Motor Evoked Potentials in Supratentorial Glioma Surgery

Primary brain tumors, that is gliomas, are frequently located close to or within functional motor areas and motor tracts and therefore represent a major neurosurgical challenge. Preservation of the patients’ motor functions, while achieving a maximum resection of tumor, can be only achieved by monitoring and locating motor areas and motor tracts intraoperatively. The intraoperative use of motor evoked potentials (MEPs) represents the current gold standard to do so. However, intraoperative MEP monitoring and mapping can be quite challenging and require a profound knowledge of the MEP technique, brain anatomy and physiology and anesthesia. In this chapter, a systematic review of PubMed listed literature on MEP monitoring and mapping in glioma surgery is presented. The benefits, limitations, technical pearls and pitfalls are discussed from the perspective of an experienced neurosurgical/neurophysiological team

SpikeDeeptector: A deep-learning based method for detection of neural spiking activity

Objective. In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. Approach. We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. Main results. We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. Significance. The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. Clinical trial registration number. The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation

SpikeDeep-classifier: a deep-learning based fully automatic offline spike sorting algorithm

Objective. Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called "SpikeDeep-Classifier" is proposed. The values of hyperparameters remain fixed for all the evaluation data. Approach. The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning. Main Results. We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results. Significance. The results demonstrate that "SpikeDeep-Classifier" possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting

SpikeDeeptector: A deep-learning based method for detection of neural spiking activity

Objective. In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. Approach. We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. Main results. We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. Significance. The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. Clinical trial registration number. The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation

Evaluation and discussion of handmade face-masks and commercial diving-equipment as personal protection in pandemic scenarios.

ObjectivePandemic scenarios like the current Corona outbreak show the vulnerability of both globalized markets and just-in-time production processes for urgent medical equipment. Even usually cheap personal protection equipment becomes excessively expensive or is not deliverable at all. To avoid dangerous situations especially to medical professionals, but also to affected patients, 3D-printer and maker-communities have teamed up to develop and print shields, masks and adapters to help the medical personnel. In this study, we investigate three home-made respiratory masks for filter and protection efficacy and discuss the results and legal aspects.Materials and methodsA home-printed respiratory mask with a commercial filter, a scuba-diving mask with a commercial filter and a mask sewn from a vacuum cleaner bag were investigated with 99mTc-labeled NaCl-aerosol, and the respective filter-efficacy was measured under a scintigraphic camera.ResultsThe sewn mask from a vacuum cleaner bag had a filter efficacy of 69.76%, the 3D-printed mask of 39.27% and the scuba-diving mask of 85.07%.ConclusionHome-printed personal protection equipment can be a-yet less efficient-alternative against aerosol in case professional masks are not available, but legal aspects of their use and distribution have to be kept in mind in order to avoid compensation claims

Validating EEG, MEG and Combined MEG and EEG Beamforming for an Estimation of the Epileptogenic Zone in Focal Cortical Dysplasia

MEG and EEG source analysis is frequently used for the presurgical evaluation of phar-macoresistant epilepsy patients. The source localization of the epileptogenic zone depends, among other aspects, on the selected inverse and forward approaches and their respective parameter choices. In this validation study, we compare the standard dipole scanning method with two beamformer approaches for the inverse problem, and we investigate the influence of the covariance estimation method and the strength of regularization on the localization performance for EEG, MEG, and combined EEG and MEG. For forward modelling, we investigate the difference between calibrated six-compartment and standard three-compartment head modelling. In a retrospective study, two patients with focal epilepsy due to focal cortical dysplasia type IIb and seizure freedom following le-sionectomy or radiofrequency-guided thermocoagulation (RFTC) used the distance of the localization of interictal epileptic spikes to the resection cavity resp. RFTC lesion as reference for good localization. We found that beamformer localization can be sensitive to the choice of the regularization parameter, which has to be individually optimized. Estimation of the covariance matrix with averaged spike data yielded more robust results across the modalities. MEG was the dominant modality and provided a good localization in one case, while it was EEG for the other. When combining the modalities, the good results of the dominant modality were mostly not spoiled by the weaker modality. For appropriate regularization parameter choices, the beamformer localized better than the standard dipole scan. Compared to the importance of an appropriate regularization, the sensitivity of the localization to the head modelling was smaller, due to similar skull conductivity modelling and the fixed source space without orientation constraint.publishedVersionPeer reviewe

Evaluation and discussion of handmade face-masks and commercial diving-equipment as personal protection in pandemic scenarios

$\bf Objective$ Pandemic scenarios like the current Corona outbreak show the vulnerability of both globalized markets and just-in-time production processes for urgent medical equipment. Even usually cheap personal protection equipment becomes excessively expensive or is not deliverable at all. To avoid dangerous situations especially to medical professionals, but also to affected patients, 3D-printer and maker-communities have teamed up to develop and print shields, masks and adapters to help the medical personnel. In this study, we investigate three home-made respiratory masks for filter and protection efficacy and discuss the results and legal aspects. $\textbf {Materials and methods}$ A home-printed respiratory mask with a commercial filter, a scuba-diving mask with a commercial filter and a mask sewn from a vacuum cleaner bag were investigated with 99mTc-labeled NaCl-aerosol, and the respective filter-efficacy was measured under a scintigraphic camera. $\bf Results$ The sewn mask from a vacuum cleaner bag had a filter efficacy of 69.76%, the 3D-printed mask of 39.27% and the scuba-diving mask of 85.07%. $\bf Conclusion$ Home-printed personal protection equipment can be a–yet less efficient–alternative against aerosol in case professional masks are not available, but legal aspects of their use and distribution have to be kept in mind in order to avoid compensation claims

Lesion guided stereotactic radiofrequency thermocoagulation for palliative, in selected cases curative epilepsy surgery

Introduction Resective epilepsy surgery is an established treatment option in patients with pharmacoresistant, lesion related epilepsy. Yet, if the presurgical work-up proves multi-focal organization of the epileptogenic zone, or the area of intended resection is close to eloquent brain areas, patients may decide against resections because of an unfavorable risk–benefit-ratio. We assess if lesion guided cortical stereotactic radiofrequency thermocoagulation (L-RFTC) is a potential surgical alternative in these patients. Methods We performed seven procedures of L-RFTC. Three patients had monofocal epilepsy arising close to eloquent structures; in four, invasive pre-surgical workup documented monofocal seizure onset but strong interictal epileptic activity also independent and distant from the seizure onset zone. L-RFTC was restricted to the lesional area (=seizure onset site). Results 12 to 37 months after RFTC worthwhile seizure improvement was achieved in 6 patients. One patient became seizure free following complete coagulation of a focal cortical dysplasia, two had had 1–2 auras under tapered but not under continued medication. In one patient only subclinical seizures persisted. In one patient hypermotor seizures were transformed into milder short tonic seizures and another one had a seizure reduction by 50%. Only one patient did not profit at all. One patient developed a persisting neurological deficit. Significance In patients with complex epileptogenic zones L-RFTC can lead to worthwhile seizure reduction. This qualifies this procedure as a palliative surgical technique with potential good risk–benefit ratio. In patients with small focal cortical dysplasias L-RFTC may even allow minimal-invasive surgery with curative intention