Bewegungsdekodierung für elektrophysiologisch gestützte intelligente adaptive tiefen Hirnstimulation bei der Parkinson-Krankheit

Deep Brain stimulation is an effective treatment for movement disorders such as Parkinson’s disease or essential tremor. Current therapy protocols do not adjust in real-time to the present need for treatment but instead rely on constant stimulation parameters. A novel concept called intelligent adaptive deep brain stimulation triggers stimulation based on decoding of a predefined state, such as movement, in a demand-driven way. Invasive Brain Computer Interfaces were previously presented for decoding behavioral states both using local field potential recordings from depth electrodes, primarily in movement disorder patients, and using electrocorticographic signals in epilepsy patients. Future brain implants may successfully treat different movement disorders using both modalities. A systematic brain signal decoding comparison of the two recording sites within patients was lacking. In this work, we analyzed invasive intraoperative recordings from Parkinson’s disease patients undergoing deep brain stimulation therapy. Subthalamic local field potentials and simultaneous electrocorticographic signals were recorded while the patients were performing a hand-gripping force task. We used these signals to develop a real-time-enabled feature estimation and decoding framework and investigated different hyperparameter-optimized machine learning approaches for the prediction of movement strength. We identified optimal temporal, spatial, and oscillatory decoding components. Our analysis showed for the first time that movement decoding performances of cortical recordings were superior to subcortical ones using different machine learning methods. We found that gradient-boosted decision trees showed the best performances for electrocorticographic recordings, while Wiener filters were optimal for subthalamic signals. Models from single electrode contacts were better performing than methods that combine data from multiple contacts. Decoding performances were negatively correlated to Parkinson's disease-specific symptom scores. Previously, subthalamic beta oscillations were reported to reflect Parkinson’s disease symptom severity, here we found that decoding performances were negatively correlated to elevated subthalamic beta oscillations. Additionally, we developed a movement decoding network that predicted contact-specific movement decoding performances using functional and structural connectivity profiles. In conclusion, we propose a computational framework based on invasive neurophysiology for brain signal decoding and highlight interactions of decoding performances with Parkinson’s disease symptom states, pathological symptom biomarkers, and whole-brain connectivity. This thesis, therefore, constitutes a significant contribution to the development of intelligent personalized medicine for adaptive deep brain stimulation.Tiefe Hirnstimulation ist eine effektive Behandlung von Bewegungsstörungen wie bei der Parkinson-Krankheit oder dem Essentiellen Tremor. Derzeitige Protokolle passen sich nicht in Echtzeit dem aktuellen Behandlungsbedarf an, sondern beruhen auf konstanten Stimulationsparametern. In einem neuen Therapieverfahren, der „intelligenten adaptiven tiefen Hirnstimulation“, wird die Stimulation bedarfsgerecht anhand eines vordefinierten Zustands, wie beispielsweise der Bewegung, angepasst. Invasive Brain Computer Interfaces konnten in vorigen Studien Verhaltenszustände mit elektrophysiologischen Aufnahmen dekodieren. Hier wurden entweder lokale Feldpotentiale, abgeleitet von Elektroden in tiefen Hirnregionen bei Patient*innen mit Bewegungsstörungen, oder elektrokortikographische Signale, bei Epilepsie-Patient*innen, verwendet. Beide Signal-Modalitäten könnten für zukünftige Hirnimplantate genutzt werden. Ein systematischer Vergleich der jeweiligen Dekodierleistung wurde bei denselben Patient*innen bisher nicht durchgeführt. Hier analysierten wir deshalb intraoperative Aufzeichnungen subthalamischer lokaler Feldpotentiale und gleichzeitige elektrokortikographische Ableitungen von Parkinson-Patient*innen während der Implantation des tiefen Hirnstimulators. Die Patient*innen führten Handbewegungen mit unterschiedlicher Greifkraft aus. Mittels echtzeitfähiger Feature Berechnung und Dekodierung untersuchten wir verschiedene Hyperparameter-optimierte maschinelle Lernverfahren zur Vorhersage der Bewegungsstärke. Wir identifizierten optimale temporale, oszillatorische und lokalisationsspezifische Parameter der Dekodierung. Unsere Studie zeigt zum ersten Mal, dass die Dekodierleistung von kortikalen gegenüber subkortikalen Signalen anhand von verschiedenen maschinellen Lernmethoden deutlich überlegen war. Gradient-boosted decision trees waren für elektrokortikographische Aufzeich-nungen die beste Dekodiermethode, während Wiener Filter für subthalamische Signale am geeignetsten waren. Modelle aus einzelnen Elektrodenkontakten zeigten bessere Dekodierleistungen als Modelle die Daten mehrerer Kontakte kombinierten. Die Dekodierleistung korrelierte negativ mit der Parkinson-Symptomschwere, und korrelierte zusätzlich negativ mit erhöhten subthalamischen Beta-Oszillationen, von denen bereits berichtet wurde, dass sie den Parkinson-Schweregrad widerspiegeln. Zusätzlich entwickelten wir ein Netzwerk für die Vorhersage der kontaktspezifischen Dekodierleistungen anhand von funktionellen und strukturellen Konnektivitätsprofilen. Zusammenfassend stellen wir ein computerbasiertes, neurophysiologisches Framework für die invasive Hirnsignal-Dekodierung vor. Wechselwirkungen der Dekodierleistung wurden mit der Parkinson-Symptomschwere, elektrophysiologischen Biomarkern pathologischer Symptome und der Konnektivität des gesamten Gehirns identifiziert. Diese Dissertation unterstützt daher die Entwicklung intelligenter, personalisierter Medizin für die adaptive tiefe Hirnstimulation

Clinical applications of magnetic resonance imaging based functional and structural connectivity

Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective

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

Neurosurgery in Obsessive Compulsive Disorder:From targets to treatment to tracts and back again

People with obsessive compulsive disorder (OCD) suffer from obsessive thoughts and/or behavior, with a constant presence that can hardly be ignored. A range of interventions is effective in the management of OCD including behavioral therapy, cognitive therapy and cognitive behavioral therapy (CBT). In addition, a large body of evidence advocate on the use of selective serotonin reuptake inhibitors (SSRIs) and clomipramine, a tricyclic antidepressant, in the treatment of OCD, often used in combination with CBT. However, 40-60% of patients remain treatment-refractory, defined as a less than 25% reduction in Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) score. This scale is used to determine the severity of the disorder. The resistance of such a big amount of patients to therapy may urge the need for alternative treatment strategies, such as deep brain stimulation (DBS) of subcortical structures or gamma knife ventral capsulotomy (GVC), a noninvasive procedure using gamma rays to destroy certain brain tissues. The first part of this thesis aimed at identifying fiber bundles associated with clinical response to DBS or GVC. OCD patients consistently underperform across multiple cognitive domains. The second part of this thesis was focused on the neuropsychological outcome of OCD DBS in order to identify a cognitive pattern associated with a good outcome or that would (in part) help explain the functional mechanism of OCD-DBS. The third part focused on several postoperative aspects of (OCD)-DBS patients including surgical and hardware related adverse events of DBS and reviewing the effectiveness, timing and procedural aspects of CBT after DBS with the aim to provide clinical recommendations

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