biostudies-literatureLin HDeutscher Akademischer AustauschdienstNCATS NIH HHSRadiological Society of North AmericaNIDA NIH HHSDoris Duke Charitable FoundationFoundation of the American Society of NeuroradiologyNINDS NIH HHSNational Institutes of Health1138670https://www.ebi.ac.uk/biostudies/studies/S-EPMC9992191biostudies-literatureUnknown17<h4>Objectives</h4>Leveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information.<h4>Methods</h4>From the Adolescents Behavior Cognitive Development (ABCD) database, we included 1,798 children with ADHD diagnosis and 6,007 without ADHD. In multivariate logistic regression adjusted for age and sex, we examined the association of ADHD with different neuroimaging metrics. The neuroimaging metrics included fractional anisotropy (FA), neurite density (ND), mean-(MD), radial-(RD), and axial diffusivity (AD) of white matter (WM) tracts, cortical region thickness and surface areas from T1-MPRAGE series, and functional network connectivity correlations from resting-state fMRI.<h4>Results</h4>Children with ADHD showed markers of pervasive reduced microstructural integrity in white matter (WM) with diminished neural density and fiber-tracks volumes - most notable in the frontal and parietal lobes. In addition, ADHD diagnosis was associated with reduced cortical volume and surface area, especially in the temporal and frontal regions. In functional MRI studies, ADHD children had reduced connectivity among default-mode network and the central and dorsal attention networks, which are implicated in concentration and attention function. The best performing combination of feature selection and machine learning classifier could achieve a receiver operating characteristics area under curve of 0.613 (95% confidence interval = 0.580-0.645) to predict ADHD diagnosis in independent validation, using a combination of multimodal imaging metrics and clinical variables.<h4>Conclusion</h4>Our study highlights the neurobiological implication of frontal lobe cortex and associate WM tracts in pathogenesis of childhood ADHD. We also demonstrated possible potentials and limitations of machine learning models to assist with ADHD diagnosis in a general population cohort based on multimodal neuroimaging metrics.Frontiers in neurosciencePopulation level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children.PMC9992191K23 NS118056U24 DA041123U01 DA041117UL1 TR001863U24 DA041147U01 DA041174U01 DA041134U01 DA041156U01 DA0410932020097U01 DA051037U01 DA041106U01 DA041028K23NS118056U01 DA041089U01 DA041022U01 DA041120U01 DA041148RR2141U01 DA041048U01 DA041025U01 DA051039U01 DA051038U01 DA051016U01 DA051018U01 DA050988U01 DA050987U01 DA050989Constable RTMozayan AKonrad KScheinost DMent LRMalhotra ALin HHaider SPKaltenhauser SPayabvash SfalsePopulation level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children.<h4>Objectives</h4>Leveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information.<h4>Methods</h4>From the Adolescents Behavior Cognitive Development (ABCD) database, we included 1,798 children with ADHD diagnosis and 6,007 without ADHD. In multivariate logistic regression adjusted for age and sex, we examined the association of ADHD with different neuroimaging metrics. The neuroimaging metrics included fractional anisotropy (FA), neurite density (ND), mean-(MD), radial-(RD), and axial diffusivity (AD) of white matter (WM) tracts, cortical region thickness and surface areas from T1-MPRAGE series, and functional network connectivity correlations from resting-state fMRI.<h4>Results</h4>Children with ADHD showed markers of pervasive reduced microstructural integrity in white matter (WM) with diminished neural density and fiber-tracks volumes - most notable in the frontal and parietal lobes. In addition, ADHD diagnosis was associated with reduced cortical volume and surface area, especially in the temporal and frontal regions. In functional MRI studies, ADHD children had reduced connectivity among default-mode network and the central and dorsal attention networks, which are implicated in concentration and attention function. The best performing combination of feature selection and machine learning classifier could achieve a receiver operating characteristics area under curve of 0.613 (95% confidence interval = 0.580-0.645) to predict ADHD diagnosis in independent validation, using a combination of multimodal imaging metrics and clinical variables.<h4>Conclusion</h4>Our study highlights the neurobiological implication of frontal lobe cortex and associate WM tracts in pathogenesis of childhood ADHD. We also demonstrated possible potentials and limitations of machine learning models to assist with ADHD diagnosis in a general population cohort based on multimodal neuroimaging metrics.2023-01-01T00:00:00Z20232026-03-17T16:03:51.362Z2025-04-04T18:51:14.355ZS-EPMC99921913690878010.3389/fnins.2023.1138670