Abstract #4875

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

Renzo Phellan¹, Lívia Rodrigues², Gustavo Retuci Pinheiro², Andrés Quiroga Soto^3,4, 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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