Mask_explorer: A tool for exploring brain masks in fMRI group analysis.

Comput Methods Programs Biomed. 2016 Oct;134:155-63

Authors: Gajdoš M, Mikl M, Mareček R

Abstract
BACKGROUND AND OBJECTIVE: Functional magnetic resonance imaging (fMRI) studies of the human brain are appearing in increasing numbers, providing interesting information about this complex system. Unique information about healthy and diseased brains is inferred using many types of experiments and analyses. In order to obtain reliable information, it is necessary to conduct consistent experiments with large samples of subjects and to involve statistical methods to confirm or reject any tested hypotheses. Group analysis is performed for all voxels within a group mask, i.e. a common space where all of the involved subjects contribute information. To our knowledge, a user-friendly interface with the ability to visualize subject-specific details in a common analysis space did not yet exist. The purpose of our work is to develop and present such interface.
METHODS: Several pitfalls have to be avoided while preparing fMRI data for group analysis. One such pitfall is spurious non-detection, caused by inferring conclusions in the volume of a group mask that has been corrupted due to a preprocessing failure. We describe a MATLAB toolbox, called the mask_explorer, designed for prevention of this pitfall.
RESULTS: The mask_explorer uses a graphical user interface, enables a user-friendly exploration of subject masks and is freely available. It is able to compute subject masks from raw data and create lists of subjects with potentially problematic data. It runs under MATLAB with the widely used SPM toolbox. Moreover, we present several practical examples where the mask_explorer is usefully applied.
CONCLUSIONS: The mask_explorer is designed to quickly control the quality of the group fMRI analysis volume and to identify specific failures related to preprocessing steps and acquisition. It helps researchers detect subjects with potentially problematic data and consequently enables inspection of the data.

PMID: 27480740 [PubMed - in process]