Accurate activation map detection using bootstrap resampling of single fMRI data

Functional magnetic resonance imaging (fMRI) is an effective method for measuring the brain neuronal activities. Numerous statistical methods are used for fMRI analyzing. However, determining the true activated regions among the whole apparent activated voxels is a vital but challenging task. The activation pattern of fMRI data analysis is affected under the presence of source of variations such as noise, artifacts, and physiological fluctuations. Finding an accurate and reliable activation map from a single data analysis is essential for true interpretation of an individual data especially when it should be used in neurosurgical planning. We introduced a resampling process (called bootstrapping) through the original EPI data, with the aim of evaluating the reproducibility of the activation changes throughout a task-related signal variation.