Event Abstract

Modeling global brain dynamics in brain tumor patients using The Virtual Brain

  • 1 Ghent University, Department of Data Analysis, Belgium

Increasingly, computational models of brain activity are applied to investigate the relation between structure and function. In addition, biologically interpretable dynamical models may be used as unique predictive tools to investigate the impact of structural connectivity damage on brain dynamics. That is, individually modeled biophysical parameters could inform on alterations in patients' local and large-scale brain dynamics, which are invisible to brain-imaging devices. In this study, we compared global biophysical model parameters between brain tumor patients and healthy controls. To this end, The Virtual Brain (TVB; [1]) was applied, a neuroinformatics platform that utilizes subjects’ empirical structural connectivity to create personalized dynamical models of brain activity. Fifteen glioma patients (WHO grade II and III, mean age 43.7yo, 5 females, 5 from open access dataset [2]), 13 meningioma patients (mean age 60.2y, 11 females) and 11 healthy partners (mean age 58.6y, 4 females) were included in this study. From all participants, diffusion MRI, resting-state fMRI and T1-weighted MRI data were acquired. In addition, cognitive functions were assessed using the Cambridge Neuropsychological Test Automated Battery (CANTAB®; Cambridge Cognition, 2017; www.cantab.com) in all participants, except the ones from the open access dataset. MRI data were preprocessed and converted to subject-specific structural and functional connectivity matrices using a modified version of the TVB preprocessing pipeline [3]. In order to simulate brain dynamics, the reduced Wong-Wang model [4] was used. This is a dynamical mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network. A subject-specific parameter space exploration was conducted to obtain an optimal correspondence between the individual's simulated and empirical functional connectivity matrix. To this end, values of the global scaling factor G and the local feedback inhibitory synaptic coupling Ji were varied. Values of G and Ji yielding optimal correspondence were then compared between the brain tumor patient groups and healthy controls. Furthermore, optimal G and Ji values were related to neurocognitive performance and functional network topology. In particular, the first two models assessed the association between reaction time, sustained attention, working memory capacity and planning accuracy on the one hand, and G and Ji on the other hand. The next pair of models were used to investigate the relation between functional network metrics derived from graph theory (global efficiency, modularity, betweenness centrality and strength) and G and Ji. In all four models, important demographic (age, sex, lesion volume) and fMRI parameters (mean displacement during fMRI acquisition, TR of fMRI protocol) were taken into account. Correction for multiple testing across the four models was done using Bonferroni correction, with resulting alpha level of 0.0125. The distribution of optimal values for G and Ji per group (controls, meningioma and glioma patients) is depicted in Figure 1. Visually, no clear group differences are apparent, which is confirmed using Kruskal-Wallis tests (X²(2)=0.43, p=0.83 for G; X²(2)=1.456, p=0.48 for Ji). Subsequent analyses revealed several interesting associations between neurocognitive performance, graph theory metrics, demographic and fMRI parameters on the one hand, and the modeling parameters G and Ji on the other hand. In Table 1, the effects with p < 0.0125 are summarized, together with their effect size. At first glance, it is evident that the cognitive measures were more strongly related to the modeling parameters, compared to the functional network measures. In particular, results showed significant effects of reaction time, as well as its association with sex and lesion volume, on both G and Ji. Further, interactions were found between age and planning accuracy, and between lesion volume and working memory capacity, on the values of G. In addition, the interaction between age and sex, and between sex and lesion volume was also related to G. With respect to Ji values, sustained attention, working memory capacity and its interaction with sex had a significant effect, besides the already mentioned effects of reaction time. Concerning the models including the functional network metrics, a significant effect of mean displacement on both G and Ji was found, with greater motion yielding higher values of both model parameters. In addition, modularity of the functional network was found to relate to Ji. In this study, we have demonstrated the feasibility of simulating realistic brain activity in the presence of (possibly large) structural lesions. Results revealed that the individually optimized global scaling factor G and local feedback inhibitory synaptic coupling Ji did not differ significantly between control subjects, meningioma and glioma patients. However, subsequent analyses did show significant associations between cognitive performance, age, sex, lesion volume, the amount of empirical fMRI motion, and modularity of the functional network on the optimal G and Ji values. Although preliminary, these results suggest computational lesion modeling might have potential clinical applications. In future studies, larger sample sizes will be utilized in order to obtain more reliable and robust results, as data collection is still ongoing and more efforts to data sharing across labs are undertaken. In addition, local model parameter alterations in the vicinity of the lesion will be examined, since global model parameters might not be sufficiently sensitive to capture local lesion effects.

Figure 1

References

1. P Sanz Leon, S A Knock, M M Woodman, L Domide, J Mersmann, A R McIntosh, V K Jirsa. The Virtual Brain: A simulator of primate brain network dynamics. Frontiers in Neuroinformatics 2013, 7:1-23.
2. C Pernet, K Gorgolewski, I Whittle. UK Data Archive. [http://dx.doi.org/10.5255/UKDA-SN-851861]
3. M Schirner, S Rothmeier, V K Jirsa, A R McIntosh, P Ritter. An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data. NeuroImage 2015, 117:343–357.
4. G Deco, A Ponce-Alvarez, P Hagmann, G L Romani, D Mantini, M Corbetta. How local excitation-inhibition ratio impacts the whole brain dynamics. The Journal of Neuroscience 2014, 34:7886-7898.

Keywords: computational modeling, connectomics, brain tumor, graph theory, Cognition

Conference: 12th National Congress of the Belgian Society for Neuroscience, Gent, Belgium, 22 May - 22 May, 2017.

Presentation Type: Poster Presentation

Topic: Novel Methods and Technology Development

Citation: Aerts H and Marinazzo D (2019). Modeling global brain dynamics in brain tumor patients using The Virtual Brain. Front. Neurosci. Conference Abstract: 12th National Congress of the Belgian Society for Neuroscience. doi: 10.3389/conf.fnins.2017.94.00101

Received: 20 Apr 2017; Published Online: 25 Jan 2019.

* Correspondence: Ms. Hannelore Aerts, Ghent University, Department of Data Analysis, Gent, 9000, Belgium, hannelore.aerts@ugent.be

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