Intraoperative Localization of Subthalamic Nucleus during Deep Brain Stimulation Surgery using Machine Learning Algorithms

This thesis presents a novel technique for localizing the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery. DBS is an accepted treatment for individuals living with Parkinson\u27s Disease (PD). This surgery involves implantation of a permanent electrode inside the STN to deliver electrical current. The STN is a small grey matter structure within the brain, which makes accurate placement a challenging task for the surgical team. Prior to placement of the permanent electrode, intraoperative microelectrode recordings (MERs) of neural activity are used to localize the STN. The placement of the permanent electrode and the success of the stimulation therapy depend on accurate localization. In this study, an objective approach was implemented to help the surgical team in localizing the STN. This is achieved by processing the MER signals and extracting features during the surgery to be used in a Machine Learning algorithm for defining the electrophysiological borders of the STN. A classification approach that can detect the borders of the STN during the operation is proposed. MER signals from 100 PD patients were recorded and used to validate the performance of the proposed method. The results show that by extracting wavelet transformation features from MER signals and using a deep neural network architecture, it is possible to detect the border of the STN with an accuracy of 92%. The proposed method can be implemented in real-time during the surgery to assist the surgical team with the goal of enhancing the accuracy and consistency of electrode placement in the STN

Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; AlemaniaFil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Harvard Medical School; Estados UnidosFil: Köhler, Richard. Charité – Universitätsmedizin Berlin; AlemaniaFil: Haufe, Stefan. Charité – Universitätsmedizin Berlin; AlemaniaFil: Richardson, R. Mark. Harvard Medical School; Estados UnidosFil: Neumann, Wolf Julian. Charité – Universitätsmedizin Berlin; Alemani

Neuroimaging and electrophysiology meet invasive neurostimulation for causal interrogations and modulations of brain states

Deep brain stimulation (DBS) has developed over the last twenty years into a highly effective evidenced-based treatment option for neuropsychiatric disorders. Moreover, it has become a fascinating tool to provide illustrative insights into the functioning of brain networks. New anatomical and pathophysiological models of DBS action have accelerated our understanding of neurological and psychiatric disorders and brain functioning. The description of the brain networks arose through the unique ability to illustrate long-range interactions between interconnected brain regions as derived from state-of-the-art neuroimaging (structural, diffusion, and functional MRI) and the opportunity to record local and large-scale brain activity at millisecond temporal resolution (microelectrode recordings, local field potential, electroencephalography, and magnetoencephalography). In the first part of this review, we describe how neuroimaging techniques have led to current understanding of DBS effects, by identifying and refining the DBS targets and illustrate the actual view on the relationships between electrode locations and clinical effects. One step further, we discuss how neuroimaging has shifted the view of localized DBS effects to a modulation of specific brain circuits, which has been possible from the combination of electrode location reconstructions with recently introduced network imaging methods. We highlight how these findings relate to clinical effects, thus postulating neuroimaging as a key factor to understand the mechanisms of DBS action on behavior and clinical effects. In the second part, we show how invasive electrophysiology techniques have been efficiently integrated into the DBS set-up to precisely localize the neuroanatomical targets of DBS based on distinct region-specific patterns of neural activity. Next, we show how multi-site electrophysiological recordings have granted a real-time window into the aberrant brain circuits within and beyond DBS targets to quantify and map the dynamic properties of rhythmic oscillations. We also discuss how DBS alters the transient synchrony states of oscillatory networks in temporal and spatial domains during resting, task-based and motion conditions, and how this modulation of brain states ultimately shapes the functional response. Finally, we show how a successful decoding and management of electrophysiological proxies (beta bursts, phase-amplitude coupling) of aberrant brain circuits was translated into adaptive DBS stimulation paradigms for a targeted and state-dependent invasive electrical neuromodulation

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

Computationally efficient algorithms and implementations of adaptive deep brain stimulation systems for Parkinson's disease

Clinical deep brain stimulation (DBS) is a tool used to mitigate pharmacologically intractable neurodegenerative diseases such as Parkinson's disease (PD), tremor and dystonia. Present implementations of DBS use continuous, high frequency voltage or current pulses so as to mitigate PD. This results in some limitations, among which there is stimulation induced side effects and shortening of pacemaker battery life. Adaptive DBS (aDBS) can be used to overcome a number of these limitations. Adaptive DBS is intended to deliver stimulation precisely only when needed. This thesis presents work undertaken to investigate, propose and develop novel algorithms and implementations of systems for adapting DBS. This thesis proposes four system implementations that could facilitate DBS adaptation either in the form of closed-loop DBS or spatial adaptation. The first method involved the use of dynamic detection to track changes in local field potentials (LFP) which can be indicative of PD symptoms. The work on dynamic detection included the synthesis of validation dataset using mainly autoregressive moving average (ARMA) models to enable the evaluation of a subset of PD detection algorithms for accuracy and complexity trade-offs. The subset of algorithms consisted of feature extraction (FE), dimensionality reduction (DR) and dynamic pattern classification stages. The combination with the best trade-off in terms of accuracy and complexity consisted of discrete wavelet transform (DWT) for FE, maximum ratio method (MRM) for DR and k-nearest neighbours (k-NN) for classification. The MRM is a novel DR method inspired by Fisher's separability criterion. The best combination achieved accuracy measures: F1-score of 97.9%, choice probability of 99.86% and classification accuracy of 99.29%. Regarding complexity, it had an estimated microchip area of 0.84 mm² for estimates in 90 nm CMOS process. The second implementation developed the first known PD detection and monitoring processor. This was achieved using complementary detection, which presents a hardware-efficient method of implementing a PD detection processor for monitoring PD progression in Parkinsonian patients. Complementary detection is achieved by using a combination of weak classifiers to produce a classifier with a higher consistency and confidence level than the individual classifiers in the configuration. The PD detection processor using the same processing stages as the first implementation was validated on an FPGA platform. By mapping the implemented design on a 45 nm CMOS process, the most optimal implementation achieved a dynamic power per channel of 2.26 μW and an area per channel of 0.2384 mm². It also achieved mean accuracy measures: Mathews correlation coefficient (MCC) of 0.6162, an F1-score of 91.38%, and mean classification accuracy of 91.91%. The third implementation proposed a framework for adapting DBS based on a critic-actor control approach. This models the relationship between a trained clinician (critic) and a neuro-modulation system (actor) for modulating DBS. The critic was implemented and validated using machine learning models, and the actor was implemented using a fuzzy controller. Therapy is modulated based on state estimates obtained through the machine learning models. PD suppression was achieved in seven out of nine test cases. The final implementation introduces spatial adaptation for aDBS. Spatial adaptation adjusts to variation in lead position and/or stimulation focus, as poor stimulation focus has been reported to affect therapeutic benefits of DBS. The implementation proposes dynamic current steering systems as a power-efficient implementation for multi-polar multisite current steering, with a particular focus on the output stage of the dynamic current steering system. The output stage uses dynamic current sources in implementing push-pull current sources that are interfaced to 16 electrodes so as to enable current steering. The performance of the output stage was demonstrated using a supply of 3.3 V to drive biphasic current pulses of up to 0.5 mA through its electrodes. The preliminary design of the circuit was implemented in 0.18 μm CMOS technology

Mining Biomarkers Of Epilepsy From Large-Scale Intracranial Electroencephalography

Epilepsy is a chronic neurological disorder characterized by seizures. Affecting over 50 million people worldwide, the quality of life of a patient with uncontrolled epilepsy is degraded by medical, social, cognitive, and psychological dysfunction. Fortunately, two-thirds of these patients can achieve adequate seizure control through medications. Unfortunately, one-third cannot. Improving treatment for this patient population depends upon improving our understanding of the underlying epileptic network. Clinical therapies modulate this network to some degree of success, including surgery to remove the seizure onset zone or neuromodulation to alter the brain\u27s dynamics. High resolution intracranial EEG (iEEG) is often employed to study the dynamics of cortical networks, from interictal patterns to more complex quantitative features. These interictal patterns include epileptiform biomarkers whose detection and mapping, along with seizures and neuroimaging, form the mainstay of data for clinical decision making around drug therapy, surgery, and devices. They are also increasingly important to assess the effects of epileptic physiology on brain functions like behavior and cognition, which are not well characterized. In this work, we investigate the significance and trends of epileptiform biomarkers in animal and human models of epilepsy. We develop reliable methods to quantify interictal patterns, applying state of the art techniques from machine learning, signal processing, and EEG analysis. We then validate these tools in three major applications: 1. We study the effect of interictal spikes on human cognition, 2. We assess trends of interictal epileptiform bursts and their relationship to seizures in prolonged recordings from canines and rats, and 3. We assess the stability of long-term iEEG spanning several years. These findings have two main impacts: (1) they inform the interpretation of interictal iEEG patterns and elucidate the timescale of post-implantation changes. These findings have important implications for research and clinical care, particularly implantable devices and evaluating patients for epilepsy surgery. (2) They provide an analytical framework to enable others to mine large-scale iEEG datasets. In this way we hope to make a lasting contribution to accelerate collaborative research not only in epilepsy, but also in the study of animal and human electrophysiology in acute and chronic conditions

Determinación mediante minería de datos del núcleo subtalámico utilizando registros MER de cirugías para la implantación de neuroestimuladores en pacientes con Parkinson

La enfermedad de Parkinson (EP) es una degeneración del sistema nervioso central (SNC) caracterizada por un deterioro progresivo de funciones motoras como lentitud de movimientos, temblor, rigidez e inestabilidad postura. Un estimulador cerebral profundo (DBS por sus siglas en inglés) es una tecnología novedosa para el tratamiento de los trastornos motores de la EP en los casos donde la medicación no es efectiva o cuando dichos fármacos generan efectos secundarios muy discapacitantes como las discenesias. Básicamente el DBS consiste en un neuroestimulador implantado o marcapasos del cerebro que mediante 2 microelectrodos estimula con pulsos de alta frecuencia al núcleo subtalámico (STN por sus siglas en inglés) de ambos hemisferios cerebrales para reducir la hiperactividad crónica de las neuronas involucradas. Para la implantación del DBS se emplean procedimientos estereotáctico con el objetivo de realizar una cirugía lo menos invasiva posible para el paciente. Utilizando un instrumento fijo al cráneo del paciente, es posible localizar tridimensionalmente una estructura cerebral de referencia. Para localizar la zona STN y dependiendo de la tecnología disponible en cada institución se utilizan: imágenes de resonancia previas a la cirugía, atlas genéricos que en algunos casos se superponen a las imágenes obtenidas, imágenes de tomografía realizadas durante la cirugía, reconstrucción 3D de la fusión de imágenes de resonancia y tomografía (corregistro), en el análisis de ritmo beta del STN y en el análisis visual y acústico de las señales obtenidas mediante microelectrodos de registro (MER por sus siglas en inglés). Los MER tienen menos de 100 micrómetros de diámetro y mediante el sistema estereotáctico mecanizado van ingresando al cerebro atravesando distintas estructuras funcionales del mismo como son el tálamo anterior (TH), la zona incerta (ZI), el núcleo subtalámico (STN) y la sustancia negra pars reticulata (SNr). Cada una de estas zonas presenta registros eléctricos específicos. El análisis de los registros MER para determinar si los electrodos están en la zona de implantación es uno de los procedimientos más utilizado en las instituciones de salud. La detección del área objetivo (STN) es una tarea compleja y la exactitud con que se cumpla depende del éxito del tratamiento con un DBS. Recientemente se han publicado los primeros trabajos que utilizando la minería de datos mediante algoritmos individuales de clasificación supervisados determinan si los registros obtenidos mediante los MER corresponden al STN. En la presente tesis se plantea como hipótesis que es posible obtener modelos de clasificación supervisados con un buen rendimiento, haciendo uso de la combinación de clasificadores y una adecuada selección de características, con el fin de que puedan ser utilizados como herramienta de soporte para detectar las señales MER correspondientes al STN durante una cirugía para implantar un DBS. Se adquirieron señales MER de 22 pacientes con Parkinson a los cuales se les realizó una cirugía bilateral para la implantación de un dispositivo DBS en STN. Las cirugías fueron desarrolladas en su totalidad en el Hospital Universitario y Politécnico La Fe de Valencia, España. Dos especialistas experimentados analizaron durante la intervención quirúrgica y para cada nivel de profundidad, si las señales MER visualizadas en el monitor se correspondían al STN u a otra zona del cerebro. Con la idea de minimizar posibles errores humanos en el proceso de etiquetación de los registros se analizaron posteriormente a cada cirugía las imágenes de coregistros realizadas durante la cirugía (al inicio y al final de la misma) que permiten determinar mediante imágenes la profundidad a la cual se ingresó al STN. Cada registro se dividió en ventanas de 1 s solapadas al 50% a las cuales se calcularon y estandarizaron off line 16 características temporales utilizando el software Matlab®. Las características utilizadas permiten describir las principales variables temporales de las señales obtenidas mediante los MER asociadas a la actividad de fondo sin considerar las espigas y otras que sólo caracterizan a las espigas. La eliminación automática de registros asociados a ruidos de movimiento, mecánicos o eléctricos fue el único pre procesamiento que se aplicó a los registros MER utilizados, asegurado que la base de datos represente situaciones semejantes de la vida real. Con la base de datos obtenida se procedió a entrenar los clasificadores con los datos de 21 pacientes (datos de entrenamiento) dejando los datos del paciente excluido para validación (datos de validación). Este proceso se repitió 22 veces dejando un paciente distinto por vez en cada conjunto de entrenamiento-validación realizando una generación de conjuntos mediante validación cruzada “leave one out” por paciente. Se configuraron y entrenaron algoritmos de clasificación individuales: K veninos más cercanos (KNN) y árboles de decisión tipo CART y CHAID. También se trabajó con las siguientes estrategias combinadas: bagging, boosting, random forest y stacking. Para todos los casos se seleccionaron los parámetros que permitieron configurar cada algoritmo para minimizar el error de clasificación mediante optimización Bayesiana. Cuando se utilizaron las 16 características temporales para entrenamiento y validación stacking obtuvo los mejores resultados utilizando 3 clasificadores diferentes como base (nivel 0): KNN, árboles de decisión tipo CART y bagging y como meta clasificador (nivel 1) a random forest. Este algoritmo combinado se denominó Stack2 y presentó una exactitud promedio porcentual (ACC) del 94,6%, una especificidad (ESP) del 95,6% y una sensibilidad (SEN) del 95,6%. Se ensayaron algoritmos de selección de características tipo filtro, de envoltura e integrados. Branch and bound resultó el mejor para los datos de la tesis y permitió seleccionar 6 características de las 16 totales (4 vinculadas a la actividad de fondo y 2 a las espigas) que mejoraron el rendimiento de Stack2 en lo que se denominó Stack2_BBound, pasando ACC al 95%, ESP al 96% y SEN al 94%. Este clasificador tuvo el mejor desempeño de todos los analizados en la presente tesis indicando que dicho clasificador presenta mayor poder de discriminación de clases mediante las características vinculadas a la actividad de fondo. Con el desarrollo de las investigaciones realizadas en la presente tesis doctoral se pudo obtener un modelo combinado de clasificación supervisada mediante la metodología stacking, cuyos indicadores de desempeño y tiempos de validación indican que puede ser utilizado, con excelente rendimiento, en un proceso de clasificación automática para detectar señales del STN a partir de procesar señales eléctricas cerebrales provenientes de micro electrodos de registros durante cirugías para implantar un estimulador cerebral profundo en paciente con Parkinson. Los indicadores de desempeño obtenidos son superiores a los reportados por otros trabajos de investigación para este mismo tipo de aplicación. Los resultados de la presente tesis contribuyen con un modelo novedoso (Stack2_BBound) que constituye el primer paso para un sistema de clasificación automático que trabaje en el quirófano como herramienta de soporte a los neurofisiólogos y neurocirujanos al momento de definir la localización óptima del electrodo de estimulación de un sistema DBS en pacientes con Parkinson. Un sistema de éstas características permitirá reducir los tiempos de una cirugía de esta naturaleza además de brindar un resultado de clasificación objetivo.A deep brain stimulator (DBS) is an implantable neuro stimulator for the treatment of motor disorders of Parkinson's disease (PD). Using minimally invasive stereotactic surgery techniques, two stimulation electrodes are usually implanted in the Subthalamic nuclei (STN) of both cerebral hemispheres. The detection of STN is a complex and crucial task for the success of the therapy with a DBS, which is carried out in most institutions through the analysis of the patterns of signals acquired using microelectrodes register (MER). The hypothesis of the present thesis is defined like it is possible to obtain supervised classification models with a good performance, making use of the ensemble classifiers and an adequate selection of characteristics, in order that they can be used as a support tool to detect the MER signals corresponding to the STN during surgery to implant a DBS. MER signals were acquired from 22 patients with PD who underwent bilateral surgery for the implantation of a DBS in STN. As the MER descended towards the STN, whose initial coordinates were established by magnetic resonance imaging, two specialists established whether the records observed at each depth level corresponded to 'STN' or 'non-STN'. After the surgery, each record was divided into windows of 1 s overlapped to 50%, which were calculated and standardized off line 16 temporal characteristics. With the database obtained, the classifiers were trained with the data of 21 patients (training data) leaving the patient's data excluded for validation (validation data). This process was repeated 22 times leaving a different patient at a time in each training-validation set. The trained stacking algorithm with the 16 temporal characteristics obtained the best results using 3 different classifiers as a base: K- nearest neighbors, CART and bagging decision trees and random forest as meta classifier. This stacking classifier obtained an average accuracy (ACC) of 94.6%, a specificity (ESP) of 95.6% and a sensitivity (SEN) of 95.6%. The branch and bound feature selection algorithm was the best for the thesis data and allowed to select 6 characteristics of the total 16 that improved the stacking performance, which one obtained an ACC of 95%, ESP of 96% and SEN of 94%. A combined model of supervised classification was obtained using the stacking and branch and bound methodology, whose performance and validation times indicate that it can be used, with excellent performance, in an automatic classification process to detect STN signals from the temporal characteristics of cerebral electrical signals of the MER