Florey Department of Neuroscience and Mental Health - Theses
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ItemArtefact reduction methods for EEG-fMRI and fMRI( 2023-01)Advances in technology have led to increasing use of neuroimaging methods, such as electroencephalography (EEG), magnetoencephalography (MEG) and functional MRI (fMRI), that allow researchers to study brain function in a non-invasive way. However, functional neuroimaging studies are frequently contaminated by noise, or artefact, present in the data, and therefore, methods to remove or minimise artefact are crucial for obtaining accurate, reproducible results. This thesis examines artefact reduction in two different functional neuroimaging modalities - fMRI, and simultaneous EEG-fMRI. Firstly, a systematic review of artefact reduction for EEG-fMRI is presented, which contains two sub-reviews: a review of artefact reduction methods available, and a review of artefact reduction methods used in contemporary studies. The first review successfully distils all published artefact reduction methods from a twenty-year period into clear recommendations for researchers using this imaging modality. The second review found that from EEG-fMRI papers published over a four-year period, most users were selecting one or two similar methods, with up to 15% of users not adequately describing their methods used. A key finding from the work was that hardware-based methods of recording artefact are preferable to data-driven approaches, yet data-driven approaches are most commonly used. The second part of this thesis looks at motion artefact in fMRI studies - specifically, the utility of novel hardware - carbon wire loops (CWL) - for detecting head motion. We aimed to determine whether CWL could detect sub-volume motion onset and if so, whether CWL would suggest further data censoring when using a commonly used data-driven method, Framewise Displacement (FD). We hypothesised that the volume prior to motion onset in FD may often be motion affected, due to motion occurring part-way through volume acquisition, and that this motion would be detected by CWL. The results showed that CWL successfully detects motion onset at a sub-volume (slice-based) level. In addition, CWL detected motion in the volume prior to FD onset in an average of 42% of cases. It was concluded that censoring the volume prior to FD onset should only be done when rigorous motion rejection is necessary and CWL are not available. Together, both these works extend the current knowledge and methods for reducing artefact in neuroimaging studies. The first work provides guidelines for researchers using EEG-fMRI, to reduce artefact and successfully report their methods when publishing. The second work shows a proof of concept that CWL can detect motion in fMRI studies, thus laying the groundwork for more sophisticated motion removal algorithms for fMRI in the future.