Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Oct;13(10):e3219.
doi: 10.1002/brb3.3219. Epub 2023 Aug 16.

Considerations on brain age predictions from repeatedly sampled data across time

Max Korbmacher  1   2   3 , Meng-Yun Wang  3   4 , Rune Eikeland  3   4 , Ralph Buchert  5 , Ole A Andreassen  2   6 , Thomas Espeseth  7   8 , Esten Leonardsen  2   7 , Lars T Westlye  2   7 , Ivan I Maximov  1   2 , Karsten Specht  3   4   9
Affiliations

Considerations on brain age predictions from repeatedly sampled data across time

Max Korbmacher et al. Brain Behav. 2023 Oct.

Abstract

Introduction: Brain age, the estimation of a person's age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for the same individual at various time points and validate our findings with age-matched healthy controls.

Methods: We used densely sampled T1-weighted MRI data from four individuals (from two densely sampled datasets) to observe how brain age corresponds to age and is influenced by acquisition and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pretrained deep learning model.

Results: We found small within-subject correlations between age and brain age. We also found evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data and inconclusive effects of scan quality.

Conclusion: The absence of maturation effects for the age range in the presented sample, brain age model bias (including training age distribution and field strength), and model error are potential reasons for small relationships between age and brain age in densely sampled longitudinal data. Clinical applications of brain age models should consider of the possibility of apparent biases caused by variation in the data acquisition process.

Keywords: T1-weighted; brain age; densely sampled MRI; field strength; magnetic resonance imaging; scan quality.

PubMed Disclaimer

Conflict of interest statement

O. O. A. has received a speaker's honorarium from Lundbeck and is a consultant to Coretechs.ai.

Figures

FIGURE 1
FIGURE 1
Intraindividual correlations between brain age and chronological age at 3T for BBSC1–3 and FTHP1. Dot color was gray, with overlapping dots presented as darker.
FIGURE 2
FIGURE 2
Intraindividual correlations between brain age and chronological age at 1.5T and 3T for FTHP1. Dot color was gray, with overlapping dots presented darker.
FIGURE 3
FIGURE 3
Standardized quality control metrics at 3T per subject. For an overview of scan quality control metrics at 1.5T (only applicable for FTHP1), see Supplement 2.
See this image and copyright information in PMC

References

    1. Beck, D. , Lange, A.‐M. G. , Pedersen, M. L. , Alnæs, D. , Maximov, I. I. , Voldsbekk, I. , Richard, G. , Sanders, A.‐M. , Ulrichsen, K. M. , Dørum, E. S. , Kolskår, K. K. , Høgestøl, E. A. , Steen, N. E. , Djurovic, S. , Andreassen, O. A. , Nordvik, J. E. , Kaufmann, T. , & Westlye, L. T. (2022). Cardiometabolic risk factors associated with brain age and accelerate brain ageing. Human Brain Mapping, 43(2), 700–720. 10.1002/hbm.25680 - DOI - PMC - PubMed
    1. Clerx, L. , Gronenschild, E. H. B. M. , Echavarri, C. , Verhey, F. , Aalten, P. , & Jacobs, H. I. L. (2015). Can FreeSurfer compete with manual volumetric measurements in Alzheimer's disease? Current Alzheimer Research, 12(4), 358–367. 10.2174/1567205012666150324174813 - DOI - PubMed
    1. Dadar, M. , Fonov, V. S. , & Collins, D. L , & Alzheimer's Disease Neuroimaging Initiative . (2018). A comparison of publicly available linear MRI stereotaxic registration techniques. Neuroimage, 174, 191–200. 10.1016/j.neuroimage.2018.03.025 - DOI - PubMed
    1. Dörfel, R. P. , Arenas‐Gomez, J. M. , Fisher, P. M. , Ganz, M. , Knudsen, G. M. , Svensson, J. , & Plaven‐Sigray, P. (2023). Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test‐retest reliability of publicly available software packages. BioRxiv, 2023–01. 10.1101/2023.01.26.525514 bioRxiv - DOI - PMC - PubMed
    1. Espeseth, T. , Christoforou, A. , Lundervold, A. J. , Steen, V. M. , Le Hellard, S. , & Reinvang, I. (2012). Imaging and cognitive genetics: The Norwegian Cognitive NeuroGenetics sample. Twin Research and Human Genetics, 15(3), 442–452. 10.1017/thg.2012.8 - DOI - PubMed

Publication types