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36. Quantifying Anatomical and Functional Heterogeneity in Big Datasets, Using Machine Learning Methods Towards a Dimensional Neuroimaging Framework

      Neuropsychiatric disorders are characterized by highly heterogeneous and frequently overlapping clinical phenotypes. Understanding the neurobiological underpinnings of these clinical symptoms has been central in neuropsychiatric research and has been largely facilitated by MRI and associated analytical methods that have found reproducible neuroanatomical abnormalities. Multivariate machine learning approaches have been quite successful in capturing complex imaging patterns that have diagnostic and predictive value. However, the neuroanatomical heterogeneity in neuropsychiatric disorders is high, therefore attempting to find a unique neuroanatomical signature of a complex neuropsychiatric disorder using commonly used current techniques is hampered by such heterogeneity. Personalized disease treatment calls for fine quantification of heterogeneity and for more precise placement of each individual patient into a multi-dimensional spectrum of neuroanatomical alterations found in neuropsychiatric disorders.
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