Fetal brain activity and the free energy principle

Yasunari Miyagi; Toshiyuki Hata; Takahito Miyake

doi:10.1515/jpm-2023-0092

Article

Fetal brain activity and the free energy principle

Yasunari Miyagi , Toshiyuki Hata and Takahito Miyake

Published/Copyright: April 26, 2023

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information Explore this Subject

From the journal Journal of Perinatal Medicine Volume 51 Issue 7

Abstract

Objectives

To study whether the free energy principle can explain fetal brain activity and the existence of fetal consciousness via a chaotic dimension derived using artificial intelligence.

Methods

In this observational study, we used a four-dimensional ultrasound technique obtained to collect images of fetal faces from pregnancies at 27–37 weeks of gestation, between February and December 2021. We developed an artificial intelligence classifier that recognizes fetal facial expressions, which are thought to relate to fetal brain activity. We then applied the classifier to video files of facial images to generate each expression category’s probabilities. We calculated the chaotic dimensions from the probability lists, and we created and investigated the free energy principle’s mathematical model that was assumed to be linked to the chaotic dimension. We used a Mann–Whitney test, linear regression test, and one-way analysis of variance for statistical analysis.

Results

The chaotic dimension revealed that the fetus had dense and sparse states of brain activity, which fluctuated at a statistically significant level. The chaotic dimension and free energy were larger in the sparse state than in the dense state.

Conclusions

The fluctuating free energy suggests consciousness seemed to exist in the fetus after 27 weeks.

Keywords: artificial intelligence; chaos; facial recognition; fetus; free energy; ultrasonography

Corresponding author: Yasunari Miyagi, MD, PhD, Director, Department of Gynecology, Miyake Ofuku Clinic, 393-1 Ofuku, Minami Ward, Okayama, Okayama 701-0204, Japan; and Representative, Medical Data Labo, 289-48 Yamasaki, Naka Ward, Okayama, Okayama 703-8267, Japan, Phone: +81-86-281-2020, Fax: +81-86-281-7575, E-mail: ymiyagi@mac.com

Research funding: None.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. The roles of the authors were as follows. Yasunari Miyagi: Conceptualization, methodology, software, formal analysis, investigation, data curation, writing original draft, reviewing and editing, visualization, supervision. Toshiyuki Hata: Validation, resources, data curation, review and editing, supervision, project administration. Takahito Miyake: Validation, resources, review and editing, supervision, project administration.
Competing interests: The authors have no conflicts of interest.
Informed consent: Informed consent was obtained from all individuals included in this study. The study was approved by the Institutional Review Board of Miyake Clinic (Institutional Review Board number 2019–10).
Ethical approval: Research involving human subjects complied with all relevant national regulations, institutional policies and is in accordance with the tenets of the Helsinki Declaration (as revised in 2013), and has been approved by the authors’ Institutional Review Board (IRB No. 2019–10).
Copyright: Manuscripts are accepted on condition of transfer of copyright to the publisher. Once the manuscript has been accepted, it may not be published elsewhere without the consent of the copyright holders.

References

1. Hata, T, Kanenishi, K, Hanaoka, U, Marumo, G. HDIive and 4D ultrasound in the assessment of fetal facial expressions. Donald Sch J Ultrasound Obstet Gynecol 2015;9:44–50.10.5005/jp-journals-10009-1388Search in Google Scholar

2. AboEllail, MAM, Hata, T. Fetal face as important indicator of fetal brain function. J Perinat Med 2017;45:729–36. https://doi.org/10.1515/jpm-2016-0377.Search in Google Scholar PubMed

3. Hata, T, Dai, SY, Marumo, G. Ultrasound for evaluation of fetal neurobehavioural development: from 2-D to 4-D ultrasound. Infant Child Dev 2010;19:99–118. https://doi.org/10.1002/icd.659.Search in Google Scholar

4. Hata, T. Current status of fetal neurodevelopmental assessment: 4D ultrasound study. J Obstet Gynaecol Res 2016;42:1211–21. https://doi.org/10.1111/jog.13099.Search in Google Scholar PubMed

5. Miyagi, Y, Hata, T, Bouno, S, Koyanagi, A, Miyake, T. Recognition of facial expression of fetuses by artificial intelligence (AI). J Perinat Med 2021;49:596–603. https://doi.org/10.1515/jpm-2020-0537.Search in Google Scholar PubMed

6. Miyagi, Y, Hata, T, Bouno, S, Koyanagi, A, Miyake, T. Recognition of fetal facial expressions using artificial intelligence deep learning. Donald Sch J Ultrasound Obstet Gynecol 2021;15:223–8. https://doi.org/10.5005/jp-journals-10009-1710.Search in Google Scholar

7. Miyagi, Y, Hata, T, Bouno, S, Koyanagi, A, Miyake, T. Artificial intelligence to understand fluctuation of fetal brain activity by recognizing facial expressions. Int J Gynecol Obstet 2022;1–9. https://doi.org/10.1002/ijgo.14569.Search in Google Scholar PubMed

8. Friston, K, Kilner, J, Harrison, L. A free energy principle for the brain. J Physiol Paris 2006;100:70–87. https://doi.org/10.1016/j.jphysparis.2006.10.001.Search in Google Scholar PubMed

9. Friston, K. The free-energy principle: a rough guide to the brain? Trends Cognit Sci 2009;13:293–301. https://doi.org/10.1016/j.tics.2009.04.005.Search in Google Scholar PubMed

10. Friston, KJ, Daunizeau, J, Kilner, J, Kiebel, SJ. Action and behavior: a free-energy formulation. Biol Cybern 2010;102:227–60. https://doi.org/10.1007/s00422-010-0364-z.Search in Google Scholar PubMed

11. Friston, KJ, Daunizeau, J, Kiebel, SJ. Reinforcement learning or active inference? PLoS One 2009;4:e6421. https://doi.org/10.1371/journal.pone.0006421.Search in Google Scholar PubMed PubMed Central

12. Joyce, JM. Kullback-leibler divergence. In: Lovric, M, editor. International encyclopedia of statistical science. Berlin, Heidelberg: Springer; 2011:720–2 pp.10.1007/978-3-642-04898-2_327Search in Google Scholar

13. Inui, T. The free-energy principle: a unified theory of brain functions. Brain Neural Network 2018;25:123–34. https://doi.org/10.3902/jnns.25.123.Search in Google Scholar

14. Miyagi, Y, Miyagi, Y, Terada, S, Kudo, T. Variations of multifractal structure in the fetal heartbeats. Acta Med Okayama 2003;57:49–52. https://doi.org/10.18926/AMO/32821.Search in Google Scholar PubMed

15. Grassberger, P, Procaccia, I. Characterization of strange attractors. Phys Rev Lett 1983;50:346–9. https://doi.org/10.1103/physrevlett.50.346.Search in Google Scholar

16. Grassberger, P, Procaccia, I. Dimensions and entropies of strange attractors from a fluctuating dynamics approach. Physica D 1984;13:34–54. https://doi.org/10.1016/0167-2789(84)90269-0.Search in Google Scholar

17. Farmer, JD, Ott, E, York, JA. The dimension of chaotic attractors. Physica D 1983;7:153–80. https://doi.org/10.1016/0167-2789(83)90125-2.Search in Google Scholar

18. Halsey, TC, Jensen, MH, Kadanoff, LP, Procaccia, I, Shraiman, BI. Fractal measures and their singularities: the characterization of strange sets. Phys Rev A 1986;33:1141–51. https://doi.org/10.1103/physreva.33.1141.Search in Google Scholar PubMed

19. Hentschel, HE, Procaccia, I. The infinite number of generalized dimensions of fractals and strange attractors. Phys Nonlinear Phenom 1983;8:435–44. https://doi.org/10.1016/0167-2789(83)90235-x.Search in Google Scholar

20. Isomura, T, Shimazaki, H, Friston, KJ. Canonical neural networks perform active inference. Commun Biol 2022;5:55. https://doi.org/10.1038/s42003-021-02994-2.Search in Google Scholar PubMed PubMed Central

21. Friston, K. Am i self-conscious? (or does self-organization entail self-consciousness?). Front Psychol 2018;9:579. https://doi.org/10.3389/fpsyg.2018.00579.Search in Google Scholar PubMed PubMed Central

22. Yoshida, M, Taguchi, S. Free energy principle and visual consciousness. Brain Neural Networs 2018;25:53–70. https://doi.org/10.3902/jnns.25.53.Search in Google Scholar

23. Kim, J, Gulati, T, Ganguly, K. Competing roles of slow oscillations and delta waves in memory consolidation versus forgetting. Cell 2019;179:514–26. https://doi.org/10.1016/j.cell.2019.08.040.Search in Google Scholar PubMed PubMed Central

24. Yoshida, K, Toyoizumi, T. Information maximization explains state-dependent synaptic plasticity and memory reorganization during non-rapid eye movement sleep. PNAS Nexus 2023;2:pgac286. https://doi.org/10.1093/pnasnexus/pgac286.Search in Google Scholar PubMed PubMed Central

Received: 2023-03-06

Accepted: 2023-04-12

Published Online: 2023-04-26

Published in Print: 2023-09-26

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.1515/jpm-2023-0092

Keywords for this article

artificial intelligence; chaos; facial recognition; fetus; free energy; ultrasonography