Skip to main content
SpringerLink
Log in

Reconstruction of the Electrical Structure of the Human Body Using Spectral Functional Tomography

  • SCIENTIFIC SCHOOL OF THE INSTITUTE OF MATHEMATICAL PROBLEMS OF BIOLOGY OF THE RUSSIAN ACADEMY OF SCIENCES–THE BRANCH OF KELDYSH INSTITUTE OF APPLIED MATHEMATICS OF RUSSIAN ACADEMY OF SCIENCES, PUSHCHINO, MOSCOW REGION, THE RUSSIAN FEDERATION
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

The functional tomography method, based on the spectral analysis of multichannel time series of long duration, has been used to study the distribution of electrical sources in the human body. The spontaneous activity of various organs and tissues has been studied. The spatial distribution and directions of elementary sources of alpha rhythm in the brain have been examined. Spontaneous brain activity has been studied in mental disorders. Using a cardiogram, the functional structure of the heart has been found, and using myography data, working skeletal muscles have been reconstructed. The spatial distribution of moving magnetic nanoparticles was also found. The coincidence of the results with the anatomical and physical structure of the complex systems being studied confirms the high promise of the proposed method in various fundamental and applied problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13
Fig. 14.
Fig. 15.
Fig. 16.
Fig. 17.
Fig. 18.
Fig. 19.
Fig. 20.
Fig. 21.
Fig. 22.
Fig. 23.
Fig. 24.
Fig. 25.
Fig. 26.
Fig. 27.
Fig. 28.
Fig. 29.
Fig. 30.
Fig. 31.
Fig. 32.
Fig. 33.
Fig. 34.

REFERENCES

  1. P. L. Agren, H. Goranson, H. Jonsson, and L. Bergfeldt, “Magnetocardiographic and Magnetic Resonance Imaging for Noninvasive Localization of Ventricular Arrhythmia Origin in a Model of Nonischemic Cardiomyopathy,” Pacing Clin. Electrophysiology 25, 161–166 (2002). https://doi.org/10.1046/j.1460-9592.2002.00161.x

    Article  Google Scholar 

  2. E. Barzegaran, V. Y. Vildavski, and M. G. Knyazeva, “Fine structure of posterior alpha rhythm in human EEG: Frequency components, their cortical sources, and temporal behavior,” Sci. Rep. 7, 8249 (2017). https://doi.org/10.1038/s41598-017-08421-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. E. Başar, “A review of alpha activity in integrative brain function: Fundamental physiology, sensory coding, cognition and pathology,” Int. J. Psychophysiology 86, 1–24 (2012). https://doi.org/10.1016/j.ijpsycho.2012.07.002

    Article  Google Scholar 

  4. H. Berger, “Über das Elektrenkephalogramm des Menschen,” Arch. Psychiatrie und Nervenkrankheiten 87, 527–570 (1929). https://doi.org/10.1007/bf01797193

    Article  Google Scholar 

  5. H. Bin Yoo, E. O. d. l. Concha, D. De Ridder, B. A. Pickut, and S. Vanneste, “The functional alterations in top-down attention streams of Parkinson’s disease measured by EEG,” Sci. Rep. 8, 10609 (2018). https://doi.org/10.1038/s41598-018-29036-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. A. V. Bocharov, G. G. Knyazev, A. N. Savostyanov, T. N. Astakhova, and S. S. Tamozhnikov, “EEG dynamics of spontaneous stimulus-independent thoughts,” Cognit. Neurosci. 10, 77–87 (2019). https://doi.org/10.1080/17588928.2018.1534820

    Article  Google Scholar 

  7. A. I. Boyko, S. D. Rykunov, and M. N. Ustinin, “A software package for the modeling of electrophysiological activity data,” Math. Biol. Bioinf. 17 (1), 1–9 (2022). https://doi.org/10.17537/2022.17.1

    Article  Google Scholar 

  8. D. Brisinda, A. M. Meloni, and R. Fenici, “First 36‑channel magnetocardiographic study of CAD patients in an unshielded laboratory for interventional and intensive cardiac care,” in Functional Imaging and Modeling of the Heart, Ed. by I. E. Magnin, J. Montagnat, P. Clarysse, J. Nenonen, and T. Katila, Lecture Notes in Computer Science, Vol. 2674 (Springer, Berlin, 2003), pp. 122–131. https://doi.org/10.1007/3-540-44883-7_13

    Book  Google Scholar 

  9. P. J. Broser, S. Knappe, D. Kajal, N. Noury, O. Alem, V. Shah, and C. Braun, “Optically pumped magnetometers for magneto-myography to study the innervation of the hand,” IEEE Trans. Neural Syst. Rehabilitation Eng. 26, 2226–2230 (2018). https://doi.org/10.1109/tnsre.2018.2871947

    Article  Google Scholar 

  10. Yi-Ch. Chang, Ch.-Ch. Wu, Ch.-H. Lin, Ye.-W. Wu, Yi.-Ch. Yang, T.-J. Chang, Yi-D. Jiang, and L.-M. Chuang, “Early myocardial repolarization heterogeneity is detected by magnetocardiography in diabetic patients with cardiovascular risk factors,” PLoS One 10, e0133192 (2015). https://doi.org/10.1371/journal.pone.0133192

  11. R. Chowdhury, M. Reaz, M. Ali, A. Bakar, K. Chellappan, and T. Chang, “Surface electromyography signal processing and classification techniques,” Sensors 13, 12431–12466 (2013). https://doi.org/10.3390/s130912431

    Article  PubMed  PubMed Central  Google Scholar 

  12. J. Clarke and N. E. Goldstein, “Magnetotelluric measurements,” in SQUID Applications to Geophysics, Ed. by H. Weinstock and W. C. Overton (Society of Exploration Geophysicists, Tulsa, Okla., 1980), p. 99. https://doi.org/10.1190/1.9781560802518

    Book  Google Scholar 

  13. D. Cohen and E. Givler, “Magnetomyography: Magnetic fields around the human body produced by skeletal muscles,” Appl. Phys. Lett. 21, 114–116 (1972). https://doi.org/10.1063/1.1654294

    Article  Google Scholar 

  14. D. Cohen, E. A. Edelsack, and J. E. Zimmerman, “Magnetocardiograms taken inside a shielded room with a superconducting point-contact magnetometer,” Appl. Phys. Lett. 16, 278–280 (1970). https://doi.org/10.1063/1.1653195

    Article  Google Scholar 

  15. R. J. Dinger, J. H. Claassen, and S. A. Wolf, “SQUIDS in a marine environment,” in SQUID Applications to Geophysics, Ed. by H. Weinstock and W. C. Overton (Society of Exploration Geophysicists, Tulsa, Okla., 1980), р. 49. https://doi.org/10.1190/1.9781560802518

  16. K. van den Doel, U. M. Ascher, and D. K. Pai, “Computed myography: Three-dimensional reconstruction of motor functions from surface EMG data,” Inverse Probl. 24, 065010 (2008). https://doi.org/10.1088/0266-5611/24/6/065010

  17. J. M. Van Egeraat, R. N. Friedman, and J. P. Wikswo, “Magnetic field of a single muscle fiber. First measurements and a core conductor model,” Biophys. J. 57, 663–667 (1990). https://doi.org/10.1016/s0006-3495(90)82585-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. M. A. C. Garcia and O. Baffa, “Magnetic fields from skeletal muscles: A valuable physiological measurement?,” Front. Physiol. 6, 228 (2015). https://doi.org/10.3389/fphys.2015.00228

    Article  PubMed  PubMed Central  Google Scholar 

  19. D. Goldman, “The clinical use of the ‘average’ reference electrode in monopolar recording,” Electroencephalography Clin. Neurophysiology 2, 209–212 (1950). https://doi.org/10.1016/0013-4694(50)90039-3

    Article  CAS  Google Scholar 

  20. M. D. Gregory and D. E. Mandelbaum, “Evidence of a faster posterior dominant EEG rhythm in children with autism,” Res. Autism Spectrum Disord. 6, 1000–1003 (2012). https://doi.org/10.1016/j.rasd.2012.01.001

    Article  Google Scholar 

  21. H. Heidari, S. Zuo, A. Krasoulis, and K. Nazarpour, “CMOS magnetic sensors for wearable magnetomyography,” in 2018 40th Annu. Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, Hawaii, 2018 (IEEE, 2018), pp. 2116–2119. https://doi.org/10.1109/embc.2018.8512723

  22. C. J. Holmes, R. Hoge, L. Collins, R. Woods, A. W. Toga, and A. C. Evans, “Enhancement of MR images using registration for signal averaging,” J. Comput. Assisted Tomography 22, 324–333 (1998). https://doi.org/10.1097/00004728-199803000-00032

    Article  CAS  Google Scholar 

  23. E. Honaga, R. Ishii, R. Kurimoto, L. Canuet, K. Ikezawa, H. Takahashi, T. Nakahachi, M. Iwase, I. Mizuta, T. Yoshimine, and M. Takeda, “Post-movement beta rebound abnormality as indicator of mirror neuron system dysfunction in autistic spectrum disorder: An MEG study,” Neurosci. Lett. 478, 141–145 (2010). https://doi.org/10.1016/j.neulet.2010.05.004

    Article  CAS  PubMed  Google Scholar 

  24. H. H. Jasper, “Report of the committee on methods of clinical examination in electroencephalography,” Electroencephalography Clin. Neurophysiology 10, 370–375 (1958). https://doi.org/10.1016/0013-4694(58)90053-1

    Article  Google Scholar 

  25. Yo. Kimura, H. Takaki, Yu. Y. Inoue, Ya. Oguchi, T. Nagayama, T. Nakashima, S. Kawakami, S. Nagase, T. Noda, T. Aiba, W. Shimizu, S. Kamakura, M. Sugimachi, S. Yasuda, H. Shimokawa, and K. Kusano, “Isolated late activation detected by magnetocardiography predicts future lethal ventricular arrhythmic events in patients with arrhythmogenic right ventricular cardiomyopathy,” Circ. J. 82, 78–86 (2018). https://doi.org/10.1253/circj.cj-17-0023

    Article  CAS  Google Scholar 

  26. W. Klimesch, M. Doppelmayr, J. Schwaiger, P. Auinger, and T. H. Winkler, “`Paradoxical’ alpha synchronization in a memory task,” Cognit. Brain Res. 7, 493–501 (1999). https://doi.org/10.1016/s0926-6410(98)00056-1

    Article  CAS  Google Scholar 

  27. P. Krauss, A. Schilling, J. Bauer, K. Tziridis, C. Metzner, H. Schulze, and M. Traxdorf, “Analysis of multichannel EEG patterns during human sleep: A novel approach,” Front. Hum. Neurosci. 12, 121 (2018). https://doi.org/10.3389/fnhum.2018.00121

    Article  PubMed  PubMed Central  Google Scholar 

  28. Y. Li, Z. Che, W. Quan, R. Yuan, Y. Shen, Z. Liu, W. Wang, H. Jin, and G. Lu, “Diagnostic outcomes of magnetocardiography in patients with coronary artery disease,” Int. J. Clin. Exp. Med. 8, 2441–2446 (2015).

  29. Ya. Liu, Yo. Ning, S. Li, P. Zhou, W. Z. Rymer, and Yi. Zhang, “Three-dimensional innervation zone imaging from multi-channel surface EMG recordings,” Int. J. Neural Syst. 25, 1550024 (2015). https://doi.org/10.1142/s0129065715500240

  30. R. R. Llinás and M. N. Ustinin, “Frequency-pattern functional tomography of magnetoencephalography data allows new approach to the study of human brain organization,” Front. Neural Circuits 8, 43 (2014). https://doi.org/10.3389/fncir.2014.00043

    Article  PubMed  PubMed Central  Google Scholar 

  31. R. R. Llinás and M. N. Ustinin, “Precise frequency-pattern analysis to decompose complex systems into functionally invariant entities,” US Patent 20140107979 (2014).

  32. R. R. Llinás, M. N. Ustinin, S. D. Rykunov, A. I. Boyko, V. V. Sychev, K. D. Walton, G. M. Rabello, and J. Garcia, “Reconstruction of human brain spontaneous activity based on frequency-pattern analysis of magnetoencephalography data,” Front. Neurosci. 9, 373 (2015). https://doi.org/10.3389/fnins.2015.00373

    Article  PubMed  PubMed Central  Google Scholar 

  33. R. R. Llinás, M. Ustinin, S. Rykunov, K. D. Walton, G. M. Rabello, J. Garcia, A. Boyko, and V. Sychev, “Noninvasive muscle activity imaging using magnetography,” Proc. Natl. Acad. Sci. U. S. A. 117, 4942–4947 (2020). https://doi.org/10.1073/pnas.1913135117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. R. R. Llinás, “The intrinsic electrophysiological properties of mammalian neurons: Insights into central nervous system function,” Science 242, 1654–1664 (1988). https://doi.org/10.1126/science.3059497

    Article  PubMed  Google Scholar 

  35. R. R. Llinás and D. Paré, “Of dreaming and wakefulness,” Neuroscience 44, 521–535 (1991). https://doi.org/10.1016/0306-4522(91)90075-y

    Article  PubMed  Google Scholar 

  36. E. A. Lushchekina, O. Yu. Khaerdinova, V. Yu. Novototskii-Vlasov, V. S. Lushchekin, and V. B. Strelets, “Synchronization of EEG rhythms in baseline conditions and during counting in children with autism spectrum disorders,” Neurosci. Behav.al Physiol. 46, 382–389 (2017). https://doi.org/10.1007/s11055-016-0246-5

    Article  Google Scholar 

  37. Magnetism in Medicine: A Handbook, Ed. by W. Andra and H. Nowak, 2nd ed. (Wiley, 2007). https://doi.org/10.1002/9783527610174

    Book  Google Scholar 

  38. J. Malmivuo and R. Plonsey, Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields (Oxford Univ. Press, New York, 1995). https://doi.org/10.1093/acprof:oso/9780195058239.001.0001

    Book  Google Scholar 

  39. I. Manshanden, J. C. De Munck, N. R. Simon, and F. H. Lopes Da Silva, “Source localization of MEG sleep spindles and the relation to sources of alpha band rhythms,” Clin. Neurophysiology 113, 1937–1947 (2002). https://doi.org/10.1016/s1388-2457(02)00304-8

    Article  Google Scholar 

  40. Yu. V. Maslennikov, M. A. Primin, V. Yu. Slobodtchikov, I. V. Nedayvoda, V. A. Krymov, V. V. Khanin, G. G. Ivanov, N. A. Bulanova, S. Yu. Kuznetsova, and V. N. Gunaeva, “SQUID-based magnetometric systems for cardiac diagnostics,” Biomed. Eng. 51, 153–156 (2017). https://doi.org/10.1007/s10527-017-9704-9

    Article  Google Scholar 

  41. Yu. V. Maslennikov, “Magnetocardiographic diagnostic complexes based on the MAG-SKAN SQUIDs,” J. Commun. Technol. Electron. 56, 991–999 (2011). https://doi.org/10.1134/S1064226911050093

    Article  Google Scholar 

  42. Yu. V. Maslennikov, M. A. Primin, V. Yu. Slobodchikov, V. V. Khanin, I. V. Nedayvoda, V. A. Krymov, A. V. Okunev, E. A. Moiseenko, A. V. Beljaev, V. S. Rybkin, A. V. Tolcheev, and A. V. Gapelyuk, “The DC-SQUID-based magnetocardiographic systems for clinical use,” Phys. Procedia 36, 88–93 (2012). https://doi.org/10.1016/j.phpro.2012.06.218

    Article  Google Scholar 

  43. J. McCubbin, J. Vrba, P. Spear, D. McKenzie, R. Willis, R. Loewen, S. E. Robinson, and A. A. Fife, “Advanced electronics for the CTF MEG system,” Neurol. Clin. Neurophysiol. 2004, 69 (2004).

    CAS  PubMed  Google Scholar 

  44. D. A. Menassa, S. Braeutigam, A. Bailey, and C. M. Falter-Wagner, “Frontal evoked γ activity modulates behavioural performance in autism spectrum disorders in a perceptual simultaneity task,” Neurosci. Lett. 665, 86–91 (2018). https://doi.org/10.1016/j.neulet.2017.11.045

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. C. M. Michel and D. Brunet, “EEG source imaging: A practical review of the analysis steps,” Front. Neurol. 10, 325 (2019). https://doi.org/10.3389/fneur.2019.00325

    Article  PubMed  PubMed Central  Google Scholar 

  46. W. Moshage, S. Achenbach, K. Göhl, and K. Bachmann, “Evaluation of the non-invasive localization accuracy of cardiac arrhythmias attainable by multichannel magnetocardiography (MCG),” Int. J. Cardiac Imaging 12, 47–59 (1996). https://doi.org/10.1007/bf01798116

    Article  CAS  Google Scholar 

  47. W. Moshage, S. Achenbach, K. Göhl, A. Weikl, K. Bachmann, P. Wegener, S. Schneider, and W. Härer, “Biomagnetic localization of ventricular arrhythmias,” Radiology 180, 685–692 (1991). https://doi.org/10.1148/radiology.180.3.1714612

    Article  CAS  PubMed  Google Scholar 

  48. G. Niso, C. Rogers, J. T. Moreau, L. Chen, C. Madjar, S. Das, E. Bock, F. Tadel, A. C. Evans, P. Jolicoeur, and S. Baillet, “OMEGA: The Open MEG Archive,” NeuroImage 124, 1182–1187 (1182). https://doi.org/10.1016/j.neuroimage.2015.04.028

  49. P. L. Nunez, B. M. Wingeier, and R. B. Silberstein, “Spatial-temporal structures of human alpha rhythms: Theory, microcurrent sources, multiscale measurements, and global binding of local networks,” Hum. Brain Mapping 13, 125–164 (2001). https://doi.org/10.1002/hbm.1030

    Article  CAS  Google Scholar 

  50. E. Olejarczyk, P. Bogucki, and A. Sobieszek, “The EEG split alpha peak: Phenomenological origins and methodological aspects of detection and evaluation,” Front. Neurosci. 11, 506 (2017). https://doi.org/10.3389/fnins.2017.00506

    Article  PubMed  PubMed Central  Google Scholar 

  51. N. M. Pankratova, S. D. Rykunov, A. I. Boyko, D. A. Molchanova, and M. N. Ustinin, “Localization of encephalogram spectral features in psychic disorders,” Math. Biol. Bioinf. 13, 322–336 (2018). https://doi.org/10.17537/2018.13.322

    Article  Google Scholar 

  52. M. A. Polikarpov, M. N. Ustinin, S. D. Rykunov, A. Y. Yurenya, S. P. Naurzakov, A. P. Grebenkin, and V. Y. Panchenko, “3D imaging of magnetic particles using the 7-channel magnetoencephalography device without pre-magnetization or displacement of the sample,” J. Magn. Magn. Mater. 427, 139–143 (2017). https://doi.org/10.1016/j.jmmm.2016.10.055

    Article  CAS  Google Scholar 

  53. M. A. Polikarpov, M. N. Ustinin, S. D. Rykunov, A. Y. Yurenya, S. P. Naurzakov, A. P. Grebenkin, and V. Y. Panchenko, “Study of anisotropy of magnetic noise, generated by magnetic particles in geomagnetic field,” J. Magn. Magn. Mater. 475, 620–626 (2019). https://doi.org/10.1016/j.jmmm.2018.12.011

    Article  CAS  Google Scholar 

  54. M. A. Primin, Yu. V. Maslennikov, I. V. Nedayvoda, and Yu. V. Gulyaev, “Magnetocardiographic technology of human heart investigations,” Biomed. Radioelektronika, No. 3, 3–22 (2016).

  55. Project BCI—EEG motor activity data set Brain Computer Interface research at NUST Pakistan. https://sites.google.com/site/projectbci/

  56. M. B. I. Reaz, M. S. Hussain, and F. Mohd-Yasin, “Techniques of EMG signal analysis: Detection, processing, classification and applications,” Biol. procedures online 8, 11–35 (2006). https://doi.org/10.1251/bpo115

  57. G. Rizzolatti and L. Craighero, “The mirror-neuron system,” Annu. Rev. Neurosci. 27, 169–192 (2004). https://doi.org/10.1146/annurev.neuro.27.070203.144230

    Article  CAS  PubMed  Google Scholar 

  58. S. D. Rykunov, M. N. Ustinin, A. G. Polyanin, V. V. Sychev, and R. R. Llinás, “Software for the partial spectroscopy of human brain,” in Matematicheskaya Biologiya i Bioinformatika (2016), Vol. 11, pp. 127–140. Math. Biol. Bioinf. 11(1), 127–140 (2016).https://doi.org/10.17537/2016.11.127

  59. S. D. Rykunov, E. D. Rykunova, A. I. Boyko, and M. N. Ustinin, “VirtEl—Software for magnetic encephalography data analysis by the method of virtual electrodes,” Math. Biol. Bioinf. 14, 340–354 (2019). https://doi.org/10.17537/2019.14.340

    Article  Google Scholar 

  60. S. D. Rykunov, E. S. Oplachko, and M. N. Ustinin, “FTViewer application for analysis and visualization of functional tomograms of complex systems,” Pattern Recognit. Image Anal. 30, 716–725 (2020). https://doi.org/10.1134/s1054661820040227

    Article  Google Scholar 

  61. M. Saarinen, P. J. Karp, T. E. Katila, and P. Siltanen, “The magnetocardiogram in cardiac disorders,” Cardiovasc. Res. 8, 820–834 (1974). https://doi.org/10.1093/cvr/8.6.820

    Article  CAS  PubMed  Google Scholar 

  62. M. Saarinen, P. Siltanen, P. J. Karp, and T. E. Katila, “The normal magnetocardiogram: I. Morphology,” Ann. Clin. Res 10 (Suppl. 21), 1–43 (1978).

    PubMed  Google Scholar 

  63. J. Sarvas, “Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem,” Phys. Med. Biol. 32, 11–22 (1987). https://doi.org/10.1088/0031-9155/32/1/004

    Article  CAS  PubMed  Google Scholar 

  64. S. Schneider, E. Hoenig, H. Reichenberger, K. Abraham-Fuchs, W. Moshage, A. Oppelt, H. Stefan, A. Weikl, and A. Wirth, “Multichannel biomagnetic system for study of electrical activity in the brain and heart,” Radiology 176, 825–830 (1990). https://doi.org/10.1148/radiology.176.3.2389043

    Article  CAS  PubMed  Google Scholar 

  65. J. J. Schulman, R. Cancro, S. Lowe, F. Lu, K. D. Walton, and R. R. Llinás, “Imaging of thalamocortical dysrhythmia in neuropsychiatry,” Front. Hum. Neurosci. 5, 69 (2011). https://doi.org/10.3389/fnhum.2011.00069

    Article  PubMed  PubMed Central  Google Scholar 

  66. U. Smailovic and V. Jelic, “Neurophysiological markers of Alzheimer’s disease: Quantitative EEG approach,” Neurol. Ther. 8 (S2), 37–55 (2019). https://doi.org/10.1007/s40120-019-00169-0

    Article  PubMed  PubMed Central  Google Scholar 

  67. S. J. M. Smith, “EEG in the diagnosis, classification, and management of patients with epilepsy,” J. Neurol., Neurosurgery Psychiatry 76 (Suppl. 2), ii2–ii7 (2005). https://doi.org/10.1136/jnnp.2005.069245

    Article  Google Scholar 

  68. M. N. Ustinin, Yu. V. Maslennikov, S. D. Rykunov, and V. A. Krymov, “Reconstruction of the human heart functional structure based on a few-channel magnetocardiogram,” Math. Biol. Bioinf. 13, 392–401 (2018). https://doi.org/10.17537/2018.13.392

    Article  Google Scholar 

  69. M. N. Ustinin, S. D. Rykunov, A. I. Boyko, O. A. Maslova, K. D. Walton, and R. R. Llinás, “Estimation of the directions of alpha rhythm elementary sources using the method of human brain functional tomography based on the magnetic encephalography data,” Math. Biol. Bioinf. 13, 426–436 (2018). https://doi.org/10.17537/2018.13.426

    Article  Google Scholar 

  70. M. N. Ustinin, S. D. Rykunov, M. A. Polikarpov, A. Y. Yurenya, S. P. Naurzakov, A. P. Grebenkin, and V. Y. Panchenko, “Reconstruction of the human hand functional structure based on a magnetomyogram,” Math. Biol. Bioinf. 13, 480–489 (2018). https://doi.org/10.17537/2018.13.480

    Article  Google Scholar 

  71. M. N. Ustinin, A. I. Boyko, and S. D. Rykunov, “Correlation of the brain compartments in the attention deficit and hyperactivity disorder calculated by the method of virtual electrodes from magnetic encephalography data,” Math. Biol. Bioinf. 15, 471–486 (2020). https://doi.org/10.17537/2020.15.471

    Article  Google Scholar 

  72. M. N. Ustinin, V. V. Sychev, K. D. Walton, and R. R. Llinás, “New methology for the analysis and representation of human brain function: MEGMRIAn,” Math. Biol. Bioinf. 9, 464–481 (2014). https://doi.org/10.17537/2014.9.464

    Article  Google Scholar 

  73. V. M. Verkhlyutov, Yu. V. Shchuchkin, V. L. Ushakov, V. B. Ctrelets, and Yu. A. Pirogov, “Assessing the localizatoin and dipole moment of alpha and theta rhythm sources in EEG using the cluster analysis in health patients and patients with schizophrenia,” Zh. Vyssh. Nervn. Deyat. 56 (1), 47–55 (2006).

    Google Scholar 

  74. A. Wacker-Gussmann, H. Paulsen, K. Stingl, J. Braendle, R. Goelz, and J. Henes, “Atrioventricular conduction delay in the second trimester measured by fetal magnetocardiography,” J. Immunol. Res. 2014, 753953 (2014). https://doi.org/10.1155/2014/753953

  75. K. Yoshida, K. Ogata, T. Inaba, Yo. Nakazawa, Yo. Ito, I. Yamaguchi, A. Kandori, and K. Aonuma, “Ability of magnetocardiography to detect regional dominant frequencies of atrial fibrillation,” J. Arrhythmia 31, 345–351 (2015). https://doi.org/10.1016/j.joa.2015.05.003

    Article  Google Scholar 

  76. H. Zhang, A. J. Watrous, A. Patel, and J. Jacobs, “Theta and alpha oscillations are traveling waves in the human neocortex,” Neuron 98, 1269–1281.e4 (2018). https://doi.org/10.1016/j.neuron.2018.05.019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

Author information

Authors and Affiliations

  1. Keldysh Institute of Applied Mathematics of the Russian Academy of Sciences, 125047, Moscow, Russian Federation

    S. D. Rykunov, A. I. Boyko & M. N. Ustinin

Authors
  1. S. D. Rykunov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. I. Boyko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. N. Ustinin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to S. D. Rykunov, A. I. Boyko or M. N. Ustinin.

Ethics declarations

The authors of this work declare that they have no conflicts of interest.

Additional information

Stanislav Dmitrievich Rykunov, born in 1986. He graduated from the Lomonosov Moscow State University of Instrument Engineering and Informatics in 2012, defended his Candidate thesis in 2016. Senior Researcher at the Institute of Mathematical Problems of Biology of the Russian Academy of Sciences—a Branch of the Keldysh Institute for Problems of Mathematics. Research interests: magnetic encephalography, data processing and analysis, parallel computing. He has published over 40 scientific papers.

Anna Ivanovna Boyko. Year of birth: 1966. Graduated from Lomonosov Moscow State University in 1988. Researcher at the Institute of Mathematical Problems of Biology of the Russian Academy of Sciences, a Branch of the Keldysh Institute for Problems of Mathematics. Scientific interests: processing and analysis of biological and medical data. Author of over 40 articles.

Mikhail Nikolaevich Ustinin. Year of birth: 1957. Graduated from Lomonosov Moscow State University in 1981, defended his Candidate dissertation in 1990, and defended his Doctoral dissertation in 2004. Deputy Director for Research at the Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, leads the Branch Institute of Mathematical Problems of Biology, Russian Academy of Sciences. Scientific interests: creation of data analysis methods and their application in biology and medicine. Author of over 190 articles and two monographs. Member of the International Society for Neuroscience.

Publisher’s Note.

Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rykunov, S.D., Boyko, A.I. & Ustinin, M.N. Reconstruction of the Electrical Structure of the Human Body Using Spectral Functional Tomography. Pattern Recognit. Image Anal. 33, 1315–1343 (2023). https://doi.org/10.1134/S1054661823040387

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1054661823040387

Key words:

Search

Navigation