Members

Dr Alex Casson is a Reader in the Materials, Devices and Systems division of the Department of Electrical and Electronic Engineering at the University of Manchester. His research focuses on non-invasive bioelectronic interfaces: the design and application of wearable sensors, and skin-conformal flexible sensors, for human body monitoring and data analysis from highly artefact prone naturalistic situations. This work is highly multi-disciplinary and he has research expertise in:
– Ultra low power microelectronic circuit design at the discrete and fully custom microchip levels.
– Sensor signal processing and machine learning for power and time constrained motion artefact rich environments.
– Manufacturing using 3D printing, screen printing, and inkjet printing.
He has particular interests in closed loop systems: those which are tailored to the individual by personalised manufacturing via printing; and tailored to the individual by adjusting non-invasive stimulation (light, sound, electrical current) using data driven responses/outputs from real-time signal processing. Dr Casson’s ultra low power sensors work is mainly for health and wellness applications, with a strong background in brain interfacing (EEG and transcranial current stimulation) and heart monitoring. Applications focus on both mental health situations including chronic pain, sleep disorders, and autism, and physical health/rehabilitation applications including diabetic foot ulceration, and chronic kidney disease.

Electrical stimulation therapies represent nonpharmacologic treatment options for chronic pain management. However, we do not understand how these therapies work and this knowledge gap continues to limit the success of these technologies. Therefore, our research group implements a patient-specific approach that integrates detailed clinical mechanistic testing with computational models. We believe that this systematic approach will improve our scientific understanding of neurostimulation for chronic pain and provide scientific guidance to individualize and optimize several components of these neurostimulation technologies.

How does the brain represent, process, and regulate pain and affective experiences? The goal of our lab research is to answer this question to better understand pain and emotions, thereby promoting the physical and psychological well-being of people who suffer from pain and emotional distress. Specific aims include 1) understanding the brain mechanisms of pain and emotion dynamics (mechanisms), 2) developing integrated brain models of human affective experiences and clinical outcomes (biomarkers), 3) translating basic and computational neuroscience findings into clinical applications (translation), and 4) building a life- and neuroscience-inspired artificial intelligence (AI) to better understand pain, affect, and human intelligence.

My main interest is in understanding how our body representation changes in the brain (brain plasticity). Our primary model for brain plasticity is hand function and dysfunction, and how we could use technology to increase hand functionality in able and disabled individuals at all ages.

Although some AI models look or act like a brain, they do not necessarily think like a brain. My research focuses on understanding how the brain learns and makes inferences from the machine learning perspective. To address this question, I have put together ideas from AI and computational neuroscience. The approach is two-fold: 1) “Brain↦AI” aimed at understanding how the brain learns from a machine learning standpoint, and 2) “AI↦Brain” aimed at understanding why such neural processes occur.

I investigate the functionality of brain and spinal cord sensory circuits in healthy/chronic pain rodents and humans. My labs translational experiments focus on addressing the problem of failure when it comes to the discovery of novel analgesics. To address invalid targets, our pre-clinical work focuses on defining circuitry in health/pinpointing dysfunction in disease. To address limitations of currently used methods to assess pain, our clinical work focuses on translational paradigms and appropriate stratification of patients into cohorts.

Flavia Mancini is an MRC Career Development Award fellow and head of a multidisciplinary research group, called the Nox Lab, at the Department of Engineering, University of Cambridge. The Nox Lab includes a mix of computational neuroscientists, information and biomedical engineers, united by a shared passion for the development of open-source computational methods to understand brain function and improve human health. Their work is motivated by neuroscience questions relating to how neural activity generates perception and behaviour, mostly in humans. They use a combination of neuroimaging, physiological, behavioural and computational methods for the processing of neural signals and behavioural/clinical data.

The Nox Lab’s current work has a primary application to chronic pain. They take an innovative information engineering approach to understanding the neural processing and regulation of pain. Nox Lab’s research is split into a basic research line, aiming to understand the computational and neural mechanisms of pain inference, learning and control, and a translational research line in which they translate this knowledge into digital and neurotechnology tools for precision medicine, pain prevention and treatment.

My research focuses on the neurobiology of decision-making and learning. I use multimodal neuroimaging and neurostimulation methods to uncover the roles of multiple areas in the brain, predominantly the prefrontal cortex. Recently, I have shown that transcranial ultrasound neuromodulation can safely change neural activity in precise parts of the brain, both in non-human primates and humans. I am now working towards bringing this technology forward and apply it to mental health challenges.

My lab seeks to investigate the multisensory bodily basis for self-consciousness. We do this by creating ‘out of body’ illusions using virtual reality setups, and by measuring the integration of multisensory exteroceptive and interoceptive bodily signals in neurotypical participants, participants with autism, and participants living with chronic pain and depersonalisation.