Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Jul;82(1):67-78.
doi: 10.1002/ana.24974.

Connectivity Predicts Deep Brain Stimulation Outcome in Parkinson Disease

Affiliations
Free PMC article

Connectivity Predicts Deep Brain Stimulation Outcome in Parkinson Disease

Andreas Horn et al. Ann Neurol. .
Free PMC article

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. Ann Neurol 2017;82:67-78.

Conflict of interest statement

Potential Conflicts of Interest

M.R., A.K., R.N., J. Vol., and A.A.K. have business relationships with Medtronics, St Judes, and Boston Scientific, which are makers of DBS devices, but none is related to the current work. M.D.F. has submitted a patent on using connectivity imaging to identify the ideal site for brain stimulation; the processing stream is different from this work.

Figures

FIGURE 1
FIGURE 1
Method for identifying deep brain stimulation (DBS) connectivity. Processing steps include acquiring pre-/postoperative imaging (A), localizing DBS electrodes in standard space (B), calculating the volume of tissue activated (VTA) based on stimulation parameters (C), then calculating functional (D) and structural (E) connectivity from the VTA to the rest of the brain using high-quality normative connectome data. Our processing stream using the connectome datasets defined in healthy subjects is shown and was used in all primary analyses. For functional connectivity, positive correlations are shown in warm colors whereas negative correlations (anticorrelations) are shown in cool colors (color version available online). GPe = globus pallidus externus; GPi = globus pallidus internus; HCP = Human Connectome Project; STN = subthalamic nucleus.
FIGURE 2
FIGURE 2
Deep brain stimulation electrode localization and cohort information of training and test dataset. The Berlin dataset shown on the left (B1–4) was used for training and cross-validation (applying a leave-one-subcohort-out design). The final model was then confirmed by applying it to the Würzburg test dataset (W, right).
FIGURE 3
FIGURE 3
Connectivity predictive of clinical improvement in the Berlin training dataset. Results from analyses using the connectome defined in healthy subjects are shown. Functional connectivity (top row) and structural connectivity (bottom row) associated with clinical improvement were identified using a weighted average (first column), correlation with clinical outcome (R maps, second column), and a combination of these two maps (third column). Using the combined map, clinical outcome was predicted for each patient using the full dataset (fourth column) and leave-one-cohort-out design (last column). Dot color (color version available online) represents subcohorts as specified in Figure 2.
FIGURE 4
FIGURE 4
Validation of connectivity profiles on an independent dataset. Connectivity maps generated using the Berlin training dataset (B1–4 combined) for both functional connectivity (top row) and structural connectivity (bottom row) predict clinical outcome in the independent Würzburg dataset.
FIGURE 5
FIGURE 5
The topography of connectivity associated with clinical response is consistent across datasets. Combined maps (weighted average and R map) are based on the Berlin dataset (left), Würzburg dataset (middle), and both datasets together (right).
FIGURE 6
FIGURE 6
Clinical outcome prediction in individual patients based on deep brain stimulation (DBS) connectivity. Connectivity between individual DBS sites and the rest of the brain is shown for 3 patients using functional connectivity (left) and structural connectivity (middle). Clinical response (right) was predicted based on the match of each patient’s connectivity profile to that associated with good DBS response. Selected examples include a good responder with accurate prediction (top), a poor responder with accurate prediction (middle), and a poor responder with inaccurate prediction at the primary endpoint (+32%; bottom) who later improved to match our prediction with treatment of depression (−10.4%).

Comment in

Similar articles

See all similar articles

Cited by 63 articles

See all "Cited by" articles
Feedback