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 gray matter volume have been extensively studied, less has been done using newer morphological indexes, 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 indexes probably reflecting specific histological changes occurring during aging.

Introduction

Normal brain aging is characterized by an overall cerebral atrophy. This atrophy is associated with shrinkage of gray matter (GM) and white matter (WM) volumes and enlargement of the cerebrospinal fluid (CSF) spaces. As shown by postmortem studies, histological changes underlying these age-related macroscopic variations are more likely related to a loss of neuropil associated with a reduction of dendrites and synapses, and a loss of nerve-fibers, rather than related to a direct loss of neurons which is relatively limited with age (Pakkenberg et al., 2003, Peters et al., 1998). In vivo studies using Magnetic Resonance Imaging (MRI) have consistently shown an age-related decrease in GM and WM volume concomitant with an increase in CSF volume (Courchesne et al., 2000, Good et al., 2001, Lemaitre et al., 2005, Raz et al., 1997, Smith et al., 2007, Walhovd et al., 2005). Studies looking at the effect of age using either manual drawing of regions of interest (ROI) (Allen et al., 2005, Raz et al., 1997, Raz et al., 2004) or automated/semi-automated methods, such as voxel-based morphometry (VBM) (Good et al., 2001, Smith et al., 2007, Tisserand et al., 2002) have reported regionally variable vulnerability to aging across the whole brain (Raz and Rodrigue, 2006, Walhovd et al., 2005).

The literature related to regional heterogeneity in age-related changes in brain morphometry can be grouped into two categories based on the regions involved. The first reflects the vulnerability of the prefrontal region to aging (Tisserand and Jolles, 2003). This region has been shown to be one of the most affected with advancing age (Abe et al., 2008, Allen et al., 2005, Good et al., 2001, Raz et al., 1997, Tisserand et al., 2002). Prefrontal vulnerability to aging is supported by evidence of pronounced age-related decline in several cognitive processes, such as speed of processing, working memory, cognitive control, including response inhibition and interference suppression all of which depend on the integrity of the prefrontal cortex (Raz et al., 1998, West, 1996). The second set of regions repeatedly implicated in normal aging includes the hippocampus and the medial temporal lobe. Similar to the prefrontal cortex, numerous studies have found an age-related reduction in the volume of the hippocampal region (Bigler et al., 2002, Du et al., 2006, Walhovd et al., 2005). The medial temporal lobe has received much attention as it is involved in both normal as well as pathological aging (Fox et al., 2001, Killiany et al., 2002). The growing interest of the affect of aging on this region is also due to its important role in episodic memory, which has been shown to decline with age (Verhaeghen et al., 1993). Studies have also shown that atrophy of medial temporal structures predicts future memory decline in healthy aging (Rodrigue and Raz, 2004, Rusinek et al., 2003). Reliable characterization of the extent and rate of atrophy, therefore, is an important tool for understanding normal human aging and therapeutic interventions targeted at disorders associated with accelerated atrophy, such as the dementias, making a study of the methodology used to quantify atrophy in humans in vivo important.

VBM is a classical quantitative method based purely on a volumetric representation of the brain. The local amount of tissue is simply measured as the intensity within each voxel and can be influenced by local cortical folding as well as local cortical thickness. Computational advances in surface reconstruction of inner and outer cortical boundaries have allowed the development of surface-based morphometry (SBM) methods which provide more specific morphological measures, such as cortical thickness, surface area of the cortical mantle and cortical volume by-product of these two first measurements (Dale et al., 1999, Fischl and Dale, 2000, Fischl et al., 1999). SBM has been shown to be more sensitive to age-related decline than VBM which within the cortex provides a local measure of gray matter dependent on both cortical folding and thickness (Hutton et al., 2009). The SBM approach may lead to better understanding of global and regional structural changes in the brain associated with normal aging. Sorting out the different cortical features given by SBM on the entorhinal and the posterior parahippocampal cortices, Dickerson et al. (2009) reported that while volume reduction was seen in both normal aging and Alzheimer's disease (AD), surface and thickness reductions were exclusively associated with normal aging and AD respectively. Thus, it appears that exploring age-related changes in thickness and surface area, the two components of volume, independently may be more informative. In a whole brain study of cortical thickness, Salat et al. (2004) reported marked age-related reductions in prefrontal cortex thickness and relative conservation of temporal and parahippocampal cortical thickness. They also reported thinning of the precentral and calcarine cortices. These results have since been replicated by other groups measuring cortical thickness (Fjell et al., 2009, Ziegler et al., 2008).

To date, no study has directly assessed over the whole brain the effect of age on the different cortical measurements provided by a surface-based reconstruction approach. In the present study, we proposed to examine age-related cortical thickness, surface area, and cortical gray matter volume changes across all cortical regions in a sample of 216 healthy volunteers ranging from age 18 to 87 years.