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Variational Bayesian causal connectivity analysis for fMRI.

Luessi M, Babacan SD, Molina R, Booth JR, Katsaggelos AK - Front Neuroinform (2014)

Bottom Line: The ability to accurately estimate effective connectivity among brain regions from neuroimaging data could help answering many open questions in neuroscience.We propose a method which uses causality to obtain a measure of effective connectivity from fMRI data.Due to the employed modeling, it is possible to efficiently estimate all latent variables of the model using a variational Bayesian inference algorithm.

View Article: PubMed Central - PubMed

Affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital Charlestown, MA, USA ; Department of Electrical Engineering and Computer Science, Northwestern University Evanston, IL, USA.

ABSTRACT
The ability to accurately estimate effective connectivity among brain regions from neuroimaging data could help answering many open questions in neuroscience. We propose a method which uses causality to obtain a measure of effective connectivity from fMRI data. The method uses a vector autoregressive model for the latent variables describing neuronal activity in combination with a linear observation model based on a convolution with a hemodynamic response function. Due to the employed modeling, it is possible to efficiently estimate all latent variables of the model using a variational Bayesian inference algorithm. The computational efficiency of the method enables us to apply it to large scale problems with high sampling rates and several hundred regions of interest. We use a comprehensive empirical evaluation with synthetic and real fMRI data to evaluate the performance of our method under various conditions.

No MeSH data available.


Related in: MedlinePlus

Average d-Accuracy calculated over 50 simulations for a network with two nodes and a single connection for varying neuronal delays (40 steps between 5 and 300 ms) and TR values of the fMRI scanner (40 steps between 50 ms and 2 s). The HRF is held constant for all simulations, the signal-to-noise ratio is 0 dB. The proposed method is denoted VBCCA and we use P = 1, whereas WGCA(P) denotes the Wiener–Granger causality method, for which we use AR model orders of 1, 5, and an order between 1 and 20 selected using the Bayesian information criterion (BIC).
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Figure 7: Average d-Accuracy calculated over 50 simulations for a network with two nodes and a single connection for varying neuronal delays (40 steps between 5 and 300 ms) and TR values of the fMRI scanner (40 steps between 50 ms and 2 s). The HRF is held constant for all simulations, the signal-to-noise ratio is 0 dB. The proposed method is denoted VBCCA and we use P = 1, whereas WGCA(P) denotes the Wiener–Granger causality method, for which we use AR model orders of 1, 5, and an order between 1 and 20 selected using the Bayesian information criterion (BIC).

Mentions: Results for the first simulation, in which the HRF is held constant, are shown in Figure 7. The results confirm previous findings (Seth et al., 2013) that downsampling confounds WGC. One might intuitively expect that when the neuronal delay is held constant, a lower TR will lead to a higher d-Accuracy. However, our simulations show that this is not necessarily the case; For very low delay and TR values, the WGCA method has d-Accuracy to zero, i.e., it consistently estimates a causal influence with the opposite direction of the true influence, while it approaches the chance level (0.5) when TR is increased. The proposed method shows a similar behavior, but for TR values below 300 ms the d-Accuracy is close to 1.0. While it is difficult to assess the origin of this transition, it is likely caused by increased aliasing that occurs for larger TR values. Together with the consistent causality inversion of WGC for low TR values, it shows that causal information is still present in the data.


Variational Bayesian causal connectivity analysis for fMRI.

Luessi M, Babacan SD, Molina R, Booth JR, Katsaggelos AK - Front Neuroinform (2014)

Average d-Accuracy calculated over 50 simulations for a network with two nodes and a single connection for varying neuronal delays (40 steps between 5 and 300 ms) and TR values of the fMRI scanner (40 steps between 50 ms and 2 s). The HRF is held constant for all simulations, the signal-to-noise ratio is 0 dB. The proposed method is denoted VBCCA and we use P = 1, whereas WGCA(P) denotes the Wiener–Granger causality method, for which we use AR model orders of 1, 5, and an order between 1 and 20 selected using the Bayesian information criterion (BIC).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4017144&req=5

Figure 7: Average d-Accuracy calculated over 50 simulations for a network with two nodes and a single connection for varying neuronal delays (40 steps between 5 and 300 ms) and TR values of the fMRI scanner (40 steps between 50 ms and 2 s). The HRF is held constant for all simulations, the signal-to-noise ratio is 0 dB. The proposed method is denoted VBCCA and we use P = 1, whereas WGCA(P) denotes the Wiener–Granger causality method, for which we use AR model orders of 1, 5, and an order between 1 and 20 selected using the Bayesian information criterion (BIC).
Mentions: Results for the first simulation, in which the HRF is held constant, are shown in Figure 7. The results confirm previous findings (Seth et al., 2013) that downsampling confounds WGC. One might intuitively expect that when the neuronal delay is held constant, a lower TR will lead to a higher d-Accuracy. However, our simulations show that this is not necessarily the case; For very low delay and TR values, the WGCA method has d-Accuracy to zero, i.e., it consistently estimates a causal influence with the opposite direction of the true influence, while it approaches the chance level (0.5) when TR is increased. The proposed method shows a similar behavior, but for TR values below 300 ms the d-Accuracy is close to 1.0. While it is difficult to assess the origin of this transition, it is likely caused by increased aliasing that occurs for larger TR values. Together with the consistent causality inversion of WGC for low TR values, it shows that causal information is still present in the data.

Bottom Line: The ability to accurately estimate effective connectivity among brain regions from neuroimaging data could help answering many open questions in neuroscience.We propose a method which uses causality to obtain a measure of effective connectivity from fMRI data.Due to the employed modeling, it is possible to efficiently estimate all latent variables of the model using a variational Bayesian inference algorithm.

View Article: PubMed Central - PubMed

Affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital Charlestown, MA, USA ; Department of Electrical Engineering and Computer Science, Northwestern University Evanston, IL, USA.

ABSTRACT
The ability to accurately estimate effective connectivity among brain regions from neuroimaging data could help answering many open questions in neuroscience. We propose a method which uses causality to obtain a measure of effective connectivity from fMRI data. The method uses a vector autoregressive model for the latent variables describing neuronal activity in combination with a linear observation model based on a convolution with a hemodynamic response function. Due to the employed modeling, it is possible to efficiently estimate all latent variables of the model using a variational Bayesian inference algorithm. The computational efficiency of the method enables us to apply it to large scale problems with high sampling rates and several hundred regions of interest. We use a comprehensive empirical evaluation with synthetic and real fMRI data to evaluate the performance of our method under various conditions.

No MeSH data available.


Related in: MedlinePlus