Uncovering the underlying mechanisms and whole-brain dynamics of deep brain stimulation for Parkinson’s disease

Abstract

Deep brain stimulation (DBS) for Parkinson’s disease is a highly effective treatment in controlling otherwise debilitating symptoms. Yet the underlying brain mechanisms are currently not well understood. Whole-brain computational modeling was used to disclose the effects of DBS during resting-state functional Magnetic Resonance Imaging in ten patients with Parkinson’s disease. Specifically, we explored the local and global impact that DBS has in creating asynchronous, stable or critical oscillatory conditions using a supercritical bifurcation model. We found that DBS shifts global brain dynamics of patients towards a Healthy regime. This effect was more pronounced in very specific brain areas such as the thalamus, globus pallidus and orbitofrontal regions of the right hemisphere (with the left hemisphere not analyzed given artifacts arising from the electrode lead). Global aspects of integration and synchronization were also rebalanced. Empirically, we found higher communicability and coherence brain measures during DBS-ON compared to DBS-OFF. Finally, using our model as a framework, artificial in silico DBS was applied to find potential alternative target areas for stimulation and whole-brain rebalancing. These results offer important insights into the underlying large-scale effects of DBS as well as in finding novel stimulation targets, which may offer a route to more efficacious treatments.

Reliable local dynamics in the brain across sessions are revealed by whole‐brain modeling of resting state activity

“…In this work, we developed and applied a supercritical Hopf model (Deco, Kringelbach, et al, ; Jobst et al, ) that uses functional and structural connectivity (SC) information to simulate whole‐brain activity. Importantly, this model has been applied to disease (Saenger et al, ) and altered states of consciousness (Jobst et al, ), revealing important characteristics of the resting brain architecture. As shown in Figure , the model uses brain dynamics from fMRI data as well as the SC from diffusion weighted imaging (DWI) data to construct an interconnected network (Figure a).…”

Neurofeedback: Principles, appraisal, and outstanding issues

“…Such questions are of interest for all brain stimulations techniques, not only from a basic science viewpoint, but also from a technological one, as their answer is essential to things such as device development and optimization. For instance, the mechanisms through which DBS exerts its effects, for example, in the treatment for Parkinson's disease, have been investigated both computationally and experimentally (Saenger et al, 2017;Santaniello et al, 2015;Wang, Hutchins, & Kaiser, 2015). While DBS's basic physics is known by construction, it has been suggested to improve motor symptoms by activating efferent fibres (Hashimoto, Elder, Okun, Patrick, & Vitek, 2003), by modifying of oscillatory activity (Vitek, 2008), or by decoupling oscillations within the basal ganglia (Moran, Stein, Tischler, & Bar-Gad, 2012).…”

Personalized brain network models for assessing structure–function relationships

“…Personalized BNM also offer advantages when modeling the effects of diseases such as schizophrenia [43,44], Parkinson's Disease [45,46], and epilepsy [17,19,28,31,47]. For example, epilepsy is a disorder that is known to be associated with both structural and dynamical changes in the brain [48,49].…”