title: Aberrant Cross-Brain Network Interaction in Children With Attention-Deficit/Hyperactivity Disorder and Its Relation to Attention Deficits: A Multisite and Cross-Site Replication Study.
abstract: Attention-deficit/hyperactivity disorder (ADHD) is increasingly viewed as a disorder stemming from disturbances in large-scale brain networks, yet the exact nature of these impairments in affected children is poorly understood. We investigated a saliency-based triple-network model and tested the hypothesis that cross-network interactions between the salience network (SN), central executive network, and default mode network are dysregulated in children with ADHD. We also determined whether network dysregulation measures can differentiate children with ADHD from control subjects across multisite datasets and predict clinical symptoms.
  
  
  
  Functional magnetic resonance imaging data from 180 children with ADHD and control subjects from three sites in the ADHD-200 database were selected using case-control design. We investigated between-group differences in resource allocation index (RAI) (a measure of SN-centered triple network interactions), relation between RAI and ADHD symptoms, and performance of multivariate classifiers built to differentiate children with ADHD from control subjects.
  
  
  
  RAI was significantly lower in children with ADHD than in control subjects. Severity of inattention symptoms was correlated with RAI. Remarkably, these findings were replicated in three independent datasets. Multivariate classifiers based on cross-network coupling measures differentiated children with ADHD from control subjects with high classification rates (72% to 83%) for each dataset. A novel cross-site classifier based on training data from one site accurately (62% to 82%) differentiated children with ADHD on test data from the two other sites.
  
  
  
  Aberrant cross-network interactions between SN, central executive network, and default mode network are a reproducible feature of childhood ADHD. The triple-network model provides a novel, replicable, and parsimonious systems neuroscience framework for characterizing childhood ADHD and predicting clinical symptoms in affected children.
authors.affiliation: Departments of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California. Electronic address: wdcai@stanford.edu.
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.affiliation: Departments of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California.
authors.forename: Tianwen
authors.initials: T
authors.lastname: Chen
authors.affiliation: Departments of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Department of Automation and Applied Informatics (LS), Budapest University of Technology and Economics, Budapest, Hungary.
authors.forename: Luca
authors.initials: L
authors.lastname: Szegletes
authors.affiliation: Departments of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California.
authors.forename: Kaustubh
authors.initials: K
authors.lastname: Supekar
authors.affiliation: Departments of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, California; Stanford Neuroscience Institute (VM), Stanford University School of Medicine, Stanford, California.
authors.forename: Vinod
authors.initials: V
authors.lastname: Menon
keywords: Cognitive control; Cross-site classification; Disorder; Functional connectivity; Independent component analysis; Support vector machine; fMRI