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Computer Aided Diagnosis of ADHD Using Brain Magnetic Resonance Images

  • B. S. Mahanand
  • R. Savitha
  • S. Suresh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8272)

Abstract

This paper presents a pilot study on the development of an automated diagnostic tool for Attention Deficiency Hyperactivity Disorder (ADHD) based on regional anatomy of the child brain. For the pilot study, amygdala and cerebellar vermis are chosen from magnetic resonance images obtained from ADHD-200 consortium data set. These regions play a vital role in the control of emotional response and behavior/locomotion, respectively. The images are preprocessed, registered by transforming each image to the space of the population average. The gray matter tissue probability values of amygdala and cerebellar vermis are obtained by applying a region-of-interest mask. These values are then used to train a Projection Based Learning algorithm for a Meta-cognitive Radial Basis Function Network (PBL-McRBFN) for the diagnosis of ADHD and prediction of its subtype. Performance results show that the PBL-McRBFN diagnoses ADHD and predicts its subtypes based on these regions with an accuracy of approx. 65% and 62%, respectively.

Keywords

Attention Deficient Hyperactivity Disorder Meta-cognitive Radial Basis Function Network Projection Based Learning Region-of-Interest Magnetic Resonance Imaging 

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References

  1. 1.
    Polanczyk, G., de Lima, M.S., Horta, B.L., Biederman, J., Rohde, L.A.: The worldwide prevalence of ADHD: a systematic review and metaregression analysis. American Journal of Psychiatry 164(6), 942–948 (2007)CrossRefGoogle Scholar
  2. 2.
    Diagnostic and statistical manual of mental disorders, IV edition, Text Revision. American Psychiatric Association, Washington D. C (2000)Google Scholar
  3. 3.
    Banaschewski, T., Becker, K., Scherag, S., Franke, B., Coghill, D.: Molecular genetics of attention-deficit/hyperactivity disorder: An overview. European Child and Adolescent Psychiatry 19(3), 237–257 (2010)CrossRefGoogle Scholar
  4. 4.
    Cortese, S.: The neurobiology and genetics of Attention-Deficit/Hyperactivity Disorder (ADHD): What every clinician should know. European Journal of Paediatric Neurology 16(5), 422–433 (2012)CrossRefGoogle Scholar
  5. 5.
    Cherkasova, M.V., Hechtman, L.: Neuroimaging in attention deficit hyperactivity disorder: beyond the frontostriatal circuitry. Canadian Journal of Psychiatry 54(10), 651–664 (2009)Google Scholar
  6. 6.
    Giedd, J.N., Rapoport, J.L.: Structural MRI of pediatric brain development: what have we learned and where are we going? Neuron. 67(5), 728–734 (2010)CrossRefGoogle Scholar
  7. 7.
    Ivanov, I., Bansal, R., Hao, X., Zhu, H., Kellendonk, C., Miller, L., Sanchez-Pena, J., Miller, A.M., Chakravarty, M.M., Klahr, K., Durkin, K., Greenhill, L.L., Peterson, B.S.: Morphological abnormalities of the thalamus in youths with attention deficit hyperactivity disorder. Americal Journal of Psychiatry 167(4), 397–408 (2010)CrossRefGoogle Scholar
  8. 8.
    Shaw, P., Lerch, J., Greenstein, D., Sharp, W., Clasen, L., Evans, A., Giedd, J., Castellanos, F.X., Rapoport, J.: Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-deficit/hyperactivity disorder. Archives of General Psychiatry 63(5), 540–549 (2006)CrossRefGoogle Scholar
  9. 9.
    Jack, C.R., Petersen, R.C., O’Brien, P.C., Tangalos, E.G.: MR-based hippocampal volumetry in the diagnosis of Alzheimer’s disease. Neurology 42(1), 183–188 (1992)CrossRefGoogle Scholar
  10. 10.
    Milham, P.M., Damien, F., Maarten, M., Stewart, H.M.: The ADHD-200 consortium: A model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers in Systems Neuroscience 6, 1–5 (2012)Google Scholar
  11. 11.
    Maldjian, J.A., Laurienti, P.J., Kraft, R.A., Burdette, J.H.: An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage 19(3), 1233–1239 (2003)CrossRefGoogle Scholar
  12. 12.
    Babu, G.S., Suresh, S.: Meta-cognitive rbf network and its projection based learning algorithm for classification problems. Applied Soft. Computing Journal 13(1), 654–666 (2013)CrossRefGoogle Scholar
  13. 13.
    Joysula, D.P., Vadali, H., Donahue, J., Hughes, F.C.: Modeling meta-cognition for learning in artificial systems. In: World Congress on Nature and Biologically Inspired Computing, pp. 1419–1424 (2009)Google Scholar
  14. 14.
    Suresh, S., Dong, K., Kim, H.: A sequential learning algorithm for self-adaptive resource allocation network classifier. Neurocomputing 73(16-18), 3012–3019 (2010)CrossRefGoogle Scholar
  15. 15.
    Mahanand, B.S., Suresh, S., Sundararajan, N., Kumar, M.A.: Identification of brain regions responsible for Alzheimer’s disease using a self-adaptive resource allocation network. Neural Networks 32, 313–322 (2012)CrossRefGoogle Scholar
  16. 16.
    Babu, G.S., Suresh, S.: Sequential projection-based metacognitive learning in a radial basis function network for classification problems. IEEE Transactions on Neural Networks and Learning Systems 24(2), 194–206 (2013)CrossRefGoogle Scholar
  17. 17.
    Babu, G.S., Suresh, S., Mahanand, B.S.: A novel PBL-McRBFN-RFE approach for identification of critical brain regions responsible for Parkinson’s disease. Expert Systems with Applications (2013), doi:10.1016/j.eswa.2013.07.073Google Scholar
  18. 18.
    Subramanian, K., Suresh, S., Sundararajan, N.: A meta-cognitive neuro-fuzzy inference system (McFIS) for sequential classification problems. IEEE Transactions Fuzzy Systems (2013), doi:10.1109/TFUZZ.2013.2242894Google Scholar
  19. 19.
    Nelson, T.O., Narens, L.: Metamemory: A theoretical framework and new findings. Psychology of Learning and Motivation 26, 125–173 (1990)CrossRefGoogle Scholar
  20. 20.
    Babu, G.S., Suresh, S.: Metacognitive neural network for classification problems in a sequential learning framework. Neurocomputing 81(1), 86–96 (2011)Google Scholar
  21. 21.
    Suresh, S., Savitha, R., Sundararajan, N.: A sequential learning algorithm for complex valued self regulating resource allocation network-CSRAN. IEEE Transactions Neural Networks 22(7), 1061–1072 (2011)CrossRefGoogle Scholar
  22. 22.
    Savitha, R., Suresh, S., Sundararajan, N.: Metacognitive learning in a fully complex-valued radial basis function neural network. Neural Computation 24(5), 1297–1328 (2012)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Savitha, R., Suresh, S., Sundararajan, N.: A meta-cognitive learning algorithm for a fully complex-valued relaxation network. Neural Networks 32, 209–218 (2012)CrossRefzbMATHGoogle Scholar
  24. 24.
    Subramanian, K., Suresh, S.: A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system. Applied Soft. Computing 12(11), 3603–3614 (2012)CrossRefGoogle Scholar
  25. 25.
    Suresh, S., Subramanian, K.: A sequential learning algorithm for meta-cognitive neuro-fuzzy inference system for classification problems. In: Proceedings of International Joint Conference on Neural Networks, pp. 2507–2512 (2011)Google Scholar
  26. 26.
    Ashburner, J.: A fast diffeomorphic image registration algorithm. NeuroImage 38(1), 95–113 (2007)CrossRefGoogle Scholar
  27. 27.
    Frodl, T., Skokauskas, N.: Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects. Acta Psychiatrica Scandinavica 125(2), 114–126 (2012)CrossRefGoogle Scholar
  28. 28.
    Bledsoe, J.C., Semrud-Clikeman, M., Pliszka, S.R.: Neuroanatomical and neuropsychological correlates of the cerebellum in children with attention-deficit/ hyperactivity disorder-combined type. Journal of the American Academy of Child and Adolescent Psychiatry 50(6), 593–601 (2011)CrossRefGoogle Scholar
  29. 29.
    Colby, J.B., Rudie, J.D., Brown, J.A., Douglas, P.K., Cohen, M.S., Shehzad, Z.: Insights into multimodal imaging classification of ADHD. Frontiers in Systems Neuroscience 6(59), 1–18 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • B. S. Mahanand
    • 1
  • R. Savitha
    • 2
  • S. Suresh
    • 2
  1. 1.Department of Information Science and EngineeringSri Jayachamarajendra College of EngineeringMysoreIndia
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingapore

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