Analysis of fMRI time series with mutual information

• Published in: Medical image analysis Vol. 16; no. 2; pp. 451 - 458
• Main Authors: Gómez-Verdejo, Vanessa, Martínez-Ramón, Manel, Florensa-Vila, José, Oliviero, Antonio
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.02.2012
• Subjects: Adult; Algorithms; Brain Mapping - methods; Data Interpretation, Statistical; Evoked Potentials, Motor - physiology; Female; fMRI; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Middle Aged; Motor Cortex - physiology; Mutual information; Reproducibility of Results; Sensitivity and Specificity; Statistical parametric mapping; Subtraction Technique; fMRI; Statistical parametric mapping; Mutual information
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2011.11.002

Multisubject brain response estimated using the three methods under study, KNN maps are shown in serial and parallel versions. Raw maps (left panel) and their responses after thresholding them (at a 95% level) are displayed. Talairach coordinates of axial slices are 66mm, 55mm, −20mm and −23mm. [Display omitted] ► A mutual information method is used to identify specific effects produced by a task. ► Two MI estimators are proposed for fMRI brain mapping: Parzen windows and KNN. ► A statistical measure has been introduced to automatically threshold the MI maps. ► MI estimators outperform SPM in single subject studies. ► KNN MI shows improved performance in multisubject studies. Neuroimaging plays a fundamental role in the study of human cognitive neuroscience. Functional magnetic resonance imaging (fMRI), based on the Blood Oxygenation Level Dependent signal, is currently considered as a standard technique for a system level understanding of the human brain. The problem of identifying regionally specific effects in neuroimaging data is usually solved by applying Statistical Parametric Mapping (SPM). Here, a mutual information (MI) criterion is used to identify regionally specific effects produced by a task. In particular, two MI estimators are presented for its use in fMRI data. The first one uses a Parzen probability density estimation, and the second one is based on a K Nearest Neighbours (KNN) estimation. Additionally, a statistical measure has been introduced to automatically detect the voxels which are relevant to the fMRI task. Experiments demonstrate the advantages of MI estimators over SPM maps; firstly, providing more significant differences between relevant and irrelevant voxels; secondly, presenting more focalized activation; and, thirdly, detecting small areas related to the task. These findings, and the improved performance of KNN MI estimator in multisubject and multistimuli studies, make the proposed methods a good alternative to SPM.