Neurophysical programmable model for imaging the loci of mental functions in the brain

A.L. Eryomin; N.M. Bogatov; A. Reyes Moncada

doi:10.18127/j15604136-202501-01

Neurophysical programmable model for imaging the loci of mental functions in the brain

A.L. Eryomin¹, N.M. Bogatov², A. Reyes Moncada³

research article UDC 004.81 Pages: 6-17 doi.org/10.18127/j15604136-202501-01

^1,2,3 Kuban State University (KubSU) (Krasnodar, Russia)

Keywords: Modeling neurophysics psychophysics fMRI mental functions brain LEDs stereolithography 3D-model

Abstract

Developments are presented and reviewed:

tabular classification matrix "standards of mental functions according to the International Classification of Functioning WHO – experimental data of functional magnetic resonance imaging – loci in the three – dimensional structure of the brain";

techniques for placing LEDs at precise coordinates in a physical model of the brain structure to reflect the loci of specific mental functions;

image segmentation and reconstruction techniques for obtaining a three-dimensional brain grid of high resolution and accuracy;

3D-printed realistic model of the human brain using additive technologies of layer-by-layer printing and laser stereolithography;

technologies for creating a multilayer structure with arrays of LEDs of different color reproduction and assembly inside a 3D-model of the brain.

The developed methodology and practical implementation of the structural and functional physical model of localization of mental functions in the three-dimensional space of the brain is an innovative approach that, when implemented in practice, can contribute to improvement and optimization in: teaching and research in the physiology of higher nervous activity, psychiatry, psychology, neurology, neuroscience and neurotechnology, biophysics, medical physics, psychophysics, neurophysics, bioengineering; medical imaging, diagnosis and treatment of nervous and mental diseases, planning in neurosurgery and radiotherapy.

Received: 15.11.2024
Approved after review: 29.11.2024
Accepted: 15.01.2025

How to cite

Еремин А.Л., Богатов Н.М., Рейес Монкада А . Нейрофизическая программируемая модель визуализации локусов умственных функций в мозге // Биомедицинская радиоэлектроника. 2025. T. 28. № 1. С. 6−17. DOI: https://doi.org/10.18127/j15604136-202501-01

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[1] Axer M., Amunts K. Scale matters: the nested human connectome. Science. 2022. V. 378. № 6619. P. 500–504.

[2] Adams A. et al. International brain initiative: an innovative framework for coordinated global brain research efforts. Neuron. 2020. V. 105. № 2. P. 212–216.

[3] Anoxin K.V. Kognitom: v poiskax fundamental`noj nejronauchnoj teorii soznaniya. Zhurnal vy`sshej nervnoj deyatel`nosti im. IP Pavlova. 2021. T. 71. № 1. S. 39–71.

[4] Mezhdunarodnaya klassifikaciya funkcionirovaniya, ogranichenij zhiznedeyatel`nosti i zdorov`ya. Zheneva: Vsemirnaya Organizaciya Zdravooxraneniya. 2001.

[5] Eremin A.L., Rejes Monkada A. Matricza lokusov umstvenny`x funkcij v trexmernom prostranstve mozga po nejrovizualizacii fMRT / Sovremenny`e problemy` fiziki, biofiziki i infokommunikacionny`x texnologij. Krasnodar: CzNTI. 2023. S. 84–100.

[6] Zinchenko O., Arsalidou M. Brain responses to social norms: Meta‐analyses of MRI studies. Human brain mapping. 2018. V. 39. № 2. P. 955–970.

[7] Owen A.M. et al. Detecting awareness in the vegetative state. Science. 2006. V. 313. № 5792. P. 1402–1402.

[8] Boly M. et al. When thoughts become action: an fMRI paradigm to study volitional brain activity in non-communicative brain injured patients. Neuroimage. 2007. V. 36. № 3. P. 979–992.

[9] Ameller A. Les troubles de la familiarité dans la schizophrénie. Lille: Université du Droit et de la Santé-Lille II. 2014.

[10] Yang Z. et al. Using fMRI to decode true thoughts independent of intention to conceal. NeuroImage. 2014. V. 99. P. 80–92.

[11] Harlé K.M., Sanfey A.G. Social economic decision-making across the lifespan: An fMRI investigation. Neuropsychologia. 2012. V. 50. № 7. P. 1416–1424.

[12] Hao X. et al. Enhancing insight in scientific problem solving by highlighting the functional features of prototypes: An fMRI study. Brain research. 2013. V. 1534. P. 46–54.

[13] Rilling J.K. et al. A neural basis for social cooperation. Neuron. 2002. V. 35. № 2. P. 395–405.

[14] Franzan M.E. Activación cerebral diferencial en la prueba de reconocimiento de emociones faciales UNCEEF mediante la utilización de resonancia magnética functional. Buenos Aires: Instituto Universitario Hospital Italiano de Buenos Aires. 2019.

[15] Lévesque J. Neuroanatomie fonctionnelle sous-tendant la régulation consciente et volontaire de la tristesse chez l'enfant et l'adulte sains. 2005.

[16] Fusar-Poli P. et al. Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies. Journal of psychiatry and neuroscience. 2009. V. 34. № 6. P. 418–432.

[17] Wu Y. et al. Religious beliefs influence neural substrates of self-reflection in Tibetans. Social Cognitive and Affective Neuroscience. 2010. V. 5. № 2-3. P. 324–331.

[18] Han S. et al. Neural consequences of religious belief on self-referential processing. Social Neuroscience. 2008. V. 3. № 1. P. 1–15.

[19] Christensen J.F. et al. Roman Catholic beliefs produce characteristic neural responses to moral dilemmas. Social Cognitive and Affective Neuroscience. 2014. V. 9. № 2. P. 240–249.

[20] Beauregard M., Paquette V. Neural correlates of a mystical experience in Carmelite nuns. Neuroscience letters. 2006. V. 405. № 3. P. 186–190.

[21] Poldrack R.A., Mumford J.A., Nichols T.E. Handbook of functional MRI data analysis. Cambridge University Press. 2024.

Abstract

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