally, primarily due to an aging population, positioning NDs as an increasing significant public health concern. Despite intensive research, few effective therapies that prevent or delay ND progression have been developed. Mounting evidence indicates that one of the well-defined risk factors for NDs is type 2 diabetes mellitus, and insulin resistance has also been proven to be related to cognitive decline. Certain antidiabetic drugs, such as glucagon-like peptide-1 receptor agonists, peroxisome proliferator-activated receptor gamma agonists, and metformin, have shown promise in offering neuroprotective benefits and alleviating ND symptoms beyond their glucose-lowering effects. Although the exact mechanisms remain elusive, these drugs offer a promising novel strategy for managing cognitive disorders. In this review, we first highlight the benefits and specific neuroprotective effects of multiple antidiabetic drugs and discuss the main mechanisms of action of antidiabetic drugs in treating NDs. These mechanisms include reducing protein aggregation and improving apoptosis, mitochondrial dysfunction, oxidative stress, and neuroinflammation. Finally, we summarize clinical trials evaluating these drugs for treating NDs.

Alzheimer's disease (AD), the most common form of dementia, is a progressive neurodegenerative disease characterized by cognitive deficits, β-amyloid (Aβ) accumulation-induced amyloid plaques, and tau hyperphosphorylation-induced neurofibrillary tangles.¹ Interestingly, emerging evidence suggests other factors may contribute to Aβ-associated pathologies.² β2-microglobulin (β2M), one of the major histocompatibility complex class I molecules, is a short peptide with seven antiparallel β-strands. It is elevated in AD brains and has recently been detected in the amyloid plaque core.³ Therefore, increasing evidence suggests β2M may be a potential factor that promotes Aβ aggregation and neurotoxicity.

A recent study in Nature Neuroscience conducted by Zhao et al. found that β2M may be a possible factor involved in amyloid pathologies.³ The authors characterized the pathological changes of β2M and elucidated the functional involvement of β2M in amyloid deposition and spreading and in boosting Aβ neurotoxicity.³ They concluded that β2M is an essential coaggregation factor with Aβ in amyloid pathology and β2M-Aβ coaggregation is a therapeutic target for AD. In addition, their findings indirectly support the amyloid hypothesis and provide additional information underlying Aβ aggregation and Aβ neurotoxicity. In the past 2 decades, all clinical trials based on the amyloid hypothesis on AD have failed, prompting reconsideration of the amyloid hypothesis.³ However, the current study performed by Zhao et al. confirmed that inhibition of Aβ deposition significantly improves cognitive function, indirectly supporting this hypothesis. More importantly, their findings revealed that β2M expressed in the central nervous system and peripheral tissues are potential targets for alleviating amyloid pathology and Aβ neurotoxicity. Disrupting the β2M-Aβ interactions ameliorated Aβ deposition and Aβ-associated pathogenesis, exhibiting a tremendous therapeutic potential for AD treatment. Overall, although Zhao et al. cannot exclude the possibility that MHC class I contributes to β2M-dependent neurotoxicity, their study identifies a previously undefined role of β2M in Aβ aggregation and neurotoxicity and offers a novel therapeutic strategy for AD by inhibiting peripheral β2M (Figure 1).

Meanwhile, their findings also raise several intriguing questions that deserve further investigation. First, Zhao et al. discovered β2M is mainly present in microglia, suggesting it would be interesting to study further the relationship between microglial β2M and microglial function. For example, it is of great interest to investigate the role of β2M in microglial-mediated phagocytosis, synapse pruning, and neuroinflammatory response in AD and other brain disorders. Moreover, single-cell technologies have found various phenotypes

Advanced technologies that can establish intimate, long-lived functional interfaces with neural systems have attracted increasing interest due to their wide-ranging applications in neuroscience, bioelectronic medicine, and the associated treatment of neurodegenerative diseases. A critical challenge of significance remains in the development of electronic platforms that offer conformal contact with soft brain tissue for the sensing or stimulation of brain activities and chronically stable operation in vivo, at scales that range from cellular-level resolution to macroscopic areas. This review summarizes recent advances in this field, with an emphasis on the use of demonstrated concepts, constituent materials, engineered designs, and system integration to address the current challenges. The article begins with an overview of recent bioelectronic platforms with unique form factors, ranging from filamentary probes to conformal sheets and three-dimensional frameworks for alleviating the mechanical mismatch between interface materials and neural tissues. Next, active interfaces which utilize inorganic/organic semiconductor-enabled devices are reviewed, highlighting various working principles of recording mechanisms including capacitively and conductively coupled sensing enabled by high transistor matrices at high spatiotemporal resolution. The subsequent section presents approaches to biological integration which use active materials for multiplexed addressing, local amplification and multimodal operation with high-channel-count and large-scale electronic systems in a safe fashion that provides multi-decade stable performance in both animal models and human subjects. The advances summarized in this review will guide the future direction of this technology and provide a basis for next-generation chronic neural interfaces with long-lived high-performance operation.

Neuromorphic computing, benefitting from its integration of computing with memory, enables highly efficient parallel-computing capabilities. While artificial intelligence chips are expensive due to their large area and power consumption, neuromorphic devices have shown energy efficiency and compatibility with complementary metal-oxide-semiconductor transistor technology.¹ Complex neuronal circuits with feedforward and feedback topologies are the foundation for nonlinear information integration and processing in the human brain. In addition, the nonlinear integration of neuronal signals as the basic functions of the human brain's nervous system is also essential to implement machine learning. However, artificial neurons still face the challenge of nonlinearly integrating feedforward and feedback signals. It is crucial to develop bio-plausible neurons capable of those functions, including nonlinearity and integration of excitatory and inhibitory postsynaptic signals. Writing in Nature Nanotechnology, G. S. Syed and coworkers recently reported a major step toward bio-plausible optomemristive feedback neurons, enabling the simultaneous existence of separate feedforward and feedback paths within a neural network.²

The authors designed a delicate capacitor-like device with a 2D vertical heterostructure in which WS₂/MoS₂ and graphene served as the neuronal membrane and soma (Figure 1B), respectively. Generally, trapped electrons and holes in the WS₂/MoS₂ heterostructure recombine upon a positive back gate voltage (Figure 1A). The conductance state of p-doped graphene would further increase, representing an excitatory operation. In this work, the electron-hole carriers in the WS₂/MoS₂ heterostructure are easily separated upon illumination (Figure 1C), and the electrons are injected into graphene. The Fermi-level movement toward the Dirac point decreases the conductance of graphene, having an inhibitory effect. Specifically, graphene's gradual conductance change can be separately modulated through electrical and optical means (Figure 1D), mimicking excitatory and inhibitory functionalities. 2D memristors have been investigated to emulate leaky-integrate-and-fire feedforward neurons.³ The synergistic effect of both input signals mimics a competitive neuron and enables the simultaneous existence of separate feedforward and feedback paths within the neural network.

The winner-take-all (WTA) neural network is a critical computational model for artificial neural networks, which can be used to implement unsupervised competitive learning and cooperative learning. The traditional memristors make it difficult to separately process feedforward and feedback neuronal signals, necessitating peripheral circuits or software to mimic inhibition behavior. The developed optomemristive feedback neuron can respond to both el

The genus Gelsemium belongs to the family Loganiaceae, one of the traditional Chinese herbs. Gelsemium is traditionally used to treat rheumatoid and neuropathic pain. Its root extracts were found to protect against anxiety, especially the alkaloids koumine and gelsemine. Indeed, koumine and gelsemine can act as positive agonists of the glycine receptor (GlyR), which reduces neuronal excitability through chloride influx and can also increase neuroactive steroid content by enhancing 3alpha-hydroxysteroid oxidoreductase (3α-HSOR) expression. The latter can activate the excitation-inhibitory response via the γ-aminobutyric acid type A receptor (GABA_AR), reduce the abnormal corticotropin-releasing hormone (CRH) increase in the hypothalamus, inhibit adrenocorticotropic hormone (ACTH) secretion, and effectively inhibit the abnormal ACTH and corticosterone increases in the circulation. In addition, koumine and gelsemine inhibited the expression of the NLR family pyrin domain containing 3 (NLRP3) inflammasome, inhibiting the release of inflammatory factors and regulating anxiety-related neural circuits. Gelsemine also inhibited the overexpression of brain-derived neurotrophic factor (BDNF) and cAMP response element-binding protein (CREB) in the hypothalamus to maintain the plasticity of brain neurons and protect neurogenesis to achieve anxiety regulation. In general, this article reviews the recent studies on Gelsemium in the anxiety field, discusses its possible antianxiety mechanism, and confirms the potential of Gelsemium as a therapeutic drug for anxiety-related diseases.

The quest for conscious machines and questions raised by the prospect of self-aware artificial intelligence (AI) fascinate some humans. OpenAI's ChatGPT, celebrated for its human-like comprehension and conversational abilities, is a milestone in that quest.^{1, 2} Early AI models were basic and rule-driven and mainly completed tasks like checking spelling and correcting grammar. Then, in 2010, recurrent neural network language models were trained to understand and generate text. ChatGPT, using transformer neural networks, produces coherent text and exemplifies this new kind of language model.³ Silicon Valley leaders claimed that these models and similar AI technologies will revolutionize various sectors and raised ethical and societal questions. As we explore AI's potential, we must navigate these implications and emphasize the necessity of using it responsibly. AI is a promising dream, but society must prepare to address the challenges likely to arise from wielding its transformative power.

Curious and skeptical, we explored a set of outputs ChatGPT produced when asked about the enigmatic concept of human consciousness. We began with a conceptual inquiry, asking ChatGPT to define consciousness (Figure S1). It eloquently described consciousness as “the reflection of being aware of oneself and the surrounding world” and acknowledged that the true nature of consciousness remains a mystery. The definition ChatGPT provided resembles the idea that consciousness is a state of wakefulness and self-awareness. Philosophers, neuroscientists, and psychologists are currently debating whether AI products are conscious and have yet to reach a consensus on criteria for determining when a machine is exercising judgment.⁴

After defining consciousness, ChatGPT described humans as conscious beings and emphasized that consciousness enables humans to perceive and cognize the world in complex ways. ChatGPT also acknowledged the uniqueness of human consciousness and highlighted that it is more advanced than that of other animals and AI systems. Human consciousness encompasses perception, cognition, emotions, and subjective experiences and enables people to recognize their existence, understand the external world, process information, and undergo unique conscious experiences. Its nature remains a subject of debate, and scholars in fields like philosophy, psychology, and neuroscience are working to understand it.

The conversation then turned to animal consciousness, which ChatGPT characterized as an ongoing research and philosophical puzzle. While some studies suggest that animals may exhibit a degree of awareness or self-awareness, ChatGPT underscored the difference between human and animal consciousness. Human cognition, with its capacity for reasoning and moral contemplation, stands apart from the instinct-driven fight-or-flight responses observed in animals.

The dialog cul

Proteins are often secreted and transited through cells or multiple organelles in physiological and pathological processes. Various interacting proteins are highly dynamic. Many proteins transiently interact with adjacent proteins with low affinity. This requires highly sensitive equipment for detection. For example, to monitor protein subcellular localization, transport, and interactions, we typically apply routine methods, such as imaging with high-resolution microscopy, to monitor fluorescently tagged proteins in live or formaldehyde-fixed cells. To detect the secreted target protein, we used enzyme-linked immunosorbent assays and western blotting. Because these methods are not often applied to detect dynamic changes in various proteins, researchers cannot perform protein profiling under diverse conditions. Most technologies can hardly decipher endogenous proteins that transit between specific organelles or cells. Professor Alice Y. Ting from Stanford University recently developed a novel technique called TransitID, and this technique can be expanded to several new applications, especially in neuroscience.¹

TransitID is based on proximity labeling (PL) and involves recombining various unrestrained enzymes, such as BioID, TurboID, and APEX2. These recombined enzymes label prey protein molecules near the fusion protein in the vicinity of the spatial region, allowing them to covalently connect known chemical groups, such as biotin or alkyne-phenol (AP), to nearby proteins, thus capturing prey proteins through the purification of reactive groups. PL has been widely used in vitro and in vivo cell systems to monitor and detect protein trafficking or interactions but has not been widely used in neuroscience, except in a few studies to investigate proteins that interact between cell membranes, secreted proteomic profiling, and so on.^{2, 3} Professor Ting's team combined dual-labeled proteins using PL enzymes to distinguish which proteins transited from the “source” location (the first labeling) to the “destination” location (the second labeling) via mass spectrometry. However, the TransitID system, a more delicate technique, has not been used in neuroscience thus far.

Researchers have developed four cellular applications: mapping cytosol-to-nucleus proteome shuttling, mapping proteome trafficking between the nucleolus and stress granules (SGs), mapping local versus cytosolic translation of mitochondrial proteins, and mapping exchanged endogenous proteins between two different types of cells. TurboID is expressed in the “source” location, and APEX2 is expressed in the “destination” location. Ting et al. found that TurboID can link biotin to substrate proteins. AP can also perform click-based derivatization of APEX2-tagged proteins. AP and biotin have specific affinity, membrane permeability, stability, and efficiency without having issues, such as apparent cytotoxicity, low recovery, or incom