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Brain Connect. 2018 Nov 15. doi: 10.1089/brain.2018.0620. [Epub ahead of print]

Quantifying differences between passive and task-evoked intrinsic functional connectivity in a large-scale brain simulation.

Author information

1
National Institute on Deafness and Other Communication Disorders, 35041, Section on Brain Imaging and Modeling, Bethesda, Maryland, United States.
2
Neural Bytes LLC, 466356, Washington, District of Columbia, United States ; antonio.ulloa@alum.bu.edu.
3
National Institute on Deafness and Other Communication Disorders, 35041, Section on Brain Imaging and Modeling, Bethesda, Maryland, United States ; horwitzb@mail.nih.gov.

Abstract

Establishing a connection between intrinsic and task-evoked brain activity 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 non-task neural elements while giving us the effects of task execution on non-task regions of interest at the neuroimaging level. The quantification of those effects in a computational model would represent a step towards elucidating the intrinsic versus task-evoked connection. Here 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 ten subjects performing passive fixation (PF), passive viewing (PV) and delay match-to-sample (DMS) tasks. We used the simulated BOLD fMRI 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 functional connectivity during task execution. Our results represent a step towards establishing a connection between intrinsic and task-related brain activity.

KEYWORDS:

Blood oxygen level dependent (BOLD) signal; Connectome; Graph theory; Modeling; Visual system

PMID:
30430844
DOI:
10.1089/brain.2018.0620

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