Abstract
This article proposes the image intraclass correlation (I2C2) coefficient as a global measure of reliability for imaging studies the I2C2 generalizes the classic intraclass correlation (ICC) coefficient to the case when the data of interest are images, thereby providing a measure that is both intuitive and convenient. Drawing a connection with classical measurement error models for replication experiments, the I2C2 can be computed quickly, even in high-dimensional imaging studies. A nonparametric bootstrap procedure is introduced to quantify the variability of the I2C2 estimator. Furthermore, a Monte Carlo permutation is utilized to test reproducibility versus a zero I2C2, representing complete lack of reproducibility. Methodologies are applied to three replication studies arising from different brain imaging modalities and settings: regional analysis of volumes in normalized space imaging for characterizing brain morphology, seed-voxel brain activation maps based on resting-state functional magnetic resonance imaging (fMRI), and fractional anisotropy in an area surrounding the corpus callosum via diffusion tensor imaging. Notably, resting-state fMRI brain activation maps are found to have low reliability, ranging from.2 to.4. Software and data are available to provide easy access to the proposed methods.
Original language | English (US) |
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Pages (from-to) | 714-724 |
Number of pages | 11 |
Journal | Cognitive, Affective and Behavioral Neuroscience |
Volume | 13 |
Issue number | 4 |
DOIs | |
State | Published - Dec 2013 |
Keywords
- DTI
- Intraclass correlation coefficient
- RAVENS
- Replication studies
- fMRI
ASJC Scopus subject areas
- Cognitive Neuroscience
- Behavioral Neuroscience