Multi-Region Risk-Sensitive Cognitive Ensembler for Accurate Detection of Attention-Deficit/Hyperactivity Disorder

  • Vasily SachnevEmail author
  • Sundaram Suresh
  • Narasimman Sundararajan
  • Belathur Suresh Mahanand
  • Muhammad W. Azeem
  • Saras Saraswathi


In this paper, we present a multi-region ensemble classifier approach (MRECA) using a cognitive ensemble of classifiers for accurate identification of attention-deficit/hyperactivity disorder (ADHD) subjects. This approach is developed using the features extracted from the structural MRIs of three different developing brain regions, viz., the amygdala, caudate, and hippocampus. For this study, the structural magnetic resonance imaging (sMRI) data provided by the ADHD-200 consortium has been used to identify the following three classes of ADHD, viz., ADHD-combined, ADHD-inattentive, and the TDC (typically developing control). From the sMRIs of the amygdala, caudate, and hippocampus regions of the brain from the ADHD-200 data, multiple feature sets were obtained using a feature-selecting genetic algorithm (FSGA), in a wraparound approach using an extreme learning machine (ELM) basic classifier. An improved crossover operator for the FSGA has been developed for obtaining higher accuracies compared with other existing crossover operators. From the multiple feature sets and the corresponding ELM classifiers, a classifier-selecting genetic algorithm (CSGA) has been developed to identify the top performing feature sets and their ELM classifiers. These classifiers are then combined using a risk-sensitive hinge loss function to form a risk-sensitive cognitive ensemble classifier resulting in a simultaneous multiclass classification of ADHD with higher accuracies. Performance evaluation of the multi-region ensemble classifier is presented under the following three scenarios, viz., region-based individual (best) classifier, region-based ensemble classifier, and finally a multiple-region-based ensemble classifier. The study results clearly indicate that the proposed “multi-region ensemble classification approach” (MRECA) achieves a much higher classification accuracy of ADHD data (normally a difficult problem because of the variations in the data) compared with other existing methods.


Attention-deficit/hyperactivity disorder (ADHD) Neuroimaging structural magnetic resonance imaging (sMRI) Extreme learning machine (ELM) Genetic algorithm (GA) 



The authors would like to thank the ADHD-200 consortium and the Neuro Bureau for making the MRI data available. We would like to thank the ADHD-200 Pre-processed initiative and Dr. Carlton Chu for the Burner pipeline.

Funding information

This work was supported by Catholic University of Korea, Research Funds 2016, National Research Foundation of Korea, grant #2011-0013695.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Research Involving Human Participants and/or Animals/Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Diagnostic and Statistical Manual of Mental Disorders, fifth edn. (American Psychiatric Association).Google Scholar
  2. 2.
    Polanczyk G, DeLima MS, Horta BL, Biederman J, Rohde LA. The worldwide prevalence of ADHD: a systematic review and metaregression analysis. Am J Psychiatr. 2007;164:942–8.CrossRefGoogle Scholar
  3. 3.
    Biederman J. Attention-deficit/hyperactivity disorder: a life-span perspective. J Clin Psychiatry. 1998;59(7):4–16.Google Scholar
  4. 4.
    Frodl T, Skokauskas N. Meta-analysis of structural MRI studies in children and adults with attention-deficit hyperactivity disorder indicates treatment effects. Acta Psychiatr Scand. 2012;125:114–26.CrossRefGoogle Scholar
  5. 5.
    Doshi JA, Hodgkins P, Kahle J, Sikirica V, Cangelosi MJ, Setyawan J, et al. Economic impact of childhood and adult attention-deficit/hyperactivity disorder in the United States. J Am Acad Child Adolesc Psychiatry. 2012;51(10):990–1002.CrossRefGoogle Scholar
  6. 6.
    Davenport N, Karatekin C, White T, Lim KO. Differential fractional anisotropy abnormalities in adolescents with ADHD or schizophrenia. Psychiatry Res. 2010;181:193–8.CrossRefGoogle Scholar
  7. 7.
    Selikowitz M. ADHD. Oxford press; 2009.Google Scholar
  8. 8.
    Fuente ADL, Xia S, Branch C, Li X. A review of attention-deficit/hyperactivity disorder from the perspective of brain networks. Front Hum Neurosci. 2013;7(192):1–6.Google Scholar
  9. 9.
    Wolosin SM, Richardson ME, Hennessey JG, Denckla MB, Mostofsky SH. Abnormal cerebral cortex structure in children with ADHD. Hum Brain Mapp. 2009;30(1):175–84.CrossRefGoogle Scholar
  10. 10.
    Giedd JN, Rapoport JL. Structural MRI of pediatric brain development: what have we learned and where are we going? Neuron. 2010;67:728–34.CrossRefGoogle Scholar
  11. 11.
    Whalen PJ, Phelps EA. The human amygdala. Guilford Press, 2009.Google Scholar
  12. 12.
    Markowitsch H. Differential contribution of right and left amygdala to affective information processing. Behav Neurol. 1998;11(4):233–44.CrossRefGoogle Scholar
  13. 13.
    Nestler EJ, Hyman SE, Holtzman DM, Malenka RC. Molecular neuropharmacology: a foundation for clinical neuroscience (3d ed.). McGraw Hill; 2009.Google Scholar
  14. 14.
    Martin JH Neuroanatomy: text and atlas, 4th edn. McGraw Hill; 2003.Google Scholar
  15. 15.
    Castellanos FX, Giedd JN, Marsh WL, Hamburger SD, Vaituzis AC, Dickstein DP, et al. Quantitative brain magnetic resonance 560 imaging in attention-deficit hyperactivity disorder. Arch Gen Psychiatry. 1996;53:607–16.CrossRefGoogle Scholar
  16. 16.
    Kyeong S, Park S, Cheon K-A, Kim J-J, Song D-H, Kim E. A new approach to investigate the association between brain functional connectivity and disease characteristics of attention-deficit/hyperactivity disorder: topological neuroimaging data analysis. PLoS One. 2015;10(9):e0137296.CrossRefGoogle Scholar
  17. 17.
    Nachamai M. Sub-type discernment of attention-deficit hyperactive disorder in children using a cluster partitioning algorithm. 2016; Indian J Sci Technol 9(8).Google Scholar
  18. 18.
    Wang J-B, Zheng L-J, Cao Q-J, Wang Y-F, Sun L, Zang Y-F, et al. Inconsistency in abnormal brain activity across cohorts of ADHD-200 in children with attention-deficit hyperactivity disorder. Front Neurosci. 2017;11:320.CrossRefGoogle Scholar
  19. 19.
    Bellec P, Chu C, Chouinard-Decorte F, Benhajali Y, Margulies DS, Craddock RC. The Neuro Bureau ADHD-200 preprocessed repository. NeuroImage. 2017;144(Part B):275–86.CrossRefGoogle Scholar
  20. 20.
    Madureira DQM, Carvalho LAV, Cheniaux E. Focus modulated by mesothalamic dopamine: consequences in Parkinson’s disease and attention-deficit hyperactivity disorder. Cogn Comput. 2010;2(1):31–49.CrossRefGoogle Scholar
  21. 21.
    Zou L, Xu S, Ma Z, Lu J, Su W. Attentional automatic removal of artifacts from attention-deficit hyperactivity disorder electroencephalograms based on independent component analysis. Cogn Comput. 2013;5(2):225–33.CrossRefGoogle Scholar
  22. 22.
    Anderson A, Douglas PK, Kerr WT, Haynes VS, Yuille AL, Xie J, et al. Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD. NeuroImage. 2014;19(102).Google Scholar
  23. 23.
    Iannaccone R, Hauser TU, Ball J, Brandeis D, Walitza S, Brem S. Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging. Eur Child Adolesc Psychiatry. 2015;24(10):1279–89.CrossRefGoogle Scholar
  24. 24.
    Chang CW, Ho CC and Chen JH. ADHD classification by a texture analysis of anatomical brain MRI data, Front Syst Neurosci. 2012; 6.Google Scholar
  25. 25.
    Kobel M, Bechtel N, Specht K, Klarhofer M, Weber P, Scheffler K, et al. Structural and functional imaging approaches in attention-deficit/hyperactivity disorder: does the temporal lobe play a key role? Psychiatry Res. 2010;183:230–6.CrossRefGoogle Scholar
  26. 26.
    Cao M, Shu N, Cao Q, Wang Y, He Y. Imaging functional and structural brain connectomics in attention-deficit/hyperactivity disorder. Mol Neurobiol. 2014;50(3):1111–23.CrossRefGoogle Scholar
  27. 27.
    Rangarajan B, Suresh S, Mahanand BS. Identification of potential biomarkers in the hippocampus region for the diagnosis of ADHD using PBL-McRBFN approach, Proceeding of 2014 13th International Conference on Control Automation Robotics and Vision (ICARCV) 2014; 2, 17–22.Google Scholar
  28. 28.
    Sabuncu MR, Konukoglu E. Clinical prediction from structural brain MRI scans: a large-scale empirical study. Neuroinformatics. 2015;13(1):31–46.CrossRefGoogle Scholar
  29. 29.
    Mahanand BS, Savitha R, Suresh S. Computer aided diagnosis of ADHD using brain magnetic resonance images. Advances in Artificial Intelligence. 2013; 386–395.Google Scholar
  30. 30.
    Sato JR, Hoexter MQ, Fujita A, Rohde LA. Evaluation of pattern recognition and feature extraction methods in ADHD prediction, Front Syst Neurosci. 2012; 6.Google Scholar
  31. 31.
    Eloyan A, Muschelli J, Nebel MB, Liu H, Han F, Zhao T, Barber AD, Joel S, Pekar JJ, Mostofsky SH, Caffo B. Automated diagnoses of attention-deficit hyperactive disorder using magnetic resonance imaging., Front Syst Neurosci 2012; 6.Google Scholar
  32. 32.
    Colby JB, Rudie JD, Brown JA, Douglas PK, Cohen MS, Shehzad Z. Insights into multimodal imaging classification of ADHD. Front Syst Neurosci (59) (6) 1–18.Google Scholar
  33. 33.
    Peng X, Lin P, Zhang T, Wang J. Extreme learning machine-based classification of ADHD using brain structural MRI data. PLoS One. 2013;8:e79476.CrossRefGoogle Scholar
  34. 34.
    Sachnev V. An efficient classification scheme for ADHD problem based on binary-coded genetic algorithm and McFIS, Proceeding on 2015 International Conference on Cognitive Computing and Information Processing (CCIP) 2015; 1–6.Google Scholar
  35. 35.
    Maldjian J, Laurienti P, Kraft R, Burdette J. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage. 2003;19(3):1233–9.CrossRefGoogle Scholar
  36. 36.
    Murugan MS, Suresh S, Ganguli R, Mani V. Target vector optimization of composite box beam using real-coded genetic algorithm: a decomposition approach. Struct Multidiscip Optim. 2007;33:131–46.CrossRefGoogle Scholar
  37. 37.
    Suresh S, Babu V, Sundararajan N. Image quality measurement using sparse extreme learning machine classifier, 2006 9th International Conference on Control, Automation, Robotics and Vision 2006; pp. 1–6.Google Scholar
  38. 38.
    Huang G-B, Zhu QY, Siew CK. Extreme learning machine: theory and application. Neurocomputing. 2006;70:489–501.CrossRefGoogle Scholar
  39. 39.
    Huang G-B. An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput. 2014;6:376–90.CrossRefGoogle Scholar
  40. 40.
    Huang G-B. What are extreme learning machines? Filling the gap between frank Rosenblatt’s dream and John von Neumann’s puzzle. Cogn Comput. 2015;7:263–78.CrossRefGoogle Scholar
  41. 41.
    Sachnev V, Savitha R, Suresh S, Kim HJ, Hwang HJ. A cognitive ensemble of extreme learning machines for steganalysis based on risk-sensitive hinge loss function. Cogn Comput. 2014;7(1):103–10.CrossRefGoogle Scholar
  42. 42.
    Milham MP, Fair D, Mennes M, Mostofsky SH. The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front Syst Neurosci. 2012;6(62):1–5.Google Scholar
  43. 43.
    Friston K, Ashburner J, Kiebel S, Nichols T, Penny W (eds.). Statistical parametric mapping: the analysis of functional brain images. Academic Press; 2007).Google Scholar
  44. 44.
    Ashburner J. A fast diffeomorphic image registration algorithm. NeuroImage. 2007;38(1):95–113.CrossRefGoogle Scholar
  45. 45.
    Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. U Michigan Press; 1975.Google Scholar
  46. 46.
    Suresh S, Saraswathi S, Sundararajan N. Performance enhancement of extreme learning machine for multi-category sparse data classification problems. Eng Appl Artif Intell. 2010;23(7):1149–57.CrossRefGoogle Scholar
  47. 47.
    Suresh S, Omkar SN, Mani V, Prakash TNG. Lift coefficient prediction at high angle of attack using recurrent neural network. Aerosp Sci Technol. 2003;7:595–602.CrossRefGoogle Scholar
  48. 48.
    Rong H-J, Jia Y-X, Zhao G-S. Aircraft recognition using modular extreme learning machine. Neurocomputing. 2014;128:166–74.CrossRefGoogle Scholar
  49. 49.
    Rong H-J, Ong Y-S, Tan A-H, Zhu Z. A fast pruned-extreme learning machine for classification problem. Neurocomputing. 2008;72:1–3.CrossRefGoogle Scholar
  50. 50.
    Liu N, Wang H. Ensemble Based extreme learning machine. IEEE Signal Processing Letters. 2010;17(8):754–7.CrossRefGoogle Scholar
  51. 51.
    Yu Q, van Heeswijk M, Miche Y, Nian R, He B, Séverin E, et al. Ensemble delta test-extreme learning machine (DT-ELM) for regression. Neurocomputing. 2014;129(10):153–8.CrossRefGoogle Scholar
  52. 52.
    Cao J, Lin Z, Huang G-B, Liu N. Voting based extreme learning machine. Inf Sci. 2012;185(1):66–77.CrossRefGoogle Scholar
  53. 53.
    Qureshi MNI, Min B, Jo HJ, Lee B. Multiclass classification for the differential diagnosis on the ADHD subtypes using recursive feature elimination and hierarchical extreme learning machine: structural MRI study. PLoS One. 2016;6(4):249–68.Google Scholar
  54. 54.
    Dai D, Wang J, Hua J, He H. Classification of ADHD children through multimodal magnetic resonance imaging. Front Syst Neurosci. 2012;6(63):1–8.Google Scholar
  55. 55.
    Oishi K, Faria AV, Yoshida S, Chang L, Mori S. Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging. Int J Dev Neurosci. 2013;31(7):512–24.CrossRefGoogle Scholar
  56. 56.
    Davatzikos C. Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. NeuroImage. 2004;23(1):17–20.CrossRefGoogle Scholar
  57. 57.
    Chiapponi C, Piras F, Piras F, Fagioli S, Caltagirone C, Spalletta G. Cortical grey matter and subcortical white matter brain microstructural changes in schizophrenia are localized and age independent: a case-control diffusion tensor imaging study. PLoS One. 2013;8(10):e75115.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Information, Communication and Electronics EngineeringCatholic University of KoreaSeoulSouth Korea
  2. 2.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.Department of Information Science and EngineeringSri Jayachamarajendra College of EngineeringMysuruIndia
  4. 4.Psychiatry DivisionSidra MedicineDohaQatar
  5. 5.Department of Children’s Clinical Management GroupSidra MedicineDohaQatar

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