Multivariate approaches

Many of the 'higher-order' cognitive tasks that are likely to be of greatest interest to psychiatric research do not activate a single modular region of the brain. Instead, they typically activate several spatially distinct or distributed regions that together comprise a large-scale neurocognitive network for performance of the task. It may then be of interest to investigate functional integration between different regions or nodes of the network. The simplest way to do this is by estimating the correlation between a pair of fMRI time series observed at different voxels or regions. Large correlations, whether negative or positive, may be described as evidence for functional connectivity. Psychiatric disorders may be characterized by abnormal functional relationships between coactivated regions, or functional dysconnectivity.

More sophisticated techniques for investigating functional relationships between large numbers of voxels or regions include multivariate methods such as principal component analysis, discriminant analysis, and path analysis. These methods are equally applicable to structural MRI data, where they may provide indirect evidence for anatomical connectivity between regions.

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