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Connectivity concordance mapping: A new tool for model-free analysis of fMRI data of the human brain

MPS-Authors
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Lohmann,  Gabriele
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Margulies,  Daniel S.
Berlin School of Mind and Brain, Humboldt University Berlin, Germany;
Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Villringer,  Arno
Berlin School of Mind and Brain, Humboldt University Berlin, Germany;
Center for Stroke Research, Charité University Medicine Berlin, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Turner,  Robert
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Lohmann_2012_Connectivity.pdf
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Citation

Lohmann, G., Ovadia-Caro, S., Jungehülsing, G. J., Margulies, D. S., Villringer, A., & Turner, R. (2012). Connectivity concordance mapping: A new tool for model-free analysis of fMRI data of the human brain. Frontiers in Systems Neuroscience, 6: 13. doi:10.3389/fnsys.2012.00013.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-515A-2
Abstract
Functional magnetic resonance data acquired in a task-absent condition (“resting state”) require new data analysis techniques that do not depend on an activation model. Here, we propose a new analysis method called Connectivity Concordance Mapping (CCM). The main idea is to assign a label to each voxel based on the reproducibility of its whole-brain pattern of connectivity. Specifically, we compute the correlations of time courses of each voxel with every other voxel for each measurement. Voxels whose correlation pattern is consistent across measurements receive high values. The result of a CCM analysis is thus a voxel-wise map of concordance values. Regions of high inter-subject concordance can be assumed to be functionally consistent, and may thus be of specific interest for further analysis. Here we present two fMRI studies to demonstrate the possible applications of the algorithm. The first is a eyes-open/eyes-closed paradigm designed to highlight the potential of the method in a relatively simple domain. The second study is a longitudinal repeated measurement of a patient following stroke. Longitudinal clinical studies such as this may represent the most interesting domain of applications for this algorithm.