, Volume 4, Issue 1, pp 81–94 | Cite as

Mutual information-based feature selection in studying perturbation of dendritic structure caused by TSC2 inactivation

  • Xiaobo Zhou
  • Jinmin Zhu
  • Kuang-Yu Liu
  • Bernardo L. Sabatini
  • Stephen T. C. Wong
Original Article


In this study, the effect of protein Tuberous sclerosis 2 (TSC2) on the dendritic spine density and length was demonstrated by using TSC2-RNA inactivation. In addition, the role of rapamycin, an antagonist of the molecular target of rapamycin, in the morphological changes of spine caused by TSC2 silencing was investigated. The features were extracted from high-resolution three-dimensional image stacks collected by two-photon laser scanning microscopy of green fluorescing pyramidal cells expressing TSC2-RNA interference (RNAi), or TSC2-RNAi and rapamycin treatment in rat hippocampal slice cultures. We proposed to apply the lognormal distribution method for feature extraction. The extracted features of three cases under investigation, namely, (1) green-fluorescent protein GFP vs TSC2-RNAi, (2) GFP vs TSC2-RNAi and rapamycin, and (3) TSC2-RNAi vs TSC2-RNAi and rapamycin, were analyzed by mutual information-based feature selection and evaluated by three classifiers, K-nearest neighbor, Perceptron, and two-layer neural networks. The results showed that both the spine density and length have significant morphological changes after TSC2-RNAi treatment. However, rapamycin treatment could reverse the effect of TSC2-RNAi on spine length but not on spine density. These results are consistent with the results reported in the scientific literature. Finally, we explored the application of pattern recognition method in a small sample with richer feature properties, namely bootstrap mutual information estimation and a mutual information-based feature selection method.

Index Entries

GFP TSC2-RNAi spine density spine length mutual information feature selection neuron classification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Battiti, R. (1994) Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Netw. 5, 537–550.CrossRefGoogle Scholar
  2. Cover, T. M. and Thomas J. A. (1991) Elements of Information Theory. Wiley, New York.Google Scholar
  3. Daniel, W. W. (1999) Biostatistics: A Foundation For Analysis in the Health Sciences. Wiley, New York.Google Scholar
  4. Denk, W. and Svoboda, K. (1997) Photon upmanship: why multiphoton imaging is more than a gimmick. Neuron 18, 351–357.CrossRefGoogle Scholar
  5. Dougherty, E. R. (2001) Small sample issues for microarray-based classification. Comp. Functional Genom. 2, 28–34.CrossRefGoogle Scholar
  6. Duda, R. O., Hart, P. E., and Stork, D. G. (2001) Pattern Classification (2nd ed). John Wiley and Sons, New York.Google Scholar
  7. Garey, L. J., Ong, W. Y., Patel, T. S., et al. (1998) Reduced dendritic spinedensity on cerebral cortical pyramidal neurons in schizophrenia, J. Neurol. Neurosurg. Psychiatry 65, 446–453.CrossRefGoogle Scholar
  8. Inoki, K., Li, Y., Zhu, T., Wu, J., and Guan, K. L. (2002) TSC2 is phosphorylated and inhibited by Akt and suppresses mTOR signaling. Nat. Cell Biol. 4, 648–657.CrossRefGoogle Scholar
  9. Irwin, S. A., Patel, B., Idupulapati, M., Harris, J. B., and Crisostomo, R. A. (2001) Abnormal dendritic spine characteristics in the temporal and visual cortices of patients with fragile-X syndrome: a quantitative examination. Am. J. Med. Genet. 98, 161–167.CrossRefGoogle Scholar
  10. Koh, I. Y., Lindquist, W. B., Zito, K., Nimchinsky, E. A., and Svoboda, K. (2002) An image analysis algorithm for dendritic spines. Neural Comput. 14, 1283–1310.CrossRefGoogle Scholar
  11. Lendvai, B., Stern, E. A., Chen, B., and Svoboda, K. (2000) Experience-dependent plasticity of dendritic spines in the developing rat barrel cortex in vivo. Nature 404, 876–881.CrossRefGoogle Scholar
  12. Lo, D. C., McAllister, A. K., and Katz, L. C. (1994) Neuronal transfection in brain slices using particle-mediated gene transfer. Neuron 13, 1263–1268.CrossRefGoogle Scholar
  13. Machado-Salas, J. P. (1984) Abnormal dendritic patterns and aberrant spine development in Bourneville's disease—a Golgi survey. Clin. Neuropathol. 3, 52–58.Google Scholar
  14. Marin-Padilla, M. (1976) Pyramidal cell abnormalities in the motor cortex of a child with Down's syndrome, A Golgi Study. J. Comp. Neurol. 167, 63–81.CrossRefGoogle Scholar
  15. McManus, E. J. and Alessi, D. R. (2002) TSC1-TSC2: a complex tale of PKB-mediated S6K regulation. Nat. Cell Biol. 4, E214-E216.CrossRefGoogle Scholar
  16. Moser, M. B., Trommald, M., and Anderson, P. (1994) An increase in dendritic spine density on hippocampal CA1 pyramidal cells following spatial learning in adult rats suggests the formation of new synapses. Proc. Natl. Acad. Sci. USA 91, 12,673–12,675.CrossRefGoogle Scholar
  17. Nimchinsky, E. A., Sabatini, B. L., and Svoboda, K. (2002) Structure and function of dendritic spines. Ann. Rev. Physiol. 64, 313–353.CrossRefGoogle Scholar
  18. Potter, C. J., Pedraza, L. G., and Xu, T. (2002) Akt regulates growth by directly phosphorylating Tsc2. Nat. Cell Biol. 4, 658–665.CrossRefGoogle Scholar
  19. Rudelli, R. D., Brown, W. T., Wisniewski, K., et al. (1985) Adult fragile X syndrome. Cliniconeuropathologic findings. Acta Neuropathol. 67, 289–295.CrossRefGoogle Scholar
  20. Stoppini, L., Buchs, P. A., and Muller, D. (1991) A simple method for organotypic cultures of nervous tissue. J. Neurosci. Methods 37, 173–182.CrossRefGoogle Scholar
  21. Tavazoie, S. F., Alvarez, V. A., Ridenour, D. A., Kwiatkowski, D. J., and Sabatini, B. L. (2005) Regulation of neuronal morphology and function by the tumor suppressors TSC1 and TSC2. Nat. Neurosci. 8(12), 1727–1734.CrossRefGoogle Scholar
  22. Troyanskaya, O., Cantor, M., Sherlock, G., et al. (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525.CrossRefGoogle Scholar
  23. Yuste, R. and Denk, W. (1995) Dendritic spines as basic functional units of neuronal integration. Nature 375, 682–684.CrossRefGoogle Scholar
  24. Zhou, X., Wang, X., and Dougherty, E. R. (2003a) Binarization of microarray data based on a mixture model. Mol. Cancer Ther. 2, 679–684.Google Scholar
  25. Zhou, X., Wang, X., and Dougherty, E. R. (2003b) Missing value estimation based on linear and nonlinear regression with Bayesian gene selection. Bioinformatics 19, 2302–2307.CrossRefGoogle Scholar
  26. Zhou, X., Wang, X., and Dougherty, E. R. (2004) Nonlinear-probit gene classification using mutual-information and wavelet based feature selection. J. Biol. Syst. 12, 371–386.CrossRefGoogle Scholar
  27. Zhou, X., Cao, X., Perlman, Z., and Wong, S. T. C. (2005a) A computerized cellular imaging system for high content analysis in Monastrol suppressor screens. J. Biomedical Informat., 38 (in Press).Google Scholar
  28. Zhou, X., Liu, K. L., Bradley, P., Perrimon, N., and Wong, S. T. C. (2005b) Towards automated cellular image segmentation for RNAi genome-wide screening. Lecture Notes in Computer Science (MICCAI2005), 3749, 885–892.Google Scholar
  29. Zoubir, A. M. and Boashash, B. (1998) The bootstrap and its application in signal processing. IEEE Signal Process. Magazine 15, 56–76.CrossRefGoogle Scholar

Copyright information

© Humana Press Inc 2006

Authors and Affiliations

  • Xiaobo Zhou
    • 1
  • Jinmin Zhu
    • 1
  • Kuang-Yu Liu
    • 1
  • Bernardo L. Sabatini
    • 3
  • Stephen T. C. Wong
    • 1
    • 2
  1. 1.Harvard Center for Neurodegeneration and Repair-Center for BioinformaticsHarvard Medical SchoolBostonUSA
  2. 2.Functional & Molecular Imaging Center, Radiology DepartmentBrigham and Women's HospitalBostonUSA
  3. 3.Department of NeurobiologyHarvard Medical SchoolBostonUSA

Personalised recommendations