Longitudinal changes in cortical thickness associated with normal aging

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Longitudinal changes in cortical thickness associated with normal aging

M. Thambisetty, J. Wan, A. Carass, Y. An, J.L. Prince and S.M. Resnick

Overview

Imaging studies of anatomic changes in regional gray matter volumes and cortical thickness have documented age effects in many brain regions, but the majority of such studies have been cross-sectional investigations of individuals studied at a single point in time. In this study, using serial imaging assessments of participants in the Baltimore Longitudinal Study of Aging (BLSA), we investigate longitudinal changes in cortical thickness during aging in a cohort of 66 older adults (mean age 68.78; sd. 6.6; range 60–84 at baseline) without dementia. We used the Cortical Reconstruction Using Implicit Surface Evolution CRUISE suite of algorithms to automatically generate a reconstruction of the cortical surface and identified twenty gyral based regions of interest per hemisphere. Using mixed effects regression, we investigated longitudinal changes in these regions over a mean follow-up interval of 8 years. The main finding in this study is that age-related decline in cortical thickness is widespread, but shows an anterior–posterior gradient with frontal and parietal regions, in general, exhibiting greater rates of decline than temporal and occipital. There were fewer regions in the right hemisphere showing statistically significant age-associated longitudinal decreases in mean cortical thickness. Males showed greater rates of decline in the middle frontal, inferior parietal, parahippocampal, postcentral, and superior temporal gyri in the left hemisphere, right precuneus and bilaterally in the superior parietal and cingulate regions. Significant nonlinear changes over time were observed in the postcentral, precentral, and orbitofrontal gyri on the left and inferior parietal, cingulate, and orbitofrontal gyri on the right.


Introduction

Neuroimaging methods to assess brain atrophy have been extensively applied to track the onset and progression of neurodegenerative conditions such as Alzheimer's disease (AD) [1], [2], [3], [4]). Longitudinal analyses have proven especially useful in delineating changes in brain volume during normal aging (Resnick et al., 2003), as well as in evaluating the temporal progression of neuropathology in AD (Driscoll et al., 2009 I. Driscoll, C. Davatzikos, Y. An, X. Wu, D. Shen, M. Kraut and S.M. Resnick, Longitudinal pattern of regional brain volume change differentiates normal aging from MCI, Neurology 72 (2009), pp. 1906–1913. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (10)[Driscoll et al., 2009], [Fox et al., 2000], [Jack et al., 2004], [Misra et al., 2009], [Mungas et al., 2005] and [Schott et al., 2005]). In older individuals, longitudinal decreases in gray and white matter volumes are widespread, and these declines are observed even in very healthy subjects during normal aging (Resnick et al., 2003). In AD, the rates of whole brain atrophy are several times greater than age-matched controls and differentiate the two groups with sensitivity greater than 90% (Fox and Freeborough, 1997). Medial temporal lobe structures such as the hippocampus and entorhinal cortex are especially vulnerable to early atrophic changes in AD ([Du et al., 2004], [Du et al., 2003] and [Jack et al., 2004]), and accelerated longitudinal tissue loss in these structures has been shown to precede the onset of cognitive impairment in subjects at risk (Fox et al., 1996).

We have recently shown that spatial patterns of regional atrophy provide better discrimination between MRI scans of cognitively normal and impaired individuals than a global or single regional atrophy measure alone (Davatzikos et al., 2008a). Moreover, these high-dimensional pattern classification approaches may have additional utility in the differentiation between sub-types of dementia (Davatzikos et al., 2008b). Subsequently, others have reported concordance between patterns of spatial atrophy detected in ante-mortem MRI studies and the distribution of neurofibrillary pathology in the brain at autopsy (Whitwell et al., 2008).

Recent studies suggest that the measurement of cortical thickness in vulnerable brain regions may also be a useful tool to detect perturbations in brain structure in cognitively normal subjects at risk for development of AD (Burggren et al., 2008) and in subjects with mild cognitive impairment (MCI) (Singh et al., 2006). Furthermore, decreases in cortical thickness appear to correlate well with severity of clinical impairment even in the earliest stages of AD (Dickerson et al., 2008). These data indicate that cortical thickness may represent a more sensitive, and perhaps complementary, measure of early pathological change than standard MRI-based volumetry in subjects at risk for subsequent cognitive decline. However, these studies, while suggestive, are cross-sectional and are therefore limited in their ability to address the effects of age-related changes in cortical thickness over time.

We have previously reported cross-sectional age differences and 4-year longitudinal age changes in mean cortical thickness within eight sulcal regions in a subset of 35 older adults from the Baltimore Longitudinal Study of Aging (BLSA) (Rettmann et al., 2006). In a cross-sectional study that also included young and middle-aged individuals, global cortical thinning was detectable by middle age with similar patterns of age differences in cortical thickness in both males and females (Salat et al., 2004). In the present study, we extend these investigations of cortical thickness through analysis of longitudinal changes in 66 older BLSA participants with up to eight serial imaging assessments. Furthermore, we determine whether age and sex influence rates of change in cortical thickness in older individuals during normal aging.


references

  1. ^ D.J. Callen, S.E. Black, F. Gao, C.B. Caldwell, and J.P. Szalai, "Beyond the hippocampus: MRI volumetry confirms widespread limbic atrophy in AD", Neurology, 57:669–1674, 2001.
  2. ^ S. De Santi, M.J. de Leon, H. Rusinek, A. Convit, C.Y. Tarshish, A. Roche, W.H. Tsui, E. Kandil, M. Boppana, K. Daisley, G.J. Wang, D. Schlyer, and J. Fowler, "Hippocampal formation glucose metabolism and volume losses in MCI and AD", Neurobiol. Aging, 22:529–539, 2001.
  3. ^ A.T. Du, N. Schuff, D. Amend, M.P. Laakso, Y.Y. Hsu, W.J. Jagust, K. Yaffe, J.H. Kramer, B. Reed, D. Norman, H.C. Chui, and M.W. Weiner, "Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer's disease", J. Neurol. Neurosurg. Psychiatry, 71:441–447, 2001.
  4. ^ H.S. Soininen, K. Partanen, A. Pitkanen, P. Vainio, T. Hanninen, M. Hallikainen, K. Koivisto, and P.J. Riekkinen Sr., "Volumetric MRI analysis of the amygdala and the hippocampus in subjects with age-associated memory impairment: correlation to visual and verbal memory", Neurology, 44:1660–1668, 1994.