Dataset Information

Method for localizing intraoperative recordings from deep brain stimulation surgery using post-operative structural MRI.

ABSTRACT:

Background

Implantation of deep brain stimulation (DBS) electrodes for the treatment of involuntary movement disorders, such as Parkinson's disease, routinely relies on the use of intraoperative electrophysiological confirmation to identify the optimal therapeutic target in the brain. However, only a few options exist to visualize the relative anatomic localization of intraoperative electrophysiological recordings with respect to post-operative imaging. We have developed a novel processing pipeline to visualize intraoperative electrophysiological signals registered to post-operative neuroanatomical imaging.

New method

We developed a processing pipeline built on the use of ITK-SNAP and custom MATLAB scripts to visualize the anatomical localization of intraoperative electrophysiological recordings mapped onto the post-operative MRI following implantation of DBS electrodes. This method combines the user-defined relevant electrophysiological parameters measured during the surgery with a manual segmentation of the DBS electrode from post-operative MRI; mapping the microelectrode recording (MER) depths along the DBS lead track.

Results

We demonstrate the use of our processing pipeline on data from Parkinson's disease patients undergoing DBS implantation targeted to the subthalamic nucleus (STN). The primary processing components of the pipeline are: extrapolation of the lead wire and alignment of intraoperative electrophysiology.

Conclusion

We describe the use of a processing pipeline to aid clinicians and researchers engaged in deep brain stimulation work to correlate and visualize the intraoperative recording data with the post-operative DBS trajectory.

SUBMITTER: Winter M

PROVIDER: S-EPMC6205403 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Method for localizing intraoperative recordings from deep brain stimulation surgery using post-operative structural MRI.

Winter McKenzie M Costabile Jamie D JD Abosch Aviva A Thompson John A JA

NeuroImage. Clinical 20181016

<h4>Background</h4>Implantation of deep brain stimulation (DBS) electrodes for the treatment of involuntary movement disorders, such as Parkinson's disease, routinely relies on the use of intraoperative electrophysiological confirmation to identify the optimal therapeutic target in the brain. However, only a few options exist to visualize the relative anatomic localization of intraoperative electrophysiological recordings with respect to post-operative imaging. We have developed a novel processi ...[more]

PMID: 30380519

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Project description:INTRODUCTION:Twin-to-twin transfusion syndrome (TTTS) is a potentially lethal condition that affects pregnancies in which twins share a single placenta. The definitive treatment for TTTS is fetoscopic laser photocoagulation, a procedure in which placental blood vessels are selectively cauterized. Challenges in this procedure include difficulty in quickly identifying placental blood vessels due to the many artifacts in the endoscopic video that the surgeon uses for navigation. We propose using deep-learned segmentations of blood vessels to create masks that can be recombined with the original fetoscopic video frame in such a way that the location of placental blood vessels is discernable at a glance. METHODS:In a process approved by an institutional review board, intraoperative videos were acquired from ten fetoscopic laser photocoagulation surgeries performed at Yale New Haven Hospital. A total of 345 video frames were selected from these videos at regularly spaced time intervals. The video frames were segmented once by an expert human rater (a clinician) and once by a novice, but trained human rater (an undergraduate student). The segmentations were used to train a fully convolutional neural network of 25 layers. RESULTS:The neural network was able to produce segmentations with a high similarity to ground truth segmentations produced by an expert human rater (sensitivity?=?92.15%?±?10.69%) and produced segmentations that were significantly more accurate than those produced by a novice human rater (sensitivity?=?56.87%?±?21.64%; p?<?0.01). CONCLUSION:A convolutional neural network can be trained to segment placental blood vessels with near-human accuracy and can exceed the accuracy of novice human raters. Recombining these segmentations with the original fetoscopic video frames can produced enhanced frames in which blood vessels are easily detectable. This has significant implications for aiding fetoscopic surgeons-especially trainees who are not yet at an expert level.

Project description:The clinical utility of monitoring behavioral changes during intraoperative testing of subcallosal cingulate deep brain stimulation is unknown.To characterize the structural connectivity correlates of deep brain stimulation-evoked behavioral effects using probabilistic tractography in depression.Categorization of acute behavioral effects was conducted in 9 adults undergoing deep brain stimulation implantation surgery for chronic treatment-resistant depression in a randomized and blinded testing session at Emory University. Patients were studied from September 1, 2011, through June 30, 2013. Post hoc analyses of the structural tractography patterns mediating distinct categories of evoked behavioral effects were defined, including the best response overall. Data analyses were performed from May 1 through July 1, 2015.Categorization of stimulation-induced transient behavioral effects and delineation of the shared white matter tracts mediating response subtypes.Among the 9 patients, 72 active and 36 sham trials were recorded. The following stereotypical behavior patterns were identified: changes in interoceptive (noted changes in body state in 30 of 72 active and 4 of 36 sham trials) and in exteroceptive (shift in attention from patient to others in 9 of 72 active and 0 sham trials) awareness. The best response was a combination of exteroceptive and interoceptive changes at a single left contact for all 9 patients. Structural connectivity showed that the best response contacts had a pattern of connections to the bilateral ventromedial frontal cortex (via forceps minor and left uncinate fasciculus) and to the cingulate cortex (via left cingulum bundle), whereas behaviorally salient but nonbest contacts had only cingulate involvement. The involvement of the 3 white matter bundles during stimulation of the best contacts suggests a mechanism for the observed transient "depression switch."This analysis of transient behavior changes during intraoperative deep brain stimulation of the subcallosal cingulate and the subsequent identification of unique connectivity patterns may provide a biomarker of a rapid-onset depression switch to guide surgical implantation and to refine and optimize algorithms for the selection of contacts in long-term stimulation for treatment-resistant depression.

Dataset Information

Method for localizing intraoperative recordings from deep brain stimulation surgery using post-operative structural MRI.

Background

New method

Results

Conclusion

Publications

Method for localizing intraoperative recordings from deep brain stimulation surgery using post-operative structural MRI.

Similar Datasets

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