Literature DB >> 30430844

Quantifying Differences Between Passive and Task-Evoked Intrinsic Functional Connectivity in a Large-Scale Brain Simulation.

Antonio Ulloa1,2, Barry Horwitz1.   

Abstract

Establishing a connection between intrinsic and task-evoked brain activities is critical because it would provide a way to map task-related brain regions in patients unable to comply with such tasks. A crucial question within this realm is to what extent the execution of a cognitive task affects the intrinsic activity of brain regions not involved in the task. Computational models can be useful to answer this question because they allow us to distinguish task from nontask neural elements while giving us the effects of task execution on nontask regions of interest at the neuroimaging level. The quantification of those effects in a computational model would represent a step toward elucidating the intrinsic versus task-evoked connection. In this study we used computational modeling and graph theoretical metrics to quantify changes in intrinsic functional brain connectivity due to task execution. We used our large-scale neural modeling framework to embed a computational model of visual short-term memory into an empirically derived connectome. We simulated a neuroimaging study consisting of 10 subjects performing passive fixation (PF), passive viewing (PV), and delayed match-to-sample (DMS) tasks. We used the simulated blood oxygen level-dependent functional magnetic resonance imaging time series to calculate functional connectivity (FC) matrices and used those matrices to compute several graph theoretical measures. After determining that the simulated graph theoretical measures were largely consistent with experiments, we were able to quantify the differences between the graph metrics of the PF condition and those of the PV and DMS conditions. Thus, we show that we can use graph theoretical methods applied to simulated brain networks to aid in the quantification of changes in intrinsic brain FC during task execution. Our results represent a step toward establishing a connection between intrinsic and task-related brain activities.

Entities:  

Keywords:  connectome; fMRI; functional connectivity; graph theory; neural modeling; visual short-term memory

Mesh:

Year:  2018        PMID: 30430844      PMCID: PMC6308294          DOI: 10.1089/brain.2018.0620

Source DB:  PubMed          Journal:  Brain Connect        ISSN: 2158-0014


  65 in total

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3.  Investigating the neural basis for functional and effective connectivity. Application to fMRI.

Authors:  Barry Horwitz; Brent Warner; Julie Fitzer; M-A Tagamets; Fatima T Husain; Theresa W Long
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2005-05-29       Impact factor: 6.237

4.  Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems.

Authors:  Michael D Fox; Maurizio Corbetta; Abraham Z Snyder; Justin L Vincent; Marcus E Raichle
Journal:  Proc Natl Acad Sci U S A       Date:  2006-06-20       Impact factor: 11.205

5.  Predicting human resting-state functional connectivity from structural connectivity.

Authors:  C J Honey; O Sporns; L Cammoun; X Gigandet; J P Thiran; R Meuli; P Hagmann
Journal:  Proc Natl Acad Sci U S A       Date:  2009-02-02       Impact factor: 11.205

6.  Collective dynamics of 'small-world' networks.

Authors:  D J Watts; S H Strogatz
Journal:  Nature       Date:  1998-06-04       Impact factor: 49.962

7.  Using the virtual brain to reveal the role of oscillations and plasticity in shaping brain's dynamical landscape.

Authors:  Dipanjan Roy; Rodrigo Sigala; Michael Breakspear; Anthony Randal McIntosh; Viktor K Jirsa; Gustavo Deco; Petra Ritter
Journal:  Brain Connect       Date:  2014-12

8.  Excitatory and inhibitory interactions in localized populations of model neurons.

Authors:  H R Wilson; J D Cowan
Journal:  Biophys J       Date:  1972-01       Impact factor: 4.033

9.  Correspondence between evoked and intrinsic functional brain network configurations.

Authors:  Taylor Bolt; Jason S Nomi; Mikail Rubinov; Lucina Q Uddin
Journal:  Hum Brain Mapp       Date:  2017-01-04       Impact factor: 5.038

Review 10.  Predictions not commands: active inference in the motor system.

Authors:  Rick A Adams; Stewart Shipp; Karl J Friston
Journal:  Brain Struct Funct       Date:  2012-11-06       Impact factor: 3.270

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