Parametric Modeling Analysis of Optical Imaging Data on Neuronal Activities in the Brain

Part of the Springer Optimization and Its Applications book series (SOIA, volume 38)


An optical imaging technique using a voltage-sensitive dye (voltage imaging) has been widely applied to the analyses of various brain functions. Because optical signals in voltage imaging are small and require several kinds of preprocessing, researchers who use voltage imaging often conduct signal averaging of multiple trials and correction of signals by cutting the noise near the baseline in order to improve the apparent signal–noise ratio. However, a noise cutting threshold level that is usually set arbitrarily largely affects the analyzed results. Therefore, we aimed to develop a new method to objectively evaluate optical imaging data on neuronal activities. We constructed a parametric model to analyze optical time series data. We have chosen the respiratory neuronal network in the brainstem as a representative system to test our method. In our parametric model we assumed an optical signal of each pixel as the input and the inspiratory motor nerve activity of the spinal cord as the output. The model consisted of a threshold function and a delay transfer function. Although it was a simple nonlinear dynamic model, it could provide precise estimation of the respiratory motor output. By classifying each pixel into five types based on our model parameter values and the estimation error ratio, we obtained detailed classification of neuronal activities. The parametric modeling approach can be effectively employed for the evaluation of voltage-imaging data and thus for the analysis of the brain function.


Optical Signal Nonlinear Optimization Method Respiratory Motor Spinal Cord Preparation Voltage Imaging 



This research was executed by grant-in-aid for scientific research (spadework (A) No19200021) of The Ministry of Education, Culture, Sports, Science, and Technology.


  1. 1.
    Baker, B., Kosmidis, E., Vucinic, D., Falk, C., Cohen, L., Djurisic, M., Zecevic, D. Imaging brain activity with voltage-and calcium-sensitive dyes. Cell Mol Neurobiol 25, 245–282 (2005)CrossRefGoogle Scholar
  2. 2.
    Buzàki, G. Large-scale recording of neuronal ensembles. Nat Neurosci 7, 446–451 (2004)CrossRefGoogle Scholar
  3. 3.
    Cohen, L., Keynes, R., Hille, B. Light scattering and birefringence changes during nerve activity. Nature 218, 438–441 (1968)CrossRefGoogle Scholar
  4. 4.
    Cohen, L., Salzberg, B. Optical measurement of membrane potential. Rev Physiol Biochem Pharmacol 83, 35–88 (1978)Google Scholar
  5. 5.
    Ebner, T., Chen, G. Use of voltage-sensitive dyes and optical recordings in the central nervous system. Prog Neurobiol 46, 463–506 (1995)CrossRefGoogle Scholar
  6. 6.
    Ezure, K. Reflections on respiratory rhythm generation. Prog Brain Res 143, 67–74 (2004)CrossRefGoogle Scholar
  7. 7.
    Feldman, J., Mitchell, G., Nattie, E. Breathing: Rhythmicity, plasticity, chemosensitivity. Annu Rev Neurosci 26, 239–266 (2003)CrossRefGoogle Scholar
  8. 8.
    Feldman, J., Negro, C.D. Looking for inspiration: New perspectives on respiratory rhythm. Nat Rev Neurosci 7, 232–242 (2006)CrossRefGoogle Scholar
  9. 9.
    Fisher, J., Marchenko, V., Yodh, A., Rogers, R. Spatiotemporal activity patterns during respiratory rhythmogenesis in the rat ventrolateral medulla. J Neurophysiol 95, 1982–1991 (2006)CrossRefGoogle Scholar
  10. 10.
    Fujii, M., Umezawa, K., Arata, A. Dopamine desynchronizes the pace-making neuronal activity of rat respiratory rhythm generation. Eur J Neurosci 23, 1015–1027 (2006)CrossRefGoogle Scholar
  11. 11.
    Fukuda, K., Okada, Y., Yoshida, H., Aoyama, R., Nakamura, M., Chiba, K., Toyama, Y. Ischemia-induced disturbance of neural network function in the rat spinal cord analyzed by voltage-imaging. Neuroscience 140, 1453–1465 (2006)CrossRefGoogle Scholar
  12. 12.
    Fukunishi, K., Murai, N. Temporal coding in the guinea-pig auditory cortex as revealed by optical imaging and its pattern-time-series analysis. Biol Cybern 72, 463–473 (1995)MATHCrossRefGoogle Scholar
  13. 13.
    Gill, P., Murray, W., Saunders, M., Wright, M. Procedures for optimization problemswith a mixture of bounds and general linear constraints. ACM Trans Math Software 10, 282–298 (1984)MathSciNetMATHCrossRefGoogle Scholar
  14. 14.
    Gill, P., Murray, W., Wright, M. Practical Optimization. Academic Press, London (1981)Google Scholar
  15. 15.
    Kamino, K. Optical studies of early developing cardiac and neural activities using voltage-sensitive dyes. Jpn J Physiol 40, 443–461 (1990)CrossRefGoogle Scholar
  16. 16.
    Lindsey, B., Morris, K., Segers, L., Shannon, R. Respiratory neuronal assemblies. Respir Physiol 122, 183–196 (2000)CrossRefGoogle Scholar
  17. 17.
    Okada, Y., Chen, Z., Yoshida, H., Kuwana, S., Jiang, W., Maruiwa, H. Optical recording of the neuronal activity in the brainstem-spinal cord: Application of a voltage-sensitive dye. Adv Exp Med Biol 499, 113–118 (2001)CrossRefGoogle Scholar
  18. 18.
    Okada, Y., Kuwana, S., Masumiya, H., Kimura, N., Chen, Z., Oku, Y. Chemosensitive neuronal network organization in the ventral medulla analyzed by dynamic voltage-imaging. Adv Exp Med Biol 605, 353–358 (2007)CrossRefGoogle Scholar
  19. 19.
    Okada, Y., Masumiya, H., Tamura, Y., Oku, Y. Respiratory and metabolic acidosis differentially affect the respiratory neuronal network in the ventral medulla of neonatal rats. Eur J Neurosci 26, 2834–2843 (2007)CrossRefGoogle Scholar
  20. 20.
    Okada, Y., Mückenhoff, K., Holtermann, G., Acker, H., Scheid, P. Depth profiles of ph and p02 in the isolated brainstem-spinal cord of the neonatal rat. Respir Physiol 93, 315–326 (1993)CrossRefGoogle Scholar
  21. 21.
    Oku, Y., Kimura, N., Masumiya, H., Okada, Y. Spatiotemporal organization of frog respiratory neurons visualized on the ventral medullary surface. Resp Physiol Neurobiol 161, 281–290 (2010)CrossRefGoogle Scholar
  22. 22.
    Oku, Y., Masumiya, H., Okada, Y. Postnatal developmental changes in activation profiles of the respiratory neuronal network in the rat ventral medulla. J Physiol 585, 175–186 (2007)CrossRefGoogle Scholar
  23. 23.
    Onimaru, H., Homma, I. A novel functional neuron group for respiratory rhythm generation in the ventral medulla. J Neurosci 23, 1478–1486 (2003)Google Scholar
  24. 24.
    Ramirez, J., Richter, D. The neuronal mechanisms of respiratory rhythm generation. Curr Opin Neurobiol 6, 817–825 (1996)CrossRefGoogle Scholar
  25. 25.
    Segers, L., Shannon, R., Saporta, S., Lindsey, B. Functional associations among simultaneously monitored lateral medullary respiratory neurons in the cat. I. Evidence for excitatory and inhibitory actions of inspiratory neurons. J Neurophysiol 57, 1078–1100 (1987)Google Scholar
  26. 26.
    Smith, J., Ellenberger, H., Ballanyi, K., Richter, D., Feldman, J. Pre-Bötzinger complex: A brainstem region that may generate respiratory rhythm in mammals. Science 254, 726–729 (1991)CrossRefGoogle Scholar
  27. 27.
    Suzue, T. Respiratory rhythm generation in the in vitro brainstem-spinal cord preparation of the neonatal rat. J Physiol 354, 173–183 (1984)Google Scholar
  28. 28.
    Tasaki, I., Watanabe, A., Sandlin, R., Carnay, L. Changes in fluorescence, turbidity, and birefringence associated with nerve excitation. Proc Natl Acad Sci 61, 883–888 (1968)CrossRefGoogle Scholar
  29. 29.
    Tominaga, T., Tominaga, Y., Yamada, H., Matsumoto, G., Ichikawa, M. Quantification of optical signalswith electrophysiological signals in neural activities of di-4-anepps stained rat hippocampal slices. J Neurosci Methods 102, 11–23 (2000)CrossRefGoogle Scholar
  30. 30.
    Yoshida, T., Sakagami, M., Katura, T., Yamazaki, K., Tanaka, S., Iwamoto, M., Tanaka, N. Anisotropic spatial coherence of ongoing and spontaneous activities in auditory cortex. Neurosci Res 61, 49–55 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.The Graduate University for Advanced StudiesMinato-kuJapan
  2. 2.Hyogo College of MedicineNishinomiyaJapan
  3. 3.Keio University Tsukigase Rehabilitation CenterIzuJapan
  4. 4.Chiba UniversityInage-kuJapan
  5. 5.The Institute of Statistical MathematicsMinato-kuJapan

Personalised recommendations