Comparing Offline Decoding Performance in Physiologically Defined Neuronal Classes

Objective: Recently, several studies have documented the presence of a bimodal distribution of spike waveform widths in primary motor cortex. Although narrow and wide spiking neurons, corresponding to the two modes of the distribution, exhibit different response properties, it remains unknown if these differences give rise to differential decoding performance between these two classes of cells. Approach: We used a Gaussian mixture model to classify neurons into narrow and wide physiological classes. Using similar-size, random samples of neurons from these two physiological classes, we trained offline decoding models to predict a variety of movement features. We compared offline decoding performance between these two physiologically defined populations of cells. Main results: We found that narrow spiking neural ensembles decode motor parameters better than wide spiking neural ensembles including kinematics, kinetics, and muscle activity. Significance: These findings suggest that the utility of neural ensembles in brain machine interfaces may be predicted from their spike waveform widths

Low-frequency local field potentials in primate motor cortex and their application to neural interfaces

PhD ThesisFor patients with spinal cord injury and paralysis, there are currently very limited options for clinical therapy. Brain-machine interfaces (BMIs) are neuroprosthetic devices that are being developed to record from the motor cortex in such patients, bypass the spinal lesion, and use decoded signals to control an effector, such as a prosthetic limb. The ideal BMI would be durable, reliable, totally predictable, fully-implantable, and have generous battery life. Current, state-of-the-art BMIs are limited in all of these domains; partly because the typical signals used—neuronal action potentials, or ‘spikes’—are very susceptible to micro-movement of recording electrodes. Recording spikes from the same neurons over many months is therefore difficult, and decoder behaviour may be unpredictable from day-today. Spikes also need to be digitized at high frequencies (~104 Hz) and heavily processed. As a result, devices are energy-hungry and difficult to miniaturise. Low-frequency local field potentials (lf-LFPs; < 5 Hz) are an alternative cortical signal. They are more stable and can be captured and processed at much lower frequencies (~101 Hz). Here we investigate rhythmical lf-LFP activity, related to the firing of local cortical neurons, during isometric wrist movements in Rhesus macaques. Multichannel spike-related slow potentials (SRSPs) can be used to accurately decode the firing rates of individual motor cortical neurons, and subjects can control a BMI task using this synthetic signal, as if they were controlling the actual firing rate. Lf-LFP–based firing rate estimates are stable over time – even once actual spike recordings have been lost. Furthermore, the dynamics of lf-LFPs are distinctive enough, that an unsupervised approach can be used to train a decoder to extract movement-related features for use in biofeedback BMIs. Novel electrode designs may help us optimise the recording of these signals, and facilitate progress towards a new generation of robust, implantable BMIs for patients.Research Studentship from the MRC, and Andy Jackson’s laboratory (hence this work) is supported by the Wellcome Trust

Machine Learning for Multi-Layer Open and Disaggregated Optical Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

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

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Brain-Computer Interfaces using Electrocorticography and Surface Stimulation

The brain connects to, modulates, and receives information from every organ in the body. As such, brain-computer interfaces (BCIs) have vast potential for diagnostics, medical therapies, and even augmentation or enhancement of normal functions. BCIs provide a means to explore the furthest corners of what it means to think, to feel, and to act—to experience the world and to be who you are. This work focuses on the development of a chronic bi-directional BCI for sensorimotor restoration through the use of separable frequency bands for recording motor intent and providing sensory feedback via electrocortical stimulation. Epidural cortical surface electrodes are used to both record electrocorticographic (ECoG) signals and provide stimulation without adverse effects associated with penetration through the protective dural barrier of brain. Chronic changes in electrode properties and signal characteristics are discussed, which inform optimal electrode designs and co-adaptive algorithms for decoding high-dimensional information. Additionally, a multi-layered approach to artifact suppression is presented, which includes a systems-level design of electronics, signal processing, and stimulus waveforms. The results of this work are relevant to a wider range of applications beyond ECoG and BCIs that involve closed-loop recording and stimulation throughout the body. By enabling simultaneous recording and stimulation through the techniques described here, responsive therapies can be developed that are tuned to individual patients and provide precision therapies at exactly the right place and time. This has the potential to improve targeted therapeutic outcomes while reducing undesirable side effects