The general linear model (glm) provides a general framework for a large set of models whose common goal is to explain or predict a quantitative dependent variable by a set of independent variables which can be categorical of quantitative. The glm encompasses techniques such as Student’s t test, simple and multiple linear regression, analysis of variance, and covariance analysis. The glm is adeq...

functional magnetic resonance imaging (fmri) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. the technique has become a ubiquitous tool in basic, clinical and cognitive neuroscience. this method can measure little metabolism changes that occur in active part of the brain. we process the fmri data to be able to find the parts of br...

In this review, we first set out the general linear model (GLM) for the non technical reader, as a tool able to do both linear regression and ANOVA within the same flexible framework. We present a short history of its development in the fMRI community, and describe some interesting examples of its early use. We offer a few warnings, as the GLM relies on assumptions that may not hold in all situ...

In this chapter, we introduce general linear models (GLM) that have been widely used in brain imaging applications. The GLM is a very flexible and general statistical framework encompassing a wide variety of fixed effect models such as the multiple regressions, the analysis of variance (ANOVA), the multivariate analysis of variance (MANOVA), the analysis of covariance (ANCOVA) and the multivari...

A statistical parameter map of fMRI group analyses relies on the assumptions of the General Linear Model (GLM). The assumptions at the single subject level are that the noise is stationary and unbiased for every voxel of every subject. In the mixed level model, the group level GLM assumes that all subjects are drawn randomly from a population, and that the estimates brought up from the single s...

A review of the different approaches using the General Linear Model (GLM) to analyse multi-subject fMRI studies is presented. The first part of the paper attempts to expose the approaches with the least amount of statistic knowledge whilst the second part embeds those approaches in the GLM framework necessitating more statistical mathematical awareness, but enabling more advanced applications. ...

We apply sparse Bayesian learning methods, automatic relevance determination (ARD) and predictive ARD (PARD), to Alzheimer's disease (AD) classification to make accurate prediction and identify critical imaging markers relevant to AD at the same time. ARD is one of the most successful Bayesian feature selection methods. PARD is a powerful Bayesian feature selection method, and provides sparse m...

Local canonical correlation analysis (CCA) is a multivariate method that simultaneously analyzes the timecourses of a group of neighboring voxels and has been demonstrated to be more sensitive than the conventional univariate GLM approach. However, unlike the general linear model (GLM), an arbitrary linear contrast of the temporal regressors has not been so far incorporated in the CCA formalism...

Statistical procedures based on the general linear model (GLM) share much in common with one another, both conceptually and practically. The use of structural equation modeling path diagrams as tools for teaching the GLM as a body of connected statistical procedures is presented. A heuristic data set is used to demonstrate a variety of univariate and multivariate statistics as structural models...