Recent advances in whole-brain, connectome-based neurophysiological modelling of brain stimulation

Deep brain stimulation (DBS) has been successfully applied in various neurodegenerative diseases as an effective symptomatic treatment. However, its mechanisms of action within the brain network are still poorly understood. Many virtual DBS models analyze a subnetwork around the basal ganglia and its dynamics as a spiking network with their details validated by experimental data. However, connectomic evidence shows widespread effects of DBS affecting many different cortical and subcortical areas. From a clinical perspective, various effects of DBS besides the motoric impact have been demonstrated.
We will introduce the neuroinformatics platform The Virtual Brain (TVB), which has previously been used to model pharmacological effects and virtual transcranial direct current stimulation (tDCS). TVB offers a modeling framework allowing us to virtually perform stimulation, including DBS, and forecast the outcome from a dynamic systems perspective prior to invasive surgery with DBS lead placement. For an accurate prediction of the effects of DBS, we implement a detailed spiking model of the basal ganglia, which we combine with TVB via our previously developed co-simulation environment. This 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 this 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 provide proof of concept for virtual DBS in a co-simulation environment with TVB.
Furthermore, we will show first successful validation steps of our multiscale model using multimodal data of individual whole-brain functional MRI BOLD signals and subthalamic nucleus local field potential data recorded by the DBS lead of Parkinson’s disease patients, both in DBS ON and OFF stimulation states. The developed modeling approach has the potential to optimize DBS lead placement and configuration and forecast the success of DBS treatment for individual patients. Further, we will give an outlook on our ongoing non-invasive stimulation modeling work of virtual tDCS and transcranial magnetic stimulation (TMS) using TVB. 

In recent years, the possibility to noninvasively interact with the human brain has led to unprecedented diagnostic and therapeutic opportunities. However, the vast majority of approved interventions and approaches still rely on anatomical landmarks and rarely on the individual structure of networks in the brain, drastically reducing the potential efficacy of neuromodulation. Here we implemented a target search algorithm leveraging on mathematical tools from Network Control Theory (NCT) and whole brain connectomics analysis. By means of computational simulations, we aimed to identify the optimal stimulation target(s) - at the individual brain level - capable of reaching maximal engagement of the stimulated networks’ nodes. At the model level, in silico predictions suggest that stimulation of NCT-derived cerebral sites might induce significantly higher network engagement, compared to traditionally employed neuromodulation sites, demonstrating NCT to be a useful tool in guiding brain stimulation. Indeed, NCT allows us to computationally model different stimulation scenarios tailored on the individual structural connectivity profiles and initial brain states. The use of NCT to computationally predict TMS pulse propagation suggests that individualized targeting is crucial for more successful network engagement. Future studies will be needed to verify such prediction in real stimulation scenarios. 

The brain is a complex, nonlinear, multiscale, and intricately interconnected physical system, whose laws of motion and principles of organization have proven challenging to understand with currently available measurement techniques. In such epistemic circumstances, application of spatially and temporally synchronized systematic perturbations, and measurement of their effects, is a central tool in the scientific armoury. For human brains, the technological combination that best supports this non-invasive perturbation-based modus operandi is concurrent transcranial magnetic stimulation (TMS) and electroencephalography (EEG). Spatiotemporally complex and long-lasting TMS-EEG evoked potential (TEP) waveforms are believed to result from recurrent, re-entrant activity that propagates broadly across multiple cortical and subcortical inter-connected regions, dispersing from and later re-converging on, the primary stimulation site. However, if we loosely understand the TEP of a TMS-stimulated region as the impulse response function of a noisy underdamped harmonic oscillator, then multiple later activity components (waveform peaks) should be expected even for an isolated network node in the complete absence of recurrent inputs. Thus emerges a critically important question for basic and clinical research on human brain dynamics: what parts of the TEP are due to purely local dynamics, what parts are due to reverberant, re-entrant network activity, and how can we distinguish between the two? To disentangle this, we have conducted several studies to establish the contribution of functional and structural connectivity in predicting TMS-induced signal propagation after perturbation of two distinct brain networks.
To disentangle this, we introduce a novel approach to addressing these questions around the physiological basis and spatiotemporal network dynamics of neural activity evoked by noninvasive brain stimulation, using a combination of empirical TMS-EEG data analyses and whole-brain, connectome-based neurophysiological modelling. The logic proceeds as follows: In a first step, we fit a connectome-based model to individual-subject TEP data, achieving accurate replication of the measured channel- and source-level TMS-EEG patterns. Then, we introduce to the model a series of spatially and temporally specific ‘virtual lesions’ by setting to zero the weights of all connections leaving from and returning to the primary stimulation site, at specific times. These virtual lesions isolate the TMS-stimulated region from the rest of the brain for delineated periods, and allow us to ask what its dynamics would look like with and without recurrent feedback from downstream brain areas. Activity patterns at the stimulated node that are unchanged by a given virtual lesion that suppresses recurrent inputs are thus independent of those inputs, and can be understood as a ‘local echo’ of the stimulation that persists in time for long periods (dozens to hundreds of milliseconds). For modelling the empirical TMS-EEG TEP data following the investigative line described above, we use a newly-developed numerical simulation approach that draws on recent technical advances from the field of machine learning (Griffiths et al., 2022). Our novel modelling methodology allows accurate and robust individual subject-level TEP waveform fitting, allowing us to present here the first ever subject-specific, cortex-wide, connectome-based neurophysiological model of TEP generation. Compared to late (100ms after the TMS pulse) virtual lesions, and compared to the control condition where no damage was applied, early (20 ms, 50 ms) damage of essential nodes' afferent and efferent pathways significantly reduced the amplitude of the 100 ms TEP component at the stimulation site and its neighbouring regions. In these early lesion conditions some residual activity in the left M1 area was still observed at around 100 ms, indicating that a local echo of the TMS stimulus does indeed persist for tens to hundreds of milliseconds after stimulation. Overall, these indicate that recurrent network feedback begins to drive TEP responses from 100 ms post-stimulation, with earlier TEP components being attributable to local reverberatory activity within the stimulated region. Moreover, subject-specific estimation of neurophysiological parameters additionally indicated an important role for inhibitory GABAergic neural populations in scaling cortical excitability levels, as reflected in TEP waveform characteristics. Finally, both the parameters and the populations’ trajectories of the model were able to predict the efficacy a repetitive TMS treatment in a cohort of patients with major depressive disorder. The novel discoveries and new software technologies introduced here should be of broad utility in basic and clinical neuroscience research.
 

While transcranial electrical stimulation (tES) is rapidly emerging as a powerful therapeutic tool in a number of neuropsychiatric diseases, recent advances across multiple fields indicate that computational modeling is poised to play a key role in breakthroughs in both basic and translational neuroscience. And with it, the possibility of addressing a crucial fact: each head, each brain is different. Our vision is first that research using individualized computational models will reveal new insights in basic neuroscience. Second, that they will be used routinely to reduce risk and uncertainty in diagnosis and lead to breakthroughs in the design of personalized therapies based on individual biophysical and physiological characteristics. This advancement will represent a milestone in the fields of neuroscience and neuropsychiatry, where the lack of mechanistic, quantitative approaches, together with inter-individual variability, have hindered progress and created a barrier to the introduction of new therapies such as tES. In this talk I will first provide an overview of our operational modeling pipeline (Stimweaver): personalized biophysical head models that take into account the electrical properties of head tissues and their thickness and geometry are currently used by our clinical operations team for dose analysis and montage planning. In parallel, our group is currently developing a modeling framework to integrate the mechanisms of interaction of electric fields with active, dynamical brain networks. The budding Neurotwin pipeline will be capable of assimilating diverse neuroimaging data streams – much as in numerical weather prediction – to characterize and design protocols to reshape the individual brain’s dynamical landscape with electric fields – exploiting natural plasticity to restore healthy connectivity. In this presentation, I will present an overview of both pipelines and how they can be used to optimize tES treatment protocols for epilepsy and Alzheimer’s disease.