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Edge-centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment 以边缘为中心的功能网络揭示了早期轻度认知障碍的新时空生物标志物
Pub Date : 2023-10-12 DOI: 10.1002/brx2.35
Weiping Wang, Ruiying Du, Zhen Wang, Xiong Luo, Haiyan Zhao, Ping Luan, Jipeng Ouyang, Song Liu

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.

大多数关于早期轻度认知障碍(EMCI)发病机制的神经影像学研究都依赖于以节点为中心的网络模型,该模型只计算大脑区域之间的相关性。考虑到大脑区域对之间的低阶相关性的相互作用,我们使用以边缘为中心的网络模型来研究高阶功能网络相关性。在这里,我们计算边缘时间序列(eTS)来获得重叠的社区,并研究空间中子网络和社区之间的关系。然后,基于重叠的群落,我们计算归一化熵来测量每个节点的多样性。接下来,我们计算eTS的高振幅共同波动,以探索具有时间精度的大脑活动模式。我们的研究结果表明,正常对照组和EMCI患者在大脑区域、子网络和整个大脑中存在差异。特别是,熵值逐渐降低,脑网络协同波动随着疾病进展而增加。我们的研究首次从时空灵活性和基于高阶边缘连接性的认知多样性的角度研究了EMCI的发病机制,进一步表征了大脑动力学,为寻找EMCI的生物标志物提供了新的见解。
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引用次数: 0
Understanding the role of inflammation in sensorineural hearing loss: Current goals and future prospects 了解炎症在感音神经性听力损失中的作用:当前目标和未来展望
Pub Date : 2023-10-12 DOI: 10.1002/brx2.34
Liling Li, Dan Chen, Xuexin Lin, Jia Luo, Jingqian Tan, Peng Li

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.

感觉神经性听力损失(SNHL)是一种常见的耳科疾病,由影响传播途径的毛细胞和螺旋神经节神经元损伤引起。这些细胞中的大多数不能再生,内耳细胞的再生技术也没有突破。SNHL发病率高,可引起多种临床症状,极大地影响人们的日常生活。由于临床治疗有限,迫切需要寻找关键靶点。研究表明,炎症在SNHL的发病机制中普遍存在,并在其中发挥着重要作用。炎症是一种正常的身体防御反应,系统抗炎方法是不可取的。至关重要的是,我们要确定SNHL中潜在的炎症靶点,并采取措施专门针对那些对系统影响最小的靶点。本文首先描述了炎症在各种类型SNHL中的作用,然后概述了炎症与耳蜗免疫、耳蜗微循环、血管痉挛和谷氨酸代谢之间的相互作用,最后全面考察了这些相互作用中靶点的可行性。本文有望促进SNHL靶向抗炎药的开发,并为临床治疗SNHL提供策略和方法。
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引用次数: 0
Reactive oxygen species targeted biomaterials for spinal cord injury therapy 活性氧靶向生物材料用于脊髓损伤治疗
Pub Date : 2023-10-10 DOI: 10.1002/brx2.32
Yanming Zuo, Yibo Ying, Zhiyang Huang, Jiamen Shen, Xiaokun Li, Zhouguang Wang

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.

脊髓损伤(SCIs)经常导致个人遭受痛苦的疾病和衰弱的残疾。损伤组织中过多的活性氧(ROS)生成阻碍了治疗效果。不幸的是,目前还没有确定的治疗SCI的临床药物,特别是与ROS相关的损伤。然而,材料科学技术领域取得了显著进展,开发出了一系列具有独特活性氧调节特性的生物材料。这篇综述旨在总结专门用于治疗SCIs的ROS靶向生物材料的最新进展。还讨论了ROS靶向神经保护策略进化过程中的关键科学挑战。我们预计,这一全面的总结将对新的研究人员有价值,并强调未来的具体研究途径,有助于ROS靶向的SCI治疗生物材料的进一步发展。
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引用次数: 0
Novel brain-inspired optomemristive feedback neuron for neuromorphic computing 一种用于神经形态计算的新型大脑启发的视觉反馈神经元
Pub Date : 2023-10-04 DOI: 10.1002/brx2.39
Jialin Meng, Lin Chen, Tianyu Wang, David Wei Zhang

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

基于可重构的柔性和视觉神经的记忆更新,通过合作学习视觉神经网络解决了旅行推销员问题的组合优化问题。总之,对于WTA计算,包括竞争学习和合作学习,光刺激神经元表现出校正类型的非线性和兴奋性和抑制性电位的整合能力。与传统的基于电荷的光电探测器和基于加热器的忆阻器不同,5个光阻神经元在高效神经形态计算方面显示出巨大的潜力。由于原子薄异质结(MoS2/WS2/石墨烯)的设计,光刺激神经元可以被全局光抑制和局部电兴奋信号激活。2D光反射神经元的这些关键特性确保了WTA计算可以通过原子薄的电子设备来实现。从我们的角度来看,传统集成电路面临着尺寸缩减和冯·诺依曼计算瓶颈的问题,这激发了对具有原子厚度和高效计算能力的新型2D光致发光神经元的需求。Harish Bhaskaran提出的2D视神经刺激神经元可以极大地促进高密度和小型神经形态计算系统的发展。尽管光反射神经元的详细参数需要进一步研究才能在系统级应用,但我们相信所提出的原子薄光反射反馈神经元为硬件机器学习提供了一种新的方法。孟:概念化;可视化;书写——原始草稿。林晨:回顾与编辑。王天宇:评论与编辑。张大卫:评论与编辑。作者声明没有利益冲突。
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引用次数: 0
Understanding the heterogeneous immune repertoire of brain metastases for designing next-gen therapeutics 了解脑转移瘤的异质性免疫系统以设计下一代治疗方法
Pub Date : 2023-10-01 DOI: 10.1002/brx2.33
Zongjie Wang, Kangfu Chen

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.

大约20%的癌症患者在晚期经历脑转移,因为循环肿瘤细胞迁移到脑微血管并定植于脑微血管。由于与活检相关的挑战,我们对脑转移瘤的肿瘤微环境和异质性的了解仍然有限,阻碍了系统检测和治疗方法的发展。新出现的证据表明,特异性脑转移诱导了相当水平的免疫激活和浸润,这为设计针对脑转移的特异性免疫疗法提供了机会。这一观点旨在总结使用基于活检的方法对脑转移免疫库进行分子分析的最新进展,重点是肿瘤反应性T细胞。此外,我们还讨论了替代组织和技术的潜力,这些组织和技术为跟踪肿瘤动力学提供了改进的时间分辨率、吞吐量和保真度。
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引用次数: 0
The mechanism of bone healing after traumatic brain injury 颅脑损伤后骨愈合的机制
Pub Date : 2023-09-28 DOI: 10.1002/brx2.31
Yuan Xiong, Wenbin Zhong, Bobin Mi

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.

越来越多的证据表明,与孤立骨折的患者相比,经历创伤性脑损伤的患者表现出明显更短的愈合期。然而,这一现象背后的确切潜在机制尚不清楚。最近的研究揭示了源自下丘脑的激素信号和神经回路的潜在作用,它们在调节骨骼系统中发挥着至关重要的作用。尽管取得了这些进展,但缺乏全面的研究来总结骨愈合在创伤性脑损伤中的关键作用及其潜在机制。这篇综述旨在探索在TBI患者中观察到的加速骨愈合的潜在机制,特别关注TBI和骨重塑之间复杂的串扰。此外,我们全面讨论和总结了TBI对骨骼系统的有益作用,并研究了TBI诱导的导致骨折愈合和骨重塑加速的信号通路。通过剖析这些途径,我们旨在确定干预和促进骨修复的潜在靶点。
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引用次数: 0
Direct neuronal reprogramming for central nervous system regeneration 中枢神经系统再生的直接神经元重编程
Pub Date : 2023-09-28 DOI: 10.1002/brx2.36
Peng-Yuan Wang, Weihong Song

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

中枢神经系统的残疾,包括慢性退化,威胁着人类的生命。基于细胞的治疗是一种很有前途的治疗策略,但获得足够的功能神经元并通过手术移植是临床神经科学的主要障碍。近年来,细胞重编程技术打破了对细胞生物学的传统理解,取得了突飞猛进的发展。成体细胞可以被重新编程为诱导多能干细胞(iPSC),并转化为来自不同谱系的体细胞,如诱导神经元(iNs)。直接神经元重编程(dNR)是一种新兴的生物技术,具有产生功能性iNs的重大生物医学潜力。1获得用于成人中枢神经系统治疗的功能性神经元的方法有限,主要依赖于干细胞分化。2006年首次报道的iPSC重编程为获得胚胎干细胞(ESC)样细胞打开了大门。从那时起,由于iPSC的风险和成本,直接细胞重编程(转分化或转化)的方案已被广泛测试。这些方法迫使细胞在不经过多能状态的情况下从一个谱系改变到另一个谱系,激发了对生物学的新理解,并开创了细胞技术的新时代。dNR主要基于各种转录因子(TF)的过表达。实验室中提出了不同的TF配方,如Ascl1/Brn2/Mit1L(将人成纤维细胞转化为多巴胺能iNs)和Sox2/Ascl1(将人周细胞转化为iNs)。TF如Sox2不仅改变转录组图谱,而且改变染色质结构;因此,它们在细胞重编程中具有重要影响。另一方面,生物化学家使用小分子(SM)进行dNR。机制研究表明,用各种SM连续治疗可以触发各种信号通路,从而提高重编程效率或直接产生iNs。然而,对SM触发的dNR的理解还不够,例如潜在的生物学机制、部分电生理功能和神经元递质的产生。使用生物化学和生物物理方法,在dNR期间观察到表观遗传学调节。表观遗传学状态的波动可以诱导一定程度的细胞身份紊乱;因此可以触发dNR(所谓的表观遗传重编程)。染色质和启动细胞代谢的额外调节可以提高dNR的效率。据报道,通过表观遗传学调节,生物物理力,如细胞挤压2和底物形貌3,可以促进dNR并调节iN亚型的比率。使用生物物理力的优点是这些刺激器定义明确,不会进入细胞。它们通过细胞骨架和细胞核变形产生独特的机械转导信号,这有利于简化原始方案。生物物理刺激可以被可溶性激活剂或抑制剂取代,然后与TF和SM结合。神经元亚型,如胆碱能神经元,通过释放神经递质发挥特定功能。非神经元细胞,如胶质细胞和星形胶质细胞,与中枢神经系统微环境中的神经元协调。这些神经元和非神经元细胞的数量和比例对大脑功能至关重要。因此,使用dNR技术生成特定神经元亚型的能力至关重要。不幸的是,当前的dNR协议通常会产生混合细胞亚型,尽管这一点没有提及,而且通常只有一种神经元亚型被表征。因此,监控整个dNR过程至关重要;例如,现在可以使用活细胞成像系统和单细胞RNA测序。CRISPRa等精确基因编辑技术可以提高所需iN亚型的效率和纯度。值得注意的是,非特异性标记或细胞融合可能导致误导性追踪。在应用iNs.dNR之前,细胞纯化或细胞分选是必要的。已经在小鼠大脑中报道了dNR,但只有少数方案成功。体内dNR允许将局部非神经元细胞,如星形胶质细胞,甚至受损/老化的神经元,转化为功能性iNs。有趣的是,体内dNR配方与体外方案有很大不同。例如,单个TF,如NeuroD1,可以在体内将周细胞、星形胶质细胞或胶质细胞重新编程为功能性突触iNs。已经测试了各种TF配方来提高效率和神经元电路集成,结果很有希望。另一方面,使用SM,成年小鼠大脑中的星形胶质细胞可以转化为iNs。4然而,SM诱导的dNR具有相当大的风险。例如,染色质修饰和代谢变化发生在dNR过程中。dNR过程中染色质的布线错误可能导致异常命运。在iPSC重编程中已经报道了3D染色质环的变化。
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引用次数: 0
The anatomy of the brain constrains its function 大脑的解剖结构限制了它的功能
Pub Date : 2023-09-27 DOI: 10.1002/brx2.38
Haofuzi Zhang, Xiaofan Jiang

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

人类大脑是科学界已知的最复杂、最神秘的系统之一。尽管在过去几十年里神经科学取得了重大进展,但我们对大脑如何工作的理解仍然有限。理解大脑功能的关键挑战之一是确定其与大脑结构的关系。然而,最近发表在《自然》杂志上的一篇题为《人脑功能的几何约束》的文章为理解大脑结构和功能之间的复杂相互作用提供了一种创新的方法。1文章认为,大脑的物理结构对其功能施加了几何约束。具体而言,作者提出,神经连接的排列和结构在决定大脑的功能能力方面发挥着至关重要的作用。这篇文章描述了如何将大脑视为一个由相互连接的节点和边缘组成的网络,节点代表神经元,边缘代表神经元之间的连接。作者列举了几个支持这一概念的例子。他们展示了某些大脑区域如何具有更高程度的连接,而其他区域则表现出更多的功能定位。例如,大脑中负责运动控制的区域具有更高的连接性,而那些介导感觉处理的区域则更专业。文章还讨论了由于疾病或损伤导致的连接变化如何导致功能损伤。例如,与空间意识有关的顶叶皮层损伤会影响个体驾驭周围环境的能力。同样,参与处理情绪的杏仁核连接的变化也会导致情绪障碍和焦虑。文章中提出的另一个有趣的概念是,如何针对特定功能(如对象识别或语言处理)优化神经连接的几何结构。作者提出,这种优化可以通过大脑中具有不同几何形状的子网络的连接来实现。这篇文章的优势之一是使用数学模型和模拟来检验所提出的假设。作者开发了一组模型,展示了神经连接的几何形状如何在不同场景中影响大脑功能,例如运动任务的执行或物体的识别。这些模拟提供了证据,支持大脑的功能能力在某种程度上由其物理几何形状决定的假设。然而,也应当注意到该条的一些局限性。首先,这篇文章严重依赖数学建模和模拟,可能无法准确反映大脑的复杂性。其次,这项研究对大脑物理结构的关注可能忽略了其他因素在塑造大脑功能方面的作用,如遗传和环境影响。最后,虽然文章提出了有趣的假设,但还需要进一步的实证研究来检验它们。尽管如此,这篇文章仍然是对神经科学领域的创新和重大贡献。它为概念化大脑结构和功能之间的关系提供了一种令人兴奋的新方法,并为未来的研究提出了有趣的问题。结构-功能耦合是指生物系统的物理结构与其执行的功能之间的关系。2这种理解对于确定结构变化(无论是由遗传因素还是环境因素引起)如何影响功能并最终导致疾病至关重要。疾病状态的个体差异可能受到结构-功能耦合变化的显著影响。例如,基因突变可能会改变蛋白质的结构,从而可能影响其功能并导致疾病。类似地,组织或器官的结构变化(例如,由于损伤、衰老或环境压力源)会破坏正常功能并导致疾病发展。此外,结构-功能耦合的个体差异会影响疾病在不同个体中的表现和进展。3这可以解释为什么患有相同疾病的个体可能表现出不同的症状或对相同治疗的反应不同。这篇文章对理解神经连接几何结构的贡献之一是潜在的神经疾病新治疗方法的开发。例如,如果疾病或损伤导致的连接模式变化会导致功能损伤,那么旨在恢复神经连接正常几何形状的干预措施可能是有益的。此外,正如作者所指出的,大脑几何结构和连接的基本原理可能会为设计更强大、更强大的人工智能系统提供信息。
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引用次数: 0
ChatGPT: The cognitive effects on learning and memory ChatGPT:认知对学习和记忆的影响
Pub Date : 2023-09-20 DOI: 10.1002/brx2.30
Long Bai, Xiangfei Liu, Jiacan Su

As the integration of artificial intelligence (AI) into our daily lives and educational environments becomes increasingly prevalent, it is necessary to understand the way in which these technologies impact cognitive functions. AI models such as ChatGPT hold immense promise for advancing the field of education, making it easier than ever for educators to support personalized learning and for students to access information. However, there are risks associated with increased AI engagement; individuals may become over-reliant on AI, resulting in a reduced capacity for critical thinking, or a decline in memory retention. This article provides a comprehensive survey of these potential impacts, emphasizing the need for the judicious utilization of AI, and advocating for an integration approach that supplements, rather than supplants, human cognitive functions. The paper concludes by encouraging further research into the long-term cognitive effects of interacting with advanced AI models such as ChatGPT.

随着人工智能(AI)融入我们的日常生活和教育环境变得越来越普遍,有必要了解这些技术对认知功能的影响。ChatGPT等人工智能模型在推进教育领域有着巨大的前景,使教育工作者比以往任何时候都更容易支持个性化学习和学生获取信息。然而,人工智能参与度的提高也存在风险;个体可能会过度依赖人工智能,导致批判性思维能力下降,或记忆力下降。本文对这些潜在影响进行了全面调查,强调了明智利用人工智能的必要性,并倡导一种补充而非取代人类认知功能的整合方法。论文最后鼓励进一步研究与ChatGPT等先进人工智能模型交互的长期认知影响。
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引用次数: 1
Prediction of the hand function part of the Fugl-Meyer scale after stroke using an automatic quantitative assessment system 用自动定量评估系统预测中风后Fugl-Meyer量表的手功能部分
Pub Date : 2023-08-22 DOI: 10.1002/brx2.26
Shugeng Chen, Xiaolei Lin, Jianghong Fu, Yeye Qian, Zihang Chen, Zhanbo Huang, Qiang Liu, Xiaofeng Lu, Jie Jia

Hand function assessment is an essential component of the process of stroke rehabilitation because of the high incidence of hand motor dysfunction. In terms of the manual evaluation of hand function, the Fugl-Meyer scale is a recommended scale with high reliability and validity. However, the need for accurate assessments and increasing developments in technology has led to the promotion of automatic quantitative assessment systems for the hand. In this study, we collected quantitative data on hand function with an automatic system and the upper limb Fugl-Meyer assessment (FMA) from 79 people with stroke. We developed decision tree (DT) and gradient-boosted decision tree (GBDT) predictive models for the Fugl-Meyer score using features extracted from the Hand Automatic Quantitative Assessment System (HAQAS). Predictive performances were compared between these models regarding the predictive accuracy and Cohen's kappa. There were high correlations between features automatically collected by the HAQAS and the Fugl-Meyer scale in all the sub-items, with the maximal correlations all being over 0.5, indicating the high validity of the HAQAS in automatic FMA prediction. Hand functions were more highly correlated (average correlation coefficient 0.90) with HAQAS features than wrist functions (average correlation coefficient 0.54), and the GBDT achieved higher predictive accuracies and agreement than the DT algorithm. We conclude that the HAQAS is feasible for stroke patients with hand dysfunction and convenient for clinicians and therapists. This study was registered in the Chinese Clinical Trial Registry (ChiCTR1800019098).

手功能评估是中风康复过程中的一个重要组成部分,因为手部运动功能障碍的发生率很高。就手动评估手功能而言,Fugl-Meyer量表是一种推荐的量表,具有较高的信度和有效性。然而,对准确评估的需求和技术的不断发展导致了手部自动定量评估系统的推广。在这项研究中,我们使用自动系统收集了79名中风患者的手功能和上肢Fugl-Meyer评估(FMA)的定量数据。我们使用从手动定量评估系统(HAQAS)中提取的特征,为Fugl-Meyer评分开发了决策树(DT)和梯度增强决策树(GBDT)预测模型。比较了这些模型在预测准确性和Cohenκ方面的预测性能。在所有子项中,HAQAS和Fugl-Meyer量表自动收集的特征之间存在高度相关性,最大相关性均超过0.5,表明HAQAS在FMA自动预测中具有较高的有效性。与手腕功能(平均相关系数0.54)相比,手功能与HAQAS特征的相关性更高(平均相关性系数0.90),GBDT比DT算法实现了更高的预测精度和一致性。我们的结论是,HAQAS对手功能障碍的中风患者是可行的,并且对临床医生和治疗师来说是方便的。本研究已在中国临床试验注册中心(ChiCTR1800019098)注册。
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引用次数: 0
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