Deakin-Aston Cotutelle - Predicting the neuropsychological outcomes of children after brain injury

This is a doctoral cotutelle project between Deakin University (Australia) and Aston University (UK)

The successful PhD Student will be awarded a scholarship from Deakin University with the supervision team being drawn from Deakin University and Aston University. The PhD Student will graduate with two testamurs, one from Deakin University and one from Aston University, each of which recognises that the program was carried out as part of a jointly supervised doctoral program. The PhD Student is anticipated to spend a minimum 6 months of the total period of the program at Aston University, including for 8 consecutive weeks in their final year of study to comply with UK Research Excellence Framework (REF) regulations, with the remainder of the program based at Deakin University.

The program is for a duration of 3 years and scheduled to commence in October 2025.

A cutting-edge project leveraging MRI (magnetic resonance imaging) and machine learning to predict neurodevelopmental outcomes in children with brain injuries. The long-term goals of this work are to support early intervention and help shape personalized care to maximize potential and improve lives.

Childhood brain insults can arise from many types of injury, and have profound effects on cognition and behaviour, impacting families and straining healthcare, education, and welfare systems. These injuries are a leading cause of disability in young people. Early intervention improves cognitive and behavioural outcomes by altering neural pathways and is more cost-effective than addressing issues later in life ^{(Anderson, Spencer-Smith & Wood, 2011)}. Identifying predictors of long-term outcomes is essential to help children reach their potential.

While early support can help prevent developmental delays compared to their typically-developing peers, precise methods to predict high-risk cases are lacking. Currently, up to 56% of children with brain injuries develop long-term psychological impairments, underscoring the need for better predictive tools to guide effective allied-health intervention and clinical management.

Regardless of cause or type of injury, brain insults often lead to issues with attention, memory, executive function, and behaviour which are enduring. These common functional outcomes are likely a result of common disruptions to neural networks. Previous work has used this to understanding outcomes but relies on course measures such as age at injury and lesion indicators (type, location, size). These may lack the fine detail necessary to discriminate patients in terms of long-term outcome.

Instead, direct MRI measurements of brain structure offer a more accurate measure of injury burden and could predict individual cognitive outcomes. Though models we have developed such as developmental divergence and BRAINAGE ^{(Griffiths-King et al., 2023; King et al., 2020)} have shown promise, the efficacy of these techniques, beyond the cohorts in which we developed them, remains unclear.

Applications close 5pm, Sunday 2 February 2025

This scholarship is supported by Deakin University, is available over 3 years and includes:

• Stipend of $35,550 per annum  tax exempt (2025 rate)
• Full Tuition Fee Waiver for up to 4 years
• Funding to support single travel between Deakin University and Aston University.
• Travel insurance support during residency at partner institution.

To be eligible you must:

• be a domestic candidate. Domestic includes candidates with Australian Citizenship, Australian Permanent Residency or New Zealand Citizenship.
• meet the PhD entry requirements of both Deakin University and Aston University, including English language proficiency requirements
• hold a Bachelor's degree with research experience or Master's degree (research) in a relevant subject area
• be enrolling full time
• be able to physically locate to both Aston University (UK) and Deakin University (Australia)

Please refer to the research degree entry pathways page and Aston’s research entry criteria page for further information.

Other desirable criteria:

• experience working with children in a clinical or health or educational setting
• experience with quantitative brain image analysis
• strong quantitative methodology skills or experience (e.g. data science, statistics, mathematics).