High-resolution Virtual Brain Modeling Personalizes Deep Brain Stimulation for Treatment-Resistant Depression: Spatiotemporal Response Characteristics Following Stimulation of Neural Fiber Pathways

Over the past 15 years, deep brain stimulation (DBS) has been actively investigated as a groundbreaking therapy for patients with treatment-resistant depression (TRD); nevertheless, outcomes have varied from patient to patient, with an average response rate of ∼50%. The engagement of specific fiber tracts at the stimulation site has been hypothesized to be an important factor in determining outcomes, however, the resulting individual network effects at the whole-brain scale remain largely unknown. Here we provide a computational framework that can explore each individual's brain response characteristics elicited by selective stimulation of fiber tracts. We use a novel personalized in-silico approach, the Virtual Big Brain, which makes use of high-resolution virtual brain models at a mm-scale and explicitly reconstructs more than 100 000 fiber tracts for each individual. Each fiber tract is active and can be selectively stimulated. Simulation results demonstrate distinct stimulus-induced event-related potentials as a function of stimulation location, parametrized by the contact positions of the electrodes implanted in each patient, even though validation against empirical patient data reveals some limitations (i.e., the need for individual parameter adjustment, and differential accuracy across stimulation locations). This study provides evidence for the capacity of personalized high-resolution virtual brain models to investigate individual network effects in DBS for patients with TRD and opens up novel avenues in the personalized optimization of brain stimulation.

[What can we expect from clinical trials in psychiatry?]

Clinical trials in psychiatry allow to build the regulatory dossiers for market authorization but also to document the mechanism of action of new drugs, to build pharmacodynamics models, evaluate the treatment effects, propose prognosis, efficacy or tolerability biomarkers and altogether to assess the impact of drugs for patient, caregiver and society. However, clinical trials have shown some limitations. Number of recent dossiers failed to convince the regulators. The clinical and biological heterogeneity of psychiatric disorders, the pharmacokinetic and pharmacodynamics properties of the compounds, the lack of translatable biomarkers possibly explain these difficulties. Several breakthrough options are now available: quantitative system pharmacology analysis of drug effects variability, pharmacometry and pharmacoepidemiology, Big Data analysis, brain modelling. In addition to more classical approaches, these opportunities lead to a paradigm change for clinical trials in psychiatry.

Systematic approximations of neural fields through networks of neural masses in the virtual brain

Full brain network models comprise a large-scale connectivity (the connectome) and neural mass models as the network's nodes. Neural mass models absorb implicitly a variety of properties in their constant parameters to achieve a reduction in complexity. In situations, where the local network connectivity undergoes major changes, such as in development or epilepsy, it becomes crucial to model local connectivity explicitly. This leads naturally to a description of neural fields on folded cortical sheets with local and global connectivities. The numerical approximation of neural fields in biologically realistic situations as addressed in Virtual Brain simulations (see http://thevirtualbrain.org/app/ (version 1.0)) is challenging and requires a thorough evaluation if the Virtual Brain approach is to be adapted for systematic studies of disease and disorders. Here we analyze the sampling problem of neural fields for arbitrary dimensions and provide explicit results for one, two and three dimensions relevant to realistically folded cortical surfaces. We characterize (i) the error due to sampling of spatial distribution functions; (ii) useful sampling parameter ranges in the context of encephalographic (EEG, MEG, ECoG and functional MRI) signals; (iii) guidelines for choosing the right spatial distribution function for given anatomical and geometrical constraints.