Selective increase of cortical thickness in high-performing elderly - Structural indices of optimal cognitive aging

University of Oslo, Department of Psychology, Norway.
NeuroImage (Impact Factor: 6.36). 03/2006; 29(3):984-94. DOI: 10.1016/j.neuroimage.2005.08.007
Source: PubMed


The aim of this study was to identify cortical areas important for optimal cognitive aging. 74 participants (20-88 years) went through neuropsychological tests and two MR sessions. The sample was split into two age groups. In each, every participant was classified as "high" or "average" on fluid ability tests and on neuropsychological tests related to executive function. The groups were compared with regard to thickness on a point-by-point basis across the entire cortical mantle. The old high fluid performers had thicker cortex than the average performers in large areas of cortex, while there was minimal difference between the groups of high vs. average executive function. Furthermore, the old group with high fluid function had thicker cortex than the young participants in the posterior cingulate and adjacent areas. Further analyses showed that the latter was a result of a complex aging pattern, differing between the two performance groups, with decades of cortical thickening and subsequent thinning.

Download full-text


Available from: Anders M Dale
  • Source
    • "However, results from the Baltimore Longitudinal Study of Aging provide evidence of longitudinal increases in cortical thickness in multiple frontal, temporal, and parietal regions (Thambisetty et al., 2010) despite an overall pattern of decreased cortical thickness with age in most regions. Another study reported selective increases in cortical thickness in older adults who performed well on fluid intelligence measures (Fjell et al., 2006), suggesting that sample differences in cognitive abilities may contribute to the different findings across studies. The mechanisms underlying age-related increases in cortical thickness are unclear, but increases in low-grade inflammation with age may contribute, as inflammatory markers have been associated with cortical thickening (Sörös, 2010;Krishnadas et al., 2013). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Age is associated with reductions in surface area and cortical thickness, particularly in prefrontal regions. There is also evidence of greater thickness in some regions at older ages. Non-linear age effects in some studies suggest that age may continue to impact brain structure in later decades of life, but relatively few studies have examined the impact of age on brain structure within middle-aged to older adults. We investigated age differences in prefrontal surface area and cortical thickness in healthy adults between the ages of 51 and 81 years. Participants received a structural 3-Tesla magnetic resonance imaging scan. Based on a priori hypotheses, primary analyses focused on surface area and cortical thickness in the dorsolateral prefrontal cortex, anterior cingulate cortex, and orbitofrontal cortex. We also performed exploratory vertex-wise analyses of surface area and cortical thickness across the entire cortex. We found that older age was associated with smaller surface area in the dorsolateral prefrontal and orbitofrontal cortices but greater cortical thickness in the dorsolateral prefrontal and anterior cingulate cortices. Vertex-wise analyses revealed smaller surface area in primarily frontal regions at older ages, but no age effects were found for cortical thickness. Results suggest age is associated with reduced surface area but greater cortical thickness in prefrontal regions during later decades of life, and highlight the differential effects age has on regional surface area and cortical thickness.
    Full-text · Article · Jan 2016 · Frontiers in Aging Neuroscience
  • Source
    • "In other domains such as memory recall (Walhovd et al. 2006), intelligence (Narr et al. 2007; Choi et al. 2008), or cognitive performance in the elderly (Fjell et al. 2006), neocortical thickness has been shown to predict individual differences in cognition. "
    [Show abstract] [Hide abstract]
    ABSTRACT: We report that preexisting individual differences in the cortical thickness of brain areas involved in a perceptual learning task predict the subsequent perceptual learning rate. Participants trained in a motion-discrimination task involving visual search for a "V"-shaped target motion trajectory among inverted "V"-shaped distractor trajectories. Motion-sensitive area MT+ (V5) was functionally identified as critical to the task: after 3 weeks of training, activity increased in MT+ during task performance, as measured by functional magnetic resonance imaging. We computed the cortical thickness of MT+ from anatomical magnetic resonance imaging volumes collected before training started, and found that it significantly predicted subsequent perceptual learning rates in the visual search task. Participants with thicker neocortex in MT+ before training learned faster than those with thinner neocortex in that area. A similar association between cortical thickness and training success was also found in posterior parietal cortex (PPC). © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail:
    Full-text · Article · Jan 2015 · Cerebral Cortex
  • Source
    • "As we get older, we decline in a broad range of behavioral measures, from simple processing speed (Salthouse, 1996) to overall performance on batteries of intelligence tests (Kaufman, Reynolds, & McLean, 1989). More recently there has arisen a growing body of evidence describing changes in neurophysiological variables that might account for these declines, such as gray matter reduction (Fjell et al., 2006)—which could be associated with neuron death, neuron atrophy, or dendritic shrinkage—myelin damage (Peters & Sethares, 2002; Sullivan, Adalsteinsson, & Pfefferbaum, 2006), loss of connectivity (Goh, 2011; Madden, Bennett, & Song, 2009), dedifferentiation of neural representations (Park, Carp, Hebrank, Park, & Polk, 2010; Park et al., 2004), and reduced neurotransmitter efficiency (Kaasinen et al., 2000). However, the challenge in testing such hypotheses is that when investigating these alternatives in elderly subjects, most if not all of these factors are present to some degree. "
    [Show abstract] [Hide abstract]
    ABSTRACT: We present a spiking neural model capable of solving a popular test of intelligence, Raven's Advanced Progressive Matrices (RPM). The central features of this model are its ability to dynamically generate the rules needed to solve the RPM and its biologically detailed implementation in spiking neurons. We describe the rule generation processes, and demonstrate the model's ability to use the resulting rules to solve the RPM with similar performance and error patterns to human subjects. Investigating the rules in more detail, we show that they successfully capture abstract patterns in the data, enabling them to generalize to novel matrices. We also show that the same model can be used to solve a separate reasoning task, and demonstrates the expected positive correlation in performance across tasks. Finally, we demonstrate the advantages of the biologically detailed implementation by using the model to connect behavioral and neurophysiological data. Specifically, we investigate two neurophysiological explanations of cognitive decline in aging: neuron loss and representational “dedifferentiation”. We show that manipulations to the model that reflect these neurophysiological hypotheses result in performance changes that match observed human behavioral data.
    Full-text · Article · Feb 2014 · Intelligence
Show more