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

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 middleand 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.

A history of previous childbirths is linked to women’s white matter brain age in midlife and older age

“…While some structural brain changes revert post parturition [6], recent studies indicate that some effects of pregnancy may be long-lasting [2, 5], potentially influencing brain trajectories later in life [7, 8, 9, 10]. However, neuroimaging studies of the maternal brain have largely focused on grey matter (GM) volume [1, 2, 10, 11, 12, 13, 4, 14] and cortical thickness [3, 15], and less is known about the effects of pregnancy on brain white matter (WM) microstructure.…”

“…In the current study, we utilised four diffusion models (DTI, DKI, WMTI, SMT) to build predictive models of WM brain ageing, and investigated associations between brain-age estimates and previous childbirths in a sample of 8,895 UK Biobank women (mean age ± standard deviation = 62.45 ± 7.26). In line with studies suggesting that distinct and regional brain-age prediction models may provide additional detail [11, 35, 36, 37], we used separate models to estimate i) global WM ageing, ii) global GM ageing to test for modality-specific contributions, and iii) WM ageing in 12 major WM tracts in order to identify regions of particular importance for maternal brain ageing.…”

Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

“…However, given the limited age range of participants in the current dataset, the number and the patterns of the brain developmental subsystems are unknown. Unlike some recent studies that conducted separate brain age prediction based on imaging modality (Hwang et al, 2020; Truelove-Hill et al, 2020) or clustered imaging features based on their similarity across participants (Lange et al, 2020), we first ran robust regression to extract the coefficients that contained information of developmental pattern of each feature. Then, K-means cluster analysis was conducted based on the coefficients to groups features into clusters with different developmental patterns.…”

“…The current study proposed a multidimensional brain-age prediction model based on brain-imaging features in different clusters with divergent developmental trajectories. Recent research has taken a similar approach except that they performed the cluster analysis on the original brain imaging features rather than the coefficients of developmental trajectories (Lange et al, 2020). In addition, their work examined altered maternal brain development on subjects between 45-82 years.…”

“…Given that we had a relatively large sample size and balanced distribution across age, the estimated regression coefficients were used to approximate the developmental trajectory for each brain imaging feature. Other studies have taken a similar approach to approximate the temporal trajectory with cross-sectional data (Brown et al, 2012; Lange et al, 2020; Smith, Vidaurre, Alfaro-Almagro, Nichols, & Miller, 2019). To reduce the correlation between the linear and quadratic terms, age was standardized.…”