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Construct validation of a DCM for resting state fMRI.

Razi A, Kahan J, Rees G, Friston KJ - Neuroimage (2014)

Bottom Line: Dynamic causal modelling (DCM) is a framework that allows for the identification of the causal (directed) connections among neuronal systems--known as effective connectivity.We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences.We show that spectral DCM was not only more accurate but also more sensitive to group differences.

View Article: PubMed Central - PubMed

Affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK; Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan. Electronic address: a.razi@ucl.ac.uk.

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Related in: MedlinePlus

Summary of empirical time series used for the illustrative analysis. The time series (right-hand panels) from four regions are the principal eigenvariates of regions identified using seed connectivity analyses (upper left insert) for a typical subject. These time series we used to invert the DCMs (both spectral and stochastic) with the (fully-connected) architecture shown in the lower left panel.
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f0045: Summary of empirical time series used for the illustrative analysis. The time series (right-hand panels) from four regions are the principal eigenvariates of regions identified using seed connectivity analyses (upper left insert) for a typical subject. These time series we used to invert the DCMs (both spectral and stochastic) with the (fully-connected) architecture shown in the lower left panel.

Mentions: To identify nodes of the DMN, the resting state was modelled using a GLM containing a discrete cosine basis set with frequencies ranging from 0.0078 to 0.1 Hz (Fransson, 2005; Kahan et al., 2014), in addition to the aforementioned nuisance regressors. Data were high-pass filtered to remove any slow frequency drifts (< 0.0078 Hz) in the normal manner. An F-contrast was specified across the discrete cosine transforms (DCT), producing an SPM that identified regions exhibiting BOLD fluctuations within the frequency band. Our DMN graph comprised of four nodes; the posterior cingulate cortex (PCC), the right and left intraparietal cortex (R/LIPC), and the medial prefrontal cortex (mPFC). The PCC node was identified using this GLM: the principal eigenvariate of a (8 mm radius) sphere was computed (adjusted for confounds), centred on the peak voxel of the aforementioned F-contrast. The ensuing region of interest was masked by a (8 mm radius) sphere centred on previously reported MNI coordinates for the PCC [0, − 52, 26] (Di and Biswal, 2014). The remaining DMN nodes were identified using a standard seed-based functional connectivity analysis, using the PCC as the reference time series in an independent GLM containing the same confounds. A t-contrast on the PCC time series was specified, and the resulting SPM was masked by spheres centred on previously reported coordinates for the RIPC [48, − 69, 35], LIPC [− 50, − 63, 32], and mPFC [3, 54, − 2] (Di and Biswal, 2014). The principal eigenvariate from a (8 mm radius) sphere centred on the peak t-value from each region was computed for each region and corrected for confounds. The time series extracted from each of the four regions – for typical subject – are shown in Fig. 9.


Construct validation of a DCM for resting state fMRI.

Razi A, Kahan J, Rees G, Friston KJ - Neuroimage (2014)

Summary of empirical time series used for the illustrative analysis. The time series (right-hand panels) from four regions are the principal eigenvariates of regions identified using seed connectivity analyses (upper left insert) for a typical subject. These time series we used to invert the DCMs (both spectral and stochastic) with the (fully-connected) architecture shown in the lower left panel.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4295921&req=5

f0045: Summary of empirical time series used for the illustrative analysis. The time series (right-hand panels) from four regions are the principal eigenvariates of regions identified using seed connectivity analyses (upper left insert) for a typical subject. These time series we used to invert the DCMs (both spectral and stochastic) with the (fully-connected) architecture shown in the lower left panel.
Mentions: To identify nodes of the DMN, the resting state was modelled using a GLM containing a discrete cosine basis set with frequencies ranging from 0.0078 to 0.1 Hz (Fransson, 2005; Kahan et al., 2014), in addition to the aforementioned nuisance regressors. Data were high-pass filtered to remove any slow frequency drifts (< 0.0078 Hz) in the normal manner. An F-contrast was specified across the discrete cosine transforms (DCT), producing an SPM that identified regions exhibiting BOLD fluctuations within the frequency band. Our DMN graph comprised of four nodes; the posterior cingulate cortex (PCC), the right and left intraparietal cortex (R/LIPC), and the medial prefrontal cortex (mPFC). The PCC node was identified using this GLM: the principal eigenvariate of a (8 mm radius) sphere was computed (adjusted for confounds), centred on the peak voxel of the aforementioned F-contrast. The ensuing region of interest was masked by a (8 mm radius) sphere centred on previously reported MNI coordinates for the PCC [0, − 52, 26] (Di and Biswal, 2014). The remaining DMN nodes were identified using a standard seed-based functional connectivity analysis, using the PCC as the reference time series in an independent GLM containing the same confounds. A t-contrast on the PCC time series was specified, and the resulting SPM was masked by spheres centred on previously reported coordinates for the RIPC [48, − 69, 35], LIPC [− 50, − 63, 32], and mPFC [3, 54, − 2] (Di and Biswal, 2014). The principal eigenvariate from a (8 mm radius) sphere centred on the peak t-value from each region was computed for each region and corrected for confounds. The time series extracted from each of the four regions – for typical subject – are shown in Fig. 9.

Bottom Line: Dynamic causal modelling (DCM) is a framework that allows for the identification of the causal (directed) connections among neuronal systems--known as effective connectivity.We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences.We show that spectral DCM was not only more accurate but also more sensitive to group differences.

View Article: PubMed Central - PubMed

Affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK; Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan. Electronic address: a.razi@ucl.ac.uk.

Show MeSH
Related in: MedlinePlus