AUTHOR=Lin Huang , Haider Stefan P. , Kaltenhauser Simone , Mozayan Ali , Malhotra Ajay , Constable R. Todd , Scheinost Dustin , Ment Laura R. , Konrad Kerstin , Payabvash Seyedmehdi 

TITLE=Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1138670

DOI=10.3389/fnins.2023.1138670

ISSN=1662-453X

ABSTRACT=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. From the Adolescents Behavior Cognitive Development (ABCD) database, we included 1798 children with ADHD diagnosis and 6007 without ADHD. 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 (ROC) area under curve (AUC) 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. In this study, we could identify the morphological, microstructural, and functional correlates of ADHD in the developing brain, based on a large cohort of demographically representative 9-to-10-year-old American children. These findings can shed light on multifaceted neurobiology of 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.