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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

This figure summarises the results of simulating fMRI responses to endogenous fluctuations over 512 time points (scans) with a TR of 2 s — here we only show initial 256 time bins. The simulation was based upon a simple four-region hierarchical network or graph, shown on the lower right, with positive effective connectivity (black) in the forward or ascending direction (and lateral direction) and negative (red) in the backward or descending direction. The four regions were driven by endogenous fluctuations (upper right panel) generated from an AR(1) process with autoregressive coefficient of one half (and scaled to a standard deviation of one quarter). These fluctuations caused distributed perturbations in neuronal states and consequent changes in haemodynamic states (shown as cyan) in the upper right panel, which produce the final fMRI response in the lower left panel.
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f0005: This figure summarises the results of simulating fMRI responses to endogenous fluctuations over 512 time points (scans) with a TR of 2 s — here we only show initial 256 time bins. The simulation was based upon a simple four-region hierarchical network or graph, shown on the lower right, with positive effective connectivity (black) in the forward or ascending direction (and lateral direction) and negative (red) in the backward or descending direction. The four regions were driven by endogenous fluctuations (upper right panel) generated from an AR(1) process with autoregressive coefficient of one half (and scaled to a standard deviation of one quarter). These fluctuations caused distributed perturbations in neuronal states and consequent changes in haemodynamic states (shown as cyan) in the upper right panel, which produce the final fMRI response in the lower left panel.

Mentions: In this section, we address the face validity of the two schemes, comparing the models based on deterministic and stochastic modelling of neuronal fluctuations. Simulated time series were generated from a four node graph (producing data over 512 time bins with a repetition time of 2 s) with known effective connectivity (see Eq. (13) and Fig. 1). Smooth neuronal fluctuations (resp. observation noise) driving each node were independently generated based on an AR(1) process with an autoregressive coefficient of one half, scaled to a standard deviation of one fourth (resp. one eighth). The equations of motion in Eq. (1), together with haemodynamic observation Eq. (2) were used to generate synthetic slow-varying time series, reminiscent of BOLD data acquired at rest (Fig. 1). With these parameters, we produce a maximum fMRI signal change of around 2%. The upper panels in Fig. 1 show the variations in the amplitude of endogenous fluctuations that drive the changes in the hidden and haemodynamic states (cyan), which in turn produce the observed BOLD response. It is worth noting that the haemodynamic signal is much smoother than the neuronal variations – that reflect the low-pass filter-like effect of the haemodynamic transfer function – with a time constant of several seconds.


Construct validation of a DCM for resting state fMRI.

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

This figure summarises the results of simulating fMRI responses to endogenous fluctuations over 512 time points (scans) with a TR of 2 s — here we only show initial 256 time bins. The simulation was based upon a simple four-region hierarchical network or graph, shown on the lower right, with positive effective connectivity (black) in the forward or ascending direction (and lateral direction) and negative (red) in the backward or descending direction. The four regions were driven by endogenous fluctuations (upper right panel) generated from an AR(1) process with autoregressive coefficient of one half (and scaled to a standard deviation of one quarter). These fluctuations caused distributed perturbations in neuronal states and consequent changes in haemodynamic states (shown as cyan) in the upper right panel, which produce the final fMRI response in the lower left panel.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

f0005: This figure summarises the results of simulating fMRI responses to endogenous fluctuations over 512 time points (scans) with a TR of 2 s — here we only show initial 256 time bins. The simulation was based upon a simple four-region hierarchical network or graph, shown on the lower right, with positive effective connectivity (black) in the forward or ascending direction (and lateral direction) and negative (red) in the backward or descending direction. The four regions were driven by endogenous fluctuations (upper right panel) generated from an AR(1) process with autoregressive coefficient of one half (and scaled to a standard deviation of one quarter). These fluctuations caused distributed perturbations in neuronal states and consequent changes in haemodynamic states (shown as cyan) in the upper right panel, which produce the final fMRI response in the lower left panel.
Mentions: In this section, we address the face validity of the two schemes, comparing the models based on deterministic and stochastic modelling of neuronal fluctuations. Simulated time series were generated from a four node graph (producing data over 512 time bins with a repetition time of 2 s) with known effective connectivity (see Eq. (13) and Fig. 1). Smooth neuronal fluctuations (resp. observation noise) driving each node were independently generated based on an AR(1) process with an autoregressive coefficient of one half, scaled to a standard deviation of one fourth (resp. one eighth). The equations of motion in Eq. (1), together with haemodynamic observation Eq. (2) were used to generate synthetic slow-varying time series, reminiscent of BOLD data acquired at rest (Fig. 1). With these parameters, we produce a maximum fMRI signal change of around 2%. The upper panels in Fig. 1 show the variations in the amplitude of endogenous fluctuations that drive the changes in the hidden and haemodynamic states (cyan), which in turn produce the observed BOLD response. It is worth noting that the haemodynamic signal is much smoother than the neuronal variations – that reflect the low-pass filter-like effect of the haemodynamic transfer function – with a time constant of several seconds.

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