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Large-scale DCMs for resting state fMRI

Razi, A; Seghier, ML; Zhou, Y; McColgan, P; Zeidman, P; Park, H-J; Sporns, O; ... Friston, KJ; + view all (2017) Large-scale DCMs for resting state fMRI. Network Neuroscience , 1 (3) pp. 222-241. 10.1162/NETN_a_00015. Green open access

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Abstract

This paper considers the identification of large directed graphs for resting state brain networks based on biophysical models of distributed neuronal activity; i.e. effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting state functional MRI (fMRI). We use spectral dynamic causal modelling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterisation of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterisation of large DCMs. We show that spectral DCM – with functional connectivity priors – is ideally suited for directed graph theoretic analyses of resting state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

Type: Article
Title: Large-scale DCMs for resting state fMRI
Open access status: An open access version is available from UCL Discovery
DOI: 10.1162/NETN_a_00015
Publisher version: http://doi.org/10.1162/NETN_a_00015
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
URI: http://discovery.ucl.ac.uk/id/eprint/1562663
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