A brain-health decision system.
Neurological disease mechanisms are heterogeneous and often unclear – a severe limitation for patient treatment. Finding strategies for better individualized treatment has therefore become an important goal in medicine.
Two closely related research directions seem especially promising for clinical research with The Virtual Brain.
One approach is to use brain network modelling in order to create a better understanding of the cell, circuit, and network mechanisms underlying an illness, with the goal to design and develop better diagnostic and therapeutic tools.
Another approach is to fit TVB models with empirical data in order to find parameters that can be used as health status indicators for diagnosis, in order to predict therapeutic outcomes – to decide between therapy alternatives or to guide surgical intervention specifically and individually for each patient.
An impressive recent example of TVB entering into clinics is the EPINOV project: a currently running clinical trial over a cohort of 365 patients in 13 French hospitals where TVB is used to help clinicians plan surgery strategies for drug-resistant epilepsy, which affects about one third of all 50 million epilepsy cases worldwide.
For these patients, surgical removal of the area from which the seizure first emerges and then spreads is the only remaining option. Therefore, it's critical to localize and delineate this area as precisely as possible – which is a major challenge when being restricted to current methods.
The study is the first example where the effectiveness of personalized brain simulation is assessed in a clinical cohort. Given that success rates of predicting surgical outcome have remained at only around 60% for the last 30 years, finding improved localization methods is paramount and urgent.
Previous pilot studies showed promising results of the TVB approach to simulate seizure propagation and customizing "brain avatars" to individual patients: in a cohort of retrospective surgery patients the predictions of TVB correlated well with the actual surgical outcomes (Jirsa et al., 2017; Olmi et al., 2019; Proix et al., 2017, 2018).
Translational studies identified biomarkers of stroke recovery in adults and children (Adhikari et al., 2015; Falcon et al., 2015) and functional mechanisms of recovery after chronic stroke (Falcon et al., 2016).
Parameter estimates indicated that, compared with controls, stroke patients demonstrated a consistent reduction in conduction velocity, increased local dynamics and reduced local inhibition. The values of these parameters were then associated with motor recovery in 20 patients, indicating that TVB coupling parameters may be individual biomarkers of stroke outcome.
In a similar approach estimated TVB parameters were used to characterize brain network dynamics in Alzheimer's disease (AD) patients (Zimmermann et al., 2018). Individual models from 124 study participants across the behavioral spectrum from healthy aging, to amnesic mild cognitive impairment, to AD were fit with resting-state functional connectivity.
Optimized model parameters correlated significantly with cognition and may therefore be used as biomarkers for disease severity. Interestingly, TVB parameters were better predictors of cognition than empirical structural and functional connectivity, which indicates that the knowledge embedded in the model combined with empirical data can help to identify key features of SC and FC that reflect individual variability.
The potential of TVB to fuse multimodal neuroimaging data with theoretical models to gain a better understanding of pathology mechanism is also demonstrated in a recent study by Stefanovski et al. (2019).
In this study, TVB was combined with positron emission tomography (PET) to simulate hyperexcitability in AD related to the protein amyloid beta (Abeta). To personalize TVB models, each patient's individual Abeta burden was estimated from PET images and included in the model as a parameter that modulates local excitation-inhibition balance, leading to local hyperexcitation in patients where Abeta loads were high.
The study showed that the measured heterogenous Abeta loads were crucial for simulations to produce the typical slowing of EEG observed in AD patients as it was absent in control models with homogeneous Abeta distributions.
Crucially, the study also showed that introducing the NMDA receptor antagonist memantine reversed the slowing of EEG and provided a potential mechanistic explanation for the effectiveness of memantine and a general testbed to test the efficacy of treatment strategies.
In a similar manner TVB offers an in-silico testbed for the effects of brain stimulation and development of therapeutic applications. For example, Kunze et al. (2016) systematically explored how transcranial direct current stimulation modulates functional connectivity, inter-areal synchronization, EEG power spectra and the emergence of network states.
TVB brain models are also used to understand the effects of ageing with results indicating that the decreased multi-scale entropy observed in older adults' resting-state fMRI time series can be best explained as a departure of the system from its optimal dynamical working point (Nakagawa et al., 2013).
Aerts et al. (2018, 2020) used TVB to model brain dynamics in patients before and after tumor resection. In the patient-specific models local and global parameters were individually optimized. Optimized parameters in presurgical models differentiated between regions directly affected by a tumor, regions distant from a tumor and regions in a healthy brain, which may help to better delineate eloquent tissue in the vicinity of the lesion to spare during surgery.
In a next step, prediction of postsurgical outcome was performed in order to test whether individual patient models can reliably predict postsurgical FC using only information that is available at this time, which would allow presurgical virtual exploration of different neurosurgical approaches and identification of optimal surgical strategies.
Indeed, it was found that the fitted parameters remained relatively stable in patients before and after surgery and that a "virtual neurosurgery", where the patients' actual surgeries were simulated by removing white matter fibers in the resection region, improved the fit with postsurgical brain dynamics in three out of four patients.
Costa-Klein et al. (2020; 2020), examined 96 healthy carriers of single-nucleotide polymorphisms (SNP) in the NRG1 gene that were previously associated with schizophrenia in order to study the influence of this polymorphism on E/I balance.
By tuning excitatory and inhibitory coupling parameters it was found that G/G-carriers in the rs3924999 NRG-1 SNP exhibit lower excitatory recurrence and global coupling, and higher excitatory synaptic coupling and feedback inhibition as compared to other allele carriers, which confirms previous animal research and provided further corroboration for the central role of the NRG-1 pathway for the moderation of E/I-balance.
Currently, several lines of clinical research with TVB are ongoing that aim to uncover pathomechanisms underlying
Together with 17 European research and neuroinformatics partners, the Brain Simulation Section of Charité University Medicine in Berlin has set out to develop and validate all components of a decision-making system for early diagnosis and therapy development of neurodegenerative diseases (NDD): the VirtualBrainCloud. This project contributes to the implementation of the European Open Science Cloud (EOSC).
This project is funded by the prestigious EU H2020 program for societal challenges because NDD affect every third senior and kill more people than breast cancer and prostate cancer combined. The causes for this group of diseases (predominantly Alzheimer’s and Parkinson’s) are still not understood well, there are no reliable therapies and, just in the United States alone, the cost for disease management will balloon to $1.1 trillion per year.
Earlier research clearly suggests that NDD have no single cause but rather a complex network of interactions between scales and systems that define disease risk and mitigation. Combining the granularity of semantic network modeling on the subcellular level with the wholistic integration of TVB’s large-scale brain dynamics modeling will lead to a better understanding of how the balance between local and global brain activity relates to cognitive changes in NDD.
Michael Schirner, Lia Domide, Dionysios Perdikis, Paul Triebkorn, Leon Stefanovski, Roopa Pai, Paula Popa, Bogdan Valean, Jessica Palmer, Chloê Langford, André Blickensdörfer, Michiel van der Vlag, Sandra Diaz-Pier, Alexander Peyser, Wouter Klijn, Dirk Pleiter, Anne Nahm, Oliver Schmid, Marmaduke Woodman, Lyuba Zehl, Jan Fousek, Spase Petkoski, Lionel Kusch, Meysam Hashemi, Daniele Marinazzo, Jean-François Mangin, Agnes Flöel, Simisola Akintoye, Bernd Carsten Stahl, Michael Cepic, Emily Johnson, Gustavo Deco, Anthony R. McIntosh, Claus C. Hilgetag, Marc Morgan, Bernd Schuller, Alex Upton, Colin McMurtrie, Timo Dickscheid, Jan G. Bjaalie, Katrin Amunts, Jochen Mersmann, Viktor Jirsa, Petra Ritter
Brain simulation as a cloud service: The Virtual Brain on EBRAINS
Falcon, M. I., Riley, J. D., Jirsa, V., McIntosh, A. R., Chen, E. E., & Solodkin, A.
Functional mechanisms of recovery after chronic stroke: Modeling with the virtual brain
The Journal of Neuroscience, 2015
Adhikari, M. H., Beharelle, A. R., Griffa, A., Hagmann, P., Solodkin, A., McIntosh, A. R., Small, S. L., & Deco, G.
Computational Modeling of Resting-State Activity Demonstrates Markers of Normalcy in Children with Prenatal or Perinatal Stroke
Frontiers in Neurology, 6, 2015
Falcon, M. I., Riley, J. D., Jirsa, V., McIntosh, A. R., Shereen, A. D., Chen, E. E., & Solodkin, A.
The Virtual Brain: Modeling Biological Correlates of Recovery after Chronic Stroke
Aerts, H., Schirner, M., Dhollander, T., Jeurissen, B., Achten, E., Van Roost, D., Ritter, P., & Marinazzo, D.
Modeling brain dynamics after tumor resection using The Virtual Brain
Aerts, H., Schirner, M., Jeurissen, B., Van Roost, D., Achten, E., Ritter, P., & Marinazzo, D. (2018)
Modeling brain dynamics in brain tumor patients using the virtual brain
Costa-Klein, P., Ettinger, U., Schirner, M., Ritter, P., Falka, P., Koutsouleris, N., & Kambeitz, J.
Brain network simulations indicate effect of neuregulin-1 genotype on excitation-inhibition balance in cortical dynamics
Biological Psychiatry, 2020
Costa-Klein, P., Ettinger, U., Schirner, M., Ritter, P., Falkai, P., Koutsouleris, N., & Kambeitz, J.
Investigating the Effect of the Neuregulin-1 Genotype on Brain Function Using Brain Network Simulations
Nakagawa, T. T., Jirsa, V. K., Spiegler, A., McIntosh, A. R., & Deco, G.
Bottom up modeling of the connectome: Linking structure and function in the resting brain and their changes in aging
PLoS Computational Biology, 2019
Olmi, S., Petkoski, S., Guye, M., Bartolomei, F., & Jirsa, V.
Controlling seizure propagation in large-scale brain networks
Nature Communications, 2018
Proix, T., Jirsa, V. K., Bartolomei, F., Guye, M., & Truccolo, W.
Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy
Jirsa, V. K., Proix, T., Perdikis, D., Woodman, M. M., Wang, H., Bernard, C., Bénar, C., Chauvel, P., Bartolomei, F., Bartolomei, F., Guye, M., Gonzalez-Martinez, J., & Chauvel, P.
The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread
Proix, T., Bartolomei, F., Guye, M., & Jirsa, V. K.
Individual brain structure and modelling predict seizure propagation
Kunze, T., Hunold, A., Haueisen, J., Jirsa, V., & Spiegler, A.
Transcranial direct current stimulation changes resting state functional connectivity: A large-scale brain network modeling study
Frontiers in Computational Neuroscience, 2019
Stefanovski, L., Triebkorn, P., Spiegler, A., Diaz-Cortes, M. A., Solodkin, A., Jirsa, V., McIntosh, A. R., & Ritter, P.
Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer’s Disease
NeuroImage: Clinical, 2018
Zimmermann, J., Perry, A., Breakspear, M., Schirner, M., Sachdev, P., Wen, W., Kochan, N. A., Mapstone, M., Ritter, P., McIntosh, A. R., & Solodkin, A.
Differentiation of Alzheimer’s disease based on local and global parameters in personalized Virtual Brain models