European Child & Adolescent Psychiatry

, Volume 24, Issue 10, pp 1279–1289 | Cite as

Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging

  • Reto Iannaccone
  • Tobias U. Hauser
  • Juliane Ball
  • Daniel Brandeis
  • Susanne Walitza
  • Silvia Brem
Original Contribution


Attention-deficit/hyperactivity disorder (ADHD) is a common disabling psychiatric disorder associated with consistent deficits in error processing, inhibition and regionally decreased grey matter volumes. The diagnosis is based on clinical presentation, interviews and questionnaires, which are to some degree subjective and would benefit from verification through biomarkers. Here, pattern recognition of multiple discriminative functional and structural brain patterns was applied to classify adolescents with ADHD and controls. Functional activation features in a Flanker/NoGo task probing error processing and inhibition along with structural magnetic resonance imaging data served to predict group membership using support vector machines (SVMs). The SVM pattern recognition algorithm correctly classified 77.78 % of the subjects with a sensitivity and specificity of 77.78 % based on error processing. Predictive regions for controls were mainly detected in core areas for error processing and attention such as the medial and dorsolateral frontal areas reflecting deficient processing in ADHD (Hart et al., in Hum Brain Mapp 35:3083–3094, 2014), and overlapped with decreased activations in patients in conventional group comparisons. Regions more predictive for ADHD patients were identified in the posterior cingulate, temporal and occipital cortex. Interestingly despite pronounced univariate group differences in inhibition-related activation and grey matter volumes the corresponding classifiers failed or only yielded a poor discrimination. The present study corroborates the potential of task-related brain activation for classification shown in previous studies. It remains to be clarified whether error processing, which performed best here, also contributes to the discrimination of useful dimensions and subtypes, different psychiatric disorders, and prediction of treatment success across studies and sites.


ADHD fMRI Classification Attention Adolescence 



This study was supported by Swiss National Science Foundation grant (No. 320030_130237) and the Hartmann Mueller Foundation (No. 1,460). We thank Julia Frey and Maya Schneebeli for assistance with MR-measurements, recruitment and administrative work, Carolin Knie for help with clinical interviews, and Philipp Staempfli and Marco Piccirelli for their helpful inputs on our MR-sequence. We thank the anonymous reviewers for their helpful suggestions to improve this article.

Conflict of interest

S. Walitza received speakers’ honoraria from Eli Lilly, Opo-Pharma, Janssen-Cilag and AstraZeneca in the last 5 years.

Supplementary material

787_2015_678_MOESM1_ESM.pdf (3 mb)
Supplementary material 1 (PDF 3,120 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Reto Iannaccone
    • 1
    • 2
  • Tobias U. Hauser
    • 1
    • 3
    • 4
  • Juliane Ball
    • 1
  • Daniel Brandeis
    • 1
    • 3
    • 5
    • 6
  • Susanne Walitza
    • 1
    • 3
    • 5
  • Silvia Brem
    • 1
    • 3
  1. 1.University Clinic for Child and Adolescent Psychiatry (UCCAP)University of ZurichZurichSwitzerland
  2. 2.PhD Program in Integrative Molecular MedicineUniversity of ZurichZurichSwitzerland
  3. 3.Neuroscience Center ZurichUniversity of Zurich and ETH ZurichZurichSwitzerland
  4. 4.Wellcome Trust Centre for NeuroimagingUniversity College LondonLondonUK
  5. 5.Zurich Center for Integrative Human PhysiologyUniversity of ZurichZurichSwitzerland
  6. 6.Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental HealthMedical Faculty Mannheim/Heidelberg UniversityMannheimGermany

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