Analysis of fMRI time series with mutual information
| Autor: | Manel Martínez-Ramón, Antonio Oliviero, Vanessa Gómez-Verdejo, José Florensa-Vila |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Adult
Male Computer science Health Informatics Cognitive neuroscience computer.software_genre Statistical parametric mapping Machine learning Sensitivity and Specificity Task (project management) Neuroimaging Voxel Image Interpretation Computer-Assisted medicine Humans Radiology Nuclear Medicine and imaging Brain Mapping Radiological and Ultrasound Technology medicine.diagnostic_test business.industry Motor Cortex Estimator Reproducibility of Results Mutual information Middle Aged Evoked Potentials Motor Image Enhancement Computer Graphics and Computer-Aided Design Magnetic Resonance Imaging Data Interpretation Statistical Subtraction Technique Female Computer Vision and Pattern Recognition Artificial intelligence Functional magnetic resonance imaging business computer Algorithms |
| Zdroj: | Medical image analysis. 16(2) |
| ISSN: | 1361-8423 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect