Digital healthcare for the management of functional neurological disorders

This talk will address some of the structural and illness-specific challenges of delivering effective healthcare for people with FND, and what the opportunities might be for digital technologies to improve healthcare process and outcomes.

Professor Mark Edwards, King's College London, UK

Professor Mark Edwards, King's College London, UK

Mark Edwards (MBBS, BSc (Hons), PhD, FRCP) is Professor of Neurology and Interface Disorders at King’s College London and Honorary Consultant Neurologist at The Maudsley and King’s College Hospitals. He has a specialist clinical and research interest in Movement Disorders and Functional Neurological Disorder (FND). He did his PhD with Professor John Rothwell and Professor Kailash Bhatia at the UCL Institute of Neurology, studying the pathophysiology of genetic dystonia. Following completion of neurology training he became a Senior Lecturer and Honorary Consultant Neurologist at UCL and the National Hospital for Neurology. After moving to St George’s in 2015, he expanded this work to develop one of the first integrated diagnostic and treatment services for FND alongside continued research work into the pathophysiology of the disorder and development and testing of novel treatments, including involvement the first randomised trial of specialist physiotherapy for functional movement disorders.

Professor Rohit Shankar, University of Plymouth

Chronic stress is the cause of many adverse changes in the brain: increased release of glucocorticoids, widespread inflammation, changes in neuronal morphology such as myelination in prefrontal cortex (PFC) and hippocampus (HC), reduced levels of neurotrophic factors such as Fibroblast Growth Factor2, alterations in brain connectivity especially within PFC-amygdalo-HPC networks, and circulation disruption. Chronic stress has also been found to be an antecedent of cognitive impairment and dementia. 

Affiliative tactile interactions buffer social mammals against neurobiological and behavioural effects of stress, and with the recent COVID pandemic, where for the first time in human evolution affective touch interactions were significantly restricted, we saw the adverse consequences on people’s stress levels and subsequent mental and physical health. But what's the mechanism? Here a case is made for the role of a relatively recently discovered (in humans) population of cutaneous low threshold thermo-mechanosensitive c-fibres called c-low threshold mechanoreceptors (C-LTMR) as a neurobiological substrate responsible for regulating resilience to stress. These 1st order neurones are velocity tuned, preferentially encoding gentle, dynamic touch delivered at ~3 cm/sec, projecting centrally to dorsal posterior insular cortex and ACC. C-LTMRs are now being understood to provide a singular vital protective function, from the nurturing touch of the mother. Deprivation of nurturing touch causes life-long adverse neurodevelopmental consequences. There is now evidence of C-LTMRs putative role in dementia. By analogy with the vital role vitamins play in physical health, affective touch and its neuronal substrate the C-LTMRs, can be described as ‘Vitamin-T’ for the social brain.

Professor Francis McGlone, Manchester Metropolitan University, UK

Professor Francis McGlone, Manchester Metropolitan University, UK

Francis McGlone is Professor in Neuroscience, Visiting Professor, Manchester Metropolitan University, and Aalto University, Finland. He has a long-term interest in the function of the different classes of sensory nerves innervating the skin, particularly those that code for touch, temperature, pain, itch (for which an Ig Nobel prize was awarded) and the recent discovery of a population of unmyelinated gentle touch sensitive c-fibres that are hypothesised to be the neurobiological substrate for ‘affective touch’. He has focussed the past ~25 years, in collaboration with his Swedish colleagues, to characterising the functional properties of this system of nerves, C-Low Threshold Mechanoreceptors, and their emerging role across the lifespan in underpinning physical and mental health.  Techniques used in this research span single unit recordings with microneurography, psychophysical measurements, functional neuroimaging, behavioural measures, and psychopharmacological approaches. He is Founder & President of the International Association for the Study of Affective Touch (IASAT).

Functional Neurological Disorder (FND) is often associated with significant morbidity. The estimated incidence is 4–10/100,000 and is more common in adults. In South Asian countries, the prevalence is as high as 31% and twice as common in women than in men. The economic burden of clinic-based care is high due to the involvement of multiple specialties and extensive investigations. Studies showed an excess annual cost associated with FND (range $4,964-$86,722 2021 US dollars), which consisted of both direct and large indirect costs. Studies showed promise that interventions, including the provision of a definitive diagnosis, could reduce this cost. The assessment and therapy may be stretched over longer periods and contribute to care costs. Hence, innovations for a definitive diagnosis and effective monitoring and management can reduce the care burden.

The digital tools appear promising in the detection of triggering factors, promoting positive behavioural changes, self-monitoring, and therapy and networking for online community support and teleconsultation. The limiting factors to access appropriate care appear to be affordability, access to devices, literacy, and privacy in lower resource settings. In lower and middle countries, patriarchal and collectivistic societies having access is a challenge. According to a telecommunications report, there are up to 50% of Indians, with internet access. The smartphone penetration in India, however, reached 71% in the financial year 2023. Thus, with rising mobile phone penetration, and reduced data charges, digital tools especially mobile applications hold a promise for effective management of FND symptoms in lower resource settings.

The Rosa Burden Centre provides a 3 week multi-disciplinary programme for people with FND symptoms. We have reviewed symptoms-based outcome measures on admission and discharge, together with 3 and 6 month follow up. I will also present information about our novel FND Liaison Practitioner role.

Dr Elizabeth Mallam, Southmead Hospital, UK

Dr Elizabeth Mallam, Southmead Hospital, UK

Dr Mallam trained in Edinburgh, London, and Bristol. She qualified as a Neurology Consultant in 2017 and has delivered 2 FND clinics a week since then. Dr Mallam is based at the Rosa Burden Centre at Southmead Hospital in Bristol. The Rosa Burden Centre has a long history of treating people with FND. Dr Mallam and the FND team were delighted to recently secure funding to expand the FND service at the Rosa Burden Centre to include a novel FND Liaison Practitioner role and expand their multi-disciplinary inpatient and outpatient programmes incorporating neurophysiotherapy, occupational therapy, and psychotherapy.

This talk will present an overview of the applications of robotics and immersive technologies in neuro-rehabilitation research. Robotics technology and virtual reality systems found multiple useful applications in neuro-rehabilitation research and clinical practice. Rehabilitation robotics and assistive technology enable clinicians to perform complex, monotonic and time-consuming physiotherapy tasks more easily supporting faster and more efficient patient recovery. The talk will specifically address the issue of rehabilitation technology accessibility, and design aspects that can improve its affordability and breadth of applications. To address the technology affordability, examples of modular robotic systems designed for the upper limb will be presented and the potential of at-home based tele-rehabilitation will be discussed.

Ildar Farkhatdinov, King's College London, UK

Ildar Farkhatdinov, King's College London, UK

Dr Ildar Farkhatdinov is a Senior Lecturer in Healthcare Engineering (Robotics and Mechatronics) at King’s College London, UK. He is an internationally leading expert in assistive robotics and human-machine interaction with applications to rehabilitation, neurosciences and immersive environments. He is a principal investigator of several projects on wearable robotics, mobility assistance and haptic interfaces. Several of his research works were recognised as the best paper or finalists for best paper awards at leading robotics conferences. Before joining King's, he was an academic at Queen Mary University of London and a researcher at Imperial College London, UK. He earned PhD in Robotics in 2013 (Sorbonne University, UPMC, France), MSc in Mechanical Engineering in 2008 (KoreaTech, South Korea) and BSc in Automation and Control in 2006 (Moscow University, Russia). He is a co-founder of Human Robotix Ltd, a London-based start-up aiming to develop novel robotic technologies for human neuro-muscular system research.

Children with neuromuscular disorders suffer from a combination of motor control deficits and lower limb weakness resulting in a range of gait impairments. Robotic exoskeletons offer a potential solution to improve mobility in these individuals; their modularity in design and implementation enable the ability to tailor devices to meet individual user needs. Initial cohort studies of these technologies, performed mostly in clinical settings, demonstrate their effectiveness as assistive devices to improve gait biomechanics. Their promise as therapeutic interventions to improve long term function is less clear. This talk will highlight key advances made by the Neurorobotics Research Group at the US National Institutes of Health to explore this application, including novel approaches to the use of exoskeletons to resist or challenge limb movement during walking as a form of gait training. The design of and initial results from a randomized crossover study to determine whether 12 weeks of this overground gait training delivered in the community setting is effective will also be presented. Finally, this presentation will consider the context of other relevant advances within the field and discuss future directions for improving functional recovery in children with neurologic disorders.

Dr Thomas C Bulea, National Institutes of Health Clinical Center, USA

Dr Thomas C Bulea, National Institutes of Health Clinical Center, USA

Thomas C Bulea received the BS in mechanical engineering from The Ohio State University and MS and PhD in biomedical engineering from Case Western Reserve University. He completed post-doctoral fellowships at the University of Houston and the National Institutes of Health Clinical Center. He joined the NIH Clinical Center in 2014 as a staff scientist and since 2021 has been a tenure track investigator in the Neurorehabilitation and Biomechanics Research Section of the Rehabilitation Medicine Department, where he leads the Neurorobotics Research Group. 

Dr Bulea’s research interests include functional neuroimaging, neural interfacing, and rehabilitation robotics and exoskeletons for treatment of movement disorders. Dr Bulea also serves the Training Director for the Rehabilitation Medicine Department and as an Associate Editor of Wearable Technologies and IEEE Transactions on Neural Systems and Rehabilitation Engineering.

In this talk a mathematical framework for understanding transitions between apparently healthy brain states and seizure-like states is introduced. A dynamic network model consists of a mathematical model that describes how the dynamics within a node varies over time with nodes coupled together through directed or undirected graphs. The developed framework is used to describe mechanistic properties of the network that make seizures more (or less) likely. Algorithms are introduced from which properties of the mathematical model can be informed directly from clinical imaging data such as electroencephalography (EEG). In this context, nodes now represent brain regions (as reflected from channels of the EEG) and edges determined by functional connectivity. Those mechanistic properties that best explain transitions to seizure states are considered as candidate biomarkers from which a statistical classifier is developed and applied in a large cohort of clinically non-contributory EEG recordings from both people with epilepsy and other conditions. The potential for the approach to provide meaningful clinical decision support is discussed.

Professor John Terry, University of Birmingham, UK

Professor John Terry, University of Birmingham, UK

John is an Interdisciplinary Professorial Fellow at the University of Birmingham, a role he holds jointly across mathematics, computer science and medicine. Holding a prestigious EPSRC Established Career Fellowship, John and his team develop and apply mathematical models and computer algorithms to develop mechanistic understanding of biomedical and clinical systems. John is internationally renowned for his work in epilepsy and neuroendocrinology, having published over 80 pieces of original research and built teams of over 30 researchers in Exeter and more recently Birmingham. John is motivated by the translation of research and is co-founder and managing director of the multi-award-winning startup Neuronostics, who have developed a UKCA-marked clinical decision support tool for diagnosis of epilepsy and differential conditions.

Making ‘real-world’ BCI, ie a wearable BCI system that can be used in the home, poses specific challenges in managing and extracting useable information from systems that by design are smaller and less intrusive and, usually, have fewer recording channels which are most probably scalp (EEG) based. This talk addresses the issues that arise when trying to extract meaningful information from single or few channel, noisy, recordings. The talk pays particular attention to data-driven methods to information extraction, specifically in blind source separation where information is extracted from recordings using particular criteria such as, for example, statistical independence, which is the case in Independent Component Analysis. This process is made more difficult due to the low-channel count and the limited spatial/ contextual information; the talk will address ways in which this can be addressed. Overall, it is possible to see that despite low channel counts and challenging recording environments it is possible to extract meaningful and useful information from noisy data using well-structured and powerful assumptions about the data and its acquisition.

Professor Christopher James

Professor Christopher James

Professor James is a biomedical engineer and neuroscientist researching techniques for use in brain and behaviour analysis. His work spans the development of signal processing techniques applied to human brain activity, to behaviour analysis in humans and other model organisms. He has published over 170 papers in varied biomedical engineering journals and refereed conferences and has regular interaction with the press through newspaper articles, opinion pieces, radio interviews and TV programmes. In November 2012 he gave the prestigious IET Wheatstone Lecture on Brain-to-brain Communication in Chennai, India. In 2013 he was awarded the IEEE MGA Achievement Award and in 2012 the IET Sir Monty Finniston Medal. He is founding editor-in-chief of the IET Healthcare Technology Letters journal, founder and CEO of EMbody Biosignals Ltd, as well as founding director of Augmented Insights Ltd, a spinout from the University of Warwick creating pattern recognition algorithms extracting behaviour information from health data.

Wearable devices, allowing the monitoring of a wide range of physiological parameters, are having a transformative impact on health and wellbeing applications. We are now able to collect longitudinal data in a way never previously possible. At the same time, current wearables have a wide range of open opportunities for making them even better, from improving accuracy, to improving comfort, to embedding real-time machine learning bases analyses. This talk will provide an overview of these different topics. It will start with a high-level overview and roadmap for future wearable hardware, with particular emphasis on advances being enabled by emerging flexible electronics. I will then present examples of our work on data science and machine learning applied to wearable data.

Professor Alex Casson, University of Manchester, UK

Professor Alex Casson, University of Manchester, UK

Alex Casson is Professor of Biomedical Engineering at the University of Manchester. He is a specialist in 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. Professor 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. 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.

Emerging data-driven modelling methods are affording new opportunities for building bio-inspired models for neural activity. Of particular interest is understanding how neural pathways from input stimuli to behavioural responses function. Importantly, data-driven models are capable of framing neurosensory integration into a modern framework, allowing for an improved understanding of the role of network structure and function. Specifically, the encoding space of neural activity can be extracted from modern mathematical methods with a goal of improved scientific understanding.

J Nathan Kutz, University of Washington, USA

J Nathan Kutz, University of Washington, USA

Nathan Kutz is the Yasuko Endo and Robert Bolles Professor of Applied Mathematics and Electrical and Computer Engineering at the University of Washington, having served as chair of applied mathematics from 2007-2015. He is also the Director of the AI Institute in Dynamic Systems.  He received the BS degree in physics and mathematics from the University of Washington in 1990 and the PhD in applied mathematics from Northwestern University in 1994. He was a post doctorate in the applied and computational mathematics program at Princeton University before taking his faculty position. He has a wide range of interests, including neuroscience to fluid dynamics where he integrates physics-informed and biophysics informed machine learning with dynamical systems and control. 

When we read, we move our eyes every 250 milliseconds. This means there is very little time between eye movements to process the words we are fixate at, decide where to look next, and move our eyes. To better understand the brain mechanisms of the fast attentional mechanisms supporting natural reading, we combined MEG and eye-tracking recordings. Participants were asked to read sentences while their brain activity and eye movements were recorded. We found that parafoveal words being saccade goals are processed surprisingly fast at the semantic level. This previewing predicted individual reading speed. To investigate these mechanisms in children learning to read, we are adapting our OPM/MEG system such that it can be used for paediatric recordings. The aim is to gain insight into the reading mechanisms that need to be developed in children to support proficient reading.

Professor Ole Jensen, University of Birmingham, UK

Professor Ole Jensen, University of Birmingham, UK

Ole Jensen received his MSc in Electrical Engineering in 1993 from the Technical University of Denmark and his PhD in Neuroscience in 1998 from Brandeis University, specialising in computational modelling of oscillatory networks. He was a then postdoctoral fellow at Helsinki University of Technology, using magnetoencephalography (MEG) to explore brain dynamics. In 2002, he became head of the MEG laboratory at the Donders Institute and principal investigator in 2003. He was appointed professor at Radboud University Nijmegen in 2013, and in 2016, professor in translational neuroscience at the University of Birmingham, co-founding the Centre for Human Brain Health. His research focuses on linking oscillatory brain activity to cognition, particularly memory and attention, and developing a paediatric OPM-MEG system.

It is now widely recognised that physical and psychological well-being are dependent on good sleep. Sleep is marked by clearly identifiable physiological states that define stages of sleep, with different stages involved in different physiological and cognitive processes. An important sleep stage that may be involved in brain clearance of toxins and memory formation is slow-wave sleep (also known as deep sleep). We present evidence that slow-wave sleep can be enhanced through use of a portable, user-friendly device deployed in the home. This closed-loop transcranial electrical stimulation technology detects when a person is about to enter slow-wave sleep and injects low-level current to sustain and enhance this sleep stage. The ability to selectively enhance slow-wave sleep has significant implications for health.

Phan Luu, Brain Electrophysiology Laboratory, USA

Phan Luu, Brain Electrophysiology Laboratory, USA

Phan Luu is Chief Scientist at BEL and Neurosom. He is an active scientist whose scientific publications cover brain evolution, brain mechanisms of self-regulation, sleep, transcranial electrical stimulation and brain imaging. He has over 25 years of experience in industry directing commercial development of research and clinical technologies. He is also experienced with clinical trials and the regulatory process of bringing new technologies to clinical practice.

Discussion panel formed of Professor Siddhartha Bandyopdhyay, University of Birmingham, Professor Charles Phelps, University of Rochester, USA, and Professor Darius Lakdawalla, USC Price School of Public Policy, USA.