Biology subjects

Brandmaier, A. M.

Publications and source records attributed to Brandmaier, A. M..

8 recordsLinked to original sources

bioRxiv2025

Distinguishing Lifelong Individual Differences from Divergent Aging Trajectories of Adult Brain Volumes

Individual differences in the volumes of brain structures are often linked to various conditions, including Alzheimers disease, schizophrenia, and overall brain health. However, it remains unclear to what extent these differences reflect individual levels present from young adulthood or diverging aging trajectories from later ages. In this study, we analyze the aging dynamics of the volumes of six brain structures based on magnetic resonance imaging (MRI) scans from a large cross-cohort longitudinal sample of cognitively healthy adults (n = 8,311 with 18,520 MRIs, ages from 18 to 97 years). From general assumptions about structural brain dynamics and measurement noise, a stochastic dynamical model was fitted to the data to estimate both the variability and persistence of structural changes across adulthood. Using this model, we calculated how much of the variance of volumetric differences between individuals can be attributed to stable levels from young adulthood versus systematic changes at older ages, as well as the theoretical sensitivity of longitudinal studies to detect individual differences in change. The findings were as follows: 1) Before age 60 years, inter-individual differences in neuroanatomical volumes almost exclusively reflect stable differences between individuals, while the influence from systematic differences in rate-of-change increases thereafter; up to 50 % of the variation being due to differences in change at 80 years. In contrast, ventricular volume reflects differences in change from early adulthood. 2) Current brain-age models are unlikely to be sensitive to detect differences in aging trajectories. 3) Imaging studies have low reliability in detecting inter-individual brain changes before age 60. After 60 years, the study reliability increases sharply with longer intervals between scans and more modestly with additional intermediate observations. In conclusion, our results reinforce the view that it is critical to distinguish stable early-adulthood levels from systematic differences in change when studying adult brain aging.

neuroscience↗

bioRxiv2025

Vulnerability to memory decline in aging. A mega-analysis of structural brain change.

Brain atrophy is a key factor behind episodic memory loss in aging, but the nature and ubiquity of this relationship remains poorly understood. This study leveraged 13 longitudinal datasets, including 3,737 cognitively healthy adults (10,343 MRI scans; 13,460 memory assessments), to determine whether brain change-memory change associations are more pronounced with age and genetic risk for Alzheimers Disease. Both factors are associated with accelerated brain decline, yet it remains unclear whether memory loss is exacerbated beyond what atrophy alone would predict. Additionally, we assessed whether memory decline aligns with a global pattern of atrophy or stems from distinct regional contributions. Our mega-analysis revealed a nonlinear relationship between memory decline and brain atrophy, primarily affecting individuals with above-average brain structural decline. The associations were stronger in the hippocampus but also spread across diverse cortical and subcortical regions. The associations strengthened with age, reaching moderate associations in participants in their eighties. While APOE {varepsilon}4 carriers exhibited steeper brain and memory loss, genetic risk had no effect on the change-change associations. These findings support the presence of common biological macrostructural substrates underlying memory function in older age which are vulnerable to multiple age-related factors, even in the absence of overt pathological changes.

neuroscience↗

bioRxiv2024

Reliability of structural brain change in cognitively healthy adult samples.

In neuroimaging research, tracking individuals over time is key to understanding the interplay between brain changes and genetic, environmental, or cognitive factors across the lifespan. Yet, the extent to which we can estimate the individual trajectories of brain change over time with precision remains uncertain. In this study, we estimated the reliability of structural brain change in cognitively healthy adults from multiple samples and assessed the influence of follow-up time and number of observations. Estimates of cross-sectional measurement error and brain change variance were obtained using the longitudinal FreeSurfer processing stream. Our findings showed, on average, modest longitudinal reliability with two years of follow-up. Increasing the follow-up time was associated with a substantial increase in longitudinal reliability while the impact of increasing the number of observations was comparatively minor. On average, 2-year follow-up studies require {approx}2.7 and {approx}4.0 times more individuals than designs with follow-ups of 4 and 6 years to achieve comparable statistical power. Subcortical volume exhibited higher longitudinal reliability compared to cortical area, thickness, and volume. The reliability estimates were comparable to those estimated from empirical data. The reliability estimates were affected by both the cohorts age where younger adults had lower reliability of change, and the preprocessing pipeline where the FreeSurfers longitudinal stream was notably superior than the cross-sectional. Suboptimal reliability inflated sample size requirements and compromised the ability to distinguish individual trajectories of brain aging. This study underscores the importance of long-term follow-ups and the need to consider reliability in longitudinal neuroimaging research.

neuroscience↗

bioRxiv2021

Reliability of quantitative multiparameter maps is high for MT and PD but attenuated for R1 and R2* in healthy young adults

We investigate the reliability of individual differences of four quantities measured by magnetic resonance imaging based multiparameter mapping (MPM): magnetization transfer (MT), proton density (PD), longitudinal relaxation rate (R1), and effective transverse relaxation rate (R2*). A total of four MPM datasets, two on each of two consecutive days, were acquired in healthy young adults. On Day 1, no repositioning occurred; on Day 2, participants were repositioned between MPM datasets. Using intra-class correlation effect decomposition (ICED), we assessed the contributions of session-specific, day-specific, and residual sources of measurement error. For whole-brain gray and white matter, all four MPM parameters showed high reproducibility and high reliability, as indexed by the coefficient of variation (CoV) and the intra-class correlation (ICC). However, MT, PD, R1, and R2* differed markedly in the extent to which reliability varied across brain regions. MT and PD showed high reliability in almost all regions. In contrast, R1 and R2* showed low reliability in some regions outside the basal ganglia, such that the sum of the measurement error estimates in our structural equation model was higher than estimates of between-person differences. In sum, in this sample of healthy young adults, the four MPM parameters showed very little variability over four measurements over two days but differed in how well they could assess between-person differences. We conclude that R1 and R2* might carry only limited person-specific information in samples of healthy young adults, and, by implication, might be of restricted utility for studying associations to between-person differences in behavior.

neuroscience↗

bioRxiv2021

Brain age relates to early life factors but not to accelerated brain aging

Brain age is a widely used index for quantifying individuals brain health as deviation from a normative brain aging trajectory. Higher than expected brain age is thought partially to reflect above-average rate of brain aging. We explicitly tested this assumption in two large datasets and found no association between cross-sectional brain age and steeper brain decline measured longitudinally. Rather, brain age in adulthood was associated with early-life influences indexed by birth weight and polygenic scores. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.

neuroscience↗

bioRxiv2020

Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer's Disease

Normal aging and Alzheimers Disease (AD) are accompanied by large-scale alterations in brain organization that undermine brain function. Although hemispheric asymmetry is a global organizing feature of cortex thought to promote brain efficiency, current descriptions of cortical thinning in aging and AD have largely overlooked cortical asymmetry. Consequently, the foundational question of whether and where the cerebral hemispheres change at different rates in aging and AD remains open. First, applying vertex-wise data-driven clustering in a longitudinal discovery sample (aged 20-89; 2577 observations; 1851 longitudinal) we identified cortical regions exhibiting similar age-trajectories of asymmetry across the adult lifespan. Next, we sought replication in 4 independent longitudinal aging cohorts. We show that higher-order regions of cortex that exhibit pronounced asymmetry at age ~20 also show asymmetry change in aging. Results revealed that both leftward and rightward asymmetry is progressively lost on a similar time-scale across adult life. Hence, faster thinning of the (previously) thicker homotopic hemisphere is a feature of aging. This simple organizational principle showed high consistency across multiple aging cohorts in the Lifebrain consortium, and both the topological patterns and temporal dynamics of asymmetry-loss were markedly similar across replicating samples. Finally, we show that regions exhibiting gradual asymmetry-loss over healthy adult life exhibit faster asymmetry-change in AD. Overall, our results suggest a system-wide breakdown in the adaptive asymmetric organization of cortex across adult life which is further accelerated in AD, and may implicate thickness asymmetry as a viable marker for declining hemispheric specialization in aging and AD. SignificanceThe brain becomes progressively disorganized with age, and brain alterations accelerated in Alzheimers disease may occur gradually over the lifespan. Although hemispheric asymmetry aids efficient network organization, efforts to identify structural markers of age-related decline have largely overlooked cortical asymmetry. Here we show the hemisphere that is thicker when younger, thins faster. This leads to progressive system-wide loss of regional thickness asymmetry across life. In multiple aging cohorts, asymmetry-loss showed high reproducibility topologically across cortex and similar timing-of-change in aging. Asymmetry-change was further accelerated in AD. Our findings uncover a new principle of brain aging - thicker homotopic cortex thins faster - and suggest we may have unveiled a structural marker for a widely-hypothesized decline in hemispheric specialization in aging and AD.

neuroscience↗

bioRxiv2020

Hippocampal and parahippocampal grey matter structural integrity assessed by multimodal imaging is associated with episodic memory in old age

Maintained structural integrity of hippocampal and cortical grey matter may explain why some older adults show rather preserved episodic memory. However, viable measurement models for estimating individual differences in grey matter structural integrity are lacking; instead, findings rely on fallible single indicators of integrity. Here, we introduce multitrait-multimethod (MTMM) methodology to capture individual differences in grey matter integrity, based on multimodal structural imaging in a large sample of 1,522 healthy adults aged 60 to 88 years from the Berlin Aging Study II, including 331 participants who underwent MR imaging. Structural integrity factors expressed the common variance of voxel-based morphometry (VBM), mean diffusivity (MD), and magnetization transfer ratio (MT) for each of four regions of interest (ROI): hippocampus, parahippocampal gyrus, prefrontal cortex, and precuneus. Except for precuneus, the integrity factors correlated with episodic memory. Associations with hippocampal and parahippocampal integrity persisted after controlling for age, sex, and education. Our results support the proposition that episodic memory ability in old age benefits from maintained structural integrity of hippocampus and parahippocampal gyrus. Exploratory follow-up analyses on sex differences showed that this effect is restricted to men. Multimodal factors of structural brain integrity might help improve our biological understanding of human memory aging.

neuroscience↗

bioRxiv2019

Hippocampal subfields and limbic white matter jointly predict learning rate in older adults

Age-related memory impairments have been linked to differences in structural brain parameters, including cerebral white matter (WM) microstructure and hippocampal (HC) volume, but their combined influences are rarely investigated. In a population-based sample of 337 older participants 61-82 years of age (Mage=69.66, SDage=3.92 years) we modeled the independent and joint effects of limbic WM microstructure and HC subfield volumes on verbal learning. Participants completed a verbal learning task over five learning trials and underwent magnetic resonance imaging (MRI), including structural and diffusion scans. We segmented three HC subregions on high-resolution MRI data and sampled mean fractional anisotropy (FA) from bilateral limbic WM tracts identified via deterministic fiber tractography. Using structural equation modeling, we evaluated the associations between learning rate and latent factors representing FA sampled from limbic WM tracts, and HC subfield volumes, as well as their latent interaction. Results showed limbic WM and the interaction of HC and WM - but not HC volume alone - predicted verbal learning rates. Model decomposition revealed HC volume is only positively associated with learning rate in individuals with higher levels of WM anisotropy. We conclude that structural characteristics of limbic WM regions and HC volume jointly contribute to verbal learning in older adults.

neuroscience↗