Voxel-based analysis derived from fractional anisotropy images of white matter volume changes with aging

www.elsevier.com/locate/ynimg NeuroImage 41 (2008) 657 – 667

Voxel-based analysis derived from fractional anisotropy images of white matter volume changes with aging Elisabetta Pagani,a Federica Agosta,a Maria A. Rocca,a Domenico Caputo,b and Massimo Filippia,⁎ a

Neuroimaging Research Unit, Scientific Institute and University Ospedale San Raffaele, Milan, Italy Department of Neurology, Scientific Institute Fondazione Don Gnocchi, Milan, Italy

b

Received 12 December 2007; revised 26 February 2008; accepted 15 March 2008 Available online 26 March 2008

Although age-related effects on brain volume have been extensively investigated post mortem and in vivo using magnetic resonance imaging (MRI), regional and temporal patterns of white matter (WM) volume changes with aging are not defined yet. The aim of this study was to assess the topographical distribution of age-related WM volume changes using a recently developed voxel-based method to obtain estimates of WM fiber bundle volumes using diffusion tensor (DT) MRI. Brain conventional and DT MRI were obtained from 84 healthy subjects (mean age = 44 years, range = 13–70). Linear and non-linear relationships between age and WM fiber bundle volume changes were tested. A negative linear correlation was found between age and WM volume decline in the corona radiata, anterior cingulum, body and crus of the fornix and left superior cerebellar peduncle. A positive linear correlation was found between age and volume increase of the right deep temporal association fibers. The non-linear regression analysis also showed age-related changes of the genu of the corpus callosum and fitted better the volume changes of the right deep temporal association fibers. WM volume decline with age is unevenly distributed across brain regions. Our approach holds promise to gain additional information on the pathological changes associated to neurological disorders of the elderly. © 2008 Elsevier Inc. All rights reserved.

Introduction Age-related effects on the volume of the human brain tissues have been extensively studied both at post mortem (Rees, 1976; Meier-Ruge et al., 1992; Kemper, 1994; Aboitiz et al., 1996; Pakkenberg and Gundersen, 1997; Tang et al., 1997; Marner et al., 2003) and in vivo using magnetic resonance imaging (MRI) (Sowell ⁎ Corresponding author. Neuroimaging Research Unit, Department of Neurology, Scientific Institute and University Ospedale San Raffaele, Via Olgettina, 60, 20132 Milan, Italy. Fax: +39 02 2643 3054. E-mail address: [email protected] (M. Filippi). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2008.03.021

et al., 2004; Raz and Rodrigue, 2006). The most shared hypothesis is that grey matter (GM) volume declines linearly with age (Sowell et al., 2004; Raz and Rodrigue, 2006), while white matter (WM) volume essentially remains steady or increases slowly through adulthood, peaking at the 40–50 year range (Courchesne et al., 2000; Bartzokis et al., 2001, 2004; Jernigan et al., 2001; Ge et al., 2002; Allen et al., 2005; Fotenos et al., 2005; Walhovd et al., 2005), followed by a precipitous decline starting around 60 years of age (Guttmann et al., 1998; Salat et al., 1999; Courchesne et al., 2000; Bartzokis et al., 2001, 2004; Jernigan et al., 2001; Ge et al., 2002; Liu et al., 2003; Allen et al., 2005; Fotenos et al., 2005; Walhovd et al., 2005). The topographic patterns of age-related GM decline have been investigated using both global and regional MR-based approaches (Sowell et al., 2004; Raz and Rodrigue, 2006), whereas the actual topographic distribution of WM changes with aging is still controversial (Raz and Rodrigue, 2006). The remarkable heterogeneity between studies regarding WM volume changes with aging might be due to at least three reasons. First, these studies differ in the sample size and age ranges studied. In this context, the inclusion of adolescents should serve to clarify the impact of ongoing progressive volume changes that can be thought of as continuous with brain maturational effects (Sowell et al., 1999, 2002, 2004). This is particularly important for those WM fiber bundles that post mortem (Yakovlev and Lecours, 1967; Benes et al., 1994; Kemper, 1994) and in vivo MRI (Jernigan et al., 1991; Pfefferbaum et al., 1994; Reiss et al., 1996; Giedd et al., 1999; Courchesne et al., 2000; Bartzokis et al., 2001, 2004; Sowell et al., 2002) studies have shown to progressively increase in size throughout childhood and into young adulthood. Second, the lack of a correlation between subjects’ age and whole WM volume might relate to an uneven distribution of WM loss across different brain regions (Salat et al., 1999; Bartzokis et al., 2001, 2004; Jernigan et al., 2001; Allen et al., 2005; Lemaitre et al., 2005; Walhovd et al., 2005; Abe et al., 2008; Brickman et al., 2007; Smith et al., 2007). As a consequence, these regional changes might go undetected when using a global approach (Good et al.,

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2001; Sullivan et al., 2004; Abe et al., 2008; Agosta et al., 2007; Smith et al., 2007). Third, in spite of the optimization of methods to map GM density (Good et al., 2001), a careful standardization of MRI methods to assess WM morphometry is still lacking. To date, the majority of the studies evaluating age-related WM volume regional changes (Salat et al., 1999; Bartzokis et al., 2001; Jernigan et al., 2001; Good et al., 2001; Sato et al., 2003; Taki et al., 2004; Tisserand et al., 2004; Allen et al., 2005; Lemaitre et al., 2005; Walhovd et al., 2005; Abe et al., 2008; Brickman et al., 2007; Smith et al., 2007) were based on sequences and post-processing algorithms used for GM volumetry assessment, which may not represent the best approach to image the well-known pathological heterogeneity of the WM (Sullivan and Pfefferbaum 2006). Diffusion tensor (DT) MRI is a non-invasive method which is sensitive to features of tissue microstructure, such as axonal density and axonal fiber orientational coherence. Diffusion is anisotropic in WM, and DT-derived maps allow to visualize anisotropic structures that are consistent with the anatomy of the major WM fiber bundles (Pierpaoli et al., 1996). Recently, we developed a voxel-based (VB) method to obtain estimates of WM fiber bundles volumetry using DT MRI (Pagani et al., 2007). Such an approach, which provides an index of atrophy derived from the transformation between a fractional anisotropy (FA) atlas (resuming average morphometry of a reference population) and individual subjects FA maps, has been shown to be sensitive to fiber bundles volume changes related to disease (Pagani et al., 2007). Against this background, the aim of this study was to investigate the age-related changes of WM fiber bundles volumes at a VB resolution, using DT MRI obtained from a large sample of healthy subjects, spanning six decades of life. Since there is recent evidence that WM changes with aging may not be linear (Courchesne et al., 2000; Bartzokis et al., 2001, 2004; Jernigan et al., 2001; Ge et al., 2002; Allen et al., 2005; Walhovd et al., 2005), we also tested for non-linear effects in the age-functions observed. Materials and methods Subjects We studied 84 healthy volunteers (36 women and 48 men, mean age = 44 years, range = 13–70 years), with no previous history of neurological dysfunction and a normal neurological exam. Eighty-one subjects (96.4%) were right-handers and three subjects (3.6%) were left-handers, according to the Edinburgh Handedness Questionnaire (Oldfield, 1971). All subjects had normal Mini-Mental State Examination scores (Folstein et al., 1975), after correction for age and education. All subjects were assessed clinically by a single neurologist, who was unaware of the MRI results. Table 1 reports the main demographic characteristics of the subjects in each age group. Subjects were recruited by means of advertisements distributed in the community. Local Ethical Committee approval and written informed consent from all subjects were obtained prior to study initiation. MRI data acquisition Using a 1.5 T scanner (Avanto, Siemens, Erlangen, Germany), the following scans of the brain were obtained: 1) dual-echo (DE) turbo spin echo (SE) (TR = 3460 ms, TE = 27/109 ms, echo train length [ETL] = 5, forty-four contiguous axial slices, with 3 mm slice thickness, 512 × 512 matrix and 250 × 250 mm2 field of view [FOV], acquisition time = 6 min), and 2) pulsed-gradient SE single

Table 1 Main demographic and conventional MRI characteristics of subjects in different age groups Subject age group [years]

Number of subjects

Men/ women

Mean age [years] (range)

WMH load [ml] (SD)

b20 21–30 31–40 41–50 51–60 61–70 All

10 14 10 13 21 16 84

7/3 4/10 5/5 4/9 11/10 5/11 36/48

16 26 34 47 56 66 44

0.00 0.00 0.19 ± 0.06 0.35 ± 0.34 0.48 ± 0.38 4.45 ± 6.70 2.09 ± 4.71

(13–20) (21–30) (31–39) (42–50) (51–60) (61–70) (13–70)

Abbreviations: WMH = white matter hyperintensities; SD = standard deviation.

shot echo-planar sequence (PGSE-SS-EPI) (inter-echo spacing = 0.69 ms, bandwidth = 1860 Hz/pixel, TR = 2700 ms, TE = 71 ms, flip angle = 90°, 18 contiguous axial slices, with 4 mm slice thickness, 128 × 128 matrix, pixel size= 1.87 mm2, 240 × 240 mm2 FOV, number of acquisitions [NEX] = 2, acquisition time= 1 min and 41 s), with diffusion-encoding gradients applied in 12 noncollinear directions (b factor = 900 s/mm2) and a modified sensitivity encode of the kspace (reduction factor R = 2). Diffusion-weighted images were positioned with the same orientation as the DE scan, with the central slice positioned to match the central slice of the DE set. MRI data analysis Structural MRI analysis was performed by a single experienced observer, unaware to whom the scans belonged. WM hyperintensities (WMHs) were identified on the DE scans, and the WMH loads were measured using a local thresholding segmentation technique (Rovaris et al., 1997). From diffusion-weighted images, the DT was estimated by a nonlinear regression (Marquardt–Levenberg method), assuming a mono-exponential relationship between signal intensity and the bmatrix components (Basser et al., 1994). After diagonalization of the estimated tensor matrix, the FA index (Pierpaoli and Basser 1996), a scalar invariant of the tensor, was derived for every pixel. Then, using the VTK CISG Registration Toolkit (Hartkens et al., 2002), the rigid transformation needed to correct for position between the b = 0 images (T2-weighted, but not diffusion-weighted) and T2-weighted images as well as the affine transformation between the T2-weighted scan and the Montreal Neurological Institute (MNI) atlas (Mazziotta et al., 2001) was calculated. Normalized mutual information (Studholme et al., 1999) was the similarity measure used for image matching. The two consecutive transformations were then applied to the FA images to compensate for gross differences, including head size, before applying the non-linear registration. This allowed: a) to split the transformation so that, when the Jacobian is calculated, global expansion due to head size or global atrophy is not retained; and b) to help the convergence of the deformation algorithm. According to previous data which showed that WM volumes essentially remain steady or increase slowly through adulthood (Courchesne et al., 2000; Bartzokis et al., 2001, 2004; Jernigan et al., 2001; Ge et al., 2002; Allen et al., 2005; Fotenos et al., 2005; Walhovd et al., 2005), an FA atlas was first created using the data from healthy subjects ranging from 21 to 40 age years of age (reference group). An individual subject’s FA image was chosen randomly as a temporary atlas, and all other FA images were then

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registered to it using a non-linear transformation (Rohde et al., 2003). Next, the average of the registered FA images was re-sampled with the inverse of the average deformation field to achieve a morphological (shape) mean as well as an intensity (FA) mean of the group. The new average image was used as a target atlas during the following iteration. To reduce the influence of the first template on the final atlas, three iterations were used to create the final FA atlas (Guimond et al., 2000). The non-linear transformations between FA maps of all subjects and the atlas were then calculated as well as the determinants of the Jacobian of the transformations. This scalar index summarizes the point-wise volume changes produced by the deformation: values less than unity reflect atrophy, whereas values greater than unity reflect hypertrophy (Studholme et al., 2004; Pagani et al., 2007). Before entering the statistical analysis, Jacobian determinant maps were smoothed with 8 mm Full Width Half Maximum Gaussian kernel. In order to have a complete picture of the spatial distribution of volumes changes and to avoid attributing to the WM changes related to GM and cerebrospinal fluid (CSF) because of smoothing, no masks were applied to the data to limit the analysis to the WM. Lesion masks were also created in the DE space from a regionof interest analysis and transformed into the MNI space using the affine transformation calculated between the T2-weighted scans and the MNI atlas. After averaging, a map containing information about the degree of occurrence of lesions at a voxel level was produced. This map was used to assess the spatial correlation between volume changes and lesions distribution. Statistical analysis Statistical analysis was performed using the statistical parametric mapping (SPM2) software. To assess correlations between subjects’ age and volume changes a general linear model (Friston et al., 1995) was used. Gender entered the analysis as a nuisance variable, since it has been shown to be an important factor in brain shape analysis (Good et al., 2001). We first considered subjects’ age as the only covariate of interest, making the a priori hypothesis of a linear

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relationship between subjects’ age and volume changes. Then, we used a multi-linear regression approach with second-order polynomial expansion, as suggested by Büchel et al. (1996). With this method, the regression model is non-linear in the explanatory variables, but not in the unknown parameters: this is obtained by including age itself and its squared value as explanatory variables of interest. To test the overall significance of the effects of interest, we tested the null hypotheses that the introduction into the analysis of age and age squared as explanatory variables did not result in a reduction of error variance; this was achieved by using an F statistic and estimating thresholds of statistical significance with False Discovery Rate (FDR) correction (Genovese et al., 2002) with a threshold of p b 0.05. To compare the linear and non-linear approaches, regression plots were produced to visually assess the best fitting; this was done since the difference in degrees of freedom of the two approaches makes F values not directly comparable (Büchel et al., 1996). Also, an F-test was performed by considering age squared only as an effect of interest. Figures were also produced to help in the anatomical localization of clusters: over the FA atlas, supra-threshold F values corresponding to negative and positive slopes of the linear model were displayed separately, using colour lookup tables. The same procedure was applied for the non-linear model, displaying all the suprathreshold F values. For those voxels showing a significant linear relation with age, the correlation coefficients (r values) were also calculated. To assess that the F-test was applied correctly, the assumptions of normal distribution of the error term in the regression model and the independency of standard deviation of the sample were checked; a modified Kolmogorof–Smirnov test (matlab function: “lillietest”) was used to test the null hypothesis that the error terms are normally distributed at a significance level of 0.01. To assess that errors have constant variance, the residuals were plotted versus age to be visually checked. Results One or more WMHs were seen on the T2-weighted MRI scans from 31 subjects (37%). Mean WMH loads per each age year

Table 2 Cluster extensions (Ke), parameter estimates of regressor equations (β), F and p values for the linear and non-linear (quadratic) effects of age on volume changes Region

Side

Linear Ke

Corpus callosum (genu) Corona radiata Anterior cingulum bundles Fornix (body) Fornix (crus) Temporal association fibers Superior cerebellar peduncle Putamen Thalamus Third ventricle Lateral ventricles (posterior horns)

R L R L – R L R L R L R L R L

67 169 35 8 492 541 61 12 551 504 492 541 94 49 341

Non-linear β

F

pFDR.corr

r

Ke

β1

β2

F

pFDR.corr

– −3.12 −4.56 −2.71 −2.86 −3.77 −4.26 −3.20 3.31 −2.69 −6.12 −5.55 −3.64 −4.61 5.35 4.76 6.27

– 16.76 26.12 17.21 15.84 13.93 22.44 18.75 15.40 16.00 101.3 86.33 20.81 31.01 46.40 17.96 29.98

– 0.008 0.001 0.007 0.011 0.020 b0.001 b0.001 0.012 0.010 b0.001 b0.001 b0.001 b0.001 b0.001 0.006 b0.001

– −0.42 −0.50 −0.42 −0.41 −0.39 −0.47 −0.43 0.40 −0.41 −0.75 −0.72 −0.45 −0.53 0.63 0.42 0.49

27 119 107 98

6.65 3.20 5.56 0.61 −8.05 – 1.48 −0.28 16.90 – −7.57 −6.44 −1.65 0.19 −4.67 −9.09 −15.76

−0.97 −0.07 0.02 −0.04 0.06 – −0.07 −0.03 −0.17 – 0.02 0.01 −0.02 −0.06 0.12 0.16 0.26

9.41 9.58 13.00 8.98 9.02 – 11.89 9.56 15.52 – 50.23 42.70 10.40 16.03 27.09 11.30 19.74

0.016 0.006 0.002 0.021 0.020 – b0.001 b0.001 0.001 – b0.001 b0.001 0.001 0.001 b0.001 0.006 b0.001

– 358 360 91 – 598 435 358 360 364 120 463

Correlation coefficients (r) are reported for significant linear correlations between subjects' age and the Jacobian determinant. Abbreviations: FDR = False Discovery Rate; R = right; L = left. See text for further details.

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decade are reported in Table 1. The characteristics of the brain lesions were always consistent with those of non-specific changes in the WM. No other major abnormalities such as infarct, vascular malformation, or tumor were found. Table 2 reports parameter estimates of regressor equations, cluster sizes (i.e., the number of contiguous voxels passing the height threshold), F values, and p values for the two approaches used to test linear and non-linear effects of age on volume changes. For regions where a significant linear correlation was found, correlation coefficients are also reported. The parametric analysis showed a significant linear relation between subjects’ age and the Jacobian determinants of the right

prefrontal and left fronto-parietal fibers of the corona radiata, the anterior cingulum bundles, bilaterally, the body and crus of the fornix, the left superior cerebellar peduncle, and, in the GM, of the putamen and the thalamus, bilaterally (Fig. 1). For all these structures the slope (parameter β in Table 2) of the linear fitting was negative, indicating a volume decrease with aging. A positive correlation was found between subjects’ age and the mean Jacobian determinants of the right deep temporal association fibers and with the size of the third ventricle and the posterior horns of the lateral ventricles, bilaterally (Fig. 2). With the exception of the body of the fornix and the left superior cerebellar peduncle, all the previous regions were detected by the analysis using a second-order polynomial expansion as the regressor

Fig. 1. Brain regions showing a negative correlation between subjects' age and volume changes. Supra-threshold F values (p b 0.05 corrected with False Discovery Rate) were mapped over the fractional anisotropy template using a blue lookup table. Images are in neurological convention. See text for further details.

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Fig. 2. Brain regions showing a positive linear correlation between subjects' age and volume changes. Supra-threshold F values (p b 0.05 corrected with False Discovery Rate) were mapped over the fractional anisotropy template using a red lookup table. Images are in neurological convention. See text for further details.

(Table 2). In addition, this analysis showed the involvement of the genu of the corpus callosum (Fig. 3), which was not detected by the analysis based on the linear effect (Table 2). The plots for the linear and non-linear regressions between subjects’ age and volumetry changes (Fig. 4) showed a better fit of the non-linear function for the right deep temporal association fibers, the third ventricle and the posterior horns of the lateral ventricles. This was confirmed by the test for a significant reduction of the error variance with age squared in the model. This was true for uncorrected threshold only at p = 0.001. No overlap was found between volume changes and lesions distribution at a voxel level (Fig. 5).

The test for the normal distribution of the residuals showed that this assumption was true for all the voxels when both the two models were used. On the contrary the assumption was rejected for the voxel in the left lateral ventricle (posterior horns) when the linear model was used. Plots of residuals versus age, appeared as a random cloud of points, centered at 0, without showing any specific pattern of distribution. Discussion Previous reports on the relationship between age and brain WM volume changes have provided conflicting results, since some

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Fig. 3. Brain regions showing a quadratic correlation between subjects' age and volume changes. Supra-threshold F values (p b 0.05 corrected with False Discovery Rate), mapped in red over the fractional anisotropy template: genu of the corpus callosum (A), right deep temporal association fibers (B). Images are in neurological convention. See text for further details.

studies have reported a decrease in WM volume with aging (Guttmann et al., 1998; Salat et al., 1999; Courchesne et al., 2000; Bartzokis et al., 2001, 2004; Jernigan et al., 2001; Ge et al., 2002; Liu et al., 2003; Allen et al., 2005; Fotenos et al., 2005; Lemaitre et al., 2005; Walhovd et al., 2005; Abe et al., 2008; Benedetti et al., 2006; Brickman et al., 2007; Smith et al., 2007), while others did not (Good et al., 2001; Sato et al., 2003; Sullivan et al., 2004; Taki et al., 2004; Tisserand et al., 2004; Agosta et al., 2007). Several factors might contribute to explain this discrepancy, including: a) the different age ranges of subjects enrolled into previous studies (Courchesne et al., 2000; Bartzokis et al., 2001, 2004; Jernigan et al., 2001; Allen et al., 2005; Fotenos et al., 2005; Walhovd et al., 2005; Abe et al., 2008), b) the heterogeneous distribution of WM volume loss among different brain regions (Salat et al., 1999; Bartzokis et al., 2001; Jernigan et al., 2001; Allen et al., 2005; Lemaitre et al., 2005; Walhovd et al., 2005; Abe et al., 2008), which might go undetected when using a “global” approach, and c) the lack of standardized methods to assess WM morphometry. In an attempt to overcome these limitations, we used a VB method specifically designed to obtain WM fiber bundles volumetry using DT MRI in a large sample of healthy subjects spanning six decades of life. Since there is evidence that WM volume changes are more age-dependent than those of GM (Courchesne et al., 2000; Bartzokis et al., 2001, 2004; Jernigan et al., 2001; Ge et al., 2002; Allen et al., 2005; Walhovd et al., 2005), we also assessed the hypothesis of non-linearity in the WM volume changes with aging. The most intriguing finding of this study is the in vivo demonstration that aging has a heterogeneous topographical effect on volume changes of WM structures of the human brain, with a predominant involvement of regions located in the fronto-parietal lobes and the superior cerebellar peduncle. These regional volume changes did not correspond to the presence of macroscopic T2visible lesions, suggesting that they are not due to a reduction in the FA values secondary to the presence of focal hyperintensity changes. Clearly, since we do not have a direct histopathological confirmation, we can only speculate on the pathological nature of the damage underlying the observed atrophy. Nevertheless, it is

conceivable that age-related microscopic abnormalities that have been described by post mortem studies (Yakovlev and Lecours 1967; Lamantia and Rakic, 1990; Benes et al., 1994; Meier-Ruge et al., 1992; Kemper 1994; Aboitiz et al., 1996; Huttenlocher and Dabholkar 1997; Pakkenberg and Gundersen 1997; Tang et al., 1997; Cruz-Sanchez et al., 1998; Marner et al., 2003), and which include the breakdown of myelin and the cytoskeleton, reduction in axon density, and decline in the number and length of myelinated fibers (Meier-Ruge et al., 1992; Kemper 1994; Aboitiz et al., 1996; Pakkenberg and Gundersen 1997; Marner et al., 2003) are likely to play a role. Clearly, these abnormalities might be the result of aging per se or the result of an accumulation of remote effects from lesions scattered throughout the brain. The uneven distribution of WM volume change we observed is likely to reflect spatially specific vulnerability rooted in development. Human WM is characterized by a “heterochronologic” development since some regions myelinate on a different timeline than others (Huttenlocher and Dabholkar 1997), with heavily myelinated connections serving primary sensorimotor functions completing myelination first and having a high ratio of oligodendrocytes to myelinated fibers. Conversely, association fibers have longer period of myelin formation and one oligodendrocyte may myelinate multiple small fibers, resulting in thinner myelination (Bartzokis, 2004). A well-known pathological hallmark of the aging brain is the reduction of small diameter myelinated fibers (Lamantia and Rakic, 1990; Tang et al., 1997), which are also more susceptible to amyloid β peptide-related pathology (Braak et al., 2000). The increased vulnerability to aging of association cortices rather than sensory regions as well as the anterior–posterior gradient of such vulnerability supports the theory that the impact of aging is predominant to the frontal lobes (Pfefferbaum et al., 2005; Raz et al., 1997; Buckner, 2004). The relatively few studies investigating the relationship among age, normal cognition, and brain morphology (Raz et al., 1998; MacLullich et al., 2002; Gunning-Dixon and Raz 2003; Tisserand et al., 2004; Van Petten et al., 2004; Brickman et al., 2006, 2007) suggested that loss of both GM and WM volumes with aging is involved with age-related cognitive changes. Our finding of a reduced integrity of the major “cognitive-related” WM fiber

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Fig. 4. Regression plots between age and the Jacobian determinants of volume changes for the linear and non-linear models. Measured values are displayed as grey squares for voxels in the genu of the corpus callosum (A), the right (B) and left (C) corona radiata, the right anterior cingulum bundles (D), the body (E) and right crus (F) of the fornix, the left superior cerebellar peduncle (G), the right deep temporal association fibers (H), the right putamen (I), the right thalamus (L), the third ventricle (M), and the right (N) lateral ventricles. Volume changes predicted from age by the linear model are displayed by the grey lines, whereas changes predicted by the quadratic model are displayed by the black lines.

bundles with aging adds to the growing evidence that normal aging may be accompanied by a loss of cortico-subcortical connectivity, which might affect the use of resources needed for the maintenance of a normal cognitive function (Buckner 2004; Raz and Rodrigue, 2006; Sullivan and Pfefferbaum 2006). We found a linear relation between age and WM atrophy in the fronto-parietal bundles of the corona radiata, the anterior cingulum,

the fornix and the superior cerebellar peduncle. The selective disruption of prefrontal and fronto-parietal associative fibers is likely to be the structural substrate of age-related cognitive decline dependent on functioning of the prefrontal circuitry (Buckner 2004; Raz and Rodrigue, 2006; Sullivan and Pfefferbaum 2006). The cingulum is involved in the maintenance of a wide range of motivational and emotional aspects of behaviour and, also, of

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Fig. 5. The percentage of T2-visible lesions occurrence in 31 subjects (red lookup table) and supra-threshold F values (p b 0.05 corrected with False Discovery Rate) for linear correlation between subjects' age and volume changes (blue lookup table) superimposed on a representative coronal, sagittal and axial slice of the fractional anisotropy template. Images are in neurological convention. See text for further details.

working memory functions (Devinsky et al., 1995). The fornix provides the main cholinergic input to the hippocampus and it also projects from the hippocampus to the anterior thalamic nuclei, mammillary bodies, striatum, and prefrontal cortex (Copenhaver et al., 2006), playing a key role in episodic memory (Copenhaver et al., 2006). In agreement with this, recent DT MRI (Stadlbauer et al., 2008) and volumetric (Copenhaver et al., 2006) studies suggested a possible degradation of WM limbic structures with aging. Finally, the superior cerebellar peduncle contains efferent fibers running from the dentate nucleus via the red nucleus to the contralateral ventrolateral thalamus and from there to various cortical areas including the prefrontal, posterior parietal, and oculomotor cortex (Paviour et al., 2005). Atrophy of the superior cerebellar peduncle is a common pathological (Dickson et al., 2007) and neuroimaging (Paviour et al., 2005) feature of neurodegenerative disorders, such as progressive supranuclear palsy. The non-linear regression analysis disclosed a quadratic pattern of age-related WM decrease in the genu of the corpus callosum, which was not detected when using the linear regression method. In this region, the non-linear regression curve showed an increase of WM volume until the age of 30 years, a steadiness between the 30 and 40 year decade, and a decline by the age of 50 years. In addition, the non-linear function improved, albeit weakly, the relation between prefrontal callosal fibers volume and age. Small connecting fibers of the anterior corpus callosum have been suggested to be particularly vulnerable to aging by pathogical (Lamantia and Rakic, 1990; Tang et al., 1997) and in vivo MRI studies (Sullivan et al., 2002; Bartzokis et al., 2004; Allen et al., 2005; Lemaitre et al., 2005; Brickman et al., 2007; Smith et al., 2007). In addition, in adulthood, the genu of the corpus callosum, which connects the prefrontal lobes, has up to 20–30% of its axons unmyelinated compared to less than 7% in the splenium (Lamantia and Rakic, 1990). This higher proportion of unmyelinated axons might yet be an additional reason for the increased vulnerability of this portion of the corpus callosum compared to others. An additional evidence of the goodness of the approach used in the present study is the finding of the loss of association fibers in the right deep temporal lobe with aging. Using a linear regression model, we found a progressive volume increase in a cluster corresponding to these fiber bundles along the whole age range considered (data not shown). However, the quadratic fitting showed the possible dynamic of WM volume changes of such connections,

which are characterized by an increase until the 40–50 year decade followed by a rapid decline. This is in agreement with the recent demonstration of a significant FA increase in these regions in children and adolescents (Eluvathingal et al., 2007) and suggests that our method might be useful to monitor not only the age-related WM loss but also the ongoing maturational process. It is worth noting that the effective behaviour of volume changes of these connections would have been misinterpreted by simply using the linear regression analysis. Because of the complex trajectories of association fibers through the temporal lobe (Catani et al., 2002; Makris et al., 2007), we can only speculate on the anatomy of the atrophic WM paths that we found in this region. In this case, we are probably observing an area of WM loss where both the inferior fronto-occipital and the uncinate fasciculi are packed together (Catani et al., 2002). The inferior fronto-occipital and the uncinate fasciculi travel along the anterior–posterior axis of the brain, connecting the frontal cortex with the temporal cortex and the occipital lobe (Catani et al., 2002; Makris et al., 2007). They have a role in visuospatial functions and enable the interaction between emotion and cognition (Makris et al., 2007). Although the analysis of volume changes in non-WM regions was not the scope of the present study, we also detected volume changes in the putamen and thalamus, and the ventricular system. These findings further support the goodness of our approach. Indeed, the linear relationship between thalamic and putamen volume loss and age is in line with previous results (Raz et al., 1997, 2001; Jernigan et al., 2001; Sullivan et al., 2004; Van Der Werf et al., 2001; Walhovd et al., 2005; Nunnemann et al., in press). The same applies for the non-linear relation found between volume enlargements of the ventricles with age (Walhovd et al., 2005). The inclusion of a large sample of subjects spanning a wide range of ages, coupled with the comparison between linear and quadratic fitting, allowed us to collect evidence that aging of the WM is characterized by a considerable variation from a region to another and that part of this variation is likely to be related to maturational aspects of the different WM fiber bundles. In addition to agedependent neuronal loss which has long been considered central to age-related cognitive decline, our study suggests that changes in brain WM with aging also need to be considered to have a more complete picture of the aging brain, which may be useful to gain additional pieces of information on the pathological changes associated to neurological disorders of the elderly.

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