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. The engagement of specific fibre 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 a computational framework that can explore each individual's brain response characteristics was provided, elicited by selective stimulation of fibre tracts. A novel personalised in silico approach was used, the Virtual Big Brain, which makes use of high-resolution virtual brain models at an mm-scale and explicitly reconstructs more than 100,000 fibre tracts for each individual. Each fibre 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. This study provides evidence for the capacity of personalised high-resolution virtual brain models to investigate individual network effects in DBS for patients with TRD and opens up novel avenues in the personalised optimization of brain stimulation.
High-resolution virtual brain modeling personalizes deep brain stimulation for treatment-resistant depression: Spatiotemporal response characteristics following stimulation of neural fiber pathways
Viktor K. Jirsa(1), Sora An(2), Andrea B. Protzner(3)
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