Cerebellum and Neurodegeneration

The Cerebellum & Neurodegeneration Research Group uses neuroimaging, fluid biomarkers, and digital assessment tools to understand brain and behavioural changes in people with cerebellar diseases, other forms of neurodegeneration, and aging.



We use a range of magnetic resonance imaging (MRI) and positron emission tomography (PET) approaches to investigate and track brain changes in people with neurodegenerative diseases, including:

  • Large-scale international data aggregation: We coordinate the international ENIGMA-Ataxia international working group, a consortium of over 20 sites globally who aggregate MRI data from people with hereditary ataxias in order to overcome research limitation inherent to working with rare diseases.
  • Mechanistic Imaging: Multimodal MRI/PET imaging to investigate pathological processes and sub-cellular measures of disease expression and progression. The techniques we employ are sensitive to neuroinflammation, oxidative stress/ferroptosis, iron homeostasis, vascular health, and systems-level brain structure and function.
  • Artificial Intelligence for Image Processing and Disease Prediction: Developing deep-learning approaches for automated segmentation of cerebellar and brainstem structures and using rich longitudinal multi-modal datasets to develop AI models for prediction of future individual-level disease progression rates.

We also use a range of web-based digital assessments for monitoring psychomotor, cognitive, and speech function, mood, and quality of life in individuals with hereditary ataxias. We seek to improve the detection and understanding of non-motor symptoms in these diseases.

  • SCA-RemoteA longitudinal, remote monitoring study of motor, cognitive, speech, and mood in people with spinocerebellar ataxia types 1, 2, 3, and 6 from around the world. Participants completely monthly assessments for 12-24 months.
  • FA App: We have implemented the remote monitoring battery into the FA App for collection of longitudinal data in individuals with Friedreich ataxia.

Our team also develop and support research capacity building initiatives, including:

  • Australian Cerebellar Ataxia Registry, a contact list of individuals with cerebellar ataxia who are willing to be contacted for participation in research studies and clinical trial.
  • Standardisation of approaches for quantifying cerebellar structural changes using MRI data in neurological and psychiatric disease (ENIGMA-Cerebellum), and development of novel tools for automatic segmentation of the dentate nucleus from quantitative susceptibility mapping (QSM) images.
  • Spinocerebellar Ataxias
  • Friedreich Ataxia
  • Huntington’s Disease
  • Ageing


  • Nithin Manchery, Research Assistant

External Collaborators

  • Dr Rebecca Kerestes, Monash University
  • Dr Kishore Kumar, The University of Sydney
  • Professor Paul Lockhart, Murdoch Childrens Research Institute
  • Dr Susmita Saha, Monash University
  • Dr Louisa Selvadurai, Monash University
  • Associate Professor David Szmulewicz, Royal Eye and Ear Hospital
  • Professor Adam Vogel, The University of Melbourne

We gratefully acknowledge the support from the following funding agencies:

  • National Health and Medical Research Council
  • Medical Research Future Fund
  • Friedreich Ataxia Research Alliance (USA)
  • Friedreich Ataxia Research Alliance (Australia)
  • National Ataxia Foundation (USA)
  • Telethon Fondazione (Italy)


Neuroimaging big data in rare neurodegenerative diseases: an international collaboration

This project is suitable for Honours, Masters, MD, or PhD students. Background Hereditary Cerebellar Ataxias (HCAs) are rare neurodegenerative diseases that are associated with profound and extensive motor control impairments, predominantly affecting the cerebellum and brainstem. Neuroimaging provides a powerful tool to investigate the functional and structural alterations occurring in HCAs, and ultimately advance our […]

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Human neuroimaging and blood biomarkers for inherited neurodegenerative diseases

This project is suitable for PhD students. Background Can new human brain imaging techniques allow us to better understand, track, and treat neurodegenerative diseases? Can blood-based proteomics/metabolomics help us to better define and predict heterogeneity in the onset and progression of neurodegeneration? Can machine learning be applied to complex, multi-domain clinical and biological data to […]

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