Characterising Mild Cognitive Impairment Using Multimodal Neuroimaging

This project will use advanced neuroimaging techniques, including MRI and PET, to characterise mild cognitive impairment (MCI) using data from the Monash Cognition Imaging Study (MCIS), led by Assoc/Prof. Lucy Vivash (ACTRN12620001246976). MCI is an important target for early intervention research, as it represents a critical window between healthy ageing and dementia, yet its underlying neurobiology remains incompletely understood. This project aims to address that gap by leveraging a rich, multimodal imaging dataset to build a more complete picture of the structural, microstructural, vascular, functional, and metabolic changes associated with MCI. The project offers scope to investigate several complementary aspects of brain health, including brain volumetry (structural MRI), tissue microstructure (diffusion MRI), vascular health (structural and arterial spin labelling MRI), brain connectivity (diffusion and functional MRI), and brain metabolism (FDG-PET). A key strength of this project is the availability of these diverse neuroimaging outcomes alongside comprehensive demographic and neuropsychological testing data, enabling analyses that link imaging findings directly to cognitive and clinical profiles. Where appropriate, students will also have the opportunity to explore multimodal integration, combining multiple imaging modalities to derive a more holistic characterisation of MCI than any single method could provide alone. Students undertaking this project will gain hands-on experience across the full research pipeline, including neuroimaging data analysis, quality control, statistical analysis, and scientific write-up. This provides excellent preparation for further research or clinical work in neuroscience, neurology, or psychiatry, as well as a strong technical grounding in neuroimaging methods that are highly transferable across research settings. The scope, complexity, and duration of the project can be tailored to suit the requirements of Honours, Masters, or PhD candidates, as well as the student's existing level of expertise. Prior experience with neuroimaging data analysis (e.g. FSL, FreeSurfer, SPM, or similar software) is desirable, particularly for PhD applicants, but is not required. Students with a strong interest in neuroscience, cognitive ageing, and quantitative data analysis are encouraged to apply.