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      The maternal brain: Region‐specific patterns of brain aging are traceable decades after childbirth

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          Abstract

          Pregnancy involves maternal brain adaptations, but little is known about how parity influences women's brain aging trajectories later in life. In this study, we replicated previous findings showing less apparent brain aging in women with a history of childbirths, and identified regional brain aging patterns linked to parity in 19,787 middle‐ and older‐aged women. Using novel applications of brain‐age prediction methods, we found that a higher number of previous childbirths were linked to less apparent brain aging in striatal and limbic regions. The strongest effect was found in the accumbens—a key region in the mesolimbic reward system, which plays an important role in maternal behavior. While only prospective longitudinal studies would be conclusive, our findings indicate that subcortical brain modulations during pregnancy and postpartum may be traceable decades after childbirth.

          Abstract

          In this study, we identified regional brain‐aging patterns linked to parity in 19,787 middle‐ and older‐aged women. A higher number of previous childbirths were linked to less apparent brain aging in striatal and limbic regions. The strongest effect was found in the accumbens—a key region in the mesolimbic reward system, which plays an important role in maternal behavior.

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          Most cited references68

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

            In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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              Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.

              We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimer's disease.
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                Author and article information

                Contributors
                ann-marie.delange@psych.ox.ac.uk
                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                1065-9471
                1097-0193
                07 August 2020
                November 2020
                : 41
                : 16 ( doiID: 10.1002/hbm.v41.16 )
                : 4718-4729
                Affiliations
                [ 1 ] Department of Psychiatry University of Oxford Oxford UK
                [ 2 ] Department of Psychology University of Oslo Oslo Norway
                [ 3 ] NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction Oslo University Hospital Oslo Norway
                [ 4 ] Wellcome Centre for Integrative Neuroimaging University of Oxford Oxford UK
                [ 5 ] KG Jebsen Centre for Neurodevelopmental Disorders University of Oslo Oslo Norway
                Author notes
                [*] [* ] Correspondence

                Ann‐Marie G. de Lange, Department of Psychiatry, University of Oxford, Oxford, UK.

                Email: ann-marie.delange@ 123456psych.ox.ac.uk

                Author information
                https://orcid.org/0000-0002-5150-6656
                https://orcid.org/0000-0001-6544-0945
                https://orcid.org/0000-0001-8644-956X
                Article
                HBM25152
                10.1002/hbm.25152
                7555081
                32767637
                e7c4639e-4a9d-4978-9cfc-eb7dd6640204
                © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 May 2020
                : 30 June 2020
                : 16 July 2020
                Page count
                Figures: 5, Tables: 11, Pages: 4, Words: 10071
                Funding
                Funded by: H2020 European Research Council , open-funder-registry 10.13039/100010663;
                Award ID: 802998
                Funded by: HDH Wills 1965 Charitable Trust
                Award ID: 1117747
                Funded by: Helse Sør‐Øst RHF , open-funder-registry 10.13039/501100006095;
                Award ID: 2015073
                Award ID: 2019107
                Funded by: Medical Research Council , open-funder-registry 10.13039/501100007155;
                Award ID: G1001354
                Funded by: Norges Forskningsråd , open-funder-registry 10.13039/501100005416;
                Award ID: 223273
                Award ID: 249795
                Award ID: 273345
                Award ID: 276082
                Award ID: 286838
                Award ID: 298646
                Award ID: 300768
                Funded by: the Alzheimer's Society
                Award ID: 441
                Funded by: Wellcome Trust , open-funder-registry 10.13039/100010269;
                Award ID: 203139/Z/16/Z
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                November 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.2 mode:remove_FC converted:14.10.2020

                Neurology
                brain‐age prediction,maternal brain aging,neuroimaging,parity
                Neurology
                brain‐age prediction, maternal brain aging, neuroimaging, parity

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