Accuracy of different three-dimensional subcortical human brain atlases for DBS –lead localisation

Nowacki, Andreas; Nguyen, T. A. Khoa; Tinkhauser, Gerd; Petermann, Katrin; Debove, Ines; Wiest, Roland; Pollo, Claudio

doi:10.1016/j.nicl.2018.09.030

Cited by 40 publications

(35 citation statements)

References 35 publications

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“…Therefore, inferior congruence of the manual MRI based atlases with the manual segmentation compared to the MRI-based DA might be caused by the different origins of the atlases. Furthermore, it has recently been shown that DA shows significant discrepancies compared to electrophysiologically defined STN [18]. Therefore, it is questionable whether this atlas should be used as reference.…”

Section: Discussion/conclusionmentioning

confidence: 99%

Comparison of Automatic Segmentation Algorithms for the Subthalamic Nucleus

Polanski

Zolal

Sitoci-Ficici

et al. 2020

Stereotact Funct Neurosurg

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Introduction: Various automatic segmentation algorithms for the subthalamic nucleus (STN) have been published recently. However, most of the available software tools are not approved for clinical use. Objective: The aim of this study is to evaluate a clinically available automatic segmentation tool of the navigation planning software Brainlab Elements (BL-E) by comparing the output to manual segmentation and a nonclinically approved research method using the DISTAL atlas (DA) and the Horn electrophysiological atlas (HEA). Methods: Preoperative MRI data of 30 patients with idiopathic Parkinson's disease were used, resulting in 60 STN segmentations. The segmentations were created manually by two clinical experts. Automatic segmentations of the STN were obtained from BL-E and Advanced Normalization Tools using DA and HEA. Differences between manual and automatic segmentations were quantified by Dice and Jaccard coefficient, target overlap, and false negative/positive value (FNV/FPV) measurements. Statistical differences between similarity measures were assessed using the Wilcoxon signed-rank test with continuity correction, and comparison with interrater results was performed using the Mann-Whitney U test. Results: For manual segmentation, the mean size of the segmented STN was 133 ± 24 mm 3. The mean size of the STN was 121 ± 18 mm 3 for BL-E, 162 ± 21 mm 3 for DA, and 130 ± 17 mm 3 for HEA. The Dice coefficient for the interrater comparison was 0.63 and 0.54 ± 0.12, 0.59 ± 0.13, and 0.52 ± 0.14 for BL-E, DA, and HEA, respectively. Significant differences between similarity measures were found for Dice and Jaccard coefficient, target overlap and FNV between BL-E and DA; and FPV between BL-E and HEA. However, none of the differences were significant compared to interrater variability. The analysis of the center of gravity of the segmentations revealed that the BL-E STN ROI was located more medially, superior and posterior compared to other segmentations. Regarding the target overlap for beta power within the STN ROI included with the HEA, the BL-E segmentation showed a significantly higher value compared to manual segmentation. Conclusion: Automatic image segmentation by means of the clinically approved software BL-E provides Witold H. Polanski and Amir Zolal contributed equally to this work.

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Section: Discussion/conclusionmentioning

confidence: 99%

Comparison of Automatic Segmentation Algorithms for the Subthalamic Nucleus

Polanski

Zolal

Sitoci-Ficici

et al. 2020

Stereotact Funct Neurosurg

View full text Add to dashboard Cite

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“…Recently, others confirmed accuracy of the atlas based on intraoperative microelectrode recordings and showed that it matched electrophysiological data better than three other atlases of the STN (Nowacki et al, 2018). Finally, the motor functional zone of the atlas was defined using diffusion weighted imaging data acquired on specialized MR hardware (Setsompop et al, 2013) and cross-validated using both healthy controls and PD data (Ewert, Plettig, et al, 2018).…”

Section: Limitationsmentioning

confidence: 89%

Modulating the human functional connectome using deep brain stimulation

Horn

Wenzel

Irmen

et al. 2019

Preprint

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Neuroimaging has seen a paradigm shift from a formal description of local activity patterns toward studying distributed brain networks. The recently defined framework of the 'human connectome' allows to globally analyse parts of the brain and their interconnections. Deep brain stimulation (DBS) is an invasive therapy for patients with severe movement disorders aiming to retune abnormal brain network activity by local high frequency stimulation of the basal ganglia. Beyond clinical utility, DBS represents a powerful research platform to study functional connectomics and the modulation of distributed brain networks in the human brain. We acquired resting-state functional MRI in twenty Parkinson's disease (PD) patients with subthalamic DBS switched ON and OFF. An age-matched control cohort of fifteen subjects was acquired from an open data repository. DBS lead placement in the subthalamic nucleus (STN) was localized using a state-of-the art pipeline that involved brain shift correction, multispectral image registration and use of a precise subcortical atlas. Based on a realistic 3D model of the electrode and surrounding anatomy, the amount of local impact of DBS was estimated using a finite element method approach. On a global level, average connectivity increases and decreases throughout the brain were estimated by contrasting ON and OFF DBS scans on a voxel-wise graph comprising eight thousand nodes. Local impact of DBS on the motor STN explained half the variance in global connectivity increases within the motor network (R = 0.711, p < 0.001). Moreover, local impact of DBS on the motor STN could explain the degree of how much voxel-wise average brain connectivity normalized toward healthy controls (R = 0.713, p < 0.001). Finally, a network based statistics analysis revealed that DBS attenuated specific couplings that are known to be pathological in PD. Namely, coupling between motor thalamus and motor cortex was increased and striatal coupling with cerebellum, external pallidum and STN was decreased by DBS.Our results show that rs-fMRI may be acquired in DBS ON and OFF conditions on clinical MRI hardware and that data is useful to gain additional insight into how DBS modulates the functional connectome of the human brain. We demonstrate that effective DBS increases overall connectivity in the motor network, normalizes the network profile toward healthy controls and specifically strengthens thalamo-cortical connectivity while reducing striatal control over basal ganglia and cerebellar structures.

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“…Still, the amount of error introduced by nonlinear registrations remains unclear. Some validation of electrode localizations in standard space were made using electrophysiology by various groups (Horn et al, 2017b;Neumann et al, 2017;Nowacki et al, 2018;Tinkhauser et al, 2019;van Wijk et al, 2017). For instance, agreement between electrophysiology-defined and atlas-defined boundaries in standard space showed high agreement across 303 microelectrode recording sites when localizing recording-sites using Lead-DBS (Rappel et al, 2019).…”

Section: Limitationsmentioning

confidence: 99%

Deep Brain Stimulation: Imaging on a group level

Treu

Strange

Oxenford

et al. 2020

Preprint

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Highlights We present a novel toolbox to carry out DBS imaging analyses on a grouplevel  Group electrodes are visualized in 2D and 3D and related to clinical regressors  A favorable target and connectivity profiles for the treatment of PD are validated Abstract Deep Brain Stimulation (DBS) is an established treatment option for movement disorders and is investigated to treat a growing number of other brain disorders. It has been shown that DBS effects are highly dependent on exact electrode placement, which is especially important when probing novel indications or stereotactic targets. Thus, considering precise electrode placement is crucial when investigating efficacy of DBS targets. To measure clinical improvement as a function of electrode placement, neuroscientific methodology and specialized software tools are needed. Such tools should have the goal to make electrode placement comparable across patients and DBS centers, and include statistical analysis options to validate and define optimal targets. Moreover, to allow for comparability across different research sites, these need to be performed within an algorithmically and anatomically standardized and openly available group space. With the publication of Lead-DBS software in 2014, an open-source tool was introduced that allowed for precise electrode reconstructions based on pre-and postoperative neuroimaging data. Here, we introduce Lead Group, implemented within the Lead-DBS environment and specifically designed to meet aforementioned demands. In the present article, we showcase the various processing streams of Lead Group in a retrospective cohort of 51 patients suffering from Parkinson's disease, who were implanted with DBS electrodes to the subthalamic nucleus (STN). Specifically, we demonstrate various ways to visualize placement of all electrodes in the group and map clinical improvement values to subcortical space. We do so by using active coordinates and volumes of tissue activated, showing converging evidence of an optimal DBS target in the dorsolateral STN. Second, we relate DBS outcome to the impact of each electrode on local structures by measuring overlap of stimulation volumes with the STN. Finally, we explore the software functions for connectomic mapping, which may be used to relate DBS outcomes to connectivity estimates with remote brain areas. We isolate a specific fiber bundlewhich structurally resembles the hyperdirect pathwaythat is associated with good clinical outcome in the cohort.The manuscript is accompanied by a walkthrough tutorial through which users are able to reproduce all main results presented in the present manuscript. All data and code needed to reproduce results are openly available.

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Accuracy of different three-dimensional subcortical human brain atlases for DBS –lead localisation

Cited by 40 publications

References 35 publications

Comparison of Automatic Segmentation Algorithms for the Subthalamic Nucleus

Comparison of Automatic Segmentation Algorithms for the Subthalamic Nucleus

Modulating the human functional connectome using deep brain stimulation

Deep Brain Stimulation: Imaging on a group level

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