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| Home > Publications database > Impact of brain parcellation on parameter optimization of the whole-brain dynamical models |
| Poster (Other) | FZJ-2019-04669 |
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2019
Please use a persistent id in citations: http://hdl.handle.net/2128/23264
Abstract: Recent progress in neuroimaging techniques has advanced our understanding of structural and functional properties of the brain. Resting-state functional connectivity (FC) analysis has brought new insights to the inter-individual variability [1]. Using diffusion-weighted magnetic resonance imaging, one can retrieve the basic features of the anatomical architecture of brain networks, i.e. structural connectivity (SC) [2]. Empirical SC (eSC) and FC (eFC) can be used to build and validate large-scale mathematical models of the brain dynamics being in the focus of research nowadays [3, 4]. In this work, we set out to investigate the impact of different brain atlases on the dynamics of the whole-brain computational models and their optimal parameters fitted to the neuroimaging data, resulting in the optimal agreement between empirical and simulated data. We considered a sample of 23 healthy subjects from the Human Connectome Project database [5] and 2 different brain atlases, the Harvard-Oxford structural atlas and the Schaefer functional atlas [6]. The large-scale network model of brain activity is based on an informed by eSC Kuramoto model [8] and is simulated using The Virtual Brain (TVB) platform [7], with an optimized code from TVB-HPC adequate for high-performance clusters computing. We found that the two considered atlases are in good agreement with respect to the optimal parameters (e.g. global coupling strength K) and the corresponding values of the correlation coefficient of the best correspondence between sFC and eSC. Moreover, the considered model can demonstrate relatively strong correlations between eSC and sFC matrices whereas the correspondence between eFC and sFC matrices is, however, weaker for both atlases [9].References[1] Park H. J. and Friston K. J. (2013). Structural and functional brain networks: from connections to cognition. Science 342: 1238411.[2] Maier-Hein K. H., Neher P. F., Houde J.-C., Côté M.-A., Garyfallidis E., Zhong J., et al. (2017). The challenge of mapping the human connectome based on diffusion tractography. Nat. Commun. 8: 1349.[3] Popovych O. V., Manos T., Hoffstaedter F. and Eickhoff S. B. (2019). What can computational models contribute to neuroimaging data analytics? Frontiers in Systems Neuroscience (in press).[4] Deco G. and Kringelbach M. (2016). Metastability and Coherence: Extending the Communication through Coherence Hypothesis Using a Whole-Brain Computational Perspective. Trends in Neurosciences. 39(6):432[5] McNab J. A., Edlow B. L., Witzel T., Huang S. Y., Bhat H., Heberlein K., Feiweier T., Liu K., Keil B., Cohen-Adad J., Tisdall M. D., Folkerth R. D., Kinney H. C., Wald L. L. (2013). The Human Connectome Project and beyond: initial applications of 300 mT/m gradients. NeuroImage 80:234.[6] Schaefer A., Kong R., Gordon E. M., Laumann T. O., Zuo X. N., Holmes A. J., Eickhoff S. B., and Yeo B. T. T. (2017). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI, Cereb. Cortex, 28(9): 3095.[7] Sanz Leon P., Knock S. A., Woodman M. M., Domide L., Mersmann J., McIntosh A. R. and Jirsa V. (2013). The Virtual Brain: a simulator of primate brain network dynamics. Front. Neuroinform. 7:10 (TVB-HPC: https://gitlab.thevirtualbrain.org/tvb/hpc).[8] Kuramoto Y. (1984). Chemical oscillations, waves, and turbulence, Springer, Berlin.[9] Manos T., Diaz-Pier S., Hoffstaedter F., Schreiber J., Eickhoff S. B. and Popovych O. V. (in preparation).
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