Connectivity Predicts deep brain stimulation outcome in Parkinson disease

Abstract

Objective The benefit of deep brain stimulation (DBS) for Parkinson disease (PD) may depend on connectivity between the stimulation site and other brain regions, but which regions and whether connectivity can predict outcome in patients remain unknown. Here, we identify the structural and functional connectivity profile of effective DBS to the subthalamic nucleus (STN) and test its ability to predict outcome in an independent cohort. Methods A training dataset of 51 PD patients with STN DBS was combined with publicly available human connectome data (diffusion tractography and resting state functional connectivity) to identify connections reliably associated with clinical improvement (motor score of the Unified Parkinson Disease Rating Scale [UPDRS]). This connectivity profile was then used to predict outcome in an independent cohort of 44 patients from a different center. Results In the training dataset, connectivity between the DBS electrode and a distributed network of brain regions correlated with clinical response including structural connectivity to supplementary motor area and functional anticorrelation to primary motor cortex (p<0.001). This same connectivity profile predicted response in an independent patient cohort (p<0.01). Structural and functional connectivity were independent predictors of clinical improvement (p<0.001) and estimated response in individual patients with an average error of 15% UPDRS improvement. Results were similar using connectome data from normal subjects or a connectome age, sex, and disease matched to our DBS patients. Interpretation Effective STN DBS for PD is associated with a specific connectivity profile that can predict clinical outcome across independent cohorts. This prediction does not require specialized imaging in PD patients themselves.

Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation

“…VTA-Madler and VTA-Astrom were chosen because they are being utilized by a broader community of researchers studying DBS [e.g. Vanegas-Arroyave et al, 2016; Horn et al, 2017]. And, we chose DF-Peterson because it is the most accurate and generalizable DF-based method currently available [Peterson et al, 2011].…”

“…For combinations of PW and D that were not provided in the tables, we estimated E T,th by using a fitted bivariate decaying rational function to interpolate and extrapolate values (Figure S2). The concept of E T,th -based VTAs are currently employed by the Lead-DBS software tool [Horn et al, 2017]. …”

“…In particular, subthalamic DBS has already been the focus of numerous PAM studies [e.g. Coenen et al, 2011; Chaturvedi et al, 2012; Groppa et al, 2014; Sweet et al, 2014; Accolla et al, 2016; Vanegas-Arroyave et al, 2016; Gunalan et al, 2017; Horn et al, 2017; Akram et al, 2017]. However, the various PAM analyses have employed a wide range of different methods without much in the way of documentation or validation of their accuracy.…”

Automatic localization of the subthalamic nucleus on patient‐specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation

“…Standard atlases that are well defined in a normalized space could be of great value for retrospective population studies and are highly appreciated in the field (Horn, Kühn, et al, 2017; Horn, Neumann, et al, 2017; Horn, Reich, et al, 2017; Keuken et al, 2014). However, uncertainty in registration needs to be addressed when using such atlases for patient-specific STN-DBS targeting.…”

Data-Driven Prediction of the Therapeutic Window during Subthalamic Deep Brain Stimulation Surgery

“…Therefore, functional atlases have been developed to predict the DBS outcome. These atlases could be coordinate-based [20,21], enhanced with biophysical models [22], or more recently can adopt a connectomic approach [23,24]. Main limitations are the discrepancies between the methods used that prevent the transfer between medical centers and the lack of prospective validation.…”