The University of Adelaide Forecasting Impairment and Neurodegenerative Disease risk following Traumatic Brain Injury (FIND-TBI): A computational neurology-driven method to predict long-term prognosis.
Our group is proud to be part of two projects funded by the Medical Research Future Fund (MRFF) this year.
Led by the University of Adelaide – $1,987,160.
This study will use a suite of innovative neuroimaging techniques, including quantitative magnetisation transfer imaging of the locus coeruleus, Nigrosome-1 visualisation and PET imaging of neuroinflammation, to track the progression of brain pathology as a function of both initial severity and time since injury in individuals who have experienced a TBI compared to those with established idiopathic PD. This will be coupled with functional assessment using a custom-designed cognitive and motor testing battery, as well as a comprehensive panel of neuroinflammatory and cell stress markers, in order to assess patterns of change over time. Machine learning and computational neurology techniques will be used to generate a risk algorithm. Ultimately, this will improve our ability to predict an individual’s long-term prognosis following TBI, allowing for earlier, more targeted therapeutic interventions.
Team members involved: Dr Wayne Leifert and Dr Maxime François
PREDICT-TBI – PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury: the value of Magnetic Resonance Imaging.
Led by the University of Queensland – $1,765,000.
The PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury (PREDICT-TBI) study seeks to address and optimise support and personalisation of care for patients after moderate to severe traumatic brain injury to achieve the best possible outcomes. The PREDICT-TBI patient outcome model will combine Computed Tomography (CT) and magnetic resonance imaging (MRI), detailed clinical data, blood biospecimens including CSIROs novel circulating cell-free DNA (ccfDNA) assay, detailed clinical outcomes, and sophisticated artificial intelligence (AI) models to predict neurological outcome at 3 and 6 months post-injury and better inform patient management.
Team members involved: Dr Jason Ross and Dr Warwick Locke