Personalized whole brain simulations: linking connectomics and dynamics in the human brain

Inferring multi-scale neural mechanisms with brain network modelling (Schirner et al 2018, Schirner et al. 2022): The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. We developed connectome-based brain network models that integrate individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.

Brain Dynamics in Aging (Battaglia et al 2020): FC during resting-state or task conditions is not static but inherently dynamic. Dynamic FC has a complex structure endowed with long-range sequential correlations that give rise to transient slowing and acceleration epochs in the continuous flow of reconfiguration. Our analysis for fMRI data in healthy elderly revealed that dynamic FC tends to slow down and becomes less complex as well as more random with increasing age. These effects appear to be strongly associated with age-related changes in behavioural and cognitive performance.

Motor control in aging: (Chettouf et al. 2020, Chettouf et al. 2022): Sensorimotor coordination requires orchestrated network activity in the brain, mediated by inter- and intra-hemispheric interactions that may be affected by aging-related changes. We adopted a theoretical model, according to which intra-hemispheric inhibition from premotor to primary motor cortex is mandatory to compensate for inter-hemispheric excitation through the corpus callosum. To test this as a function of age we acquired electroencephalography (EEG) simultaneously with functional magnetic resonance imaging (fMRI) in two groups of healthy adults (younger N = 13: 20–25 year and older N = 14: 59–70 year) while learning a unimanual motor task. On average, quality of performance of older participants stayed significantly below that of the younger ones. Accompanying decreases in motor-event-related EEG β-activity were lateralized toward contralateral motor regions, albeit more so in younger participants. In this younger group, the mean β-power during motor task execution was significantly higher in bilateral premotor areas compared to the older adults. In both groups, fMRI blood oxygen level dependent (BOLD) signals were positively correlated with source-reconstructed β-amplitudes: positive in primary motor and negative in premotor cortex. This suggests that β-amplitude modulation is associated with primary motor cortex “activation” (positive BOLD response) and premotor “deactivation” (negative BOLD response). Although the latter results did not discriminate between age groups, they underscore that enhanced modulation in primary motor cortex may be explained by a β-associated excitatory crosstalk between hemispheres.

Multi-scale co-simulation of deep brain stimulation, DBS (Meier et al. 2022): Our multiscale co-simulation approach builds on the extensive previous literature of spiking models of the basal ganglia while simultaneously offering a whole-brain perspective on widespread effects of the stimulation going beyond the motor circuit. In the first demonstration of our model, we show that virtual DBS can move the firing rates of a Parkinson's disease patient's thalamus - basal ganglia network towards the healthy regime while, at the same time, altering the activity in distributed cortical regions with a pronounced effect in frontal regions. Thus, we provided proof of concept for virtual DBS in a co-simulation environment with TVB. The developed modeling approach has the potential to optimize DBS lead placement and configuration and forecast the success of DBS treatment for individual patients.

Personalized brain simulation in the Cloud (Schirner et al. 2022): The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation.

Integrative brain models that connect separate mechanisms across levels of description and spatiotemporal scales and link them with cognitive function. They capture multiple experimental phenomena of the brain and link them mechanistically – across methods and species.

Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease (Stefanovski et al. 2019): We demonstrate how TVB enables the simulation of systems effects caused by pathogenetic molecular candidate mechanisms in human virtual brains.

Brain simulation augments machine-learning–based classification of dementia (Triebkorn et al. 2022): Computational brain network modeling using TVB simulation platform acts synergistically with machine learning (ML) and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer's disease (AD).

Personalized multi-scale brain simulation and virtual intervention such as brain sTimulation hold the potential for individualized in-silico therapy planning for patients with brain disease.

Brain simulation workflows have been made available as GDPR compliant Cloud service on the EBRAINS infrastructure at ebrains.eu.

Periodic Reporting for period 4 - BrainModes (Personalized whole brain simulations: linking connectomics and dynamics in the human brain)

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