Brain Aging Comprises Many Modes of Structural and Functional Change with Distinct Genetic and Biophysical Associations

Overview

Journal Elife

Specialty Biology

Date 2020 Mar 6

PMID 32134384

Citations 79

Authors

Stephen M Smith

Lloyd T Elliott

Fidel Alfaro-Almagro

Paul McCarthy

Thomas E Nichols

Gwenaelle Douaud

Karla L Miller

Affiliations

Soon will be listed here.

Abstract

Brain imaging can be used to study how individuals' brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single 'brain age' is estimated per subject, whereas here we identified 62 modes of subject variability, from 21,407 subjects' multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease.

Citing Articles

Genetically supported targets and drug repurposing for brain aging: A systematic study in the UK Biobank.

Yi F, Yuan J, Somekh J, Peleg M, Zhu Y, Jia Z Sci Adv. 2025; 11(11):eadr3757.

PMID: 40073132 PMC: 11900869. DOI: 10.1126/sciadv.adr3757.

Reliability of structural brain change in cognitively healthy adult samples.

Vidal-Pineiro D, Sorensen O, Stromstad M, Amlien I, Anderson M, Baare W bioRxiv. 2025; .

PMID: 40027710 PMC: 11870432. DOI: 10.1101/2024.06.03.592804.

An Update on Neuroaging on Earth and in Spaceflight.

Kuznetsov N, Statsenko Y, Ljubisavljevic M Int J Mol Sci. 2025; 26(4).

PMID: 40004201 PMC: 11855577. DOI: 10.3390/ijms26041738.

Temporal autocorrelation is predictive of age-An extensive MEG time-series analysis.

Stier C, Balestrieri E, Fehring J, Focke N, Wollbrink A, Dannlowski U Proc Natl Acad Sci U S A. 2025; 122(8):e2411098122.

PMID: 39977317 PMC: 11873822. DOI: 10.1073/pnas.2411098122.

Characterizing Brain-Cardiovascular Aging Using Multiorgan Imaging and Machine Learning.

Amirmoezzi Y, Cropley V, Mansour L S, Seguin C, Zalesky A, Tian Y J Neurosci. 2025; 45(8).

PMID: 39971581 PMC: 11841759. DOI: 10.1523/JNEUROSCI.1440-24.2024.

References

Lambert J, Ibrahim-Verbaas C, Harold D, Naj A, Sims R, Bellenguez C . Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet. 2013; 45(12):1452-8. PMC: 3896259. DOI: 10.1038/ng.2802. View

Vinke E, De Groot M, Venkatraghavan V, Klein S, Niessen W, Ikram M . Trajectories of imaging markers in brain aging: the Rotterdam Study. Neurobiol Aging. 2018; 71:32-40. DOI: 10.1016/j.neurobiolaging.2018.07.001. View

Hoglinger G, Melhem N, Dickson D, Sleiman P, Wang L, Klei L . Identification of common variants influencing risk of the tauopathy progressive supranuclear palsy. Nat Genet. 2011; 43(7):699-705. PMC: 3125476. DOI: 10.1038/ng.859. View

OFlaherty E . Modeling normal aging bone loss, with consideration of bone loss in osteoporosis. Toxicol Sci. 2000; 55(1):171-88. DOI: 10.1093/toxsci/55.1.171. View

Simon-Sanchez J, Schulte C, Bras J, Sharma M, Gibbs J, Berg D . Genome-wide association study reveals genetic risk underlying Parkinson's disease. Nat Genet. 2009; 41(12):1308-12. PMC: 2787725. DOI: 10.1038/ng.487. View

Cole J, Poudel R, Tsagkrasoulis D, Caan M, Steves C, Spector T . Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage. 2017; 163:115-124. DOI: 10.1016/j.neuroimage.2017.07.059. View

Wang K, Li M, Hakonarson H . ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010; 38(16):e164. PMC: 2938201. DOI: 10.1093/nar/gkq603. View

Battle A, Brown C, Engelhardt B, Montgomery S . Genetic effects on gene expression across human tissues. Nature. 2017; 550(7675):204-213. PMC: 5776756. DOI: 10.1038/nature24277. View

Coultas L, Terzano S, Thomas T, Voss A, Reid K, Stanley E . Hrk/DP5 contributes to the apoptosis of select neuronal populations but is dispensable for haematopoietic cell apoptosis. J Cell Sci. 2007; 120(Pt 12):2044-52. PMC: 2795636. DOI: 10.1242/jcs.002063. View

10.

Cruchaga C, Kauwe J, Harari O, Jin S, Cai Y, Karch C . GWAS of cerebrospinal fluid tau levels identifies risk variants for Alzheimer's disease. Neuron. 2013; 78(2):256-68. PMC: 3664945. DOI: 10.1016/j.neuron.2013.02.026. View

11.

Liem F, Varoquaux G, Kynast J, Beyer F, Masouleh S, Huntenburg J . Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage. 2016; 148:179-188. DOI: 10.1016/j.neuroimage.2016.11.005. View

12.

Ruddy R, Adams K, Morshead C . Age- and sex-dependent effects of metformin on neural precursor cells and cognitive recovery in a model of neonatal stroke. Sci Adv. 2019; 5(9):eaax1912. PMC: 6739114. DOI: 10.1126/sciadv.aax1912. View

13.

Kessler D, Angstadt M, Sripada C . Growth Charting of Brain Connectivity Networks and the Identification of Attention Impairment in Youth. JAMA Psychiatry. 2016; 73(5):481-9. PMC: 5507181. DOI: 10.1001/jamapsychiatry.2016.0088. View

14.

Groves A, Smith S, Fjell A, Tamnes C, Walhovd K, Douaud G . Benefits of multi-modal fusion analysis on a large-scale dataset: life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure. Neuroimage. 2012; 63(1):365-80. DOI: 10.1016/j.neuroimage.2012.06.038. View

15.

Deary V, Hagenaars S, Harris S, Hill W, Davies G, Liewald D . Genetic contributions to self-reported tiredness. Mol Psychiatry. 2017; 23(3):609-620. PMC: 5822465. DOI: 10.1038/mp.2017.5. View

16.

Chesi A, Mitchell J, Kalkwarf H, Bradfield J, Lappe J, McCormack S . A trans-ethnic genome-wide association study identifies gender-specific loci influencing pediatric aBMD and BMC at the distal radius. Hum Mol Genet. 2015; 24(17):5053-9. PMC: 4527490. DOI: 10.1093/hmg/ddv210. View

17.

Mollink J, Smith S, Elliott L, Kleinnijenhuis M, Hiemstra M, Alfaro-Almagro F . The spatial correspondence and genetic influence of interhemispheric connectivity with white matter microstructure. Nat Neurosci. 2019; 22(5):809-819. PMC: 6517273. DOI: 10.1038/s41593-019-0379-2. View

18.

Salat D, Lee S, van der Kouwe A, Greve D, Fischl B, Rosas H . Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. Neuroimage. 2009; 48(1):21-8. PMC: 2750073. DOI: 10.1016/j.neuroimage.2009.06.074. View

19.

Miller K, Alfaro-Almagro F, Bangerter N, Thomas D, Yacoub E, Xu J . Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci. 2016; 19(11):1523-1536. PMC: 5086094. DOI: 10.1038/nn.4393. View

20.

Moers A, Nurnberg A, Goebbels S, Wettschureck N, Offermanns S . Galpha12/Galpha13 deficiency causes localized overmigration of neurons in the developing cerebral and cerebellar cortices. Mol Cell Biol. 2007; 28(5):1480-8. PMC: 2258794. DOI: 10.1128/MCB.00651-07. View