scholarly journals Brain connectivity dynamics: Multilayer network switching rate predicts brain performance

2018 ◽  
Author(s):  
Mangor Pedersen ◽  
Andrew Zalesky ◽  
Amir Omidvarnia ◽  
Graeme D. Jackson
Keyword(s):  
Large Scale ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Dynamics ◽  
Model Parameters ◽  
Brain Areas ◽  
Multilayer Network ◽  
Time Resolved ◽  
Brain Functions ◽  
Human Connectome Project

AbstractLarge-scale brain dynamics measures repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood and whether switching between these states is important for behavior has been little studied. Our aim here is to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved functional MRI (fMRI) connectivity from one-hour long data recordings in 1003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data was used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching which we define as the rate at which each brain region transits between different fMRI networks. We found i) an inverse relationship between network switching and connectivity dynamics –defined as the difference in variance between time-resolved fMRI connectivity signals and phase randomized surrogates–; ii) brain connectivity was lower during intervals of network switching; iii) brain areas with frequent network switching had greater temporal complexity; iv) brain areas with high network switching were located in association cortices; and v) using cross-validated Elastic Net regression, network switching predicted inter-subject variation in working memory performance, planning/reasoning and amount of sleep. Our findings shed new light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.

scholarly journals Multilayer network switching rate predicts brain performance

2018 ◽  
Vol 115 (52) ◽  
pp. 13376-13381 ◽  
Author(s):  
Mangor Pedersen ◽  
Andrew Zalesky ◽  
Amir Omidvarnia ◽  
Graeme D. Jackson
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Dynamics ◽  
Model Parameters ◽  
Brain Areas ◽  
Multilayer Network ◽  
Healthy Human ◽  
Time Resolved ◽  
Brain Functions ◽  
Human Connectome Project

Large-scale brain dynamics are characterized by repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood, and whether switching between these states is important for behavior has been little studied. Our aim was to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved fMRI connectivity in 1,003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data were used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching, which we define as the rate at which each brain region transits between different networks. We found (i) an inverse relationship between network switching and connectivity dynamics, where the latter was defined in terms of time-resolved fMRI connections with variance in time that significantly exceeded phase-randomized surrogate data; (ii) brain connectivity was lower during intervals of network switching; (iii) brain areas with frequent network switching had greater temporal complexity; (iv) brain areas with high network switching were located in association cortices; and (v) using cross-validated elastic net regression, network switching predicted intersubject variation in working memory performance, planning/reasoning, and amount of sleep. Our findings shed light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.

scholarly journals Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions

2021 ◽  
Author(s):  
Elvira Pirondini ◽  
Nawal Kinany ◽  
Cécile Le Sueur ◽  
Joseph C. Griffis ◽  
Gordon L. Shulman ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Cognitive Functions ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Dynamics ◽  
Time Resolved ◽  
Post Stroke ◽  
Temporal Properties ◽  
Fmri Analysis

AbstractFunctional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. While large-scale spatial patterns of these networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. These temporal alterations were associated with white matter damage and were behavior-specific. Specifically, restoration of healthy brain dynamics paralleled recovery of cognitive functions, but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.Significance StatementUnderstanding the pathophysiology of a disorder is pivotal to design effective treatment. In this regard, recent advances in stroke research settled a new clinical concept: connectional diaschisis, which suggested that post-stroke impairments arise from both focal structural changes (tied to the injury) and widespread alterations in functional connectivity. fMRI time-resolved methods consider structural and temporal properties of brain networks as interdependent features. They are, thus, better suited to capture the intertwine between structural and functional changes. Here we leveraged a dynamic functional connectivity framework based on the clustering of hemodynamic-informed transients in a large and heterogeneous stroke population assessed longitudinally. We showed that lesions led to an unbalance in the brain dynamics that was associated with white matter fibers disruption and was restored as deficits recovered. Our work showed the potential of a time-resolved method to reveal clinically relevant dynamics of large-scale brain networks.

scholarly journals Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rieke Fruengel ◽  
Timo Bröhl ◽  
Thorsten Rings ◽  
Klaus Lehnertz
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Theoretical Concept ◽  
Global Network ◽  
Time Resolved ◽  
Functional Connections ◽  
Node Centrality ◽  
Network Mechanism

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.

scholarly journals Functional Connectome of the Five-Factor Model of Personality

2018 ◽  
Vol 1 ◽  
Author(s):  
Nicola Toschi ◽  
Roberta Riccelli ◽  
Iole Indovina ◽  
Antonio Terracciano ◽  
Luca Passamonti
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Factor Model ◽  
Brain Connectivity ◽  
Five Factor Model ◽  
Mechanistic Explanation ◽  
Data Sets ◽  
Personality Differences ◽  
Human Connectome Project ◽  
Maximal Information Coefficient

Abstract A key objective of the emerging field of personality neuroscience is to link the great variety of the enduring dispositions of human behaviour with reliable markers of brain function. This can be achieved by analysing big data-sets with methods that model whole-brain connectivity patterns. To meet these expectations, we exploited a large repository of personality and neuroimaging measures made publicly available via the Human Connectome Project. Using connectomic analyses based on graph theory, we computed global and local indices of functional connectivity (e.g., nodal strength, efficiency, clustering, betweenness centrality) and related these metrics to the five-factor model (FFM) personality traits (i.e., neuroticism, extraversion, openness, agreeableness, and conscientiousness). The maximal information coefficient was used to assess for linear and nonlinear statistical dependencies across the graph “nodes”, which were defined as distinct large-scale brain circuits identified via independent component analysis. Multivariate regression models and “train/test” approaches were used to examine the associations between FFM traits and connectomic indices as well as to assess the generalizability of the main findings, while accounting for age and sex variability. Conscientiousness was the sole FFM trait linked to measures of higher functional connectivity in the fronto-parietal and default mode networks. This offers a mechanistic explanation of the behavioural observation that conscientious people are reliable and efficient in goal-setting or planning. Our study provides new inputs to understanding the neurological basis of personality and contributes to the development of more realistic models of the brain dynamics that mediate personality differences.

scholarly journals Centralized and distributed cognitive task processing in the human connectome

2019 ◽  
Vol 3 (2) ◽  
pp. 455-474 ◽  
Author(s):  
Enrico Amico ◽  
Alex Arenas ◽  
Joaquín Goñi
Keyword(s):  
Cognitive Processing ◽  
Resting State ◽  
Large Scale ◽  
Cognitive Task ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Cognitive Changes ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
Jensen Shannon Divergence

A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectivity (FC). A systematic approach to measure pairwise functional distance at different brain states is lacking. This would provide a straightforward way to quantify differences in cognitive processing across tasks; also, it would help in relating these differences in task-based FCs to the underlying structural network. Here we propose a framework, based on the concept of Jensen-Shannon divergence, to map the task-rest connectivity distance between tasks and resting-state FC. We show how this information theoretical measure allows for quantifying connectivity changes in distributed and centralized processing in functional networks. We study resting state and seven tasks from the Human Connectome Project dataset to obtain the most distant links across tasks. We investigate how these changes are associated with different functional brain networks, and use the proposed measure to infer changes in the information-processing regimes. Furthermore, we show how the FC distance from resting state is shaped by structural connectivity, and to what extent this relationship depends on the task. This framework provides a well-grounded mathematical quantification of connectivity changes associated with cognitive processing in large-scale brain networks.

A Data Structure for real-time Aggregation Queries of Big Brain Networks

2018 ◽  
Author(s):  
Florian Ganglberger ◽  
Joanna Kaczanowska ◽  
Wulf Haubensak ◽  
Katja Bühler
Keyword(s):  
Gene Expression ◽  
Data Structure ◽  
Real Time ◽  
Large Scale ◽  
State Of The Art ◽  
Brain Networks ◽  
Spatial Context ◽  
Brain Areas ◽  
Public Data ◽  
Aggregation Queries

AbstractRecent advances in neuro-imaging allowed big brain-initiatives and consortia to create vast resources of brain data that can be mined by researchers for their individual projects. Exploring the relationship between genes, brain circuitry, and behavior is one of key elements of neuroscience research. This requires fusion of spatial connectivity data at varying scales, such as whole brain correlated gene expression, structural and functional connectivity. With ever-increasing resolution, those exceed the past state-of-the art in several orders of magnitude in size and complexity. Current analytical workflows in neuroscience involve time-consuming manual aggregation of the data and only sparsely incorporate spatial context to operate continuously on multiple scales. Incorporating techniques for handling big connectivity data is therefore a necessity.We propose a data structure to explore heterogeneous neurobiological connectivity data for integrated visual analytics workflows. Aggregation Queries, i.e. the aggregated connectivity from, to or between brain areas allow experts the comparison of multimodal networks residing at different scales, or levels of hierarchically organized anatomical atlases. Executed on-demand on volumetric gene expression and connectivity data, they enable an interactive dissection of networks, with billions of edges, in real-time, and based on their spatial context. The data structure is optimized to be accessed directly from the hard disk, since connectivity of large-scale networks typically exceed the memory size of current consumer level PCs. This allows experts to embed and explore their own experimental data in the framework of public data resources without large-scale infrastructure.Our novel data structure outperforms state-of-the-art graph engines in retrieving connectivity of local brain areas experimentally. We demonstrate the application of our approach for neuroscience by analyzing fear-related functional neuroanatomy in mice. Further, we show its versatility by comparing multimodal brain networks linked to autism. Importantly, we achieve cross-species congruence in retrieving human psychiatric traits networks, which facilitates selection of neural substrates to be further studied in mouse models.

scholarly journals Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks

2014 ◽  
Vol 369 (1653) ◽  
pp. 20130531 ◽  
Author(s):  
Petra E. Vértes ◽  
Aaron Alexander-Bloch ◽  
Edward T. Bullmore
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Large Scale ◽  
Hebbian Learning ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Network ◽  
Generative Models ◽  
Adaptive Behaviour ◽  
Topological Term

Rich clubs arise when nodes that are ‘rich’ in connections also form an elite, densely connected ‘club’. In brain networks, rich clubs incur high physical connection costs but also appear to be especially valuable to brain function. However, little is known about the selection pressures that drive their formation. Here, we take two complementary approaches to this question: firstly we show, using generative modelling, that the emergence of rich clubs in large-scale human brain networks can be driven by an economic trade-off between connection costs and a second, competing topological term. Secondly we show, using simulated neural networks, that Hebbian learning rules also drive the emergence of rich clubs at the microscopic level, and that the prominence of these features increases with learning time. These results suggest that Hebbian learning may provide a neuronal mechanism for the selection of complex features such as rich clubs. The neural networks that we investigate are explicitly Hebbian, and we argue that the topological term in our model of large-scale brain connectivity may represent an analogous connection rule. This putative link between learning and rich clubs is also consistent with predictions that integrative aspects of brain network organization are especially important for adaptive behaviour.

scholarly journals Modeling brain dynamics in brain tumor patients using The Virtual Brain

2018 ◽  
Author(s):  
Hannelore Aerts ◽  
Michael Schirner ◽  
Ben Jeurissen ◽  
Dirk Van Roost ◽  
Rik Achten ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Cognitive Performance ◽  
Network Topology ◽  
Large Scale ◽  
Brain Dynamics ◽  
Model Parameters ◽  
Suitable Model ◽  
Tumor Patients ◽  
Structural Network ◽  
Optimized Model

AbstractPresurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, non-invasive neuroimaging techniques such as functional MRI and diffusion weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex non-linear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics.As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed.Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

scholarly journals Evaluating test-retest reliability and sex/age-related effects on temporal clustering coefficient of dynamic functional brain networks

2021 ◽  
Author(s):  
Yicheng Long ◽  
Chaogan Yan ◽  
Zhipeng Wu ◽  
Xiaojun Huang ◽  
Hengyi Cao ◽  
...  
Keyword(s):  
Intraclass Correlation ◽  
Brain Networks ◽  
Clustering Coefficient ◽  
Retest Reliability ◽  
Brain Functions ◽  
Temporal Clustering ◽  
Age Related ◽  
Human Connectome Project ◽  
Wide Range ◽  
Test Retest Reliability

The multilayer dynamic network model has been proposed as an effective method to understand how the brain functions dynamically. Specially, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of topological stability of dynamic brain networks and shows potential in predicting altered brain functions in both normal and pathological conditions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a publicly available dataset from the Human Connectome Project consisting of 337 young healthy adults (157 males/180 females; 22 to 37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels; (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, in particular within the default-mode and subcortical regions; (3) temporal clustering coefficient of the subcortical subnetwork was negatively correlated with age in young adults. Our findings suggest that temporal clustering coefficient is a reliable and reproducible approach for the identification of individual differences in brain function, and provide evidence for sex and age effects on human brain dynamic connectome.

scholarly journals Individualized perturbation of the human connectome reveals reproducible biomarkers of network dynamics relevant to cognition

2020 ◽  
Vol 117 (14) ◽  
pp. 8115-8125 ◽  
Author(s):  
Recep A. Ozdemir ◽  
Ehsan Tadayon ◽  
Pierre Boucher ◽  
Davide Momi ◽  
Kelly A. Karakhanyan ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Resting State ◽  
Cognitive Abilities ◽  
Large Scale ◽  
Brain Networks ◽  
Blood Oxygenation ◽  
Neuronal Firing ◽  
Brain Dynamics ◽  
High Temporal Resolution ◽  
Indirect Measure

Large-scale brain networks are often described using resting-state functional magnetic resonance imaging (fMRI). However, the blood oxygenation level-dependent (BOLD) signal provides an indirect measure of neuronal firing and reflects slow-evolving hemodynamic activity that fails to capture the faster timescale of normal physiological function. Here we used fMRI-guided transcranial magnetic stimulation (TMS) and simultaneous electroencephalography (EEG) to characterize individual brain dynamics within discrete brain networks at high temporal resolution. TMS was used to induce controlled perturbations to individually defined nodes of the default mode network (DMN) and the dorsal attention network (DAN). Source-level EEG propagation patterns were network-specific and highly reproducible across sessions 1 month apart. Additionally, individual differences in high-order cognitive abilities were significantly correlated with the specificity of TMS propagation patterns across DAN and DMN, but not with resting-state EEG dynamics. Findings illustrate the potential of TMS-EEG perturbation-based biomarkers to characterize network-level individual brain dynamics at high temporal resolution, and potentially provide further insight on their behavioral significance.

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