Assessing causal interaction in human brain using conditional mutual information and transfer entropy
-
1
National Taiwan University, Institute of Biomedical Engineering, Taiwan
Elucidating directional information flow among brain areas during tasks and cognition is of critical importance in understanding complex human brain function. Such casual interaction between brain regions can be estimated from time series of functional magnetic resonance imaging (fMRI) or Magnetoencephalography (MEG) measurements. Under the stationary and linearity assumptions, Granger causality among brain areas can be estimated using an auto-regressive model. Based on the information theory, we propose here a model-free approach to estimate causality. Specifically, mutual information is first calculated to quantify coherence between two time series. Conditional mutual information and transfer entropy are then respectively used to detect causality from time series without and with strong coherence [1, 2]. Numerical simulations suggest that conditional mutual information is more suitable for fMRI time series causality analysis because of their similar hemodynamic waveforms. Distinct MEG time courses are more efficiently analyzed by transfer entropy. Statistical inference of the estimated causality can be derived using permutations [3].
References
1. T. Schreiber, Phys. Rev. Lett. 85, 461 (2000).
2. M. Paluš et al., Physical Review E 63, 046211 (2001).
3. M. Paluš, and M. Vejmelka, Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) 75, 056211 (2007).
Conference:
Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010.
Presentation Type:
Poster Presentation
Topic:
MEG Modeling
Citation:
Chu
Y and
Lin
F
(2010). Assessing causal interaction in human brain using conditional mutual information and transfer entropy.
Front. Neurosci.
Conference Abstract:
Biomag 2010 - 17th International Conference on Biomagnetism .
doi: 10.3389/conf.fnins.2010.06.00062
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
21 Mar 2010;
Published Online:
21 Mar 2010.
*
Correspondence:
Ying-Hua Chu, National Taiwan University, Institute of Biomedical Engineering, Taipei, Taiwan, eva2004@gmail.com