Relationship of critical dynamics, functional connectivity, and states of consciousness in large-scale human brain networks

Background: Despite accumulated evidence for adult brain plasticity, the temporal relationships between large-scale functional and structural connectivity changes in human brain networks remain unclear. Methods: By analysing a unique richly detailed 19-week longitudinal neuroimaging dataset, we tested whether macroscopic functional connectivity changes lead to the corresponding structural alterations in the adult human brain, and examined whether such time lags between functional and structural connectivity changes are affected by functional differences between different large-scale brain networks. Results: In this single-case study, we report that, compared to attention-related networks, functional connectivity changes in default-mode, fronto-parietal, and sensory-related networks occurred in advance of modulations of the corresponding structural connectivity with significantly longer time lags. In particular, the longest time lags were observed in sensory-related networks. In contrast, such significant temporal differences in connectivity change were not seen in comparisons between anatomically categorised different brain areas, such as frontal and occipital lobes. These observations survived even after multiple validation analyses using different connectivity definitions or using parts of the datasets. Conclusions: Although the current findings should be examined in independent datasets with different demographic background and by experimental manipulation, this single-case study indicates the possibility that plasticity of macroscopic brain networks could be affected by cognitive and perceptual functions implemented in the networks, and implies a hierarchy in the plasticity of functionally different brain systems.

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Dynamic Reconfiguration of Human Brain Networks Across Altered States of Consciousness

Behavioural Brain Research ◽

10.1016/j.bbr.2021.113685 ◽

2021 ◽

pp. 113685

Author(s):

Haiyang Liu ◽

Ke Hu ◽

Yingjie Peng ◽

Xiaohan Tian ◽

Meng Wang ◽

...

Keyword(s):

Human Brain ◽

Brain Networks ◽

Dynamic Reconfiguration ◽

Altered States Of Consciousness ◽

Altered States ◽

States Of Consciousness

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Development and Emergence of Individual Variability in the Functional Connectivity Architecture of the Preterm Human Brain

Cerebral Cortex ◽

10.1093/cercor/bhy302 ◽

2018 ◽

Vol 29 (10) ◽

pp. 4208-4222 ◽

Cited By ~ 7

Author(s):

Yuehua Xu ◽

Miao Cao ◽

Xuhong Liao ◽

Mingrui Xia ◽

Xindi Wang ◽

...

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Network Architecture ◽

Brain Networks ◽

Individual Variability ◽

Distance Constraint ◽

Behavioral Differences ◽

Sensorimotor Network ◽

Middle Range ◽

Brain Functional Connectivity

Abstract Individual variability in human brain networks underlies individual differences in cognition and behaviors. However, researchers have not conclusively determined when individual variability patterns of the brain networks emerge and how they develop in the early phase. Here, we employed resting-state functional MRI data and whole-brain functional connectivity analyses in 40 neonates aged around 31–42 postmenstrual weeks to characterize the spatial distribution and development modes of individual variability in the functional network architecture. We observed lower individual variability in primary sensorimotor and visual areas and higher variability in association regions at the third trimester, and these patterns are generally similar to those of adult brains. Different functional systems showed dramatic differences in the development of individual variability, with significant decreases in the sensorimotor network; decreasing trends in the visual, subcortical, and dorsal and ventral attention networks, and limited change in the default mode, frontoparietal and limbic networks. The patterns of individual variability were negatively correlated with the short- to middle-range connection strength/number and this distance constraint was significantly strengthened throughout development. Our findings highlight the development and emergence of individual variability in the functional architecture of the prenatal brain, which may lay network foundations for individual behavioral differences later in life.

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Multi-modal analysis of functional connectivity and cerebral blood flow reveals shared and unique effects of propofol in large-scale brain networks

NeuroImage ◽

10.1016/j.neuroimage.2016.12.080 ◽

2017 ◽

Vol 148 ◽

pp. 130-140 ◽

Cited By ~ 20

Author(s):

Maolin Qiu ◽

Dustin Scheinost ◽

Ramachandran Ramani ◽

R. Todd Constable

Keyword(s):

Blood Flow ◽

Cerebral Blood Flow ◽

Functional Connectivity ◽

Modal Analysis ◽

Large Scale ◽

Brain Networks

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Large-Scale Brain Networks and Functional Connectivity

ADHD as a Model of Brain-Behavior Relationships - SpringerBriefs in Neuroscience ◽

10.1007/978-1-4614-8382-3_10 ◽

2013 ◽

pp. 23-27

Author(s):

Leonard F. Koziol ◽

Deborah Ely Budding ◽

Dana Chidekel

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Brain Networks

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Attentional Fluctuations Influence the Neural Fidelity and Connectivity of Stimulus Representations

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01306 ◽

2018 ◽

Vol 30 (9) ◽

pp. 1209-1228 ◽

Cited By ~ 6

Author(s):

David Rothlein ◽

Joseph DeGutis ◽

Michael Esterman

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Brain Networks ◽

Relevant Information ◽

Mental States ◽

Attention Task ◽

Default Mode ◽

Neural Representations ◽

Similarity Structure ◽

Similarity Matrices

Attention is thought to facilitate both the representation of task-relevant features and the communication of these representations across large-scale brain networks. However, attention is not “all or none,” but rather it fluctuates between stable/accurate (in-the-zone) and variable/error-prone (out-of-the-zone) states. Here we ask how different attentional states relate to the neural processing and transmission of task-relevant information. Specifically, during in-the-zone periods: (1) Do neural representations of task stimuli have greater fidelity? (2) Is there increased communication of this stimulus information across large-scale brain networks? Finally, (3) can the influence of performance-contingent reward be differentiated from zone-based fluctuations? To address these questions, we used fMRI and representational similarity analysis during a visual sustained attention task (the gradCPT). Participants ( n = 16) viewed a series of city or mountain scenes, responding to cities (90% of trials) and withholding to mountains (10%). Representational similarity matrices, reflecting the similarity structure of the city exemplars ( n = 10), were computed from visual, attentional, and default mode networks. Representational fidelity (RF) and representational connectivity (RC) were quantified as the interparticipant reliability of representational similarity matrices within (RF) and across (RC) brain networks. We found that being in the zone was characterized by increased RF in visual networks and increasing RC between visual and attentional networks. Conversely, reward only increased the RC between the attentional and default mode networks. These results diverge with analogous analyses using functional connectivity, suggesting that RC and functional connectivity in tandem better characterize how different mental states modulate the flow of information throughout the brain.

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

eLife ◽

10.7554/elife.32696 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 4

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.

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Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity

International Journal of Neural Systems ◽

10.1142/s0129065720500471 ◽

2020 ◽

Vol 30 (09) ◽

pp. 2050047

Author(s):

Lubin Wang ◽

Xianbin Li ◽

Yuyang Zhu ◽

Bei Lin ◽

Qijing Bo ◽

...

Keyword(s):

High Risk ◽

Functional Connectivity ◽

Symptom Severity ◽

Large Scale ◽

Brain Networks ◽

Brain Regions ◽

Support Vector ◽

Healthy Controls ◽

Intrinsic Functional Connectivity ◽

Ultra High Risk

Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.

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Characterizing Complex Networks Using Entropy-Degree Diagrams: Unveiling Changes in Functional Brain Connectivity Induced by Ayahuasca

Entropy ◽

10.3390/e21020128 ◽

2019 ◽

Vol 21 (2) ◽

pp. 128 ◽

Cited By ~ 6

Author(s):

Aline Viol ◽

Fernanda Palhano-Fontes ◽

Heloisa Onias ◽

Draulio de Araujo ◽

Philipp Hövel ◽

...

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Resting State ◽

Structural Information ◽

Brain Connectivity ◽

Functional Brain ◽

States Of Consciousness ◽

Functional Brain Networks ◽

Empirical Networks ◽

Emergent Behaviors

With the aim of further advancing the understanding of the human brain’s functional connectivity, we propose a network metric which we term the geodesic entropy. This metric quantifies the Shannon entropy of the distance distribution to a specific node from all other nodes. It allows to characterize the influence exerted on a specific node considering statistics of the overall network structure. The measurement and characterization of this structural information has the potential to greatly improve our understanding of sustained activity and other emergent behaviors in networks. We apply this method to study how the psychedelic infusion Ayahuasca affects the functional connectivity of the human brain in resting state. We show that the geodesic entropy is able to differentiate functional networks of the human brain associated with two different states of consciousness in the awaking resting state: (i) the ordinary state and (ii) a state altered by ingestion of the Ayahuasca. The functional brain networks from subjects in the altered state have, on average, a larger geodesic entropy compared to the ordinary state. Finally, we discuss why the geodesic entropy may bring even further valuable insights into the study of the human brain and other empirical networks.

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