www.elsevier.com/locate/ynimg NeuroImage 26 (2005) 900 – 911 Age- and sex-related effects on the neuroanatomy of healthy elderly Herve Lemaıtre,a Fabrice Crivello,a Blandine Grassiot,a Annick Alperovitch,b ´ ˆ ´ b a,c,d,T Christophe Tzourio, and Bernard Mazoyer a Groupe d’Imagerie Neurofonctionnelle, UMR 6194, CNRS, CEA, Universites de Caen et Paris 5, GIP Cyceron, BP5229, F-14074 Caen, France ´ INSERM U360, Hopital Pitie-Salpetriere, 75013 Paris, France ˆ ´ ˆ ` c Unite IRM, CHU de Caen, 14000 Caen, France ´ d Institut Universitaire de France, 75005 Paris, France b Received 16 December 2004; revised February 2005; accepted 24 February 2005 Available online 13 April 2005 Effects of age and sex, and their interaction on the structural brain anatomy of healthy elderly were assessed thanks to a cross-sectional study of a cohort of 662 subjects aged from 63 to 75 years T1- and T2weighted MRI scans were acquired in each subject and further processed using a voxel-based approach that was optimized for the identification of the cerebrospinal fluid (CSF) compartment Analysis of covariance revealed a classical neuroanatomy sexual dimorphism, men exhibiting larger gray matter (GM), white matter (WM), and CSF compartment volumes, together with larger WM and CSF fractions, whereas women showed larger GM fraction GM and WM were found to significantly decrease with age, while CSF volume significantly increased Tissue probability map analysis showed that the highest rates of GM atrophy in this age range were localized in primary cortices, the angular and superior parietal gyri, the orbital part of the prefrontal cortex, and in the hippocampal region There was no significant interaction between bSexQ and bAgeQ for any of the tissue volumes, as well as for any of the tissue probability maps These findings indicate that brain atrophy during the seventh and eighth decades of life is ubiquitous and proceeds at a rate that is not modulated by bSexQ D 2005 Elsevier Inc All rights reserved Keywords: Brain; Aging; Sex; Voxel-based morphometry; MRI Introduction The increase of life expectancy during the last century has led to a growing number of dementia cases in the aging population Prevalence studies suggested that, in 2000, the number of persons with Alzheimer’s disease in the United States was 4.5 million and predicted to rise to 13.2 million by 2050 (Hebert et al., 2003) This dementia incidence upsurge has reinforced the importance of T Corresponding author Groupe d’ Imagerie Neurofonctionnelle ´ UMR6194, CNRS, CEA, Universites de Caen et Paris 5, GIP Cyceron, BP5229, F-14074 Caen, France Fax: +33 231 470 271 E-mail address: mazoyer@cyceron.fr (B Mazoyer) Available online on ScienceDirect (www.sciencedirect.com) 1053-8119/$ - see front matter D 2005 Elsevier Inc All rights reserved doi:10.1016/j.neuroimage.2005.02.042 characterizing the mechanisms of the human brain aging during the seventh and eighth decades of life Indeed, a better understanding of the normal neuroanatomical aging could be of high interest for dissociating processes specifically associated with pathologic brain changes from those associated to normal changes During the past two decades, several studies have investigated the effect of aging on the human brain More often than not, these studies investigated cerebral changes over life span (from 20 up to 80 years) Their findings have led to a large consensus regarding the global morphological changes due to aging First, postmortem studies have described, starting at the fourth decade, a decrease of the brain weight and an increase of the cerebrospinal fluid volume (CSF) (Dekaban, 1978) Then, studies using Magnetic Resonance Imaging (MRI) have confirmed and refined these findings by showing that the gray matter (GM) volume starts to decrease earlier in the life (at the end of the first decade), whereas the white matter (WM) volume starts to decrease at the fourth decade (Courchesne et al., 2000; Pfefferbaum et al., 1994) There seems to exist, however, a large variability in the way the different brain areas are reacting to aging These selective agerelated neuroanatomical changes could be explained by several aging theories One of them is based on brain ontogeny and phylogeny and states that the age-related changes of the various cerebral regions follow a time pattern that is the reverse sequence of their maturation during development (Braak et al., 1999; Raz et al., 1997) According to this model, late maturating unimodal or high-order heteromodal associative cortices are the first and the most age-sensitive, while early maturating primary areas are subject to later and smaller age-related changes In agreement with this model, several studies have specifically focused on associative cortices and have shown a preferential atrophy of the regions belonging to the prefrontal cortex (Coffey et al., 1992; Jernigan et al., 2001; Salat et al., 2001) Other studies have reported focal atrophy localized into the temporal lobe (Bigler et al., 2002) including the hippocampus (Raz et al., 2004b; Tisserand et al., 2000) However, other aging hypotheses based on the dysfunction of the principal neurotransmitter systems could also explain the affliction of these cerebral regions in healthy elderly H Lemaıtre et al / NeuroImage 26 (2005) 900–911 ˆ subjects Indeed, the age-related decline of dopaminergic (Volkow et al., 2000) and cholinergic (Podruchny et al., 2003) systems, which project on the frontal and limbic structures, respectively, could be associated to this cerebral pattern of atrophy Meanwhile, using whole brain exploratory approaches, several other studies were aimed at identifying other potential targets of normal aging These studies have found an age-related atrophy of associative cortices but, more surprisingly, an implication of several primary cortices normally considered as spared by aging (Good et al., 2001; Sowell et al., 2003; Van Laere and Dierckx, 2001) For example, Salat et al (2004) found that regional cortical thinning with age (which has been found highly correlated with regional GM density, Narr et al., in press) is widespread over large parts of the cortex including motor, auditory, and visual primary areas, as well as association cortices such as the inferior lateral prefrontal cortex Interestingly, a few recent studies have specifically focused on the seventh and subsequent decades, a period of life where maturation processes no longer interfere with aging, and have reported a similar pattern of regional age-related atrophy (Resnick et al., 2003; Tisserand et al., 2004) Beside age, sex is another major player of the inter-individual brain morphology variability and several studies have been interested in the potential impact of sex on age-related brain changes As a rule, these studies concluded that men exhibited larger age-related brain atrophy and CSF increase than women over the entire life span (Coffey et al., 1998; Gur et al., 1999; Yue et al., 1997), this effect being enhanced in the frontal and temporal lobes (Gur et al., 2002; Murphy et al., 1996; Raz et al., 1997, 2004a) Conversely, reports of regional age-related atrophy higher in women than in men are rare, although larger reduction of gray matter in women have been reported in the visual cortex (Raz et al., 1993), the parietal lobes and the hippocampus (Murphy et al., 1996) Actually, as the majority of these studies were based on large age range cohorts, little is actually known about the effect of sex on age-related changes in brain structure of healthy elderly subjects In the present study, we have investigated this issue by taking advantage of a large epidemiology study dealing with vascular aging for which a large cohort of subjects in their seventh or eighth decades were recruited and examined with MRI 901 At 4-year follow-up, MRI examination was proposed to all subjects and 88% of them agreed to participate This sub-sample did not differ from the rest of the cohort in terms of age, sex ratio, hypertension, and cognitive performances Due to financial limitations, MRI could be performed in 845 subjects only, among whom 32 had to be excluded because of the poor technical quality of their scans, and 11 others because of previous history of stroke as confirmed by a neurologist Left with a sample of 802 subjects (471 women, 331 men), we randomly selected 331 women in order to obtain groups of men and women with identical size Basic demographic statistics are presented in Table At the time of their MRI, the 331 men and 331 women did not differ for age However, the men group had a higher mean level of education, a larger proportion of hypertensive subjects, and a smaller proportion of right-handed subjects, than women ANCOVA reveals no effect of Sex (P = 0.14) or Age (P = 0.29) on the cohort MMSE scores Rather, we found a significant bSex by AgeQ interaction (P = 0.0012), the age-related decrease of MMSE being larger in women than in men MRI imaging MRI acquisition MR images were acquired between November 1995 and September 1997, using the same machine (1.0 T Magnetom Expert, Siemens, Erlangen) and a standardized acquisition protocol Exclusion criteria were conventional: (1) carrying a cardiac pacemaker, valvular prosthesis, or other internal electrical/magnetic device; (2) history of neurosurgery or aneurysm; (3) presence of metal fragments in the eyes, brain, or spinal cord; (4) claustrophobia MRI acquisition was performed after the biological/psychological testing The MRI acquisition which consisted of a three-dimensional (3D) high-resolution T1-weighted brain volume was first acquired using a 3D inversion recovery spoiled-gradient echo sequence (3D IR-SPGR; TR = 97 ms; TE = ms; TI = 300 ms; sagittal acquisition) The 3D volume matrix size was 128  256  256, with a 1.4  0.89  0.89 mm3 voxel size T2- and PD- (proton density) weighted brain volumes were also acquired during the same sequence using a 2D axial turbo spin-echo sequence with two echo times (TR = 3500 ms; TE1 = 15 ms; TE2 = 85 ms; 23 cm field of view) T2 and PD acquisitions consisted of 26 contiguous Methods Subjects The sample of subjects who participated to the present protocol is a sub-sample of the EVA (Epidemiology of Vascular Aging) cohort (n = 1389), a longitudinal study on vascular aging and cognitive decline in healthy elderly subjects, the characteristics of which have been described elsewhere (Dufouil et al., 2001) Subjects, born between 1922 and 1932, were recruited from electoral rolls in Nantes (West of France) from June 1991 to June 1993 All participants gave their written informed consent to the EVA study protocol, which was approved by the Ethic committee of the Kremlin-Bicetre hospital A number of biological and ˆ sociological parameters were collected from each subject including age, sex, hypertension, education level (number of schooling years), and handedness Subject’s global cognitive performances were assessed using the Mini-Mental State Examination (MMSE) (Folstein et al., 1985) Table Sample characteristics Men Number of subjects Age (years) Education level (years) Hypertensive subjects (%) Right-handed subjects (%) MMSE score (max = 30) Women P value 331 69.52 (3.09) [63.77, 75.60] 11.3 (3.8) [4, 20] 331 69.56 (2.95) [63.69, 75.47] 10.2 (3.0) [4, 20] 0.00051y 48.0% 37.4% 0.0075z 89.7% 95.1% 0.012z 27.7 (2.0) 27.4 (2.0) 0.14# 0.87# Mean (standard deviation); [range]; MMSE: Mini-mental state examination # Student’s t test y Wilcoxon rank sum and signed rank test z Pearson’s chi-squared test 902 H Lemaıtre et al / NeuroImage 26 (2005) 900–911 ˆ 5-mm-thick axial slices (13.0 cm axial field of view), having a 256  256 matrix size, and a 0.89  0.89 mm2 in-plane resolution Positioning in the magnet was based on a common landmark for all subjects, namely, the orbito-meatal line, so that the entire brain, including cerebellum and mid-brain, was contained within the field of view of both T1 and T2/PD acquisitions Data sets (T1, T2, and PD) were readily reconstructed, visually checked for major artifacts, before further analysis Finally, only the T1- and T2weighted images were used in the framework of our study Image processing The T1- and T2-weighted images of each subject were first aligned to each other (Woods et al., 1992) and then analyzed with SPM99 (http://www.fil.ion.ucl.ac.uk/spm/) We used the so-called optimized Voxel-Based Morphometry (VBM) protocol (Good et al., 2001) that we slightly modified in two ways in order to account for the structural characteristics of aged brains (see Fig 1) First, GM, WM, and CSF templates specific to our database (EVA priors) were used for tissue segmentation Second, segmentation of the CSF class was refined using T2 images Creating EVA priors Tissue templates specific to our database (EVA priors) were created using a sub-sample of 120 randomly selected subjects (60 men and 60 women) matched for age, hypertension frequency, handedness, and education level with the entire group Each of the 120 subject T1 volumes was segmented using the MNI priors available in SPM, providing 120 individual GM, WM, and CSF tissue maps The 120 GM images were then non-linearly spatially normalized to the GM MNI template (7   non-linear basis functions in the three orthogonal directions) The normalization parameters (deformation fields) obtained from the GM warping step were then reapplied to the WM and CSF partition images, the resulting images being further interpolated as mm3 isotropic voxel volume Individual GM, WM, and CSF image volumes were further smoothed with an 8-mm full-width at half-maximum isotropic Gaussian kernel Finally, EVA priors were obtained by computing GM, WM, and CSF probability maps based on the set of 120 GM, WM, and CSF partition volumes, respectively Processing individual images of the 662 subjects cohort Each subject T1 volume image was first segmented using MNI priors in order to obtain a GM partition image in his native space This GM volume was then non-linearly spatially normalized to the EVA GM template using   nonlinear basis functions in the three orthogonal directions Corresponding normalization parameters (deformation fields) were reapplied to the subject original brain T1 and T2 images, the resulting images being further interpolated (1 mm3 isotropic voxel) The resulting normalized T1 volume was then segmented using the EVA priors thereby providing GM, WM, and CSF partition images (see Fig 1, left side) Optimizing the CSF partition image Obtaining a good segmentation of the CSF compartment requires an accurate definition of its borders Accordingly, we proceeded to a multi-spectral segmentation of both the T1 and T2 volumes, again using the EVA priors An optimized CSF partition image was obtained by subtracting the GM and WM partition images provided by the first mono-spectral T1 segmentation from the sum of the GM, WM, and CSF partition images provided by Fig Flow chart of the image processing protocol The blue part is equivalent to the optimized VBM protocol proposed by Good et al (2001), whereas the red part describes how T2-weighted MR images were incorporated in order to optimize the CSF tissue segmentation MRI: whole brain images in their native space GM, WM, and CSF: gray matter, white matter, and cerebrospinal fluid tissue images, respectively The prefixes bnQ and bmQ denote images in the stereotactic space after normalization and modulation, respectively T1 and T1T2 indices refer to mono-spectral (T1) and multi-spectral (T1 and T2) segmentations, respectively The figure shows four images corresponding to the same axial slice of the same subject: nT1 and nT2 (gray-scaled) are the normalized T1- and T2-weighted images, respectively, whereas nCSFT1 and nCSFOpt (color-scaled) are the CSF tissue images without and with optimization, respectively The skull inner and outer limits were derived as iso-intensity contours in the normalized T2 image (nT2) and superimposed on both CSF tissue images this second segmentation (see Fig 1, right side) In summary, the final CSF partition images were derived from a multi-spectral segmentation combining T1 and T2 volumes, while the final GM and WM partition images were derived from the segmentation of the T1 volumes only (see Fig for a detailed description of the pipeline procedure) The improvement provided by this modified CSF segmentation scheme was quantified by comparing the absolute CSF and total intracranial volumes (see below for tissue volumes estimation) obtained either without or with T2 image inclusion in the segmentation process Image modulation Finally, we applied a so-called bmodulationQ to each cerebral partition image, adjusting their voxel intensities for the strength of the deformation they were submitted to during the spatial H Lemaıtre et al / NeuroImage 26 (2005) 900–911 ˆ 903 Tissue partition maps ANCOVA was applied to modulated and smoothed GM, WM, and CSF probability maps as implemented in SPM (Friston et al., 1995), using two different intensity normalizations: voxels of each tissue partition map were scaled to either TIV value, adjusting for head size, or to absolute cerebral compartment volume, searching for local variations within each cerebral compartment A map-wise threshold of P b 0.05 corrected for multiple comparisons was used for each tissue map analysis Results A brain atlas for healthy elderly Fig shows selected slices through the average T1 volume, and the GM, WM, and CSF probability maps computed over the sample of 662 subjects Such maps constitute a probabilistic brain atlas in healthy elderly human subjects aged between 63 and 75 years GM and WM atrophy, and CSF enlargement, are the most prominent features of these maps when compared with their counterparts in young healthy adults As such maps could be of value for others working with anatomical/functional brain images of aged subjects, they will be made available to the neuroimaging community on the Internet Evaluation of the optimized CSF tissue segmentation Fig Selected slices through the average (n = 662) normalized T1 volumes and corresponding gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) probability maps The gray scale applies to GM, WM, and CSF tissue images and gives the probability for a voxel to belong to the considered tissue The location of the five axial slices is shown on a three-dimensional rendering of the average T1 volume (z = 49, 31, 15, À1, and À17 mm from the biÀcommissural plane, respectively) normalization process (Good et al., 2001) Modulation preserves the subject’s original tissue quantity after its transfer to the reference space Finally, all cerebral partition images were smoothed with a 12-mm full-width at half-maximum isotropic Gaussian kernel Volume estimation For each subject, GM, WM, and CSF volumes were computed as the integral of the voxel intensities over the corresponding modulated tissue partition image Total Intracranial Volume (TIV) was computed as the sum of the GM, WM, and CSF volumes, and fractional cerebral compartment volumes as the ratios of tissue absolute volumes to TIV Statistical analysis Volumetry TIV and GM, WM, CSF absolute and fractional volumes were analyzed using the same ANCOVA design, with bSexQ as the main factor, bAgeQ as the covariate, including a bSex by AgeQ interaction Significance level set at P b 0.05 for each tissue volume analysis Slopes of the linear regressions of cerebral compartment volumes with age were estimated separately for men and for women Using a multi-spectral rather than a mono-spectral segmentation led to smaller average volumes both for the CSF (357 F 58 cm3 vs 494 F 68 cm3, mean F SD, n = 662) and for TIV (1371 F 132 cm3 vs 1515 F 134 cm3) It also gave a larger age-related CSF volume increase (3.6 cm3/year vs 2.3 cm3/year) and a smaller agerelated TIV decrease (0.4 cm3/year vs 1.7 cm3/year) This last finding constitutes a clear indication that including T2 images improved the CSF segmentation since one cannot expect TIV to significantly decrease over such a short age range In the subsequent results, we will thus only consider the CSF volume obtained with the multi-spectral segmentation only Table Sex and age effects and bSex by AgeQ interaction on absolute cerebral compartment volumes Men TIV Slope GM Slope WM Slope CSF Slope Women Sex effect ( P value) Age effect ( P value) Sex by Age ( P value) 1454 (107) À0.059ns 575 (44) À1.73* 491 (46) À1.67* 387 (51) 3.34** 1288 (100) À0.28ns 532 (38) À2.67** 428 (43) À1.62* 327 (49) 4.01** b0.001 0.90 0.93 b0.001 b0.001 0.37 b0.001 0.0043 0.97 b0.001 b0.001 0.60 Mean (standard deviation) of absolute cerebral compartment volumes (in cm3) in men and women (upper line) and slopes of their regression on age (in cm3/year) with their significance levels (lower line): ns: non-significant, *P b 0.05, **P b 0.001 The last three columns give the P values of the Sex and Age effects as well as the bSex by AgeQ interaction of the ANCOVA analysis TIV: total intracranial volume; GM: gray matter; WM: white matter; CSF: cerebrospinal fluid 904 H Lemaıtre et al / NeuroImage 26 (2005) 900–911 ˆ Table Sex and age effects and bSex by AgeQ interaction on fractional cerebral compartment volumes Men GM fraction 0.396 (0.021) Slope À0.12* WM fraction 0.337 (0.016) Slope À0.11** CSF fraction 0.266 (0.027) Slope 0.23** GM/WM 1.18 (0.08) Slope 0.00042ns Women Sex effect Age Sex by ( P value) effect Age ( P value) ( P value) 0.414 (0.021) À0.20** 0.332 (0.017) À0.11** 0.253 (0.028) 0.32** 1.25 (0.09) À0.0015ns b0.001 b0.001 0.11 b0.001 b0.001 0.89 b0.001 b0.001 0.19 b0.001 0.65 0.38 Mean (standard deviation) of fractional cerebral compartment volumes (relative to TIV) and of the gray to white matter ratio in men and women (upper line) and slopes of their regression on age (in %/year) with their significance levels (lower line): ns: non-significant, *P b 0.01, **P b 0.001 The last three columns give the P values of the Sex and Age effects as well as the bSex by AgeQ interaction of the ANCOVA analysis GM: gray matter; WM: white matter; CSF: cerebrospinal fluid Volumetric data Results regarding absolute and fractional brain tissue volumes are shown in Tables and 3, respectively As expected, TIV, GM, WM, and CSF absolute volumes were larger in men than in women There was no bSex by AgeQ interaction for any of the absolute cerebral compartment volumes For the 662 subjects, TIV was found to be unaffected by age, while GM (2.2 cm3/year) and WM (1.7 cm3/year) volume significantly decreased with age, their decreases being compensated by an equivalent increase of CSF volume (3.6 cm3/year) Note, however, that the rate of GM loss was somewhat smaller in men than in women whereas the rate of WM loss was identical for both sexes Nevertheless, the GM to WM volume ratio did not vary with age and stayed higher in women (1.25) than in men (1.18) The GM fraction was found higher in women than in men, whereas both the WM and CSF fractions were higher in men than in women There was a significant effect of age on all cerebral compartment fractions, with no bSex by AgeQ interaction for any of them but, again, the GM fraction decrease was somewhat larger in women (0.20% per year) than in men (0.12% per year) For WM, men and women exhibited the same rate of fractional volume decrease (0.11% per year) The GM and WM fraction losses were compensated by a rate of CSF fraction increase of 0.23% per year for men and of 0.32% per year for women Voxel-based morphometry Adjusted either by TIV or by cerebral compartment volumes, the regional regression coefficients with age for the GM, WM, and CSF compartments were not statistically different between men and women (P b 0.05 corrected for multiple comparisons) As no bSex by AgeQ interaction was found in any of the three compartment maps, age-related effects on tissue distribution are presented for the entire sample of 662 subjects Note that a trend for a larger (albeit not significant) age effect in women was observed in the GM and CSF TIV-adjusted maps, similar to what was reported above for cerebral compartment volumes when expressed as TIV fractions However, this trend vanished when the tissue maps were adjusted for tissue volumes rather than for TIV Age-related changes in tissue probability maps corrected for TIV The age-related variations of GM, WM, and CSF probability maps corrected for TIV are depicted in the Fig The rate of GM loss was highest in primary cortices, including the Heschl’s gyrus, the cortex surrounding the Calcarine fissure and the preand postcentral gyri Rates of GM losses were also very high in the angular and superior parietal gyri, in the orbital part of the prefrontal cortex, and in the hippocampal region By contrast, the rate of GM losses appeared marginal in areas such as the lateral and medial surfaces of the superior frontal gyri, the median cingulate gyrus, and the inferior temporal gyrus Interestingly, we found positive regression slopes with age in the subcortical gray nuclei bordering the third and lateral ventricles, namely, the caudate nuclei, putamen, pallidum, and thalami For the white matter, the general pattern brought out high WM losses in the corpus callosum and in the major pathways surrounding the lateral ventricles such as the anterior and posterior callosal fibers, the optical tracts, and the posterior limb of the internal capsule By contrast, smaller WM fasciculi, close to the cortical surface, did not show any significant variations with age Finally, increase of CSF with age was highest in the third and lateral ventricles, and in the interhemispheric and Sylvian fissures Age-related changes in tissue probability maps corrected for absolute cerebral compartment volumes The effect of age on GM, WM, or CSF maps corrected for their absolute tissue volumes is summarized in Fig and Table Variability of cranial vault was implicitly accounted for in these analyses since each global cerebral compartment volume was highly correlated with TIV (r = 0.81, 0.91, and 0.77 for GM, WM, and CSF, respectively, P b 0.001 in all three cases) The results show, for each cerebral compartment, the regions in which the agerelated rate of local volume variation exceeds that of the global tissue volume Significantly higher reductions of GM with age were found in the Heschl’s, precentral, postcentral, middle frontal (orbital part), and superior parietal gyri, as well as in the hippocampus Meanwhile, the rate of WM losses was significantly higher in the bundle of fibers running alongside the lateral ventricles and in the genu of the corpus callosum By contrast, the increase of CSF was homogeneous over the entire compartment as no significant regional age-related increase was found in the CSF map of subjects when adjusted for their CSF global volume Discussion Enhanced CSF compartment using multi-spectral segmentation in the elderly Including T2 images in the tissue segmentation procedure resulted in a better characterization of the outer border of the CSF compartment and a more realistic CSF probability values in the ventricles and major sulci This was expected since T2 images exhibit a good contrast between the subarachnoidal CSF and the dura mater adhering to the inner skull surface However, the larger H Lemaıtre et al / NeuroImage 26 (2005) 900–911 ˆ 905 Fig Age-related gray matter, white matter, and cerebrospinal fluid volume regression maps (after correction for total intracranial volume) Regression maps are superimposed onto their corresponding tissue probability maps and displayed without statistical threshold The hot (green to red) and cold (green to blue) color scales represent the negative and positive slopes with age, respectively The location of the axial slices is shown on a three-dimensional rendering of the average T1 volume [z = 59, 49, 39, 31, 23, 15, 7, 0, À9, and À17 mm from the bi-commissural plane (pink box), respectively] L: Left; R: Right slice thickness of the original T2 images (5 mm) as compared to the original T1 images (1.4 mm) induced an important partial volume effect, which affected the quality of the multi-spectral segmentation For this reason, multi-spectral segmentation was only used to classify the voxels belonging to the CSF compartments, while the GM and WM compartments were obtained with a mono-spectral segmentation of T1 images Note that the CSF volumes so estimated are consistent both with another in vivo study that also used a multi-spectral segmentation (Courchesne et al., 2000) and with postmortem data (Blinkov and Glezer, 1968) Actually, mono-spectral segmentation leads to an underestimation of the CSF volume in the oldest subjects (i.e., those who present the largest atrophy) Consequently, when estimated using a monospectral segmentation, TIV appears to decrease with age in the elderly while it stays roughly constant when estimated with a multi-spectral segmentation Note that a previous study using the same optimized VBM approach and T1-weighted image segmentation only, also reported a linear decline of TIV with age for men but not for women (Good et al., 2001) As the age of the subjects of this latter study spread over seven decades, these authors interpreted the TIV decrease as a secular trend of increasing cranial vault over the last century Obviously, such an explanation does not hold for our findings since they were observed over a single decade (cranial perimeter and height of our subjects did not vary with age) The fact that TIV decrease with age could be corrected by including T2-weighted images in the segmentation leads us to conclude that it was an artifact of the mono-spectral segmentation Age effects in cross-sectional versus longitudinal studies Before discussing our results in details, it is also worthwhile discussing the intrinsic limitations of cross-sectional studies, such as ours, where age effects on neuroanatomy are measured at a single time across a sample of subjects having different ages The limited age range of our cohort does limit potential secular effects on brain volumes that could severely bias cross-sectional studies performed on the entire span of life (such as the increase in the height, and as a result, the TIV, of subjects born between 1920 and 1990, for example) A short age range does not, however, reduce the between-subject variability and statistical power loss that characterize cross-sectional studies and make longitudinal studies preferable Conversely, very large samples are more manageable in cross-sectional than in longitudinal studies, which can compensate the statistical power difference between the two designs For instance, Davatzikos and Resnick (2002) found that age effects on 906 H Lemaıtre et al / NeuroImage 26 (2005) 900–911 ˆ Fig Areas of age-related reductions in gray matter and white matter after correction for global tissue volume Student’s t maps are superimposed onto their corresponding tissue probability maps and displayed at a P b 0.05 significance level corrected for multiple comparisons The x and z coordinates (in mm) give the slice locations in the stereotactic space L: left; R: right white matter connectivity in elderly were significant both in crosssectional and longitudinal studies, but that longitudinal findings were more pronounced than cross-sectional ones Amazingly, the same authors performed a longitudinal study of 116 healthy elderly subjects aged from 59 to 85 years, and did not find any detectable changes in global or regional brain volumes over year, while they found rates of tissue loss of 1.4 cm3/year and 1.9 cm3/year for the GM and WM, respectively, using a cross-sectional analysis on the same sample (Resnick et al., 2000) These authors invoked, here, the limits of their image processing accuracy when only subtle cerebral changes are expected over a short period of time Note, however, that very short longitudinal investigation can be sufficient to highlight neuroanatomical differences in pathological processes such Alzheimer’s disease (Fox et al., 2001) Interestingly, reanalyzing 92 subjects among their initial 116 ones over a 4-year period, Resnick et al (2003) found a 71% and 63% increase of the GM and WM rate of atrophy as compared to the rates they estimated in their previous cross-sectional analysis, showing that when a larger period of time (3 to years) separates two MRI examinations of a longitudinal study, higher age-related effects on brain atrophy rates are found in longitudinal analysis as compared to cross-sectional ones rate of brain tissue loss we found was somewhat different from that of studies based over the entire life span Postmortem studies have reported an age-related decrease of brain volume close to cm3/ year between the third and eighth decades (Dekaban, 1978; Pakkenberg and Gundersen, 1997) In addition, the average of atrophy rates reported by MRI studies performed over the entire life span sets at 2.5 cm3/year (range from 1.5 to 4.2 cm3/year) (Blatter et al., 1995; Good et al., 2001; Guttmann et al., 1998; Jernigan et al., 2001; Liu et al., 2003; Van Laere and Dierckx, 2001) Actually, according to some authors, the GM volume linearly decreases starting from the second decade, whereas the Table Regional gray matter reduction with age Anatomical label Frontal L L R Parietal L R L Temporal L R L Global age-related cerebral volume changes in healthy elderly We observed a loss of 3.9 cm3/year of brain tissue (GM plus WM), in agreement with previous studies dealing with elderly subjects (Liu et al., 2003; Resnick et al., 2000, 2003) In fact, the latter studies reported a loss of 4.4 cm3/year on average (range from 3.2 to 5.4 cm3/year), a value very close to ours However, the x Limbic Precentral gyrus Middle frontal gyrus, orbital part Middle frontal gyrus, orbital part Postcentral gyrus Postcentral gyrus Superior parietal gyrus Heschl’s gyrus Heschl’s gyrus Hippocampus y z t value À53 À45 10 52 43 À2 5.4 5.2 43 47 À7 4.6 À56 50 À32 À13 À12 À70 46 36 53 6.2 5.4 5.7 À43 42 À32 À16 À20 À40 À2 10 À2 6.0 6.0 5.5 t value: Student’s t value (P b 0.05 corrected for multiple comparisons); x y z: MNI space stereotactic coordinates in mm; L: left; R: right H Lemaıtre et al / NeuroImage 26 (2005) 900–911 ˆ WM volume increases until the fourth decade and, then, decreases in the following decades (Courchesne et al., 2000; Jernigan et al., 2001) Thus, one should expect the annual rate of brain tissue loss to increase in elderly Our findings are consistent with this hypothesis and confirm that brain shrinkage is a non-linear phenomenon over the life span that accelerates after the sixth decade We found that GM and WM almost equally contributed to brain shrinkage, no significant difference being observed between the annual atrophy rates of these two brain compartments (P = 0.58) This is in agreement with the findings of two previous studies in elderly (Resnick et al., 2000, 2003), and with those of another study dealing with a larger age range sample (Good et al., 2001) However, the regression slopes we found for GM (2.2 cm3/year) and WM (1.7 cm3/year) not fit with the supposed larger WM loss rate proposed by other authors (Guttmann et al., 1998; Liu et al., 2003) Difference in study designs (i.e., cross-sectional vs longitudinal) is an unlikely explanation given the short age range of the samples of the Liu study and of ours Rather, even though we could not find in the two above reports whether or not the atrophy rates of GM and WM were significantly different (both reports state that the rate of atrophy is significant for WM only), we believe that the use of different segmentation procedure could be at the origin of these discrepant findings First, note that Guttmann et al (1998) used T2- and PD-weighted images only for the segmentation step which renders the GM/WM limit hard to define Second, in elderly subjects aged from 57 to 77 years, Liu et al (2003) reported an annual loss of brain tissue (GM plus WM) that did not match the corresponding annual increase of CSF in the same sample, the unexplained 1.4 cm3/year difference being possibly the consequence of an inaccurate tissue segmentation It seems thus reasonable to assume that GM and WM contributions to brain shrinkage are similar during the seventh and eighth decades, but additional studies focusing on the following decades are needed to check whether this holds later in life Voxel-wise age-related changes in healthy elderly The regional distribution of age-related reduction of GM volume was found to be very heterogeneous, some areas seeming particularly vulnerable, others being relatively spared Interestingly, the largest rates of atrophy were found in the primary auditory, somatosensory, and motor cortices (see Fig 4) Highly negative regression slopes of GM density with age were also observed in the primary visual cortex but failed to reach significance after adjustment for the global GM rate of atrophy We believe this lack of significance to be the consequence of higher residual standard errors of the regression slope estimated in this region (about twofold the average residual standard error computed over the whole GM map as indicated by analysis of the residual variance image) This is likely to be due to the high residual anatomical variance given both the large spatial variability of the Calcarine fissure (Thompson et al., 1996) and the relative small cortical thickness (Von Economo, 1929) observed in the primary visual cortex as compared to other regions (see also the GM probability map in Fig 2) Thus, notwithstanding the lack of significant findings, we believe that the primary visual cortex should be considered as a focus of age-related GM reduction, as well as others primary cortices More generally, it should be stressed that VBM findings are influenced by the amount of residual anatomical variability between 907 subjects after spatial normalization (Crivello et al., 2002; Good et al., 2001) since this procedure does not perfectly align cerebral structures between subjects However, we believe this bias source to have a weak impact on our findings First, the smoothing applied to our images (FWHM = 12 mm) dramatically reduces the interindividual misalignment of cerebral structures after spatial normalization Second, the very large number of subjects included in our study, as opposed to studies performed on relative small samples, acts as a supplementary image smoothing process, compensating in part the anatomical residual variability As a matter of fact, inspection of the residual variance image, that partly reflects the spatial distribution of the inter-individual anatomical variability, revealed that the occipital cortex was the only region presenting a high negative regression coefficient associated with a high residual variance Meanwhile, the same image also revealed that many associative regions presented small residual variances, a pattern also shared by the primary cortices (except the primary visual one) These findings allow to refute the idea that the strong age effect found on primary cortices could be explained by a weaker interindividual anatomical variability in these regions Note that primary cortices have been previously reported as spared by the aging processes (Jernigan et al., 2001; Raz et al., 1997), as predicted with the classical blast in, first outQ brain area aging theory (Raz, 2001) Actually, a close look at the most recent literature reveals that several studies, using a voxel-based approach similar to ours, have mentioned primary cortices as the seat of large rates of atrophy (Good et al., 2001; Resnick et al., 2003; Salat et al., 2004; Tisserand et al., 2004) Concerning the age-related decline in perisylvian regions such as insula and Heschl’s gyrus, Tisserand et al (2004) suggested that cerebral regions with complex anatomical boundaries for manual tracing have been largely ignored in aging studies using classical ROI approach This assumption may partially explain why we found in the present study some new cerebral regions vulnerable to aging Such converging results require reconsidering the status of the primary cortices in normal aging One could postulate that, whereas the associative cortices are particularly affected in pathological aging such as Alzheimer’s disease, the same associative cortices would be distinctly less affected and primary cortices more vulnerable in normal aging This hypothesis is consistent with reports of cognitive decline of the lowest echelons of sensory and motor systems in healthy elderly subjects (Kaye et al., 1994) Moreover, several studies have shown that, in absence of peripheral sensor age-related changes, hearing loss, visual decline, as well as motor slowness during aging could be associated to an affliction of their respective primary cortices (Mendelson and Ricketts, 2001; Schmolesky et al., 2000; Yordanova et al., 2004) The other areas where preferential age-related GM reduction was observed, included the hippocampus and the orbital part of the middle frontal gyri and are more classically found in studies dealing with normal and/or pathological brain aging (Petersen et al., 2000; Salat et al., 2001) The prefrontal cortex is usually considered as the structure most affected during normal aging, all age ranges taken together (Jernigan et al., 2001; Raz et al., 1997), and therefore is a key region of the frontal aging theory (relating that the major part of cognitive aging is related to a structural deficit of the prefrontal cortex, West, 1996) In recent whole brain exploratory studies, GM reduction with age was also found in the left middle frontal gyrus (Good et al., 2001), the orbital and inferior frontal cortex (Resnick et al., 2003), the frontal pole and dorsolateral prefrontal cortex 908 H Lemaıtre et al / NeuroImage 26 (2005) 900–911 ˆ (Tisserand et al., 2004), or the inferior lateral prefrontal cortex (Salat et al., 2004) Therefore, taking into account nomenclature differences, the orbital part of the middle frontal gyrus appears to be a preferential target for age-related decrease of GM in healthy elderly Note that this area has been reported in functional neuroimaging studies as mainly involved in maintaining information in working memory (see Tisserand and Jolles, 2003 for review) In this context, increased atrophy rates in this area in healthy elderly may constitute an early neural correlate of future diminished performances in executive functions Nevertheless, because of the importance of the prefrontal cortex in cognitive aging, future imaging studies are clearly needed to better differentiate the specific functions of the different frontal regions in relation to aging Regarding the hippocampus, although it is a key target of agerelated memory changes, previous studies have experienced difficulty to demonstrate significant hippocampal atrophy with age in absence of Alzheimer’s disease (Jack et al., 2002) Interestingly, Raz et al (2004a) recently showed a non-linear relationship between the hippocampus volume and age, the rate of atrophy in this region being small until the sixth decade, while larger atrophy rate occurs afterwards This model fits with our findings, observed in a sample of subjects aged between 63 and 75 years, as well as with those of two other studies dealing with subjects over 50 years (Resnick et al., 2003; Tisserand et al., 2004) The biological mechanisms driving the differential age vulnerability of the various cortical regions remain unclear Age-related impairment of specific neurotransmitter systems, such as the dopaminergic or cholinergic systems (Kaasinen and Rinne, 2002; Mesulam, 1995), may be put forward As a matter of fact, key structures of these two systems (the substantia nigra and the nucleus basalis of Meynert, respectively) show a loss of dopaminergic/ cholinergic neurons with age (Rehman and Masson, 2001) This could in turn trigger atrophy in the cortical structures on which these subcortical nuclei mainly project, such as the prefrontal cortex and the hippocampus (Goldman-Rakic and Brown, 1981; Volkow et al., 2000; Wenk et al., 1989) However, further investigations are clearly needed to determine the exact link between regional atrophy and the impairment of the neurotransmitter systems Surprisingly, regional GM analysis also revealed some foci of age-related increase which were localized bilaterally in the caudate, putamen and pallidum, and thalami, a phenomenon previously reported by others (Good et al., 2001) Although these areas may be less affected than others by aging, we agree with others that they must also be the seat of a normal age-related shrinkage (GunningDixon et al., 1998) Thus, we believe that what we observed in these areas could be an artifact due to the presence of particular GM/CSF and GM/WM interfaces First, the age-related ventricle enlargement due to brain atrophy could lead to a displacement of adjacent gray nuclei simulating an artificial increase of GM with age in voxel-based approaches Secondly, the volume left by the loss of myelin in the WM fibers of the internal capsule (Abe et al., 2002) could be replaced by putamen and pallidum neuron cell bodies, producing an apparent spatial expansion of GM Alternately, Ylikoski et al (1995) reported in healthy elderly an agerelated increase of white matter hyperintensities (WMH) in the periventricular areas This type of lesion, observed with a hyposignal in T1-weighted images, could be potentially misclassified as GM and imitate an increase of GM with age This remark is all the more right as several subjects were hypertensive and as hypertension has been significantly associated with an increased severity of WMH in our cohort of subjects (Dufouil et al., 2001) Finally, as opposed to what was found for the GM, there were only few areas of accelerated WM atrophy with age after removal of the global age-related WM volume reduction In fact, accelerated WM atrophy rates were observed almost exclusively in the corpus callosum, in agreement with the findings of a previous study in healthy subjects aged between 70 and 82 years (Sullivan et al., 2002) Such age-related WM reduction could be attributed to the micro-structural deterioration of the WM identified in diffusion imaging studies (Pfefferbaum et al., 2000), which was interpreted as a demyelination of WM fibers during aging (MeierRuge et al., 1992) Otherwise, the ventricular enlargement in aging could determine partly the age-related changes in WM fibers surrounding ventricles by a simple mechanical force (Peterson et al., 2001) Global versus voxel-wise age-related brain changes The results obtained in the TIV-adjusted VBM analysis show, at the voxel level, the same age-related trends that those observed at the cerebral volumetric level Such concordance is explained by the fact that the TIV-adjusted VBM analysis did not take into account the age effect on the cerebral volumes Therefore, the age-related changes estimated in the fractional cerebral volumes reflect the global outcome of all age-related variations identified at the voxel level By contrast, adjusting VBM analysis for absolute cerebral volumes rather than TIV provided quasi-identical age-related regression maps of GM, WM, and CSF compartments between men and women This means that the regional pattern of agerelated changes were similar in men and women for each tissue taken separately More generally, the age effects on global cerebral volumes and on tissue maps not necessarily match since VBM findings are highly dependent on the kind of adjustment used (TIV or cerebral volumes for instance) Thus, several scenarios can be envisaged On the one hand, if a VBM analysis is not adjusted for a global effect, this global effect naturally spreads over regionally, and as a consequence, the volumetric and VBM findings are well related On the other hand, if the global effect is modeled and adjusted for in a VBM analysis, regional changes due to this effect (i.e., regional changes greater that the global one) could be highlighted or not, leading to related or discrepant findings between volumetric and VBM findings Sex effect on structural brain aging The neuroanatomical sexual dimorphism we observed in healthy elderly is in close agreement with previous observation in younger adults (Gur et al., 1999) In addition, we did not find any significant bSex by AgeQ interaction either on global cerebral compartment volumes (either absolute or fractional) or in tissue probability maps, although a trend for larger rate of GM loss and CSF increase was present in women (associated with a larger agerelated decline of MMSE score in women) These findings are in contradiction with the common idea that men brains are more vulnerable to aging (Coffey et al., 1998) In a sample of elderly aged from 66 to 96 years, these authors reported an increase of sulcal CSF volume in men only Taking a sub-sample of subjects aged from 65 to 75 years, the same authors highlighted an annual rate of sulcal CSF increase for men and women of 2.1 and 0.06 cm3/year, respectively By contrast, we estimated an annual rate of CSF increase (including sulcal and ventricular CSF compartments) for men and women of 3.3 and 4.0 cm3/year, respectively H Lemaıtre et al / NeuroImage 26 (2005) 900–911 ˆ A possible explanation of this discrepancy could come from differences in hypertensive subject proportion or education level between men and women in our cohort Indeed, some studies have reported the effect of these two factors on the neuroanatomical aging For example, concerning the hypertension, Strassburger et al (1997) reported a greater cerebral atrophy in occipital and temporal regions for hypertensive elderly subjects as compared to normotensive elderly subjects Concerning the education level, Coffey et al (1999) highlighted a positive correlation between the number of years of education and the peripheral CSF volume in healthy elderly subjects However, including these variables, as confounding factors in the analysis, did not modify our results One could also raise the issue of using a common normalization template, including both men and women, with the possible ensuing bias of reproducing a similar atrophy scheme in men and women However, using a specific template for each sex did not significantly modify our results Rather, Coffey et al (1998) also reported no sex effect on brain atrophy on the same sample, what seems contradictory with their findings concerning the CSF and may indicate a problem in volume estimation that could possibly originate from the manual tissue segmentation performed in this study As other recent studies (Resnick et al., 2000, 2003), based on automated image segmentation rather than manual tracing, also reported no bSex by AgeQ interaction in healthy elderly, one is led to admit that in their seventh and eighth decades, men brain are not more, if not less, vulnerable to aging than that of women Arguments in favor of this hypothesis may be found in several studies of white matter lesions that have shown a larger prevalence of this type of lesions in women compared to men aged over 60 years (Sijens et al., 2001; Wen and Sachdev, 2004), which may be due to a larger agerelated decrease of the brain choline level in women (Sijens et al., 2003) The drastic changes in circulating hormone concentrations due to menopause in women around age 50 years could be one cause of such phenomenon (Lamberts, 2002; Raz et al., 2004c), but this assertion requires further investigations to be validated Conclusion Modifications of brain anatomy in the seventh and eighth decades appear to be characterized by (1) a shrinkage due to approximate equal loss of gray and white matter, (2) an inhomogeneous cortical pattern of atrophy rates, larger rates 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GM, WM, and CSF tissue images and gives the probability for a voxel to belong to the considered tissue The location of the five axial slices is shown on a three-dimensional rendering of the average... subtracting the GM and WM partition images provided by the first mono-spectral T1 segmentation from the sum of the GM, WM, and CSF partition images provided by Fig Flow chart of the image processing... functions in the three orthogonal directions) The normalization parameters (deformation fields) obtained from the GM warping step were then reapplied to the WM and CSF partition images, the resulting