scholarly journals Attention Networks and the Intrinsic Network Structure of the Human Brain

2021 ◽  
Author(s):  
Sebastian Markett ◽  
David Nothdurfter ◽  
Antonia Focsa ◽  
Martin Reuter ◽  
Philippe Jawinski
Keyword(s):  
Network Structure ◽  
Network Theory ◽  
Large Scale ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Relevant Information ◽  
Attention Network ◽  
Attention Networks ◽  
Large Scale Networks ◽  
The Brain

Attention network theory states that attention is not a unified construct but consists of three independent systems that are supported by separable distributed networks: an alerting network to deploy attentional resources in anticipation of upcoming events, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. It is well established that the brain is intrinsically organized into several large-scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks with highest spatial precision at the level of cifti-grayordinates. Resulting group maps were compared to the group-level topology of 23 intrinsic networks which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto-parietal and midcingulo-insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.

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

2017 ◽  
Vol 1 (2) ◽  
pp. 69-99 ◽  
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.

scholarly journals Cerebral cartography and connectomics

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140173 ◽  
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.

scholarly journals From static to temporal network theory – applications to functional brain connectivity

2016 ◽  
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 ◽  
Python Package

AbstractNetwork neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, there has been a growing interest to examine the temporal dynamics of the brain's network activity. While different approaches to capture fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. Temporal network theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences and engineering. The objective of this paper is twofold: (i) to present a detailed description of the central tenets and outline measures from temporal network theory; (ii) apply these measures to a resting-state fMRI dataset to illustrate their utility. Further, 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 paper are freely available as a python package Teneto.

scholarly journals BRAPH: A Graph Theory Software for the Analysis of Brain Connectivity

2017 ◽  
Author(s):  
Mite Mijalkov ◽  
Ehsan Kakaei ◽  
Joana B. Pereira ◽  
Eric Westman ◽  
Giovanni Volpe ◽  
...  
Keyword(s):  
Graph Theory ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Large Scale ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Brain Network ◽  
Local Network ◽  
Healthy Controls ◽  
The Brain

AbstractThe brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH – BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment.

Direct Causal Networks for the Study of Transcranial Magnetic Stimulation Effects on Focal Epileptiform Discharges

2015 ◽  
Vol 25 (05) ◽  
pp. 1550006 ◽  
Author(s):  
Dimitris Kugiumtzis ◽  
Vasilios K. Kimiskidis
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Network Structure ◽  
Magnetic Stimulation ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Epileptic Focus ◽  
Sham Stimulation ◽  
Focal Seizures ◽  
Epileptiform Discharges ◽  
The Brain

Background: Transcranial magnetic stimulation (TMS) can have inhibitory effects on epileptiform discharges (EDs) of patients with focal seizures. However, the brain connectivity before, during and after EDs, with or without the administration of TMS, has not been extensively explored. Objective: To investigate the brain network of effective connectivity during ED with and without TMS in patients with focal seizures. Methods: For the effective connectivity a direct causality measure is applied termed partial mutual information from mixed embedding (PMIME). TMS-EEG data from two patients with focal seizures were analyzed. Each EEG record contained a number of EDs in the majority of which TMS was administered over the epileptic focus. As a control condition, sham stimulation over the epileptogenic zone or real TMS at a distance from the epileptic focus was also performed. The change in brain connectivity structure was investigated from the causal networks formed at each sliding window. Conclusion: The PMIME could detect distinct changes in the network structure before, within, and after ED. The administration of real TMS over the epileptic focus, in contrast to sham stimulation, terminated the ED prematurely in a node-specific manner and regained the network structure as if it would have terminated spontaneously.

Attentional load modulates large-scale functional brain connectivity beyond the core attention networks

NeuroImage ◽  
2015 ◽  
Vol 109 ◽  
pp. 260-272 ◽  
Author(s):  
Dag Alnæs ◽  
Tobias Kaufmann ◽  
Geneviève Richard ◽  
Eugene P. Duff ◽  
Markus H. Sneve ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Connectivity ◽  
Attention Networks ◽  
Functional Brain ◽  
The Core ◽  
Attentional Load

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

scholarly journals Brain network dynamics are hierarchically organized in time

2017 ◽  
Vol 114 (48) ◽  
pp. 12827-12832 ◽  
Author(s):  
Diego Vidaurre ◽  
Stephen M. Smith ◽  
Mark W. Woolrich
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Network ◽  
Specific Measure ◽  
Brain Areas ◽  
Neuronal Populations ◽  
Subject Specific ◽  
Large Scale Networks ◽  
Network Patterns ◽  
The Brain

The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.

scholarly journals Oxytocin modulates the intrinsic dynamics between attention-related large scale networks

2018 ◽  
Author(s):  
Fei Xin ◽  
Feng Zhou ◽  
Xinqi Zhou ◽  
Xiaole Ma ◽  
Yayuan Geng ◽  
...  
Keyword(s):  
Attentional Control ◽  
Large Scale ◽  
Functional Integration ◽  
Network Dynamics ◽  
Resting State Fmri ◽  
Neural Systems ◽  
Functional Dynamics ◽  
Fmri Study ◽  
N 93 ◽  
Large Scale Networks

AbstractAttention and salience processing have been linked to the intrinsic between- and within-network dynamics of large scale networks engaged in internal (default mode network, DN) and external attention allocation (dorsal attention, DAN, salience network, SN). The central oxytocin (OXT) system appears ideally organized to modulate widely distributed neural systems and to regulate the switch between internal attention and salient stimuli in the environment. The current randomized placebo (PLC) controlled between-subject pharmacological resting-state fMRI study in N = 187 (OXT, n = 94; n = 93; single-dose intranasal administration) healthy male and female participants employed an independent component analysis (ICA) approach to determine the modulatory effects of OXT on the within- and between-network dynamics of the DAN-SN-DN triple network system. OXT increased the functional integration between subsystems within SN and DN and increased functional segregation of the DN with the SN and DAN engaged in attentional control. Whereas no sex differences were observed, OXT effects on the DN-SN interaction were modulated by autism traits. Together, the findings suggest that OXT may facilitate efficient attentional allocation towards social cues by modulating the intrinsic functional dynamics between DN components engaged in social processing and large-scale networks involved in external attentional demands (SN, DAN).

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