scholarly journals Criticality Creates a Functional Platform for Network Transitions Between Internal and External Processing Modes in the Human Brain

2021 ◽  
Vol 15 ◽  
Author(s):  
Minkyung Kim ◽  
Hyoungkyu Kim ◽  
Zirui Huang ◽  
George A. Mashour ◽  
Denis Jordan ◽  
...  

Continuous switching between internal and external modes in the brain appears important for generating models of the self and the world. However, how the brain transitions between these two modes remains unknown. We propose that a large synchronization fluctuation of brain networks, emerging only near criticality (i.e., a balanced state between order and disorder), spontaneously creates temporal windows with distinct preferences for integrating the network’s internal information or for processing external stimuli. Using a computational model, electroencephalography (EEG) analysis, and functional magnetic resonance imaging (fMRI) analysis during alterations of consciousness in humans, we report that synchronized and incoherent networks, respectively, bias toward internal and external information with specific network configurations. In the brain network model and EEG-based network, the network preferences are the most prominent at criticality and in conscious states associated with the bandwidth 4−12 Hz, with alternating functional network configurations. However, these network configurations are selectively disrupted in different states of consciousness such as general anesthesia, psychedelic states, minimally conscious states, and unresponsive wakefulness syndrome. The network preference for internal information integration is only significant in conscious states and psychedelic states, but not in other unconscious states, suggesting the importance of internal information integration in maintaining consciousness. The fMRI co-activation pattern analysis shows that functional networks that are sensitive to external stimuli–such as default mode, dorsal attentional, and frontoparietal networks–are activated in incoherent states, while insensitive networks, such as global activation and deactivation networks, are dominated in highly synchronized states. We suggest that criticality produces a functional platform for the brain’s capability for continuous switching between two modes, which is crucial for the emergence of consciousness.

2020 ◽  
Author(s):  
Minkyung Kim ◽  
Hyoungkyu Kim ◽  
Zirui Huang ◽  
George A. Mashour ◽  
Denis Jordan ◽  
...  

AbstractContinuous switching between internal and external modes in the brain is a key process of constructing inner models of the outside world. However, how the brain continuously switches between two modes remains elusive. Here, we propose that a large synchronization fluctuation of the brain network emerging only near criticality (i.e., a balanced state between order and disorder) spontaneously creates temporal windows with distinct preferences for integrating internal information of the network and external stimuli. Using a computational model and empirical data analysis during alterations of consciousness in human, we present that synchronized and incoherent networks respectively bias toward internal and external information with specific network configurations. The network preferences are the most prominent in conscious states; however, they disrupt in altered states of consciousness. We suggest that criticality produces a functional platform of the brain’s capability for continuous switching between two modes, which is crucial for the emergence of consciousness.


Author(s):  
Yingxu Wang ◽  
Bernard Carlos Widrow ◽  
Bo Zhang ◽  
Witold Kinsner ◽  
Kenji Sugawara ◽  
...  

The contemporary wonder of sciences and engineering has recently refocused on the beginning point of: how the brain processes internal and external information autonomously and cognitively rather than imperatively like conventional computers. Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. This paper reports a set of eight position statements presented in the plenary panel of IEEE ICCI’10 on Cognitive Informatics and Its Future Development contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.


2020 ◽  
Author(s):  
D. Lu ◽  
I. Pappas ◽  
D. K. Menon ◽  
E. A. Stamatakis

AbstractHuman brains interpret external stimuli based on internal representations. One untested hypothesis is that the default-mode network (DMN) while responsible for internally oriented cognition can also encode externally oriented information. The unique neuroanatomical and functional fingerprint of the posterior part of the DMN supports a prominent role for the precuneus in this process. By utilising imaging data during two tasks from 100 participants, we found that the precuneus is functionally divided into dorsal and ventral subdivisions, each one differentially connecting to internally and externally oriented networks. The strength and direction of their connectivity is modulated by task difficulty in a manner dictated by the balance of internal versus external cognitive demands. Our study provides evidence that the medial posterior part of the DMN may drive interactions between large-scale networks, potentially allowing access to stored representations for moment to moment interpretation of an ever-changing environment.


Author(s):  
Yingxu Wang ◽  
Bernard Carlos Widrow ◽  
Bo Zhang ◽  
Witold Kinsner ◽  
Kenji Sugawara ◽  
...  

The contemporary wonder of sciences and engineering has recently refocused on the beginning point of: how the brain processes internal and external information autonomously and cognitively rather than imperatively like conventional computers. Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. This paper reports a set of eight position statements presented in the plenary panel of IEEE ICCI’10 on Cognitive Informatics and Its Future Development contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.


2020 ◽  
Vol 4 (1) ◽  
pp. 155-173 ◽  
Author(s):  
MinKyung Kim ◽  
UnCheol Lee

Brains in unconsciousness are characterized by significantly limited responsiveness to stimuli. Even during conscious wakefulness, responsiveness is highly dependent on ongoing brain activity, specifically, of alpha oscillations (∼10 Hz). We hypothesized that the variety of brain responses to external stimuli result from the interaction between state-specific and transient alpha oscillations and stimuli. To justify this hypothesis, we simulated various alpha oscillations in the human brain network, modulating criticality (a balanced state between order and disorder), and investigated specific alpha oscillation properties (instantaneous amplitude, phase, and global synchronization) that induce a large or small response. As a result, we found that the alpha oscillations near a critical state show a more complex and long-lasting response, which is more prominent when stimuli are given to globally desynchronized and low-amplitude oscillations. We also found specific phases of alpha oscillation that barely respond to stimuli, which implies the presence of temporal windows in the alpha cycle for a large or small response. The results explain why brain responses are so variable across conscious and unconscious states and across time windows even during conscious wakefulness, and emphasize the importance of considering ongoing alpha oscillations for effective brain stimulation.


2018 ◽  
Vol 29 (1) ◽  
pp. 340-364 ◽  
Author(s):  
Kangkang Yu ◽  
Ben Nanfeng Luo ◽  
Xue Feng ◽  
Jianing Liu

Purpose Supply chain flexibility is crucial for firms to respond to uncertain circumstances caused by environmental factors, such as the diversity of customer demands, problems of product safety, and adjustments of industrial policies. To investigate the approach to enhance supply chain flexibility, the purpose of this paper is to propose that both internal and external information integration contribute to reactive and proactive supply chain flexibilities, which elicit high operational performance. Design/methodology/approach Using a sample of 84 food companies that have been listed three years in China and content analysis based on their annual reports, evidence was collected to test the hypotheses through hierarchical regressions. Findings The results reveal that external information integration results in both reactive and proactive flexibilities, which further improve operational performance. Internal information integration positively affects both reactive and proactive flexibilities, but its mediating role was not determined in the study. Originality/value The research provides insights into how supply chain flexibility mediates the effect of supply chain information integration on operational performance in the context of the Chinese food industry.


Author(s):  
Moriah E. Thomason ◽  
Ava C. Palopoli ◽  
Nicki N. Jariwala ◽  
Denise M. Werchan ◽  
Alan Chen ◽  
...  

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.


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