TY - JOUR
T1 - A Bayesian method for inference of effective connectivity in brain networks for detecting the Mozart effect
AU - van Esch, Rik J. C.
AU - Shi, Shengling
AU - Bernas, Antoine
AU - Zinger, Svitlana
AU - Aldenkamp, Albert P.
AU - Van den Hof, Paul M. J.
N1 - Funding Information:
This project has received funding from the European Research Council (ERC), Advanced Research Grant SYSDYNET, under the European Union's Horizon 2020 research and innovation programme (grant agreement No 694504). Shengling Shi would like to thank Maarten Schoukens for his input into this work.
Funding Information:
This project has received funding from the European Research Council (ERC) , Advanced Research Grant SYSDYNET , under the European Union's Horizon 2020 research and innovation programme (grant agreement No 694504 ). Shengling Shi would like to thank Maarten Schoukens for his input into this work.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/12
Y1 - 2020/12
N2 - Several studies claim that listening to Mozart music affects cognition and can be used to treat neurological conditions like epilepsy. Research into this Mozart effect has not addressed how dynamic interactions between brain networks, i.e. effective connectivity, are affected. The Granger-causality analysis is often used to infer effective connectivity. First, we investigate if a new method, Bayesian topology identification, can be used as an alternative. Both methods are evaluated on simulation data, where the Bayesian method outperforms the Granger-causality analysis in the inference of connectivity graphs of dynamic networks, especially for short data lengths. In the second part, the Bayesian method is extended to enable the inference of changes in effective connectivity between groups of subjects. Next, we apply both methods to fMRI scans of 16 healthy subjects, who were scanned before and after the exposure to Mozart's sonata K448 at least 2 hours a day for 7 days. Here, we investigate if the effective connectivity of the subjects significantly changed after listening to Mozart music. The Bayesian method detected changes in effective connectivity between networks related to cognitive processing and control in the connection from the central executive to the superior sensori-motor network, in the connection from the posterior default mode to the fronto-parietal right network, and in the connection from the anterior default mode to the dorsal attention network. This last connection was only detected in a subgroup of subjects with a longer listening duration. Only in this last connection, an effect was found by the Granger-causality analysis.
AB - Several studies claim that listening to Mozart music affects cognition and can be used to treat neurological conditions like epilepsy. Research into this Mozart effect has not addressed how dynamic interactions between brain networks, i.e. effective connectivity, are affected. The Granger-causality analysis is often used to infer effective connectivity. First, we investigate if a new method, Bayesian topology identification, can be used as an alternative. Both methods are evaluated on simulation data, where the Bayesian method outperforms the Granger-causality analysis in the inference of connectivity graphs of dynamic networks, especially for short data lengths. In the second part, the Bayesian method is extended to enable the inference of changes in effective connectivity between groups of subjects. Next, we apply both methods to fMRI scans of 16 healthy subjects, who were scanned before and after the exposure to Mozart's sonata K448 at least 2 hours a day for 7 days. Here, we investigate if the effective connectivity of the subjects significantly changed after listening to Mozart music. The Bayesian method detected changes in effective connectivity between networks related to cognitive processing and control in the connection from the central executive to the superior sensori-motor network, in the connection from the posterior default mode to the fronto-parietal right network, and in the connection from the anterior default mode to the dorsal attention network. This last connection was only detected in a subgroup of subjects with a longer listening duration. Only in this last connection, an effect was found by the Granger-causality analysis.
KW - fMRI
KW - Neurodynamics
KW - Resting-state networks
KW - Bayesian model selection
KW - Mozart effect
KW - ICA
KW - MUSIC
KW - MODELS
KW - FMRI
KW - CHILDREN
U2 - 10.1016/j.compbiomed.2020.104055
DO - 10.1016/j.compbiomed.2020.104055
M3 - Article
C2 - 33157484
SN - 0010-4825
VL - 127
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 104055
ER -