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ISSN: 1023-5086


ISSN: 1023-5086

Scientific and technical

Opticheskii Zhurnal

A full-text English translation of the journal is published by Optica Publishing Group under the title “Journal of Optical Technology”

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DOI: 10.17586/1023-5086-2022-89-08-104-109

УДК: 612.087

Quantitative evaluation of the functional activity in multilayered brain structures using histological sections

For Russian citation (Opticheskii Zhurnal):

Алексеенко С.В., Солнушкин С.Д., Чихман В.Н. Количественная оценка функциональной активности в многослойных структурах головного мозга по гистологическим срезам // Оптический журнал. 2022. Т. 89. № 8. С. 104–109.


Alekseenko S.V., Solnushkin S.D., Chikhman V.N. Quantitative evaluation of the functional activity in multilayered brain structures using histological sections [in Russian] // Opticheskii Zhurnal. 2022. V. 89. № 8. P. 104–109.

For citation (Journal of Optical Technology):

S. V. Alekseenko, S. D. Solnushkin, and V. N. Chikhman, "Quantitative evaluation of the functional activity in multilayered brain structures using histological sections," Journal of Optical Technology. 89(8), 502-505 (2022).


Subject of study. We develop an algorithm and software to estimate optical density as a correlate of neural activity in multilayered brain structures. Aim of study. We develop a method and program algorithm for quantitative evaluation of the optical density in the layers of neural structures from images of histological brain preparations that correlates with the functional activity of the cells. Method. Optical density was measured locally in successive areas of a layer or several layers of the neural structure, which are curvilinear-shaped in the brain slices. An expert researcher selects the trajectory of optical density measurement in the digitized image of the slice in an interactive mode. The results of density measurements were plotted in an orthogonal coordinate system. Capabilities enabling automated curve smoothing, extremum seeking, and calculation of distances between extrema were developed. Results. An algorithm for implementing the developed program is presented. The successive stages of operation are illustrated through the evaluation of the optical density in the visual cortex layer from an image of a brain slice as an example. In addition, the evaluation results of the optical density in layers of a lateral geniculate body are presented. Practical significance. The developed method and program algorithm are required for reconstructing the functional activity of the curved layers of different brain structures to investigate and simulate the organization patterns of the neural networks of the human brain.


image processing, histological sections of brain, curvilinear trajectory of measurements


The research was supported by the State program 47 "Scientific and Technological Development of the Russian Federation" (2019-2030), theme No. 0134-2019-0005.

OCIS codes: 110.2960, 330.7326, 330.4060, 330.5510


1. G. H. Kageyama and M. Wong-Riley, “Laminar and cellular localization of cytochrome oxidase in the cat striate cortex,” J. Comp. Neurol. 245(2), 137–159 (1986).
2. M. Wong-Riley, “Changes in the visual system of monocularly sutured or enucleated cats demonstrable with cytochrome oxidase histochemistry,” Brain Res. 171(1), 11–28 (1979).
3. R. Gonzalez and R. Woods, Digital Image Processing (Tekhnosfera, Moscow, 2005).
4. S. V. Ablameiko and A. M. Nedz’ved’, Processing Digital Images of Cellular Structures in Medicine (OIPI NAN Belarusi, Minsk, 2005).
5. A. S. Astakhov and V. V. Bumagin, “Analysis of the efficiency of the image processing algorithms for biological micro-objects recognition in the histological sections,” Inzh. Vestn. Dona (4), 4515 (2017).
6. D. H. Hubel and T. N. Wiesel, Brain and Visual Perception (Oxford University Press, 2005).
7. P. Y. Shkorbatova and S. V. Alekseenko, “Eye-rotation-induced spatial reorganization of horizontal connections in field 17 of the cat cortex,” Neurosci. Behav. Physiol. 36(5), 495–497 (2006).
8. P. Yu. Shkorbatova, S. N. Toporova, F. N. Makarov, and S. V. Alekseenko, “Intracortical connections of ocular dominance columns of areas 17 and 18 in cats with experimental strabismus,” Sens. Sist. 20(4), 309–318 (2006).
9. S. V. Alekseenko, S. N. Toporova, and P. Y. Shkorbatova, “Neuronal connections of eye-dominance columns in the cat cerebral cortex after monocular deprivation,” Neurosci. Behav. Physiol. 38(7), 669–675 (2008).
12. S. V. Alekseenko and P. Yu. Shkorbatova, “Deprivation and strabismic amblyopia: abnormalities in geniculocortical visual pathways,” Al’m. Klin. Med. 36, 97–100 (2015).
13. S. V. Alekseenko and P. Yu. Shkorbatova, “The time course of abnormalities in the brain subcortical visual centre following early impairment of binocular experience,” Al’m. Klin. Med. 44(3), 351–357 (2016).
14. S. V. Alekseenko, “The neural networks that provide stereoscopic vision,” J. Opt. Technol. 85(8), 482–487 (2018) [Opt. Zh. 85(8), 46–53 (2018)].