IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG)

Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.Peer reviewe

Separating Signal from Noise in High-Density Diffuse Optical Tomography

High-density diffuse optical tomography (HD-DOT) is a relatively new neuroimaging technique that detects the changes in hemoglobin concentrations following neuronal activity through the measurement of near-infrared light intensities. Thus, it has the potential to be a surrogate for functional MRI (fMRI) as a more naturalistic, portable, and cost-effective neuroimaging system. As in other neuroimaging modalities, head motion is the most common source of noise in HD-DOT data that results in spurious effects in the functional brain images. Unlike other neuroimaging modalities, data quality assessment methods are still underdeveloped for HD-DOT. Therefore, developing robust motion detection and motion removal methods in its data processing pipeline is a crucial step for making HD-DOT a reliable neuroimaging modality. In particular, our lab is interested in using HD-DOT to study the brain function in clinical populations with metal implants that cannot be studied using fMRI due to their contraindications. Two of these populations are patients having movement disorders (Parkinson Disease or essential tremor) with deep brain stimulation (DBS) implants and individuals with cochlear implants (CI). These two groups both receive tremendous benefit from their implants at the statistical level; however, there is significant single-subject variability. Our overarching goal is to use HD-DOT to find the relationships between the neuronal function and the behavioral measures in these populations to optimize the contact location of these implant surgeries. However, one of the challenges in analyzing the data in these subjects, especially in patients with DBS, is their high levels of motion due to tremors when their DBS implant is turned off. This further motivates the importance of the methods presented herein for separating signal from noise in HD-DOT data. To this end, I will first assess the efficacy of state-of-the-art motion correction methods introduced in the fNIRS literature for HD-DOT. Then, I will present a novel global metric inspired by motion detection methods in fMRI called GVTD (global variance of the temporal derivatives). Our results show that GVTD-based motion detection not only outperforms other comparable motion detection methods in fNIRS, but also outperforms motion detection with accelerometers. I will then present my work on collecting and processing HD-DOT data for two clinical populations with metal implants in their brain and the preliminary results for these studies. Our results in PD patients show that HD-DOT can reliably map neuronal activity in this group and replicate previously published results using PET and fMRI. Our results in the CI users provide evidence for the recruitment of the prefrontal cortex in processing speech to compensate for the decreased activity in the temporal cortex. These findings support the theory of cognitive demand increase in effortful listening situations. In summary, the presented methods for separating signal from noise enable direct comparisons of HD-DOT images with those of fMRI in clinical populations with metal implants and equip this modality to be used as a surrogate for fMRI

The magnetic properties of electrical pulses delivered by deep-brain stimulation systems

The aim of this article is to analyze the magnetic field properties for both the monopolar and bipolar electrode configurations of deep-brain stimulation electrodes using 3-D magnetic field measurements and to investigate if the magnetic measurements enable a localization of the electrode as a proof of concept. Therefore, a simplified head phantom with an integrated deep-brain stimulation electrode was created to measure the magnetic flux densities in all the three dimensions with a fluxgate magnetometer over a sensor trajectory of measuring points inside the magnetically shielded chamber. The magnitude of the magnetic flux density for monopolar stimulation and bipolar stimulation is in the nT and pT ranges for the frequency 160 Hz, depending on the stimulation amplitude and on the distance between the sensor and the electrode. The field distributions show a linear decline in the magnetic field for the monopolar and a quadratic decline for the bipolar stimulation. We were able to reconstruct the magnetic field using multiple recording sites. As the magnetic field of deep-brain stimulation can be measured and its field strength can be reconstructed, it is feasible to estimate the strength of the field within the limits of programmable stimulation parameters and distance between the sensor and the electrode. The presented results are intended as preliminary work for the further development of electrode localization methods using magnetic measurements. As an example of the feasibility of electrode localization, this article presents a bipolar measurement that creates a more focused spatial field distribution and results in an accurate localization

Technological advances in deep brain stimulation:Towards an adaptive therapy

Parkinson's disease (PD) is neurodegenerative movement disorder and a treatment method called deep brain stimulation (DBS) may considerably reduce the patient’s motor symptoms. The clinical procedure involves the implantation of a DBS lead, consisting of multiple electrode contacts, through which continuous high frequency (around 130 Hz) electric pulses are delivered in the brain. In this thesis, I presented the research which had the goal to improve current DBS technology, focusing on bringing the conventional DBS system a step closer to adaptive DBS, a personalized DBS therapy. The chapters in this thesis can be seen as individual building blocks for such an adaptive DBS system. After the general introduction, the first two chapters, two novel DBS lead designs are studied in a computational model. The model showed that both studied leads were able to exploit the novel distribution of the electrode contacts to shape and steer the stimulation field to activate more neurons in the chosen target compared to the conventional lead, and to counteract lead displacement. In the fourth chapter, an inverse current source density (CSD) method is applied on local field potentials (LFP) measured in a rat model. The pattern of CSD sources can act as a landmark within the STN to locate the potential stimulation target. The fifth and final chapter described the last building block of the DBS system. We introduced an inertial sensors and force sensor based measurement system, which can record hand kinematics and joint stiffness of PD patients. A system which can act as a feedback signal in an adaptive DBS system

Guiding deep brain stimulation neurosurgery with optical spectroscopy

Savoir différiencier les différentes types de tissus représente un aspect important lors d’interventions médicales, que ce soit pour aider au diagnostic d’une maladie ou pour le guidage chirurgical. Il est généralement très difficile de distinguer les tissus sains des tissus pathologiques à l’oeil nu et la navigation chirurgicale peut parfois être difficile dans les grands organes où la structure ciblé se trouve enfouie profondément. De nouvelles méthodes susceptibles d’accroître la réussite de telles interventions médicales suscitent actuellement de l’intérêt chez les professionnels de la santé. La spectroscopie optique, en analysant les interactions lumière-tissu dans une plage spectrale définie, est un outil permettant de différencier les tissus avec une résolution et une sensibilité bien supérieures à celles de l’oeil humain. Tout au long de cette thèse, je détaillerai comment la spectroscopie optique a été utilisée pour créer et améliorer un système de guidage optique utilisé pour la stimulation cérébrale profonde en neurochirurgie, en particulier pour le traitement de la maladie de Parkinson. Pour commencer, je montrerai comment les informations spectroscopiques peuvent fournir une rétroaction peropératoire en temps réel à un neurochirurgien, au cours de la phase d’implantation de la procédure, avec une sonde qui n’induit aucune invasion supplémentaire. Je présenterai l’investigation de deux modalités spectroscopiques différentes pour la discrimination tissulaire pour le guidage, soit la spectroscopie à réflectance diffuse et la spectroscopie de diffusion Raman anti-Stokes cohérente. Les avantages et les inconvénients des deux techniques, ainsi que leurs aptitude à la traduction prometteuse pour cette application seront abordés. Par la suite, je présenterai une nouvelle technique d’analyse de données pour extraire l’oxygénation des tissus à partir de spectres de réflectance diffus dans le but d’améliorer la précision de mesure en spectroscopie rétinienne et ultimement de porter un diagnostique. Bien que conçu pour la rétine, l’algorithme peut également être utilisé pour analyser les spectres acquis lors d’une neurochirurgie afin de fournir des informations à la fois discriminantes et diagnostiques. Finalement, je montrerai des preuves de diffusion anisotrope de la lumière dans les axones myélinisés de la moelle épinière et discuterai des conséquences que cela pourrait avoir sur les simulations actuelles de la propagation des photons dans le cerveau, qui feront partie intégrante d’un guidage optique efficace.Differentiating tissue types is an important aspect of guiding medical interventions whether it be for disease diagnosis or for surgical guidance. However, diseased and healthy tissues are often hard to discriminate by human vision alone and surgical navigation can be difficult to accomplish in large organs where the target structure lies deep within the body. New methods that can increase certainty in such medical interventions are therefore of great interest to healthcare professionals. Optical spectroscopy is a tool which can be exploited to probe discriminatory information in tissue by analyzing light-tissue interactions with a spectral range, resolution and sensitivity much greater than the human eye. Throughout this thesis, I will explain how I have leveraged optical spectroscopy to create, and improve, an optical guidance system for deep brain stimulation neurosurgery, specifically for the treatment of Parkinson’s disease. I will begin by describing how spectroscopic information can provide real-time feedback to a surgeon during the procedure, in the hopes of ultimately improving treatment outcome. To this end, I will present the investigation of two different spectroscopic modalities for optical guidance: diffuse reflectance spectroscopy, and coherent anti-Stokes Raman scattering spectroscopy. The advantages and disadvantages of both techniques will be discussed along with their promising translatability for this application. Following this, I will present a novel data analysis technique for extracting the tissue oxygenation from diffuse reflectance spectra with the aim of improved diagnostic information in retinal spectroscopy. While designed for the retina, the algorithm can also be used to analyze spectra acquired during a neurosurgery to provide both discriminatory and diagnostic information. Lastly, I will show evidence of anisotropic light scattering in the myelinated axons of the spinal cord and discuss the implications this may have on current photon propagation simulations in the brain, which will be integral for effective optical guidance

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others