Methods for the Analysis of Missing Data in FMRI Studies

Functional neuroimaging has provided fundamental advances in our understanding of human brain function and is increasingly used clinically for defining atypical function and surgical planning. For example, functional imaging with blood oxygenation level dependent (BOLD) contrast as a response measure is used as a clinical tool for defining atypical development, pathology, surgical planning, and evaluating treatment outcomes. Despite years of statistical advances in the analysis of complete whole brain data, there has been a limited statistical advance to address the pronounced missingness in many functional imaging studies that use large discovery or small clinical case data. For example, functional magnetic resonance imaging (fMRI) analyses do not always include the entire brain due to image acquisition space limitations and susceptibility artifacts (a loss and spatial distortion of signal that results from a disruption in the magnetic field). The consequence is ‘no data’ or ‘bad data’, respectively. No data occurs when the image acquisition doesn’t cover the whole head which leads to no values. In addition to susceptibility artifacts, bad data can occur across the brain because of motion artifacts. Because statistic maps with applied effect size or significance thresholds do not typically include information about which voxels were omitted from analyses, missing data can result in Type II errors for regions that were not tested. Missing data in fMRI studies can therefore undermine the benefits provided by high quality imaging technology used to generate data testing predictions about brain function.