Estimating biological accuracy of DSM for attention deficit/hyperactivity disorder based on multivariate analysis for small samples

Abstract

Objective To estimate whether the “Diagnostic and Statistical Manual of Mental Disorders” (DSM) is biologically accurate for the diagnosis of Attention Deficit/ Hyperactivity Disorder (ADHD) using a biological-based classifier built by a special method of multivariate analysis of a large dataset of a small sample (much more variables than subjects), holding neurophysiological, behavioral, and psychological variables. Methods Twenty typically developing boys and 19 boys diagnosed with ADHD, aged 10–13 years, were examined using the Attentional Network Test (ANT) with recordings of event-related potentials (ERPs). From 774 variables, a reduced number of latent variables (LVs) were extracted with a clustering of variables method (CLV), for further reclassification of subjects using the k-means method. This approach allowed a multivariate analysis to be applied to a significantly larger number of variables than the number of cases. Results From datasets including ERPs from the mid-frontal, mid-parietal, right frontal, and central scalp areas, we found 82% of agreement between DSM and biological-based classifications. The kappa index between DSM and behavioral/psychological/neurophysiological data was 0.75, which is regarded as a “substantial level of agreement”. Discussion The CLV is a useful method for multivariate analysis of datasets with much less subjects than variables. In this study, a correlation is found between the biological-based classifier and the DSM outputs for the classification of subjects as either ADHD or not. This result suggests that DSM clinically describes a biological condition, supporting its validity for ADHD diagnostics.

Keywords

• ADHD
• Diagnostic
• Diagnostic and statistical manual of mental disorders
• Multivariate analysis
• Clustering of variables around latent components
• Event-related potentials