Limits...
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

Sections of the true neuronal signal (blue) and estimated neuronal signals for one node in a simulation with N = 5, SNR = 0 dB in the first experiment. The neuronal signal estimated by the proposed method is shown in red (“Approx.”), while the neuronal signal estimated by the proposed method without using the approximate time series z (t) is shown in green (“Direct”).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4017144&req=5

Figure 5: Sections of the true neuronal signal (blue) and estimated neuronal signals for one node in a simulation with N = 5, SNR = 0 dB in the first experiment. The neuronal signal estimated by the proposed method is shown in red (“Approx.”), while the neuronal signal estimated by the proposed method without using the approximate time series z (t) is shown in green (“Direct”).

Mentions: where s (t) and are the true and the estimated neuronal signals, respectively. It can be seen that the use of the neuronal approximation does not have a negative influence on the performance in terms of AUC while the MSE is slightly lower when the approximation is not used. The small difference in terms of MSE implies that both methods estimate the neuronal signal with similar estimation quality. This is also apparent from Figure 5, which shows the time neuronal series for one region estimated with and without the approximation.


Variational Bayesian causal connectivity analysis for fMRI.

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

Sections of the true neuronal signal (blue) and estimated neuronal signals for one node in a simulation with N = 5, SNR = 0 dB in the first experiment. The neuronal signal estimated by the proposed method is shown in red (“Approx.”), while the neuronal signal estimated by the proposed method without using the approximate time series z (t) is shown in green (“Direct”).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Sections of the true neuronal signal (blue) and estimated neuronal signals for one node in a simulation with N = 5, SNR = 0 dB in the first experiment. The neuronal signal estimated by the proposed method is shown in red (“Approx.”), while the neuronal signal estimated by the proposed method without using the approximate time series z (t) is shown in green (“Direct”).
Mentions: where s (t) and are the true and the estimated neuronal signals, respectively. It can be seen that the use of the neuronal approximation does not have a negative influence on the performance in terms of AUC while the MSE is slightly lower when the approximation is not used. The small difference in terms of MSE implies that both methods estimate the neuronal signal with similar estimation quality. This is also apparent from Figure 5, which shows the time neuronal series for one region estimated with and without the approximation.

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