Modeling intra-subject correlation among repeated scans in positron emission tomography (PET) neuroimaging data

• Published in: Human brain mapping Vol. 20; no. 2; pp. 59 - 70
• Main Authors: Bowman, F. DuBois, Kilts, Clinton
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.10.2003; Wiley-Liss
• Subjects: Biological and medical sciences; Cerebrovascular Circulation - physiology; correlation maps; covariance model; Humans; Investigative techniques, diagnostic techniques (general aspects); Linear Models; Medical sciences; missing data; mixed effects model; Models, Psychological; Nervous system; non-sphericity; Phobic Disorders - diagnostic imaging; Phobic Disorders - physiopathology; Phobic Disorders - psychology; Radionuclide investigations; repeated measures; restricted maximum likelihood; social anxiety disorder; Tomography, Emission-Computed - methods; Radionuclide study; Human; Emission; Exploration; Modeling; missing data; covariance model; mixed effects model; repeated measures; restricted maximum likelihood; social anxiety disorder; non-sphericity; Positron; Emission tomography; correlation maps
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.10127

Many in vivo positron emission tomography (PET) neuroimaging studies record correlates of regional cerebral blood flow (rCBF) in a series of scans for each individual, usually under different experimental conditions. Typical methods for statistical analysis involve fitting voxel‐specific general linear models (GLM) that assume spherical normal errors, implying that all voxel‐specific rCBF measurements are independent and arise from identical normal probability distributions. While the spherical GLM provides a unified and computationally efficient approach to estimation, the likely correlations among an individual's repeated scans and heteroscedasticity between conditions prompt the use of extended statistical methodology. We outline a more general method to analyze PET data using random effects and correlated errors to model unequal variances across conditions as well as covariances (correlations) among the repeated scans for each individual. We introduce correlation maps to display intra‐subject correlations between an individual's rCBF measurements from different scans. We illustrate the application of our model using data from a study of social anxiety and highlight analytical advantages over the spherical GLM. Hum. Brain Mapping 20:59–70, 2003. © 2003 Wiley‐Liss, Inc.