Skip to main content
SpringerLink
Log in

Characteristics of the Involvement of Hidden Nodes in the Activity of Human Brain Systems Revealed on fMRI Data

  • Published:
Human Physiology Aims and scope Submit manuscript

Abstract—

Currently, it is generally accepted that brain functions are based on the systemic principle. At the same time, knowledge about the principles and mechanisms of the functioning of the brain system remains scarce. In the present study these principles were studied within the framework of ideas about the so-called hidden nodes of the brain systems. Previously, according to fMRI data, ìit was shown that some brain structures could be involved in work without changing their energy consumption (reflected by the blood oxygenation level-dependent signal). Their involvement was found only due to a change in the distant functional connectivity. The analysis of systemic brain activity using functional connectivity data made it possible to reveal hidden nodes that were inaccessible to detection using the standard activation approach. This study is aimed at clarifying the extent and nature of the involvement of hidden nodes in the brain maintenance of various activities using open fMRI data from the Human Connectome Project. It has been shown that the brain systems ensuring current activity are provided with a much larger number of nodes than was previously believed, and the vast majority of them are hidden. For the first time, this result clearly showed the actual scale of the brain systems providing current activity. Mental activity is actually provided by almost the entire brain working and not a minor part of it, as is usually observed in functional tomographic studies. As a result, it is shown that the idea of the existence of hidden nodes is confirmed by analyzing the activity of the human brain at the macrolevel and shows similarities with the characteristics of the microlevel activity of individual neuronal populations, confirming the previously formulated neurophysiological ideas about the systemic organization of brain activity.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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.

REFERENCES

  1. Bekhtereva, N.P., Some fundamental issues of studying the neurophysiological basis of mental phenomena in humans, in Glubokiye struktury mozga cheloveka v norme i patologii (Deep Structures of the Human Brain in Normal and Pathological Conditions), Leningrad: Nauka, 1966, p. 18.

  2. Bekhtereva, N.P., Gogolitsyn, Yu.P., Kropotov, Yu.D., and Medvedev, S.V., Neyrofiziologicheskiye mekhanizmy myshleniya (Neurophysiological Mechanisms of Thinking), Leningrad: Nauka, 1985.

  3. Roy, C.S. and Sherrington, C.S., On the regulation of the blood-supply of the brain, J. Physiol., 1890, vol. 11, nos. 1—2, p. 85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Bechtereva, N.P., Abdullaev, Y.G., Melnichuk, K.V., et al., Psychophysiological micro-mapping of the human brain, Int. J. Psychophysiol., 1989, vol. 8, no. 2, p. 107.

    Article  CAS  PubMed  Google Scholar 

  5. Bekhtereva, N.P., Neirofiziologicheskie aspekty psikhicheskoi deyatel’nosti cheloveka (Neurophysiological Aspects of Human Mental Activity), Leningrad: Meditsina, 1974.

  6. Medvedev, S.V. and Pakhomov, S.V., Dinamicheskaya organizatsiya mozgovykh system (Dynamic Organization of Brain Systems), Leningrad: Nauka, 1989.

  7. Muller, A.M. and Meyer, M., Language in the brain at rest: new insights from resting state data and graph theoretical analysis, Front. Hum. Neurosci., 2014, vol. 8, p. 228.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Liu, L., Li, H., Ren, Z., et al., The “two-brain” approach reveals the active role of task-deactivated default mode network in speech comprehension, Cereb. Cortex, 2022. https://doi.org/10.1093/cercor/bhab521

  9. Hong, C., Fallon, J., and Friston, K., fMRI evidence for default mode network deactivation associated with rapid eye movements in sleep, Brain Sci., 2021, vol. 11, no. 11, p. 1528.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Medvedev, S.V., Korotkov, A.D., and Kireev, M.V., Hidden nodes of the brain systems, Hum. Physiol., 2019, vol. 45, no. 5, p. 552. https://doi.org/10.1134/S0362119719050104

    Article  Google Scholar 

  11. Masharipov, R., Knyazeva, I., Nikolaev, Y., et al., Providing evidence for the null hypothesis in functional magnetic resonance imaging using group-level Bayesian inference, Front. Neuroinform., 2021, vol. 15, p. 738342.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kireev, M.V., Masharipov, R.S., Korotkov, A.D., et al., Functional role of hidden nodes of brain systems responsible for visual identification and categorization. Hum. Physiol., 2021, vol. 47, no. 6, p. 595. https://doi.org/10.1134/S0362119721050066

    Article  Google Scholar 

  13. Cole, M.W., Reynolds, J.R., Power, J.D., et al., Multi-task connectivity reveals flexible hubs for adaptive task control, Nat. Neurosci., 2013, vol. 16, no. 9, p. 1348.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Gratton, C., Laumann, T.O., Gordon, E.M., et al., Evidence for two independent factors that modify brain networks to meet task goals, Cell Rep., 2016, vol. 17, no. 5, p. 1276.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ito, T., Hearne, L.J., and Cole, M.W., A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales, NeuroImage, 2020, vol. 221, p. 117141.

    Article  PubMed  Google Scholar 

  16. Telesford, Q.K., Simpson, S.L., Burdette, J.H., et al., The brain as a complex system: using network science as a tool for understanding the brain, Brain Connect., 2011, vol. 1, no. 4, p. 295.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Quante, L., Kluger, D.S., Burkner, P.C., et al., Graph measures in task-based fMRI: functional integration during read-out of visual and auditory information, PloS One, 2018, vol. 13, no. 11. e0207119.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Lighthall, N.R., Karwowski, W., and Lighthall, N.R., Application of graph theory for identifying connectivity patterns in human brain networks: a systematic review, Front. Neurosci., 2019, vol. 13, p. 583.

    Google Scholar 

  19. Wang, Z., Xin, J., Wang, Z., et al., Brain functional network modeling and analysis based on fMRI: a systematic review, Cognit. Neurodyn., 2021, vol. 15, no. 3, p. 389.

    Article  CAS  Google Scholar 

  20. Rubinov, M. and Sporns, O., Complex network measures of brain connectivity: uses and interpretations, NeuroImage, 2010, vol. 52, no. 3, p. 1059.

    Article  PubMed  Google Scholar 

  21. Barch, D.M., Burgess, G.C., Harms, M.P., et al., Function in the human connectome: task-fMRI and individual differences in behavior, NeuroImage, 2013, vol. 80, p. 169.

    Article  PubMed  Google Scholar 

  22. Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., et al., The minimal preprocessing pipelines for the Human Connectome Project, NeuroImage, 2013, vol. 80, p. 105.

    Article  PubMed  Google Scholar 

  23. Binder, J.R., Gross, W.L., Allendorfer, J.B., et al., Mapping anterior temporal lobe language areas with fMRI: a multicenter normative study, NeuroImage, 2011, vol. 54, no. 2, p. 1465.

    Article  PubMed  Google Scholar 

  24. Castelli, F., Happe, F., Frith, U., and Frith, C., Movement and mind: a functional imaging study of perception and interpretation of complex intentional movement patterns, NeuroImage, 2000, vol. 12, no. 3, p. 314.

    Article  CAS  PubMed  Google Scholar 

  25. Wheatley, T., Milleville, S.C., and Martin, A., Understanding animate agents: distinct roles for the social network and mirror system, Psychol. Sci., 2007, vol. 18, no. 6, p. 469.

    Article  PubMed  Google Scholar 

  26. Drobyshevsky, A., Baumann, S.B., and Schneider, W., A rapid fMRI task battery for mapping of visual, motor, cognitive, and emotional function, NeuroImage, 2006, vol. 31, no. 2, p. 732.

    Article  PubMed  Google Scholar 

  27. Caceres, A., Hall, D.L., Zelaya, F.O., et al., Measuring fMRI reliability with the intra-class correlation coefficient, NeuroImage, 2009, vol. 45, no. 3, p. 758.

    Article  PubMed  Google Scholar 

  28. Friston, K., Penny, W., Phillips, C., et al., Classical and Bayesian inference in neuroimaging: theory, NeuroImage, 2002, vol. 16, no. 2, p. 465.

    Article  CAS  PubMed  Google Scholar 

  29. Friston, K.J. and Penny, W., Posterior probability maps and SPMs, NeuroImage, 2003, vol. 19, no. 3, p. 1240.

    Article  CAS  PubMed  Google Scholar 

  30. Friston, K.J., Williams, S., Howard, R., et al., Movement-related effects in fMRI time-series, Magn. Reson. Med., 1996, vol. 35, no. 3, p. 346.

    Article  CAS  PubMed  Google Scholar 

  31. Kruschke, J.K., Rejecting or accepting parameter values in Bayesian estimation, Adv. Methods Pract. Psychol. Sci., 2018, vol. 1, no. 2, p. 270.

    Article  Google Scholar 

  32. McLaren, D.G., Ries, M.L., Xu, G., and Johnson, S.C., A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches, NeuroImage, 2012, vol. 61, no. 4, p. 1277.

    Article  PubMed  Google Scholar 

  33. Gitelman, D.R., Penny, W.D., Ashburner, J., and Friston, K.J., Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution, NeuroImage, 2003, vol. 19, no. 1, p. 200.

    Article  PubMed  Google Scholar 

  34. Seitzman, B.A., Gratton, C., Marek, S., et al., A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum, NeuroImage, 2020, vol. 206, p. 116290.

    Article  PubMed  Google Scholar 

  35. Zalesky, A., Fornito, A., and Bullmore, E., Network-based statistic: identifying differences in brain networks, NeuroImage, 2010, vol. 53, no. 4, p. 1197.

    Article  PubMed  Google Scholar 

  36. Di, X, and Biswal, B.B., Toward task connectomics: examining whole-brain task modulated connectivity in different task domains, Cereb. Cortex, 2018, vol. 29, no. 4, p. 1572.

    Article  PubMed Central  Google Scholar 

  37. Bekhtereva, N.P., Medvedev, S.V., Abdullaev, Ya.G., et al., Neuronal activity in central gyri of the human recognition of semantic and grammatical features of speech, Dokl. Akad. Nauk. SSSR, 1989, vol. 309, no. 5. p. 1247.

    CAS  PubMed  Google Scholar 

Download references

Funding

The study was financially supported by the Russian Science Foundation (grant no. 19-18-00454).

Author information

Authors and Affiliations

  1. Institute of the Human Brain, Russian Academy of Sciences, St. Petersburg, Russia

    S. V. Medvedev, R. S. Masharipov, A. D. Korotkov & M. V. Kireev

  2. St. Petersburg State University, St. Petersburg, Russia

    M. V. Kireev

  3. Institute of Biomedical Problems, Russian Academy of Sciences, Moscow, Russia

    S. V. Medvedev

Authors
  1. S. V. Medvedev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. S. Masharipov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. D. Korotkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. V. Kireev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. V. Kireev.

Ethics declarations

Conflict of interest. The authors declare that they do not have a conflict of interest.

Statement of compliance with standards of research involving humans as subjects. All procedures performed in studies involving human participants were in accordance with the 1964 Helsinki Declaration and its later amendments. Open access data were used from Human Connectome Project database. Informed consent was obtained from all individual participants involved in the study.

Additional information

Translated by E. Larina

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Medvedev, S.V., Masharipov, R.S., Korotkov, A.D. et al. Characteristics of the Involvement of Hidden Nodes in the Activity of Human Brain Systems Revealed on fMRI Data. Hum Physiol 49, 1–11 (2023). https://doi.org/10.1134/S0362119722700141

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0362119722700141

Keywords:

Search

Navigation