Characterising motor unit properties and behaviours and clinical outcomes using artificial intelligence in humans with spinal cord injury
https://www.findaphd.com/phds/project/characterising-motor-unit-properties-and-behaviours-and-clinical-outcomes-using-artificial-intelligence-in-humans-with-spinal-cord-injury/?p166033
About the Project
Full project title: Characterising motor unit properties and behaviours of the paraspinal musculature and clinical outcomes using pattern learning and matching techniques in artificial intelligence in humans with spinal cord injury.
Project overview
Spinal cord injury (SCI) has significant impact on patients’ physical and psychological well-being. There are an estimated 1.2 million new cases worldwide each year. Such injuries disrupt the communication between the brain and body, causing loss of motor function below the injury. In the early stages following injury, repair processes are extensive in the spinal cord to restore these communication channels. Studies in rodents with SCI showed injury-induced axonal sprouting and functional recovery. With advanced technology, we are now able to measure axonal sprouting and reinnervation in human SCI using non-invasive high-density surface electromyography (HDEMG) system. Paraspinal muscles are directly innervated by the spinal nerves near the spinal cord where the injury is. We hypothesise that properties and behaviours of motor unit action potentials in the paraspinal muscles, assessed via HDEMG, in human with SCI can reveal the repair processes in the spinal circuits and predict outcomes of recovery. The project offers research training in a variety of techniques and methods, clinical training, and research networks across institutions nationally and internationally. The supervisory team provides divers expertise; the host institution is a leading research-intensive University in the UK. The student will be well supported and developed through the programme.
Person Specification
Applicants should have a strong background in Research, and ideally a background in medicine, neuroscience, biomedical sciences, biomedical engineering, or a relevant field . They should have a commitment to research in humans with spinal cord injury and hold or realistically expect to obtain at least an Upper Second Class Honours Degree in a relevant subject. Applicants should have a willingness to travel since data collection from patients with spinal cord injury will take place at partner NHS hospitals Governance and regulation for working in the NHS will be strictly followed (i.e., enhanced DBS check, occupational health check).
Informal enquiries should be directed to the project supervisor(s): Dr Shin-Yi (Chloe) Chiou s.chiou@bham.ac.uk
Applicant webinar
The DTP Leads are holding two applicant webinars for prospective candidates interested in applying to the DTP on Monday 11 December at 18:30 GMT and Wednesday 13 December 2023 at 11:00 GMT. Both sessions will last no longer than 1 hour and will be held via Zoom. If you are thinking of applying to the DTP, you are encouraged to attend one of these sessions. As well as having the opportunity to ask questions, the session will provide information on the application process as well as information about the AIM DTP.
To register your place, visit PhD Opportunities – MRC AIM (bham.ac.uk)
How to apply
To apply, please visit PhD Opportunities – MRC AIM (bham.ac.uk) and follow the instruction to complete your application.
The deadline for submitting applications is midday (GMT) Friday 12 January 2024. Please ensure that your application is submitted with all required documentation as incomplete applications will not be considered.
Funding Notes
If you are successful, you will receive a stipend (currently £18,622 per year for 2023/24) and a tuition fee waiver for 4 years.
Successful candidates will also receive an allowance for a laptop, a travel and conference allowance and an allowance for laboratory/PhD running costs.
References
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3. Merletti R, Holobar A, Farina D. Analysis of motor units with high-density surface electromyography. J Electromyogr Kinesiol. 2008; 18:879-90.
4. Nishikawa Y, Holobar A, Watanabe K, Takahashi T, Ueno H, Maeda N, Maruyama H, Tanaka S, Hyngstrom AS. Detecting motor unit abnormalities in amyotrophic lateral sclerosis using high-density surface EMG. Clin Neurophysiol. 2022;142:262-272.
5. Clarke AK, Atashzar SF, Vecchio AD, Barsakcioglu D, Muceli S, Bentley P, Urh F, Holobar A, Farina D. Deep Learning for Robust Decomposition of High-Density Surface EMG Signals. IEEE Trans Biomed Eng. 2021;68:526-534.
6. Chiou SY, Clarke E, Lam C, Harvey T, Nightingale TE. Effects of Arm-Crank Exercise on Fitness and Health in Adults With Chronic Spinal Cord Injury: A Systematic Review. Front Physiol. 2022;13:831372.
7. The World Health Organisation. Fact sheets – spinal cord injury, 2013. https://www.who.int/news-room/factsheets/detail/spinal-cord-injury
8. Mohebian MR, Marateb HR, Karimimehr S, Mañanas MA, Kranjec J, Holobar A. Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points. Front Comput Neurosci. 2019;13:14.
9. Pérez-Sanpablo AI, Quinzaños-Fresnedo J, Romero-Ixtla M, Aguirre-Güemez AV, Rodríguez-Reyes G, Pérez-Zavala R, Barrera-Ortiz A, Quijano-González Y. Validation of inertial measurement units for the assessment of trunk control in subjects with spinal cord injury. J Spinal Cord Med. 2021: 1-10.
10. Jia X, Thorley A, Chen W, Qiu H, Shen L, Styles IB, Chang HJ, Leonardis A, de Marvao A, O’Regan DP, Rueckert D, Duan J. Learning a Model-Driven Variational Network for Deformable Image Registration. IEEE Trans Med Imaging. 2022;41:199-212.
