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Abstract #4875

Automatic detection of age- and sex-related differences in human brain morphology

Renzo Phellan1, Lívia Rodrigues2, Gustavo Retuci Pinheiro2, Andrés Quiroga Soto3,4, Igor Duarte Rodrigues5, Leticia Rittner2, Ricardo Ferrari6, Matthew R G Brown7, Nils D Forkert8, Roberto Medeiros9, and Mariana Bento9

1Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada, 2School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil, 3Institute of Physics IFGW, University of Campinas, Campinas, Brazil, 4Brazilian Institute of Neuroscience and Neurotechnology, University of Campinas, Campinas, Brazil, 5Institute of Systems Engineering and Information Technology, Universidade Federal de Itajubá, Itajubá, Brazil, 6Department of Computer Science, Universidade Federal de São Carlos, São Carlos, Brazil, 7Department of Computing Science, University of Alberta, Edmonton, AB, Canada, 8Department of Radiology, University of Calgary, Calgary, AB, Canada, 9Seaman Family Centre, University of Calgary, Calgary, AB, Canada

Research on neurological and mental disorders has shown the diagnostic potential of volumetric brain analysis, also evidencing differences of human brain structures regarding sex and aging in normal subjects. This study aims at identifying the most important volumetric sex- and age-related differences of brain structures using machine learning approaches. It was found that the most important brain structures were different for age- and sex-related differences, which should be taking into account when diagnosing neurological and mental disorders based on morphological features.

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