Population-based neuroimaging reveals traces of childbirth in the maternal brain

Lange, Ann-Marie G. de; Kaufmann, Tobias; Meer, Dennis van der; Maglanoc, Luigi A.; Alnæs, Dag; Moberget, Torgeir; Douaud, Gwenaëlle; Andreassen, Ole A.; Westlye, Lars T.

doi:10.1073/pnas.1910666116

Cited by 123 publications

(82 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Though application of brain-aging algorithms in a midlife population is still relatively novel, previous literature using a similar technique to analyze brain-PAD (brainageR, https://github.com/james-cole/brainageR ) has shown that accelerated brain aging may be observable in midlife women in relation to lifestyle factors which impact hormone levels, in this case number of childbirths. Further, their findings are consistent with observed patterns of parity and risk of AD, where increased number of childbirths conveys lower risk of neurocognitive decline (de Lange et al, 2019 ). This provides additional evidence that brain-PAD at midlife could relate to later life outcomes and that female populations experience hormonal changes throughout their life that convey unique risk factors for neurocognitive decline.…”

Section: Discussionsupporting

confidence: 83%

Network Modeling Sex Differences in Brain Integrity and Metabolic Health

Foret

Dekhtyar

Cole

et al. 2021

Front. Aging Neurosci.

View full text Add to dashboard Cite

Hypothesis-driven studies have demonstrated that sex moderates many of the relationships between brain health and cardiometabolic disease, which impacts risk for later-life cognitive decline. In the present study, we sought to further our understanding of the associations between multiple markers of brain integrity and cardiovascular risk in a midlife sample of 266 individuals by using network analysis, a technique specifically designed to examine complex associations among multiple systems at once. Separate network models were constructed for male and female participants to investigate sex differences in the biomarkers of interest, selected based on evidence linking them with risk for late-life cognitive decline: all components of metabolic syndrome (obesity, hypertension, dyslipidemia, and hyperglycemia); neuroimaging-derived brain-predicted age minus chronological age; ratio of white matter hyperintensities to whole brain volume; seed-based resting state functional connectivity in the Default Mode Network, and ratios of N-acetyl aspartate, glutamate and myo-inositol to creatine, measured through proton magnetic resonance spectroscopy. Males had a sparse network (87.2% edges = 0) relative to females (69.2% edges = 0), indicating fewer relationships between measures of cardiometabolic risk and brain integrity. The edges in the female network provide meaningful information about potential mechanisms between brain integrity and cardiometabolic health. Additionally, Apolipoprotein ϵ4 (ApoE ϵ4) status and waist circumference emerged as central nodes in the female model. Our study demonstrates that network analysis is a promising technique for examining relationships between risk factors for cognitive decline in a midlife population and that investigating sex differences may help optimize risk prediction and tailor individualized treatments in the future.

show abstract

Section: Discussionsupporting

confidence: 83%

Network Modeling Sex Differences in Brain Integrity and Metabolic Health

Foret

Dekhtyar

Cole

et al. 2021

Front. Aging Neurosci.

View full text Add to dashboard Cite

show abstract

“…Physical activity was measured using ankle-worn accelerometer across on average 7 days (range 3 to 9) and physical capabilities were operationalized through grip strength, walking speed, and postural control as a measure of balance. Based on recent implementations and the use of an independent training set, we estimated individual brain age using global and subcortical gray matter from T1-weighted MRI (de Lange & Cole, 2020; de Lange et al, 2019; Kaufmann et al, 2019). We used an independent training set comprising MRI data from 2407 healthy individuals for brain age prediction, and applied the cross-validated models in our unseen test set.…”

Section: Introductionmentioning

confidence: 99%

Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults

Sanders

Richard

Kolskår

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Maintaining high levels of daily activity and physical capability have been proposed as important constituents to promote healthy brain and cognitive aging. Studies investigating the associations between brain health and physical activity in late life have, however, mainly been based on self-reported data or measures designed for clinical populations. In the current study, we examined cross-sectional associations between physical activity, recorded by an ankle-positioned accelerometer for seven days, physical capability (grip strength, postural control, and walking speed), and neuroimaging based surrogate markers of brain health in 122 healthy older adults aged 65-88 years. We used a multimodal brain imaging approach offering two complementary structural MRI based indicators of brain health: white matter diffusivity and coherence based on diffusion tensor imaging and subcortical and global brain age based on brain morphology inferred from T1-weighted MRI data. The analyses revealed a significant association between global white matter fractional anisotropy (FA) and walking speed, indicating higher white matter coherence in people with higher pace. We also found a significant interaction between sex and brain age on number of daily steps, indicating younger-appearing brains in more physically active women, with no significant associations among men. These results provide insight into the intricate associations between different measures of brain and physical health in old age, and corroborate established public health advice promoting physical activity.

show abstract

“…The first PC captures the largest amount of variation in the computed PRSs and thus could have better discrimination of the phenotype we are testing. This strategy was recently implemented in studies of polygenic effects on schizophrenia as well as brain imaging (Alnaes et al, 2019;Bergen et al, 2019;De Lange et al, 2019;Maglanoc et al, 2019). This unsupervised approach incorporates all computed scores across a range of tuning parameters and, importantly, is agnostic regarding the outcome of interest and thereby maintains correct Type I error.…”

Section: Introductionmentioning

confidence: 99%

A principal component approach to improve association testing with polygenic risk scores

Coombes

Ploner

Bergen

et al. 2020

Genetic Epidemiology

View full text Add to dashboard Cite

Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. Optimization can lead to inflated Type I error. Permutation procedures can correct this, but they can be computationally intensive. Alternatively, a single parameter setting can be chosen a priori for the PRS, but choosing suboptimal settings results in loss of power. We propose computing PRSs under a range of parameter settings, performing principal component analysis (PCA) on the resulting set of PRSs, and using the first PRS–PC in association tests. The first PC reweights the variants included in the PRS to achieve maximum variation over all PRS settings used. Using simulations and a real data application to study PRS association with bipolar disorder and psychosis in bipolar disorder, we compare the performance of the proposed PRS–PCA approach with a permutation test and an a priori selected p‐value threshold. The PRS–PCA approach is simple to implement, outperforms the other strategies in most scenarios, and provides an unbiased estimate of prediction performance.

show abstract

Population-based neuroimaging reveals traces of childbirth in the maternal brain

Cited by 123 publications

References 52 publications

Network Modeling Sex Differences in Brain Integrity and Metabolic Health

Network Modeling Sex Differences in Brain Integrity and Metabolic Health

Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults

A principal component approach to improve association testing with polygenic risk scores

Contact Info

Product

Resources

About