Journal article
M. Gilson, G. Zamora-López, V. Pallarés, Mohit H. Adhikari, M. Senden, A. T. Campo, D. Mantini, M. Corbetta, G. Deco, A. Insabato
bioRxiv, 2019
bioRxiv, 2019
Semantic Scholar
DOI
PubMedCentral
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APA
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Gilson, M., Zamora-López, G., Pallarés, V., Adhikari, M. H., Senden, M., Campo, A. T., … Insabato, A. (2019). Model-based whole-brain effective connectivity to study distributed cognition in health and disease. BioRxiv.
Chicago/Turabian
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Gilson, M., G. Zamora-López, V. Pallarés, Mohit H. Adhikari, M. Senden, A. T. Campo, D. Mantini, M. Corbetta, G. Deco, and A. Insabato. “Model-Based Whole-Brain Effective Connectivity to Study Distributed Cognition in Health and Disease.” bioRxiv (2019).
MLA
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Gilson, M., et al. “Model-Based Whole-Brain Effective Connectivity to Study Distributed Cognition in Health and Disease.” BioRxiv, 2019.
BibTeX Click to copy
@article{m2019a,
title = {Model-based whole-brain effective connectivity to study distributed cognition in health and disease},
year = {2019},
journal = {bioRxiv},
author = {Gilson, M. and Zamora-López, G. and Pallarés, V. and Adhikari, Mohit H. and Senden, M. and Campo, A. T. and Mantini, D. and Corbetta, M. and Deco, G. and Insabato, A.}
}
Abstract
Neuroimaging techniques are now widely used to study human cognition. The functional associations between brain areas have become a standard proxy to describe how cognitive processes are distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of measures like functional connectivity, via the estimation of directional interactions between brain areas. This opinion article provides an overview of our model-based whole-brain effective connectivity (MOU-EC) to analyze fMRI data, which is named so because our framework relies on the multivariate Ornstein-Uhlenbeck (MOU). We also discuss it with respect to other established methods. Once the model tuned, the directional MOU-EC estimate reflects the dynamical state of BOLD activity. For illustration purpose, we focus on two applications on task-evoked fMRI data. First, MOU-EC can be used to extract biomarkers for task-specific brain coordination. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools present some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective manner, bridging with network analysis. Our framework provides a comprehensive set of tools to study distributed cognition, as well as neuropathologies.
