| Summary: | Computational neuroanatomical techniques that are used to evaluate the structural correlates of disorders in the brain typically measure regional differences in gray matter or white matter, or measure regional differences in the deformation fields required to warp individual datasets to a standard space. Our aim in this study was to combine measurements of regional tissue composition and of deformations in order to characterize a particular brain disorder (here, major depressive disorder). We use structural Magnetic Resonance Imaging (MRI) data from young adults in a first episode of depression, and from an age- and sex-matched group of non-depressed individuals, and create population gray matter (GM) and white matter (WM) tissue average templates using DARTEL groupwise registration. We obtained GM and WM tissue maps in the template space, along with the deformation fields required to co-register the DARTEL template and the GM and WM maps in the population. These three features, reflecting tissue composition and shape of the brain, were used within a joint independent-components analysis (jICA) to extract spatially independent joint sources and their corresponding modulation profiles. Coefficients of the modulation profiles were used to capture differences between depressed and non-depressed groups. The combination of hippocampal shape deformations and local composition of tissue (but neither shape nor local composition of tissue alone) was shown to discriminate reliably between individuals in a first episode of depression and healthy controls, suggesting that brain structural differences between depressed and non-depressed individuals do not simply reflect chronicity of the disorder but are there from the very outset.
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