Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach

• Published in: Frontiers in human neuroscience Vol. 5; p. 28
• Main Author: Monti, Martin
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 2011; Frontiers Media S.A
• Subjects: autocorrelation; Blood; Blood Oxygenation Level-Dependent (BOLD); Cognition; Data analysis; Fixed Effects; Functional magnetic resonance imaging; functional magnetic resonance imaging (fMRI); General Linear Model (GLM); Metabolism; Mixed Effects; Neuroscience; NMR; Nuclear magnetic resonance; Physiology; Statistical analysis; multicollinearity; fixed effects; mixed effects; blood oxygenation level-dependent; ordinary least squares; functional magnetic resonance imaging; general linear model; autocorrelation
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2011.00028

Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making.