| Functional magnetic resonance imaging (fMRI) is a non-invasive imaging modality that enables scientists to study human brain function and how brain function may change with age or injury. fMRI data has a poor functional signal-to-noise ratio (fSNR), making it difficult to detect which regions of the brain become active as a result of some experiment. This thesis investigates the impact of intensity normalization, one pre-processing tool used to improve fSNR, on a diverse multi-task dataset with both young and old subjects. This research shows that pre-processing decisions can significantly affect analysis results, and furthermore, that the impact of specific preprocessing tools varies as a function of the data analysed. A better understanding of fMRI pre-processing tools may enable researchers to shorten experiment durations and develop tasks more sensitive to human brain function. This would potentially aid the development of fMRI as a clinical tool in neurological assessment and treatment. |
