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Merging computational models and multimodal imaging data using The Virtual Brain
Zitatschlüssel Schirner2012
Autor Ritter, P., Schirner, M., Reinacher, M., Rothmeier, S., Schwarz, J., McIntosh, A., and Jirsa, V.
Jahr 2012
Journal Human Brain Mapping
Monat June
Zusammenfassung Introduction: Cognition emerges from dynamic functional connectivity changes around a relatively stable structural skeleton [Deco et al. 2011]. The exact principles yielding those functionally relevant changes in neuronal interactions yet remain elusive. Hence, as a first step, we here aim to reveal theoretical mechanisms underlying the dynamical organization of intrinsic functional connectivity assessed by simultaneous EEG-fMRI [Ritter and Villringer 2006] combined with a large-scale neural simulation [Jirsa et al. 2010]. Methods: We acquired 22min of resting-state functional (simultaneous EEG-fMRI) and structural (DTI) imaging data of 10 adult human subjects (mean age 24.6y, range 18-30y, 5 male) using a 3-T Siemens Tim Trio MR scanner and a 61-channel EEG BrainAmp MR-Plus. EEG data underwent artifact correction and down-sampling using BrainVision Analyzer and ICA (Infomax) decomposition using EEGLab. FMRI data underwent preprocessing in SPM (motion correction, normalization, smoothing) and ICA decomposition in FSL. Combining the individual DTI data with axonal tract tracing animal data yielded structural connectivity (fiber length and capacity) matrices of 96 regions including thalamus and basal ganglia [Bezgin et al. 2011]. For computational modeling we used a Stefanescu-Jirsa neural population model [Stefanescu and Jirsa 2008] as implemented in The Virtual Brain http://thevirtualbrain.org [Jirsa et al. 2010]. The modeled large-scale network architecture consisted of 96 nodes whose coupling was constrained by individual fiber lengths -affecting transmission delays- and capacities obtained from DTI-tractography. Resulting node dynamics were transformed into simulated EEG and fMRI signals employing an EEG forward solution and convolution with a hemodynamic response function. Time series, power spectra and topologies of simulated and experimental data were compared. Results: By systematically varying the free parameters of the node model (degree of membrane excitability, coupling strength within excitatory subpopulations and between excitatory and inhibitory subpopulations, number of excitatory/inhibitory neurons, noise terms), we identified parameter sets yielding predictions that well fit the experimental data. Fig. 1 shows neurophysiological (upper panel) and fMRI (lower pannel) amplitude time series of simulated and experimental data for a selected brain region of an exemplary subject. Fig. 2 shows the corresponding power spectra. Note the 1/f distribution of the simulated neurophysiological data and the slow around 0.1 Hz peaks in simulated fMRI – both typical features of real resting-state data. Fig. 3 depicts the individual connectivity matrix (capacity and fiber length) that constrains the model.
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