%0 Conference Paper
%A Popovych, Oleksandr
%A Manos, Thanos
%A Diaz, Sandra
%A Hoffstaedter, Felix
%A Schreiber, Jan
%A Eickhoff, Simon
%T Enrichment of data analytics by whole-brain computational models
%I Heinrich Heine University Düsseldorf
%M FZJ-2020-05029
%D 2020
%X Introduction: Processing of neuroimaging data and extraction of the structural andfunctional brain signals relies on plethora of setting and parameters whose values arelargely conjectured. This problem is crucial for simulating the whole-brain dynamics bymathematical models derived from and validated against empirical data, which has attracteda great interest during the last decade. For example, considering a certain brainparcellation is essential for defining a model network, but there is practically no empiricalevidence for the effect of a particular atlas choice. We address this problem using acomputational modeling approach, where the resting-state brain dynamics as reflected byfMRI BOLD is simulated by a dynamical model of coupled oscillators. We compare themodeling outcome for two brain atlases based on functional and anatomical parcellations.We also consider different frequency bands of the empirical BOLD signals used to extractthe natural frequencies of the model oscillators as another parameter of data processing.Methods: We considered 272 healthy subjects of the Human Connectome Project.The brain was parcellated into regions according to the functional Schaefer atlas with100 and 200 cortical parcels (S100 and S200) and anatomical Harvard-Oxford atlas with 96parcels (HO96). Empirical functional connectivity (eFC) was calculated from the meanBOLD signals extracted for each brain region using FSL. Empirical structural connectivity(eSC) was computed by counting the number of streamlines and evaluating the averagedpath lengths between pairs of brain regions from the whole-brain tractography with 10Mstreamlines obtained by the probabilistic tractography with using MRtrix. The model ofcoupled phase oscillators was simulated, where the coupling weights and delayswere extracted from eSC. The oscillators’ natural frequencies were calculated by peakdetection in the spectral power of the non-filtered (NF) BOLD signals as well filtered in thebroad- (BF, [0.02, 0.07] Hz), low- (LF, [0.02, 0.04] Hz) and high- (HF, [0.04, 0.07] Hz)frequency bands. The model was validated by finding the optimal model parameters of theglobal coupling and delay, where the strongest Pearson’s correlation betweensimulated functional connectivity (sFC) and eFC denoted by Fit(sFC,eFC) orbetween sFC and eSC denoted by Fit(sFC,eSC) is achieved.Results: The maximal similarity Fit(sFC, eFC) does not demonstrate any pronounceddifference (relative changes of 1%-5%) for varying frequency band for any fixed atlas. An increase of up to 12% can be observed when comparing S200 to S100, and astrong enhancement of the model fitting (70% and more) is apparent when comparing HO96to S100 and S200. For the structure-function relationship Fit(sFC, eSC), theordering NF < BF < LF < HF can be observed, but the differences between atlases are lessconsistent. We found that HF condition demonstrates distinct distributions of theoptimal model parameters than other frequency bands, especially, for S100 or for Fit(sFC,eSC). Furthermore, the fitting results Fit(sFC, eFC) and Fit(sFC, eSC) for theHF case less correlate with those obtained for other frequency bands across subjects. Finally, the dynamics of the validated model can result in pronounced bimodaldistributions of corr(sFC,eSC) and the order parameter R(t) for S100 and S200, but not for HO96. Conclusion: We showed that a choice of a particular brain parcellation and variation ofBOLD frequency band can cause a significant impact on the quality of the model fitting,dynamics of the validated model and their interrelations. The main impact of the brain atlasescan be observed for the fitting of sFC to eFC. The frequency bands of the BOLD filteringmostly affect structure-function relationships and can also influence the reliability of themodel validation.
%B The 2020 Annual Meeting Organization for Human Brain Mapping
%C 23 Jun 2020 - 3 Jul 2020, Virtual (Canada)
Y2 23 Jun 2020 - 3 Jul 2020
M2 Virtual, Canada
%F PUB:(DE-HGF)24
%9 Poster
%U https://juser.fz-juelich.de/record/888565