scholarly journals Criticality as a Determinant of Integrated Information Φ in Human Brain Networks

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 981 ◽  
Author(s):  
Kim ◽  
Lee
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Brain Network ◽  
Necessary Condition ◽  
Network Configuration ◽  
Irreducible Constituent ◽  
Level Of Consciousness ◽  
The Brain ◽  
Integrated Information Theory ◽  
Integrated Information

Integrated information theory (IIT) describes consciousness as information integrated across highly differentiated but irreducible constituent parts in a system. However, in a complex dynamic system such as the brain, the optimal conditions for large integrated information systems have not been elucidated. In this study, we hypothesized that network criticality, a balanced state between a large variation in functional network configuration and a large constraint on structural network configuration, may be the basis of the emergence of a large Φ, a surrogate of integrated information. We also hypothesized that as consciousness diminishes, the brain loses network criticality and Φ decreases. We tested these hypotheses with a large-scale brain network model and high-density electroencephalography (EEG) acquired during various levels of human consciousness under general anesthesia. In the modeling study, maximal criticality coincided with maximal Φ. The EEG study demonstrated an explicit relationship between Φ, criticality, and level of consciousness. The conscious resting state showed the largest Φ and criticality, whereas the balance between variation and constraint in the brain network broke down as the response rate dwindled. The results suggest network criticality as a necessary condition of a large Φ in the human brain.

scholarly journals Amplification and Suppression of Distinct Brain-wide Activity Patterns by Catecholamines

2018 ◽  
Author(s):  
RL van den Brink ◽  
S Nieuwenhuis ◽  
TH Donner
Keyword(s):  
Spatial Distribution ◽  
Human Brain ◽  
Spatial Patterns ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Heterogeneous Distribution ◽  
Neurotransmitter Systems ◽  
The Brain ◽  
Catecholamine Levels

ABSTRACTThe widely projecting catecholaminergic (norepinephrine and dopamine) neurotransmitter systems profoundly shape the state of neuronal networks in the forebrain. Current models posit that the effects of catecholaminergic modulation on network dynamics are homogenous across the brain. However, the brain is equipped with a variety of catecholamine receptors with distinct functional effects and heterogeneous density across brain regions. Consequently, catecholaminergic effects on brain-wide network dynamics might be more spatially specific than assumed. We tested this idea through the analysis of functional magnetic resonance imaging (fMRI) measurements performed in humans (19 females, 5 males) at ‘rest’ under pharmacological (atomoxetine-induced) elevation of catecholamine levels. We used a linear decomposition technique to identify spatial patterns of correlated fMRI signal fluctuations that were either increased or decreased by atomoxetine. This yielded two distinct spatial patterns, each expressing reliable and specific drug effects. The spatial structure of both fluctuation patterns resembled the spatial distribution of the expression of catecholamine receptor genes: α1 norepinephrine receptors (for the fluctuation pattern: placebo > atomoxetine), ‘D2-like’ dopamine receptors (pattern: atomoxetine > placebo), and β norepinephrine receptors (for both patterns, with correlations of opposite sign). We conclude that catecholaminergic effects on the forebrain are spatially more structured than traditionally assumed and at least in part explained by the heterogeneous distribution of various catecholamine receptors. Our findings link catecholaminergic effects on large-scale brain networks to low-level characteristics of the underlying neurotransmitter systems. They also provide key constraints for the development of realistic models of neuromodulatory effects on large-scale brain network dynamics.SIGNIFICANCE STATEMENTThe catecholamines norepinephrine and dopamine are an important class of modulatory neurotransmitters. Because of the widespread and diffuse release of these neuromodulators, it has commonly been assumed that their effects on neural interactions are homogenous across the brain. Here, we present results from the human brain that challenge this view. We pharmacologically increased catecholamine levels and imaged the effects on the spontaneous covariations between brain-wide fMRI signals at ‘rest’. We identified two distinct spatial patterns of covariations: one that was amplified and another that was suppressed by catecholamines. Each pattern was associated with the heterogeneous spatial distribution of the expression of distinct catecholamine receptor genes. Our results provide novel insights into the catecholaminergic modulation of large-scale human brain dynamics.

scholarly journals Stochastic synchronization of dynamics on the human connectome

2020 ◽  
Author(s):  
James C. Pang ◽  
Leonardo L. Gollo ◽  
James A. Roberts
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Brain Network ◽  
Scale Model ◽  
Stochastic Perturbations ◽  
Stochastic Synchronization ◽  
Large Scale Model ◽  
Synchronization Effect ◽  
Global And Local ◽  
The Brain

AbstractSynchronization is a collective mechanism by which oscillatory networks achieve their functions. Factors driving synchronization include the network’s topological and dynamical properties. However, how these factors drive the emergence of synchronization in the presence of potentially disruptive external inputs like stochastic perturbations is not well understood, particularly for real-world systems such as the human brain. Here, we aim to systematically address this problem using a large-scale model of the human brain network (i.e., the human connectome). The results show that the model can produce complex synchronization patterns transitioning between incoherent and coherent states. When nodes in the network are coupled at some critical strength, a counterintuitive phenomenon emerges where the addition of noise increases the synchronization of global and local dynamics, with structural hub nodes benefiting the most. This stochastic synchronization effect is found to be driven by the intrinsic hierarchy of neural timescales of the brain and the heterogeneous complex topology of the connectome. Moreover, the effect coincides with clustering of node phases and node frequencies and strengthening of the functional connectivity of some of the connectome’s subnetworks. Overall, the work provides broad theoretical insights into the emergence and mechanisms of stochastic synchronization, highlighting its putative contribution in achieving network integration underpinning brain function.

scholarly journals Tracking Neuronal Connectivity from Electric Brain Signals to Predict Performance

2018 ◽  
Vol 25 (1) ◽  
pp. 86-93 ◽  
Author(s):  
Fabrizio Vecchio ◽  
Francesca Miraglia ◽  
Paolo Maria Rossini
Keyword(s):  
Human Brain ◽  
Brain Network ◽  
Connection Matrix ◽  
Network Configuration ◽  
Eeg Signals ◽  
Online Evaluation ◽  
Brain Signals ◽  
Global Organization ◽  
Time Frames ◽  
The Brain

The human brain is a complex container of interconnected networks. Network neuroscience is a recent venture aiming to explore the connection matrix built from the human brain or human “Connectome.” Network-based algorithms provide parameters that define global organization of the brain; when they are applied to electroencephalographic (EEG) signals network, configuration and excitability can be monitored in millisecond time frames, providing remarkable information on their instantaneous efficacy also for a given task’s performance via online evaluation of the underlying instantaneous networks before, during, and after the task. Here we provide an updated summary on the connectome analysis for the prediction of performance via the study of task-related dynamics of brain network organization from EEG signals.

scholarly journals Criticality as a determinant of integrated information Φ in human brain networks

2019 ◽  
Author(s):  
Hyoungkyu Kim ◽  
Anthony G. Hudetz ◽  
George A. Mashour ◽  
UnCheol Lee
Keyword(s):  
Large Scale ◽  
Brain Network ◽  
Functional Network ◽  
Necessary Condition ◽  
Optimal Network ◽  
Functional Brain Network ◽  
Structural Network ◽  
Structural Brain Network ◽  
Variation Of Functional ◽  
Integrated Information

AbstractIntegrated information theory (IIT) postulates that consciousness arises from the cause-effect structure of a system but the optimal network conditions for this structure have not been elucidated. In the study, we test the hypothesis that network criticality, a dynamically balanced state between a large variation of functional network configurations and a large constraint of structural network configurations, is a necessary condition for the emergence of a cause-effect structure that results in a large Φ, a surrogate of integrated information. We also hypothesized that if the brain deviates from criticality, the cause-effect structure is obscured and Φ diminishes. We tested these hypotheses with a large-scale brain network model and high-density electroencephalography (EEG) acquired during various levels of human consciousness during general anesthesia. In the modeling study, maximal criticality coincided with maximal Φ. The constraint of the structural network on the functional network is maximized in the maximal criticality. The EEG study demonstrated an explicit relationship between Φ, criticality, and level of consciousness. Functional brain network significantly correlated with structural brain network only in conscious states. The results support the hypothesis that network criticality maximizes Φ.

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

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.

scholarly journals Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction

2015 ◽  
Vol 9 ◽  
Author(s):  
Xerxes D. Arsiwalla ◽  
Riccardo Zucca ◽  
Alberto Betella ◽  
Enrique Martinez ◽  
David Dalmazzo ◽  
...  
Keyword(s):  
Human Brain ◽  
Real Time ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Large Scale Simulation ◽  
Time Interaction

Large-scale plastic changes of the brain network in an animal model of neuropathic pain

NeuroImage ◽  
2014 ◽  
Vol 98 ◽  
pp. 203-215 ◽  
Author(s):  
Chang-Eop Kim ◽  
Yu Kyeong Kim ◽  
Geehoon Chung ◽  
Jae Min Jeong ◽  
Dong Soo Lee ◽  
...  
Keyword(s):  
Animal Model ◽  
Neuropathic Pain ◽  
Large Scale ◽  
Brain Network ◽  
Plastic Changes ◽  
The Brain

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

Interaction Model of Brain and Consciousness

2018 ◽  
pp. 105-126
Author(s):  
Georg Northoff
Keyword(s):  
Empirical Evidence ◽  
Vegetative State ◽  
Interaction Model ◽  
Necessary Condition ◽  
Additive Interaction ◽  
Fine Grained ◽  
Nancy Cartwright ◽  
Level Of Consciousness ◽  
The Brain

In addition to the spectrum model, I also introduced an interaction model to characterize the brain’s neural activity (chapter 2). Is the interaction model of brain also relevant for consciousness? That is the focus in the present chapter. I here present various lines of empirical evidence focusing on disorders of consciousness like vegetative state, anesthesia, and sleep. Based on empirical evidence, I show that the degree of non-additive interaction between spontaneous and stimulus-induced activity indexes the level of consciousness in a seemingly rather fine-grained way; for that reason, it may be considered a neural correlate of the level of consciousness, i.e., NCC. In contrast, the spontaneous activity and its spatiotemporal structure is rather a necessary condition of possible consciousness, that is, a neural predisposition of consciousness (NPC). The concept of NPC is further enriched by the concept of capacities for which I recruit Nancy Cartwright. I suggest that the brain’s non-additive interaction including the subsequent association of stimulus-induced activity with consciousness is based on the spontaneous activity’s capacity. Since that very same capacity, operating as NPC, can be traced to the spontaneous activity’s spatiotemporal features, I speak of “spatiotemporal capacity”. I conclude that the empirical data suggest a capacity-based approach (rather than law-based approach) to the brain and how it is related to consciousness.

The Power of Poetry

2021 ◽  
pp. 182-187
Author(s):  
Eugen Wassiliwizky ◽  
Winfried Menninghaus
Keyword(s):  
Human Brain ◽  
Experimental Psychology ◽  
Large Scale ◽  
Multidisciplinary Approach ◽  
Scientific Investigation ◽  
Poetic Language ◽  
Social And Emotional ◽  
Parent Child Interactions ◽  
The Brain ◽  
Parent Child

From prehistory onward, poetic language has been widely used in the context of great personal, social, and emotional significance, reaching from large scale events, such as religious ceremonies, political occasions (including inaugurations of American presidents), and artistic contexts to more private gatherings, such as birthday parties, declarations of love, and parent–child interactions. Poetic language is capable of reaching deeply into the phylogenetically ancient structures of the human brain and providing profound aesthetic pleasures to its recipients. Yet a thorough scientific investigation of the workings of poetic language in the brain is only at its very beginnings. In the article under discussion, the authors review a study that focused on the emotional power of poetic language. In this project, they strived to integrate and interrelate perspectives from experimental psychology, neuroscience, rhetoric/poetics, psychophysiology, and philosophy. They argue that such a multidisciplinary approach is key to unraveling the mysteries of human aesthetic processing.

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