It takes a highly experienced team to develop cutting-edge brain imaging. Our scientists, radiography and radiology professionals, and research technicians support our pioneering technologies.
We join forces with principal investigators from around Australia and the world. They lead large- and small-scale research projects while our staff support them, often collaborating on these ventures.
Our staff also conduct their own methodological research. They publish their findings in leading peer-reviewed journals.
We support research stages from project conception to project design and logistical planning. We understand how research grant applications work.
In addition, we can offer advice on research ethics and data management. Our expert training in data analysis and computing helps others get the most from our advanced systems and facilities.
- motor control
- sensorimotor integration
- recollection of memories
Multimodal imaging case study
The brain has tremendous ability to recover after acute or progressive brain injury. It can also react to developmental brain impairments.
However, functional recovery often occurs on a different timescale to structural recovery. This makes it difficult to predict outcomes or to design effective interventions.
Multimodal data sheds light on the interplay between functional and structural connectivity across the whole-brain network. Both researchers and clinicians benefit from that knowledge.
It allows them to improve rehabilitation in varied conditions. These include:
- traumatic brain injury
- degenerative brain diseases.
We're studying this functional-structural interplay at Swinburne. Our research into magnetoencephalography (MEG) and magnetic resonance imaging (MRI) can make people's lives better.
As an example, it's helping us to understand and improve sensorineural hearing loss (SNHL). The condition affects thousands of Australians. We’re using our MEG-MRI research to investigate the remedial effects of hearing aid use in SNHL.
In this project, MRI scans determine the forward model of volume conduction for MEG beamformer source modelling. This permits the localisation of deep, rapid brain activity and connectivity, which is thought to underpin SNHL.
We are increasingly recognising that to better understand mental health, we will need to understand both how the brain affects physical health and how physical health affects the brain.
Our 3T whole-body magnetic resonance imaging (MRI) scanner helps us to explore the connections. Our radiographers can image not only the brain’s structure and function but also that of the body. We can scan the heart and body vasculature, the digestive tract, the musculoskeletal system, and the lungs.
Swinburne has expertise in peripheral psychophysiology and its integration with MRI, magnetoencephalography (MEG) and electroencephalography (EEG).
In real time, we can combine measures of peripheral physiology with those of brain activity. This opens up opportunities to study the brain and body in health and disease.
Case study: Brain-body imaging
A current Swinburne project is exploring the potential pleiotropic impact of omega-3 fatty acids and flavonoids on brain (particularly hippocampal) function. We're looking at older adults with mild cognitive impairment or subjective memory impairment.
The individual molecular targets of omega-3 fatty acids and flavonoids suggest that omega-3s and flavonoids may be synergistic, as well as additive. This would argue for their co-administration.
In this randomised controlled trial, we integrate neuroimaging measures of brain atrophy, microstructure, cerebral blood flow and neurometabolites with measures of brain activity. We use:
- magnetic resonance imaging (MRI) structural imaging to measure brain atrophy
- diffusion-weighted MRI to measure microstructure
- MRI arterial spin labelling to measure cerebral blood flow
- magnetic resonance spectroscopy to measure neurometabolites
- magnetoencephalography (MEG) during virtual spatial learning, plus resting state functional magnetic resonance imaging (fMRI) to measure brain activity.
This gives us a more complete assessment of brain aging. Our particular focus is on the integrity of the hippocampus, a brain region heavily implicated in age-related cognitive decline.
The trial is also collecting detailed assessments of:
- cognitive performance
- cardiovascular health
- diet and physical activity
- biochemical markers of physiological processes linked with aging, such as inflammation and glucoregulation.
In addition, we analyse gut microbiome information.
We integrate these measures with neuroimaging outcomes to help understand the broader interaction of body and brain aging.
Swinburne Neuroimaging has a new suite of fully integrated data management and analysis systems in place. They’re based around open, internationally established biomedical imaging standards. They include:
- Digital Imaging and Communications in Medicine (DICOM)
- Brain Imaging Data Structure (BIDS).
Simple, accessible and portable interactive computing on virtual machines complements our high-performance workflow computing. Our wide range of analysis software know-how covers SPM, FSL, AFNI, MRTrix, MNE, FieldTrip, BrainStorm, EEGLab, and BrainVision Analyzer.
For the researcher, we offer expertise in advanced containerised pipelines for large scale image analysis. This cuts time and resource requirements, both computational and human. It also makes the results of the analysis more reliable and easier to reproduce.
We’re integrated with the OzStar supercomputer as well as National Collaborative Research Infrastructure Strategy (NCRIS) capabilities such as Nectar Cloud. This opens up exciting possibilities for collaboration with data scientists and artificial intelligence (AI) experts. Together we can model the complex interactions of neural systems.
Case study: Neuroinformatics
We've developed automated pipelines for processing large datasets, with hundreds or thousands of participants. We use high-performance computing facilities like our Ozstar supercomputer.
These pipelines can integrate the processing of multimodal BIDS-formatted data for subsequent integrated analyses, using the latest analysis tools. Pipelines are made available for all NIF researchers.
To this end, the pipelines are built so that users can set default parameters within minutes. After that, processing is done in a single job. This cuts processing time down from (potentially) years to overnight, depending on the data type or dataset size being processed.
As an example, we are conducting large projects investigating the neural basis of auditory verbal hallucinations (AVHs) in patients with psychotic illnesses. These include schizophrenia and bipolar disorder.
In under 24 hours we can produce pre-processed data for task and rest functional connectivity, voxel-based morphometry and cortical thickness analyses and diffusion imaging (diffusion tensor, fixel or connectome analyses) in the same stereotactic space, ready for separate or integrated analyses.
Use of the OzStar supercomputer allows this to be run in parallel for tens or hundreds of participants or patients, saving days or weeks of analysis time.