Primary supervisor: Dr Alejandra Aranceta Garza
Other supervisors: Dr Eric Hall, Prof Francesco Negro, Dr Ian Morrison
Location: University of Dundee
Project description
Motor neuron disease (MND) is a rapidly progressive, fatal neurodegenerative disorder causing debilitating muscle weakness and paralysis. Diagnosis currently takes months, significantly impacting care delivery given the mean survival of 3 years from symptom onset.
This project will explore the development of innovative, non-invasive digital biomarkers to aid earlier diagnosis and predict MND progression. You will longitudinally quantify motor neurone deterioration by examining clinically relevant action potentials–such as amplitude, duration, length, shape, and phases–using advanced signal processing and statistical methods. The study will involve repeated assessments of ~20 MND patients and age-matched controls every 3-4 months over 12 months, giving you access to a rich, longitudinal dataset of neurophysiological measures.
You will integrate demographic, clinical, cognitive and neurophysiological data using modern statistical and machine-learning techniques to develop and validate predictive algorithms, creating a blueprint for MND care stratification. This approach will provide more sensitive diagnostic markers whilst exploring disease sub-classifications beyond current major classifications. By addressing the variability in disease progression, particularly in Amyotrophic Lateral Sclerosis (the most common MND subtype), you will develop objective tools to enhance prognostic accuracy for individual patients.
This research aims to shorten the time to diagnosis, support earlier interventions, and provide personalised prognostic information, helping clinicians and people with MND to plan care using a precision-medicine approach. You will work closely with clinical neurologists, engineers, and data scientists, gaining experience of translational research in a clinical setting.
References
1. Farina D, Negro F. Common Synaptic Input to Motor Neurons, Motor Unit Synchronization, and Force Control. Exercise and sport sciences reviews 2015;43(1):23-33. doi: 10.1249/jes.0000000000000032
2. Negro F, Orizio C. Robust estimation of average twitch contraction forces of populations of motor units in humans. Journal of Electromyography and Kinesiology 2017;37:132-40. doi: https://doi.org/10.1016/j.jelekin.2017.10.005
3. Negro F, Muceli S, Castronovo AM, et al. Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation. Journal of neural engineering 2016;13(2):026027. doi: 10.1088/1741-2560/13/2/026027
4. Carvalho Md, Barkhaus PE, Nandedkar SD, et al. Motor unit number estimation (MUNE): Where are we now? Clinical Neurophysiology 2018;129:1507-16
5. Felice KJ. A longitudinal study comparing thenar motor unit number estimates to other quantitative tests in patients with amyotrophic lateral sclerosis. Muscle & Nerve: Official Journal of the American Association of Electrodiagnostic Medicine 1997;20(2):179-85.
Suitable first degree subjects
Medicine, Mathematics, Statistics, Engineering, Computing, Neuroscience, Data Science
Essential/desirable skills and experience
Essential: Some element of coding (python/Matlab).
Desirable: Participant/patient testing experience.
