MICA: A toolkit for multimodal image coupling analysis

Abstract

BackgroundAnalytical methods of brain research involving across-voxel correlation between multimodal images are currently tedious and slow due to the amount of manual interaction required. We have developed a new software package to automate and simplify many of these tasks. New method and resultsOur software performs four primary functions to aid in research. First, it helps with consistent renaming of files preprocessed with other software, enabling more accurate analysis. Second, it automates ROI extraction using data from existing and custom brain atlases. Third, it performs coupling analysis to obtain across-voxel Pearson correlation coefficients between images of different modalities based on these brain atlases or custom ROIs. Fourth, it automatically performs multiple comparison correction to correct the P-value using two false discovery rate (FDR) methods and a Bonferroni method to reduce the false-positive rate. Comparison with existing methodsPrevious researchers have investigated the couplings between blood supply and brain functional topology in healthy brains and those from patients with type 2 diabetes, chronic migraine, and schizophrenia. These studies conducted analyses of both the whole and parts of the brain in terms of neuronal activity and blood perfusion, but the procedures were laborious and time-consuming. ConclusionWe have developed a convenient and time-saving software package using MATLAB 2014a to automate the data preparation and analysis of across-voxel coupling between multimodal images.