Defining benchmark outcomes for mesial temporal lobe epilepsy surgery: A global multicenter analysis of 1119 cases.

OBJECTIVE: Benchmarking has been proposed to reflect surgical quality and represents the highest standard reference values for desirable results. We sought to determine benchmark outcomes in patients after surgery for drug-resistant mesial temporal lobe epilepsy (MTLE). METHODS: This retrospective multicenter study included patients who underwent MTLE surgery at 19 expert centers on five continents. Benchmarks were defined for 15 endpoints covering surgery and epilepsy outcome at discharge, 1 year after surgery, and the last available follow-up. Patients were risk-stratified by applying outcome-relevant comorbidities, and benchmarks were calculated for low-risk ("benchmark") cases. Respective measures were derived from the median value at each center, and the 75th percentile was considered the benchmark cutoff. RESULTS: A total of 1119 patients with a mean age (range) of 36.7 (1-74) years and a male-to-female ratio of 1:1.1 were included. Most patients (59.2%) underwent anterior temporal lobe resection with amygdalohippocampectomy. The overall rate of complications or neurological deficits was 14.4%, with no in-hospital death. After risk stratification, 377 (33.7%) benchmark cases of 1119 patients were identified, representing 13.6%-72.9% of cases per center and leaving 742 patients in the high-risk cohort. Benchmark cutoffs for any complication, clinically apparent stroke, and reoperation rate at discharge were ≤24.6%, ≤.5%, and ≤3.9%, respectively. A favorable seizure outcome (defined as International League Against Epilepsy class I and II) was reached in 83.6% at 1 year and 79.0% at the last follow-up in benchmark cases, leading to benchmark cutoffs of ≥75.2% (1-year follow-up) and ≥69.5% (mean follow-up of 39.0 months). SIGNIFICANCE: This study presents internationally applicable benchmark outcomes for the efficacy and safety of MTLE surgery. It may allow for comparison between centers, patient registries, and novel surgical and interventional techniques.

Educational stereoscopic representation of a step-by-step brain white fiber dissection according to Klingler's method.

BACKGROUND: Understanding and teaching the three-dimensional architecture of the brain remains difficult because of the intricate arrangement of grey nuclei within white matter tracts. Although cortical area functions have been well studied, educational and three-dimensional descriptions of the organization of deep nuclei and white matter tracts are still missing. OBJECTIVE: We propose herein a detailed step-by-step dissection of the lateral aspect of a left hemisphere using the Klingler method and provide high-quality stereoscopic views with the aim to help teach medical students or surgeons the three-dimensional anatomy of the brain. METHODS: Three left hemispheres were extracted and prepared. Then, according to the Klingler method, dissections were carried out from the lateral aspect. Photographs were taken at each step and were modified to provide stereoscopic three-dimensional views. RESULTS: Gray and white structures were described: cortex, claustrum, putamen, pallidum, caudate nucleus, amygdala; U-fibers, external and internal capsules, superior longitudinal fasciculus, frontal aslant fasciculus, uncinate fasciculus, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, corticospinal fasciculus, corona radiata, anterior commissure, and optic radiations. CONCLUSION: This educational stereoscopic presentation of an expert dissection of brain white fibers and basal ganglia would be of value for theoretical or hands-on teaching of brain anatomy; labeling and stereoscopy could, moreover, improve the teaching, understanding, and memorizing of brain anatomy. In addition, this could be also used for the creation of a mental map by neurosurgeons for the preoperative planning of brain tumor surgery.

Voxel-based diktiometry: Combining convolutional neural networks with voxel-based analysis and its application in diffusion tensor imaging for Parkinson's disease.

Extracting population-wise information from medical images, specifically in the neurological domain, is crucial to better understanding disease processes and progression. This is frequently done in a whole-brain voxel-wise manner, in which a population of patients and healthy controls are registered to a common co-ordinate space and a statistical test is performed on the distribution of image intensities for each location. Although this method has yielded a number of scientific insights, it is further from clinical applicability as the differences are often small and altogether do not permit for a high-performing classifier. In this article, we take the opposite approach of using a high-performing classifier, specifically a traditional convolutional neural network, and then extracting insights from it which can be applied in a population-wise manner, a method we call voxel-based diktiometry. We have applied this method to diffusion tensor imaging (DTI) analysis for Parkinson's disease (PD), using the Parkinson's Progression Markers Initiative database. By using the network sensitivity information, we can decompose what elements of the DTI contribute the most to the network's performance, drawing conclusions about diffusion biomarkers for PD that are based on metrics which are not readily expressed in the voxel-wise approach.

Subthalamic nucleus oscillations during vocal emotion processing are dependent of the motor asymmetry of Parkinson's disease.

The subthalamic nucleus (STN) is involved in different aspects of emotional processes and more specifically in emotional prosody recognition. Recent studies on the behavioral effects of deep brain stimulation (DBS) in patients with Parkinson's disease (PD) have uncovered an asymmetry in vocal emotion decoding in PD, with left-onset PD patients showing deficits for the processing of happy voices. Whether and how PD asymmetry affects STN electrophysiological responses to emotional prosody, however, remains unknown. In the current study, local field potential activity was recorded from eight left- and six right-lateralized motor-onset PD patients (LOPD/ROPD) undergoing DBS electrodes implantation, while they listened to angry, happy and neutral voices. Time-frequency decomposition revealed that theta (2-6 Hz), alpha (6-12 Hz) and gamma (60-150 Hz) band responses to emotion were mostly bilateral with a differential pattern of response according to patient's sides-of onset. Conversely, beta-band (12-20 Hz and 20-30 Hz) emotional responses were mostly lateralized in the left STN for both patient groups. Furthermore, STN theta, alpha and gamma band responses to happiness were either absent (theta band) or reduced (alpha and gamma band) in the most affected STN hemisphere (contralateral to the side-of onset), while a late low-beta band left STN happiness-specific response was present in ROPD patients and did not occur in LOPD patients. Altogether, in this study, we demonstrate a complex pattern of oscillatory activity in the human STN in response to emotional voices and reveal a crucial influence of disease laterality on STN low-frequency oscillatory activity.

Subthalamic nucleus local field potentials recordings reveal subtle effects of promised reward during conflict resolution in Parkinson's disease.

Cognitive action control depends on cortical-subcortical circuits, involving notably the subthalamic nucleus (STN), as evidenced by local field potentials recordings (LFPs) studies. The STN consistently shows an increase in theta oscillations power during conflict resolution. Some studies have shown that cognitive action control in Parkinson's disease (PD) could be influenced by the occurrence of monetary reward. In this study, we investigated whether incentive motivation could modulate STN activity, and notably STN theta activity, during response conflict resolution. To achieve this objective, we recorded STN LFPs during a motivated Simon task in PD patients who had undergone deep brain stimulation surgery. Behavioral results revealed that promised rewards increased the difficulty in resolving conflict situations, thus replicating previous findings. Signal analyses locked on the imperative stimulus onset revealed the typical pattern of increased theta power in a conflict situation. However, this conflict-related modulation of theta power was not influenced by the size of the reward cued. We nonetheless identified a significant effect of the reward size on local functional organization (indexed by inter-trial phase clustering) of theta oscillations, with higher organization associated with high rewards while resolving conflict. When focusing on the period following the onset of the reward cue, we unveiled a stronger beta power decrease in higher reward conditions. However, these LFPs results were not correlated to behavioral results. Our study suggests that the STN is involved in how reward information can influence computations during conflict resolution. However, considering recent studies as well as the present results, we suspect that these effects are subtle.

Data-Driven Prediction of the Therapeutic Window during Subthalamic Deep Brain Stimulation Surgery.

BACKGROUND: Moving from awake surgery under local anesthesia to asleep surgery under general anesthesia will require to precisely predict the outcome of deep brain stimulation. OBJECTIVE: To propose a data-driven prediction of both the therapeutic effect and side effects of the surgery. METHODS: The retrospective intraoperative data from 30 patients operated on in the subthalamic nucleus were used to train an artificial neural network to predict the deep brain stimulation outcome. A leave-one-out validation was undertaken to give a predictive performance that would reflect the performance of the predictive model in clinical practice. Three-dimensional coordinates and the amount of current of the electrodes were used to train the model. RESULTS: 130 electrode positions were reviewed. The areas under the curve were 0.902 and 0.89 for therapeutic and side effects, respectively. The mean sensitivity and specificity were 93.07% (SD 0.95) and 69.24% (SD 5.27) for the therapeutic effect, 73.47% (SD 10.55) and 91.82% (SD 0.12) for the side effect. CONCLUSION: Data-driven prediction could be an additional modality to predict deep brain stimulation outcome. Further validation is needed to precisely use this method for performing surgery under general anesthesia.

Intradiploic growing skull fracture: review of mechanisms and literature.

PURPOSE: A rare case of intradiploic growing fracture of the occipital bone is presented. Examination of this case and literature review was carried out to find significant specific features of such a rare condition. CASE: A 4-year-old boy was operated on for a huge occipital intradiploic growing skull fracture. His medical history, surgical management, and postoperative course are presented. LITERATURE REVIEW: A computer literature search using the Medline database revealed 31 well-documented cases. CONCLUSIONS: Special attention must be paid to skull fractures in childhood when occurring near CSF large spaces, especially if ventricle enlargement, suggesting that hydrocephalus is present. Surgeons should evaluate hydrocephalus before treating such lesions in order to improve surgical result and postoperative clinical evolution.

Influence of cumulative surgical experience on the outcome of poor-grade patients with ruptured intracranial aneurysm.

BACKGROUND: The expansion of endovascular techniques for intracranial aneurysms has led to a global decrease in vascular neurosurgery activity. This situation might impact neurosurgeons' level of expertise, even though they all might have to deal with this surgically challenging pathology. In that context, we wanted to assess the impact of cumulative surgical experience on the outcome of patients with poor-grade subarachnoid haemorrhage (SAH) and intracerebral haemorrhage (ICH) treated by microsurgery. METHODS: Sixty-seven patients who underwent surgery for a ruptured aneurysm with SAH and ICH, and a WFNS scale of IV/V, were included. Surgeries were performed by five surgeons, whose experience was judged by the total number of aneurysm surgeries performed. The outcome was assessed by three indicators: intraoperative rupture (IOR), early mortality, and the modified Rankin Scale at last follow-up. The time of IOR was reported on an IOR score. The correlation between surgical experience and outcome was assessed by linear regression. Nonlinear regression was used to assess the correlation of the data with a learning curve model. RESULTS: The analysis showed an influence of surgical experience on intraoperative rupture, with no effect on long-term outcome. No influence was found on early mortality. Increased surgical experience seems to reduce IOR during aneurysm dissection and clip repositioning. Intraoperative rupture data fit Wright's learning curve model. CONCLUSION: This study suggests a direct impact of cumulative experience on the course of ruptured aneurysm surgery and pleads for the use of training and simulation programmes dedicated to neurovascular surgery.

Investigation of morphometric variability of subthalamic nucleus, red nucleus, and substantia nigra in advanced Parkinson's disease patients using automatic segmentation and PCA-based analysis.

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an effective surgical therapy to treat Parkinson's disease (PD). Conventional methods employ standard atlas coordinates to target the STN, which, along with the adjacent red nucleus (RN) and substantia nigra (SN), are not well visualized on conventional T1w MRIs. However, the positions and sizes of the nuclei may be more variable than the standard atlas, thus making the pre-surgical plans inaccurate. We investigated the morphometric variability of the STN, RN and SN by using label-fusion segmentation results from 3T high resolution T2w MRIs of 33 advanced PD patients. In addition to comparing the size and position measurements of the cohort to the Talairach atlas, principal component analysis (PCA) was performed to acquire more intuitive and detailed perspectives of the measured variability. Lastly, the potential correlation between the variability shown by PCA results and the clinical scores was explored.

Weight gain following subthalamic nucleus deep brain stimulation: a PET study.

Several hypotheses have been put forward to explain weight gain after deep brain stimulation (DBS), but none provides a fully satisfactory account of this adverse effect. We analyzed the correlation between changes in brain metabolism (using positron emission tomography [PET] imaging) and weight gain after bilateral subthalamic nucleus DBS in patients with Parkinson's disease. Body mass index was calculated and brain activity prospectively measured using 2-deoxy-2[18F]fluoro-D-glucose 3 months before and 4 months after the start of subthalamic nucleus deep brain stimulation in 23 patients with Parkinson's disease. Motor complications (United Parkinson's Disease Rating Scale [UPDRS]-IV scores) and dopaminergic medication were included in the analysis to control for their possible influence on brain metabolism. Mean ± standard deviation (SD) body mass index increased significantly by 0.8 ± 1.5 kg/m(2) (P = 0.03). Correlations were found between weight gain and changes in brain metabolism in limbic and associative areas, including the orbitofrontal cortex (Brodmann areas [BAs] 10 and 11), lateral and medial parts of the temporal lobe (BAs 20, 21, 22,39 and 42), anterior cingulate cortex (BA 32), and retrosplenial cortex (BA 30). However, we found no correlation between weight gain and metabolic changes in sensorimotor areas. These findings suggest that changes in associative and limbic processes contribute to weight gain after subthalamic nucleus DBS in Parkinson's disease.

High-frequency oscillations, extent of surgical resection, and surgical outcome in drug-resistant focal epilepsy.

PURPOSE: Removal of areas generating high-frequency oscillations (HFOs) recorded from the intracerebral electroencephalography (iEEG) of patients with medically intractable epilepsy has been found to be correlated with improved surgical outcome. However, whether differences exist according to the type of epilepsy is largely unknown. We performed a comparative assessment of the impact of removing HFO-generating tissue on surgical outcome between temporal lobe epilepsy (TLE) and extratemporal lobe epilepsy (ETLE). We also assessed the relationship between the extent of surgical resection and surgical outcome. METHODS: We studied 30 patients with drug-resistant focal epilepsy, 21 with TLE and 9 with ETLE. Two thirds of the patients were included in a previous report and for these, clinical and imaging data were updated and follow-up was extended. All patients underwent iEEG investigations (500 Hz high-pass filter and 2,000 Hz sampling rate), surgical resection, and postoperative magnetic resonance imaging (MRI). HFOs (ripples, 80-250 Hz; fast ripples, >250 Hz) were identified visually on a 5-10 min interictal iEEG sample. HFO rates inside versus outside the seizure-onset zone (SOZ), in resected versus nonresected tissue, and their association with surgical outcome (ILAE classification) were assessed in the entire cohort, and in the TLE and ETLE subgroups. We also tested the correlation of resected brain hippocampal and amygdala volumes (as measured on postoperative MRIs) with surgical outcome. KEY FINDINGS: HFO rates were significantly higher inside the SOZ than outside in the entire cohort and TLE subgroup, but not in the ETLE subgroup. In all groups, HFO rates did not differ significantly between resected and nonresected tissue. Surgical outcome was better when higher HFO rates were included in the surgical resection in the entire cohort and TLE subgroup, but not in the ETLE subgroup. Resected brain hippocampal and amygdala volumes were not correlated with surgical outcome. SIGNIFICANCE: In TLE, removal of HFO-generating areas may lead to improved surgical outcome. Less consistent findings emerge from ETLE, but these may be related to sample size limitations of this study. Size of resection, a factor that was ignored and that could have affected results of earlier studies did not influence results.

Motor Symptom Asymmetry Predicts Cognitive and Neuropsychiatric Profile Following Deep Brain Stimulation of the Subthalamic Nucleus in Parkinson's Disease: a 5-Year Longitudinal Study.

INTRODUCTION: Risk factors (e.g., motor symptom asymmetry) for short- and long-term cognitive and neuropsychiatric symptoms following deep brain stimulation (DBS) of the subthalamic nucleus (STN) in patients with Parkinson's disease have yet to be fully identified. The objectives of the present study were to determine whether motor symptom asymmetry in Parkinson's disease is one such risk factor and to identify predictors of subnormal cognitive decline. METHODS: A total of 26 patients receiving STN-DBS (13 with left-sided motor symptoms and 13 with right-sided ones) underwent follow-up neuropsychological, depression and apathy assessments over a 5-year period. Nonparametric intergroup comparisons were performed on raw scores, as well as Cox regression analyses on standardized Mattis Dementia Rating Scale scores. RESULTS: Compared with patients who had predominantly left-sided symptoms, right-sided patients scored higher on both apathy (at 3 months and 36 months) and depressive symptoms (at 6 months and 12 months) and scored lower on global cognitive efficiency (at 36 months and 60 months). Survival analyses revealed that only right-sided patients had subnormal standardized dementia scores, which were negatively associated with the number of perseverations in the Wisconsin Card Scoring Test. CONCLUSION: Right-sided motor symptoms are a risk factor for more severe short- and long-term cognitive and neuropsychiatric symptoms following STN-DBS, confirming literature findings on left hemispheric vulnerability.

Online database of clinical MR and ultrasound images of brain tumors.

PURPOSE: One of the important challenges in the field of medical imaging is finding real clinical images with which to validate new image processing algorithms. This is particularly true for tracked 3D ultrasound images of the brain. METHODS: In 2010, pre- and postoperative magnetic resonance and intraoperative ultrasound images were acquired from brain tumor patients involved in the authors' imaging study at the Montreal Neurological Institute. RESULTS: These data are available online at the Montreal Neurological Institute's Brain Images of Tumors for Evaluation database, termed here the MNI BITE database. It contains ultrasound and magnetic resonance images from 14 patients. Each patient underwent a preoperative and a postoperative T1-weighted magnetic resonance scan with gadolinium enhancement, and multiple intraoperative B-mode images were acquired before and after resection. Corresponding features were manually selected in some image pairs for validation. All images are in MINC format, the file format used at the authors' institute for image processing. The MINC tools are available for free download at packages.bic.mni.mcgill.ca. CONCLUSIONS: This is the first online database of its kind. These images can be used by image processing scientists as well as clinicians wishing to compare findings from magnetic resonance and ultrasound imaging.

Motor symptom asymmetry predicts non-motor outcome and quality of life following STN DBS in Parkinson's disease.

Risk factors for long-term non-motor symptoms and quality of life following subthalamic nucleus deep brain stimulation (STN DBS) have not yet been fully identified. In the present study, we investigated the impact of motor symptom asymmetry in Parkinson's disease. Data were extracted for 52 patients with Parkinson's disease (half with predominantly left-sided motor symptoms and half with predominantly right-sided ones) who underwent bilateral STN and a matched healthy control group. Performances for cognitive tests, apathy and depression symptoms, as well as quality-of-life questionnaires at 12 months post-DBS were compared with a pre-DBS baseline. Results indicated a deterioration in cognitive performance post-DBS in patients with predominantly left-sided motor symptoms. Performances of patients with predominantly right-sided motor symptoms were maintained, except for a verbal executive task. These differential effects had an impact on patients' quality of life. The results highlight the existence of two distinct cognitive profiles of Parkinson's disease, depending on motor symptom asymmetry. This asymmetry is a potential risk factor for non-motor adverse effects following STN DBS.

Deep brain stimulation hardware-related infections: a report of 12 cases and review of the literature.

In this monocentric study, the median delay between deep brain stimulation implantation and infection was 28 days (range, 8-820). Infections limited to generator (n = 4) required partial hardware removal, whereas infections involving frontal or retroauricular sites (n = 7) required total removal. Surgical samples yielded Staphylococcus aureus (n = 6), Staphylococcus epidermidis (n = 2), Propionibacterium acnes, and Micrococcus species.

Changes preceding interictal epileptic EEG abnormalities: comparison between EEG/fMRI and intracerebral EEG.

PURPOSE: In simultaneous scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), blood oxygen level dependent (BOLD) changes occurring before the spike have been sometimes described but could not be explained. To characterize the origin of this prespike BOLD signal change, we looked for electrographic changes in stereo-EEG (SEEG) possibly preceding the scalp spike in patients that showed early BOLD response in EEG/fMRI. METHODS: We studied four patients with drug-resistant focal epilepsy who underwent EEG/fMRI, showed a prespike BOLD response, and were then studied with depth electrodes for presurgical localization of the epileptic generator. Early BOLD responses in the region of the spike field were analyzed using models with hemodynamic response functions (HRFs) peaking from -9 to +9 s around the spike. SEEG recordings in the period and location corresponding to the early HRF responses were analyzed to detect if electrographic changes were present in the SEEG before the scalp abnormality. KEY FINDINGS: One of the four patients presented a SEEG interictal discharge in the period corresponding to the early BOLD response. In the other three, no electrographic changes were detected in the SEEG in the period corresponding to early BOLD changes. SIGNIFICANCE: Although the early BOLD activity may sometimes be explained by a synchronized neural discharge detectable with SEEG but not visible on the scalp EEG, in most cases the early BOLD response reflects a metabolic phenomenon that does not appear to result from a synchronized neuronal discharge. Prespike metabolic responses can result from synchronized or nonsynchronized neuronal activity, or from nonneuronal mechanisms including glia.

Data imputation and compression for Parkinson's disease clinical questionnaires.

Medical questionnaires are a valuable source of information but are often difficult to analyse due to both their size and the high possibility of them having missing values. This is a problematic issue in biomedical data science as it may complicate how individual questionnaire data is represented for statistical or machine learning analysis. In this paper, we propose a deeply-learnt residual autoencoder to simultaneously perform non-linear data imputation and dimensionality reduction. We present an extensive analysis of the dynamics of the performance of this autoencoder regarding the compression rate and the proportion of missing values. This method is evaluated on motor and non-motor clinical questionnaires of the Parkinson's Progression Markers Initiative (PPMI) database and consistently outperforms linear coupled imputation and reduction approaches.

PassFlow: a multimodal workflow for predicting deep brain stimulation outcomes.

PURPOSE: Deep Brain Stimulation (DBS) is a proven therapy for Parkinson's Disease (PD), frequently resulting in an enhancement of motor function. Nonetheless, several undesirable side effects can occur after DBS, which can worsen the quality of life of the patient. Thus, the clinical team has to carefully select patients on whom to perform DBS. Over the past decade, there have been some attempts to relate pre-operative data and DBS clinical outcomes, with most focused on the motor symptomatology. In this paper, we propose a machine learning-based method able to predict a large number of DBS clinical outcomes for PD. METHODS: We propose a multimodal pipeline, referred to as PassFlow, which predicts 84 clinical post-operative clinical scores. PassFlow is composed of an artificial neural network to compress clinical information, an image processing method from the state-of-the-art to extract morphological biomarkers our of T1 imaging, and an SVM to perform the regressions. We validated PassFlow on 196 PD patients who undergone a DBS. RESULTS: PassFlow showed correlation coefficients as high as 0.71 and were able to significantly predict 63 out of the 84 scores, outperforming a comparative linear method. The number of metrics that are predicted with this pre-operative information was also found to be correlated with the number of patients with this information available, indicating that the PassFlow method is still actively learning. CONCLUSION: We presented a novel, machine learning-based pipeline to predict a variety of post-operative clinical outcomes of DBS for PD patients. PassFlow took into account various bio-markers, arising from different data modalities, showing high correlation coefficients for some scores from pre-operative data only. It indicates that many clinical outcomes of DBS can be predicted agnostic to the specific simulation parameters, as PassFlow has been validated without such stimulation-related information.

Adapting the listening time for micro-electrode recordings in deep brain stimulation interventions.

PURPOSE: Deep brain stimulation (DBS) is a common treatment for a variety of neurological disorders which involves the precise placement of electrodes at particular subcortical locations such as the subthalamic nucleus. This placement is often guided by auditory analysis of micro-electrode recordings (MERs) which informs the clinical team as to the anatomic region in which the electrode is currently positioned. Recent automation attempts have lacked flexibility in terms of the amount of signal recorded, not allowing them to collect more signal when higher certainty is needed or less when the anatomy is unambiguous. METHODS: We have addressed this problem by evaluating a simple algorithm that allows for MER signal collection to terminate once the underlying model has sufficient confidence. We have parameterized this approach and explored its performance using three underlying models composed of one neural network and two Bayesian extensions of said network. RESULTS: We have shown that one particular configuration, a Bayesian model of the underlying network's certainty, outperforms the others and is relatively insensitive to parameterization. Further investigation shows that this model also allows for signals to be classified earlier without increasing the error rate. CONCLUSION: We have presented a simple algorithm that records the confidence of an underlying neural network, thus allowing for MER data collection to be terminated early when sufficient confidence is reached. This has the potential to improve the efficiency of DBS electrode implantation by reducing the time required to identify anatomical structures using MERs.