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Simulating combined monoaminergic depletions in a PD animal model through a bio-constrained differential equations system. 通过生物约束微分方程系统模拟老年痴呆症动物模型中的合并单胺能耗竭。
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-23 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1386841
Samuele Carli, Luigi Brugnano, Daniele Caligiore

Introduction: Historically, Parkinson's Disease (PD) research has focused on the dysfunction of dopamine-producing cells in the substantia nigra pars compacta, which is linked to motor regulation in the basal ganglia. Therapies have mainly aimed at restoring dopamine (DA) levels, showing effectiveness but variable outcomes and side effects. Recent evidence indicates that PD complexity implicates disruptions in DA, noradrenaline (NA), and serotonin (5-HT) systems, which may underlie the variations in therapy effects.

Methods: We present a system-level bio-constrained computational model that comprehensively investigates the dynamic interactions between these neurotransmitter systems. The model was designed to replicate experimental data demonstrating the impact of NA and 5-HT depletion in a PD animal model, providing insights into the causal relationships between basal ganglia regions and neuromodulator release areas.

Results: The model successfully replicates experimental data and generates predictions regarding changes in unexplored brain regions, suggesting avenues for further investigation. It highlights the potential efficacy of alternative treatments targeting the locus coeruleus and dorsal raphe nucleus, though these preliminary findings require further validation. Sensitivity analysis identifies critical model parameters, offering insights into key factors influencing brain area activity. A stability analysis underscores the robustness of our mathematical formulation, bolstering the model validity.

Discussion: Our holistic approach emphasizes that PD is a multifactorial disorder and opens promising avenues for early diagnostic tools that harness the intricate interactions among monoaminergic systems. Investigating NA and 5-HT systems alongside the DA system may yield more effective, subtype-specific therapies. The exploration of multisystem dysregulation in PD is poised to revolutionize our understanding and management of this complex neurodegenerative disorder.

导言:帕金森病(Parkinson's Disease,PD)的研究历来侧重于黑质紧密团多巴胺分泌细胞的功能障碍,该细胞与基底节的运动调节有关。治疗方法主要以恢复多巴胺(DA)水平为目标,虽然有效,但疗效和副作用各不相同。最近的证据表明,帕金森病的复杂性牵涉到DA、去甲肾上腺素(NA)和5-羟色胺(5-HT)系统的紊乱,这可能是治疗效果变化的原因:我们提出了一个系统级生物约束计算模型,该模型全面研究了这些神经递质系统之间的动态相互作用。该模型旨在复制实验数据,证明在帕金森病动物模型中NA和5-羟色胺耗竭的影响,为了解基底神经节区域和神经调节剂释放区域之间的因果关系提供见解:结果:该模型成功地复制了实验数据,并预测了尚未探索的大脑区域的变化,为进一步研究提供了途径。尽管这些初步研究结果还需要进一步验证,但它凸显了针对神经丘脑和背侧剑突核的替代疗法的潜在疗效。敏感性分析确定了关键的模型参数,为了解影响脑区活动的关键因素提供了见解。稳定性分析强调了我们数学表述的稳健性,增强了模型的有效性:我们的整体方法强调了帕金森病是一种多因素疾病,并为利用单胺类药物系统之间错综复杂的相互作用开发早期诊断工具开辟了前景广阔的途径。在研究DA系统的同时研究NA和5-HT系统可能会产生更有效的亚型特异性疗法。对帕金森病多系统失调的探索将彻底改变我们对这种复杂神经退行性疾病的理解和管理。
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引用次数: 0
A functional contextual, observer-centric, quantum mechanical, and neuro-symbolic approach to solving the alignment problem of artificial general intelligence: safe AI through intersecting computational psychological neuroscience and LLM architecture for emergent theory of mind 解决人工通用智能对齐问题的功能性语境、以观察者为中心、量子力学和神经符号方法:通过交叉计算心理神经科学和 LLM 架构实现安全人工智能的新兴心智理论
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-08 DOI: 10.3389/fncom.2024.1395901
Darren J. Edwards
There have been impressive advancements in the field of natural language processing (NLP) in recent years, largely driven by innovations in the development of transformer-based large language models (LLM) that utilize “attention.” This approach employs masked self-attention to establish (via similarly) different positions of tokens (words) within an inputted sequence of tokens to compute the most appropriate response based on its training corpus. However, there is speculation as to whether this approach alone can be scaled up to develop emergent artificial general intelligence (AGI), and whether it can address the alignment of AGI values with human values (called the alignment problem). Some researchers exploring the alignment problem highlight three aspects that AGI (or AI) requires to help resolve this problem: (1) an interpretable values specification; (2) a utility function; and (3) a dynamic contextual account of behavior. Here, a neurosymbolic model is proposed to help resolve these issues of human value alignment in AI, which expands on the transformer-based model for NLP to incorporate symbolic reasoning that may allow AGI to incorporate perspective-taking reasoning (i.e., resolving the need for a dynamic contextual account of behavior through deictics) as defined by a multilevel evolutionary and neurobiological framework into a functional contextual post-Skinnerian model of human language called “Neurobiological and Natural Selection Relational Frame Theory” (N-Frame). It is argued that this approach may also help establish a comprehensible value scheme, a utility function by expanding the expected utility equation of behavioral economics to consider functional contextualism, and even an observer (or witness) centric model for consciousness. Evolution theory, subjective quantum mechanics, and neuroscience are further aimed to help explain consciousness, and possible implementation within an LLM through correspondence to an interface as suggested by N-Frame. This argument is supported by the computational level of hypergraphs, relational density clusters, a conscious quantum level defined by QBism, and real-world applied level (human user feedback). It is argued that this approach could enable AI to achieve consciousness and develop deictic perspective-taking abilities, thereby attaining human-level self-awareness, empathy, and compassion toward others. Importantly, this consciousness hypothesis can be directly tested with a significance of approximately 5-sigma significance (with a 1 in 3.5 million probability that any identified AI-conscious observations in the form of a collapsed wave form are due to chance factors) through double-slit intent-type experimentation and visualization procedures for derived perspective-taking relational frames. Ultimately, this could provide a solution to the alignment problem and contribute to the emergence of a theory of mind (ToM) within AI.
近年来,自然语言处理(NLP)领域取得了令人瞩目的进步,这主要得益于利用 "注意力 "开发基于转换器的大型语言模型(LLM)的创新成果。这种方法采用掩蔽式自我注意力,(通过类似的方式)在输入的标记词序列中确定标记词(单词)的不同位置,从而根据其训练语料库计算出最合适的反应。然而,有人猜测,仅靠这种方法是否能扩大规模,开发出新兴的人工通用智能(AGI),以及是否能解决 AGI 价值与人类价值的一致性问题(称为一致性问题)。一些探索一致性问题的研究人员强调,AGI(或人工智能)需要以下三个方面来帮助解决这个问题:(1)可解释的价值规范;(2)效用函数;(3)行为的动态背景说明。在此,我们提出了一个神经符号模型,以帮助解决人工智能中的这些人类价值一致性问题,该模型扩展了基于变压器的 NLP 模型,纳入了符号推理,可使 AGI 将多层次进化和神经生物学框架所定义的视角推理(即通过 deictics 解决对行为动态语境说明的需求)纳入名为 "神经生物学和自然选择关系框架理论"(N-Frame)的后斯金纳人类语言功能语境模型。本文认为,这种方法还有助于建立一个可理解的价值体系,通过扩展行为经济学的预期效用方程来考虑功能语境主义,从而建立效用函数,甚至建立一个以观察者(或目击者)为中心的意识模型。进化论、主观量子力学和神经科学的目标是进一步帮助解释意识,并通过与 N-Frame所建议的界面对应,在LLM中实现意识。这一论点得到了超图计算层面、关系密度集群、QBism定义的意识量子层面以及现实世界应用层面(人类用户反馈)的支持。有观点认为,这种方法可以让人工智能实现意识,并发展出脱敏透视能力,从而达到人类水平的自我意识、同理心和对他人的同情心。重要的是,通过双缝意向型实验和衍生透视关系框架的可视化程序,这一意识假设可以直接进行测试,其显著性约为 5 西格玛(350 万分之一的概率是由于偶然因素造成的)。最终,这将为对齐问题提供一个解决方案,并有助于在人工智能中出现心智理论(ToM)。
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引用次数: 0
Multiscale modeling of neuronal dynamics in hippocampus CA1 海马 CA1 神经元动态的多尺度建模
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-06 DOI: 10.3389/fncom.2024.1432593
Federico Tesler, Roberta Maria Lorenzi, Adam Ponzi, Claudia Casellato, Fulvia Palesi, Daniela Gandolfi, Claudia A. M. Gandini Wheeler Kingshott, Jonathan Mapelli, Egidio D'Angelo, Michele Migliore, Alain Destexhe
The development of biologically realistic models of brain microcircuits and regions constitutes currently a very relevant topic in computational neuroscience. One of the main challenges of such models is the passage between different scales, going from the microscale (cellular) to the meso (microcircuit) and macroscale (region or whole-brain level), while keeping at the same time a constraint on the demand of computational resources. In this paper we introduce a multiscale modeling framework for the hippocampal CA1, a region of the brain that plays a key role in functions such as learning, memory consolidation and navigation. Our modeling framework goes from the single cell level to the macroscale and makes use of a novel mean-field model of CA1, introduced in this paper, to bridge the gap between the micro and macro scales. We test and validate the model by analyzing the response of the system to the main brain rhythms observed in the hippocampus and comparing our results with the ones of the corresponding spiking network model of CA1. Then, we analyze the implementation of synaptic plasticity within our framework, a key aspect to study the role of hippocampus in learning and memory consolidation, and we demonstrate the capability of our framework to incorporate the variations at synaptic level. Finally, we present an example of the implementation of our model to study a stimulus propagation at the macro-scale level, and we show that the results of our framework can capture the dynamics obtained in the corresponding spiking network model of the whole CA1 area.
大脑微电路和脑区生物现实模型的开发是当前计算神经科学领域一个非常重要的课题。此类模型面临的主要挑战之一是如何在不同尺度之间穿行,从微观尺度(细胞)到中观尺度(微电路)和宏观尺度(区域或全脑水平),同时还要保持对计算资源需求的限制。本文介绍了海马 CA1 的多尺度建模框架,海马 CA1 是大脑的一个区域,在学习、记忆巩固和导航等功能中发挥着关键作用。我们的建模框架从单细胞水平到宏观尺度,并利用本文介绍的 CA1 的新型均场模型来弥合微观和宏观尺度之间的差距。我们通过分析系统对海马体中观察到的主要大脑节律的响应,并将结果与 CA1 的相应尖峰网络模型进行比较,来测试和验证该模型。然后,我们分析了突触可塑性在我们的框架中的实现,这是研究海马在学习和记忆巩固中的作用的一个关键方面。最后,我们举例说明了如何利用我们的模型来研究刺激在宏观尺度上的传播,结果表明我们的框架可以捕捉到整个 CA1 区域相应的尖峰网络模型所获得的动态变化。
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引用次数: 0
A neural basis for learning sequential memory in brain loop structures 大脑环路结构中学习顺序记忆的神经基础
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-08-05 DOI: 10.3389/fncom.2024.1421458
Duho Sihn, Sung-Phil Kim
IntroductionBehaviors often involve a sequence of events, and learning and reproducing it is essential for sequential memory. Brain loop structures refer to loop-shaped inter-regional connection structures in the brain such as cortico-basal ganglia-thalamic and cortico-cerebellar loops. They are thought to play a crucial role in supporting sequential memory, but it is unclear what properties of the loop structure are important and why.MethodsIn this study, we investigated conditions necessary for the learning of sequential memory in brain loop structures via computational modeling. We assumed that sequential memory emerges due to delayed information transmission in loop structures and presented a basic neural activity model and validated our theoretical considerations with spiking neural network simulations.ResultsBased on this model, we described the factors for the learning of sequential memory: first, the information transmission delay should decrease as the size of the loop structure increases; and second, the likelihood of the learning of sequential memory increases as the size of the loop structure increases and soon saturates. Combining these factors, we showed that moderate-sized brain loop structures are advantageous for the learning of sequential memory due to the physiological restrictions of information transmission delay.DiscussionOur results will help us better understand the relationship between sequential memory and brain loop structures.
导言行为往往涉及一系列事件,学习和再现这些事件对于顺序记忆至关重要。大脑环路结构是指大脑中的环形区域间连接结构,如皮质-基底节-丘脑环路和皮质-小脑环路。方法在这项研究中,我们通过计算建模研究了脑环路结构中顺序记忆学习的必要条件。我们假设顺序记忆的出现是由于环路结构中信息传递的延迟,并提出了一个基本的神经活动模型,用尖峰神经网络模拟验证了我们的理论考虑。结果基于这个模型,我们描述了顺序记忆学习的因素:首先,信息传递延迟应该随着环路结构规模的增大而减小;其次,顺序记忆学习的可能性随着环路结构规模的增大而增大,并很快达到饱和。综合这些因素,我们发现由于信息传递延迟的生理限制,适度大小的大脑环路结构对顺序记忆的学习是有利的。
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引用次数: 0
Eight challenges in developing theory of intelligence 发展智力理论的八大挑战
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-07-24 DOI: 10.3389/fncom.2024.1388166
Haiping Huang
A good theory of mathematical beauty is more practical than any current observation, as new predictions about physical reality can be self-consistently verified. This belief applies to the current status of understanding deep neural networks including large language models and even the biological intelligence. Toy models provide a metaphor of physical reality, allowing mathematically formulating the reality (i.e., the so-called theory), which can be updated as more conjectures are justified or refuted. One does not need to present all details in a model, but rather, more abstract models are constructed, as complex systems such as the brains or deep networks have many sloppy dimensions but much less stiff dimensions that strongly impact macroscopic observables. This type of bottom-up mechanistic modeling is still promising in the modern era of understanding the natural or artificial intelligence. Here, we shed light on eight challenges in developing theory of intelligence following this theoretical paradigm. Theses challenges are representation learning, generalization, adversarial robustness, continual learning, causal learning, internal model of the brain, next-token prediction, and the mechanics of subjective experience.
一个好的数学美学理论比当前的任何观测都更实用,因为关于物理现实的新预测可以自洽地得到验证。这一信念适用于理解深度神经网络(包括大型语言模型)甚至生物智能的现状。玩具模型为物理现实提供了一种隐喻,可以用数学方法表述现实(即所谓的理论),并随着更多猜想的证实或反驳而更新。我们不需要在模型中呈现所有细节,而是要构建更抽象的模型,因为大脑或深层网络等复杂系统有许多马虎的维度,但对宏观观测指标有强烈影响的僵硬维度却少得多。在理解自然或人工智能的现代,这种自下而上的机理建模仍大有可为。在此,我们将阐明按照这种理论范式发展智能理论所面临的八大挑战。这些挑战包括表征学习、泛化、对抗鲁棒性、持续学习、因果学习、大脑内部模型、下一个标记预测以及主观体验的机制。
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引用次数: 0
Editorial: Neuromorphic computing: from emerging materials and devices to algorithms and implementation of neural networks inspired by brain neural mechanism 社论:神经形态计算:从新兴材料和设备到受大脑神经机制启发的算法和神经网络的实现
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-07-19 DOI: 10.3389/fncom.2024.1443758
Guohe Zhang
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引用次数: 0
EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism 利用图卷积神经网络和双重关注机制进行基于脑电图的情绪识别
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-07-19 DOI: 10.3389/fncom.2024.1416494
Wei Chen, Yuan Liao, Rui Dai, Yuanlin Dong, Liya Huang
EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism Graph Convolutional Neural Network (DAMGCN). Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features. Furthermore, we employ the self-attention mechanism of the Transformer model which allocates more electrode channel weights and signal frequency band weights to important brain regions and frequency bands. The visualization of attention mechanism clearly demonstrates the weight allocation learned by DAMGCN. During the performance evaluation of our model on the DEAP, SEED, and SEED-IV datasets, we achieved the best results on the SEED dataset, showing subject-dependent experiments’ accuracy of 99.42% and subject-independent experiments’ accuracy of 73.21%. The results are demonstrably superior to the accuracies of most existing models in the realm of EEG-based emotion recognition.
基于脑电图的情感识别在脑机接口(BCI)中变得至关重要。目前,大多数研究侧重于提高准确率,而忽视了对模型可解释性的进一步研究,我们致力于分析不同脑区和信号频段对基于图结构的情感生成的影响。因此,本文提出了一种名为双注意机制图卷积神经网络(DAMGCN)的方法。具体来说,我们利用图卷积神经网络将大脑网络建模为图,从而提取具有代表性的空间特征。此外,我们还采用了 Transformer 模型的自我注意机制,将更多的电极通道权重和信号频带权重分配给重要的脑区和频带。注意力机制的可视化清晰地展示了 DAMGCN 学习到的权重分配。在 DEAP、SEED 和 SEED-IV 数据集上对我们的模型进行性能评估时,我们在 SEED 数据集上取得了最好的结果,受试者依赖实验的准确率为 99.42%,受试者独立实验的准确率为 73.21%。在基于脑电图的情感识别领域,这些结果明显优于大多数现有模型的准确率。
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引用次数: 0
Hippocampal formation-inspired global self-localization: quick recovery from the kidnapped robot problem from an egocentric perspective 海马体形成启发的全局自我定位:从自我中心视角快速解决被绑架机器人问题
IF 3.2 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-07-18 DOI: 10.3389/fncom.2024.1398851
Takeshi Nakashima, Shunsuke Otake, Akira Taniguchi, Katsuyoshi Maeyama, Lotfi El Hafi, Tadahiro Taniguchi, Hiroshi Yamakawa
It remains difficult for mobile robots to continue accurate self-localization when they are suddenly teleported to a location that is different from their beliefs during navigation. Incorporating insights from neuroscience into developing a spatial cognition model for mobile robots may make it possible to acquire the ability to respond appropriately to changing situations, similar to living organisms. Recent neuroscience research has shown that during teleportation in rat navigation, neural populations of place cells in the cornu ammonis-3 region of the hippocampus, which are sparse representations of each other, switch discretely. In this study, we construct a spatial cognition model using brain reference architecture-driven development, a method for developing brain-inspired software that is functionally and structurally consistent with the brain. The spatial cognition model was realized by integrating the recurrent state—space model, a world model, with Monte Carlo localization to infer allocentric self-positions within the framework of neuro-symbol emergence in the robotics toolkit. The spatial cognition model, which models the cornu ammonis-1 and -3 regions with each latent variable, demonstrated improved self-localization performance of mobile robots during teleportation in a simulation environment. Moreover, it was confirmed that sparse neural activity could be obtained for the latent variables corresponding to cornu ammonis-3. These results suggest that spatial cognition models incorporating neuroscience insights can contribute to improving the self-localization technology for mobile robots. The project website is https://nakashimatakeshi.github.io/HF-IGL/.
当移动机器人在导航过程中突然被传送到一个与其信念不同的位置时,它们仍然很难继续进行准确的自我定位。在开发移动机器人的空间认知模型时融入神经科学的见解,可能会使移动机器人获得对不断变化的情况做出适当反应的能力,类似于生物体。最近的神经科学研究表明,在大鼠导航的远距离传送过程中,海马角氨-3区的位置细胞神经群会离散切换,而这些神经群是彼此稀疏的表征。在这项研究中,我们利用大脑参考架构驱动的开发方法构建了一个空间认知模型,这种方法用于开发在功能和结构上与大脑一致的大脑启发软件。空间认知模型是在机器人工具包的神经符号涌现框架内,通过将循环状态空间模型(一种世界模型)与蒙特卡洛定位推断分配中心自我位置相结合而实现的。该空间认知模型利用每个潜变量对 cornu ammonis-1 和 -3 区域进行建模,在模拟环境中展示了移动机器人在远距传物过程中自我定位性能的提高。此外,研究还证实,与 cornu ammonis-3 相对应的潜变量可以获得稀疏的神经活动。这些结果表明,结合神经科学见解的空间认知模型有助于改进移动机器人的自我定位技术。项目网站:https://nakashimatakeshi.github.io/HF-IGL/。
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引用次数: 0
The synaptic correlates of serial position effects in sequential working memory 顺序工作记忆中序列位置效应的突触相关性
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-07-15 DOI: 10.3389/fncom.2024.1430244
Jiaqi Zhou, Liping Gong, Xiaodong Huang, Chunlai Mu, Yuanyuan Mi
Sequential working memory (SWM), referring to the temporary storage and manipulation of information in order, plays a fundamental role in brain cognitive functions. The serial position effect refers to the phenomena that recall accuracy of an item is associated to the order of the item being presented. The neural mechanism underpinning the serial position effect remains unclear. The synaptic mechanism of working memory proposes that information is stored as hidden states in the form of facilitated neuronal synapse connections. Here, we build a continuous attractor neural network with synaptic short-term plasticity (STP) to explore the neural mechanism of the serial position effect. Using a delay recall task, our model reproduces the the experimental finding that as the maintenance period extends, the serial position effect transitions from the primacy to the recency effect. Using both numerical simulation and theoretical analysis, we show that the transition moment is determined by the parameters of STP and the interval between presented stimulus items. Our results highlight the pivotal role of STP in processing the order information in SWM.
顺序工作记忆(SWM)是指按顺序临时存储和处理信息,在大脑认知功能中发挥着重要作用。序列位置效应是指一个项目的回忆准确性与该项目呈现的顺序相关联的现象。序列位置效应的神经机制尚不清楚。工作记忆的突触机制认为,信息是以神经元突触连接的形式作为隐藏状态存储的。在此,我们建立了一个具有突触短期可塑性(STP)的连续吸引子神经网络,以探索序列位置效应的神经机制。通过延迟回忆任务,我们的模型再现了实验结果,即随着维持时间的延长,序列位置效应会从首要效应过渡到回顾效应。通过数字模拟和理论分析,我们发现过渡时刻是由 STP 参数和刺激项目呈现间隔决定的。我们的研究结果凸显了 STP 在 SWM 中处理顺序信息的关键作用。
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引用次数: 0
Rényi entropy-complexity causality space: a novel neurocomputational tool for detecting scale-free features in EEG/iEEG data 雷尼熵复杂因果关系空间:用于检测脑电图/电子脑电图数据中无标度特征的新型神经计算工具
IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2024-07-15 DOI: 10.3389/fncom.2024.1342985
Maurizio Mattia, Leonardo Dalla, Porta, Haroldo V. Ribeiro, Fernando Montani, Natalí Guisande
Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforward means of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed the most variation across different modes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders.
无标度大脑活动与学习、不同时间尺度的整合以及心理模型的形成有关,与认知基础的不稳定性相关。光谱斜率是无标度动态的一个关键方面,被认为是区分不同睡眠阶段的潜在指标。研究表明,大脑网络在清醒、麻醉和恢复期间保持着一致的无标度结构。虽然人们已经认识到两性对麻醉的敏感性存在差异,但这些差异在临床脑电皮层记录中并不明显。最近,人们发现神经活动幂律指数斜率的变化与雷尼熵的变化相关,雷尼熵是香农信息熵的扩展概念。这些发现使量化器成为研究大脑无标度动态的一种有前途的工具。我们的研究提出了一种名为雷尼熵-复杂性因果关系空间的新型可视化表示方法,它囊括了复杂性、排列熵和雷尼参数 q。此外,本研究还旨在探讨如何区分模仿无标度活动的不同时间序列。最后,该工具将用于检测颅内脑电图(iEEG)信号中的动态特征。为了实现这些目标,该研究采用了 Bandt 和 Pompe 方法来处理顺序模式。在此过程中,每个信号都与概率分布相关联,并根据参数 q 计算雷尼熵和复杂度的因果度量。它能有效区分相关噪声元素,并提供一种直接的方法来检查行为、特征和分类方面的差异。在 iEEG 实验数据中,快速眼动状态显示了更多显著的性别差异,而边际上回区域在不同模式和分析中的变化最大。利用这一框架探索无尺度的大脑活动可以为认知和神经系统疾病提供有价值的见解。这些结果可能对理解两性大脑功能的差异及其与神经系统疾病的可能相关性有影响。
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引用次数: 0
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Frontiers in Computational Neuroscience
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