High-resolution connectome-based neural field modelling of deep brain stimulation

Deep brain stimulation (DBS), which modulates dysfunction in the brain network by applying chronic high-frequency electrical stimulation to a specific location in the brain, is being explored as a groundbreaking therapy for drug-resistant neurological and neuropsychiatric diseases. DBS has been applied to several brain diseases such as Parkinson's disease, epilepsy, obsessive compulsive disorder, and major depressive disorder, and has demonstrated positive effects in improving symptoms. However, despite many efforts to provide personalized treatment through advanced neuroimaging techniques and accumulated clinical expertise, the therapeutic effects still vary from patient to patient. A potential reason for these inconsistent findings may be the individual variation in the brain structure and functional network organization. Because the stimulation response depends not only on the external conditions including its location, type, and parameters, but also on the dynamic state of the brain network being stimulated, a systematic approach to investigate individualized impacts of stimulation is required. Therefore, we propose a virtual brain modeling approach that enables personalized exploration of DBS. In particular, we examine the feasibility of this modeling approach in the context of treatment-resistant depression (TRD).
Virtual brain models were constructed from patient-specific structural data including brain anatomy and connectome, and then equipped with computational neural mass models for each brain region, thereby reproducing the functional dynamics of the brain. Given the recent neuroimaging findings reporting that the engagement of specific fiber tracts at the stimulation site is associated with the efficacy of DBS, we extend the existing virtual brain modeling framework to incorporate the geometry of fiber tracts and investigate stimulus-induced network effects. In particular, we combine two factors: high resolution and explicit fiber tract modeling. A high-resolution brain model at a mm-scale is built by placing neural mass models at the vertices of the brain surface mesh, regardless of brain parcellation (region). Through explicit modeling of fiber tracts considering their locations and geometries, neural mass models located at both terminals of each fiber tract are coupled taking into account connection strengths and transmission delays. This approach also allows the stimulation of segments of the fiber tract, including signal propagation along its entire length in both directions.
Numerical simulations were run examining regional activations and EEG-projected activity patterns, evoked by stimulation of specific fiber tracts by DBS. Tracts were distinguished by their elicitation of distinct spatiotemporal activity patterns in the network. Our results indicate that, while there are some challenges in validating against empirical data (e.g., individual parameter tuning and variable accuracy across stimulation sites), the high-resolution connectome-based neural field modeling with TVB effectively captures the functional network effects of DBS across different stimulation locations.
Code for replicating these results is available online, and a specially tailored, streamlined version will also be provided and presented to workshop attendees for exploration during the hands-on tutorial accompanying this talk at the end of the workshop’s afternoon session.