A voxelation-corrected non-stationary 3D cluster-size test based on random field theory

Cluster-size tests (CSTs) based on random field theory (RFT) are commonly adopted to identify significant differences in brain images. However, the use of RFT in CSTs rests on the assumption of uniform smoothness (stationarity). When images are non-stationary, CSTs based on RFT will likely lead to increased false positives in smooth regions and reduced power in rough regions. An adjustment to the cluster size according to the local smoothness at each voxel has been proposed for the standard test based on RFT to address non-stationarity, however, this technique requires images with a large degree of spatial smoothing, large degrees of freedom and high intensity thresholding. Recently, we proposed a voxelation-corrected 3D CST based on Gaussian random field theory that does not place constraints on the degree of spatial smoothness. However, this approach is only applicable to stationary images, requiring further modification to enable use for non-stationary images. In this study, we present modifications of this method to develop a voxelation-corrected non-stationary 3D CST based on RFT. Both simulated and real data were used to compare the voxelation-corrected non-stationary CST to the standard cluster-size adjusted non-stationary CST based on RFT and the voxelation-corrected stationary CST. We found that voxelation-corrected stationary CST is liberal for non-stationary images and the voxelation-corrected non-stationary CST performs better than cluster-size adjusted non-stationary CST based on RFT under low smoothness, low intensity threshold and low degrees of freedom

Development and Application of Functional Magnetic Resonance Imaging in Paediatric Focal Epilepsy

There are two major applications of fMRI in paediatric focal epilepsy. The first is mapping of eloquent cortex. The second is the use of simultaneous EEG-fMRI to map the epileptogenic zone. The main methodological issues faced by these fMRI applications are: motion, physiological noise, quality assurance, and statistical analysis. To address the issues of subject motion and physiological noise we constructed a simple analytical biophysical model of Blood Oxygenation Level Dependent (BOLD) signal capable of identifying and correcting these artefacts (named FIACH). This model was validated in a sample of children performing a language task with high motion levels. FIACH outperformed 6 other competitive methods of noise control. In the second study, we characterized how metrics of quality assurance could predict the clinical utility of EEG-fMRI. We also quantified the impact of a natural stimulus (a cartoon) on reducing subject motion. During this analysis it was noted that the corrections for multiple comparisons employed using Random Field Theory (RFT) at an individual level were overly conservative. This led to an exploration of RFT sensitivity and its relationship to image smoothing and degrees of freedom. By reviewing over 150 papers published in 2016 it was possible to estimate that 80% of studies suffer from a similar loss in sensitivity. Simulations are provided to help identify and prevent this loss in sensitivity. In the final study we sought to use EEG-fMRI to characterize the relationship between the brain’s functional organization and Interictal Epileptiform Discharges (IEDs) in paediatric focal epilepsy. Interestingly, we identified increasing connectivity of the piriform cortex and caudate to the default mode network as a function of IEDs. This suggested a mechanism by which IEDs may propagate through functional networks in the brain