Brain biomechanics group

The Brain biomechanics group combines advanced neuroimaging, computational modelling and artificial intelligence to investigate how the brain responds to injury and disease. We develop personalised digital twins that integrate MRI and sensor data to quantify brain structure and mechanical behaviour. Our long-term goal is to support more objective diagnosis, prognosis and personalised rehabilitation for concussion and neurodegenerative disease.

Brain concussion research

Since 2021, the ABI and Mātai Medical Research Institute have conducted a longitudinal study of adolescent rugby players in Aotearoa, New Zealand. Players undergo repeated MRI scans across the rugby season, while instrumented mouthguards record the frequency and magnitude of head impacts. By combining neuroimaging, head-impact kinematics, clinical assessments and subject-specific computational models, we investigate how repetitive head impacts and concussion affect the brain over time. Our goal is to develop objective methods for assessing injury severity and predicting recovery.

Tayebi, M., Kwon, E., McGeown, J., Potter, L., Taylor, D., Condron, P., . . . Holdsworth, S. (2025). Characterizing the Effect of Repetitive Head Impact Exposure and mTBI on Adolescent Collision Sports Players' Brain with Diffusion Magnetic Resonance Imaging.. Journal of neurotrauma, 42(5-6), 349-366. doi:10.1089/neu.2024.0064

Brain tissue mechanics research

Brain tissue changes often precede any visible changes in neurological diseases such as brain injury and neurodegenerative diseases. We aim to characterise this subtle change with a novel AI-based technique that combines advanced MRI and digital twin modelling. Specifically, our method can characterise brain tissue properties to characterise early changes in brain tissue before any visible signs appear. We aim to use this as an early diagnosis tool for neurodegenerative diseases.

Saludar, C.J.A., Tayebi, M., Kwon… Shim, V. A Reproducible MRI-to-FE Workflow for Generating Population-Specific and Subject-Specific Finite Element Head Models. Ann Biomed Eng (2026). https://doi.org/10.1007/s10439-026-04432-y

Wearable sensor-based detection and monitoring of brain changes

Because MRI is not readily accessible for routine monitoring, we aim to develop an MRI-free surrogate model that can provide an indication of brain health from wearable sensor data. Using large datasets such as the UK Biobank, which include both MRI and wrist-worn activity data, we are training our digital-twin-based AI model to predict indicators of brain health from wearable measurements. Depending on the application, these may include physiological measures such as heart-rate variability or head-impact signals. The aim of this project is to develop a wearable-sensor-based diagnostic and prognostic tool that may identify warning signs earlier than conventional testing methods.

Mathew, J., Saludar, C. J. A., Yeung, T., Holdsworth, S., Wang, A., Fernandez, J., . . . Shim, V. (2026). Rapid atlas-based predictions of regional brain strain simulations from wearable head kinematics using diffusion MRI-informed machine learning. Journal of biomechanics, 206, 113497. doi:10.1016/j.jbiomech.2026.113497

Members

Primary contact

Vickie Shim

Academics

Vickie Shim
Eryn Kwon
Maryam Tayebi
Samantha Holdsworth
Justin Fernandez
Alan Wang

Students

Christian Saludar
Jacob Mathew
Katze Zambo
Chun Vong
Maerdian Adili
Lu Cheng
Rohan Dhuri

Our people

Collaborators

Prof. Antoine Jerusalem (Oxford, UK)
Prof. Mehmet Kurt (University of Washington, Seattle, USA)
Dr. Lucy Hiscox (Cardiff University, UK)
Prof . Yun Jung Bae (SNU, South Korea)
Prof.. Silvia Budday (Friedirch-Alexander-Universitat)
Prof. Benedicte Vanwanseele (KU Leuven)

Funding

Marsden
Neurological Foundation
HRC
TTM RAP Funding
Royal Society of NZ Catalyst