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.

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 A whole-brain and cross-diagnostic perspective on functional brain network dysfunction

2018 ◽  
Author(s):  
Marjolein Spronk ◽  
Kaustubh Kulkarni ◽  
Jie Lisa Ji ◽  
Brian P. Keane ◽  
Alan Anticevic ◽  
...  
Keyword(s):  
Mental Illness ◽  
Resting State ◽  
Brain Network ◽  
Functional Network ◽  
Graph Distance ◽  
Network Organization ◽  
Whole Brain ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Mental Diseases

AbstractA wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations have seemed to support various theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in broad, whole-brain perspective. Using a graph distance measure – connectome-wide correlation – we found that whole-brain resting-state functional network organization in humans is highly similar across a variety of mental diseases and healthy controls. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease those differences are informative. Such small network alterations may reflect the fact that most psychiatric patients maintain overall cognitive abilities similar to those of healthy individuals (relative to, e.g., the most severe schizophrenia cases), such that whole-brain functional network organization is expected to differ only subtly even for mental diseases with devastating effects on everyday life. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases.

scholarly journals Large-Scale Functional Brain Network Architecture Changes Associated With Trauma-Related Dissociation

2020 ◽  
pp. appi.ajp.2020.1
Author(s):  
Lauren A.M. Lebois ◽  
Meiling Li ◽  
Justin T. Baker ◽  
Jonathan D. Wolff ◽  
Danhong Wang ◽  
...  
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network

Dynamic Reorganization of the Cortical Functional Brain Network in Affective Processing and Cognitive Reappraisal

2020 ◽  
Vol 30 (10) ◽  
pp. 2050051
Author(s):  
Feng Fang ◽  
Thomas Potter ◽  
Thinh Nguyen ◽  
Yingchun Zhang
Keyword(s):  
Emotion Regulation ◽  
Large Scale ◽  
Brain Networks ◽  
Cognitive Reappraisal ◽  
Brain Network ◽  
Affective Processing ◽  
Functional Brain ◽  
Affective Stimuli ◽  
Functional Brain Network ◽  
Functional Brain Networks

Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a ‘top-down’ pattern that occurs across four brain network states that arise at different time instants (0–170[Formula: see text]ms, 170–370[Formula: see text]ms, 380–620[Formula: see text]ms, and 620–1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.

scholarly journals Development of a Large-Scale Functional Brain Network during Human Non-Rapid Eye Movement Sleep

2010 ◽  
Vol 30 (34) ◽  
pp. 11379-11387 ◽  
Author(s):  
V. I. Spoormaker ◽  
M. S. Schroter ◽  
P. M. Gleiser ◽  
K. C. Andrade ◽  
M. Dresler ◽  
...  
Keyword(s):  
Eye Movement ◽  
Large Scale ◽  
Brain Network ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep ◽  
Functional Brain ◽  
Functional Brain Network

scholarly journals Graph-Based Method for Anomaly Detection in Functional Brain Network using Variational Autoencoder

2019 ◽  
Author(s):  
Jalal Mirakhorli ◽  
Mojgan Mirakhorli
Keyword(s):  
Large Scale ◽  
Functional Neuroimaging ◽  
Evaluation Criteria ◽  
Region Of Interest ◽  
Brain Network ◽  
Convolutional Network ◽  
Large Scale Data ◽  
Functional Brain Network ◽  
Variational Autoencoder ◽  
Brain Connections

AbstractFunctional neuroimaging techniques using resting-state functional MRI (rs-fMRI) have accelerated progress in brain disorders and dysfunction studies. Since, there are the slight differences between healthy and disorder brains, investigation in the complex topology of human brain functional networks is difficult and complicated task with the growth of evaluation criteria. Recently, graph theory and deep learning applications have spread widely to understanding human cognitive functions that are linked to gene expression and related distributed spatial patterns. Irregular graph analysis has been widely applied in many brain recognition domains, these applications might involve both node-centric and graph-centric tasks. In this paper, we discuss about individual Variational Autoencoder and Graph Convolutional Network (GCN) for the region of interest identification areas of brain which do not have normal connection when apply certain tasks. Here, we identified a framework of Graph Auto-Encoder (GAE) with hyper sphere distributer for functional data analysis in brain imaging studies that is underlying non-Euclidean structure, in learning of strong rigid graphs among large scale data. In addition, we distinguish the possible mode correlations in abnormal brain connections.

scholarly journals Functional brain network reconfiguration during learning in a dynamic environment

2019 ◽  
Author(s):  
Chang-Hao Kao ◽  
Ankit N. Khambhati ◽  
Danielle S. Bassett ◽  
Matthew R. Nassar ◽  
Joseph T. McGuire ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Functional Integration ◽  
Dynamic Environment ◽  
Brain Network ◽  
Uncertain Environments ◽  
Whole Brain ◽  
Functional Brain Network ◽  
Brain Pattern ◽  
Brain Functional Connectivity

AbstractWhen learning about dynamic and uncertain environments, people should update their beliefs most strongly when new evidence is most informative, such as when the environment undergoes a surprising change or existing beliefs are highly uncertain. Here we show that modulations of surprise and uncertainty are encoded in a particular, temporally dynamic pattern of whole-brain functional connectivity, and this encoding is enhanced in individuals that adapt their learning dynamics more appropriately in response to these factors. The key feature of this whole-brain pattern of functional connectivity is stronger connectivity, or functional integration, between the fronto-parietal and other functional systems. Our results provide new insights regarding the association between dynamic adjustments in learning and dynamic, large-scale changes in functional connectivity across the brain.

scholarly journals Adolescent development of multiscale cortical wiring and functional connectivity in the human connectome

2021 ◽  
Author(s):  
Bo-yong Park ◽  
Casey Paquola ◽  
Richard A.I. Bethlehem ◽  
Oualid Benkarim ◽  
Bratislav Misic ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Brain Network ◽  
Network Development ◽  
Network Function ◽  
Age Related ◽  
Mri Features ◽  
Functional Brain Network ◽  
Structural Networks ◽  
Advanced Model

Adolescence is a time of profound changes in the structural wiring of the brain and maturation of large-scale functional interactions. Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14-25 years (n = 199). Core to our work was an advanced model of cortical wiring that incorporates multimodal MRI features of (i) cortico-cortical proximity, (ii) microstructural similarity, and (iii) diffusion tractography. Longitudinal analyses assessing age-related changes in cortical wiring during adolescence identified increases in cortical wiring within attention and default-mode networks, as well as between transmodal and attention, and sensory and limbic networks, indicative of a continued differentiation of cortico-cortical structural networks. Cortical wiring changes were statistically independent from age-related cortical thinning seen in the same subjects. Conversely, resting-state functional MRI analysis in the same subjects indicated an increasing segregation of sensory and transmodal systems during adolescence, with age-related reductions in their functional connectivity alongside with an increase in structural wiring distance. Our findings provide new insights into adolescent brain network development, illustrating how the maturation of structural wiring interacts with the development of macroscale network function.

scholarly journals Age-related changes in spatial and temporal features of resting state fMRI

2017 ◽  
Author(s):  
Shruti G. Vij ◽  
Jason S. Nomi ◽  
Dina R. Dajani ◽  
Lucina Q. Uddin
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Spectral Composition ◽  
Brain Network ◽  
Functional Networks ◽  
Functional Changes ◽  
Age Related ◽  
Temporal Features ◽  
Functional Brain Network ◽  
Age Related Changes

AbstractDevelopment and aging are associated with functional changes in the brain across the lifespan. These changes can be detected in spatial and temporal features of resting state functional MRI (rs-fMRI) data. Independent vector analysis (IVA) is a whole-brain multivariate approach that can be used to comprehensively assess these changes in spatial and temporal features. We present a multi-dimensional approach to assessing age-related changes in spatial and temporal features of statistically independent components identified by IVA in a cross-sectional lifespan sample (ages 6-85 years). We show that while large-scale brain network configurations remain consistent throughout the lifespan, changes continue to occur in both local organization and in the spectral composition of these functional networks. We show that the spatial extent of functional networks decreases with age, but with no significant change in the peak functional loci of these networks. Additionally, we show differential age-related patterns across the frequency spectrum; lower frequency correlations decrease across the lifespan whereas higher-frequency correlations increase. These changes indicate an increasing stability of networks with age. In addition to replicating results from previous studies, the current results uncover new aspects of functional brain network changes across the lifespan that are frequency band-dependent.

scholarly journals Neuroimaging studies on cognitive impairment due to cerebral small vessel disease

2019 ◽  
Vol 4 (2) ◽  
pp. 99-101 ◽  
Author(s):  
Jing Du ◽  
Qun Xu
Keyword(s):  
Cognitive Impairment ◽  
Small Vessel Disease ◽  
Underlying Mechanism ◽  
Brain Network ◽  
Cerebral Small Vessel Disease ◽  
Small Vessel ◽  
Vessel Disease ◽  
Age Related ◽  
Functional Brain Network ◽  
Structural Network

Vascular cognitive impairment (VCI) is a major contributor to age-related dementing illnesses which imposes a tremendous burden on families and society. It is a heterogeneous group of brain disorders. However, cerebral small vessel disease (CSVD) accounts for about 50%–70% of VCI, which represented a more homogeneous subtype of VCI. Advanced multimodal neuroimaging techniques like brain network connectome analyses are currently applied to explore the underlying mechanism of VCI. Some progress in the field of structural and functional brain network researches on a poststroke longitudinal CSVD cohort (Renji CSVD Cohort Study) was reported. Global and regional brain network characters were compared between patients with CSVD and healthy control. It suggested that distributed brain structural network disruption may play a pivot role in the cognitive decline. The results showed that brain structural network characters have distinctive differentiating capacity on the cognition of patients with CSVD.

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