Cortical network fingerprints predict deep brain stimulation outcome in dystonia

Abstract

Background Deep brain stimulation (DBS) is an effective evidence‐based therapy for dystonia. However, no unequivocal predictors of therapy responses exist. We investigated whether patients optimally responding to DBS present distinct brain network organization and structural patterns. Methods From a German multicenter cohort of 82 dystonia patients with segmental and generalized dystonia who received DBS implantation in the globus pallidus internus, we classified patients based on the clinical response 3 years after DBS. Patients were assigned to the superior‐outcome group or moderate‐outcome group, depending on whether they had above or below 70% motor improvement, respectively. Fifty‐one patients met MRI‐quality and treatment response requirements (mean age, 51.3 ± 13.2 years; 25 female) and were included in further analysis. From preoperative MRI we assessed cortical thickness and structural covariance, which were then fed into network analysis using graph theory. We designed a support vector machine to classify subjects for the clinical response based on individual gray‐matter fingerprints. Results The moderate‐outcome group showed cortical atrophy mainly in the sensorimotor and visuomotor areas and disturbed network topology in these regions. The structural integrity of the cortical mantle explained about 45% of the DBS stimulation amplitude for optimal response in individual subjects. Classification analyses achieved up to 88% of accuracy using individual gray‐matter atrophy patterns to predict DBS outcomes. Conclusions The analysis of cortical integrity, informed by group‐level network properties, could be developed into independent predictors to identify dystonia patients who benefit from DBS. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Pre-dopa Deep Brain Stimulation: Is Early Deep Brain Stimulation Able to Modify the Natural Course of Parkinson’s Disease?

“…Hence, we proposed that STN-DBS before L-Dopa therapy could further promote a longer-lasting physiological and psychosocial functioning and positively modulate the neurodegenerative process. This hypothesis finds support on recent reports on the potential of DBS to shift the network response toward the physiological range (Kim et al, 2015;Horn et al, 2019), whereas DBS outcomes have been constantly related to the integrity of widespread brain regions connected to the stimulation sites in PD (Muthuraman et al, 2017;Koirala et al, 2018;Irmen et al, 2020) and in other diseases (Gonzalez-Escamilla et al, 2019). Thus, an effect on the neurodegenerative side is also expected.…”

Functional and Structural Connectivity Patterns Associated with Clinical Outcomes in Deep Brain Stimulation of the Globus Pallidus Internus for Generalized Dystonia

“…24 In our generalized dystonia cohort, we found a similar pattern of DBS connectivity correlating with clinical improvement, including the cerebellum and somatosensory cortex; however, we also found positive and negative correlations in multiple areas outside those interrogated by Corp et al 24 Our results revealed connectivity correlations with multiple areas outside the primary somatosensory and cerebellar regions, including the supplementary motor area, prefrontal cortex, inferior parietal lobule, lateral temporal, and lateral occipital regions. Most interesting, many of these areas were noted to exhibit cortical thickness differences between patients with poor response versus good response to GPi DBS for segmental and generalized dystonia in a recent study by Gonzalez-Escamilla et al 36 Using graph theory, their study posited that changes in centrality and the clustering coefficient in these prefrontal, parietal, temporal, and lateral occipital regions suggest that they may be a cause of network failure and decreased DBS effectiveness. 36 Our findings of negative functional connectivity correlation with UDRS improvement in many of these regions support the idea that they may play a role in limiting the neuromodulatory effect of GPi DBS for dystonia.…”

“…Most interesting, many of these areas were noted to exhibit cortical thickness differences between patients with poor response versus good response to GPi DBS for segmental and generalized dystonia in a recent study by Gonzalez-Escamilla et al 36 Using graph theory, their study posited that changes in centrality and the clustering coefficient in these prefrontal, parietal, temporal, and lateral occipital regions suggest that they may be a cause of network failure and decreased DBS effectiveness. 36 Our findings of negative functional connectivity correlation with UDRS improvement in many of these regions support the idea that they may play a role in limiting the neuromodulatory effect of GPi DBS for dystonia.…”

Proceedings of the Eighth Annual Deep Brain Stimulation Think Tank: Advances in Optogenetics, Ethical Issues Affecting DBS Research, Neuromodulatory Approaches for Depression, Adaptive Neurostimulation, and Emerging DBS Technologies

“…New techniques for the application of DBS have emerged with the advent of imaging, which has resulted in a paradigm shift toward targeted modulation of a particular network (Gonzalez-Escamilla et al, 2019, 2020Horn et al, 2019). Another emerging and as yet unresolved area is how beta oscillatory activity in the basal ganglia is affected by DBS and how this is associated with symptom improvement (Lofredi et al, 2019;Petersson et al, 2020).…”

“…In a recent review, we comprehensively describe the causal interrogations and modulations of network states using neuroimaging and electrophysiology (Gonzalez-Escamilla et al, 2020). Using structural magnetic resonance imaging (sMRI), we were further able to show in the pre-operative MRI the cortical thickness (CT) in the frontal lobe predicted the clinical improvement after STN-DBS (Muthuraman et al, 2017) and cortical atrophy in sensorimotor areas in dystonia patients (Gonzalez-Escamilla et al, 2019). In the same direction, frontal lobe network proxies can predict postoperative clinical response to STN-DBS using diffusion tensor imaging (DTI; Koirala et al, 2018).…”