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Conference Paper

Visualization of Effective Connectivity of the Brain

MPS-Authors

Wiebel,  Alexander
Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Anwander,  Alfred
Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Knösche,  Thomas R.
Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Eichelbaum, S., Wiebel, A., Hlawitschka, M., Anwander, A., Knösche, T. R., & Scheuermann, G. (2010). Visualization of Effective Connectivity of the Brain. In Proceedings of the 15th International Workshop on Vision, Modeling and Visualization (VMV) Workshop 2010 (pp. 155-162).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0011-F325-B
Abstract
Diffusion tensor images and higher-order diffusion images are the foundation for neuroscience researchers who are trying to gain insight into the connectome, the wiring scheme of the brain. Although modern imaging devices allow even more detailed anatomical measurements, these pure anatomical connections are not sufficient for understanding how the brain processes external stimuli. Anatomical connections constraint the causal influences between several areas of the brain, as they mediate causal influence between them. Therefore, neuroscientists developed models to represent the causal coherence between several pre-defined areas of the brain, which has been measured using fMRI, MEG, or EEG. The dynamic causal modeling (DCM) technique is one of these models and has been improved to use anatomical connection as informed priors to build the effective connectivity model. In this paper, we present a visualization method allowing neuroscientists to perceive both, the effective connec- tivity and the underlying anatomical connectivity in an intuitive way at the same time. The metaphor of moving information packages is used to show the relative intensity of information transfer inside the brain using a GPU based animation technique. We provide an interactive way to selectively view one or multiple effective connec- tions while conceiving their anatomical connectivity. Additional anatomical context is supplied to give further orientation cues.