Brain cortical characteristics of lifetime cognitive ageing - PubMed (original) (raw)

. 2018 Jan;223(1):509-518.

doi: 10.1007/s00429-017-1505-0. Epub 2017 Sep 6.

Simon R Cox 1 2 3, Mark E Bastin 4 5 6, David Alexander Dickie 4 5 6, Dave C Liewald 4 7, Susana Muñoz Maniega 4 5 6, Paul Redmond 7, Natalie A Royle 4 5 6, Alison Pattie 7, Maria Valdés Hernández 4 5 6, Janie Corley 7, Benjamin S Aribisala 4 6 8, Andrew M McIntosh 9, Joanna M Wardlaw 4 5 6, Ian J Deary 4 7

Affiliations

Brain cortical characteristics of lifetime cognitive ageing

Simon R Cox et al. Brain Struct Funct. 2018 Jan.

Abstract

Regional cortical brain volume is the product of surface area and thickness. These measures exhibit partially distinct trajectories of change across the brain's cortex in older age, but it is unclear which cortical characteristics at which loci are sensitive to cognitive ageing differences. We examine associations between change in intelligence from age 11 to 73 years and regional cortical volume, surface area, and thickness measured at age 73 years in 568 community-dwelling older adults, all born in 1936. A relative positive change in intelligence from 11 to 73 was associated with larger volume and surface area in selective frontal, temporal, parietal, and occipital regions (r < 0.180, FDR-corrected q < 0.05). There were no significant associations between cognitive ageing and a thinner cortex for any region. Interestingly, thickness and surface area were phenotypically independent across bilateral lateral temporal loci, whose surface area was significantly related to change in intelligence. These findings suggest that associations between regional cortical volume and cognitive ageing differences are predominantly driven by surface area rather than thickness among healthy older adults. Regional brain surface area has been relatively underexplored, and is a potentially informative biomarker for identifying determinants of cognitive ageing differences.

Keywords: Ageing; Cortex; Intelligence; MRI; Surface area; Thickness.

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Conflict of interest statement

The authors declare no competing financial or other conflicts of interests.

Figures

Fig. 1

Fig. 1

Schematic of parcellated cortical regions according to Desikan et al. (2006). FP frontal pole, DLPF dorsolateral prefrontal, IF inferior frontal, lOrbital lateral orbitofrontal, mOrbital medial orbitofrontal, CMF caudal middle frontal, RACing rostral anterior cingulate, CACing caudal anterior cingulate, TP temporal pole, ST superior temporal, MT middle temporal, IT inferior temporal, ParaHip parahippocampal, SP superior parietal, IP inferior parietal, PCing posterior cingulate, lOccipital lateral occipital, mOccipital medial occipital. See Supplementary Material for further information

Fig. 2

Fig. 2

a Regional associations between raw cortical surface area and thickness; b correlation matrix of associations between global volume, surface area, and thickness; c absence of association between cortical surface area and thickness in inferior temporal gyrus; d strongest negative association between cortical surface area and thickness is exhibited in the isthmus cingulate gyrus. For panels c and d, light blue points and solid regression lines (with shaded 95% confidence intervals) denote right sided measures, and dark blue points and broken regression lines indicate left sided measures

Fig. 3

Fig. 3

FDR-corrected associations between lifetime cognitive change between 11 and 73 (corrected for age at testing, sex) and brain cortical volume (top row), surface area (middle), and thickness (bottom). Magnitude (Pearson’s r) of association is reported for each regional cortical measure (corrected for age at scan, sex, and intracranial volume)

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