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Sex differences in prenatal development of neural complexity in the human brain

Nature Mental Health volume 2, pages 401–416 (2024)Cite this article

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Abstract

The complexity of neural activity is a commonly used readout of healthy functioning in cortical circuits. Previous work has linked neural complexity to the level of maternal care in preterm infants at risk for developing mental disorders, yet the evolution of neural complexity in early human development is largely unknown. We hypothesized that cortical dynamics would evolve to optimize information processing as birth approaches, thereby increasing the complexity of cortical activity. To test this hypothesis, we conducted a cohort study relating prenatal neural complexity to maturation. Magnetoencephalography (MEG) recordings were obtained from a sample of fetuses and newborns, including longitudinal data before and after birth. Using cortical responses to auditory irregularities, we computed several entropy measures that reflect the complexity of the MEG signal. Despite our hypothesis, neural complexity decreased significantly with maturation in both fetuses and newborns. Furthermore, we found that complexity decreased significantly faster in male fetuses for most entropy measures. Our surprising results chart the evolution of neural complexity in perinatal human development and may lay a foundation for future work that would relate fetal neural complexity to developmental phenotypes, especially in the area of perinatal risk where biomarkers are greatly needed.

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Fig. 1: Scatter plots of GA versus fetal MEG signal entropy.
Fig. 2: Effects of developmental variables and stimuli on ERSPs.
Fig. 3: Possible scenarios explaining the decline of MEG entropy with perinatal maturation.

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Data availability

Data used in this study are already publicly archived and available through Zenodo88,89. Fetal data were originally published in ref. 26 and are available at https://zenodo.org/record/4541463#.Y0a-iExByHt. Neonatal data were originally published in ref. 25 and are available at https://zenodo.org/record/4018827#.Y0a-akxByHt.

Code availability

Analysis code is publicly available at https://github.com/jfrohlich/Fetal_MEG_Entropy/. Other code relevant to the data preprocessing is publicly available at https://github.com/moser297/fMEG_data_processing/tree/main.

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Acknowledgements

We are grateful to all volunteers and families who participated in our research. We would also like to thank D. Toker for his input on methodological aspects of our study, A. DallaVecchia for her input on ‘sensory PCI’, C. Chu for useful comments on our work, J. F. Hipp for contributing analysis code, S. Ruch for assisting with a code review, and T. Bayne for many enlightening discussions on infant cognition. Additionally, we thank the fMEG team for their contributions, including F. Schleger (original study design) and M. Weiss (data collection). Finally, we gratefully acknowledge the following funders: the German Federal Ministry of Education and Research (BMBF) grants Somnia (13GW0294, A.G.), Enable (13GW0359, A.G.) and Bevares (13GW0570, A.G.), the European Union’s Joint Programme for Neurodegenerative Disease Research (EU-JPND 2022-130, A.G.) grant Recast (01ED2309, A.G.) the FET Open Luminous project (H2020 FETOPEN-2014-2015-RIA under agreement no. 686764, H.P.) as part of the European Union’s Horizon 2020 research and 2014–2018 training program, the German Federal Ministry of Education and Research (BMBF) at the German Center for Diabetes Research (DZD01GI0925, H.P.), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; 493345456, J.M.) and the Wellcome Trust (grant no. 210920/Z/18/Z, P.M.).

Author information

Author notes

  1. These authors contributed equally: Hubert Preissl, Alireza Gharabaghi.

Authors and Affiliations

  1. Institute for Neuromodulation and Neurotechnology, University Hospital and University of Tuebingen, Tuebingen, Germany

    Joel Frohlich & Alireza Gharabaghi

  2. Institute for Advanced Consciousness Studies, Santa Monica, CA, USA

    Joel Frohlich

  3. fMEG Center/Institute for Diabetes Research and Metabolic Diseases of the Hemholtz Center Munich at the University of Tuebingen, Tuebingen, Germany

    Julia Moser, Katrin Sippel & Hubert Preissl

  4. Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA

    Julia Moser

  5. Department of Internal Medicine, Division of Diabetology, Endocrinology and Nephrology, University of Tuebingen, Tuebingen, Germany

    Katrin Sippel & Hubert Preissl

  6. Department of Computing, Imperial College London, London, UK

    Pedro A. M. Mediano

  7. Department of Psychology, University of Cambridge, Cambridge, UK

    Pedro A. M. Mediano

  8. German Center for Mental Health (DZPG), Tuebingen, Germany

    Hubert Preissl & Alireza Gharabaghi

  9. Institute of Pharmaceutical Sciences, Department of Pharmacy and Biochemistry; Interfaculty Centre for Pharmacogenomics and Pharma Research, University of Tuebingen, Tuebingen, Germany

    Hubert Preissl

  10. Center for Bionic Intelligence Tuebingen Stuttgart (BITS), Tuebingen, Germany

    Alireza Gharabaghi

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Contributions

J.F., P.M., H.P. and A.G. were responsible for conceptualization of the current study’s hypotheses and analysis plan. J.M., H.P. and K.S. designed the experiments and acquired and preprocessed the data in the context of a prior study. J.F., J.M., K.S. and P.M. contributed code for data preprocessing and analysis. J.M. and K.S. performed data curation. J.F. performed the formal analysis, generated the display items, and wrote the first draft of the manuscript. All authors reviewed and edited the manuscript. H.P. and A.G. supervised the project.

Corresponding author

Correspondence to Joel Frohlich.

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The authors declare no competing interests.

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Nature Mental Health thanks Chun Meng and the other, anonymous, reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Overview of the experiment.

Musical notes denote auditory stimuli. (a) We used two block rules and the four possible permutations of local/global standard/deviant auditory sequences across both block rules. (b) Each auditory sequence consisted of four tones of 200 ms duration each, separated by a 400 ms inter-tone interval. The entire stimulus sequence was 2000 ms in duration. The fourth tone of each sequence varied during the test phase. After averaging across trials within each condition, we analyzed signals starting from 200 ms prior to the onset of the first tone to 1000 ms following the offset of the fourth tone (3200 ms duration). (c) For fetal recordings, the mother-to-be postioned her abdomen within the concavity of the MEG sensor array, with a sound balloon between her body and the SARA device delivering auditory tones. (d) Fetal MEG signals were recorded noninvasively in response to auditory tones. To correct for the influence of fetal head orientation and size on MEG signal amplitude, fetal signals were normalized. (e) After birth, a subset of subjects returned to the laboratory as newborns and were recorded from after being placed in a cradle oriented head-first toward the SARA device’s SQUID magnetometer array. Newborns wore infant-friendly headphones for stimulus delivery. (f) As with fetuses, the SARA device recorded cortical signals noninvasively from newborns. Note that photographs in c and e are adapted from ref. 23. The woman in c and the parents of the infant in e gave consent for identifiable images to be published.

Extended Data Fig. 2 Agreement between filtering.

High correlations between entropy measures computed from neonatal signals with narrow (1–10 Hz) versus broadband (1–15 Hz) filtering demonstrate that entropy is similar in both cases. We used 1–15 Hz filtering for the neonatal entropy analyzed in the manuscript.

Extended Data Fig. 3 Numbers of participants at each stage of the study, including recruitment, successful data collection, quality control, and followup visits to collect longitudinal data.

Numbers pertaining to fetal data are shown in red, whereas numbers pertaining to neonatal data are shown in blue. The Venn diagram at bottom right shows the total number of participants with usable data from before birth (N = 27), after birth (N = 4), and both before and after birth (N = 16). Note that all mothers-to-be who volunteered were found eligible to participate, thus an additional stage of confirmed eligibility is not depicted.

Extended Data Fig. 4 Scatter plots of postmenstrual age (PMA) versus neonatal MEG signal entropy.

Blue data points are taken from each session and rule/stimulus condition. For visualization purposes and R2 estimates, we took the median of data at the level of one-week time bins (magenta circles) and computed the variance explained R2 according to the least-squares fit of the smoothed data. For symmetry with Fig. 1 in the main manuscript, we plotted data separately for males (first and third columns) and females (second and fourth columns). The above data show that signal entropy generally declines with PMA in newborns. Note that some individual data points are excluded by the vertical axis scale due to the very large spread in entropy values.

Extended Data Fig. 5 Changes in entropy resulting from amplitude or non-amplitude signal changes.

Data distributions in each panel are averaged across all four experimental conditions (that is, block rule and stimulus combinations). PermEn32 and PermEn64 (middle row) showed a far larger dissociation between amplitude and non-amplitude signal properties, likely because the PermEn algorithm is sensitive to small changes in the signal which result in new ordinal rankings of data points. Effect sizes (Cohen’s d) are indicated for each decomposition.

Extended Data Fig. 6 Histograms of signal dynamics categories (stochastic or deterministic) by gestational age (fetuses, left column) and age (newborns, right column).

The first row shows results from global standards (a,b) and the second row shows results from global deviants (c,d). Both fetuses and newborns displayed a mixture of stochastic and deterministic dynamics. In fetuses, the majority of recordings were deterministic, whereas in newborns, the majority of recordings were stochastic. Dynamics were not significantly predicted by maturation in either group, though the proportion of recordings with stochastic dynamics was significantly higher in newborns than in fetuses (two-tailed chi-squared test using all data from both global standards and deviants, χ2 = 28.6, P = 9.1 × 10−8).

Extended Data Fig. 7 Correlations between MEG measures.

Entropy measures were highly correlated with one another in both fetuses (a) and newborns (b). These same entropy measures show negative correlations with spectral power at most frequencies in fetuses (c) and all frequencies in newborns (d). Subsecond CTW did not correlate strongly with subsecond spectral power after averaging across conditions in fetuses (e) or newborns (f).

Extended Data Fig. 8 Alternative calculations of correlations in fetal entropy measures.

Because our fetal data contained multiple recordings from the same fetal subjects and, moreover, the random effect term significantly increased the fits of most models predicting entropy, we did not wish to rely on Pearson correlation coefficients (a) between entropy measures from each recording, as these correlation estimates may over-represent subjects with multiple recordings. For this reason, we instead utilized standardized model coefficients (betas) from linear mixed models that predicted entropy measures from each other while accounting for random effects (b). Differences between Pearson coefficients and averaged beta coefficients (c) are very small (β − r < 0.1 in all cases). However, standardized betas in LMMs are not generally symmetrical (that is, βi,j ≠ βj,i), since they depend on the variance of the random effect, and the random effect may often contribute more to one variable or the other. To address this problem, we used the mean of βi,j and βj,i to represent the correlation between entropy measure i and j. Here, for transparency, we show the asymmetry in the standardized betas prior to averaging (d), which primarily affected correlation estimates for PermEn64.

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Frohlich, J., Moser, J., Sippel, K. et al. Sex differences in prenatal development of neural complexity in the human brain. Nat. Mental Health 2, 401–416 (2024). https://doi.org/10.1038/s44220-024-00206-4

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