Classification of aging types in healthy individuals using cross-sectional and longitudinal designs
Poster No:
895
Submission Type:
Abstract Submission
Authors:
Benita Schmitz-Koep1, Fabian Bongratz2, Vivian Schultz1, Aurore Menegaux1, Melissa Thalhammer1, Severin Schramm1, Su Hwan Kim1, Claus Zimmer1, Christian Sorg1, Christian Wachinger2, Panteleimon Giannakopoulos3, Marie-Louise Montandon4, François Herrmann4, Cristelle Rodriguez3, Sven Haller5, Dennis Hedderich1
Institutions:
1Institute for Neuroradiology, TUM University Hospital, Munich, Bavaria, 2Department of Radiology, TUM University Hospital, Munich, Bavaria, 3Department of Psychiatry, University of Geneva, Geneva, Geneva, 4Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Geneva, 5CIMC - Centre d’Imagerie Médicale de Cornavin, Geneva, Geneva
First Author:
Co-Author(s):
Marie-Louise Montandon
Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva
Geneva, Geneva
Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva
Geneva, Geneva
François Herrmann
Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva
Geneva, Geneva
Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva
Geneva, Geneva
Introduction:
Global brain aging trajectories are well-characterized in the general population, thanks to large structural magnetic resonance imaging (MRI) datasets (Bethlehem et al., 2022; Coupé et al., 2017). However, regional patterns enabling classification into distinct aging types remain less clear. While longitudinal studies are critical for understanding aging trajectories (Di Biase et al., 2023), they are scarce due to time and resource demands. This raises the question of whether cross-sectional data can capture distinct aging types. Therefore, we aim to classify aging types using cross-sectional data and assess their correspondence with longitudinal data.
Methods:
Cortical thickness (CTh) is a key biomarker in both normal aging and various pathologies. CTh was assessed in 176 cognitively healthy individuals aged 68-85 years at two time points (approx. 5 years apart) using 3T MRI. T1-weighted images were processed with longitudinal FreeSurfer v7.1.1 (Reuter et al. 2012). CTh was extracted from 68 gyral-based regions using the Desikan–Killiany Atlas (Desikan et al. 2006). Regional cortical atrophy was identified with two approaches (see Fig. 1): A) Cross-sectional: t-test comparing CTh of an individual at time point 2 with that of a small, approx. 5 years younger normative cohort at time point 1. B) Longitudinal: annualized percent change (APC) of CTh.
Cognitive performance was assessed at baseline and follow-up (approx. 5 years later) using a comprehensive neuropsychological battery (Herrmann et al. 2019). A continuous cognitive score was calculated by converting test results into z-scores, summing the number of tests with improvements and declines (≥0.5 SD), and subtracting the count of declined from improved tests (Herrmann et al. 2019).
To analyze aging types based on A) t-values and B) APC values, we used Ward's linkage hierarchical clustering method (Scipy v1.11) (Ward 1963). Initially, each subject was treated as an individual cluster. At each step, the pair of clusters with the smallest increase in total within-cluster variance was merged. This process successively created N hierarchical clustering levels. To each level, we assigned the maximum Euclidean inter-cluster distance as a metric evaluating the separation between clusters. We used the resulting dendrogram, which provides a clear visualization of the hierarchical relationships in the data, to determine the optimal number of clusters.
Multiple logistic regression models were used to evaluate predictors of cluster membership, with age, sex, education, and continuous cognitive score as independent variables.
Cognitive performance was assessed at baseline and follow-up (approx. 5 years later) using a comprehensive neuropsychological battery (Herrmann et al. 2019). A continuous cognitive score was calculated by converting test results into z-scores, summing the number of tests with improvements and declines (≥0.5 SD), and subtracting the count of declined from improved tests (Herrmann et al. 2019).
To analyze aging types based on A) t-values and B) APC values, we used Ward's linkage hierarchical clustering method (Scipy v1.11) (Ward 1963). Initially, each subject was treated as an individual cluster. At each step, the pair of clusters with the smallest increase in total within-cluster variance was merged. This process successively created N hierarchical clustering levels. To each level, we assigned the maximum Euclidean inter-cluster distance as a metric evaluating the separation between clusters. We used the resulting dendrogram, which provides a clear visualization of the hierarchical relationships in the data, to determine the optimal number of clusters.
Multiple logistic regression models were used to evaluate predictors of cluster membership, with age, sex, education, and continuous cognitive score as independent variables.
·Fig. 1: A) Cross-sectional and B) longitudinal approach to identify different aging types. Abbreviations: APC, annualized percent change; CTh, cortical thickness.
Results:
Two clearly distinguishable clusters emerged from the cross-sectional approach: one with minimal atrophy and another with more pronounced atrophy, particularly in central and prefrontal regions (see Fig. 2A). Age and sex were not significant predictors of cluster membership. Individuals with ≤12 years of education had significantly higher odds of being in the cluster with more atrophy (adjusted OR=2.58, 95% CI: 1.01-6.58, p=0.047, adjusting for age, sex, cognitive score). Furthermore, cognitive decline was associated with higher odds of being in the cluster with more atrophy (adj. OR=0.92, 95% CI: 0.85-1.00, p=0.050, adjusting for age, sex, education).
The cross-sectional and longitudinal approach revealed similar clustering patterns (see Fig.2A and B). Of 176 individuals, 114 (approx. two-thirds), were assigned to corresponding clusters in both approaches.
The cross-sectional and longitudinal approach revealed similar clustering patterns (see Fig.2A and B). Of 176 individuals, 114 (approx. two-thirds), were assigned to corresponding clusters in both approaches.
·Fig. 2: Visualization of cluster centroids in A) cross-sectional and B) longitudinal approach. Abbreviation: APC, annualized percent change.
Conclusions:
Two distinct clusters indicate that different aging types within normal aging processes can be identified using cross-sectional data. The cluster with more pronounced atrophy was linked to lower education and cognitive decline, suggesting that these cortical aging types may be functionally relevant to cognition and influenced by sociodemographic factors. Cross-sectional and longitudinal results were consistent, demonstrating robustness across different methods.
Higher Cognitive Functions:
Higher Cognitive Functions Other 2
Lifespan Development:
Aging 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Keywords:
Aging
Cognition
Cortex
Data analysis
Machine Learning
STRUCTURAL MRI
Other - Longitudinal
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Coupé, P. et al. (2017). Towards a unified analysis of brain maturation and aging across the entire lifespan: A MRI analysis. Human Brain Mapping, 38(11), 5501–5518. https://doi.org/10.1002/hbm.23743
Desikan, R. S. et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021
Di Biase, M. A. et al. (2023). Mapping human brain charts cross-sectionally and longitudinally. Proceedings of the National Academy of Sciences of the United States of America, 120(20). https://doi.org/10.1073/pnas.2216798120
Herrmann, F. R. et al. (2019). Gray Matter Densities in Limbic Areas and APOE4 Independently Predict Cognitive Decline in Normal Brain Aging. Frontiers in Aging Neuroscience, 11(JUN). https://doi.org/10.3389/fnagi.2019.00157
Reuter, M. et al. (2012). Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage, 61(4), 1402–1418. https://doi.org/10.1016/j.neuroimage.2012.02.084
Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.