Neural coupling mechanism in fMRI hemodynamics

Peng, Jun; Wang, Yihong; Wang, Rubin; Kong, Wanzeng; Zhang, Jianhai

doi:10.1007/s11071-020-06040-4

Cited by 7 publications

(16 citation statements)

References 40 publications

(61 reference statements)

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“…The highly nonlinear and complex brain dynamics as well as their various functional expressions are not only related to gene and functional genomics, biology and biochemistry, but also to solid mechanics (McIntyre et al 2001), fluid mechanics (Moore and Cao 2008), dynamics and control (Lu et al 2008a, b). Our research shows that some experimental phenomena in cognitive neuroscience can be reproduced and repeated by mechanical models (Wang et al 2015a;Li et al 2022a), and some experimental data that cannot be explained by neuroscience can also be scientifically explained by our mechanical models (Peng and Wang 2021). We also can use mechanical models to predict new experimental phenomena and new neural mechanisms not found in neuroscience .…”

Section: Introductionmentioning

confidence: 66%

“…In this research area, we have published a series of research results (Wang et al 2021aYuan et al 2021). Using the large-scale neural energy theory to reveal the neural mechanism of the hemodynamic phenomenon of the brain, it was revealed that neural energy is an important marker of the activity of the nervous system, and energy features contain information about external stimuli and neural responses (Yuan et al 2021;Peng and Wang 2021); using the neural energy theory to attain the biophysical mechanism of the mutual coupling and antagonism between the brain DMN and the working memory network, it can be demonstrated that neural energy can effectively fuse DMN s and cognitive networks to interpret and analyze the spatial information and encoded content contained in complex neural activities (Yuan et al 2021). The computational simulation results are in complete agreement with the experimental data (Piccoli et al 2015;Compte 2000;Wei et al 2012;Hsieh and Ranganath 2014;Karlsgodt et al 2005).…”

Section: Brain Theory: Exploration Of Working Mechanism Of the Brainmentioning

confidence: 99%

“…The adjustable parameters of the global functional model of brain are necessarily limited and simple; (d) this type of models can be used for the analysis of experimental data hidden behind the nature and regularity. As our neural energy model meets all of the above-mentioned requirements of the global functional model of brain, and it has already accumulated and published a series of original and innovative scientific research results (Wang et al 2008(Wang et al , 2015a(Wang et al , 2017a(Wang et al , b, 2019b(Wang et al , 2021aYuan et al 2021;Wang and Zhu 2016;Wang and Wang 2018a, b;Li et al 2022a;Peng and Wang 2021;Lu 2020;Cheng et al 2020), this has laid a firm foundation for the creation of new brain theory that can stand the testing of experimental data. The main research directions of brain theory are reflected in the following four aspects.…”

Section: Introductionmentioning

confidence: 99%

“…The main contents include the following items: (a) a new research method that can unify reductionism and holism in theory. The new method could theoretically reproduce not only the electrophysiological recordings, but also the global information of the functional neural activity using functional magnetic resonance imaging, fMRI (Yuan et al 2021;Wang et al 2015a;Peng and Wang 2021;Cheng et al 2020); (b) the global functional model of brain is established computationally, which can be used to construct, analyze, and describe the experimental models of neuroscience at various levels (Wang and Zhu 2016;Wang et al 2020). Thus, the computational results at all levels are no longer mutually inapplicable, contradictory and irrelevant; (c) if a global functional model of brain cannot explain the function and energy consumption of the default mode network (DMN) and the resting state network, and how it transitions from the default model to the cognitive network and the corresponding energy transformation under task-induced condition, then, such model is not a global neural model (Yuan et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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Brain works principle followed by neural information processing: a review of novel brain theory

Wang

et al. 2023

Artif Intell Rev

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The way the brain work and its principle of work has long been a big scientific question that scientists have dreamed of solving. However, as is known to all, the brain works at different levels, and the operation at different levels is interactional and mutually coupled. Unfortunately, until now, we still do not know how the nervous system at different levels is interacting and coupling with each other. This review provides some preliminary discussions on how to address these scientific questions, for which we propose a novel theory of the brain called neural energy. Such a theoretical and research approach can couple neural information with neural energy to address the interactions of the nervous system at various levels. Therefore, this review systematically summarizes the neural energy theories and methods proposed by our research in the field of brain science, as well as the internal relationship between mechanics and neural energy theory. Focuses on how to construct a Wang–Zhang (W–Z) neuron model equivalent to Hodgkin–Huxley (H–H) model by using the idea of analytical dynamics. Then, based on this model, we proposed a large-scale neural model and a theoretical framework of global neural coding of the brain in the field of neuroscience. It includes information processing of multiple sensory and perceptual nervous systems such as visual perception, neural mechanism of coupling between default mode network and functional network of brain, memory switching and brain state switching, brain navigation, prediction of new working mechanism of neurons, and interpretation of experimental phenomena that are difficult to be explained by neuroscience. It is proved that the new W–Z neuron model and neural energy theory have unique functions and advantages in neural modeling, neural information processing and methodology. The idea of large-scale neuroscience research with neural energy as the core will provide a potentially powerful research method for promoting the fusion of experimental neuroscience and theoretical neuroscience in the future, and propose a widely accepted brain theory system between experimental neuroscience and theoretical neuroscience. It is of great scientific significance to abandon the shortcomings of reductive and holism research methods in the field of neuroscience, and effectively integrate their respective advantages in methodology.

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Section: Introductionmentioning

confidence: 66%

Section: Brain Theory: Exploration Of Working Mechanism Of the Brainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Brain works principle followed by neural information processing: a review of novel brain theory

Wang

et al. 2023

Artif Intell Rev

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“…Previous studies had shown that neural energy theory could be effectively used on different levels of brain networks and their coupling for the construction of global neural brain models and coding (Wang and Zhu 2016 ; Wang et al 2020 ). It has been known that theoretical energy theory could successfully interpret the neural mechanism of brain hemodynamic phenomena (Peng et al 2021 ). Why the amount of data in biological visual system would significantly reduce but not affect the external world visual cognition could also be explained by energy theory (Zhong and Wang 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Biophysical mechanism of the interaction between default mode network and working memory network

2021

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Default mode network (DMN) is a functional brain network with a unique neural activity pattern that shows high activity in resting states but low activity in task states. This unique pattern has been proved to relate with higher cognitions such as learning, memory and decision-making. But neural mechanisms of interactions between the default network and the task-related network are still poorly understood. In this paper, a theoretical model of coupling the DMN and working memory network (WMN) is proposed. The WMN and DMN both consist of excitatory and inhibitory neurons connected by AMPA, NMDA, GABA synapses, and are coupled with each other only by excitatory synapses. This model is implemented to demonstrate dynamical processes in a working memory task containing encoding, maintenance and retrieval phases. Simulated results have shown that: (1) AMPA channels could produce significant synchronous oscillations in population neurons, which is beneficial to change oscillation patterns in the WMN and DMN. (2) Different NMDA conductance between the networks could generate multiple neural activity modes in the whole network, which may be an important mechanism to switch states of the networks between three different phases of working memory. (3) The number of sequentially memorized stimuli was related to the energy consumption determined by the network's internal parameters, and the DMN contributed to a more stable working memory process. (4) Finally, this model demonstrated that, in three phases of working memory, different memory phases corresponded to different functional connections between the DMN and WMN. Coupling strengths that measured these functional connections differed in terms of phase synchronization. Phase synchronization characteristics of the contained energy were consistent with the observations of negative and positive correlations between the WMN and DMN reported in referenced fMRI experiments. The results suggested that the coupled interaction between the WMN and DMN played important roles in working memory.

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