Abstract
Structural and functional MRI unveil many hidden properties of the human brain. We performed this multi-class classification study on selected subjects from the publically available attention deficit hyperactivity disorder ADHD-200 dataset of patients and healthy children. The dataset has three groups, namely, ADHD inattentive, ADHD combined, and typically developing. We calculated the global averaged functional connectivity maps across the whole cortex to extract anatomical atlas parcellation based features from the resting-state fMRI (rs-fMRI) data and cortical parcellation based features from the structural MRI (sMRI) data. In addition, the preprocessed image volumes from both of these modalities followed an ANOVA analysis separately using all the voxels. This study utilized the average measure from the most significant regions acquired from ANOVA as features for classification in addition to the multi-modal and multi-measure features of structural and functional MRI data. We extracted most discriminative features by hierarchical sparse feature elimination and selection algorithm. These features include cortical thickness, image intensity, volume, cortical thickness standard deviation, surface area, and ANOVA based features respectively. An extreme learning machine performed both the binary and multi-class classifications in comparison with support vector machines. This article reports prediction accuracy of both unimodal and multi-modal features from test data. We achieved 76.190% (p < 0.0001) classification accuracy in multi-class settings as well as 92.857% (p < 0.0001) classification accuracy in binary settings. In addition, we found ANOVA-based significant regions of the brain that also play a vital role in the classification of ADHD. Thus, from a clinical perspective, this multi-modal group analysis approach with multi-measure features may improve the accuracy of the ADHD differential diagnosis.
Highlights
The neurodevelopmental disease of attention deficit hyperactivity disorder (ADHD) is among the major health problems both in developing and developed countries of the world
We focused on the machine learningbased differential diagnosis of the subtypes of ADHD
We used analysis of variance (ANOVA) analysis to extract the features for classification in this study along with the ROI based features as proposed in a recent study (Qureshi et al, 2016)
Summary
The neurodevelopmental disease of attention deficit hyperactivity disorder (ADHD) is among the major health problems both in developing and developed countries of the world. The early and precise diagnosis of ADHD is very important. Children affected by this disorder have characteristic symptoms such as attention deficit, hyperactivity, and impulsiveness. On the other hand, according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth edition (DSM-5), there are three ADHD subtypes, based on the predominant symptoms: (1) predominantly inattentive presentation, (2) predominantly hyperactive-impulsive presentation, and (3) combined presentation (Association AP, 2013). Pattern recognition techniques have shown promising results to detect biomarkers from neuroimaging data. These techniques hold the potential to combine complementary information across different sources in an efficient way (Wolfers et al, 2015). Very few studies including (Qureshi et al, 2016) were conducted on ADHD differential diagnosis (Arbabshirani et al, 2017)
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