High-resolution Virtual Brain Modeling Towards Personalization of Deep Brain Stimulation

COEX 

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 are 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.
As a proof-of-concept study, we construct high-resolution virtual brain models for three patients diagnosed with TRD and undergoing DBS treatment and simulate brain response patterns as a function of stimulation location, parameterized by the contact location of electrodes implanted in each patient. In other words, we simulate the effects of local DBS on the patient-specific large-scale brain networks and demonstrate individual explanatory power by predicting the spatiotemporal response pattern due to stimulation. Regional activities triggered by activation of fiber tracts at the stimulation location are propagated to other regions through interactions based on the brain connectome, thereby generating a unique spatiotemporal response pattern. The simulated signals derived at each vertex are projected into the EEG sensor space and compared with empirical data measuring cortical evoked responses time-locked to stimulation at 2 Hz for each patient. Simulation results demonstrate that, despite some limitations in validation against empirical data (i.e., the need for individual parameter adjustment, and differential accuracy across stimulation locations), the virtual brain model with high resolution, in both network nodes and links, is capable of investigating the functional network effects depending on stimulation location in DBS.
This study provides evidence for the capacity of personalized high-resolution virtual brain models to explain the individual variation in up- and downstream effects of DBS and to quantify functional network contributions to DBS efficacy. The further extended modeling approaches, incorporating the longitudinal changes in the functional network induced by long-term DBS, may elucidate the modulation mechanisms of DBS and pave the new ways in the personalized optimization of brain stimulation.