Biology subjects

Armitage, O.

Publications and source records attributed to Armitage, O..

3 recordsLinked to original sources

bioRxiv2025

Multi-modal, multi-species, and multi-task latent-space model for decoding level of consciousness

Assessing the degree and characteristics of consciousness is central to caring for patients with Disorders of Consciousness (DoC), yet current standard of care is a bedside questionnaire. Using a laminar neural probe we introduce a self-supervised, multi-modal representation learning approach that constructs an interpretable 2-D latent space of brain state from continuous neural recordings. We jointly encode local field potentials (LFPs), single-unit firing rates, and a movement proxy into a time-aware autoregressive VAE (TN-VAE). In rats, pigs and humans undergoing controlled depth anesthesia experiments, the learned latent trajectories nonlinearly, but smoothly, separate four expert-defined states (awake, light, moderate, deep) at 2s resolution, exceeding the temporal granularity of behavioral scoring (15m). The latent space supports linear readout of both coarse state and individual behavioral components, and its axes align with known physiology: delta/alpha power differentiates unconscious sub-states, while gamma and unit firing distinguish wakefulness. To enable cross-species use and incomplete modality sets, we add lightweight, species-specific stitching layers. This model pretrained on tri-modal rat data and fine-tuned with unimodal (LFP) pig data and unimodal human intraoperative data, successfully separates awake versus anesthetized states in new unseen human subjects, demonstrating zero-shot multi-subject transfer without per-patient calibration. The model further generalizes in a multi-task manner to predict the results of additional stimuli. These results highlight a path toward a foundation model for DoC that generalizes across sessions, subjects, species, and stimuli to enable scalable, continuous brain-state monitoring and clinical decision support.

neuroscience↗

bioRxiv2023

Online Bayesian Optimization of Nerve Stimulation

ObjectiveIn bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs modifying their function. Stimulation devices, capable of triggering exogenous neural signals using electrical wave forms, require a complex and multi-dimensional parameter space in order to control such wave forms. Determining the best combination of parameters (wave form optimization, or dosing) for treating a particular patients illness is therefore challenging. Comprehensive parameter searching for an optimal stimulation effect is often infeasible in a clinical setting, due to the size of the parameter space. Restricting this space, however, may lead to sub-optimal therapeutic results, reduced responder rates, and adverse effects. ApproachAs an alternative to a full parameter search, we present a flexible machine learning, data acquisition and processing framework for optimizing neural stimulation parameters requiring as few steps as possible using Bayesian optimization. Such optimization builds a model of the neural and physiological responses to stimulations enabling it to optimize stimulation parameters and to provide estimates of the accuracy of the response model. The vagus nerve innervates, among other thoracic and visceral organs, the heart, thus controlling heart rate and is therefore ideal for demonstrating the effectiveness of our approach. Main results. The efficacy of our optimization approach was first evaluated on simulated neural responses, then applied to vagus nerve stimulation intraoperatively in porcine subjects. Optimization converged quickly on parameters achieving target heart rates and optimizing neural B-fibre activations despite high intersubject variability. SignificanceAn optimized stimulation waveform was achieved in real time with far fewer stimulations than required by alternative optimization strategies, thus minimizing exposure to side effects. Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that a complex set of neural stimulation parameters can be optimized in real-time for a patient to achieve a personalized precision dosing.

neuroscience↗

bioRxiv2023

Using neural biomarkers to personalize dosing of vagus nerve stimulation

BackgroundVagus nerve stimulation (VNS) is an established therapy for treating a variety of chronic diseases, such as epilepsy, depression, obesity, and for stroke rehabilitation. However, lack of precision and side-effects have hindered its efficacy and extension to new conditions. ObjectiveTo achieve a better understanding of the relationship between VNS parameters and neural and physiological responses to enable the design of personalized dosing procedures to improve precision and efficacy of VNS therapies. MethodsWe used biomarkers from recorded evoked neural activity and short-term physiological responses (throat muscle, cardiac and respiratory activity) to understand the response to a wide range of VNS parameters in anaesthetised pigs. Using signal processing, Gaussian processes (GP) and parametric regression models we analyse the relationship between VNS parameters and neural and physiological responses. ResultsFirstly, we observe inter-subject variability for both neural and physiological responses. Secondly, we illustrate how considering multiple stimulation parameters in VNS dosing can improve the efficacy and precision of VNS therapies. Thirdly, we describe the relationship between different VNS parameters and the evoked neural activity and show how spatially selective electrodes can be used to improve fibre recruitment. Fourthly, we provide a detailed exploration of the relationship between the activations of neural fibre types and different physiological effects, and show that recordings of evoked neural activity are powerful biomarkers for predicting the short-term physiological effects of VNS. Finally, based on these results, we discuss how recordings of evoked neural activity can help design VNS dosing procedures that optimize short-term physiological effects safely and efficiently. ConclusionUnderstanding of evoked neural activity during VNS provide powerful biomarkers that could improve the precision, safety and efficacy of VNS therapies.

neuroscience↗