Graph-aware linear mixed effects models for brain connectivity networks
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Neuroimaging data on functional connections in the brain are frequently represented by weighted networks. These networks share the same set of labeled nodes corresponding to a fixed atlas of the brain, while each subject's network has their own edge weights. We propose a method for modeling such brain networks via linear mixed effects models, which takes advantage of the community structure, or functional regions, known to be present in the brain. The model allows for comparing two populations, such as patients and healthy controls, globally, at functional systems level, and at individual edge level, with systems-level inference in particular allowing for a biologically meaningful interpretation. We incorporate correlation between edge weights into the model by allowing for a general variance structure, and show this leads to much more accurate inference. A thorough study comparing schizophrenics to healthy controls illustrates the full potential of our methods, and obtains results consistent with the medical literature on schizophrenia.
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