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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

Persistent corneal pain (PCP) has excellent research prospects, especially the central sensitization mechanism of the trigeminal nerve, which is involved in migraine, corneal pain, and trigeminal neuralgia. The cornea has dense sensory innervation, and repeated corneal neuropathic pain has been associated with trigeminal nerve central sensitization, which is induced in PCP. The calcitonin gene-related peptide (CGRP) is involved in corneal pain conduction, injury protection, and immune homeostasis. A high CGRP level maintains corneal pain perception and protects corneal epithelial cells. However, a persistently high CGRP level causes hypersensitivity of the corneal and trigeminal nerves, resulting in PCP. CGRP-related drugs can effectively improve trigeminal nerve sensitization and relieve central sensitization-related pain (PCP, migraine, and trigeminal neuralgia). Exploring the role of CGRP in PCP's pain sensitization mechanism is vital in the pain perception field, with the potential to improve the quality of life of patients with PCP and strengthen the understanding of CGRP's dual role in corneal pain.

Sonogenetics is a non-invasive approach that selectively modulates neural activities using ultrasound-reactive mediators.¹ An acoustic pressure gradient is generated by introducing ultrasound waves into tissues. Since optogenetics, which is currently widely used for modulating neural activities, is invasive as it requires surgeries, a physiologically safer modulation technique is in need. Sonogenetics has a high temporal resolution and is non-invasive, accurately targeting the brain region of interest without affecting other tissues.² A recent landmark study observed several beneficial bio-effects with the G22S mutant of the large conductance mechanosensitive ion channel MscL in mice.

MscL sonogenetics could accurately target deep brain circuits such as dopamine (DA) circuits by creating a dual-viral vector strategy: one containing a Cre-recombinase-dependent enhanced yellow fluorescent protein (EYFP) or MscL-G22S-EYFP fragment and the other controlling the tyrosine hydroxylase promoter modulating Cre recombinase expression. The ventral tegmental area reward circuitry was activated to test fiber photometry (FP) recording. The authors then inserted optical fibers into the nucleus accumbens (NAc) to monitor DA activity by measuring Da2m fluorescence changes. There was a rapid increase in DA2m fluorescence in the NAc of mutant McsL-G22S mice after being inserted at a 0.3 MPa pressure, but there was no increase in fluorescence for mutant EYFP mice. Therefore, MscL sonogenetics was effective for inducing DA release in neurons.

Another beneficial bio-effect for MscL sonogenetics in MscL-G22S mice was that stimulating the dorsal striatum (dSTR) neurons generated a motor response. By measuring the fluorescence changes of jRGE-CO1a (a genetically encoded calcium sensor with red fluorescence) using FP, results illustrated that applying MscL sonogenetics to the dSTR successfully induced neural activation. Mice were stimulated with ultrasound in an open-field box experiment. The results showed that MscL-G22S mice had significantly increased locomotion activity compared to EYFP mice. In addition, mobility speed and motor activity increased in the MscL-G22S mice but did not change in the EYFP mice.

Furthermore, employing MscL sonogentics show alleviation effects of Parkinson's disease (PD) symptoms in freely moving mice by injecting 6-hydroxydopamine (6-OHDA) into their brains to selectively activate neurons in the subthalamus (STN). They showed the alleviation of movement symptoms in PD mice. In baseline experiments, 6-OHDA-treated PD mice showed decreased retention time in the rotarod test. However, after US stimulation, retention time significantly increased for MscL + PD mice but not for EYFP + PD mice (control). Finally, an open-field experiment demonstrated improvement in motor functions for PD mice. The MscL + PD mice showed increased movement distances and longer mobile time. Therefor

Given the unmet medical needs for stroke rehabilitation, neurotechnologies with innovative rationales and good designs hold promise for restoring motor function in patients worldwide. These features are of unique importance in developing a more physiologically based, individualized, precise therapy to improve motor function prognoses, even after a chronic stroke. Among the emerging neurotechnologies, deep brain stimulation (DBS) enables precise modulation of specific neural circuits to enhance motor recovery for neurological disorders such as stroke.¹ In a paper recently published in Nature Medicine, Baker et al. proposed a masterful DBS approach based on the therapeutic proposition of alleviating crossed cerebellar diaschisis (CCD) to address upper-extremity hemiparesis since ascending input from the dentato-thalamo-cortical (DTC) pathway can activate the ipsilesional motor cortex and beyond, including prefrontal and parietal areas. In this first-in-human study, the authors highlighted the potential of combining DBS electrodes inserted into the contralateral dentate nucleus (DN-DBS) with rehabilitation therapy as a novel approach with clinical significance for adults with hemiparesis 1–3 years after a middle cerebral artery infarction.²

The DN-DBS protocol is grounded on elegant anatomical and neurophysiological knowledge, which provides the foundation for applying DBS to the contralateral dentate nucleus. The DTC pathway comprises the dominant ascending fibers projecting from the cerebrum into the ipsilesional motor, prefrontal, and parietal regions. Excitatory input to the cerebellar hemisphere can be reduced after a middle cerebral artery ischemia due to disruption of the corticopontocerebellar pathway. Consequently, the decreased activation of the dentate nucleus lowers its output to the ipsilesional motor-related cortices, which was shown to be associated with reduced motor performance in patients after a stroke. Therefore, neuromodulation of the dentate nucleus may enhance cortical excitability to promote motor recovery in these patients. As reported in this study, the trial intervention was feasible and well tolerated, although adverse events occurred in all patients, and the recruitment rate was relatively low.

Driven by the CCD hypothesis, the scientific rationale of this neurostimulation configuration could benefit from reporting the extent of diaschisis within the pathway.³ Consequently, inspecting the associations between cortico-cerebellar connectivity and the participants' preservation of gross motor impairment and distal dexterity would be more convincing. Notably, the latter is a crucial determinant in assessing intervention response, as the post-hoc subgroup analysis indicates. Nonetheless, incorporating structural, functional, or neuroelectrophysiological integrity measures into the scheduled visits would be highly beneficial to substantiate

Neurostimulation has emerged as a potential remedy for early mild cognitive impairment (EMCI). However, further exploration is needed on how external stimulation of brain regions promotes the transition of the brain state from EMCI to health and the selection of target locations. In this study, a functional magnetic resonance imaging dataset was used to evaluate the brain states of healthy individuals and patients with EMCI to explore the probabilistic metastable substate space, identifying abnormal manifestations of EMCI. Stimulation targets were then identified and stimulated to achieve complete controllability of the effective connection network for EMCI. A whole-brain model successfully fitted the brain state of the patients with EMCI based on diffusion tensor imaging data. Based on this whole-brain model, stimulation of the hippocampus, medial frontal gyrus, suboccipital gyrus, and fusiform gyrus can promote the transformation of the brain state from EMCI to health. The findings reveal the underlying brain mechanisms of cognitive decline in patients with EMCI and the stimulation targets of the neural mechanisms of EMCI restoration, which could help in designing more effective therapeutic interventions for EMCI.

The brain governs the behaviors of natural species (including humans and animals), which serves as a central hub integrating incoming sensory signals from the constantly changing environment. Recent cutting-edge technologies in neuroscience from behavioral¹ and neural levels² have enabled precise and comprehensive measurements. However, the environment–brain–behavior dataset is difficult to interpret because of its high-dimensional nature. To address this challenge, latent embedding has emerged as a promising technique with the property of dimensionality reduction, which can facilitate the identification of common environment–brain–behavior patterns (Figure 1).

The main idea of extracting latent embeddings is to eliminate dataset redundancy. It requires an algorithm to transform the raw dataset to a new low-dimensional feature space with little information loss. Classically, principal component analysis has been used to linearly transform raw data to an orthogonal space. However, owing to the existence of non-linear structures in nature, the linear transform cannot avoid high information loss in low dimensions. Thus, several non-linear dimensionality reduction methods (t-distributed stochastic neighbor embedding [t-SNE]⁴ and uniform manifold approximation and projection for dimension reduction [UMAP]⁵) have been developed. However, their non-linear features can reduce the interpretability. For instance, the hippocampus is responsible for representing spatial information and the direction of travel, but pure data-driven latent embeddings (t-SNE or UMAP) may confuse these two functions. These two functions are executed simultaneously, which requires interpretable hypotheses to separate them. Pure data-driven methods cannot introduce existing assumptions to refine latent embeddings. However, using a recent neural network encoder (CEBRA),³ this problem can be fully solved. CEBRA addresses this issue by incorporating both supervised and self-supervised learning approaches. By providing supervision through space or direction labels, CEBRA can identify distinct coding patterns in the neural activities of the hippocampus across different latent dimensions, ensuring dimensional alignment with interpretable prior knowledge.

The main process of CEBRA uses contrastive learning, which was developed to obtain low-dimensional embeddings that are both interpretable and exhibit high performance across various applications.³ The contrastive learning technique aims to discover common and distinguishable attributes by contrasting samples, and it optimizes joint latent embeddings from multiple sources, including sensory inputs, brain activities, and behaviors. CEBRA's non-linear encoder combines input data from multiple modalities and uses auxiliary labels to enhance the interpretability. As a result, CEBRA can

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.