Cross Validation of Experts Versus Registration Methods for Target Localization in Deep Brain Stimulation

In the last five years, Deep Brain Stimulation (DBS) has become the most popular and effective surgical technique for the treatment of Parkinsons disease (PD). The Subthalamic Nucleus (STN) is the usual target involved when applying DBS. Unfortunately, the STN is in general not visible in common medical imaging modalities. Therefore, atlas-based segmentation is commonly considered to locate it in the images. In this paper, we propose a scheme that allows both, to perform a comparison between different registration algorithms and to evaluate their ability to locate the STN automatically. Using this scheme we can evaluate the expert variability against the error of the algorithms and we demonstrate that automatic STN location is possible and as accurate as the methods currently used

Methodological considerations for neuroimaging in deep brain stimulation of the subthalamic nucleus in Parkinson’s disease patients

Deep brain stimulation (DBS) of the subthalamic nucleus is a neurosurgical intervention for Parkinson’s disease patients who no longer appropriately respond to drug treatments. A small fraction of patients will fail to respond to DBS, develop psychiatric and cognitive side-effects, or incur surgery-related complications such as infections and hemorrhagic events. In these cases, DBS may require recalibration, reimplantation, or removal. These negative responses to treatment can partly be attributed to suboptimal pre-operative planning procedures via direct targeting through low-field and low-resolution magnetic resonance imaging (MRI). One solution for increasing the success and efficacy of DBS is to optimize preoperative planning procedures via sophisticated neuroimaging techniques such as high-resolution MRI and higher field strengths to improve visualization of DBS targets and vasculature. We discuss targeting approaches, MRI acquisition, parameters, and post-acquisition analyses. Additionally, we highlight a number of approaches including the use of ultra-high field (UHF) MRI to overcome limitations of standard settings. There is a trade-off between spatial resolution, motion artifacts, and acquisition time, which could potentially be dissolved through the use of UHF-MRI. Image registration, correction, and post-processing techniques may require combined expertise of traditional radiologists, clinicians, and fundamental researchers. The optimization of pre-operative planning with MRI can therefore be best achieved through direct collaboration between researchers and clinicians

Personalized computational models of deep brain stimulation

University of Minnesota Ph.D. dissertation. December 2016. Major: Biomedical Engineering. Advisor: Matthew Johnson. 1 computer file (PDF); xii, 138 pages.Deep brain stimulation (DBS) therapy is used for managing symptoms associated with a growing number of neurological disorders. One of the primary challenges with delivering this therapy, however, continues to be accurate neurosurgical targeting of the DBS lead electrodes and post-operative programming of the stimulation settings. Two approaches for addressing targeting have been advanced in recent years. These include novel DBS lead designs with more electrodes and computational models that can predict cellular modulation during DBS. Here, we developed a personalized computational modeling framework to (1) thoroughly investigate the electrode design parameter space for current and future DBS array designs, (2) generate and evaluate machine learning feature sets for semi-automated programming of DBS arrays, (3) study the influence of model parameters in predicting behavioral and electrophysiological outcomes of DBS in a preclinical animal model of Parkinson’s disease, and (4) evaluate feasibility of a novel endovascular targeting approach to delivering DBS therapy in humans. These studies show how independent current controlled stimulation with advanced machine learning algorithms can negate the need for highly dense electrode arrays to shift, steer, and sculpt regions of modulation within the brain. Additionally, these studies show that while advanced and personalized computational models of DBS can predict many of the behavioral and electrophysiological outcomes of DBS, there are remaining inconsistencies that suggest there are additional physiological mechanisms of DBS that are not yet well understood. Finally, the results show how computational models can be beneficial for prospective development of novel approaches to neuromodulation prior to large-scale preclinical and clinical studies

Experimental and Model-based Approaches to Directional Thalamic Deep Brain Stimulation

University of Minnesota Ph.D. dissertation. September 2016. Major: Biomedical Engineering. Advisor: Matthew Johnson. 1 computer file (PDF); xii, 181 pages.Deep brain stimulation (DBS) is an effective surgical procedure for the treatment of several brain disorders. However, the clinical successes of DBS hinges on several factors. Here, we describe the development of tools and methodologies in the context of thalamic DBS for essential tremor (ET) to address three key challenges: 1) accurate localization of nuclei and fiber pathways for stimulation, 2) model-based programming of high-density DBS electrode arrays (DBSA) and 3) in vivo assessment of computational DBS model predictions. We approached the first challenge through a multimodal imaging approach, utilizing high-field (7T) susceptibility-weighted imaging and diffusion-weighted imaging data. A nonlinear image deformation algorithm was used in conjunction with probabilistic fiber tractography to segment individual thalamic sub-nuclei and reconstruct their afferent fiber pathways. We addressed the second challenge by developing subject-specific computational model-based algorithms built on maximizing population activating function values within a target region using convex optimization principles. The algorithms converged within seconds and only required as many finite-element simulations as the number of electrodes on the DBSA being modeled. For the third challenge, we recorded (in two non-human primates) unit-spike data from neurons in the vicinity of chronically implanted thalamic DBSAs before, during and after high-frequency stimulation. A novel entropy-based method was developed to quantify the degree and significance of stimulation-induced changes in neuronal firing pattern. Results indicated that neurons modulated by thalamic DBS were distributed and not confined to the immediate proximity of the active electrode. For those that were modulated by DBS, their responses increasingly shifted from firing rate modulation to firing pattern modulation with increased stimulation amplitude. Additionally, strong low-pass filtering effect was observed where <4% of DBS pulses produced phase-locked spikes in cells exhibiting significant excitatory firing pattern modulation. Finally, we quantified the spatial distribution of neurons modulated by DBS by developing a novel spherical statistical framework for analysis. Together, these tools and methodologies are poised to improve our understanding of DBS mechanisms and improve the efficacy and efficiency of DBS therapy

Imaging the subthalamic nucleus in Parkinson’s disease

This thesis is comprised of a set of work that aims to visualize and quantify the anatomy, structural variability, and connectivity of the subthalamic nucleus (STN) with optimized neuroimaging methods. The study populations include both healthy cohorts and individuals living with Parkinson's disease (PD). PD was chosen specifically due to the involvement of the STN in the pathophysiology of the disease. Optimized neuroimaging methods were primarily obtained using ultra-high field (UHF) magnetic resonance imaging (MRI). An additional component of this thesis was to determine to what extent UHF-MRI can be used in a clinical setting, specifically for pre-operative planning of deep brain stimulation (DBS) of the STN for patients with advanced PD. The thesis collectively demonstrates that i, MRI research, and clinical applications must account for the different anatomical and structural changes that occur in the STN with both age and PD. ii, Anatomical connections involved in preparatory motor control, response inhibition, and decision-making may be compromised in PD. iii. The accuracy of visualizing and quantifying the STN strongly depends on the type of MR contrast and voxel size. iv, MRI at a field strength of 3 Tesla (T) can under certain circumstances be optimized to produce results similar to that of 7 T at the expense of increased acquisition time

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