{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T15:19:04Z","timestamp":1747149544018,"version":"3.40.5"},"reference-count":28,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T00:00:00Z","timestamp":1708560000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intl J of Devlp Neuroscience"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Despite the importance of understanding how intelligence is ingrained in the function and structure of the brain in some neurological disorders, the alterations of intelligence\u2010associated neurological factors in atypical neurodevelopmental disorders, such as attention deficit\/hyperactivity disorder (ADHD), are limited. Therefore, we aimed to explore the relationship between the brain functional and morphological characteristics and the intellectual performance of 139 patients with ADHD. Resting\u2010state functional and T1\u2010weighted structural magnetic resonance imaging (MRI) data and intellectual\u2010performance data of the patients were collected. The MRI data were preprocessed to extract four indicators characterizing the participants' brain features: fractional amplitude of low\u2010frequency fluctuation, regional homogeneity, and gray and white matter volumes. Then, we used a two\u2010layer feature\u2010selection method with support vector regression models based on three kernel functions to predict the verbal and performance intelligent quotients of the patients, along with ten fold cross\u2010validation to evaluate the models' predictive performance. All models showed good performance; the correlation coefficients between the predicted and observed values for each predictive phenotypic variable were &gt;0.41, with statistical significance. The brain features that could best predict the intellectual performance of the patients were concentrated in the superior and inferior frontal gyrus of the prefrontal areas, the angular gyrus and precuneus of the parietal lobe, the inferior and middle temporal gyrus of the temporal lobe, and part of the cerebellar regions. Thus, the voxel\u2010based brain\u2010feature indicators could adequately predict the intellectual performance of patients with ADHD, providing a foundation for future neuroimaging studies of this disorder.<\/jats:p>","DOI":"10.1002\/jdn.10320","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:21:50Z","timestamp":1708647710000},"page":"217-226","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting verbal and performance intelligence quotients from multimodal data in individuals with attention deficit\/hyperactivity disorder"],"prefix":"10.1002","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0093-5570","authenticated-orcid":false,"given":"Ningning","family":"He","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics Zhoukou Normal University  Zhoukou People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Kou","sequence":"additional","affiliation":[{"name":"School of Foreign Languages Zhoukou Normal University  Zhoukou People's Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,2,22]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2014.00014"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291719003015"},{"key":"e_1_2_11_4_1","first-page":"529","article-title":"Diagnostic and statistical manual of mental disorders, fourth edition (DSM\u2010IV)","volume":"29","author":"American Psychiatric Association","year":"2009","journal-title":"Australian and New Zealand Journal of Psychiatry"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-7610.2007.01735.x"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.06.034"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnsys.2012.00059"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.10.003"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00525"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11682-019-00166-9"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1097\/00004583-199707000-00021"},{"issue":"1","key":"e_1_2_11_12_1","first-page":"86","article-title":"Rank correlation methods","volume":"25","author":"Kendall M. 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