Watch, Imagine, Attempt: Motor Cortex Single-Unit Activity Reveals Context-Dependent Movement Encoding in Humans With Tetraplegia

Planning and performing volitional movement engages widespread networks in the human brain, with motor cortex considered critical to the performance of skilled limb actions. Motor cortex is also engaged when actions are observed or imagined, but the manner in which ensembles of neurons represent these volitional states (VoSs) is unknown. Here we provide direct demonstration that observing, imagining or attempting action activates shared neural ensembles in human motor cortex. Two individuals with tetraplegia (due to brainstem stroke or amyotrophic lateral sclerosis, ALS) were verbally instructed to watch, imagine, or attempt reaching actions displayed on a computer screen. Neural activity in the precentral gyrus incorporated information about both cognitive state and movement kinematics; the three conditions presented overlapping but unique, statistically distinct activity patterns. These findings demonstrate that individual neurons in human motor cortex reflect information related to sensory inputs and VoS in addition to movement features, and are a key part of a broader network linking perception and cognition to action

Encoding of Motor Behaviors by Cortical Neuronal Networks

Performance of motor behavior requires complex coordination of neural activity across diverse regions of cortex at multiples scales. At the level of coordination across large areas of cortex, this activity is thought to be related to similarly broad concepts of movement from goal identification to motor planning to generation of motor commands. At smaller scales on the level of local populations of individual neurons in motor and premotor cortex, we observe complex non-stationary firing patterns that appear to be related to the movement itself. Our understanding of the details of this relationship are incomplete, however. Earlier work by the community largely focused on the analysis of individual units in isolation. Technological advances and changes in experimental paradigms have led to the simultaneous recording of hundreds of neurons simultaneously. From an analysis standpoint, we are observing a similar shift in focus from the individual neuron to the population as a whole. This dissertation investigates encoding and decoding techniques that handle time-varying neuronal activity from within the context of a reach-to-grasp task. The first part of this work investigates the dynamics of neural coding of reach and grasp through a series of temporally localized classifiers. The second part of this thesis proposes a semi-supervised learning approach to identifying task relevant neurons for classification purposes and for identifying communities of neurons that co-modulate their activity in correlation to a common external variable. The third part of this work proposes and demonstrates an approach to modeling the firing of individual neurons as a weighted combination of other neurons with weighting dependent on the task being performed

Enabling human physiological sensing by leveraging intelligent head-worn wearable systems

This thesis explores the challenges of enabling human physiological sensing by leveraging head-worn wearable computer systems. In particular, we want to answer a fundamental question, i.e., could we leverage head-worn wearables to enable accurate and socially-acceptable solutions to improve human healthcare and prevent life-threatening conditions in our daily lives? To that end, we will study the techniques that utilise the unique advantages of wearable computers to (1) facilitate new sensing capabilities to capture various biosignals from the brain, the eyes, facial muscles, sweat glands, and blood vessels, (2) address motion artefacts and environmental noise in real-time with signal processing algorithms and hardware design techniques, and (3) enable long-term, high-fidelity biosignal monitoring with efficient on-chip intelligence and pattern-driven compressive sensing algorithms. We first demonstrate the ability to capture the activities of the user's brain, eyes, facial muscles, and sweat glands by proposing WAKE, a novel behind-the-ear biosignal sensing wearable. By studying the human anatomy in the ear area, we propose a wearable design to capture brain waves (EEG), eye movements (EOG), facial muscle contractions (EMG), and sweat gland activities (EDA) with a minimal number of sensors. Furthermore, we introduce a Three-fold Cascaded Amplifying (3CA) technique and signal processing algorithms to tame the motion artefacts and environmental noises for capturing high-fidelity signals in real time. We devise a machine-learning model based on the captured signals to detect microsleep with a high temporal resolution. Second, we will discuss our work on developing an efficient Pattern-dRiven Compressive Sensing framework (PROS) to enable long-term biosignal monitoring on low-power wearables. The system introduces tiny on-chip pattern recognition primitives (TinyPR) and a novel pattern-driven compressive sensing technique (PDCS) that exploits the sparsity of biosignals. They provide the ability to capture high-fidelity biosignals with an ultra-low power footprint. This development will unlock long-term healthcare applications on wearable computers, such as epileptic seizure monitoring, microsleep detection, etc. These applications were previously impractical on energy and resource-constrained wearable computers due to the limited battery lifetime, slow response rate, and inadequate biosignal quality. Finally, we will further explore the possibility of capturing the activities of a blood vessel (i.e., superficial temporal artery) lying deep inside the user's ear using an ear-worn wearable computer. The captured optical pulse signals (PPG) are used to develop a frequent and comfortable blood pressure monitoring system called eBP. In contrast to existing devices, eBP introduces a novel in-ear wearable system design and algorithms to eliminate the need to block the blood flow inside the ear, alleviating the user's discomfort

Network Structure and Function in the Input Stage of the Cerebellar Cortex

It has long been recognised that neuronal networks are complex systems, whose dynamics depend on the properties of the individual synapses and neurons and the way in which they are interconnected. However, establishing clear links between network structure and function has proven difficult. To address this question I applied tools and techniques from computational neuroscience, neuroinformatics, information theory, machine learning, spatial point process theory and network theory, deploying them on a suitable HPC infrastructure where appropriate. Moreover, access to electrophysiological and anatomical data enabled me to develop biologically accurate models and to compare my theoretical predictions with analyses of raw data. In this work, I focused on the granule cell layer (GCL), the input stage of the cerebellar cortex. The GCL is particularly well suited to this type of analysis, as its structural characteristics are comparatively regular, well known and conserved across animal species, and several of its basic functions are relatively well understood. I showed that the synaptic connectivity in simple feed forward networks like the GCL governs the trade-off between information transmission and sparsification of incoming signals. This suggests a link between the functional requirements for the network and the strong evolutionary conservation of the anatomy of the cerebellar GCL. Furthermore, I investigated how the geometry of the GCL interacts with the spatial constraints of synaptic connectivity and gives rise to the statistical features of the chemically and electrically coupled networks formed by mossy fibres, granule cells and Golgi cells. Finally, I studied the influence of the spatial structure of the Golgi cell network on the robustness of the synchronous activity state it can support

Spike train similarity space (ssims): A framework for single neuron and ensemble data analysis

Increased emphasis on circuit level activity in the brain makes it necessary to have methods to visualize and evaluate large scale ensemble activity, beyond that revealed by raster-histograms or pairwise correlations. We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how Spike train SIMilarity Space (SSIMS) analysis captures the relationship between goal directions for an 8-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models