It is important to understand the influence of common artefacts when processing fMRI data, and how best to remove these in order to produce reliable outcomes. This information affects clinicians and researchers. Little is known about functional connectivity in people with genetic generalised epilepsy at present, making it an ideal cohort to examine while using a quality data processing pipeline. The present thesis discusses individual artefactual contribution, and how to deal with artefact using single subject manual independent component analysis (SSM-ICA). A systematic review of the literature (Chapter two) found people with genetic generalised epilepsy to have reduced functional connectivity in the default mode and attention networks relative to healthy controls. A methods study (Chapter three) investigated functional connectivity in juvenile absence epilepsy, a subtype of genetic generalised epilepsy. Results showed reduced connectivity strength relative to controls. Further, incremental removal of artefacts using single subject manual independent component analysis showed fewer correlations breaching the .05 threshold. These areas were more confined to default mode and attention networks, suggesting most correlations derived without the use of SSM-ICA are spurious. Signal-to-noise ratios were also significantly higher in both incremental cleaning conditions.