Brain-X最新文献

{"title":"Virtual external stimulation promotes the transformation of the brain state from early mild cognitive impairment to health","authors":"Weiping Wang,&nbsp;Weiwei Wang,&nbsp;Haiyan Zhao,&nbsp;Zhen Wang,&nbsp;Xiong Luo,&nbsp;Jipeng Ouyang","doi":"10.1002/brx2.41","DOIUrl":"https://doi.org/10.1002/brx2.41","url":null,"abstract":"<p>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.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.41","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138468624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent embeddings: An essential representation of brain–environment interactions","authors":"Yaning Han,&nbsp;Xiaoting Hou,&nbsp;Chuanliang Han","doi":"10.1002/brx2.40","DOIUrl":"https://doi.org/10.1002/brx2.40","url":null,"abstract":"<p>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 behavioral<span>¹</span> and neural levels<span>²</span> 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).</p><p>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]<span>⁴</span> and uniform manifold approximation and projection for dimension reduction [UMAP]<span>⁵</span>) 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),<span>³</span> 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.</p><p>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.<span>³</span> 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","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.40","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50136449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements and implications of semantic reconstruction of continuous language from non-invasive brain recordings","authors":"Zhao Chen,&nbsp;Ning Liang,&nbsp;Haili Zhang,&nbsp;Huizhen Li,&nbsp;Xiangwei Dai,&nbsp;Yanping Wang,&nbsp;Nannan Shi","doi":"10.1002/brx2.37","DOIUrl":"https://doi.org/10.1002/brx2.37","url":null,"abstract":"<p>Semantic reconstruction of continuous language from non-invasive brain recordings is an emerging research field that aims to decode the meaning of words, sentences,<span>¹</span> or even entire narratives from neural activity patterns recorded using non-invasive techniques like electroencephalography or magnetoencephalography.<span>²</span> Semantic reconstruction of continuous language from non-invasive brain recordings can potentially to transform our understanding of how the brain processes language.</p><p>Tang et al.<span>³</span> 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.</p><p>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.,<span>⁴</span> and prompts considerations into a richer understanding of the brain's approach to language.</p><p>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","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.37","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50136450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the brain with attention: A survey of transformers in brain sciences","authors":"Cheng Chen,&nbsp;Huilin Wang,&nbsp;Yunqing Chen,&nbsp;Zihan Yin,&nbsp;Xinye Yang,&nbsp;Huansheng Ning,&nbsp;Qian Zhang,&nbsp;Weiguang Li,&nbsp;Ruoxiu Xiao,&nbsp;Jizong Zhao","doi":"10.1002/brx2.29","DOIUrl":"https://doi.org/10.1002/brx2.29","url":null,"abstract":"<p>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.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.29","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50130471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Edge-centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment","authors":"Weiping Wang,&nbsp;Ruiying Du,&nbsp;Zhen Wang,&nbsp;Xiong Luo,&nbsp;Haiyan Zhao,&nbsp;Ping Luan,&nbsp;Jipeng Ouyang,&nbsp;Song Liu","doi":"10.1002/brx2.35","DOIUrl":"https://doi.org/10.1002/brx2.35","url":null,"abstract":"<p>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.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.35","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50130470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the role of inflammation in sensorineural hearing loss: Current goals and future prospects","authors":"Liling Li,&nbsp;Dan Chen,&nbsp;Xuexin Lin,&nbsp;Jia Luo,&nbsp;Jingqian Tan,&nbsp;Peng Li","doi":"10.1002/brx2.34","DOIUrl":"https://doi.org/10.1002/brx2.34","url":null,"abstract":"<p>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.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.34","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50130469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reactive oxygen species targeted biomaterials for spinal cord injury therapy","authors":"Yanming Zuo,&nbsp;Yibo Ying,&nbsp;Zhiyang Huang,&nbsp;Jiamen Shen,&nbsp;Xiaokun Li,&nbsp;Zhouguang Wang","doi":"10.1002/brx2.32","DOIUrl":"https://doi.org/10.1002/brx2.32","url":null,"abstract":"<p>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.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.32","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50128338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel brain-inspired optomemristive feedback neuron for neuromorphic computing","authors":"Jialin Meng,&nbsp;Lin Chen,&nbsp;Tianyu Wang,&nbsp;David Wei Zhang","doi":"10.1002/brx2.39","DOIUrl":"https://doi.org/10.1002/brx2.39","url":null,"abstract":"<p>Traditional computing architectures based on complementary metal-oxide semiconductor technology suffer from von Neumann computing bottleneck,<span>¹</span> 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.<span>²</span> 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.<span>³</span> 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.</p><p>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.</p><p>The research team of Harish Bhaskaran proposed an atomically thin optomemristive feedback neuron using a stack of MoS₂, WS₂, and graphene (Figure 1).<span>⁴</span> 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.</p><p>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","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.39","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50120696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the heterogeneous immune repertoire of brain metastases for designing next-gen therapeutics","authors":"Zongjie Wang,&nbsp;Kangfu Chen","doi":"10.1002/brx2.33","DOIUrl":"https://doi.org/10.1002/brx2.33","url":null,"abstract":"<p>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.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.33","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50119087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The mechanism of bone healing after traumatic brain injury","authors":"Yuan Xiong,&nbsp;Wenbin Zhong,&nbsp;Bobin Mi","doi":"10.1002/brx2.31","DOIUrl":"https://doi.org/10.1002/brx2.31","url":null,"abstract":"<p>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.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.31","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50155416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}