Publications by authors named "Pieter L Kubben"

45 Publications

Variability in subthalamic nucleus targeting for deep brain stimulation with 3 and 7 Tesla magnetic resonance imaging.

Neuroimage Clin 2021 Sep 16;32:102829. Epub 2021 Sep 16.

Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective surgical treatment for Parkinson's disease (PD). Side-effects may, however, be induced when the DBS lead is placed suboptimally. Currently, lower field magnetic resonance imaging (MRI) at 1.5 or 3 Tesla (T) is used for targeting. Ultra-high-field MRI (7 T and above) can obtain superior anatomical information and might therefore be better suited for targeting. This study aims to test whether optimized 7 T imaging protocols result in less variable targeting of the STN for DBS compared to clinically utilized 3 T images. Three DBS-experienced neurosurgeons determined the optimal STN DBS target site on three repetitions of 3 T-T2, 7 T-T2*, 7 T-R2* and 7 T-QSM images for five PD patients. The distance in millimetres between the three repetitive coordinates was used as an index of targeting variability and was compared between field strength, MRI contrast and repetition with a Bayesian ANOVA. Further, the target coordinates were registered to MNI space, and anatomical coordinates were compared between field strength, MRI contrast and repetition using a Bayesian ANOVA. The results indicate that the neurosurgeons are stable in selecting the DBS target site across MRI field strength, MRI contrast and repetitions. The analysis of the coordinates in MNI space however revealed that the actual selected location of the electrode is seemingly more ventral when using the 3 T scan compared to the 7 T scans.
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http://dx.doi.org/10.1016/j.nicl.2021.102829DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463907PMC
September 2021

Real-time synthesis of imagined speech processes from minimally invasive recordings of neural activity.

Commun Biol 2021 09 23;4(1):1055. Epub 2021 Sep 23.

Department of Neurosurgery, School of Mental Health and Neurosciences, Maastricht University, Maastricht, The Netherlands.

Speech neuroprosthetics aim to provide a natural communication channel to individuals who are unable to speak due to physical or neurological impairments. Real-time synthesis of acoustic speech directly from measured neural activity could enable natural conversations and notably improve quality of life, particularly for individuals who have severely limited means of communication. Recent advances in decoding approaches have led to high quality reconstructions of acoustic speech from invasively measured neural activity. However, most prior research utilizes data collected during open-loop experiments of articulated speech, which might not directly translate to imagined speech processes. Here, we present an approach that synthesizes audible speech in real-time for both imagined and whispered speech conditions. Using a participant implanted with stereotactic depth electrodes, we were able to reliably generate audible speech in real-time. The decoding models rely predominately on frontal activity suggesting that speech processes have similar representations when vocalized, whispered, or imagined. While reconstructed audio is not yet intelligible, our real-time synthesis approach represents an essential step towards investigating how patients will learn to operate a closed-loop speech neuroprosthesis based on imagined speech.
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http://dx.doi.org/10.1038/s42003-021-02578-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460739PMC
September 2021

[Clinical course of COVID-19 in the Netherlands: an overview of 2607 patients in hospital during the first wave].

Ned Tijdschr Geneeskd 2021 01 11;165. Epub 2021 Jan 11.

Maastricht UMC.

Objective: To systematically collect clinical data from patients with a proven COVID-19 infection in the Netherlands.

Design: Data from 2579 patients with COVID-19 admitted to 10 Dutch centers in the period February to July 2020 are described. The clinical data are based on the WHO COVID case record form (CRF) and supplemented with patient characteristics of which recently an association disease severity has been reported.

Methods: Survival analyses were performed as primary statistical analysis. These Kaplan-Meier curves for time to (early) death (3 weeks) have been determined for pre-morbid patient characteristics and clinical, radiological and laboratory data at hospital admission.

Results: Total in-hospital mortality after 3 weeks was 22.2% (95% CI: 20.7% - 23.9%), hospital mortality within 21 days was significantly higher for elderly patients (> 70 years; 35, 0% (95% CI: 32.4% - 37.8%) and patients who died during the 21 days and were admitted to the intensive care (36.5% (95% CI: 32.1% - 41.3%)). Apart from that, in this Dutch population we also see a risk of early death in patients with co-morbidities (such as chronic neurological, nephrological and cardiac disorders and hypertension), and in patients with more home medication and / or with increased urea and creatinine levels.

Conclusion: Early death due to a COVID-19 infection in the Netherlands appears to be associated with demographic variables (e.g. age), comorbidity (e.g. cardiovascular disease) but also disease char-acteristics at admission.
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January 2021

Machine learning prediction of motor response after deep brain stimulation in Parkinson's disease-proof of principle in a retrospective cohort.

PeerJ 2020 18;8:e10317. Epub 2020 Nov 18.

Department of Neurosurgery, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands.

Introduction: Despite careful patient selection for subthalamic nucleus deep brain stimulation (STN DBS), some Parkinson's disease patients show limited improvement of motor disability. Innovative predictive analysing methods hold potential to develop a tool for clinicians that reliably predicts individual postoperative motor response, by only regarding clinical preoperative variables. The main aim of preoperative prediction would be to improve preoperative patient counselling, expectation management, and postoperative patient satisfaction.

Methods: We developed a machine learning logistic regression prediction model which generates probabilities for experiencing weak motor response one year after surgery. The model analyses preoperative variables and is trained on 89 patients using a five-fold cross-validation. Imaging and neurophysiology data are left out intentionally to ensure usability in the preoperative clinical practice. Weak responders ( = 30) were defined as patients who fail to show clinically relevant improvement on Unified Parkinson Disease Rating Scale II, III or IV.

Results: The model predicts weak responders with an average area under the curve of the receiver operating characteristic of 0.79 (standard deviation: 0.08), a true positive rate of 0.80 and a false positive rate of 0.24, and a diagnostic accuracy of 78%. The reported influences of individual preoperative variables are useful for clinical interpretation of the model, but cannot been interpreted separately regardless of the other variables in the model.

Conclusion: The model's diagnostic accuracy confirms the utility of machine learning based motor response prediction based on clinical preoperative variables. After reproduction and validation in a larger and prospective cohort, this prediction model holds potential to support clinicians during preoperative patient counseling.
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http://dx.doi.org/10.7717/peerj.10317DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7680051PMC
November 2020

Understanding Medical Students' Attitudes Toward Learning eHealth: Questionnaire Study.

JMIR Med Educ 2020 Oct 1;6(2):e17030. Epub 2020 Oct 1.

Department of Neurosurgery, Maastricht University Medical Center, Maastricht, Netherlands.

Background: Several publications on research into eHealth demonstrate promising results. Prior researchers indicated that the current generation of doctors is not trained to take advantage of eHealth in clinical practice. Therefore, training and education for everyone using eHealth are key factors to its successful implementation. We set out to review whether medical students feel prepared to take advantage of eHealth innovations in medicine.

Objective: Our objective was to evaluate whether medical students desire a dedicated eHealth curriculum during their medical studies.

Methods: A questionnaire assessing current education, the need for education about eHealth topics, and the didactical forms for teaching these topics was developed. Questionnaire items were scored on a scale from 1 (fully disagree with a topic) to 10 (fully agree with a topic). This questionnaire was distributed among 1468 medical students of Maastricht University in the Netherlands. R version 3.5.0 (The R Foundation) was used for all statistical procedures.

Results: A total of 303 students out of 1468, representing a response rate of 20.64%, replied to our questionnaire. The aggregate statement "I feel prepared to take advantage of the technological developments within the medical field" was scored at a mean value of 4.8 out of 10. Mean scores regarding the need for education about eHealth topics ranged from 6.4 to 7.3. Medical students did not favor creating their own health apps or mobile apps; the mean score was 4.9 for this topic. The most popular didactical option, with a mean score 7.2, was to remotely follow a real-life patient under the supervision of a doctor.

Conclusions: To the best of our knowledge, this is the largest evaluation of students' opinions on eHealth training in a medical undergraduate curriculum. We found that medical students have positives attitudes toward incorporating eHealth into the medical curriculum.
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http://dx.doi.org/10.2196/17030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7563623PMC
October 2020

Serial measurements in COVID-19-induced acute respiratory disease to unravel heterogeneity of the disease course: design of the Maastricht Intensive Care COVID cohort (MaastrICCht).

BMJ Open 2020 09 29;10(9):e040175. Epub 2020 Sep 29.

Department of Intensive Care, Maastricht University Medical Center+, Maastricht, The Netherlands.

Introduction: The course of the disease in SARS-CoV-2 infection in mechanically ventilated patients is unknown. To unravel the clinical heterogeneity of the SARS-CoV-2 infection in these patients, we designed the prospective observational Maastricht Intensive Care COVID cohort (MaastrICCht). We incorporated serial measurements that harbour aetiological, diagnostic and predictive information. The study aims to investigate the heterogeneity of the natural course of critically ill patients with a SARS-CoV-2 infection.

Methods And Analysis: Mechanically ventilated patients admitted to the intensive care with a SARS-CoV-2 infection will be included. We will collect clinical variables, vital parameters, laboratory variables, mechanical ventilator settings, chest electrical impedance tomography, ECGs, echocardiography as well as other imaging modalities to assess heterogeneity of the course of a SARS-CoV-2 infection in critically ill patients. The MaastrICCht is also designed to foster various other studies and registries and intends to create an open-source database for investigators. Therefore, a major part of the data collection is aligned with an existing national intensive care data registry and two international COVID-19 data collection initiatives. Additionally, we create a flexible design, so that additional measures can be added during the ongoing study based on new knowledge obtained from the rapidly growing body of evidence. The spread of the COVID-19 pandemic requires the swift implementation of observational research to unravel heterogeneity of the natural course of the disease of SARS-CoV-2 infection in mechanically ventilated patients. Our study design is expected to enhance aetiological, diagnostic and prognostic understanding of the disease. This paper describes the design of the MaastrICCht.

Ethics And Dissemination: Ethical approval has been obtained from the medical ethics committee (Medisch Ethische Toetsingscommissie 2020-1565/3 00 523) of the Maastricht University Medical Centre+ (Maastricht UMC+), which will be performed based on the Declaration of Helsinki. During the pandemic, the board of directors of Maastricht UMC+ adopted a policy to inform patients and ask their consent to use the collected data and to store serum samples for COVID-19 research purposes. All study documentation will be stored securely for fifteen years after recruitment of the last patient. The results will be published in peer-reviewed academic journals, with a preference for open access journals, while particularly considering deposition of the manuscripts on a preprint server early.

Trial Registration Number: The Netherlands Trial Register (NL8613).
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http://dx.doi.org/10.1136/bmjopen-2020-040175DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526030PMC
September 2020

Cross-database evaluation of EEG based epileptic seizures detection driven by adaptive median feature baseline correction.

Clin Neurophysiol 2020 07 23;131(7):1567-1578. Epub 2020 Apr 23.

Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands.

Objective: In long-term electroencephalogram (EEG) signals, automated classification of epileptic seizures is desirable in diagnosing epilepsy patients, as it otherwise depends on visual inspection. To the best of the author's knowledge, existing studies have validated their algorithms using cross-validation on the same database and less number of attempts have been made to extend their work on other databases to test the generalization capability of the developed algorithms. In this study, we present the algorithm for cross-database evaluation for classification of epileptic seizures using five EEG databases collected from different centers. The cross-database framework helps when sufficient epileptic seizures EEG data are not available to build automated seizure detection model.

Methods: Two features, namely successive decomposition index and matrix determinant were extracted at a segmentation length of 4 s (50% overlap). Then, adaptive median feature baseline correction (AM-FBC) was applied to overcome the inter-patient and inter-database variation in the feature distribution. The classification was performed using a support vector machine classifier with leave-one-database-out cross-validation. Different classification scenarios were considered using AM-FBC, smoothing of the train and test data, and post-processing of the classifier output.

Results: Simulation results revealed the highest area under the curve-sensitivity-specificity-false detections (per hour) of 1-1-1-0.15, 0.89-0.99-0.82-2.5, 0.99-0.73-1-1, 0.95-0.97-0.85-1.7, 0.99-0.99-0.92-1.1 using the Ramaiah Medical College and Hospitals, Children's Hospital Boston-Massachusetts Institute of Technology, Temple University Hospital, Maastricht University Medical Centre, and University of Bonn databases respectively.

Conclusions: We observe that the AM-FBC plays a significant role in improving seizure detection results by overcoming inter-database variation of feature distribution.

Significance: To the best of the author's knowledge, this is the first study reporting on the cross-database evaluation of classification of epileptic seizures and proven to be better generalization capability when evaluated using five databases and can contribute to accurate and robust detection of epileptic seizures in real-time.
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http://dx.doi.org/10.1016/j.clinph.2020.03.033DOI Listing
July 2020

EEG based multi-class seizure type classification using convolutional neural network and transfer learning.

Neural Netw 2020 Apr 25;124:202-212. Epub 2020 Jan 25.

Department of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands.

Recognition of epileptic seizure type is essential for the neurosurgeon to understand the cortical connectivity of the brain. Though automated early recognition of seizures from normal electroencephalogram (EEG) was existing, no attempts have been made towards the classification of variants of seizures. Therefore, this study attempts to classify seven variants of seizures with non-seizure EEG through the application of convolutional neural networks (CNN) and transfer learning by making use of the Temple University Hospital EEG corpus. The objective of our study is to perform a multi-class classification of epileptic seizure type, which includes simple partial, complex partial, focal non-specific, generalized non-specific, absence, tonic, and tonic-clonic, and non-seizures. The 19 channels EEG time series was converted into a spectrogram stack before feeding as input to CNN. The following two different modalities were proposed using CNN: (1) Transfer learning using pretrained network, (2) Extract image features using pretrained network and classify using the support vector machine classifier. The following ten pretrained networks were used to identify the optimal network for the proposed study: Alexnet, Vgg16, Vgg19, Squeezenet, Googlenet, Inceptionv3, Densenet201, Resnet18, Resnet50, and Resnet101. The highest classification accuracy of 82.85% (using Googlenet) and 88.30% (using Inceptionv3) was achieved using transfer learning and extract image features approach respectively. Comparison results showed that CNN based approach outperformed conventional feature and clustering based approaches. It can be concluded that the EEG based classification of seizure type using CNN model could be used in pre-surgical evaluation for treating patients with epilepsy.
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http://dx.doi.org/10.1016/j.neunet.2020.01.017DOI Listing
April 2020

A convolutional neural network based framework for classification of seizure types.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:2547-2550

Epileptic seizures are caused by a disturbance in the electrical activity of the brain and classified as many different types of epileptic seizures based on the characteristics of EEG and other parameters. Till now research has been conducted to classify EEG as seizure and non-seizures, but the classification of seizure types has not been explored. Thus, in this paper, we have proposed the 8-class classification problem in order to classify different seizure types using convolutional neural networks (CNN). This research study suggests a CNN based framework for classification of epileptic seizure types that include simple partial, complex partial, focal non-specific, generalized non-specific, absence, tonic, and tonic-clonic, and non-seizures. EEG time series was converted into spectrogram stacks and used as input for CNN. To the best of authors knowledge, ours is the very first study that classified the seizures types using the computational algorithm. The four CNN models, namely AlexNet, VGG16, VGG19, and basic CNN model was applied to study the performance of 8-class classification problem. The proposed study showed a classification accuracy of 84.06%, 79.71%, 76.81%, and 82.14% using AlexNet, VGG16, VGG19 and basic CNN models respectively. The experimental results suggest that the proposed framework could be helpful to the neurology community for recognition of seizures types.
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http://dx.doi.org/10.1109/EMBC.2019.8857359DOI Listing
July 2019

Monitoring Parkinson's disease symptoms during daily life: a feasibility study.

NPJ Parkinsons Dis 2019 30;5:21. Epub 2019 Sep 30.

4Department of Neurosurgery, Maastricht University Medical Center, Maastricht, Netherlands.

Parkinson's disease symptoms are most often charted using the MDS-UPDRS. Limitations of this approach include the subjective character of the assessments and a discrepant performance in the clinic compared to the home situation. Continuous monitoring using wearable devices is believed to eventually replace this golden standard, but measurements often lack a parallel ground truth or are only tested in lab settings. To overcome these limitations, this study explores the feasibility of a newly developed Parkinson's disease monitoring system, which aims to measure Parkinson's disease symptoms during daily life by combining wearable sensors with an experience sampling method application. Twenty patients with idiopathic Parkinson's disease participated in this study. During a period of two consecutive weeks, participants had to wear three wearable sensors and had to complete questionnaires at seven semi-random moments per day on their mobile phone. Wearable sensors collected objective movement data, and the questionnaires containing questions about amongst others Parkinson's disease symptoms served as parallel ground truth. Results showed that participants wore the wearable sensors during 94% of the instructed timeframe and even beyond. Furthermore, questionnaire completion rates were high (79,1%) and participants evaluated the monitoring system positively. A preliminary analysis showed that sensor data could reliably predict subjectively reported OFF moments. These results show that our Parkinson's disease monitoring system is a feasible method to use in a diverse Parkinson's disease population for at least a period of two weeks. For longer use, the monitoring system may be too intense and wearing comfort needs to be optimized.
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http://dx.doi.org/10.1038/s41531-019-0093-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6768992PMC
September 2019

Stereotactic accuracy and frame mounting: A phantom study.

Surg Neurol Int 2019 24;10:67. Epub 2019 Apr 24.

Department of Neurosurgery, Maastricht University Medical Center+, P. Debyelaan 25, 6229 HX Maastricht, The Netherlands.

Background: Frame mounting is considered one of the most critical steps in stereotactic neurosurgery. In routine clinical practice, the aim is to mount the frame as symmetrical as possible, parallel to Reid's line. However, sometimes, the frame is mounted asymmetrically often due to patient-related reasons.

Methods: In this study, we addressed the question whether an asymmetrically mounted frame influences the accuracy of stereotactic electrode implantation. A Citrullus lanatus was used for this study. After a magnetic resonance imaging scan, symmetric and asymmetric mounting of the frame, which could occur in clinical scenarios, was performed with computed tomography (CT). Three different stereotactic software packages were used to analyze the results. In addition, manual calculations were performed by two different observers.

Results: Our results show that an asymmetrically mounted frame (deviated, tilted, or rotated) does not affect the accuracy in the mediolateral axis (X-coordinate) or the anteroposterior axis (Y-coordinate). However, it can lead to a clinically relevant error in the superoinferior axis (Z-coordinate). This error was largest with manual calculations.

Conclusion: These results suggest that asymmetrical frame mounting can lead to stereotactic inaccuracy in the superoinferior axis (Z coordinate).
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http://dx.doi.org/10.25259/SNI-88-2019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744823PMC
April 2019

Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier.

Comput Biol Med 2019 07 24;110:127-143. Epub 2019 May 24.

Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands.

The electroencephalogram (EEG) signal contains useful information on physiological states of the brain and has proven to be a potential biomarker to realize the complex dynamic behavior of the brain. Epilepsy is a brain disorder described by recurrent and unpredictable interruption of healthy brain function. Diagnosis of patients with epilepsy requires monitoring and visual inspection of long-term EEG by the neurologist, which is found to be a time-consuming procedure. Therefore, this study proposes an automated seizure detection model using a novel computationally efficient feature named sigmoid entropy derived from discrete wavelet transforms. The sigmoid entropy was estimated from the wavelet coefficients in each sub-band and classified using a non-linear support vector machine classifier with leave-one-subject-out cross-validation. The performance of the proposed method was tested with the Ramaiah Medical College and Hospital (RMCH) database, which consists of the 58 Hours of EEG from 115 subjects, the University of Bonn (UBonn), and CHB-MIT databases. Results showed that sigmoid entropy exhibits lower values for epileptic EEG in contrary to other existing entropy methods. We observe a seizure detection rate of 96.34%, a false detection rate of 0.5/h and a mean detection delay of 1.2 s for the RMCH database. The highest sensitivity of 100% and 94.21% were achieved for UBonn and CHB-MIT databases respectively. The performance comparison confirms that sigmoid entropy was found to be better and computationally efficient as compared to other entropy methods. It can be concluded that the proposed sigmoid entropy could be used as a potential biomarker for recognition and detection of epileptic seizures.
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http://dx.doi.org/10.1016/j.compbiomed.2019.05.016DOI Listing
July 2019

Feasibility of using a low-cost head-mounted augmented reality device in the operating room.

Surg Neurol Int 2019 28;10:26. Epub 2019 Feb 28.

Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.

Background: Augmented reality (AR) has great potential for improving image-guided neurosurgical procedures, but until recently, hardware was mostly custom-made and difficult to distribute. Currently, commercially available low-cost AR devices offer great potential for neurosurgery, but reports on technical feasibility are lacking. The goal of this pilot study is to evaluate the feasibility of using a low-cost commercially available head-mounted holographic AR device (the Microsoft Hololens) in the operating room. The Hololens is operated by performing specific hand gestures, which are recognized by the built-in camera of the device. This would allow the neurosurgeon to control the device "touch free" even while wearing a sterile surgical outfit.

Methods: The Hololens was tested in an operating room under two lighting conditions (general background theatre lighting only; and general background theatre lighting and operating lights) and wearing different surgical gloves (both bright and dark). All required hand gestures were performed, and voice recognition was evaluated against background noise consisting of two nurses talking at conversational speech level.

Results: Wearing comfort was sufficient, with and without regular glasses. All gestures were correctly classified regardless of lighting conditions or the sort of sterile gloves. Voice recognition was good. The visibility of the holograms was good if the device was configured to use high brightness for display.

Conclusions: We demonstrate that using a commercially available low-cost head-mounted holographic AR device is feasible in a sterile surgical setting, under different lighting conditions and using different surgical gloves. Given the availability of freely available software for application development, neurosurgery can benefit from new opportunities for image-guided surgery.
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http://dx.doi.org/10.4103/sni.sni_228_18DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6416754PMC
February 2019

Comparing deep brain stimulation in the ventral intermediate nucleus versus the posterior subthalamic area in essential tremor patients.

Surg Neurol Int 2018 4;9:244. Epub 2018 Dec 4.

Department of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands.

Background: The ventral intermediate nucleus (VIM) is the most commonly used target for deep brain stimulation (DBS) in patients with essential tremor (ET). Recent evidence suggests that the posterior subthalamic area (PSA) might be a better target for tremor reduction. We compared the outcome of VIM DBS with PSA DBS in our cohort of patients.

Methods: Overall, 19 ET patients with bilateral DBS were included in this retrospective study, with a total of 38 electrodes (12 located in the VIM, 12 in the PSA, and 14 in an intermediate area). The outcome was measured using the essential tremor rating scale (ETRS), the glass scale and the quality of life in essential tremor questionnaire (QUEST).

Results: Unilateral tremor-scores with items 5-6 (tremor of the upper extremity), 8-9 (tremor of the lower extremity), and 11-14 (hand function) from the ETRS showed a 63% tremor reduction in the VIM group, 47% tremor reduction in the PSA group, and 67% tremor reduction in the intermediate group after a mean follow-up of 1.6 years. After a mean follow-up of 5.8 years, there was a tremor reduction of 50%, 34%, and 45%, respectively. In our series, side effects such as dysarthria (75%), ataxia and disequilibrium (40%), and paraesthesia (15%) were assessed.

Conclusions: All aforementioned anatomical target areas are effective in reducing tremor, although no superior reduction was found with PSA stimulation. Because of intraindividual differences between left and right hemisphere regarding the stimulated anatomical target, no conclusions can be drawn regarding differences in side effects.
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http://dx.doi.org/10.4103/sni.sni_234_18DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293600PMC
December 2018

An update on adaptive deep brain stimulation in Parkinson's disease.

Mov Disord 2018 12 24;33(12):1834-1843. Epub 2018 Oct 24.

Departments of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands.

Advancing conventional open-loop DBS as a therapy for PD is crucial for overcoming important issues such as the delicate balance between beneficial and adverse effects and limited battery longevity that are currently associated with treatment. Closed-loop or adaptive DBS aims to overcome these limitations by real-time adjustment of stimulation parameters based on continuous feedback input signals that are representative of the patient's clinical state. The focus of this update is to discuss the most recent developments regarding potential input signals and possible stimulation parameter modulation for adaptive DBS in PD. Potential input signals for adaptive DBS include basal ganglia local field potentials, cortical recordings (electrocorticography), wearable sensors, and eHealth and mHealth devices. Furthermore, adaptive DBS can be applied with different approaches of stimulation parameter modulation, the feasibility of which can be adapted depending on specific PD phenotypes. Implementation of technological developments like machine learning show potential in the design of such approaches; however, energy consumption deserves further attention. Furthermore, we discuss future considerations regarding the clinical implementation of adaptive DBS in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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http://dx.doi.org/10.1002/mds.115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6587997PMC
December 2018

Infections in deep brain stimulation: Shaving versus not shaving.

Surg Neurol Int 2017 10;8:249. Epub 2017 Oct 10.

Department of Neurosurgery, Maastricht University Medical Center +, Maastricht, The Netherlands.

Background: To report our experience of infections in deep brain stimulation (DBS) surgeries comparing shaving versus no shaving of cranial hair. Nonshaving is strongly preferred by patients due to aesthetic and psychological factors.

Methods: This study is a prospective follow-up of the infection rate in 43 nonshaven DBS cases between April 2014 and December 2015 compared to our former infection rate with shaving in our center. Minimum follow-up was 6 months. All patients, except 7 epilepsy patients, received implantation of the electrodes together with the extension cables and internal pulse generator in one session.

Results: In 43 nonshaven patients, a total of 81 electrodes were implanted or revised with a mean follow-up of 16 months. One patient (2.32%) developed an infection of the implanted DBS-hardware and was treated with antibiotics.

Conclusion: In our experience nonshaving of cranial hair in DBS surgery does not lead to more infections when compared to shaving. We have changed our protocol to nonshaving based on these findings.
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http://dx.doi.org/10.4103/sni.sni_172_17DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5655757PMC
October 2017

NeuroMind: Past, present, and future.

Authors:
Pieter L Kubben

Surg Neurol Int 2017 6;8:216. Epub 2017 Sep 6.

Department of Neurosurgery, Maastricht University Medical Center, The Netherlands.

This narrative report describes the underlying rationale and technical developments of NeuroMind, a mobile clinical decision support system for neurosurgery. From the perspective of a neurosurgeon - (app) developer it explains how technical progress has shaped the world's "most rated and highest rated" neurosurgical mobile application, with particular attention for operating system diversity on mobile hardware, cookbook medicine, regulatory affairs (in particular regarding software as a medical device), and new developments in the field of clinical data science, machine learning, and predictive analytics. Finally, the concept of "computational neurosurgery" is introduced as a vehicle to reach new horizons in neurosurgery.
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http://dx.doi.org/10.4103/sni.sni_129_17DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5609441PMC
September 2017

Introducing "computational neurosurgery".

Authors:
Pieter L Kubben

Surg Neurol Int 2017 1;8:170. Epub 2017 Aug 1.

Department of Neurosurgery, Maastricht University Medical Center, The Netherlands.

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http://dx.doi.org/10.4103/sni.sni_126_17DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5551287PMC
August 2017

Perioperative Technical Complications in Deep Brain Stimulation Surgeries.

Turk Neurosurg 2018 ;28(3):483-489

Maastricht University, Department of Translational Neuroscience, Maastricht, Netherlands.

Aim: Deep brain stimulation (DBS) surgeries are multi-faceted and the various steps are interconnected. Since its first implementation, the method of DBS surgery has undergone changes. We have encountered several expected and also non-expected perioperative technical complications in the past seventeen years. Here, we describe the stereotactic frame, stereotactic localizer and planning station related complications and how we have managed them as much as possible.

Material And Methods: This study is a retrospective qualitative analysis of the documented technical events encountered during DBS surgeries from 1999 onwards. We have collected these events from a cohort of approximately 921 DBS electrodes implantations from the centers of the authors.

Results: Stereotactic frame related complications included movement related fixation problems, head anatomy related problems, and lack of maintenance related issues. Localizer related complications were compatibility issues of the stereotactic localizer and planning station, field of view effect on fiducials, air bubbles in localizers using liquid solutions, and disengaged localizer effect. Planning station related complications included image fusion failures and cerebrospinal fluid signal effect on image fusion.

Conclusion: The road to success in DBS therapy passes through the ability to cope with surgical and technical complications. Each step is unconditionally connected to the other, and detection of the problems that can be encountered in advance and preparations for these negative conditions are the key to success for the group responsible for executing the therapy. We are still learning from these events and advance our surgical approaches.
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http://dx.doi.org/10.5137/1019-5149.JTN.20042-17.1DOI Listing
August 2018

Delayed cerebral ischemia after subarachnoid hemorrhage: Comparing and integrating classification systems.

Surg Neurol Int 2017 21;8:121. Epub 2017 Jun 21.

Department of Neurosurgery, University Hospital of Essen, Essen, Germany.

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http://dx.doi.org/10.4103/2152-7806.208805DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5502297PMC
June 2017

Methodology, outcome, safety and in vivo accuracy in traditional frame-based stereoelectroencephalography.

Acta Neurochir (Wien) 2017 Sep 5;159(9):1733-1746. Epub 2017 Jul 5.

Department of Neurosurgery, Maastricht University Medical Center, Maastricht, Limburg, The Netherlands.

Background: Stereoelectroencephalography (SEEG) is an established diagnostic technique for the localization of the epileptogenic zone in drug-resistant epilepsy. In vivo accuracy of SEEG electrode positioning is of paramount importance since higher accuracy may lead to more precise resective surgery, better seizure outcome and reduction of complications.

Objective: To describe experiences with the SEEG technique in our comprehensive epilepsy center, to illustrate surgical methodology, to evaluate in vivo application accuracy and to consider the diagnostic yield of SEEG implantations.

Methods: All patients who underwent SEEG implantations between September 2008 and April 2016 were analyzed. Planned electrode trajectories were compared with post-implantation trajectories after fusion of pre- and postoperative imaging. Quantitative analysis of deviation using Euclidean distance and directional errors was performed. Explanatory variables for electrode accuracy were analyzed using linear regression modeling. The surgical methodology, procedure-related complications and diagnostic yield were reported.

Results: Seventy-six implantations were performed in 71 patients, and a total of 902 electrodes were implanted. Median entry and target point deviations were 1.54 mm and 2.93 mm. Several factors that predicted entry and target point accuracy were identified. The rate of major complications was 2.6%. SEEG led to surgical therapy of various modalities in 53 patients (69.7%).

Conclusions: This study demonstrated that entry and target point localization errors can be predicted by linear regression models, which can aid in identification of high-risk electrode trajectories and further enhancement of accuracy. SEEG is a reliable technique, as demonstrated by the high accuracy of conventional frame-based implantation methodology and the good diagnostic yield.
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http://dx.doi.org/10.1007/s00701-017-3242-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5557874PMC
September 2017

TREMOR12: An Open-Source Mobile App for Tremor Quantification.

Stereotact Funct Neurosurg 2016 9;94(3):182-6. Epub 2016 Jul 9.

Department of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands.

Background: Evaluating the effect of treatment of tremor is mostly performed with clinical rating scales. Mobile applications facilitate a more rapid, objective, and quantitative evaluation of treatment effect. Existing mobile apps do not offer raw data access, which limits algorithm development.

Objective: To develop a novel open-source mobile app for tremor quantification.

Methods: TREMOR12 is an open-source mobile app that samples acceleration, rotation, rotation speed, and gravity, each in 3 axes and time-stamped in a frequency up to 100 Hz. The raw measurement data can be exported as a comma-separated value file for further analysis in the TREMOR12P data processing module. The app was evaluated with 3 patients suffering from essential tremor, who were between 55 and 71 years of age.

Results: This proof-of-concept study shows that the TREMOR12 app is able to detect and register tremor characteristics such as acceleration, rotation, rotation speed, and gravity in a simple and nonburdensome way. The app is compatible with current regulatory oversight by the European Union (MEDDEV regulations) and the Food and Drug Administration (FDA) guidance on mobile medical applications.

Conclusion: TREMOR12 offers low-cost tremor quantification for research purposes and algorithm development, and may help to improve treatment evaluation.
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http://dx.doi.org/10.1159/000446610DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5296883PMC
September 2017

Programming for physicians: A free online course.

Authors:
Pieter L Kubben

Surg Neurol Int 2016 29;7:29. Epub 2016 Mar 29.

Department of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands.

This article is an introduction for clinical readers into programming and computational thinking using the programming language Python. Exercises can be done completely online without any need for installation of software. Participants will be taught the fundamentals of programming, which are necessarily independent of the sort of application (stand-alone, web, mobile, engineering, and statistical/machine learning) that is to be developed afterward.
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http://dx.doi.org/10.4103/2152-7806.179382DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4828953PMC
April 2016

The start and development of epilepsy surgery in Europe: a historical review.

Neurosurg Rev 2015 Jul 24;38(3):447-61. Epub 2015 May 24.

Department of Neurosurgery, Maastricht University Medical Centre, PO Box 5800, 6202 AZ, Maastricht, The Netherlands,

Epilepsy has not always been considered a brain disease, but was believed to be a demonic possession in the past. Therefore, trepanation was done not only for medical but also for religious or spiritual reasons, originating in the Neolithic period (3000 BC). The earliest documentation of trepanation for epilepsy is found in the writings of the Hippocratic Corpus and consisted mainly of just skull surgery. The transition from skull surgery to brain surgery took place in the middle of the nineteenth century when the insight of epilepsy as a cortical disorder of the brain emerged. This led to the start of modern epilepsy surgery. The pioneer countries in which epilepsy surgery was performed in Europe were the UK, Germany, and The Netherlands. Neurosurgical forerunners like Sir Victor Horsley, William Macewen, Fedor Krause, and Otfrid Foerster started with "modern" epilepsy surgery. Initially, epilepsy surgery was mainly done with the purpose to resect traumatic lesions or large surface tumours. In the course of the twentieth century, this changed to highly specialized microscopic navigation-guided surgery to resect lesional and non-lesional epileptogenic cortex. The development of epilepsy surgery in Southern Europe, which has not been described until now, will be elaborated in this manuscript. To summarize, in this paper, we provide (1) a detailed description of the evolution of European epilepsy surgery with special emphasis on the pioneer countries; (2) novel, never published information about the development of epilepsy surgery in Southern Europe; and (3) we review the historical dichotomy of invasive electrode implantation strategy (Anglo-Saxon surface electrodes versus French-Italian stereoencephalography (SEEG) model).
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http://dx.doi.org/10.1007/s10143-015-0641-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4469771PMC
July 2015

Programming for physicians: A crash course.

Authors:
Pieter L Kubben

Surg Neurol Int 2015 30;6:15. Epub 2015 Jan 30.

Department of Neurosurgery, Maastricht University Medical Center, The Netherlands, Department of Medical Information Technology, Maastricht University Medical Center, The Netherlands.

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http://dx.doi.org/10.4103/2152-7806.150460DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4314833PMC
February 2015

Intraoperative magnetic resonance imaging versus standard neuronavigation for the neurosurgical treatment of glioblastoma: A randomized controlled trial.

Surg Neurol Int 2014 15;5:70. Epub 2014 May 15.

Department of Neurosurgery, Maastricht University Medical Center, Maastricht, Netherlands ; Department of Neurosurgery, Atrium Medical Center, Heerlen, The Netherlands.

Background: Although the added value of increasing extent of glioblastoma resection is still debated, multiple technologies can assist neurosurgeons in attempting to achieve this goal. Intraoperative magnetic resonance imaging (iMRI) might be helpful in this context, but to date only one randomized trial exists.

Methods: We included 14 adults with a supratentorial tumor suspect for glioblastoma and an indication for gross total resection in this randomized controlled trial of which the interim analysis is presented here. Participants were assigned to either ultra-low-field strength iMRI-guided surgery (0.15 Tesla) or to conventional neuronavigation-guided surgery (cNN). Primary endpoint was residual tumor volume (RTV) percentage. Secondary endpoints were clinical performance, health-related quality of life (HRQOL) and survival.

Results: Median RTV in the cNN group is 6.5% with an interquartile range of 2.5-14.75%. Median RTV in the iMRI group is 13% with an interquartile range of 3.75-27.75%. A Mann-Whitney test showed no statistically significant difference between these groups (P =0.28). Median survival in the cNN group is 472 days, with an interquartile range of 244-619 days. Median survival in the iMRI group is 396 days, with an interquartile range of 191-599 days (P =0.81). Clinical performance did not differ either. For HRQOL only descriptive statistics were applied due to a limited sample size.

Conclusion: This interim analysis of a randomized trial on iMRI-guided glioblastoma resection compared with cNN-guided glioblastoma resection does not show an advantage with respect to extent of resection, clinical performance, and survival for the iMRI group. Ultra-low-field strength iMRI does not seem to be cost-effective compared with cNN, although the lack of a valid endpoint for neurosurgical studies evaluating extent of glioblastoma resection is a limitation of our study and previous volumetry-based studies on this topic.
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http://dx.doi.org/10.4103/2152-7806.132572DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4078446PMC
July 2014

Why physicians might want to learn computer programming.

Authors:
Pieter L Kubben

Surg Neurol Int 2013 22;4:30. Epub 2013 Mar 22.

Department of Neurosurgery, Maastricht University Medical Center, The Netherlands.

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http://dx.doi.org/10.4103/2152-7806.109461DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3622380PMC
April 2013

Windows 8: A promise for tablet computers in the hospital?

Authors:
Pieter L Kubben

Surg Neurol Int 2013 28;4:11. Epub 2013 Jan 28.

Department of Neurosurgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands.

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http://dx.doi.org/10.4103/2152-7806.106286DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3589842PMC
March 2013

Intraoperative magnetic resonance imaging for high grade glioma resection: Evidence-based or wishful thinking?

Surg Neurol Int 2013 15;4. Epub 2013 Jan 15.

Department of Neurosurgery, Maastricht University Medical Center, The Netherlands.

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http://dx.doi.org/10.4103/2152-7806.106114DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3589847PMC
March 2013
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