Brain Structural Differences Between 73- and 92-year Olds Matched for Childhood Intelligence, Social Background, and Intracranial Volume

Overview

Journal Neurobiol Aging

Publisher Elsevier

Specialties Geriatrics
Neurology
Physiology

Date 2017 Nov 18

PMID 29149632

Citations 6

Authors

Stuart J Ritchie

David Alexander Dickie

Simon R Cox

Maria Del C Valdes Hernandez

Ruth Sibbett

Alison Pattie

Devasuda Anblagan

Paul Redmond

Natalie A Royle

Janie Corley

Susana Munoz Maniega

Adele M Taylor

Sherif Karama

Tom Booth

Alan J Gow

John M Starr

Mark E Bastin

Joanna M Wardlaw

Ian J Deary

Affiliations

Soon will be listed here.

Abstract

Fully characterizing age differences in the brain is a key task for combating aging-related cognitive decline. Using propensity score matching on 2 independent, narrow-age cohorts, we used data on childhood cognitive ability, socioeconomic background, and intracranial volume to match participants at mean age of 92 years (n = 42) to very similar participants at mean age of 73 years (n = 126). Examining a variety of global and regional structural neuroimaging variables, there were large differences in gray and white matter volumes, cortical surface area, cortical thickness, and white matter hyperintensity volume and spatial extent. In a mediation analysis, the total volume of white matter hyperintensities and total cortical surface area jointly mediated 24.9% of the relation between age and general cognitive ability (tissue volumes and cortical thickness were not significant mediators in this analysis). These findings provide an unusual and valuable perspective on neurostructural aging, in which brains from the 8th and 10th decades of life differ widely despite the same cognitive, socioeconomic, and brain-volumetric starting points.

Citing Articles

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References

S K, Y A, Rj H, Ij D, Oc L, C L . Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds. Intelligence. 2010; 37(2):145-155. PMC: 2678742. DOI: 10.1016/j.intell.2008.09.006. View

Aribisala B, Royle N, Valdes Hernandez M, Murray C, Penke L, Gow A . Potential effect of skull thickening on the associations between cognition and brain atrophy in ageing. Age Ageing. 2014; 43(5):712-6. DOI: 10.1093/ageing/afu070. View

Panza F, Frisardi V, Capurso C, DIntrono A, Colacicco A, Imbimbo B . Late-life depression, mild cognitive impairment, and dementia: possible continuum?. Am J Geriatr Psychiatry. 2010; 18(2):98-116. DOI: 10.1097/JGP.0b013e3181b0fa13. View

Brayne C . The elephant in the room - healthy brains in later life, epidemiology and public health. Nat Rev Neurosci. 2007; 8(3):233-9. DOI: 10.1038/nrn2091. View

Lakens D . Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. 2013; 4:863. PMC: 3840331. DOI: 10.3389/fpsyg.2013.00863. View

Karama S, Ducharme S, Corley J, Chouinard-Decorte F, Starr J, Wardlaw J . Cigarette smoking and thinning of the brain's cortex. Mol Psychiatry. 2015; 20(6):778-85. PMC: 4430302. DOI: 10.1038/mp.2014.187. View

Segonne F, Dale A, Busa E, Glessner M, Salat D, Hahn H . A hybrid approach to the skull stripping problem in MRI. Neuroimage. 2004; 22(3):1060-75. DOI: 10.1016/j.neuroimage.2004.03.032. View

Bullmore E, Bassett D . Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol. 2010; 7:113-40. DOI: 10.1146/annurev-clinpsy-040510-143934. View

Widaman K, Ferrer E, Conger R . Factorial Invariance within Longitudinal Structural Equation Models: Measuring the Same Construct across Time. Child Dev Perspect. 2010; 4(1):10-18. PMC: 2848495. DOI: 10.1111/j.1750-8606.2009.00110.x. View

10.

Wardlaw J, Smith E, Biessels G, Cordonnier C, Fazekas F, Frayne R . Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013; 12(8):822-38. PMC: 3714437. DOI: 10.1016/S1474-4422(13)70124-8. View

11.

Folstein M, Folstein S, McHugh P . "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975; 12(3):189-98. DOI: 10.1016/0022-3956(75)90026-6. View

12.

Jokinen H, Lipsanen J, Schmidt R, Fazekas F, Gouw A, van der Flier W . Brain atrophy accelerates cognitive decline in cerebral small vessel disease: the LADIS study. Neurology. 2012; 78(22):1785-92. DOI: 10.1212/WNL.0b013e3182583070. View

13.

Ziegler G, Dahnke R, Jancke L, Yotter R, May A, Gaser C . Brain structural trajectories over the adult lifespan. Hum Brain Mapp. 2011; 33(10):2377-89. PMC: 6870331. DOI: 10.1002/hbm.21374. View

14.

Zijdenbos A, Forghani R, Evans A . Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging. 2003; 21(10):1280-91. DOI: 10.1109/TMI.2002.806283. View

15.

Dickie D, Job D, Gonzalez D, Shenkin S, Ahearn T, Murray A . Variance in brain volume with advancing age: implications for defining the limits of normality. PLoS One. 2013; 8(12):e84093. PMC: 3868601. DOI: 10.1371/journal.pone.0084093. View

16.

Shaw M, Abhayaratna W, Sachdev P, Anstey K, Cherbuin N . Cortical Thinning at Midlife: The PATH Through Life Study. Brain Topogr. 2016; 29(6):875-884. DOI: 10.1007/s10548-016-0509-z. View

17.

Shaw M, Sachdev P, Anstey K, Cherbuin N . Age-related cortical thinning in cognitively healthy individuals in their 60s: the PATH Through Life study. Neurobiol Aging. 2016; 39:202-9. DOI: 10.1016/j.neurobiolaging.2015.12.009. View

18.

Panizzon M, Fennema-Notestine C, Eyler L, Jernigan T, Prom-Wormley E, Neale M . Distinct genetic influences on cortical surface area and cortical thickness. Cereb Cortex. 2009; 19(11):2728-35. PMC: 2758684. DOI: 10.1093/cercor/bhp026. View

19.

Wardlaw J, Bastin M, Valdes Hernandez M, Munoz Maniega S, Royle N, Morris Z . Brain aging, cognition in youth and old age and vascular disease in the Lothian Birth Cohort 1936: rationale, design and methodology of the imaging protocol. Int J Stroke. 2011; 6(6):547-59. DOI: 10.1111/j.1747-4949.2011.00683.x. View

20.

Wardlaw J, Allerhand M, Doubal F, Hernandez M, Morris Z, Gow A . Vascular risk factors, large-artery atheroma, and brain white matter hyperintensities. Neurology. 2014; 82(15):1331-8. PMC: 4001185. DOI: 10.1212/WNL.0000000000000312. View