The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

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The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

Scientific Reports ◽

10.1038/s41598-021-94825-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rossana Mastrandrea ◽

Fabrizio Piras ◽

Andrea Gabrielli ◽

Nerisa Banaj ◽

Guido Caldarelli ◽

...

Keyword(s):

Resting State ◽

Functional Organization ◽

Functional Integration ◽

Brain Network ◽

Modular Organization ◽

Node Degree ◽

Imaging Data ◽

Brain Disorder ◽

Functional Connections ◽

The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

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Percolation Analysis of Brain Structural Network

Frontiers in Physics ◽

10.3389/fphy.2021.698077 ◽

2021 ◽

Vol 9 ◽

Author(s):

Shu Guo ◽

Xiaoqi Chen ◽

Yimeng Liu ◽

Rui Kang ◽

Tao Liu ◽

...

Keyword(s):

Failure Modes ◽

Critical Infrastructure ◽

Network Reliability ◽

Brain Connectivity ◽

Brain Networks ◽

Structural Features ◽

Brain Network ◽

Structure Design ◽

Reliability Design ◽

The Brain

The brain network is one specific type of critical infrastructure networks, which supports the cognitive function of biological systems. With the importance of network reliability in system design, evaluation, operation, and maintenance, we use the percolation methods of network reliability on brain networks and study the network resistance to disturbances and relevant failure modes. In this paper, we compare the brain networks of different species, including cat, fly, human, mouse, and macaque. The differences in structural features reflect the requirements for varying levels of functional specialization and integration, which determine the reliability of brain networks. In the percolation process, we apply different forms of disturbances to the brain networks based on metrics that characterize the network structure. Our findings suggest that the brain networks are mostly reliable against random or k-core-based percolation with their structure design, yet becomes vulnerable under betweenness or degree-based percolation. Our results might be useful to identify and distinguish brain connectivity failures that have been shown to be related to brain disorders, as well as the reliability design of other technological networks.

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From static to temporal network theory: Applications to functional brain connectivity

Network Neuroscience ◽

10.1162/netn_a_00011 ◽

2017 ◽

Vol 1 (2) ◽

pp. 69-99 ◽

Cited By ~ 28

Author(s):

William Hedley Thompson ◽

Per Brantefors ◽

Peter Fransson

Keyword(s):

Network Theory ◽

Temporal Dynamics ◽

Brain Connectivity ◽

Resting State Fmri ◽

Network Activity ◽

Temporal Network ◽

Functional Brain ◽

Brain Connectome ◽

The Brain ◽

Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

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Mindfulness Meditation Is Related to Long-Lasting Changes in Hippocampal Functional Topology during Resting State: A Magnetoencephalography Study

Neural Plasticity ◽

10.1155/2018/5340717 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 9

Author(s):

Anna Lardone ◽

Marianna Liparoti ◽

Pierpaolo Sorrentino ◽

Rosaria Rucco ◽

Francesca Jacini ◽

...

Keyword(s):

Resting State ◽

Mindfulness Meditation ◽

Brain Connectivity ◽

Brain Networks ◽

Brain Network ◽

Long Term Effects ◽

Age Related ◽

Functional Topology ◽

The Right ◽

The Brain

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks. To this aim, we used magnetoencephalography to study functional resting-state brain networks in Vipassana meditators. We observed topological modifications in the brain network in meditators compared to controls. More specifically, in the theta band, the meditators showed statistically significant (p corrected = 0.009) higher degree (a centrality index that represents the number of connections incident upon a given node) in the right hippocampus as compared to controls. Taking into account the role of the hippocampus in memory processes, and in the pathophysiology of Alzheimer’s disease, meditation might have a potential role in a panel of preventive strategies.

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Individual T1-weighted/T2-weighted ratio brain networks: Small-worldness, hubs and modular organization

International Journal of Modern Physics C ◽

10.1142/s0129183118400077 ◽

2018 ◽

Vol 29 (05) ◽

pp. 1840007

Author(s):

Huijun Wu ◽

Hao Wang ◽

Linyuan Lü

Keyword(s):

Large Scale ◽

Fluid Intelligence ◽

Brain Networks ◽

Structural Mri ◽

Modular Organization ◽

Enormous Amount ◽

Positive Correlations ◽

Law Degree ◽

Network Metrics ◽

The Brain

Applying network science to investigate the complex systems has become a hot topic. In neuroscience, understanding the architectures of complex brain networks was a vital issue. An enormous amount of evidence had supported the brain was cost/efficiency trade-off with small-worldness, hubness and modular organization through the functional MRI and structural MRI investigations. However, the T1-weighted/T2-weighted (T1w/T2w) ratio brain networks were mostly unexplored. Here, we utilized a KL divergence-based method to construct large-scale individual T1w/T2w ratio brain networks and investigated the underlying topological attributes of these networks. Our results supported that the T1w/T2w ratio brain networks were comprised of small-worldness, an exponentially truncated power–law degree distribution, frontal-parietal hubs and modular organization. Besides, there were significant positive correlations between the network metrics and fluid intelligence. Thus, the T1w/T2w ratio brain networks open a new avenue to understand the human brain and are a necessary supplement for future MRI studies.

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A covariate-constraint method to map brain feature space into lower dimensional manifolds

Network Neuroscience ◽

10.1162/netn_a_00176 ◽

2020 ◽

pp. 1-30

Author(s):

Félix Renard ◽

Christian Heinrich ◽

Marine Bouthillon ◽

Maleka Schenck ◽

Francis Schneider ◽

...

Keyword(s):

Brain Connectivity ◽

Feature Space ◽

Graph Metrics ◽

Brain Connectome ◽

Biological Phenomena ◽

Number Of Patients ◽

Population Comparison ◽

Constraint Method ◽

The Brain ◽

Insight Into

Human brain connectome studies aim at both exploring healthy brains, and extracting and analyzing relevant features associated to pathologies of interest. Usually this consists in modeling the brain connectome as a graph and in using graph metrics as features. A fine brain description requires graph metrics computation at the node level. Given the relatively reduced number of patients in standard cohorts, such data analysis problems fall in the high-dimension low sample size framework. In this context, our goal is to provide a machine learning technique that exhibits flexibility, gives the investigator grip on the features and covariates, allows visualization and exploration, and yields insight into the data and the biological phenomena at stake. The retained approach is dimension reduction in a manifold1 learning methodology, the originality lying in that one (or several) reduced variables be chosen by the investigator. The proposed method is illustrated on two studies, the first one addressing comatose patients, the second one addressing young versus elderly population comparison. The method sheds light on the differences between brain connectivity graphs using graph metrics and potential clinical interpretations of theses differences.

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A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome

10.1101/2020.12.23.424176 ◽

2020 ◽

Author(s):

Kwangsun Yoo ◽

Monica D. Rosenberg ◽

Young Hye Kwon ◽

Dustin Scheinost ◽

Robert T Constable ◽

...

Keyword(s):

Functional Organization ◽

Transformation Model ◽

Cognitive State ◽

Imaging Data ◽

State Transformation ◽

Cognitive States ◽

Brain Connectome ◽

Human Connectome Project ◽

Task Goals ◽

The Brain

The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model changes in the brain's functional organization. Here, we present a novel connectome-to-connectome (C2C) state transformation framework that enables us to model the brain's functional reorganization in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individual's task-specific connectomes from their task-free connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions. Finally, the C2C model reveals how the connectome reorganizes between cognitive states. Previous studies have reported that task-induced modulation of the brain connectome is domain-specific as well as domain-general, but did not specify how brain systems reconfigure to specific cognitive states. Our observations support the existence of reliable state-specific systems in the brain and indicate that we can quantitatively describe patterns of brain reorganization, common across individuals, in a computational model.

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Functional reorganization of brain networks across the human menstrual cycle

10.1101/866913 ◽

2019 ◽

Cited By ~ 3

Author(s):

Laura Pritschet ◽

Tyler Santander ◽

Caitlin M. Taylor ◽

Evan Layher ◽

Shuying Yu ◽

...

Keyword(s):

Menstrual Cycle ◽

Human Brain ◽

Estrogen Receptors ◽

Sex Hormones ◽

Functional Organization ◽

Brain Networks ◽

List Type ◽

Functional Architecture ◽

Human Menstrual Cycle ◽

The Brain

AbstractThe brain is an endocrine organ, sensitive to the rhythmic changes in sex hormone production that occurs in most mammalian species. In rodents and nonhuman primates, estrogen and progesterone’s impact on the brain is evident across a range of spatiotemporal scales. Yet, the influence of sex hormones on the functional architecture of the human brain is largely unknown. In this dense-sampling, deep phenotyping study, we examine the extent to which endogenous fluctuations in sex hormones alter intrinsic brain networks at rest in a woman who underwent brain imaging and venipuncture for 30 consecutive days. Standardized regression analyses illustrate estrogen and progesterone’s widespread associations with functional connectivity. Time-lagged analyses examined the temporal directionality of these relationships and suggest that cortical network dynamics (particularly in the Default Mode and Dorsal Attention Networks, whose hubs are densely populated with estrogen receptors) are preceded—and perhaps driven—by hormonal fluctuations. A similar pattern of associations was observed in a follow-up study one year later. Together, these results reveal the rhythmic nature in which brain networks reorganize across the human menstrual cycle. Neuroimaging studies that densely sample the individual connectome have begun to transform our understanding of the brain’s functional organization. As these results indicate, taking endocrine factors into account is critical for fully understanding the intrinsic dynamics of the human brain.HighlightsIntrinsic fluctuations in sex hormones shape the brain’s functional architecture.Estradiol facilitates tighter coherence within whole-brain functional networks.Progesterone has the opposite, reductive effect.Ovulation (via estradiol) modulates variation in topological network states.Effects are pronounced in network hubs densely populated with estrogen receptors.

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Evaluating Functional Connectivity Alterations in Autism Spectrum Disorder Using Network-Based Statistics

Diagnostics ◽

10.3390/diagnostics8030051 ◽

2018 ◽

Vol 8 (3) ◽

pp. 51 ◽

Cited By ~ 5

Author(s):

Aitana Pascual-Belda ◽

Antonio Díaz-Parra ◽

David Moratal

Keyword(s):

Autism Spectrum Disorder ◽

Brain Connectivity ◽

Brain Networks ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Typical Development ◽

Functional Brain ◽

Functional Brain Networks ◽

Management Functions ◽

The Brain

The study of resting-state functional brain networks is a powerful tool to understand the neurological bases of a variety of disorders such as Autism Spectrum Disorder (ASD). In this work, we have studied the differences in functional brain connectivity between a group of 74 ASD subjects and a group of 82 typical-development (TD) subjects using functional magnetic resonance imaging (fMRI). We have used a network approach whereby the brain is divided into discrete regions or nodes that interact with each other through connections or edges. Functional brain networks were estimated using the Pearson’s correlation coefficient and compared by means of the Network-Based Statistic (NBS) method. The obtained results reveal a combination of both overconnectivity and underconnectivity, with the presence of networks in which the connectivity levels differ significantly between ASD and TD groups. The alterations mainly affect the temporal and frontal lobe, as well as the limbic system, especially those regions related with social interaction and emotion management functions. These results are concordant with the clinical profile of the disorder and can contribute to the elucidation of its neurological basis, encouraging the development of new clinical approaches.

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Cerebral cartography and connectomics

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2014.0173 ◽

2015 ◽

Vol 370 (1668) ◽

pp. 20140173 ◽

Cited By ~ 39

Author(s):

Olaf Sporns

Keyword(s):

Large Scale ◽

Computational Models ◽

Network Science ◽

Temporal Dynamics ◽

Functional Organization ◽

Brain Connectivity ◽

Hierarchical Organization ◽

Large Scale Networks ◽

Definition Of ◽

The Brain

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.

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