Fetal brain activity and the free energy principle

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.

Artificial intelligence to understand fluctuation of fetal brain activity by recognizing facial expressions

OBJECTIVE

To examine whether artificial intelligence can achieve discoveries regarding fetal brain activity.

METHODS

In this observational study, the authors collected images of fetal faces using a four-dimensional ultrasound technique obtained from singleton pregnancies of outpatients in routine practice at 27 to 37 weeks of gestation between February 1 and December 31, 2021. The authors developed an artificial intelligence classifier to recognize seven facial expressions of fetuses, then applied it to video files of fetal facial images to generate the probabilities, as confidence scores, of each expression category. Discrete Fourier transform and chaotic analysis were used to investigate the scores. Mann-Whitney test, t test, variance test, and one-way analysis of variance were used for statistical analysis.

RESULTS

Facial expression changes were observed in cycles averaging 66 to 73 s. The power spectrum showed that mouthing and neutral expressions were the most prevalent. There was a difference between categories for the spectrum (p = 0.004). Two different states--dense and sparse--of confidence scores were discovered. The correlation dimension was 1.19 ± 0.22 and 1.33 ± 0.27 for dense and sparse, respectively (p = 0.047).

CONCLUSION

This method objectively and quantitatively demonstrated fetal brain activity and may provide insight into how the fetus spends its time in utero.

Recognition of facial expression of fetuses by artificial intelligence (AI)

OBJECTIVES

The development of the artificial intelligence (AI) classifier to recognize fetal facial expressions that are considered as being related to the brain development of fetuses as a retrospective, non-interventional pilot study.

METHODS

Images of fetal faces with sonography obtained from outpatient pregnant women with a singleton fetus were enrolled in routine conventional practice from 19 to 38 weeks of gestation from January 1, 2020, to September 30, 2020, with completely de-identified data. The images were classified into seven categories, such as eye blinking, mouthing, face without any expression, scowling, smiling, tongue expulsion, and yawning. The category in which the number of fetuses was less than 10 was eliminated before preparation. Next, we created a deep learning AI classifier with the data. Statistical values such as accuracy for the test dataset and the AI confidence score profiles for each category per image for all data were obtained.

RESULTS

The number of fetuses/images in the rated categories were 14/147, 23/302, 33/320, 8/55, and 10/72 for eye blinking, mouthing, face without any expression, scowling, and yawning, respectively. The accuracy of the AI fetal facial expression for the entire test data set was 0.985. The accuracy/sensitivity/specificity values were 0.996/0.993/1.000, 0.992/0.986/1.000, 0.985/1.000/0.979, 0.996/0.888/1.000, and 1.000/1.000/1.000 for the eye blinking, mouthing, face without any expression, scowling categories, and yawning, respectively.

CONCLUSIONS

The AI classifier has the potential to objectively classify fetal facial expressions. AI can advance fetal brain development research using ultrasound.

Fetal face as important indicator of fetal brain function

Fetuses show various facial expressions, such as blinking, yawning, sucking, mouthing, tongue expulsion, scowling, and smiling, assessed by four-dimensional ultrasound from the early second trimester of pregnancy. Moreover, some fetuses demonstrate emotion-like behaviors as noted in infants, children, and adults. Selective fetal responses to stimuli indicate a high degree of brain development, which is reflected in the facial reactions. The fetal face with its movements and expressions can mirror the fetal brain function and development during different stages of the fetus in utero. The fetal face might represent the key to opening the box containing the secrets of fetal brain function and development. Four-dimensional ultrasound may be an important modality in current and future research on fetal facial expressions and assist in the evaluation of the fetal brain function.

Current status of fetal neurodevelopmental assessment: Four-dimensional ultrasound study

With the latest advent of four-dimensional (4-D) ultrasound, fetal neurobehavioral or neurodevelopmental assessment can be easily and readily performed. Using this technique, typical fetal movements and behavioral patterns have become apparent in all three trimesters of pregnancy. In twin pregnancy, 4-D ultrasound facilitates the precise evaluation of inter-twin contact and intra-pair stimulation. New fetal neurobehavioral assessment tests, such as Kurjak's Antenatal Neurodevelopmental Test and the Fetal Observable Movement System, may reflect the normal and abnormal neurological development of the fetus, and will facilitate more precise assessments of fetal neurobehavior or neurodevelopment, and fetal brain and central nervous system functions. In this review article, I also discuss interesting topics regarding maternal and fetal stress, fetal pain, and fetal consciousness. Four-dimensional ultrasound has opened the door to new scientific fields, such as 'fetal neurology' and 'fetal psychology,' and fetal neurobehavioral science is at the dawn of a new era. Knowledge on fetal neurobehavior and neurodevelopment will be advanced through fetal behavioral research using this technique.

Fetal Consciousness: Four-dimensional Ultrasound Study

How to cite this article

Hata T, Kanenishi K, AboEllail MAM, Marumo G, Kurjak A. Fetal Consciousness: Four-dimensional Ultrasound Study. Donald School J Ultrasound Obstet Gynecol 2015;9(4):471-474.