Quick Project Snapshot
Artefact reduction in functional MRI
Functional MRI (fMRI) is subject to a number of noise sources such as brain pulsation and subject motion that can substantially impact the quality of results. To address this we developed an algorithm to automatically and objectively detect artefact arising from noise sources in fMRI, conservatively preserving components of likely neuronal origin, which is essential in clinical applications. The method is fully automated (no training required) and is a self-contained / stand-alone application (no external database access required). We call the algorithm the Spatially Organised Component Klassificator (SOCK).
Whilst SOCK can help reduce the effects of artefacts present in fMRI data, ideally artefacts should also be minimised at the time of data acquisition. For example, we discovered that even a small amount of breath-holding during an fMRI scan can substantially alter results. Our systematic study of this effect alerted investigators to the danger of this potential confound. The problem is best avoided: for example by instructing study participants prior to their scan to breathe normally.
Bhaganagarapu K, Jackson GD, Abbott DF. De-noising with a SOCK can improve the performance of event-related ICA. Frontiers in Neuroscience 8(285):1-9 (2014). ( doi:10.3389/fnins.2014.00285 ).
Bhaganagarapu K, Jackson GD, Abbott DF. An automated method for identifying artifact in Independent Component Analysis of resting-state fMRI. Frontiers in Human Neuroscience 7(343):1-16 (2013). ( doi:10.3389/fnhum.2013.00343 ).
Abbott DF, Opdam HI, Briellmann RS, Jackson GD. Brief breath-holding may confound functional magnetic resonance imaging studies. Human Brain Mapping 24(4):284-290 (2005). ( doi: 10.1002/hbm.20086 ).
Epilepsy Neuroinformatics Laboratory
The Neuroinformatics Laboratory undertakes advanced neuroimaging analysis methods development and applied research to further our understanding of the human brain in health and disease. Whilst the work in the laboratory is relevant to a wide range of brain mapping applications, a particular emphasis of the research is towards methods that can help better understand the causes and consequences of epileptic seizures. This includes implementation, development and application of advanced image analysis procedures for structural and functional magnetic resonance imaging (MRI, fMRI) and electroencephalography (EEG) - including simultaneous EEG & fMRI. These non-invasive imaging modalities together with advanced computational methods are capable of mapping human brain activity at millimetre spatial resolution and millisecond temporal resolution. Our scientists work collaboratively with local and international clinical research teams, sharing analysis methods and data in a multidisciplinary pursuit of discovery.
The Neuroinformatics Laboratory has a range of software publicly available. Please click below for further information and downloads.
All Projects by this LabFunctional neuroimaging analysis to identify brain abnormality in epilepsyArtefact reduction in functional MRIFunctional connectivity and the human brain functional connectomeFunctional MRI Processing PipelinesLaterality of brain functionMorphometryQuantitative voxel-based analysis of qualitative imagesSimultaneous EEG-fMRIT2 relaxometry
The Florey's Epilepsy division is a world-leading centre for epilepsy research. The division has major groups at both the Florey’s Austin and Parkville campus. The group studies mechanisms that cause epilepsy from the level of cells to the function of the whole brain. We use technologies including advanced MRI and cutting edge cellular physiology techniques to allow us to understand genetic and acquired mechanisms that give rise to epilepsy. Together with our colleagues from The University of Melbourne and across Australia we are working towards finding a cure for epilepsy.