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Association between parental age, brain structure, and behavioral and cognitive problems in children

Molecular Psychiatry volume 27, pages 967–975 (2022)Cite this article

Subjects

Abstract

Objective

To investigate the relation between parental age, and behavioral, cognitive and brain differences in the children.

Method

Data with children aged 9–11 of 8709 mothers with parental age 15–45 years were analyzed from the Adolescent Brain Cognitive Development (ABCD) study. A general linear model was used to test the associations of the parental age with brain structure, and behavioral and cognitive problems scores.

Results

Behavioral and cognitive problems were greater in the children of the younger mothers, and were associated with lower volumes of cortical regions in the children. There was a linear correlation between the behavioral and cognitive problems scores, and the lower brain volumes (r > 0.6), which was evident when parental age was included as a stratification factor. The regions with lower volume included the anterior cingulate cortex, medial and lateral orbitofrontal cortex and amygdala, parahippocampal gyrus and hippocampus, and temporal lobe (FDR corrected p < 0.01). The lower cortical volumes and areas in the children significantly mediated the association between the parental age and the behavioral and cognitive problems in the children (all p < 10−4). The effects were large, such as the 71.4% higher depressive problems score, and 27.5% higher rule-breaking score, in the children of mothers aged 15–19 than the mothers aged 34–35.

Conclusions

Lower parental age is associated with behavioral problems and reduced cognitive performance in the children, and these differences are related to lower volumes and areas of some cortical regions which mediate the effects in the children. The findings are relevant to psychiatric understanding and assessment.

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Fig. 1: The relationship between the behavioral problems and cognitive performance of the children and the maternal and paternal ages.
Fig. 2: The statistical differences of the children’s behavioral problems total score (Left triangle matrix) and cognitive performance total score (right triangle matrix) between the groups with mothers of different ages.
Fig. 3: The brain regions with their cortical areas in the children correlated with maternal and paternal age.
Fig. 4: The relation between the behavioral and cognitive problems in the children and their normalized cortical volume and area.
Fig. 5: Mediation analyses showing that cortical volume and area in the children are mediators in the relation between maternal age and behavioral problems and cognitive performance in the children.

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Data availability

The data that support the findings of this study are openly available in the ABCD Dataset Data Release 2.01 at https://nda.nih.gov/abcd.

Code availability

The data preprocessing software FreeSurfer v6.0 can be obtained from https://surfer.nmr.mgh.harvard.edu/. Qoala-T v1.2 for automatic quality control can be obtained from: https://github.com/Qoala-T/QC. The scripts used for these analyses will be made available upon publication at the following url: https://osf.io/qdb23/. Software for the mediation analysis can be obtained from https://github.com/canlab/MediationToolbox.

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Acknowledgements

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9–10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented that study and/or provided data but did not participate in the analysis for or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. WC was supported by grants from the National Natural Sciences Foundation of China (No. 82071997) and the Shanghai Rising Star Program (No. 21QA1408700). JF was supported by National Key R&D Program of China (No. 2019YFA0709502), National Key R&D Program of China (No. 2018YFC1312904), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), ZJ Lab, Shanghai Center for Brain Science and Brain-Inspired Technology, and the 111 Project (No. B18015). The authors thank Dr. Hui Wang for helpful comments and discussion.

Author information

Author notes

  1. These authors contributed equally: Jingnan Du, Edmund T. Rolls, Weikang Gong, Miao Cao.

Authors and Affiliations

  1. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China

    Jingnan Du, Edmund T. Rolls, Miao Cao, Deniz Vatansever, Jie Zhang, Jujiao Kang, Wei Cheng & Jianfeng Feng

  2. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China

    Jingnan Du, Miao Cao, Deniz Vatansever, Jie Zhang, Jujiao Kang, Wei Cheng & Jianfeng Feng

  3. Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK

    Edmund T. Rolls & Jianfeng Feng

  4. Oxford Centre for Computational Neuroscience, Oxford, UK

    Edmund T. Rolls

  5. Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK

    Weikang Gong

  6. Fudan ISTBI—ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China

    Wei Cheng & Jianfeng Feng

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Contributions

JD and WC designed the research. JD, ETR, WG and WC analyzed the data. JD and WC made the figures. JD, ETR, MC, DV, JZ, JK, JF and WC wrote and edited the manuscript. All authors approved the manuscript.

Corresponding author

Correspondence to Wei Cheng.

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Du, J., Rolls, E.T., Gong, W. et al. Association between parental age, brain structure, and behavioral and cognitive problems in children. Mol Psychiatry 27, 967–975 (2022). https://doi.org/10.1038/s41380-021-01325-5

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