Functional magnetic resonance imaging analysis of the human brain in texture recognition tasks

Kharauzov, А.К., Vasiliev, P.P., Sokolov, A.V., Fokin, V.A., Shelepin, Y.E.

Full text «Opticheskii Zhurnal»

Full text on elibrary.ru

Publication in Journal of Optical Technology

For Russian citation (Opticheskii Zhurnal):

Хараузов А.К., Васильев П.П., Соколов А.В., Фокин В.А., Шелепин Ю.Е. Анализ изображений функциональной магнитно-резонансной томографии головного мозга человека в задачах распознавания текстур // Оптический журнал. 2018. Т. 85. № 8. С. 22–28. http://doi.org/10.17586/1023-5086-2018-85-08-22-28

Kharauzov A.K., Vasiliev P.P., Sokolov A.V., Fokin V.A., Shelepin Yu.E. Functional magnetic resonance imaging analysis of the human brain in texture recognition tasks [in Russian] // Opticheskii Zhurnal. 2018. V. 85. № 8. P. 22–28. http://doi.org/10.17586/1023-5086-2018-85-08-22-28

For citation (Journal of Optical Technology):

A. K. Kharauzov, P. P. Vasil’ev, A. V. Sokolov, V. A. Fokin, and Yu. E. Shelepin, "Functional magnetic resonance imaging analysis of the human brain in texture recognition tasks," Journal of Optical Technology. 85(8), 463-467 (2018). https://doi.org/10.1364/JOT.85.000463

Abstract:

Neuroimaging and functional magnetic resonance imaging have been used to study the change in the activity of the human brain during visual spatial tests of varying complexity. It is shown that, along with an increase in activity in brain structures responsible for the task, there is a decrease in activity in other areas that are not engaged during the visual signal processing. To estimate the equilibrium of activation and deactivation processes in the brain, the number of voxels (the minimum element of a 3D image in the brain) with a significant change in activity relative to the resting state was calculated for each subject. The results of data averaging for all subjects showed that the total volume of activated regions increases with the problem complexity. The volume of brain regions that decreased their activity during the test showed a similar dependence on complexity—the more difficult the task, the greater the number of deactivated voxels. The data obtained suggest the existence of mechanisms for the redistribution of neuronal activity in the human brain to maintain a balance between the activated and deactivated regions, which makes it possible to reduce the energy expenditure of the brain with an increase in cognitive loads.

Keywords:

functional magnetic resonance imaging, images recognition, neural networks

Acknowledgements:

The research was supported by the Program of Fundamental Scientific Research of State Academies for 2013–2020 (GP-14, section 63).

OCIS codes: 330.5380, 330.5000, 100.6950

References:

1. K. D. Singh, “Which ‘neural activity’ do you mean? fMRI, MEG, oscillations and neurotransmitters,” Neuroimage 62(2), 1121–1130 (2012).
2. W. J. Bentley, J. M. Li, A. Z. Snyder, M. E. Raichle, and L. H. Snyder, “Oxygen level and LFP in task-positive and task-negative areas: bridging BOLD fMRI and electrophysiology,” Cereb. Cortex 26(1), 346–357 (2016).
3. V. Menon, “Large-scale brain networks and psychopathology: a unifying triple network model,” Trends Cognit. Sci. 15(10), 483–506 (2011).
4. T. Nekovarova, I. Fajnerova, J. Horacek, and F. Spaniel, “Bridging disparate symptoms of schizophrenia: a triple network dysfunction theory,” Front. Behav. Neurosci. 8, 171 (2014).
5. M. E. Raichle, A. M. MacLeod, A. Z. Snyder, W. J. Powers, D. A. Gusnard, and G. L. Shulman, “A default mode of brain function,” Proc. Natl. Acad. Sci. U.S.A. 98(2), 676–682 (2001).
6. M. E. Raichle, “The brain’s dark energy,” Science 314(5803), 1249–1250 (2006).
7. M. D. Fox and M. E. Raichle, “Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging,” Nat. Rev. Neurosci. 8(9), 700–711 (2007).
8. A. K. Kharauzov, P. P. Vasil’ev, A. V. Sokolov, Y. E. Shelepin, M. B. Kuvaldina, O. V. Borachuk, V. A. Fokin, and S. V. Pronin, “Image perception in visual-search tasks when dynamic noise is present,” J. Opt. Technol. 82(5), 298–307 (2015) [Opt. Zh. 82(5), 42–55 (2015)].
9. A. K. Harauzov, Y. E. Shelepin, T. Selchenkova, and S. Pronin, “Electrophysiological and psychophysical investigations of texture discrimination mechanisms,” Perception 35(suppl), 72 (2006).
10. Y. Shelepin, N. N. Krasilnikov, E. Trufanov, A. Harauzov, S. Pronin, and V. Fokin, “The principle of least action and visual perception,” Perception 35(1)(suppl), 125–136 (2006).
11. V. Fokin, E. Trufanov, A. Sevostyanov, Y. Shelepin, A. Harauzov, and S. Pronin, “Occipital-parietal interaction in incomplete pattern discrimination,” Perception 35(suppl), 18–19 (2006).
12. A. Sevostyanov, V. Fokin, A. Harauzov, Y. Shelepin, S. Pronin, and T. Selchenkova, “fMRI localization of the mechanisms underlying the incomplete patterns discrimination,” Perception 35(suppl), 72 (2006).
13. A. K. Kharauzov, Y. E. Shelepin, S. V. Pronin, T. V. Selchenkova, and Y. A. Noskov, “Electrophysiological studies of mechanisms of texture recognition,” Ross. Fiziol. Zh. im. I. M. Sechenova 93(1), 3–13 (2007).
14. Y. E. Shelepin, V. A. Fokin, A. K. Kharauzov, S. V. Pronin, and V. N. Chikhman, “Localization of the decision-making center when perceiving the form of visual stimuli,” Dokl. Akad. Nauk 429(6), 1–3 (2009).
15. A. K. Harauzov, Y. E. Shelepin, Y. A. Noskov, P. P. Vasil’ev, and N. P. Foreman, “The time course of pattern discrimination in the human brain,” Vis. Res. 125, 55–63 (2016).
16. H. R. Heekeren, S. Marrett, P. A. Bandettini, and L. G. Ungerleider, “A general mechanism for perceptual decision-making in the human brain,” Nature 431(7010), 859–862 (2004).
17. M. C. Keuken, C. Müller-Axt, R. Langner, S. B. Eickhoff, B. U. Forstmann, and J. Neumann, “Brain networks of perceptual decision-making: an fMRI ALE meta-analysis,” Front. Hum. Neurosci. 8(6), 445 (2014).
18. Y. E. Shelepin, V. A. Fokin, S. V. Men’shikova, O. V. Borachuk, S. A. Koskin, A. V. Sokolov, S. V. Pronin, A. K. Kharauzov, P. P. Vasil’ev, and O. A. Vakhrameeva, “Imaging and the brain mapping methods in assessing the functional state of the visual system,” Sens. Sist. 28(2), 63–78 (2014).
19. O. V. Borachuk, Y. E. Shelepin, A. K. Kharauzov, P. P. Vasil’ev, V. A. Fokin, and A. V. Sokolov, “Study of the influence of the role of the instruction to the observer in tasks of recognizing emotionally colored patterns,” J. Opt. Technol. 82(10), 678–684 (2015) [Opt. Zh. 82(5), 42–55 (2015)].
20. N. Krasilnikov and Y. E. Shelepin, “Functional model of vision,” Opt. Zh. 64(2), 72–82 (1997).