Dataset Information

Connectivity-based selection of optimal deep brain stimulation contacts: A feasibility study.

ABSTRACT:

Background

The selection of optimal deep brain stimulation (DBS) parameters is time-consuming, experience-dependent, and best suited when acute effects of stimulation can be observed (e.g., tremor reduction).

Objectives

To test the hypothesis that optimal stimulation location can be estimated based on the cortical connections of DBS contacts.

Methods

We analyzed a cohort of 38 patients with Parkinson's disease (24 training, and 14 test cohort). Using whole-brain probabilistic tractography, we first mapped the cortical regions associated with stimulation-induced efficacy (rigidity, bradykinesia, and tremor improvement) and side effects (paresthesia, motor contractions, and visual disturbances). We then trained a support vector machine classifier to categorize DBS contacts into efficacious, defined by a therapeutic window ?2 V (threshold for side effect minus threshold for efficacy), based on their connections with cortical regions associated with efficacy versus side effects. The connectivity-based classifications were then compared with actual stimulation contacts using receiver-operating characteristics (ROC) curves.

Results

Unique cortical clusters were associated with stimulation-induced efficacy and side effects. In the training dataset, 42 of the 47 stimulation contacts were accurately classified as efficacious, with a therapeutic window of ?3 V in 31 (66%) and between 2 and 2.9 V in 11 (24%) electrodes. This connectivity-based estimation was successfully replicated in the test cohort with similar accuracy (area under ROC = 0.83).

Conclusions

Cortical connections can predict the efficacy of DBS contacts and potentially facilitate DBS programming. The clinical utility of this paradigm in optimizing DBS outcomes should be prospectively tested, especially for directional electrodes.

SUBMITTER: Krishna V

PROVIDER: S-EPMC6649384 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

Connectivity-based selection of optimal deep brain stimulation contacts: A feasibility study.

Krishna Vibhor V Sammartino Francesco F Rabbani Qinwan Q Changizi Barbara B Agrawal Punit P Deogaonkar Milind M Knopp Michael M Young Nicole N Rezai Ali A

Annals of clinical and translational neurology 20190626 7

<h4>Background</h4>The selection of optimal deep brain stimulation (DBS) parameters is time-consuming, experience-dependent, and best suited when acute effects of stimulation can be observed (e.g., tremor reduction).<h4>Objectives</h4>To test the hypothesis that optimal stimulation location can be estimated based on the cortical connections of DBS contacts.<h4>Methods</h4>We analyzed a cohort of 38 patients with Parkinson's disease (24 training, and 14 test cohort). Using whole-brain probabilist ...[more]

PMID: 31353863

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Project description:Deep brain stimulation may be an effective therapy for select cases of severe, treatment-refractory Tourette syndrome; however, patient responses are variable, and there are no reliable methods to predict clinical outcomes. The objectives of this retrospective study were to identify the stimulation-dependent structural networks associated with improvements in tics and comorbid obsessive-compulsive behaviour, compare the networks across surgical targets, and determine if connectivity could be used to predict clinical outcomes. Volumes of tissue activated for a large multisite cohort of patients (n = 66) implanted bilaterally in globus pallidus internus (n = 34) or centromedial thalamus (n = 32) were used to generate probabilistic tractography to form a normative structural connectome. The tractography maps were used to identify networks that were correlated with improvement in tics or comorbid obsessive-compulsive behaviour and to predict clinical outcomes across the cohort. The correlated networks were then used to generate 'reverse' tractography to parcellate the total volume of stimulation across all patients to identify local regions to target or avoid. The results showed that for globus pallidus internus, connectivity to limbic networks, associative networks, caudate, thalamus, and cerebellum was positively correlated with improvement in tics; the model predicted clinical improvement scores (P = 0.003) and was robust to cross-validation. Regions near the anteromedial pallidum exhibited higher connectivity to the positively correlated networks than posteroventral pallidum, and volume of tissue activated overlap with this map was significantly correlated with tic improvement (P < 0.017). For centromedial thalamus, connectivity to sensorimotor networks, parietal-temporal-occipital networks, putamen, and cerebellum was positively correlated with tic improvement; the model predicted clinical improvement scores (P = 0.012) and was robust to cross-validation. Regions in the anterior/lateral centromedial thalamus exhibited higher connectivity to the positively correlated networks, but volume of tissue activated overlap with this map did not predict improvement (P > 0.23). For obsessive-compulsive behaviour, both targets showed that connectivity to the prefrontal cortex, orbitofrontal cortex, and cingulate cortex was positively correlated with improvement; however, only the centromedial thalamus maps predicted clinical outcomes across the cohort (P = 0.034), but the model was not robust to cross-validation. Collectively, the results demonstrate that the structural connectivity of the site of stimulation are likely important for mediating symptom improvement, and the networks involved in tic improvement may differ across surgical targets. These networks provide important insight on potential mechanisms and could be used to guide lead placement and stimulation parameter selection, as well as refine targets for neuromodulation therapies for Tourette syndrome.

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Project description:ObjectivesFirstly, to identify subthalamic region stimulation clusters that predict maximum improvement in rigidity, bradykinesia and tremor, or emergence of side-effects; and secondly, to map-out the cortical fingerprint, mediated by the hyperdirect pathways which predict maximum efficacy.MethodsHigh angular resolution diffusion imaging in twenty patients with advanced Parkinson's disease was acquired prior to bilateral subthalamic nucleus deep brain stimulation. All contacts were screened one-year from surgery for efficacy and side-effects at different amplitudes. Voxel-based statistical analysis of volumes of tissue activated models was used to identify significant treatment clusters. Probabilistic tractography was employed to identify cortical connectivity patterns associated with treatment efficacy.ResultsAll patients responded well to treatment (46% mean improvement off medication UPDRS-III [p < 0.0001]) without significant adverse events. Cluster corresponding to maximum improvement in tremor was in the posterior, superior and lateral portion of the nucleus. Clusters corresponding to improvement in bradykinesia and rigidity were nearer the superior border in a further medial and posterior location. The rigidity cluster extended beyond the superior border to the area of the zona incerta and Forel-H2 field. When the clusters where averaged, the coordinates of the area with maximum overall efficacy was X = -10(-9.5), Y = -13(-1) and Z = -7(-3) in MNI(AC-PC) space. Cortical connectivity to primary motor area was predictive of higher improvement in tremor; whilst that to supplementary motor area was predictive of improvement in bradykinesia and rigidity; and connectivity to prefrontal cortex was predictive of improvement in rigidity.InterpretationThese findings support the presence of overlapping stimulation sites within the subthalamic nucleus and its superior border, with different cortical connectivity patterns, associated with maximum improvement in tremor, rigidity and bradykinesia.

Dataset Information

Connectivity-based selection of optimal deep brain stimulation contacts: A feasibility study.

Background

Objectives

Methods

Results

Conclusions

Publications

Connectivity-based selection of optimal deep brain stimulation contacts: A feasibility study.

Similar Datasets

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