Abstract
Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and by reports from the parents and teachers. Considering its high prevalence and large economic and societal costs, a quantitative tool that aids in diagnosis by characterizing underlying neurobiology would be extremely valuable. This provided motivation for the ADHD-200 machine learning (ML) competition, a multisite collaborative effort to investigate imaging classifiers for ADHD. Here we present our ML approach, which used structural and functional magnetic resonance imaging data, combined with demographic information, to predict diagnostic status of individuals with ADHD from typically developing (TD) children across eight different research sites. Structural features included quantitative metrics from 113 cortical and non-cortical regions. Functional features included Pearson correlation functional connectivity matrices, nodal and global graph theoretical measures, nodal power spectra, voxelwise global connectivity, and voxelwise regional homogeneity. We performed feature ranking for each site and modality using the multiple support vector machine recursive feature elimination (SVM-RFE) algorithm, and feature subset selection by optimizing the expected generalization performance of a radial basis function kernel SVM (RBF-SVM) trained across a range of the top features. Site-specific RBF-SVMs using these optimal feature sets from each imaging modality were used to predict the class labels of an independent hold-out test set. A voting approach was used to combine these multiple predictions and assign final class labels. With this methodology we were able to predict diagnosis of ADHD with 55% accuracy (versus a 39% chance level in this sample), 33% sensitivity, and 80% specificity. This approach also allowed us to evaluate predictive structural and functional features giving insight into abnormal brain circuitry in ADHD.
Highlights
Attention deficit hyperactivity disorder (ADHD) is among the most common child-onset neurodevelopmental disorders in the world, with an estimated childhood prevalence of 5–10% (Wolraich et al, 1996; Swanson et al, 1998), and an estimated cost in the tens of billions of dollars per year (Pelham et al, 2007) in addition to its large personal costs
Investigations into the neurobiological basis of ADHD have found that it is highly heritable (60–75%) (Nyman et al, 2007; Faraone and Mick, 2010) and that it involves dopaminergic pathways in both the disease manifestation and the response to pharmaceutical treatment (Froehlich et al, 2011). This is consistent with observations that ADHD subjects have altered levels of dopamine (DA) transporter densities in striatal regions lateralized to the right hemisphere (McGough, 2012)
The ADHD-H subtype was the lowest represented among the three subtypes, and prior information from the ADHD-200 contest indicated that no subjects with this diagnosis would be included in the test set
Summary
Attention deficit hyperactivity disorder (ADHD) is among the most common child-onset neurodevelopmental disorders in the world, with an estimated childhood prevalence of 5–10% (Wolraich et al, 1996; Swanson et al, 1998), and an estimated cost in the tens of billions of dollars per year (Pelham et al, 2007) in addition to its large personal costs. Investigations into the neurobiological basis of ADHD have found that it is highly heritable (60–75%) (Nyman et al, 2007; Faraone and Mick, 2010) and that it involves dopaminergic pathways in both the disease manifestation and the response to pharmaceutical treatment (Froehlich et al, 2011). This is consistent with observations that ADHD subjects have altered levels of dopamine (DA) transporter densities in striatal regions lateralized to the right hemisphere (McGough, 2012). It is possible that an improved understanding of the neural underpinnings of the disease may allow us to better appreciate its variation among individuals, and lead to better-targeted individual therapies
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