Dr Sicong Tu

Dr Sicong Tu, Lenity Research Fellow and Senior Lecturer, The University of Sydney.

Sicong is the Lenity Research Fellow and Senior Lecturer based at the Brain and Mind Centre, Camperdown. His research employs leading multi-modal neuroimaging techniques (structural/functional/metabolic) to quantify changes in brain integrity and connectivity associated with neurodegeneration and cognitive dysfunction. His current focus is the discovery and translation of novel non-invasive markers for monitoring and tracking disease progression from its earliest stage in dementia and motor neuron disease. He holds particular interest in characterising regional patterns of change across key neural relay hubs as a marker of widespread cortical dysfunction and brain network abnormality.

Forefront Group:

  • BMC ForeFront Neurology Research Group


  • Neuroimaging
  • Cognitive Dysfunction

Specific Skills:

  • Structural
  • Functional
  • Diffusion MRI
  • MR Spectroscopy (1H; 31P)
  • Brain Connectivity
  • Neuropsychological Assessment

Affiliate Organisations

Brain and Mind Centre, University of Sydney

Neurodegeneration of interest:

MND, FTD, AD, Stroke, Ageing


  • 2021 – [CIA] Novel MR imaging to characterise progressive thalamic changes in neurodegenerative disorders
  • 2021 – [CI] Establishing the role of high definition -density EEG in the diagnosis and monitoring of MND
  • 2020 – [CIA] Utilising multi-modal connectivity and artificial intelligence to track disease progression in ALS

Project - Novel MR imaging to characterise progressive thalamic changes in neurodegenerative disorders

All Chief investigators and associate investigators

Sicong Tu

Research Project Abstract

Abnormality in the thalamus is increasingly recognised as an early disease feature across neurodegenerative conditions. The intrinsic complexity of individual thalamic nuclei and their differential contribution to functional decline in patients is, however, unable to be captured using standard neuroimaging sequences. The current project aims to develop a specialised MRI acquisition, analysis pipeline, and atlas for quantifying sub-thalamic integrity. Outcomes of this independent project directly address technical limitations encountered through my primary research program, namely the development of prognostic clinical markers for monitoring disease progression in FTD-MND.

Disease area:


Project - Utilising multi-modal connectivity and artificial intelligence to track disease progression in ALS

All Chief investigators and associate investigators

Sicong Tu, Matthew Kiernan, Michael Barnett, Chenyu Wang, William Huynh, Colin Mahoney

Research Project Abstract

The absence of objective clinical markers for monitoring and predicting disease progression is a significant barrier undermining clinical care and successful clinical trial outcomes in ALS. Multi-modal brain connectivity and artificial intelligence modelling are two cutting-edge techniques at the forefront of neuroscience research. This project seeks to develop these techniques in ALS to advance our understanding of disease progression and develop robust and objective clinical tools for monitoring and predicting disease trajectory to improve clinical care and provide sensitive outcome measures for future therapeutic trials.

Disease area:


Project - Neural signatures of disease spread and evolution in motor neurodegenerative syndromes

Project with a disease tag

Motor Neuron Disease

Principle investigator

Professor Matthew Kiernan

Research Project Description

Motor neuron disease (MND) is a rapidly progressive neurodegenerative disorder, linked clinically, pathologically and genetically to frontotemporal dementia. There is no significant disease modifying therapy and half of sufferers die within 3 years of diagnosis. To date, large multi-centre drug trials based on pre-clinical models of disease have all shown negative results, highlighting an urgent need for a greater understanding of disease mechanisms in the earliest stages of MND, and the development of validated markers of disease activity, including ways to identify individuals during the pre-symptomatic stage of disease.

The aim of the current project is to model stages of disease evolution in MND, in-vivo, from its earliest stage using cutting edge multi-modal neuroimaging techniques in combination with artificial intelligence-based modelling. Through our research, we have identified selective patterns of change affecting structural pathways of the brain associated with clinical symptoms and phenotypes. We are currently working on translating these findings into clinical models for predicting disease trajectory using deep learning neural networks.

Project related links:

Information on our MND research clinic can be accessed here - https://sydney.edu.au/brain-mind/patient-services/forefront-ageing-and-neurodegeneration-clinics/forefront-clinic-motor-neurone-disease-frontotemporal-dement.html