Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study

Andreas Mueller, Gian Candrian, Venke Arntsberg Grane, Juri D Kropotov, Valery A Ponomarev, Gian-Marco Baschera, Andreas Mueller, Gian Candrian, Venke Arntsberg Grane, Juri D Kropotov, Valery A Ponomarev, Gian-Marco Baschera

Abstract

Background: There are numerous event-related potential (ERP) studies in relation to attention-deficit hyperactivity disorder (ADHD), and a substantial number of ERP correlates of the disorder have been identified. However, most of the studies are limited to group differences in children. Independent component analysis (ICA) separates a set of mixed event-related potentials into a corresponding set of statistically independent source signals, which are likely to represent different functional processes. Using a support vector machine (SVM), a classification method originating from machine learning, this study aimed at investigating the use of such independent ERP components in differentiating adult ADHD patients from non-clinical controls by selecting a most informative feature set. A second aim was to validate the predictive power of the SVM classifier by means of an independent ADHD sample recruited at a different laboratory.

Methods: Two groups of age-matched adults (75 ADHD, 75 controls) performed a visual two stimulus go/no-go task. ERP responses were decomposed into independent components, and a selected set of independent ERP component features was used for SVM classification.

Results: Using a 10-fold cross-validation approach, classification accuracy was 91%. Predictive power of the SVM classifier was verified on the basis of the independent ADHD sample (17 ADHD patients), resulting in a classification accuracy of 94%. The latency and amplitude measures which in combination differentiated best between ADHD patients and non-clinical subjects primarily originated from independent components associated with inhibitory and other executive operations.

Conclusions: This study shows that ERPs can substantially contribute to the diagnosis of ADHD when combined with up-to-date methods.

Figures

Figure 1
Figure 1
Go and nogo condition grand average ERPs. Total group ERPs, assessed in response to the second stimuli of nogo (thick line) and go (thin line) trials. × axis is time in ms, y axis is amplitude in μV.
Figure 2
Figure 2
Novelty and ignore condition grand average ERPs. Total group ERPs, assessed in response to the second stimuli of novelty (thick line) and ignore (thin line) trials. × axis is time in ms, y axis is amplitude in μV.
Figure 3
Figure 3
Topographies and activation curves of go/nogo condition independent components. ICA was performed on ERPs of the ADHD group (left), on ERPs of the control group (middle) and on ERPs of the total group (right), for a time interval after the onset of the second stimuli in the go (thin line) and nogo (thick line) conditions. × axis is time in ms, y axis is amplitude in standard units.
Figure 4
Figure 4
Topographies and activation curves of novelty/ignore condition independent components. ICA was performed on ERPs of the ADHD group (left), on ERPs of the control group (middle) and on ERPs of the total group (right), for a time interval after the onset of the second stimuli in the ignore (thin line) and novelty (thick line) conditions. × axis is time in ms, y axis is amplitude in standard units.
Figure 5
Figure 5
Topographies and time courses of the preparatory-set-independent components. Time courses are based on spatial filtration and are depicted separately for control (black line) and ADHD (red line) group. × axis is time in ms, y axis is amplitude in μV. sLORETA imaging of total group is presented on the right.
Figure 6
Figure 6
Topographies and time courses of continue-set-specific (go and/or nogo) components. Time courses are based on spatial filtration and are depicted separately for control (black line) and ADHD (red line) group. Low-amplitude IC time courses in the non-dominant conditions are not presented. × axis is time in ms, y axis is amplitude in μV. sLORETA imaging of total group is presented on the right.
Figure 7
Figure 7
Topographies and time courses of discontinue-set-specific (novelty) components. Time courses are based on spatial filtration and are depicted separately for control (black line) and ADHD (red line) group. Low-amplitude IC time courses in the non-dominant ignore condition are not presented. × axis is time in ms, y axis is amplitude in μV. sLORETA imaging of total group is presented on the right.

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