Intraoperative physiology augments atlas-based data in awake deep brain stimulation.

BACKGROUND: Deep brain stimulation (DBS) is commonly performed with patients awake to perform intraoperative microelectrode recordings and/or macrostimulation testing to guide final electrode placement. Supplemental information from atlas-based databases derived from prior patient data and visualised as efficacy heat maps transformed and overlaid onto preoperative MRIs can be used to guide preoperative target planning and intraoperative final positioning. Our quantitative analysis of intraoperative testing and corresponding changes made to final electrode positioning aims to highlight the value of intraoperative neurophysiological testing paired with image-based data to optimise final electrode positioning in a large patient cohort. METHODS: Data from 451 patients with movement disorders treated with 822 individual DBS leads at a single institution from 2011 to 2021 were included. Atlas-based data was used to guide surgical targeting. Intraoperative testing data and coordinate data were retrospectively obtained from a large patient database. Medical records were reviewed to obtain active contact usage and neurologist-defined outcomes at 1 year. RESULTS: Microelectrode recording firing profiles differ per track, per target and inform the locations where macrostimulation testing is performed. Macrostimulation performance correlates with the final electrode track chosen. Centroids of atlas-based efficacy heat maps per target were close in proximity to and may predict active contact usage at 1 year. Overall, patient outcomes at 1 year were improved for patients with better macrostimulation response. CONCLUSIONS: Atlas-based imaging data is beneficial for target planning and intraoperative guidance, and in conjunction with intraoperative neurophysiological testing during awake DBS can be used to individualize and optimise final electrode positioning, resulting in favourable outcomes.

Resting-State SEEG May Help Localize Epileptogenic Brain Regions.

BACKGROUND: Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy but requires days in the hospital with interventions to trigger several seizures. OBJECTIVE: To make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings. METHODS: In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions. RESULTS: Compared to nonepileptogenic structures, we found increased functional connectivity within epileptogenic regions (P < .05) and between epileptogenic areas and other structures (P < .01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P < .01) and betweenness centrality (P < .01), and greater decay of functional connectivity with distance (P < .05, paired t-tests, corrected). Our functional connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve of 0.78 and accuracy of 80.4%. CONCLUSION: Our study represents a preliminary step towards defining resting-state SEEG functional connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings.

Towards Machine Learning Prediction of Deep Brain Stimulation (DBS) Intra-operative Efficacy Maps.

Deep brain stimulation (DBS) has the potential to improve the quality of life of people with a variety of neurological diseases. A key challenge in DBS is in the placement of a stimulation electrode in the anatomical location that maximizes efficacy and minimizes side effects. Pre-operative localization of the optimal stimulation zone can reduce surgical times and morbidity. Current methods of producing efficacy probability maps follow an anatomical guidance on magnetic resonance imaging (MRI) to identify the areas with the highest efficacy in a population. In this work, we propose to revisit this problem as a classification problem, where each voxel in the MRI is a sample informed by the surrounding anatomy. We use a patch-based convolutional neural network to classify a stimulation coordinate as having a positive reduction in symptoms during surgery. We use a cohort of 187 patients with a total of 2,869 stimulation coordinates, upon which 3D patches were extracted and associated with an efficacy score. We compare our results with a registration-based method of surgical planning. We show an improvement in the classification of intraoperative stimulation coordinates as a positive response in reduction of symptoms with AUC of 0.670 compared to a baseline registration-based approach, which achieves an AUC of 0.627 (p < 0.01). Although additional validation is needed, the proposed classification framework and deep learning method appear well-suited for improving pre-surgical planning and personalize treatment strategies.

Stereotactic EEG via multiple single-path omnidirectional trajectories within a single platform: institutional experience with a novel technique.

OBJECTIVEStereotactic electroencephalography (SEEG) is being used with increasing frequency to interrogate subcortical, cortical, and multifocal epileptic foci. The authors describe a novel technique for SEEG in patients with suspected epileptic foci refractory to medical management.METHODSIn the authors' technique, standard epilepsy evaluation and neuroimaging are used to create a hypothesis-driven SEEG plan, which informs the 3D printing of a novel single-path, multiple-trajectory, omnidirectional platform. Following skull-anchor platform fixation, electrodes are sequentially inserted according to the preoperative plan. The authors describe their surgical experience and technique based on a review of all cases, adult and pediatric, in which patients underwent invasive epilepsy monitoring via SEEG during an 18-month period at Vanderbilt University Medical Center. Platform and anatomical variables influencing localization error were evaluated using multivariate linear regression.RESULTSUsing this novel technology, 137 electrodes were inserted in 15 patients with focal epilepsy with favorable recording results and no clinical complications. The mean entry point localization error was 1.42 mm (SD 0.98 mm), and the mean target point localization error was 3.36 mm (SD 2.68 mm). Platform distance, electrode trajectory angle, and intracranial distance, but not skull thickness, were independently associated with localization error.CONCLUSIONSThe multiple-trajectory, single-path, omnidirectional platform offers satisfactory accuracy and favorable clinical results, while avoiding cumbersome frames and prohibitive up-front costs associated with other SEEG technologies.