Normal age-related brain morphometric changes: nonuniformity across cortical thickness, surface area and gray matter volume?

Abstract

Normal aging is accompanied by global as well as regional structural changes. While these age-related changes in grey matter volume have been extensively studied, less has been done using newer morphological indices such as cortical thickness and surface area. To this end, we analyzed structural images of 216 healthy volunteers, ranging from 18 to 87 years of age, using a surface-based automated parcellation approach. Linear regressions of age revealed a concomitant global age-related reduction in cortical thickness, surface area and volume. Cortical thickness and volume collectively confirmed the vulnerability of the prefrontal cortex, whereas in other cortical regions such as in the parietal cortex, thickness was the only measure sensitive to the pronounced age-related atrophy. No cortical regions showed more surface area reduction than the global average. The distinction between these morphological measures may provide valuable information to dissect age-related structural changes of the brain, with each of these indices probably reflecting specific histological changes occurring during aging.

Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia

“…Overall, in controls, our findings mirror those of a larger dataset of a normal aging population, which described relative sparing of medial temporal lobe with respect to volume (and cortical surface parameters) (Lemaitre et al, 2012) . The weak effect of age observed in the schizophrenia patients is more difficult to interpret, not least because of the potential confound of medication in this relatively small sample.…”

“…Within this study, we tested for effects of age, not least because age-related changes in grey-matter volume are reported across VBM studies (Groelsch et al, 2010;Lemaitre et al, 2012). Overall, in controls, our findings mirror those of a larger dataset of a normal aging population, which described relative sparing of medial temporal lobe with respect to volume (and cortical surface parameters) (Lemaitre et al, 2012) .…”

A common brain network links development, aging, and vulnerability to disease

“…1). The first independent component (IC1) represented the expected dominant mode of variation showing the monotonic decrease of the whole gray matter with increasing age typically reported in large-scale lifespan studies (16). Spatially, it essentially described the standard deviation across all gray matter images (explaining ∼50% of the structural variance across participants) (SI Materials and Methods), and post hoc analysis revealed that age explained 90% of IC1 variance.…”

“…First, no constraint-spatial or age-related-was imposed on the data. Second, the method allowed us to detect more subtle modes of variation over and above other global components that dominate the intersubject variability, such as seen in IC1, and that are typically reported in lifespan studies (16). This decomposition approach thus revealed this IC4 component which, while explaining only a modest amount of the structural variance across all 484 healthy subjects (3%), had a strong relationship with age (as age explained 50% of the IC4 variance) and accounted for a substantial part of the spatial variance of Alzheimer's disease and adolescent-onset schizophrenia patterns of abnormalities (30% and 23%, respectively) (Fig.…”

Structural brain markers are differentially associated with neurocognitive profiles in socially marginalized people with multimorbid illness.

“…Second of all, cortical thickness and surface area follow different developmental trajectories (Wierenga, Langen, Oranje, & Durston, 2014). Further, across the adult lifespan, cortical thickness and surface area appear to be differentially affected by the aging process, with greater reductions in thickness relative to surface area and volume (Lemaitre et al, 2012;Storsve et al, 2014) and with varying predilections for brain regions across the frontal and temporal cortices (Hogstrom et al, 2013;Storsve et al, 2014 These measures can be contrasted with gyrification. Whereas cortical thickness and surface area are considered to be dynamic across the lifespan, the gross folding patterns of the cortex begin in utero during the last trimester of pregnancy, stabilize soon after birth, and subsequently undergo only subtle changes into adolescence (Armstrong, Schleicher, Omran, Curtis, & Zilles, 1995;White, Su, Schmidt, Kao, & Sapiro, 2010).…”