Electrophysiological validation of STN-SNr boundary depicted by susceptibility-weighted MRI

McEvoy, J. D. S.; Ughratdar, Ismail; Schwarz, ST; Basu, Surajit

doi:10.1007/s00701-015-2615-1

Cited by 15 publications

(17 citation statements)

References 9 publications

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“…This may explain the low degree of correspondence in the present study. McEvoy et al [ 22 ] reported on the STN-SN border morphology using SWI, reviewing 28 MER tracks in seven patients. Their group found that SWI accurately estimated the STN-SN border within 1 mm of predicted depth of the electrophysiological STN-SN border in 85.7% of MER passes, concluding that SWI MRI and electrophysiological border coincide reliably.…”

Section: Discussionmentioning

confidence: 99%

“…However, it remains difficult to distinguish STN from other (also hypointense) adjacent structures like the ventromedial located substantia nigra (SN) and anterodorsal pallidofugal fiber pathways [ 9 , 14 ]. Susceptibility weighted imaging (SWI) has gained interest for DBS surgery because of its distinct visualization of iron-rich structures like the STN, its reported high level of contrast-to-noise ratio, and clear depiction of (superficial) veins [ 20 , 22 , 25 , 29 , 31 ]. However, a recent study has shown that STN representation on 1.5-Tesla (T) SWI showed less correspondence with lateral electrophysiological STN borders than conventional T2-weighted imaging [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Borders of STN determined by MRI versus the electrophysiological STN. A comparison using intraoperative CT

et al. 2017

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BackgroundIt is unclear which magnetic resonance imaging (MRI) sequence most accurately corresponds with the electrophysiological subthalamic nucleus (STN) obtained during microelectrode recording (MER, MER-STN). CT/MRI fusion allows for comparison between MER-STN and the STN visualized on preoperative MRI (MRI-STN).ObjectiveTo compare dorsal and ventral STN borders as seen on 3-Tesla T2-weighted (T2) and susceptibility weighted images (SWI) with electrophysiological STN borders in deep brain stimulation (DBS) for Parkinson’s disease (PD).MethodsIntraoperative CT (iCT) was performed after each MER track. iCT images were merged with preoperative images using planning software. Dorsal and ventral borders of each track were determined and compared to MRI-STN borders. Differences between borders were calculated.ResultsA total of 125 tracks were evaluated in 45 patients. MER-STN started and ended more dorsally than respective dorsal and ventral MRI-STN borders. For dorsal borders, differences were 1.9 ± 1.4 mm (T2) and 2.5 ± 1.8 mm (SWI). For ventral borders, differences were 1.9 ± 1.6 mm (T2) and 2.1 ± 1.8 mm (SWI).ConclusionsDiscrepancies were found comparing borders on T2 and SWI to the electrophysiological STN. The largest border differences were found using SWI. Border differences were considerably larger than errors associated with iCT and fusion techniques. A cautious approach should be taken when relying solely on MR imaging for delineation of both clinically relevant STN borders.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Borders of STN determined by MRI versus the electrophysiological STN. A comparison using intraoperative CT

et al. 2017

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“…Although recent imaging studies have been able to improve the differentiation between the STN and the SNr, 19 electrophysiology is still necessary to identify and verify the STN-SNr transition intraoperatively. To facilitate detection of the transition, this article describes a new automatic, reliable procedure for locating the STN exit.…”

Section: Introductionmentioning

confidence: 99%

Stop! border ahead: Automatic detection of subthalamic exit during deep brain stimulation surgery

Valsky¹,

Marmor-Levin²,

Deffains³

et al. 2016

Movement Disorders

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Background Microelectrode recordings along pre-planned trajectories are often used for accurate definition of the subthalamic nucleus (STN) borders during deep brain stimulation (DBS) surgery for Parkinson’s disease. Usually, the demarcation of the STN borders is detected manually by a neurophysiologist. The exact detection of the borders is difficult and especially detecting the transition between the STN and the substantia nigra pars reticulata. Consequently, demarcation may be inaccurate, leading to sub-optimal location of the DBS lead and inadequate clinical outcomes. Methods We present machine learning classification procedures that utilize microelectrode recordings power spectra and allow for real time, high accuracy discrimination between STN and substantia nigra pars reticulata. Results A support vector machine procedure was tested on microelectrode recordings from 58 trajectories that included both STN and substantia nigra pars reticulata that achieved a 97.6% consistency with human expert classification (evaluated by 10-fold cross validation). We used the same dataset as a training set to find the optimal parameters for a hidden Markov model using both microelectrode recordings features and trajectory history to enable a real-time classification of the ventral STN border (STN exit). Seventy-three additional trajectories were used to test the reliability of the learned statistical model in identifying the exit from the STN. The hidden Markov model procedure identified the STN exit with an error of 0.04 ± 0.18 mm and detection reliability (error < 1 mm) of 94%. Conclusion The results indicate that robust, accurate and automatic real-time electrophysiological detection of the ventral STN border is feasible.

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“…Computed tomography (CT) and MRI (T1-weighted, T2-weighted and susceptibility weighted imaging [SWI]) scans were merged and each STN was virtually reconstructed using interactive segmentation. We used susceptibility-weighted MRI to delineate the STN boundaries, as this method was validated by our team previously by microelectrode recording studies [3]. The three-dimensional (3D) skull image was modelled by manual thresholding.…”

Section: Methodsmentioning

confidence: 99%

Does trajectory matter? A study looking into the relationship of trajectory with target engagement and error accommodation in subthalamic nucleus deep brain stimulation

Steel

Basu

2017

Acta Neurochir

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BackgroundDeep brain stimulation (DBS) of the subthalamic nucleus (STN) is now a key treatment choice for advanced Parkinson’s disease. The optimum target area within the STN is well established. However, no emphasis on the impact of trajectory exists. The ellipsoid shape of the STN and the off-centre traditional target point mean that variation in the electrode inclination should affect STN engagement. Understanding of this relationship could inform trajectory selection during planning by improving STN engagements and margins for error.MethodWe simulated electrode placement at the clinical target through a set of trial trajectories. Twelve three-dimensionally reconstructed STNs were created from magnetic resonance imaging data of six patients. An appropriate target within each STN was then chosen. Each STN was approached through 56 simulated trajectories arranged in a grid covering a quadrant of skull around and in front of the coronal suture. A subset of 20 viable trajectories was reassessed for depth of engagement in each STN whilst approaching the chosen target.ResultsGroup averages for each trajectory are presented as traffic light maps and as an overlaid skull mask illustrating recommended electrode entry sites. Trajectories under 30 degrees anterior to the bregma and between 10 to 30 degrees off the midline accommodated over 2.4 degrees of wobble. A mean engagement of 6 mm was possible in half of the subset. The longest engagements are on trajectories which saddle the coronal suture, extending to 40 degrees lateral. Microelectrode tracts of 14 additional STNs were collated using the above protocol and engagement exceeded 5 mm in all central trajectories without capsular side effects, suggesting placement away from STN borders.ConclusionsTrajectory selection influences engagement and flexibility to accommodate electrode wobble or brain shift whilst approaching a chosen STN target. We recommend having the first trial trajectory 20 degrees anterior to the bregma, moving postero-laterally in successive trials to balance both error and engagement. When wider margins for error are beneficial (e.g. second side during bilateral procedures), trajectories nearer the coronal suture and around 25 degrees off the midline are advised.

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Electrophysiological validation of STN-SNr boundary depicted by susceptibility-weighted MRI

Abstract: The study demonstrates that STN morphology can be depicted using SWI MRI and coincides reliably with the electrophysiological MER boundary. Thus, this imaging modality can be used to refine STN direct targeting protocols in DBS surgery for PD.

Cited by 15 publications

References 9 publications

Borders of STN determined by MRI versus the electrophysiological STN. A comparison using intraoperative CT

Borders of STN determined by MRI versus the electrophysiological STN. A comparison using intraoperative CT

Stop! border ahead: Automatic detection of subthalamic exit during deep brain stimulation surgery

Does trajectory matter? A study looking into the relationship of trajectory with target engagement and error accommodation in subthalamic nucleus deep brain stimulation

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