mTBI-Predict

Chief Investigator of mTBI-Predict

Lt Col Professor James Mitchell is a Military Clinician Scientist and Consultant in Neurology and Rehabilitation at University Hospitals Birmingham and Defence Medical Rehabilitation Centre, and an Honorary Clinical Professor in Neurology at the University of Birmingham. Lt Col Professor Mitchell is a member of the leadership team for the Translational Brain Science Research Group and Chief Investigator of the mTBI-Predict consortium and UK MEGaBlast study. 

Co-Investigator of mTBI-Predict

Professor Lisa Hill is a Translational Neuroscientist in the Department of Biomedical Sciences at the University of Birmingham, where she serves as Pre-Clinical Lead of the Translational Brain Science Research Group. Her work bridges brain and eye health, integrating molecular discovery and experimental medicine to improve outcomes for patients with neurodegenerative and traumatic conditions.

Co-Investigator of mTBI-Predict

Professor Alex Sinclair is Professor of Neurology within the Department of Metabolism and Systems Science at the University of Birmingham, and Head of the Translational Brain Science Research Group. She is an international figure in translational research in Idiopathic Intracranial Hypertension (IIH), headache and traumatic brain injury.

Overview

Headache is the most common symptom following traumatic brain injury (TBI), occurring in up to 90% of people who have an injury. Headache following TBI is termed post-traumatic headache (PTH) and usually begins within the first week, the headache commonly resembles migraine and shares many features including a moderate/severe throbbing character, nausea and/or vomiting, light and noise sensitivity.

Post-traumatic headache is a leading cause of disability following head injury and is closely linked to other key symptoms such as balance and dizziness, cognitive function, mental health and social function. There is great variation in the character and symptoms associated with PTH. Capturing this complexity renders deep phenotyping of headache crucial to this study. There is no current specific treatment for PTH and currently treatment usually follows that of migraine. Patients with pre-injury headache have substantially worse physical and cognitive symptoms than patients with no headache. Importantly PTH intensity has an adverse impact on the ability to return to duty, work or play following injury.

Recently new headache therapies for PTH have emerged, but accurate identification of those who will go on to develop long term sequelae is still lacking and will be essential to target early treatment.

Lead Researchers

• Dr Mark Thaller – Clinical Lecturer in Neurology, University of Birmingham
• Professor Alex Sinclair – Professor of Neurology
• Professor Lisa Hill - Professor of Translational Neuroscience
• Lieutenant Colonel Professor James Mitchell - Programme Director

Overview

The prevalence of posttraumatic stress disorder (PTSD) is high often comorbid with other psychiatric disorders, such as depression and anxiety, disrupted social and occupational functioning, substance abuse and increased suicidality.

Traumatic brain injuries (TBI), often sustained during traumatic experiences, can lead to long-term sequalae, including PTSD and depression. PTSD is characterised by emotional dysregulation, hyper-arousal, avoidance behaviours, and re-experiencing of traumatic episodes.

In addition to the primary psychiatric symptoms, secondary sequalae include deficits in cognitive domains, such as inhibition and impaired executive functioning, including working memory and attention modulation.

The Mental Health workstream will work with partnered workstreams across this project to develop a computational framework for classifying TBI, PTSD, depression and other commonly comorbid mental health challenges using data-driven, machine learning approaches, neuroimaging measures and associated clinical outcome measures.

Lead Researchers

• Professor Alex Sinclair – Professor of Neurology
• Professor Lisa Hill - Professor of Translational Neuroscience 
• Dr Carl Krynicki - Assistant Professor of Clinical Psychology and Clinical Psychologist 

Overview

Vestibular dysfunction – with imbalance and/or dizziness – affects most patients with acute traumatic brain injury (TBI) across all severities.

Typically, patients are affected by multiple vestibular diagnoses. We recently conducted the first acute-prospective study in moderate-to-severe TBI showing that imbalance and deficits in vestibular perception overlap behaviourally and neuroanatomically.

Additionally, balance recovery is predicted by vestibular perceptual recovery. Similar data for mTBI is not available. We predict however that mTBI patients will also have multiple vestibular diagnoses and whose functional recovery (return to work/play) will depend upon a complex interplay between initial degree of brain injury affecting imbalance and vestibular perception, and aggravating factors including vestibular migraine, mood and sleep disorders.

We predict that in slow recovery patients, the pattern of dysfunction will differ between those with significant brain injury (with objective vestibular dysfunction) vs. those without significant brain injury manifesting without objective vestibular dysfunction.

However, since both groups’ recovery is modulated by the burden of aggravating factors, we predict that their respective degree of disability and its rate of recovery (measured by return to work/play) will not differ. The key study outcome however will be personalising treatment using the pattern of disability to inform the design of future interventional studies.

Lead Researchers

• Dr Raymond Reynolds – Senior Lecturer in Motor Control, University of Birmingham
• Professor Sam Lucas - Professor of Cerebrovascular, Exercise & Environmental Physiology, University of Birmingham

Overview

Mild to moderate traumatic brain injuries can have lasting consequences for cognition. However, the ways in which cognition is affected in any given individual is hard to predict. In part, this is because people differ in the areas of the brain that are affected and the capacities they have to adapt and recover. This variability is hard to study because people also differ substantially in their cognitive strengths even before they have a brain injury.

The cognitive work-stream of the mTBI Predict research program will use a state-of-the-art online assessment app that precisely measures different aspects of a person's cognitive faculties, such as memory, attention, planning and self-restraint, to better understand the nature of cognitive problems after traumatic brain injury. The app can be deployed on practically any tablet or smart phone device in order that patients can be assessed repeatedly at home with hardware they already own.

It will be used to assess patients repeatedly throughout the research programme, providing a detailed profile of each individual's cognitive abilities and how they change across time. These cognitive profiles will be analysed in relation to clinical records, brain images, sleep diaries and other types of data in order to better understand why people are affected differently by traumatic brain injuries and why some people recover better than others.

Cognitron App

The Cognitron app is a smartphone app which is used to help collect high-fidelity, psychological and physiological data from our participants, without the need for repeat visits. This allows us to go beyond the kinds of measures that we might see in a clinical environment, and measure changes in both state and trait over time, during recovery from TBI.

Cognitron allows us to repeatedly test measures of cognitive performance using brief well validated neurocognitive paradigms, as well as extensive questionnaire and diary data that allows us to psychological state such as mood as well as measuring physiological outcome measures such as sleep. Our participants will use the app in combination with a ‘smart watch’ which will allow us to generate a very well validated measurement of their sleep physiology as well as monitoring various other cardiovascular measures across time.

Lead Researchers

• Professor Adam Hampshire – Professor of Cognitive and Computational Neuroscience, King's College London
• Dr Davinia Fernández-Espejo – Senior Lecturer in School of Psychology, University of Birmingham
• Dr Helen Brunger – Health Psychologist, Defence Medical Rehabilitation Centre

Overview

Up to 80% of patients with military mTBI report visual symptoms, including difficulty with reading and near work, spatial perception and light sensitivity. Problems with vision are also potential confounders for the other tests after mTBI; for example, a patient performing poorly on neuropsychological assessments with visual impairment may be incorrectly diagnosed with cognitive problems when in fact the limitation is visual.

The frequency of visual symptoms in TBI reflects the high proportion (~30%) of the cerebral cortex devoted to visual function. Eye manifestations of mTBI, assessed by objective assessment of visual structure and function, can provide objective and reproducible disease biomarkers, termed “oculomics”.

TBI affecting the optic nerve results in a condition called traumatic optic neuropathy (TON). TON typically manifests with reductions in visual acuity, colour vision, pupil reactivity and visual field in the affected eye. Historic data suggests that this occurs in only 2% of patients with TBI, but more recent studies with more detailed assessment of the eyes suggests that optic nerve changes after TBI may be much more frequent.

Optical coherence tomography (OCT) provides detailed structural and blood flow information in the optic nerve and retina. Other standard tests including pupil measurement and colour vision evaluations allow assessment of function to compare with the structural OCT measurements. ​

Classical teaching is that traumatic optic neuropathy is not manifest as structural retinal change (loss of nerve cells) until 6 weeks after injury. Case reports using OCT suggest that retinal changes are detectable as early as 1-2 weeks, but this has not been systematically characterised.

Given that the earliest reported changes in retinal structure have been at 1-2 weeks after injury, we expect that initial assessment within the first 2-3 weeks after injury will have few injury-related structural retinal changes allowing comparison with this assessment as a baseline for evaluation of subsequent changes. ​

In addition to detailed assessment of optic nerve and retinal structure and function, many patients complain of problems focusing, which can be objectively measured by autorefraction.

​Lead Researchers 

• Lieutenant Colonel Richard Blanch – Defence Consultant Advisor Ophthalmology, Defence Medical Services
• Professor Alex Sinclair – Professor of Neurology
• Professor Lisa Hill - Professor of Translational Neuroscience

Overview

Sleep plays an important role in brain recovery and functioning making it a key marker to measure in the prognostication of mild traumatic brain injury (mTBI). Sleep disruption following injury can be multi-faceted, with different sleep profiles reflecting different injury types and recovery trajectories. In this work stream, we will capture different aspects of sleep using a variety of established and scalable techniques that can be captured remotely, non-invasively and cost-effectively.

Objective sleep data will be captured using wrist-worn actigraphy and simultaneous sleep diary entries via an app. This non-invasive wearable technology detects movement allowing for an estimation of sleep-wake patterns over extended periods of time (several weeks). Through the application of established algorithms to actigraphy data, we will obtain important quantifiable information about the sleep-wake cycle across the course of recovery following mTBI.

Subjective sleep data will be captured using an array of established self-report measures. The subjective perception of sleep can be particularly useful in clinical assessments, including the long-term prognosis of mTBI. Our sleep questionnaire platform will include the PSQI (sleep quality), ISI (insomnia severity), ESS (daytime sleepiness), and FSS (Fatigue severity) alongside other questionnaires. Together with our objective monitoring approaches, these questionnaires will form a complete picture of sleep health, including sleep duration, timing or regularity, sleep quality, as well as highlighting any risk of common sleep disorders.

In this workstream we aim to identify the most important sleep biomarkers for the prognostication of mTBI and determine whether we can improve mTBI prognosis overall through merging sleep biomarkers with other cutting-edge techniques captured in the program. Together, this will improve the future management of mTBI to ensure better outcomes for patients.

Lead Researchers

• Professor Clare Anderson - Professor of Sleep and Circadian Science
• Professor Alex Sinclair – Professor of Neurology
• Professor Lisa Hill - Professor of Translational Neuroscience 

Overview

Fluid biomarkers are substances we can measure in body fluids to gain some insight into a medical condition. The fluids are most commonly blood, saliva and urine due to the ease of obtaining these samples and there is increasing interest in saliva in particular due to the ease of ‘spitting into a pot’, allowing patients to collect samples themselves, in any situation, including at home, pitch side or on military operations.

There are a wide array of tests that can be performed, most commonly we use lab techniques to measure proteins released when brain cells are damaged – such as after a head injury – several of these proteins have been studied over the last 2 decades and are on the cusp of being introduced into clinical practice where they will help to inform patient care.

As well as looking at more traditional techniques the mTBI fluid biomarker team will be using new technology to look at RNA markers – these little snippets of genetic code change following injuries and can provide a wealth of information. Furthermore, hormone changes are known to be important following TBI and in mental health and headache conditions. New markers specific to headache will also allow us to gain understanding of individual symptoms affecting patients with mTBI.

We aim to utilise an array of fluid biomarkers and cutting-edge techniques to help predict outcome following mTBI allowing us treat patients in a rapid and precise fashion.

Lead Researchers

• Professor Lisa Hill - Professor of Translational Neuroscience
• Professor Alex Sinclair – Professor of Neurology
• Lieutenant Colonel James Mitchell – Programme Director

Overview 

Alterations in cerebral blood flow and its regulation are thought to play an important role in the pathophysiology underlying mTBI. More broadly, vascular responsiveness is an established biomarker of vascular dysfunction, with the sensitivity to detect the first signs of dysfunction. Given this, targeting measures of cerebrovascular responsiveness to quantify and track mTBI severity and recovery are key.

To date, studies have used different imaging modalities (Doppler, MRI, fNIRS) and stimulus-response approaches (vasoactive stimuli [e.g. inspired carbon dioxide], postural challenges [e.g. repeated stand-squats], cognitive based tasks, exercise) to assess the impact of mTBI on measures of cerebrovascular responsiveness (e.g. Cerebrovascular CO2 reactivity (CVR), cerebral autoregulation (CA), neurovascular coupling (NVC)). A number show promise as potential biomarkers, but which is the most sensitive is unclear as studies to date typically only use one modality and/or target only one regulatory process. Furthermore, a longitudinal, prognostic study has not been done to predict how any of these potential biomarkers relate to clinical outcomes. We will assess the utility of these functional responsiveness measures on the cerebral physiology across the range of imaging modalities and functional tests.

In addition, mTBI-induced alterations in vascular, metabolic and neural processes will impact on the balance between excitatory and inhibitory transmission within the brain. This can be measured on a physiological level using transcranial magnetic stimulation (TMS) to measure the excitatory state of the motor cortex. Indeed, these measures have been shown to be sensitive to changes in acute mTBI and in Post-Concussion Syndrome, although how well they relate to and predict clinical outcome in mTBI is not known.

For more information, please visit the Centre for Human Brain Health and the Sport, Exercise and Rehabilitation Sciences webpages.

Lead Researchers

• Professor Sam Lucas - Professor of Cerebrovascular, Exercise & Environmental Physiology, University of Birmingham
• Dr Ned Jenkinson – Deputy Head of Research, University of Birmingham
• Professor Ali Mazaheri - Professor of Translational Neuroscience, University of Birmingham
• Professor Hamid Dehghani – Professor of Medical Imaging, University of Birmingham 
• Professor Karen Mullinger - University of Nottingham

Overview

This workstream will use and develop mathematical modelling and computational analysis to examine the data collected in other workstreams to help identify markers that can be used to better diagnose and predict the outcomes in TBI.

In recent years our understanding of the causes of neurological disorders has changed. Simplistic concepts of single brain regions being responsible for disease are being updated with the concept that the connectivity between different brain areas (known as called connectomics) is increasingly implicated in neurological disorders. This workstream will use computer models that describe both the neural activity within brain regions, as well as the connections between them and the fMRI, MEG and EEG data collected in other workstreams.

Using algorithms that we have developed in previous work, we will construct large scale brain networks and take two approaches to interrogating these networks. In the first approach, we are not seeking an answer to a preconceived question, but we will use a range of techniques to reveal potential markers of severity of TBI and the likelihood of developing specific outcomes.

The second approach is to define network markers based on specific assumptions made from previously published studies and define mathematical models to address these questions and learn more and help predict outcomes.

Lead Researchers

• Professor Dave Smith – Professor of Applied Mathematics, University of Birmingham
• Professor Hamid Dehghani – Professor of Medical Imaging, University of Birmingham