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This study explores brain structural magnetic resonance imaging (sMRI) from two public data sets (ABIDE\u2010II and ADHD\u2010200) with healthy control (HC, <jats:italic>N<\/jats:italic>\u2009=\u2009894), autism spectrum disorder (ASD, <jats:italic>N<\/jats:italic>\u2009=\u2009251), and attention deficit hyperactivity disorder (ADHD, <jats:italic>N<\/jats:italic>\u2009=\u2009357) individuals. We used gray and white matter preprocessed via voxel\u2010based morphometry (VBM) to train a 3D convolutional neural network with a multitask learning strategy to estimate gender, age, and mental health status from structural brain differences. Gradient\u2010based methods were employed to generate attention maps, providing clinically relevant identification of most representative brain regions for models\u2019 decision\u2010making. This approach resulted in satisfactory predictions for gender and age. ADHD\u2010200\u2010trained models, evaluated in 10\u2010fold cross\u2010validation procedures on test set, obtained a mean absolute error (MAE) of 1.43 years (\u00b10.22 SD) for age prediction and an area under the curve (AUC) of 0.85 (\u00b10.04 SD) for gender classification. In out\u2010of\u2010sample validation, the best\u2010performing ADHD\u2010200 models satisfactorily predicted age (MAE\u2009=\u20091.57 years) and gender (AUC\u2009=\u20090.89) in the ABIDE\u2010II data set. The models\u2019 accuracy was in line with the current state\u2010of\u2010the\u2010art machine learning applications in neuroimaging. Key regions for models\u2019 accuracy were presented as a meaningful graphical output. New implementations, such as the use of VBM along with a 3D convolutional neural network multitask learning model and a brain imaging graphical output, reinforce the relevance of the proposed workflow.<\/jats:p>","DOI":"10.1155\/2021\/5550914","type":"journal-article","created":{"date-parts":[[2021,5,26]],"date-time":"2021-05-26T22:20:13Z","timestamp":1622067613000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Estimating Gender and Age from Brain Structural MRI of Children and Adolescents: A 3D Convolutional Neural Network Multitask Learning Model"],"prefix":"10.1155","volume":"2021","author":[{"given":"Sergio Leonardo","family":"Mendes","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Walter Hugo Lopez","family":"Pinaya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7503-9781","authenticated-orcid":false,"given":"Jo\u00e3o Ricardo","family":"Sato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,5,26]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1093\/schbul\/sbq108"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-020-0798-6"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/nm.4246"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1176\/appi.books.9780890425596"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1080\/15622975.2016.1274050"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/mp.2015.69"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/mp.2016.60"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.25096"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijdevneu.2018.08.010"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1017\/s0033291720000574"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2019.02.011"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2016.00050"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bpsc.2018.06.003"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep38897"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.24423"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.24863"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0194856"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.schres.2019.07.034"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-019-00196-1"},{"key":"e_1_2_9_21_2","unstructured":"SmilkovD. 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