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Transductive Maximum Margin Classification of ADHD Using Resting State fMRI

  • Lei Wang
  • Danping Li
  • Tiancheng He
  • Stephen T. C. Wong
  • Zhong XueEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)

Abstract

Resting-state functional magnetic resonance imaging (rs-fMRI) provides key neural imaging characteristics for quantitative assessment and better understanding of the mechanisms of attention deficit hyperactivity disorder (ADHD). Recent multivariate analysis studies showed that functional connectivity (FC) could be used to classify ADHD from normal controls at the individual level. However, there may not be sufficient large numbers of labeled training samples for a hand-on classifier especially for disease classification. In this paper, we propose a transductive maximum margin classification (TMMC) method that uses the available unlabeled data in the learning process. On one hand, the maximum margin classification (MMC) criterion is used to maximize the class margin for the labeled data; on the other hand, a smoothness constraint is imposed on both labeled and unlabeled data projection so that similar samples tend to share the same label. To evaluate the performance of TMMC, experiments on a benchmark cohort from the ADHD-200 competition were performed. The results show that TMMC can improve the performance of ADHD classification using rs-fMRI by involving unlabeled samples, even for small number of labeled training data.

Keywords

ADHD classification rs-fMRI Maximum margin classification Transductive learning 

Notes

Acknowledgements

This work was partially supported by National Natural Science Foundation of China (No. 61203137, 61401328), Natural Science Foundation of Shaanxi Province (No. 2014JQ8306, 2015JM6279), the Fundamental Research Funds for the Central Universities (No. K5051301007), and NIH 5-R03-EB018977 (ZX).

References

  1. 1.
    Cortese, S.: The neurobiology and genetics of attention-deficit/hyperactivity disorder (ADHD): what every clinician should know. Eur. J. Paediatr. Neurol. 16(5), 422–433 (2012)CrossRefGoogle Scholar
  2. 2.
    Colby, J.B., Rudie, J.D., Brown, J.A., Douglas, P.K., Cohen, M.S., Shehzad, Z.: Insights into multimodal imaging classification of ADHD. Front. Syst. Neurosci. 6(59), 1–18 (2012)Google Scholar
  3. 3.
    Teicher, M.H., Anderson, C.M., Polcari, A., Glod, C.A., Maas, L.C., Renshaw, P.F.: Functional deficits in basal ganglia of children with attention-deficit/hyperactivity disorder shown with functional magnetic resonance imaging relaxometry. Nat. Med. 6(4), 470–473 (2000)CrossRefGoogle Scholar
  4. 4.
    Durston, S., Tottenham, N.T., Thomas, K.M., Davidson, M.C., Eigsti, I.M., Yang, Y., Ulug, A.M., Casey, B.J.: Differential patterns of striatal activation in young children with and without ADHD. Biol. Psychiatry 53(10), 871–878 (2003)CrossRefGoogle Scholar
  5. 5.
    Cao, Q., Zang, Y., Sun, L., Sui, M., Long, X., Zou, Q., Wang, Y.: Abnormal neural activity in children with attention deficit hyperactivity disorder: a resting-state functional magnetic resonance imaging study. NeuroReport 17(10), 1033–1036 (2006)CrossRefGoogle Scholar
  6. 6.
    Zang, Y.F., He, Y., Zhu, C.Z., Cao, Q.J., Sui, M.Q., Liang, M., Tian, L.X., Jiang, T.Z., Wang, Y.F.: Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev. 29(2), 83–91 (2007)CrossRefGoogle Scholar
  7. 7.
    Castellanos, F.X., Margulies, D.S., Kelly, C., Uddin, L.Q., Ghaffari, M., Kirsch, A., Shaw, D., Shehzad, Z., Di Martino, A., Biswal, B., Sonuga-Barke, E.J., Rotrosen, J., Adler, L.A., Milham, M.P.: Cingulate-precuneus interactions: a new locus of dysfunction in adult attention-deficit/hyperactivity disorder. Biol. Psychiatry 63(3), 332–337 (2008)CrossRefGoogle Scholar
  8. 8.
    Lim, L., Marquand, A., Cubillo, A.A., Smith, A.B., Chantiluke, K., Simmons, A., Mehta, M., Rubia, K.: Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging. PLoS ONE 8(5), e63660 (2013)CrossRefGoogle Scholar
  9. 9.
    Sidhu, G.S., Asgarian, N., Greiner, R., Brown, M.R.: Kernel principal component analysis for dimensionality reduction in fMRI-based diagnosis of ADHD. Front. Syst. Neurosci. 6(74), 1–16 (2012)Google Scholar
  10. 10.
    Cheng, W., Ji, X., Zhang, J., Feng, J.: Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques. Front. Syst. Neurosci. 6(58), 1–11 (2012)Google Scholar
  11. 11.
    Peng, X., Lin, P., Zhang, T., Wang, J.: Extreme learning machine-based classification of ADHD using brain structural MRI data. PLoS ONE 8(11), e79476 (2013)CrossRefGoogle Scholar
  12. 12.
    Jie, B., Wee, C.Y., Shen, D., Zhang, D.: Hyper-connectivity of functional networks for brain disease diagnosis. Med. Image Anal. 32(1), 84–100 (2016)CrossRefGoogle Scholar
  13. 13.
    Liu, W., Chang, S.-F.: Robust multi-class transductive learning with graphs. In: CVPR, pp. 381–388. IEEE (2009)Google Scholar
  14. 14.
    Liu, W., Tao, D., Liu, J.: Transductive component analysis. In: ICDM, pp. 433–442. IEEE (2008)Google Scholar
  15. 15.
    Li, H., Jiang, T., Zhang, K.: Efficient and robust feature extraction by maximum margin criterion. IEEE Trans. Neural Netw. 17(1), 157–165 (2006)CrossRefGoogle Scholar
  16. 16.
    Craddock, R.C., James, G.A., Holtzheimer, P.E., Hu, X.P., Mayberg, H.S.: A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33(8), 1914–1928 (2012)CrossRefGoogle Scholar
  17. 17.
    Cai, D., He, X., Han, J.: Semi-supervised discriminant analysis. In: ICCV, pp. 1–7. IEEE (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Lei Wang
    • 1
    • 2
  • Danping Li
    • 1
    • 3
  • Tiancheng He
    • 1
  • Stephen T. C. Wong
    • 1
  • Zhong Xue
    • 1
    Email author
  1. 1.Houston Methodist Research InstituteWeill Cornell MedicineHoustonUSA
  2. 2.School of Electronic EngineeringXidian UniversityXi’anChina
  3. 3.School of Telecommunications EngineeringXidian UniversityXi’anChina

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