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) <ref name="Callen2001">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.</ref><ref name="DeSanti2001>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.</ref><ref name="Du2001">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.</ref><ref name="Soininen1994">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.</ref>. Longitudinal analyses have proven especially useful in delineating changes in brain volume during normal aging <ref name="Resnick2003">S.M. Resnick, D.L. Pham, M.A. Kraut, A.B. Zonderman, and C. Davatzikos, "Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain", J. Neurosci., 23:3295–3301, 2003.</ref>, as well as in evaluating the temporal progression of neuropathology in AD <ref name="Driscoll2009">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, 72:1906–1913, 2009.</ref><ref name="Fox2000">N.C. Fox, S. Cousens, R. Scahill, R.J. Harvey, and M.N. Rossor, "Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer disease: power calculations and estimates of sample size to detect treatment effects", Arch. Neurol., 57:339–344, 2000.</ref><ref name="Jack2004">C.R. Jack Jr., M.M. Shiung, J.L. Gunter, P.C. O'Brien, S.D. Weigand, D.S. Knopman, B.F. Boeve, R.J. Ivnik, G.E. Smith, R.H. Cha, E.G. Tangalos, and R.C. Petersen,, "Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD", Neurology, 62:591–600, 2004.</ref><ref name="Misra2009">C. Misra, Y. Fan, and C. Davatzikos, "Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI", Neuroimage, 44:1415–1422, 2009.</ref><ref name="Mungas2005">D. Mungas, D. Harvey, B.R. Reed, W.J. Jagust, C. DeCarli, L. Beckett, W.J. Mack, J.H. Kramer, M.W. Weiner, N. Schuff, and H.C. Chui, "Longitudinal volumetric MRI change and rate of cognitive decline", Neurology, 65:565–571, 2005.</ref><ref name="Schott2005">J.M. Schott, S.L. Price, C. Frost, J.L. Whitwell, M.N. Rossor and N.C. Fox, "Measuring atrophy in Alzheimer disease: a serial MRI study over 6 and 12 months", Neurology, 65:119–124, 2005.</ref>. 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<ref name="Resnick2003"/>. 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%<ref name="FoxFreeborough1997">N.C. Fox and P.A. Freeborough, "Brain atrophy progression measured from registered serial MRI: validation and application to Alzheimer's disease", J. Magn. Reson. Imaging, 7:1069–1075, 1997.</ref>. Medial temporal lobe structures such as the hippocampus and entorhinal cortex are especially vulnerable to early atrophic changes in AD<ref name="Du2004">A.T. Du, N. Schuff, J.H. Kramer, S. Ganzer, X.P. Zhu, W.J. Jagust, B.L. Miller, B.R. Reed, D. Mungas, K. Yaffe, H.C. Chui, and M.W. Weiner, "Higher atrophy rate of entorhinal cortex than hippocampus in AD", Neurology, 62:422–427, .</ref><ref name="Du2003">A.T. Du, N. Schuff, X.P. Zhu, W.J. Jagust, B.L. Miller, B.R. Reed, J.H. Kramer, D. Mungas, K. Yaffe, H.C. Chui, and M.W. Weiner, "Atrophy rates of entorhinal cortex in AD and normal aging", Neurology, 60:481–486, 2003.</ref><ref name="Jack2004"/>, and accelerated longitudinal tissue loss in these structures has been shown to precede the onset of cognitive impairment in subjects at risk<ref name="Fox1996">N.C. Fox, E.K. Warrington, P.A. Freeborough, P. Hartikainen, A.M. Kennedy, J.M. Stevens, and M.N. Rossor, "Presymptomatic hippocampal atrophy in Alzheimer's disease. A longitudinal MRI study", Brain, 119(6):2001–2007, 1996.</ref>.

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<ref name="Davatzikos2008a">C. Davatzikos, Y. Fan, X. Wu, D. Shen, and S.M. Resnick, "Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging", Neurobiol. Aging, 29:514–523, 2008.</ref>. Moreover, these high-dimensional pattern classification approaches may have additional utility in the differentiation between sub-types of dementia<ref name="Davatzikos2008b">C. Davatzikos, S.M. Resnick, X. Wu, P. Parmpi, and C.M. Clark, "Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI", Neuroimage, 41:1220–1227, 2008.</ref>. 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<ref name="Whitwell2008">J.L. Whitwell, K.A. Josephs, M.E. Murray, K. Kantarci, S.A. Przybelski, S.D. Weigand, P. Vemuri, M.L. Senjem, J.E. Parisi, D.S. Knopman, B.F. Boeve, R.C. Petersen, D.W. Dickson, and C.R. Jack Jr., "MRI correlates of neurofibrillary tangle pathology at autopsy: a voxel-based morphometry study", Neurology, 71:743–749, 2008.</ref>.

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<ref name="Burggren2008">A.C. Burggren, M.M. Zeineh, A.D. Ekstrom, M.N. Braskie, P.M. Thompson, G.W. Small, and S.Y. Bookheimer, "Reduced cortical thickness in hippocampal subregions among cognitively normal apolipoprotein E e4 carriers", NeuroImage, 41:1177–1183, 2008.</ref> and in subjects with mild cognitive impairment (MCI)<ref name="Singh2006">V. Singh, H. Chertkow, J.P. Lerch, A.C. Evans, A.E. Dorr , and N.J. Kabani, "Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer's disease", Brain, 129:2885–2893, 2006.</ref>. Furthermore, decreases in cortical thickness appear to correlate well with severity of clinical impairment even in the earliest stages of AD <ref name="Dickerson2008">B.C. Dickerson, A. Bakkour, D.H. Salat, E. Feczko, J. Pacheco, D.N. Greve, F. Grodstein, C.I. Wright, D. Blacker, H.D. Rosas, R.A. Sperling, A. Atri, J.H. Growdon, B.T. Hyman, J.C. Morris, B. Fischl, and R.L. Buckner, "The cortical signature of Alzheimer's disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals", Cereb. Cortex, 19(3):497–510, 2008.</ref>. 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)<ref name="Rettmann2006">M.E. Rettmann, M.A. Kraut, J.L. Prince, and S.M. Resnick, "Cross-sectional and longitudinal analyses of anatomical sulcal changes associated with aging", Cereb. Cortex, 16:1584–1594, 2006.</ref>. 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 <ref name="Salat2004">D.H. Salat, R.L. Buckner, A.Z. Snyder, D.N. Greve, R.S. Desikan, E. Busa, J.C. Morris, A.M. Dale, and B. Fischl, "Thinning of the cerebral cortex in aging", Cereb. Cortex, 14:721–730, 2004.</ref>. 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.

Materials and Methods

Subjects

This analysis included 66 individuals in the neuroimaging substudy of the BLSA<ref name="Resnick2000" /> all of whom completed at least 6 annual follow up scans. Most of these participants (60 individuals) completed 8 annual follow up scans. The sample included 38 males and 28 females, with 37 and 26 right-handed males and females, respectively, ranging in age at baseline from 60 to 84 years. The following exclusionary criteria were applied to all entrants at baseline; central nervous system disease (epilepsy, stroke, bipolar illness, prior diagnosis of dementia according to diagnostic and statistical manual-III-R criteria (American Psychiatric Association, 1987), severe cardiovascular disease (myocardial infarction, coronary artery disease requiring angioplasty, or bypass surgery), severe pulmonary disease, or metastatic cancer. All participants remained free of dementia throughout the duration of their involvement in the present study. The research protocol was approved by the local Institutional Review Board, and written informed consent was obtained from all participants in conjunction with each neuroimaging visit.

Image Acquisition

The MRI acquisition of the brain volumes consisted of spoiled gradient echo (SPGR) volumetric magnetic resonance images, axially acquired, with the following parameters: TR = 35ms, TE = 5ms, flip angle = 45o, Image Matrix = 256 x 256, Field of View = 24cm, NEX = 1; voxel dimensions 0.9375 x 0.9375 x 1.5mm. Each data set was acquired on one of three GE Medical Systems Signa 1.5T scanners (GE Healthcare, Waukesha, WI).

Cortical Reconstruction

To reconstruct the cortical surface from acquired MR data, we used Cortical Reconstruction Using Implicit Surface Evolution (CRUISE)<ref name="Han2004" /><ref name="Xu2000" />. This method has undergone extensive validation which is detailed in <ref name="Tosun2006" />. The CRUISE processing pipeline begins with a fuzzy tissue classification that is robust to noise and inhomogeneity artifacts<ref name="Pham2001" />. The classification provides three membership functions representing the fraction of WM, GM, and cerebrospinal fluid (CSF) present within a given voxel of the image. The next step creates masks of the ventricles and subcortical GM structures (e.g., putamen, thalamus) within the WM membership functions using<ref name="BazinPham2007" />. Next, a triangulated surface mesh lying on the GM-WM boundary is computed as the 0.5 isolevel of the WM membership function. Noise, partial volume effects, and scanner artifacts cause the WM isosurface to contain “holes” and “handles” which are removed using a graph based correction algorithm<ref name="Han2002" />. Because of the partial volume effect, opposing GM banks within narrow sulci are difficult to distinguish as separate banks. To address this problem, CRUISE automatically edits the GM membership function to create thin gaps between the banks of narrow sulci<ref name="Han2004" /><ref name="Xu2000" />. The final stage in CRUISE uses a topology preserving geometric deformable surface model<ref name="Han2003" />, initialized at the GM/WM surface, which is driven toward the pial surface using forces derived from the edited GM membership function. Figure 1 show results of typical CRUISE processing of MRI data to generation of surfaces.

references

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