Investigating the influence of maternal prenatal BMI and perinatal depressive symptoms on neonatal brain network dynamics - UTU Research Portal

A1 Refereed original research article in a scientific journal

Investigating the influence of maternal prenatal BMI and perinatal depressive symptoms on neonatal brain network dynamics

Authors: Mariani Wigley, Isabella L. C.; Lautarescu, Alexandra; Vartiainen, Elena; Pulli, Elmo P.; Hashempour, Niloofar; Merisaari, Harri; Bano, Wajiha; Luotonen, Silja; Jolly, Ashmeet; Suuronen, Ilkka; Karlsson, Linnea; Karlsson, Hasse; Cabral, Joana; Kringelbach, Morten L.; Batalle, Dafnis; Edwards, A. David; Tuulari, Jetro J.

Publisher: Springer Nature

Publication year: 2026

Journal: Pediatric Research

ISSN: 0031-3998

eISSN: 1530-0447

DOI: https://doi.org/10.1038/s41390-025-04726-2

Publication's open availability at the time of reporting: Open Access

Publication channel's open availability : Open Access publication channel

Web address : https://doi.org/10.1038/s41390-025-04726-2

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/508699547

Self-archived copy's licence: CC BY

Self-archived copy's version: Publisher`s PDF

Abstract

Background

Elevated pre-pregnancy body mass index (BMI) and perinatal depressive symptoms have been linked to neonatal alterations in brain structure and function. This study examined associations between neonatal functional brain dynamics, maternal BMI, and perinatal depressive symptoms measured by the Edinburgh Postnatal Depression Scale (EPDS) in a community-based, largely low-risk cohort.

Methods

Funcitonal MRI and Leading Eigenvector Analysis (LEiDA) were applied in a neonatal cohort (N = 437; 236 males; mean gestational age 39.6 weeks) from the developing Human Connectome Project. We assessed whether neonatal brain-state probabilities related to maternal BMI and EPDS scores (M = 5.6, SD = 4.3), testing main effects and, separately, their interaction. The sample included 291 healthy-weight (BMI < 25), 98 overweight (25 BMI < 30), and 48 obese (BMI 30) mothers.

Results

EPDS scores were low in this cohort and did not demonstrate associations with brain states or a significant BMI × EPDS interaction. Higher maternal pre-pregnancy BMI was negatively associated with the stability of a functional network encompassing superior frontal, superior parietal, and temporal regions (ß = −0.129, p = 0.006).

Conclusion

As this network is normally recruited more with age, reduced stability suggests slowed maturation of fronto-parieto-temporal systems and may signal early risk for later behavioral challenges.

Impact

Higher maternal pre-pregnancy BMI is associated with reduced stability in a neonatal frontoparietal brain state, characterized by coordinated activity in frontal, parietal, and temporal regions.
This state is one of six distinct dynamic connectivity patterns identified, reflecting core neonatal resting-state networks.
The association was robust across multiple analytic models and clustering solutions.
No significant effects were found for maternal depressive symptoms.
These findings underscore the selective impact of maternal metabolic health on early brain organization, suggesting prenatal influences on the functional architecture of the newborn brain that may shape long-term neurodevelopmental trajectories.

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s41390-025-04726-2.pdf

Funding information in the publication:
I.L.C.M.W. was supported by a postdoctoral fellowship from the Sigrid Juselius Foundation through J.J.T. fellowship. J.J.T. was supported by the Finnish Medical Foundation, the Emil Aaltonen Foundation, the Sigrid Juselius Foundation, the Signe and Ane Gyllenberg Foundation, the Hospital District of Southwest Finland State Research Grants, the Alfred Kordelin Foundation, the Juho Vainio Foundation and the Orion research Foundation. The Developing Human Connectome Project was supported by the European Research Council under the European Union’s Seventh Framework Programme (FP7/20072013)/ERC grant agreement no. 319456 (dHCP project). D.B. received support from a Wellcome Trust Seed Award in Science [217316/Z/19/Z]. D.B. acknowledges structural funding by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. J.C. is supported by the Portuguese Foundation for Science and Technology (FCT) through LARSyS funding (https://doi.org/10.54499/LA/P/0083/2020). E.P.P. is supported by the Signe and Ane Gyllenberg Foundation. The authors report no biomedical financial interests or potential conflicts of interest. Open Access funding provided by University of Turku (including Turku University Central Hospital).