Selective effects of aging on brain white matter microstructure: A diffusion tensor imaging tractography study

NeuroImage 52 (2010) 1190–1201

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Selective effects of aging on brain white matter microstructure: A diffusion tensor imaging tractography study Stijn Michielse a,e, Nick Coupland c, Richard Camicioli d, Rawle Carter c, Peter Seres a, Jennifer Sabino d, Nikolai Malykhin a,b,⁎ a

Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada Centre for Neuroscience, University of Alberta, Edmonton, Alberta, Canada Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada d Division of Neurology, University of Alberta, Edmonton, Alberta, Canada e Department of Biometrics, Zuyd University, Heerlen, The Netherlands b c

a r t i c l e

i n f o

Article history: Received 16 February 2010 Revised 17 April 2010 Accepted 7 May 2010 Available online 17 May 2010 Keywords: Aging White matter Gray matter Cerebrospinal fluid MRI Diffusion tensor imaging Tractography Corpus callosum Cingulum Fornix Uncinate fasciculus

a b s t r a c t We examined age-related changes in the cerebral white matter. Structural magnetic resonance images (MRIs) and diffusion tensor images (DTIs) were acquired from 69 healthy subjects aged 22–84 years. Quantitative DTI tractography was performed for nine different white matter tracts to determine tract volume, fractional anisotropy (FA), mean diffusivity (MD), axial, and radial diffusivities. We used automated and manual segmentation to determine volumes of gray matter (GM), white mater (WM), cerebrospinal fluid (CSF), and intracranial space. The results showed significant effects of aging on WM, GM, CSF volumes, and selective effects of aging on structural integrity of different white matter tracts. WM of the prefrontal region was the most vulnerable to aging, while temporal lobe connections, cingulum, and parieto-occipital commissural connections showed relative preservation with age. This study was cross-sectional, and therefore, additional longitudinal studies are needed to confirm our findings. © 2010 Elsevier Inc. All rights reserved.

Introduction Understanding age-related brain changes is fundamentally important and critical to understanding age-related neuropsychiatric disorders. Age-related changes in the human brain structure have been a subject of numerous studies, including postmortem, in vivo magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and, recently, diffusion tensor imaging (DTI) (Raz and Rodrigue, 2006; Sullivan and Pfefferbaum, 2006). Volumetric MRI methods have been intensively used as a valuable research tool to understand effects of aging on human brain. These methods include automated (Guttmann et al., 1998; Ge et al., 2002; Walhovd et al., 2005) and manual (Raz et al., 2004; Allen et al., 2005) segmentation of different brain regions including total gray and white matter of the major lobes (Raz et al., 2004; Allen et al., 2005), specific cortical ⁎ Corresponding author. Department of Biomedical Engineering, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada T6G 2V2. Fax: +1 780 492 8259. E-mail address: [email protected] (N. Malykhin). 1053-8119/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2010.05.019

regions (Jernigan et al., 2001; Raz et al., 2004), and subcortical structures (Jernigan et al., 2001; Walhovd et al., 2005). The fact that the volume of the gray matter decreases linearly by about 5% per decade with age, starting in early adulthood, has been replicated in several studies (Courchesne et al., 2000; Ge et al., 2002; Allen et al., 2005; Smith et al., 2007). Specific patterns of aging have also been demonstrated for several cortical regions. Frontal lobes appear to have the largest volume reduction and occipital and temporal lobes showed the least amount of volume reduction (Raz et al., 2004; Allen et al., 2005; Grieve et al., 2005), consistent with the frontal lobe theory of aging, implicating predominant frontal atrophy as the predominant feature of aging. Furthermore, relative preservation of gray matter has been reported for limbic and paralimbic structures, including the amygdala, hippocampus, thalamus, and the cingulate gyrus (Raz et al., 2004; Grieve et al., 2005; Malykhin et al., 2008a). In contrast, white matter volume increases through adulthood, reaching its peak in forth decade of life and starts to decline after 60 years (Courchesne et al., 2000; Barzokis et al., 2001; Ge et al., 2002; Allen et al., 2005, Salat et al., 2009). However, several studies did not find any significant white matter volume reduction with age

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(Pfefferbaum et al., 1994; Good et al., 2001; Smith et al., 2007). Regional patterns for white matter volume decline have been shown for several brain regions: frontal lobes showed more rapid rate of white matter volume decrease with age compared to other major lobes; temporal and occipital white matter were less affected by aging (Allen et al., 2005). Understanding the patterns of white matter deterioration with normal aging will become important background for interpretation of the age-related cognitive decline. For instance, several studies reported that age and regional white matter integrity differentially influenced cognitive performance (Kennedy and Raz, 2009). However, it remains unclear if these changes are global in nature or limited to specific white matter tracts. Until recently, white matter connections have been difficult to study due to lack of a noninvasive MRI technique specifically designed to visualize white matter tracts. With the introduction of DTI (Le Bihan, 2003; Mori and Zhang, 2006) it became possible to reliably delineate different white matter tracts in vivo (Wakana et al., 2007; Malykhin et al., 2008b). Furthermore, DTI can provide quantitative measures that reflect structural integrity of white matter fiber tracts. Several studies up to date employed DTI to study effects of aging on the brain white matter (Pfefferbaum and Sullivan, 2003; Salat et al., 2005a,b; Sullivan et al., 2006; Grieve et al., 2007; McLaughlin et al., 2007; Pagani et al., 2008; Stadlbauer et al., 2008a,b; Hasan et al., 2009a,b; Kennedy and Raz, 2009). Changes with aging have been observed in corpus callosum (Sullivan et al., 2001, 2006; McLaughlin et al., 2007; Pagani et al., 2008; Stadlbauer et al., 2008a; Salat et al., 2005a; Hasan et al., 2009a), uncinate fasciculus (Hasan et al., 2009b), internal capsule (Salat et al., 2005a), frontal (Salat et al., 2005a,b), temporal region (Salat et al., 2005a), occipital white matter (Salat et al., 2005a), corona radiata (Pagani et al., 2008), fornix (Stadlbauer et al., 2008b; Pagani et al., 2008), and cingulum bundle (Stadlbauer et al., 2008b; Pagani et al., 2008). Most of the DTI studies in aging employed a region of interest (ROI)-based approach (Sullivan et al., 2001; McLaughlin et al., 2007; Salat et al., 2005a,b; Kennedy and Raz, 2009) or voxel-based statistical mapping (Pagani et al., 2008). These methods include white matter from several different projections into the single ROI and therefore are limited when delineation of single tract is required. DTI tractography (Mori and van Zijl, 2002) allows delineating specific white matter bundles and perform quantitative evaluation of properties of the entire tract (Wakana et al., 2007; Malykhin et al., 2008b). Only few studies used DTI tractography to study changes with aging in the entire white matter tract (Sullivan et al., 2006; Stadlbauer et al., 2008a,b; Hasan et al., 2009a,b). The current cross-sectional study had two main goals. The first was to analyze the effect of age on tract specific diffusion characteristics in different white matter tracts (corpus callosum, cingulum, uncinate fasciculus, and fornix) of the human brain using reliable DTI tractography protocols in 69 subjects between the ages of 22 and 84 years. Taking together the data from previous volumetric MRI studies (Raz et al., 2004; Allen et al., 2005; Grieve et al., 2005), regional white matter changes with aging (Allen et al., 2005), and also DTI studies (Sullivan et al., 2001; McLaughlin et al., 2007; Pagani et al., 2008; Stadlbauer et al., 2008a,b; Salat et al., 2005a,b; Hasan et al., 2009a,b), we hypothesised that frontal white matter connections would be the most vulnerable to aging process and that limbic connections would show relative preservation with age. The additional goal was to examine whether tract volumes of those white matter tracts follow the same aging pattern as the entire white matter volume of the brain. Materials and methods Study sample Normative data were acquired from 69 healthy right-handed subjects (17 males, 52 females, 22–84 years old, mean age 46.9 ± 17.8 years).

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Subject characteristics are shown in Table 1. The exclusion criteria were unstable medical illness, history of psychiatric or neurological disorders, and the use of medications or nonprescribed substances that can affect brain structure. Subjects were also excluded if they showed elevated ratings of depressive symptoms (Watson et al., 1995;Yesavage, 1988), or, in the case of older subjects (N60 years), deficits on the Mini Mental State Examination (Folstein et al., 1975), Dementia Rating Scale (Brown et al., 1999), or Frontal Assessment Battery (Dubois et al., 2000). Healthy participants older than 65 years have been recruited and screened by a neurologist (R.C.) as part of a clinical imaging study in healthy controls and patients with Parkinson's disease. All MRI data sets did not show any visible lesions after examination by a neurologist with experience in imaging (R.C.) and a neuroanatomist (N.M.). Written informed consent was obtained, and the study was approved by the University of Alberta Health Research Ethics Board. MRI acquisition Images were obtained on a Siemens Sonata 1.5-T scanner (Siemens Medical Systems, South Iselin, NJ). A cradle and bilateral head supports were used to reduce subject motion. A high-resolution 3D magnetization prepared rapid gradient echo (3-D MPRAGE) sequence (TR = 1800 ms, TE= 3.82 ms, TI= 1100 ms, 1 average, flip angle= 15°, FOV = 256 mm, image matrix = 256 × 256, 128 coronal slices, 1.5 mm slice thickness, scan time 9 minutes) oriented perpendicular to the anterior–posterior commissure line (AC–PC line) and parallel to the midline was obtained first, using Turbo FLASH localizer images (15 images, 5 in each plane). Native spatial resolution was 1.5 mm× 1.0 mm× 1.0 mm, which was subsequently zero-filled to 1.5 mm× 0.5 mm× 0.5 mm. The high-resolution 3D MPRAGE volume was used for measurement of intracranial volume (ICV), total gray matter volume, and total white matter volume. DTI data sets were acquired using twice-refocused spin-echo, echo planar imaging (Reese et al., 2003) with the following parameters: relaxation time, TR=10 s, echo time, TE = 88 ms, 6 diffusion directions (1 0 1), (−1 0 1), (0 1 1), (0 1 −1), (1 1 0), and (−1 1 0), b = 1,000 s/mm2, field of view = 256 × 256 mm; image matrix = 128 × 128 (2.0 × 2.0 mm in-plane, interpolated to 1.0 mm × 1.0 mm), slice thickness= 2.0 mm, no gap, 63 axial slices, 8 averages, full brain coverage, and scan time of 9.5 minutes. Data analysis The segmentation of the three main brain tissues was performed using SPM5 (statistical parametric mapping software, Wellcome Trust Centre for Neuroimaging, London, UK; http://www.fil.ion.ucl.ac.uk/ spm) on T1-weighted MPRAGE images. The analysis was fully automated by use of a MATLAB® batch script and required about 30 minutes per MRI scan. Each T1-weighted volume data set was transferred in digital imaging and communication (DICOM) format from the MR scanner to a PC and converted to ANALYZE format by means of the DICOM conversion tool of SPM5. SPM5 employs a probabilistic framework (called “unified segmentation”), so that tissue classification, bias correction, and nonlinear registration are

Table 1 Sample characteristics (n = 69). Age (years)

Male (n)

Female (n)

Education, years, mean ± SD

22–29 30–44 45–59 60–74 75–84 Total

4 4 4 3 2 17 (25%)

9 18 14 5 6 52 (75%)

16.3 ± 1.51 15.3 ± 1.82 14.7 ± 2.24 17.0 ± 3.21 13.1 ± 2.75 15.3 ± 2.36

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integrated within the same generative model (Ashburner and Friston, 2005). By use of this framework with its default parameters, the data sets were normalized to the standard brain of the Montreal Neurological Institute (MNI) included in the SPM5 distribution and segmented using the defaults for the native space option. Intracranial volume (ICV) was calculated manually using DISPLAY software (Montreal Neurological Institute, Quebec, Canada) as previously described (Malykhin et al., 2007). To estimate the cerebrospinal fluid (CSF) volume, we subtracted the white matter (WM) and gray matter (GM) volumes from the intracranial volume. The following formula was applied: CSF = ICV − (GM + WM). All volumes (GM, WM, and CSF) for the normative sample were adjusted for ICV, according to the formula: normalized volume = (raw volume / ICV) × 1000. Normalized total brain volume (nTBV) was calculated as the sum of normalized gray matter (nGM) and normalized white matter (nWM) volumes. DTI data sets were transferred to a personal computer running DTI-studio V2.40 (Johns Hopkins University, Baltimore, MD), which employs the Fiber Assignment by Continuous Tracking (FACT) algorithm for DTI tractography (Mori et al., 1999; Jiang et al., 2006). All tracts in the data set were computed by seeding each voxel that had fractional anisotropy (FA) greater than 0.3. Tracts were propagated until they reached a voxel with FA b0.3, or an angular deviation of the propagating line N70°. Specific tracts were delineated when they penetrated 2D ROIs, based on anatomical landmarks that

were drawn on the principal direction color maps, which illustrate the main orientation of diffusion within each voxel. Tract-specific fractional anisotropy, mean diffusivity ((λ1 + λ2 + λ3) / 3), axial diffusivity (λ1), radial diffusivity ((λ2 + λ3) / 2), and tract volume were calculated using an in-house build script for Matlab (The MathWorks, Inc.) program, by tabulating their values for each voxel contributing to a tract, then averaging across these voxels. Reconstructed tract volume (RTV) was the product of voxel number and interpolated voxel size, 2.0 mm3. Normalized tract volume (NTV) was the relative volume of the specific white matter tract adjusted for individual ICV, according to the formula: normalized tract volume = (reconstructed tract volume / ICV) × 1000. Tractography protocols The details of our tractography protocols and data analysis have been previously reported (Malykhin et al., 2008b). An FA threshold of 0.3 was used to reduce the inclusion of marginal voxels with low FA due to partial volume effects. Measurements were not derived directly from the ROIs, which were used for tract selection only. All nine tracts were divided into three groups: - Corpus callosum: genu, body, splenium, and ventromedial prefrontal white matter (part of the genu that connects ventromedial prefrontal cortex) (Fig. 1a)

Fig. 1. Three-dimensional reconstructions of the three groups of white matter tracts from a 30-year-old man: corpus callosum (CC; a), cingulum bundle (b), and temporal lobe connections (b) are shown on color-coded primary diffusion maps. Although the ventromedial prefrontal white matter (vmPFWM) is a part of the genu of the corpus callosum, it was also analyzed separately.

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- Cingulum bundle (rostral, dorsal and parahippocampal parts) (Fig. 1b) - Temporal lobe connections (crus of fornix and uncinate fasciculus) (Fig. 1b) In addition to the previously published protocols, a new tractography method was developed for the body of the corpus callosum. The ROI B (body) was placed on the median sagittal slice of the corpus callosum. Its anterior border was the first coronal slice posteriorly to the ROI G (genu) (Malykhin et al., 2008b) where the genu of the corpus callosum was no longer seen in full profile. Its posterior border was the first coronal slice anterior to the ROI S (splenium) (Malykhin et al., 2008b) where the splenium of the corpus callosum was no longer seen in full profile. Statistical analyses Interrater and intrarater reliabilities were assessed on a subset of five brains, with a 1-week interval between ratings. Intraclass correlation coefficients (ICC) were calculated on 10 examples per structure: 5 brains × 2 hemispheres (Table 2). All measurements in the 69 aging study participants were performed by two raters (N.M. and S.M.). Paired t-tests were used to test inter-hemispheric differences, with P values ≤ 0.01 reported as significant. Analysis of variance was used to test the differences in intracranial volume between males and females. The correlations between age, gender, education level, ICV, gray matter-to-white matter ratio, total volumes of WM, GM, and CSF were calculated using Pearson's correlation coefficient. For normalized tissue volumes (nGM, nWM, nCSF, and nTBV), all tract-specific characteristics, including asymmetry index and various regression models, were investigated to test for the best fit (linear, quadratic, compound, or cubic polynomial) with aging. The fit with the highest correlation (adjusted R2) was used in presenting the results. An asymmetry index for tract volume was also calculated using the following formula: asymmetry index = ((right volume / left volume) × 100) − 100. Since our statistical analyses did not reveal significant changes associated with age in asymmetry index (except for the dorsal cingulum), fractional anisotropy, mean/axial/radial diffusivity values between the left and right hemispheres and their regression models, thereafter we include both left and right hemisphere data to calculate the highest correlation. Results Gray matter, white matter, and cerebrospinal fluid Brain volumes separated by tissue type are illustrated in Figs. 2a–e. Mean changes per age group for GM, WM, CSF, and TBV are shown in

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the Table 3. The ICV did not correlate with age (Pearson's correlation coefficient = 0.153, P = 0.206; Fig. 2a) in either males or females during their life span. Males demonstrated significantly larger ICV (1539 ± 140 cm3) than females (1348 ± 99 cm3; P b 0.001). Age correlated negatively with total GM volume (Pearson's correlation coefficient = −0.553, P b 0.001) and positively with total CSF volume (Pearson's correlation coefficient = 0.771, P b 0.001) but not with total WM volume (Pearson's correlation coefficient = −0.104, P = 0.394). The education level and sex did not correlate with any of the normalized volumes. The total normalized brain volume declined with age with a cubic curve providing the best fit (adjusted R2 = 0.756, P b 0.001; Fig. 2b). Changes were particularly prominent after fourth decade of life. The gray matter-to-white matter ratio significantly decreased until the fifth decade and, after that, stabilized in older subjects. This trend followed a quadratic model (adjusted R2 = 0.323, P b 0.001; Fig. 2c). The normalized GM volume gradually decreased with age with a quadratic pattern (adjusted R2 = 0.752, P b 0.001; Fig. 2d) from early adulthood throughout life. Normalized WM volume increased in volume, reaching its peak in the fifth decade of life, and then decreased with age by a quadratic model (adjusted R2 = 0.3, P b 0.001; Fig. 2e). The normalized CSF volume gradually increased with age with a cubic pattern (adjusted R2 = 0.756, P b 0.001; Fig. 2f) from the early 30s, following the opposite trend of the normalized GM.

Corpus callosum Normalized tract volumes of genu (adjusted R2 = 0.396, P b 0.001; Fig. 3a), body (adjusted R2 = 0.326, P b 0.001; Fig. 3b), and splenium (adjusted R2 = 0.224, P b 0.001; Fig. 3c) decreased with age with cubic regression. All volumes preserved their values until their mid-50s and then started to decline in their 60s, with the most prominent reduction in the late 70s. The volume of the ventromedial prefrontal white matter gradually declined from the mid-20s with a compound regression (adjusted R2 = 0.29, P b 0.001; Fig. 3d). In contrast to tract volumes, changes in FA were different for the different parts of the corpus callosum. FA of the genu and ventromedial prefrontal white matter gradually declined with age from the 20s in cubic compound (adjusted R2 = 0.328, P b 0.001; Fig. 4a) and quadratic (adjusted R2 = 0.292, P b 0.001; Fig. 4d) regression models, respectively. FA of the body of the corpus callosum remained stable with age (P = 0.318; Fig. 4b). FA of the splenium was relatively stable until mid-50s and then gradually increased with age in a cubic regression model (adjusted R2 = 0.119, P b 0.001, Fig. 4c). Mean diffusivity (Figs. 5a, b, and d), axial diffusivity (Figs. 6a, b, and d), and radial diffusivity (Figs. 7a, b, and d) in the genu of the corpus callosum (adjusted R2 values = 0.414/0.312/0.456 respectively; all P b 0.001), body of the corpus callosum (adjusted R2 values = 0.399/0.347/0.307; all

Table 2 Interrater/intrarater intra class correlation coefficients (ICC) and percent coefficient of variation (CV) for tract volume, fractional anisotropy, and mean diffusivity (n = 5a). White matter tract

Tract volume Interrater

Ventromedial prefrontal white matter Genu of the corpus callosum Splenium of the corpus callosum Body of the corpus callosum Dorsal cingulum Rostral cingulum Parahippocampal cingulum Uncinate fasciculus Crus of fornix a

Fractional anisotropy Intrarater

Interrater

Mean diffusivity Intrarater

Interrater

Intrarater

ICC

CV

ICC

CV

ICC

CV

ICC

CV

ICC

CV

ICC

CV

0.98 0.99 0.95 0.98 0.98 0.90 0.98 0.95 0.95

3.0 1.8 5.1 1.8 4.0 18.0 4.9 9.2 22.5

0.99 0.95 0.94 0.96 0.97 0.88 0.94 0.98 0.92

2.0 6.3 6.4 2.6 7.2 25.8 4.5 7.1 26.9

0.98 0.99 0.98 0.99 0.97 0.89 0.98 0.88 0.94

0.3 0.2 0.4 0.1 0.5 2.1 0.6 0.9 1.3

0.99 0.97 0.97 0.99 0.90 0.92 0.99 0.95 0.94

0.2 0.4 0.5 0.2 0.7 1.4 0.4 0.6 0.9

0.94 0.99 0.90 0.99 0.99 0.97 0.85 0.97 0.96

0.4 0.2 0.7 0.2 0.0 0.7 0.7 0.4 2.2

0.98 0.95 0.92 0.99 0.93 0.93 0.93 0.99 0.92

0.2 0.4 0.5 0.2 0.6 0.9 0.7 0.2 3.4

Interrater and intrarater data acquired from 10 measures, 5 subjects × 2 hemispheres.

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Fig. 2. Regression plots showing the relationship between age and intracranial volume (a), normalized total brain volume (b), gray matter-to-white matter ratio (c), normalized gray matter volume (d), normalized white matter volume (e), and normalized cerebrospinal fluid volume (f).

P b 0.001), and ventromedial prefrontal white matter (adjusted R2 values = 0.255/0.143/0.329; all P b 0.001) decreased slightly until the 60s and then increased dramatically with age with cubic regression model. Mean diffusivity (quadratic regression, adjusted R2 = 0.194, P b 0.001; Fig. 5c), axial diffusivity (cubic regression, adjusted R2 = 0.249, P b 0.001; Fig. 6c), and radial diffusivity (quadratic regression, adjusted R2 = 0.048, P = 0.013; Fig. 7c) of the splenium of the corpus callosum remained relatively stable until 60s and then increased with age. Cingulum bundle All parts of the cingulum bundle (rostral, quadratic regression model, adjusted R2 = 0.229, P b 0.001, Fig. 3e; dorsal, cubic regression model, adjusted R2 = 0.156, P b 0.001; Fig. 3f; and parahippocampal, quadratic regression model, adjusted R2 = 0.199, P b 0.001;

Fig. 3g) increased in volume and reached their peak in the late 40s. After that, dorsal and parahippocampal parts declined in volume, while the volume of the rostral cingulum continued to increase with age. FA values remained stable with age in rostral (P = 0.210; Fig. 4e), dorsal (P = 0.838; Fig. 4f), and parahippocampal (P = 0.319; Fig. 4g) parts of the cingulum. Mean diffusivity (Figs. 5e and f), axial (Figs. 6e and f), and radial diffusivity (Figs. 7e and f) decreased in rostral (adjusted R2 values = 0.13/0.094/0.08, respectively; all P b 0.001) and dorsal cingulum (adjusted R2 values = 0.149/0.167/0.063, respectively; all P b 0.001) with age until 60s and then increased with aging (similar to the callosal fibers) in a cubic regression model. In contrast, parahippocampal mean (P = 0.097; Fig. 5g) and axial (P = 0.746; Fig. 6g) diffusivities did not change with age. Radial diffusivity in the parahippocampal cingulum slightly increased with age in a linear model (adjusted R2 = 0.039, P = 0.012; Fig. 7g).

Table 3 Mean changes per age group for absolute and normalized to ICV volumes of gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and normalized total brain volume (nTBV). Age group (years)

ICV, cm3 (mean ± SD) GM, cm3 (mean ± SD) WM, cm3 (mean ± SD) CSF, cm3 (mean ± SD) nGM (mean ± SD) nWM (mean ± SD) nCSF (mean ± SD) nTBV (mean ± SD)

22–29

30–44

45–59

60–74

75–84

1397 ± 97 714 ± 58 445 ± 17 238 ± 30 511 ± 18 319 ± 13 170 ± 15 830 ± 15

1385 ± 127 675 ± 63 448 ± 47 262 ± 40 488 ± 18 323 ± 16 189 ± 20 811 ± 20

1358 ± 172 641 ± 75 442 ± 65 275 ± 54 473 ± 23 326 ± 17 202 ± 26 798 ± 26

1461 ± 138 609 ± 123 438 ± 177 414 ± 82 417 ± 19 301 ± 28 283 ± 40 715 ± 40

1425 ± 159 587 ± 65 418 ± 51 420 ± 89 413 ± 34 293 ± 15 294 ± 44 706 ± 44

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Fig. 3. (a–i) Regression plots showing the relationship between age and normalized tract volume. White matter tracts: genu (a), body (b), and splenium (c) of the corpus callosum; ventromedial prefrontal white matter (d); rostral (e), dorsal (f), and parahippocampal (g) parts of the cingulum; crus of fornix (h); and uncinate fasciculus (i).

Temporal lobe connections The volume of the crus of fornix decreased with age in a compound model (adjusted R2 = 0.046, P = 0.007; Fig. 3h). The volume of the uncinate fasciculus increased until the 60s and then decreased by a cubic regression model (adjusted R2 = 0.098, P = 0.001; Fig. 3i). The FA of the crus of fornix decreased with age by a compound model (adjusted R2 = 0.256, P b 0.001; Fig. 4h). The FA of the uncinate fasciculus increased until the 60s and then decreased following a cubic regression model (adjusted R2 = 0.086, P = 0.002; Fig. 4i). Mean diffusivity (adjusted R2 = 0.12, P b 0.001; Fig. 5h) and axial (adjusted R2 = 0.246, P b 0.001; Fig. 6h) diffusivities of the crus of fornix decreased with age by a cubic regression model, while radial diffusivity remained unchanged (P = 0.076; Fig. 7h). Mean diffusivity (adjusted R2 = 0.096, P = 0.001; Fig. 5i), axial diffusivity (adjusted R2 = 0.093, P = 0.001; Fig. 6i), and radial diffusivity (adjusted R2 = 0.101, P = 0.001; Fig. 7i) of the uncinate fasciculus decreased

with age until 60s and then increased with age by a cubic regression model. Asymmetry index The asymmetry indexes did not change with age in all callosal tracts, parahippocampal cingulum, and crus of fornix (all P N 0.05). Furthermore, the asymmetry indexes of the rostral cingulum and uncinate fasciculus remained stable with age (all P N 0.05). However, the asymmetry index of the dorsal cingulum increased with age by a linear model (adjusted R2 = 0.139, P = 0.001; figure is not shown). Discussion The present cross-sectional study used DTI tractography to demonstrate regional age-related changes in different white matter tracts of the human brain. Previous DTI tractography studies in normal

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Fig. 4. (a–i) Regression plots showing the relationship between age and fractional anisotropy. White matter tracts: genu (a), body (b), and splenium (c) of the corpus callosum; ventromedial prefrontal white matter (d); rostral (e), dorsal (f), and parahippocampal (g) parts of the cingulum; crus of fornix (h); and uncinate fasciculus (i).

aging were limited by the small number of subjects (Sullivan et al., 2006), included younger population (Hasan et al., 2009a,b), or analyzed entire white matter tracts without their anatomical subdivisions (Stadlbauer et al., 2008a,b). In present study we included 69 subjects aged 22–84 years and separately analyzed anatomical subdivisions of nine white matter tracts. For the first time, we investigated the effects of aging on volume of the cingulum bundle and fornix using DTI tractography. Furthermore, we demonstrated that the anatomical subdivisions of the corpus callosum and cingulum bundle have different patterns of aging. Our results confirmed our initial hypotheses that frontal white matter connections are the most vulnerable to aging process, while limbic connections are relatively preserved. Previous aging studies have suggested that the reduction in gray matter volume starts relatively early in life and continues throughout life, while white matter increases in volume until the fifth decade of life and then gradually declines. This is consistent with our finding that total brain volume remains relatively stable until the fourth decade of life. Our results are in agreement with previously published

aging studies of gray matter (Courchesne et al., 2000; Good et al., 2001; Jernigan et al., 2001; Ge et al., 2002; Allen et al., 2005; Smith et al., 2007; Walhovd et al., 2005) and white matter (Courchesne et al., 2000; Barzokis et al., 2001; Ge et al., 2002; Jernigan et al., 2001; Allen et al., 2005; Walhovd et al., 2005; Salat et al., 2009). Similar to other studies (Courchesne et al., 2000; Ge et al., 2002; Walhovd et al., 2005), we did not find any effect of aging on intracranial volume. The volume of the cerebrospinal fluid in general followed the opposite trend from that of the gray matter, and therefore, its increase may be explained by the decline of the gray matter volume which occurs simultaneously, rather than changes in white matter. Other studies have reported similar patterns of cerebrospinal fluid volume increase with aging (Guttmann et al., 1998; Courchesne et al., 2000; Good et al., 2001; Jernigan et al., 2001; Smith et al., 2007). Structural MRI studies have confirmed that aging selectively affects different cortical regions of the brain. Allen et al. (2005) reported that frontal, parietal, and occipital lobes gray matter linearly decreased across the life span. At the same time, white matter volume increases up to 50–60 years of age and thereafter declines especially

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Fig. 5. (a–i) Regression plots showing the relationship between age and mean diffusivity. White matter tracts: genu (a), body (b), and splenium (c) of the corpus callosum; ventromedial prefrontal white matter (d); rostral (e), dorsal (f), and parahippocampal (g) parts of the cingulum; crus of fornix (h); and uncinate fasciculus (i).

after 70 years. The frontal lobes appear to have the largest volume reduction with age and occipital and temporal lobes showed the least amount of volume reduction (Raz et al., 2004; Allen et al., 2005; Grieve et al., 2005), consistent with a frontal lobe theory of aging which states that many age-related changes in cognition are attributable to deterioration of the frontal lobes. Raz et al. (2004) in their volumetric study reported that lateral prefrontal cortex exhibited the greatest age-related differences, whereas significantly weaker associations were observed in the prefrontal white matter, sensory–motor, and visual association regions. The primary visual, anterior cingulate, the inferior parietal cortices, and the parietal white matter showed no age-related differences. Our results indicate that that the majority of white matter tracts follow the same developmental trajectory with aging as the global white matter volume of the brain. We found that normalized tract volumes of all parts of the corpus callosum, all parts of the cingulum bundle, and uncinate fasciculus increased with age until the mid-50s/

early 60s. Starting in early 60s, all these volumes (except rostral part of the cingulum) started to decrease with the most significant reduction occurring in the late 70s. In contrast, the volume of the rostral cingulum continued to increase during life span, without showing any sign of deterioration. However, two tracts demonstrated different patterns of volume changes with aging. The volume of the crus of fornix and the volume of the ventromedial prefrontal white matter gradually decreased with age. Despite the fact that tract volume and mean/radial/axial diffusivities of the genu, body, and splenium of the corpus callosum followed the same aging pattern, FA declined with aging only in the genu, the region that represents medial prefrontal commissural connections (Hofer and Frahm, 2006; Malykhin et al., 2008b). Furthermore, the volume of the ventromedial prefrontal white matter decreased with age earlier than the volumes of other white matter tracts. Salat et al. (2005b) reported that the ventromedial and deep prefrontal regions showed greater reduction in FA compared to other prefrontal areas.

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Fig. 6. (a–i) Regression plots showing the relationship between age and axial diffusivity. White matter tracts: genu (a), body (b), and splenium (c) of the corpus callosum; ventromedial prefrontal white matter (d); rostral (e), dorsal (f), and parahippocampal (g) parts of the cingulum; crus of fornix (h); and uncinate fasciculus (i).

Salat et al. (2005a) also demonstrated regionally selective age-related decline in FA in frontal white matter and the genu of the corpus callosum, with temporal, occipital, and postcentral white matter regions relatively preserved. Sullivan et al. (2006) reported that older subject had lower FA, higher ADC (apparent diffusion coefficient), and fewer fibers than younger subjects in frontal fiber bundles of the corpus callosum relative to posterior callosal fibers. Pagani et al. (2008) found that white matter of the genu increased until the 30 and 40 years and then declined by the age of 50. Using voxel-based morphometry Smith et al. (2007) found no significant decrease in white matter volume in healthy elderly, but the focal white matter decreased with age in the anterior corpus callosum area. At the same time, diffuse reduction of the gray matter volume was seen in most brain regions, except the medial temporal lobe and posterior cingulate. All these studies indicate the selective vulnerability of frontal callosal fibers to normal aging. In contrast, FA of the splenium of the corpus callosum that consists of parieto-occipital commissural connections (temporal connections were removed from this tract, see

Malykhin et al., 2008b) remained relatively stable until mid-50s and then gradually increased with age. We observed that radial diffusivity in the splenium increased less dramatically with aging than axial diffusivity. Such disproportional changes in diffusivities might explain the fact that FA increased in the splenium with age. In the body of the corpus callosum that consists of commissural connections from premotor and supplementary motor areas, primary motor cortex, and primary sensory cortex (Hofer and Frahm, 2006), FA remained unchanged across the life span. However, since we were not able to separate parietal from occipital fibers in the splenium of the corpus callosum, we cannot comment on differences between these two cortical regions. Other DTI studies also demonstrated a regional FA decrease in the genu but not in the splenium of the corpus callosum (Pfefferbaum et al., 2000; Sullivan et al., 2001; Abe et al., 2002; Salat et al., 2005a; Hsu et al., 2008). Sullivan et al. (2006) found that in contrast to prefrontal areas, FA values in the inferior temporal/occipital fibers were slightly higher in older group than in younger group of healthy subjects.

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Fig. 7. (a–i) Regression plots showing the relationship between age and radial diffusivity. White matter tracts: genu (a), body (b), and splenium (c) of the corpus callosum; ventromedial prefrontal white matter (d); rostral (e), dorsal (f), and parahippocampal (g) parts of the cingulum; crus of fornix (h); and uncinate fasciculus (i).

Furthermore, the authors did not report significant changes in diffusivity for λ1, λ2, and λ3 in this region. In addition, fiber length of the temporal/occipital fibers was also bigger in the old group than in the younger group. In contrast to the current study, McLaughlin et al. (2007) reported no significant difference between young adults (25– 40 years) and the elderly (60–80 years) in FA of the genu, body, and splenium of the corpus callosum. This fact might be explained by the difference in delineation of the corpus callosal parts (size of the body of the corpus callosum was relatively smaller in their study) and the difference in sample population (the oldest subjects in their study were aged 70–73 years). Hasan et al. (2009a) showed that all segments of the corpus callosum followed the same pattern with aging. Tract volume and FA in callosal fibers followed inverted Ushaped curves with aging while radial diffusivity followed U-shaped curve. Stadlbauer et al. (2008a) reported that association fibers (superior longitudinal, inferior fronto-occipital, and inferior longitudinal fasciculi) had the largest decrease both in FA and number of fiber projections per voxel per decade of age. The authors also reported that

callosal fibers had the largest increase in MD and the largest relative change in the number of fiber projections. Relative preservation of gray matter has been reported for limbic and paralimbic structures, including the amygdala, hippocampus, thalamus, and the cingulate gyrus (Raz et al., 2004; Grieve et al., 2005; Malykhin et al., 2008a). Allen et al. (2005) found that compared to other regions temporal lobe white matter appeared to have a slightly later peak in volume (N60 years). Both temporal lobe gray and white matter showed the least amount of volume reduction compared to the other major lobes. Taken together, our data support and extend previous findings by showing that the limbic white matter connections are relatively preserved with age. For instance, the parahippocampal cingulum and uncinate fasciculus showed volume increases until early 60s together with other parts of cingulum. Furthermore, these tracts showed preservation of the FA and MD (parahippocampal cingulum) or an increase in FA (uncinate fasciculus). Compared to callosal tracts, the changes in axial and radial diffusivities of the cingulum parts were relatively small. Therefore, this fact might

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explain why the FA in these white matter tracts did not change with aging. The crus of fornix was the only tract where decreases in mean and axial diffusivities have been observed across the life span and where the radial diffusivity did not change with age. This could be explained by the ability of hippocampus to produce new neurons in the adult human brain with preservation of hippocampal structure and outflow (Balu and Lucki, 2009). Stadlbauer et al. (2008b) in their study showed moderate correlations with age for number of fibers, FA, MD, and eigenvalues for the fornix but no correlation with age for the cingulum. In contracts to current study, Pagani et al. (2008) using voxel-based statistical mapping found negative linear relation between age and white matter volume decline in the anterior cingulum, body, and crus of fornix. The authors also reported positive linear correlation between age and volume increase of the right deep temporal association fibers. Hasan et al. (2009b) reported a significant negative correlation between age and FA of the uncinate fasciculus. However, these authors did not find age effects on tract volume and axial diffusivity of the uncinate fasciculus. Differences between our studies could be explained by the fact that our study included many subjects older than 70 years in whom we observed the most significant changes, whereas participants in Hasan et al. (2009b) study were all younger than 70 years. In addition, we reported that an asymmetry index (right N left) remained unchanged with age, while Hasan et al. (2009b) reported right N left asymmetry in children but not in adults. We did not find significant differences in FA, MD, axial, or radial diffusivity between left and right uncinate fasciculus, in contrast to Hasan et al. (2009b) who reported significantly larger FA and axial diffusivity on the left side in both children and adults. Our findings are consistent with our previous study on younger healthy subjects (Malykhin et al., 2008b) and with postmortem data (Highley et al., 2002) that showed a greater number of fibers on the right side, with no difference in fiber density. Furthermore, the uncinate fasciculus was the only tract (except splenium of the corpus callosum) where we observed an increase in FA and volume with age until the sixth decade of life, suggesting preserved frontotemporal connectivity with aging. The biological mechanism responsible for changes in tract-specific diffusion characteristics is currently unclear, since there has been no direct link between DTI measurements and postmortem analysis of myelin content in the human brain. In animal studies, myelin breakdown has been associated with increased radial diffusivity, while axonal degeneration is reflected in changes axial diffusivity (Song et al., 2003). The most common age-related degenerative alterations in the brain white matter are the formation of splits containing electron-dense cytoplasm, and the formation on myelin balloons (Peters, 2009). In normal aging, some myelin sheaths degenerate as a consequence of their degenerating axons, but in other cases, myelin sheaths degenerate although the axon is intact (Peters, 2009). Previous postmortem studies revealed that normal aging was associated with white mater volume loss, accompanied by expansion of the extracellular space. Meier-Ruge et al. (1992) found that the total surface area of extracellular space was significantly higher and total nerve fiber area consequently significantly lower in older than in younger subjects. In addition, this loss of total nerve fiber area in the corpus callosum was accompanied in particular by a decline in the number of myelinated nerve fibers of N1 µm in diameter and to a lesser extent in nerve fibers with a diameter of 0.4–0.2 µm. In contrast, Tang et al (1997) reported that the loss of the total nerve fiber length was accompanied in particular by a decline of the myelinated fibers with a small diameter. Marner et al. (2003) suggested that the loss of total length of the myelinated fibers with age was due to a 23% decrease in white matter volume and a 29% decrease in length density. These findings corresponded to a 10% decrease per decade or a total decrease of 45% from the age of 20 to 80 years. The authors also suggested that primarily the thinner fibers were lost with a relative preservation of the thicker ones.

Developmental DTI studies of white matter showed that the developmental increase of FA was primarily driven by decreasing perpendicular diffusivity (Snook et al., 2005; Lebel et al., 2008), while the decrease of FA may be primarily due to an increased perpendicular diffusivity associated with aging (Bhagat and Beaulieu, 2004). In present study, changes in radial diffusivity (i.e., regression models) in majority of the white matter tracts were opposite to changes in their tract volume, suggesting that there is a direct relationship between the tract volume and its radial diffusivity with aging. However, volume decline with age in several tracts, including the splenium of the corpus callosum, crus of fornix, and parahippocampal cingulum, did not correspond with significant changes in their radial diffusivity, suggesting that other factors such as myelination might contribute to this process. Changes in FA in most of the white matter tracts were not driven by changes in radial diffusivity and rather reflected a balance between radial and axial diffusivities, i.e., how simultaneously they were changing with age. We did not observe any relationship between changes in FA and tract volume. Limitations This study was cross-sectional, and therefore, additional longitudinal study is needed to confirm similar aging patterns for individual subjects. Since our number of males was limited, we did not study effects of sex on aging white matter. Lack of subjects aged 60–65 years did not allow us to predict if the changes associated with aging start in early 60s rather than in late 60s. In present study, using an FA threshold of 0.3 might lead to underestimation of the true extent of the white matter tracts. However, the use of an FA threshold 0.3, as well as boundary limits for tract propagation and data analysis, was aimed at excluding marginal voxels with low FA and more variable terminal branching portions of tracts. Our strategy was therefore to maximize reliability rather than the inclusion of all voxels. Future aging studies that combine volumetric MRI with DTI tractography will be able examine relationship between gray and white matter of the different brain regions. Conclusions In conclusion, this quantitative DTI tractography study demonstrated selective aging patterns for different white matter tracts. Our data suggest that prefrontal white matter of the brain was the most vulnerable to aging process, while temporal lobe connections, cingulum, and parieto-occipital commissural connections showed relative preservation with age. Acknowledgments Grant support: Canadian Institutes of Health Research (CIHR). Personnel support: Alberta Heritage Foundation for Medical Research (AHFMR). References Abe, O., Aoki, S., Hayashi, N., Yamada, H., Kunimatsu, A., Mori, H., Yoshikawa, T., Okubo, T., Ohtomo, K., 2002. Normal aging in the central nervous system: quantitative MR diffusion-tensor analysis. Neurobiol. Aging 23, 433–441. Ashburner, J., Friston, K.J., 2005. Unified segmentation. NeuroImage 26, 839–851. Allen, J.S., Bruss, J., Brown, C.K., Damasio, H., 2005. Normal neuroanatomical variation due to age: the major lobes and a parcellation of the temporal region. Neurobiol. Aging 26, 1245–1260. Balu, D.T., Lucki, I., 2009. Adult hippocampal neurogenesis: regulation, functional implications, and contribution to disease pathology. Neurosci. Biobehav. Rev. 33, 232–252. Barzokis, G., Beckson, M., Lu, P.H., Nuecherterlein, K.H., Edwards, N., Mintz, J., 2001. Age related changes in frontal and temporal lobes in men. Arch. Gen. Psychiatry 58, 461–465. Bhagat, Y.A., Beaulieu, C., 2004. Diffusion anisotropy in subcortical white matter and cortical gray matter: changes with aging and the role of CSF suppression. J. Magn. Reson. Imaging 20, 216–227.

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