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Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces.
Populus primaveralepensis A.Vázquez, Muñiz-Castro & Zuno sp. nov., a new species from relict gallery cloud forest in Bosque La Primavera Biosphere Reserve (Mexico), is described and illustrated. The new species belongs to P. subsect. Tomentosae Hart., and is morphologically similar to P. luziarum A.Vázquez, Muñiz-Castro & Padilla-Lepe, but differs from it in having taller trees without root suckers, white and ringed young stems and branches, a branching angle of ca 45º, leaves with higher blade to petiole ratio, leafs frequently elliptic or ovate to widely ovate (vs widely ovate to ovatedeltoid), denser inflorescences, and shorter capsules. The conservation status of the species was assessed as Critically Endangered (CR).