Unsupervised machine learning for identifying attention-deficit/hyperactivity disorder subtypes based on cognitive function and their implications for brain structure
Year: 2024
Type: article
Abstract: BACKGROUND: Structural anomalies in the frontal lobe and basal ganglia have been reported in patients with attention-deficit/hyperactivity disorder (ADHD). However, these findings have been not always consistent because of ADHD diversity. This study aimed to identify ADHD subtypes based on cognitive function and find their distinct brain structural characteristics. METHODS: Using the data of 656 children with ADHD from the Adolescent Brain Cognitive Development (ABCD) Study, we applied unsupervised machine learning to identify ADHD subtypes using the National Institutes of Health Toolbox Tasks. Moreover, we compared the regional brain volumes between each ADHD subtype and 6601 children without ADHD (non-ADHD). RESULTS: = 254, characterized by strikingly low cognitive control, working memory, episodic memory, and language ability). Structural analyses revealed that the ADHD-C type had significantly smaller volumes of the left inferior temporal gyrus and right lateral orbitofrontal cortex than the non-ADHD group, and the right lateral orbitofrontal cortex volume was positively correlated with language performance in the ADHD-C type. However, the volumes of the ADHD-A and ADHD-B types were not significantly different from those of the non-ADHD group. CONCLUSIONS: These results indicate the presence of anomalies in the lateral orbitofrontal cortex associated with language deficits in the ADHD-C type. Subtype specificity may explain previous inconsistencies in brain structural anomalies reported in ADHD.
Source: Psychological Medicine
Institutions: University of Fukui, University of Fukui Hospital
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,450
Awards: G1020259976, G1058358382, G1436251778, G1510635755, G1569016918, G1586463187, G1742849658, G1762448710, G2049360576, G2110804253, ADHD児に対するグアンファシンの神経ネットワークへの効果の解明, G2715786902, G2735337927, G2998093352, Development of ADHD Subtype Classification Using Neural Networks Based on Machine Learning, G3305455594, G343603955, G3438786812, G3563224455, G3738900353, G3755973507, G4066048162, G4180929035, G4241744443, トラベリングサブジェクト法を用いた神経発達症児の神経基盤の解明, G469525291, G5002640097, G5081895789, G5139799579, G5976565686, 幼児期における言語と思考との相互連関に関する発達的研究, Unsupervised machine learning classification of ADHD subtype by urinary levels of tryptophan and monoamine neurotransmitters, G6887863799, G6922259526, G6983123888, G7271102572, G7652160222, G771167098, G815084465, G8281824583, G8679417706, G8762821959, G9000006386, G9000008241, G9000034221, G9000042425, G9000046731, G9000061868, G9000108491, G9000112882, G9000113866, G9000163168, G9000241459, G9000286149, 9/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT U MICHIGAN, 17/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UCLA, 8/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT CHLA, 6/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT OHSU, 21/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT WUSTL, 2/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UMB, G923039674
OpenAlex ID: W4402877583