Law group

Bioinformatics Research in Artificial Intelligence and Neuroimaging (iBRAIN) research laboratory

Get in touch | Our people | Our work | Our Achievements | Publications

The iBRAIN research group is a large group featuring multiple individual research groups including the Law, Jupp, Harding, Vivash, Sinclair, Spitz, Macefield groups, all with distinct brain disease areas of interest. We use human brain imaging, image processing, and artificial intelligence to develop earlier diagnosis and new treatments for people with brain diseases, and also to find early indications of disease activity (e.g. diagnostic imaging biomarkers). We do this so that patients with brain diseases can be treated earlier and more effectively in clinical practice.

2023 iBRAIN group L-R: Back row: Lucy Vivash, Ian Harding, Sara McPhail, Katherine Kenyon, Meng Law, Mohamad Nazemzadeh, Bianca Jupp, Susmita Saha, Vaughan Macefield, Will Pham, Merran Courtney, Savindu Karunaratne, Imogen Bowden, Lara Fernandez, Joshua Lee, James Liu Front Row: Ella Rowsthorn, Cassandra Marotta, Roxanne Dilcher, Jacob Bunyamin, Ben Sinclair

Honours and Postgraduate research projects

Get in touch

Whether you want to be involved in our research, you wish to study with us, you want to collaborate with us or donate to our work, we would be delighted to hear from you.

Email us: meng.Law@monash.edu

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Our people

Group Leader

Principal Investigators

Senior Research Fellow
- Dr Gershon Spitz
Post-doctoral fellows
- Louisa Selvadurai
- Mohamad Nazemzadeh
- Lara Fernandez
- Susmita Saha
Research Assistants
- Sara Macphail
- Sarah Wallis
PhD students
- William Pham
- Merran Courtney
- Ella Rowsthorn
- Jacob Bunyamin
- Callum Taylor
- Roxane Dilcher
Honours students
- Debabrata Mishra
- Joshua Lee
- Savindu Karunaratne
Past members
- Frederique Boonstra
- Brendan Major
- Will Khan
- Scott Kolbe

Our work

We study traumatic brain injury (TBI), epilepsy, neurodegenerative diseases (e.g. Alzheimer’s disease, Ageing, Ataxias, Parkinson’s disease, Multiple Sclerosis, Brain tumours), Mental Health Diseases, Sleep Disturbance by utilising state of the art and ultra-high field magnetic resonance imaging (MRI), positron emission topography (PET), computerised topography (CT), magnetic particle imaging, and photon microscopy approaches of brain structure and pathology. We also house a data warehouse/repository of clinical images for research (Monash iBRAIN – Alfred XNAT imaging databank) and other clinical data types to be used for machine learning, deep learning, artificial intelligence applications in diagnostics and therapeutics. We perform clinical trials on novel therapeutics in neurodegenerative diseases and non-neurological diseases. We have local computing as well as access to MASSIVE, a high performance computing centre at Monash University.

Projects

Ultra-high Field 7T MRI of Cerebral Spinal Fluid, Interstitial fluid, and Lymphatic function in Neurodegenerative Diseases (Alzheimer’s DIsease, Epilepsy, Multiple Sclerosis, and Traumatic Brain Injury)
This project seeks to identify the vascular and cerebrospinal, brain interstitial and lymphatic clearance mechanisms in the brain that contribute to the pathophysiology of neurodegenerative diseases. We discovering with 7T MRI that we can image the brain’s paravascular spaces and very small lenticulostriate vessels, which we believe are biomarkers of brain lymphatic clearance. This along with other vascular contributions such as impairment of blood flow and increased blood brain permeability contribute to the deposition of tau, amyloid and other neurodegenerative toxins in the brain.
Application of Machine Learning, Deep Learning, Artificial Intelligence in the Diagnosis and Prediction of Neurodegenerative and Neurological Diseases (Alzheimer’s DIsease, Epilepsy, Multiple Sclerosis, Traumatic Brain Injury and Brain Tumours)
This project seeks to identify the application of artificial intelligence in the detection of subtle markers of disease, e.g. on MRI, which have not yet been discovered. An approach is using Generative Adversarial Network (GAN) and Convolution Neural Network (CNN) with imaging, then in combination with other data types, including genomics, to make earlier diagnoses and predict most optimal therapy and then outcome.
Investigation of novel therapeutics in pre-clinical studies and clinical trials for Neurodegenerative Diseases (e.g. Allopregnanolone in Alzheimer’s Disease and Traumatic Brain Injury)
This project seeks to investigate novel therapeutic approaches to neurological and other diseases. We look at the effects of an extremely unique regenerative therapeutic agent in Alzheimer’s Disease, namely Allopregnanolone using some very novel fiber orientation distribution (FOD) models from multi-shell diffusion spectral imaging (multi-shell DSI), resting state fMRI (rsfMRI) and structural imaging and how this correlates with cognitive regeneration.
Find out more about Dr Ben Sinclair's project: Artificial Intelligence to Improve Surgical Outcomes In Epilepsy
Find out more about Dr Ben Sinclair's project: REVIVER

Our achievements

Grants

2022-2024 - Biophysics-informed deep learning framework for magnetic resonance imaging from Australian Research Council (ARC) (Law)
2021-2026 - Clinical trial to determine the effects of statins on cognition: STAREE-Mind (Law, Harding)
2022-2023 - NIF National Mobile Magnetic Resonance Imaging Network (Law)
2021-2025 - Emerging techniques for earlier diagnosis and assessment of severity and progression of artificial stone silicosis (Law)
2020-2025 - NHMRC Centre of Research Excellence in Neuroimaging (Law)

Publications

Below are a selection of some of published studies reflecting our high impact work. For a full list of our publications, please visit Pubmed,

Gradient Patterns of Age-Related Diffusivity Changes in Cerebral White MatterGradient Patterns of Age-Related Diffusivity Changes in Cerebral White Matter. Frontiers in Neurology, 2022. Boban, J. , Thurnher, M. M. , Boban, N. , Law, M. , Jahanshad, N. , Nir, T. M. , Lendak, D. F. & Kozic, D
Machine learning approaches for imaging-based prognostication of the outcome of surgery for mesial temporal lobe epilepsy. Epilepsia, March 2022. Sinclair, B, Cahill, V, Seah, J, Kitchen, A, Vivash, LE, Chen, Z, Malpas, C, O'Shea M, Desmond P, Hicks R, Morokoff A, King K, Fabinyi G, Kaye A, Kwan P, Berokvic S, Law M, O'Brien T.
The effect of prolonged spaceflight on cerebrospinal fluid and perivascular spaces of astronauts and cosmonauts. Proceedings of the National Academy of Sciences, April 2022. Barisano, G., Sepehrband, F., Collins, H. R., Jillings, S., Jeurisse B, ... Law M, ... , Floris L Wuyts
A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging. December 2022. Pham, W. , Lynch, M. , Spitz, G. , O’Brien, T. , Vivash, L. , Sinclair, B, Law, M.
Retrospective analysis and prospective validation of an AI-based software for intracranial haemorrhage detection at a high-volume trauma centre. Scientific Reports, 2022 Zia, A., Fletcher, C., Bigwood, S., Ratnakanthan, P., Seah, J., Lee, R., Kavnoudias, H., & Law, M.
Perivascular spaces as a marker of disease severity and neurodegeneration in patients with behavioral variant frontotemporal dementia. Frontiers in Neuroscience, 2022. Moses, J., Sinclair, B., Schwartz, D. L., Silbert, L. C., O’Brien, T. J., Law, M., & Vivash, L.
Perivascular spaces as a potential biomarker of Alzheimer’s disease. Frontiers in Neuroscience, 2022 Lynch, M., Pham, W., Sinclair, B., O’Brien, T. J., Law, M., & Vivash, L.
18F-FDG-PET hypometabolism as a predictor of favourable outcome in epilepsy surgery: protocol for a systematic review and meta-analysis. BMJ Open, 2022. Courtney, M. R., Antonic-Baker, A., Sinclair, B., Nicolo, J. P., Neal, A., Law, M., Kwan, P., O'Brien, T. J., & Vivash, L.

Department of Neuroscience

Medicine, Nursing and Health Sciences