Objective: Open questions remain regarding the optimal target, or sweetspot, for deep brain stimulation (DBS) in e.g. Parkinson's Disease. Previous studies introduced different methods of mapping DBS effects to determine sweetspots. While having a direct impact on surgical targeting and postoperative programming in DBS, these methods so far have not been investigated in ground truth data. Materials & Methods: This study investigated five previously published DBS mapping methods regarding their potential to correctly identify a ground truth sweetspot. Methods were investigated in silico in eight different use case scenarios, which incorporated different types of clinical data, noise, and differences in underlying neuroanatomy. Dice coefficients were calculated to determine the overlap between identified sweetspots and the ground truth. Additionally, out of sample predictive capabilities were assessed using the amount of explained variance R-squared. Results: The five investigated methods resulted in highly variable sweetspots. Methods based on voxel-wise statistics against average outcomes showed the best performance overall. While predictive capabilities were high, even in the best of cases Dice coefficients remained limited to values around 0.5, highlighting the overall limitations of sweetspot identification. Conclusions: This study highlights the strengths and limitations of current approaches to DBS sweetspot mapping. Those limitations need to be taken into account when considering the clinical implications. All future approaches should be investigated in silico before being applied to clinical data.
Gait impairments such as freezing of gait (FOG) are among the most common and disabling symptoms of Parkinson's disease (PD). While the efficacy of deep brain stimulation (DBS) of the subthalamic nucleus (STN) in patients with early or advanced PD has been proven in large randomised controlled trials, its effect on gait impairments is still insufficiently understood. Moreover, there is uncertainty about pathways that need to be modulated to improve gait impairments. In this bi-centric study, we investigated how STN-DBS alters FOG in 47 subjects with PD. We assessed freezing prevalence and severity using the Freezing of Gait Questionnaire and Item 14 of the Unified Parkinson's Disease Rating Scale-II. Using a model of publicly available basal-ganglia pathways we determined a connectivity profile for postoperative changes in FOG. Compared to preoperative baseline, freezing of gait significantly improved six months postoperatively, marked by reduced frequency and duration of freezing episodes. We found that optimal stimulation sites for improving freezing of gait structurally connected to primary and supplementary motor areas, the dorsolateral prefrontal cortex and to the globus pallidus. Stimulation of the lenticular fasciculus was associated with worsening of freezing of gait. Our findings highlight the need for optimal identification and characterisation for network structures that can be implemented in stereotactic planning and can additionally pose a target for postoperative stimulation strategies.
Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an established therapy in advanced Parkinson's disease (PD). Motor and non-motor outcomes, however, show considerable inter-individual variability. Morphometry-based metrics have recently received increasing attention to predict treatment effects. As evidence for the prediction of non-motor outcomes is limited, we sought to investigate the association between metrics of voxel-based morphometry and short-term non-motor outcomes following STN-DBS in this prospective open-label study. 49 PD patients underwent structural MRI and a comprehensive clinical assessment at preoperative baseline and 6-month follow-up. Voxel-based morphometry was used to assess associations between cerebral volume and non-motor outcomes corrected for multiple comparisons using a permutation-based approach. We replicated existing results associating atrophy of the superior frontal cortex with subpar motor outcomes. Non-motor outcomes, however, were not associated with morphometric features, limiting its use as a marker to inform patient selection and holistic preoperative counselling.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.