White matter microstructural damage in Alzheimer's disease at different ages of onset

Neurobiology of Aging 34 (2013) 2331e2340

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White matter microstructural damage in Alzheimer’s disease at different ages of onset Elisa Canu a, Federica Agosta a, Edoardo G. Spinelli a, b, Giuseppe Magnani b, Alessandra Marcone c, Elisa Scola d, Monica Falautano b, Giancarlo Comi b, Andrea Falini d, Massimo Filippi a, b, * a

Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy Department of Clinical Neurosciences, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy d Department of Neuroradiology and CERMAC, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 9 November 2012 Received in revised form 13 March 2013 Accepted 24 March 2013 Available online 24 April 2013

White matter (WM) microstructural damage and its relationship with cortical abnormalities were explored in early-onset Alzheimer’s disease (EOAD) compared with late-onset AD (LOAD) patients. Structural and diffusion tensor magnetic resonance images were obtained from 22 EOAD patients, 35 LOAD patients, and 40 healthy controls. Patterns of WM microstructural damage and cortical atrophy, as well as their relationships, were assessed using tract-based spatial statistics, tractography and voxel-based morphometry. Compared with LOAD, EOAD patients had a more severe and distributed pattern of WM microstructural damage, in particular in the posterior fibers of cingulum and corpus callosum. In both groups with Alzheimer’s disease, but especially in LOAD patients, correlations between cingulum and corpus callosum fractional anisotropy and parietal, temporal, and frontal cortical volumes were found. In conclusion, WM microstructural damage is more severe in EOAD compared with LOAD patients. Such damage follows different patterns of topographical distribution in the 2 patient groups. Ó 2013 Elsevier Inc. All rights reserved.

Keywords: Early-onset Alzheimer’s disease Late-onset Alzheimer’s disease MRI Diffusion tensor MRI White matter damage

1. Introduction Early-onset Alzheimer’s disease (EOAD), that is, onset before 65 years of age (Rossor et al., 2010), is associated with a higher prevalence of atypical manifestations with earlier multidomain cognitive impairment, including attention, executive, language, and visuospatial deficits, compared with the more frequent late-onset (LOAD) cases (Smits et al., 2012). Episodic memory impairment can be absent in EOAD patients, at least at the earliest stages; however, it becomes common later in the course of the disease (Smits et al., 2012). Neuroimaging studies also showed more severe hypoperfusion (Hanyu et al., 1995), hypometabolism (Kim et al., 2005; Rabinovici et al., 2010), and cortical atrophy (Canu et al., 2012; Frisoni et al., 2007; Ishii et al., 2005; Migliaccio et al., 2009) in the parietal and dorsal temporal regions of EOAD when compared to LOAD patients.

E.C. and F.A. equally contributed equally to the study and should be considered as cefirst authors. * Corresponding author at: Division of Neuroscience, Neuroimaging Research Unit, Institute of Experimental Neurology, San Raffaele Scientific Institute, VitaSalute San Raffaele University, Via Olgettina, 60, 20132 Milan, Italy. Tel.: 39-0226433033; fax: 39-02-26435972. E-mail addresses: m.fi[email protected], fi[email protected] (M. Filippi). 0197-4580/$ e see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neurobiolaging.2013.03.026

Although cortical pathology is the hallmark of AD, white matter (WM) is not spared by the disease (Wisniewski et al., 1989). WM damage has been demonstrated in LOAD patients using diffusion tensor (DT) magnetic resonance imaging (MRI), which detected altered diffusion properties in several WM regions, including the posterior cingulum, corpus callosum, temporal and frontal lobes (Sexton et al., 2011). Topographically, WM abnormalities in LOAD generally follow the anatomical distribution of cortical atrophy (Agosta et al., 2011). In contrast, in patients with amnestic mild cognitive impairment (Agosta et al., 2011), as well as in presymptomatic individuals who subsequently develop this impairment (Zhuang et al., 2012), microstructural WM abnormalities are not correlated with cortical atrophy, suggesting that DT MRI might serve as an imaging marker of early AD. To date, little is known about the effect of age at AD onset on the WM damage. A few studies assessed WM atrophy in EOAD patients and showed a more distributed pattern of posterior tissue loss centered around the splenium of the corpus callosum, posterior cingulate cortex (PCC), and dorsal temporo-parietal regions relative to LOAD, with a less severe involvement of the medial temporal WM (Canu et al., 2012; Migliaccio et al., 2012). However, atrophy reflects the latest stages of the AD-related degenerative process and DT MRI might shed light into earlier patterns of WM injury that only later may become detectable by volumetric measures.

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The aim of this study was to explore the patterns of WM microstructural damage in EOAD compared with typical LOAD patients, using DT MRI and combining the unique ability of TractBased Spatial Statistics (TBSS) to perform a voxelwise analysis with that of probabilistic DT tractography to reconstruct “critical” WM structural connections. For the comparison of subjects with different ages, we had to exclude the effect of normal aging. Indeed, it is well known that aging has a strong impact on both gray matter (GM) volumes and WM integrity (Benedetti et al., 2006; Pagani et al., 2008). As done by previous studies (Frisoni et al., 2005; Frisoni et al., 2007; Hirono et al., 2002; Kim et al., 2005; Migliaccio et al., 2009), we dealt with this issue by segregating patients into 2 groups according to age at onset and contrasting each AD group to age-matched healthy controls. The second aim of the study was to investigate the effect of age of onset on the relationship between WM microstructural damage and cortical atrophy to get a hint as to whether the former is a reflection of the latter, or conversely whether it occurs independently, and whether it differs between the 2 groups of patients. Finally, we tested the correlations between WM damage, dementia clinical severity, and cognitive performance in each AD group. 2. Methods 2.1. Subjects Twenty-two EOAD patients (McKhann et al., 2011) were enrolled consecutively at the Scientific Institute and University Vita-Salute San Raffaele, Milan, Italy (Table 1). Thirty-five LOAD patients (McKhann et al., 2011) were also recruited consecutively to match EOAD cases for gender, dementia severity as measured by the Clinical Dementia Rating (CDR) (Hughes et al., 1982), and disease duration (i.e., time from disease onset to MRI scan) (Table 1). There is no overlap with the patient sample described in our previous study (Canu et al., 2012). Diagnosis of probable AD was based on a comprehensive evaluation including neurological history and examination, and neuropsychological testing. In addition, we included only patients with at least 2 abnormal AD biomarkers of any type [i.e., biomarkers of disease state or biomarkers of neuronal injury (McKhann et al., 2011)] among the following: (1) medial temporal lobe (MTL) atrophy at routine MRI; (2) temporoparietal hypometabolism at the fluorodeoxyglucose (FDG) positron emission tomography (PET) or hypoperfusion at the single photon emission computed tomography (SPECT); and abnormal cerebrospinal fluid (CSF) levels: (3) reduced amyloid b1e42 (Ab1e42), (4) increased total tau and (5) increased phosphorylated tau. Age at onset was determined based on the estimated date of first symptom

presentation as reported by patients or caregivers. Consistent with previous reports (Canu et al., 2012; Frisoni et al., 2007; Rabinovici et al., 2010), AD patients were dichotomized into early-onset (EO; age at onset <65 years) and late-onset (LO; age at onset
65 years). Atypical focal presentations of AD (i.e., logopenic variant of primary progressive aphasia and posterior cortical atrophy) were not included. Forty healthy controls (HC) were recruited among spouses of patients and by word of mouth. They were divided into younger (age <65 years) and older (age
65 years), and were age and gender matched to the corresponding patient groups (Table 1). Subjects were excluded if they had any of the following: a family history suggestive of an autosomal dominant disease; medical illnesses or substance abuse that could interfere with cognitive functioning; any other major systemic, psychiatric, or neurological illnesses; and other causes of focal or diffuse brain damage at routine MRI, including lacunae and extensive cerebrovascular disorders. An experienced observer, blinded to the clinical data, reviewed the severity of cerebrovascular disease according to the age-related WM change scale (Wahlund et al., 2001); subjects above the 90th percentile of the distribution were excluded (Galluzzi et al., 2009). Structural routine MRI showed posterior brain atrophy in all AD patients, including MTL atrophy. FDG-PET or SPECT were available in 33 patients (17 EOAD and 16 LOAD) and showed a predominant reduced glucose metabolism or hypoperfusion in the temporoparietal regions, including the precuneus and PCC, in all EOAD and 12 LOAD patients. CSF analysis was performed in 15 EOAD and 17 LOAD patients. CSF Ab1e42 was decreased in 26 patients (14 EOAD and 12 LOAD); elevated total tau was found in 18 patients (8 EOAD and 10 LOAD); and elevated phosphorylated tau was observed in 25 patients (10 EOAD and 15 LOAD). 2.2. Cognitive assessment Neuropsychological assessment was performed by an experienced neuropsychologist unaware of the MRI results, who evaluated the following: global cognitive functioning with the Mini Mental State Examination (MMSE) (Folstein et al., 1975); memory function with verbal and spatial span (Orsini et al., 1987) and Rey’s Figure Delayed Recall Test (Caffarra et al., 2002); visuo-spatial abilities with the Rey’s Figure Copy Test (Caffarra et al., 2002); reasoning and attention functions with the Raven’s Coloured Progressive (Basso et al., 1987) and the Attentive matrices (Spinnler and Tognoni, 1987); and language with the Phonemic and Semantic Fluency (Novelli et al., 1986), and Token Test (De Renzi and Faglioni, 1978). Scores on neuropsychological tests were age, gender, and education corrected by using related normative values.

Table 1 Sociodemographic and clinical features of patients classified by age at onset and age-matched healthy controls

N Age at MRI [y] Gender [female] Education [y] Age at onset [y] Disease duration [y] CDR [0.5/1/2/3] CDR-SB Wahlund scale score WM lesion load [mm3] TICV [mL]

LOAD patients

Older HC

35 75.4  4.6 23 7.6  4.3 72.2  4.7 3.2  2.0 2/24/8/1 5.7  2.5 2.6  2.4 2.3  2.8 1434  152

16 73.1  4.3 (67e81) 10 (62%) 10.3  4.0 (5e17)

0.14 0.22 0.03

1.9  2.5 (0e8) 1.2  1.4 (0e5) 1443  160 (1105e1695)

0.26 0.48 0.71

(68e84) (66%) (0e17) (65e82) (1e10) (2e12) (0e8) (0e13) (1095e1747)

p*

EOAD patients

Younger HC

p*

p**

22 59.4  4.6 11 10.1  4.5 56.0  4.6 3.4  1.8 1/16/5/0 5.5  2.4 1.4  1.5 0.9  1.1 1439  143

24 59.1  2.7 (51e64) 12 (50%) 14.2  5.6 (5e17)

0.76 1.00 0.02

0.58  1.0 (0e4) 0.7  1.2 (0e6) 1488  152 (1261e1786)

0.07 0.58 0.21

<0.001 0.28 0.05 <0.001 0.45 1.00 0.76 0.07 0.03 0.92

(48e68) (50%) (3e17) (47e64) (1e7) (2.5e12) (0e4) (0e4) (1254e1712)

Values are mean  standard deviations (ranges), or N (%). *p Values refer to the ManneWhitney U or Fischer’s exact test between *each AD group and matched healthy controls, and **EOAD and LOAD patients. Key: CDR, Clinical Dementia Rating; Disease duration, time from disease onset to MRI scan; EOAD, early-onset Alzheimer’s disease; HC, healthy controls; LOAD, late-onset Alzheimer’s disease; MRI, magnetic resonance imaging; SB, sum of boxes; TICV, total intracranial volume; WM, white matter.

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2.3. Ethics committee approval Approval was received from the local ethical standards committee on human experimentation, and written informed consent was obtained from all subjects participating in the study. 2.4. MRI acquisition Brain MRI scans were obtained using a 3.0-T scanner (Intera, Philips Medical Systems, Best, the Netherlands). The following sequences were acquired from all subjects: (1) T2-weighted spin echo (SE) (repetition time [TR] ¼ 3000 ms; echo time [TE] ¼ 85 ms; echo train length ¼ 15; flip angle ¼ 90 ; 46 contiguous, 3-mm-thick axial slices; matrix size ¼ 256  242; field of view [FOV] ¼ 230  208 mm2); (2) fluid-attenuated inversion recovery (FLAIR) (TR ¼ 11.000 ms; TE ¼ 120 ms; echo train length ¼ 21; inversion time ¼ 2800 ms; flip angle ¼ 90 ; 46 contiguous, 3-mm-thick, axial slices; matrix size ¼ 256  192; FOV ¼ 230  183 mm2); (3) 3D T1weighted fast field echo (TR ¼ 25 ms, TE ¼ 4.6 ms, flip angle ¼ 30 , FOV ¼ 230 mm2, matrix ¼ 256  256, slice thickness ¼ 1 mm, 220 contiguous axial slices, in-plane resolution ¼ 0.89  0.89 mm2); and (4) pulsed-gradient SE echo planar with sensitivity encoding (acceleration factor¼ 2.5; TR¼ 8773 ms; TE¼ 58 ms; 55 contiguous, 2.3-mm-thick axial slices; after SENSE reconstruction, the matrix dimension of each slice was 128  128, with an in-plane pixel size ¼ 1.87  1.87 mm and a FOV¼ 231  240 mm2) and diffusion gradients applied in 35 noncollinear directions using a gradient scheme that is standard on this system (gradient over-plus) and optimized to reduce echo time as much as possible. The b factor used was 900 s/mm2. 2.5. MRI analysis MRI analysis was performed by an experienced observer, blinded to subjects’ identity. WM hyperintensity (WMH) load was measured on T2 scans using the Jim software package (Version 5.0, Xinapse Systems, Northants, UK, http://www.xinapse.com). 2.5.1. DT MRI analysis 2.5.1.1. Preprocessing. DT MRI analysis was performed using the FMRIB software library (FSL) tools (http://www.fmrib.ox.ac.uk/fsl/ fdt/index.html) and the JIM5 software. The diffusion-weighted data were skull-stripped using the Brain Extraction Tool implemented in FSL. Eddy currents correction was performed using JIM5. The DT was estimated on a voxel-by-voxel basis using DTIfit provided by the FMRIB Diffusion Toolbox. Maps of mean diffusivity (MD), fractional anisotropy (FA), as well as axial (axD) and radial (radD) diffusivities were obtained. 2.5.1.2. Tract-Based Spatial Statistics. TBSS version 1.2 (http://www. fmrib.ox.ac.uk/fsl/tbss/index.html) was used to perform the multisubject DT MRI analysis (Smith et al., 2006). FA volumes were aligned to a target image using the following procedure: (1) the FSL FA template was selected as the target image; (2) the nonlinear transformation that mapped each subject’s FA to the target image was computed using the FMRIB’s Non-linear Image Registration Tool; and (3) the same transformation was used to align each subject’s FA to the standard space. A mean FA image was then created by averaging the aligned individual FA images, and thinned to create a FA skeleton representing WM tracts common to all subjects (Smith et al., 2006) and individual MD, FA, axD and radD data were projected onto this common skeleton. 2.5.1.3. Tractography. Seeds for tractography of the corpus callosum and cingulum were defined in the standard space on the FA template provided by FSL. Regions of interest (ROI) were defined

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manually on sagittal or axial slices based on a priori knowledge of the anatomy of the tracts. The seed for the corpus callosum was a sagittal ROI including the 4 median slices on which the corpus callosum is clearly visible. The seeds for the cingulum were 5 axial ROI (positioned in the anterior, body, posterior, retrosplenial, and parahippocampal portions of the tract). Masks were used to exclude fibers from neighboring tracts. The seeds and exclusion masks were transformed to each subject’s native diffusion space, using the inverse of the linear and nonlinear transformations obtained previously to align each subject’s FA to the standard space, and then binarized. Transformed seeds and exclusion masks were evaluated visually. Manual editing was performed only if masks were found to be positioned inaccurately in the native space. Fiber tracking was performed in native DT MRI space using a probabilistic tractography algorithm implemented in FSL (probtrackx) and based on a Bayesian estimation of diffusion parameters (Bedpostx) (Behrens et al., 2007). Fiber tracking was initiated from all voxels within the seed masks in the diffusion space to generate 5000 streamline samples (step length ¼ 0.5 mm, curvature threshold ¼ 0.2). Tract maps were then normalized, taking into consideration the number of voxels in the seed masks and thresholded at a value equal to 40% of the 95th percentile of the distribution of the intensity values of the voxels included in the tract. This normalization procedure allowed us to correct for possible differences between tracts due to the different sizes of the starting seeds. In this way, we also excluded the background noise and avoided an overly restrictive thresholding when the maximum intensity value was an outlier. Using a “single-seed” approach, the reconstructions of the corpus callosum and bilateral cingulum were obtained. Group probability maps of each tract were produced to visually check their anatomical accuracy across subjects. 2.5.2. Gray matter atrophy: Voxel-based morphometry Voxel-based morphometry (VBM) was performed using SPM8 and the Diffeomorphic Anatomical Registration Exponentiated Lie Algebra (DARTEL) registration method (Ashburner, 2007). This nonlinear warping technique minimizes between-subject structural variations (Ashburner, 2007). Briefly, (1) T1-weighted images were segmented to produce GM, WM, and CSF tissue probability maps in the Montreal Neurological Institute (MNI) space; (2) tissue segmentations were averaged across participants and smoothed with an 8-mm full-width at half-maximum (FWHM) Gaussian kernel to produce customized prior probability maps; (3) original T1-weighted images were segmented a second time using the customized priors to obtain new segmentation and normalization parameters; (4); the spatial transformation and segmentation parameters obtained from step 3 were imported in DARTEL; (5) the rigidly aligned version of the images previously segmented in step 3 was generated; (6) the DARTEL template was created and the obtained flow fields were applied to the rigidly aligned segments to warp them to the common DARTEL space and then modulated using the Jacobian determinants. Because the DARTEL process warps to a common space that is smaller than the MNI space, we performed an additional transformations as follows: (7) the modulated images from DARTEL were normalized to the MNI template using an affine transformation estimated from the DARTEL GM template and the a priori GM probability map without resampling (http://brainmap.wisc.edu/normalizeDARTELtoMNI). Before statistical computations, images were smoothed with an 8-mm FWHM Gaussian filter. 2.5.3. Gray matter volume measurements The volumes of 5 GM regions known to be hit by AD (Braak and Braak, 1991), i.e., right and left hippocampi, PCC, anterior cingulate cortex (ACC), precuneus, and bilateral inferior parietal cortex, were

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measured. The volumes of hippocampus and inferior parietal cortex were extracted from both sides, whereas PCC, ACC, and precuneus were segmented as single regions because of their midline anatomical position. Right and left hippocampal volumes were calculated from the T1-weighted images using FIRST (http://www.fmrib. ox.ac.uk/fsl/first/index.html), which has been validated in AD populations (Patenaude et al., 2011). Masks of the PCC, ACC, precuneus, and right and left parietal cortices were defined using the WFU Pickatlas (http://www.nitrc.org/projects/wfu_pickatlas/) in SPM8. Masks were then applied on the single-subject smoothed, normalized, and modulated GM maps obtained by VBM (step 7), and their volumes measured using the “Volumes” toolbox of SPM (http://sourceforge.net/projects/spmtools). 2.6. Statistical analysis 2.6.1. Demographic, clinical, cognitive, and volumetric data Group differences in categorial variables were assessed using Fisher’s exact test. Continuous variables were compared using analysis of covariance models (SAS release 9.1; SAS Institute, Cary, NC, USA) (p < 0.05). Age, years of education, and WM lesion load were included as covariates in the comparisons of GM volumes between AD patients and controls. 2.6.2. TBSS: Between-group comparisons DT MRI voxelwise statistics were performed using a permutation-based inference tool for nonparametric statistical thresholding [“randomize,” FSL (Nichols and Holmes, 2002)]. The number of permutations was set at 5000 (Nichols and Holmes, 2002). MD, FA, radD, and axD values within the skeleton were compared between patients and age-matched controls adjusting for age, years of education, and WM lesion load. The contrast older versus younger healthy subjects was also tested adjusting for years of education and WM lesion load. The between-group comparisons were thresholded at p < 0.05, corrected for multiple comparisons (familywise error [FWE]) at a cluster level using the threshold-free cluster enhancement option (Smith and Nichols, 2009). Regions of decreased FA and increased MD in each AD group versus the other were identified using an interaction analysis, adjusting for years of education and WM lesion load. For instance, FA interaction was performed by entering the following factors in the model: EOAD FA, younger controls FA, LOAD FA, older controls FA. The first interaction model tested the following: (EOAD < younger controls) > (LOAD < older controls), i.e., 1 1 1 1. The second interaction model tested the following: (EOAD < younger controls) < (LOAD < older controls), i.e., 1 1 1 1. In addition, tract probability maps of the corpus callosum and cingulum bilaterally were used to mask the MD and FA skeletons to perform a “tractrestricted” voxelwise interaction analysis. The corpus callosum and the cingulum bundle were selected as the main fibers known to connect the GM regions of interest (detailed in section 2.5.3). Because of the conservative nature of the interaction analysis and in agreement with previous studies (Rabinovici et al., 2007), the results of the interaction analyses were tested at a statistical threshold of p < 0.05, uncorrected. 2.6.3. VBM: Between-group comparisons Analyses of covariance were performed to assess cortical volume differences between each AD patient group and age-matched healthy controls. Total intracranial volume (TICV), age, years of education, and WM lesion load were included in the models as covariates. The comparison between older and younger healthy controls was also tested, adjusting for TICV, years of education, and WM lesion load. The statistical threshold was set at p < 0.05, FWE corrected. When no significant findings were found, results were

also tested at p < 0.001, uncorrected within at least 20 contiguous voxels. Regions of more severe atrophy in each AD group versus the other were identified using an interaction analysis, as described for TBSS. Interaction analysis was adjusted for TICV, years of education, and WM lesion load, and results tested at p < 0.001 uncorrected within at least 20 contiguous voxels. 2.6.4. Relationships between WM microstructural damage and GM volumes To assess whether the DT MRI abnormalities (i.e., FA and MD values) of the corpus callosum and cingulum were associated with GM volume loss, tract probability maps obtained from patients were used to mask the whole-brain MD and FA skeletons to obtain “tractrestricted” skeletons. Then, a “tract-restricted” TBSS correlation analysis was performed in each AD patient group using regression models in FSL. As for between-group comparisons, DT MRI voxelwise statistics were performed using “randomize” and 5000 permutations (Nichols and Holmes, 2002). Volumes of the hippocampus (left and right), PCC, ACC, precuneus, and inferior parietal cortices (left and right) entered the regression models as variables of interest. The analyses were adjusted for age and results were assessed at p < 0.05, FWE corrected. In addition, to test directly the effect of age at onset on the relationship between WM damage and GM atrophy, interaction models in FSL were performed. For instance, to investigate whether the relationship between FA values and PCC volumes was different in the 2 AD groups, we modeled FA images of AD patients as the dependent variable, and entered the following factors into the model as covariates: PCC volumes, group (i.e., EOAD or LOAD), and the product term PCC volumes  group. The results of the interaction analyses were tested at a statistical threshold of p < 0.05, uncorrected. 2.6.5. Relationships between WM microstructural damage and clinical features Using regression analyses in FSL, the relationships between FA and MD values and clinical and cognitive variables (i.e., CDR-Sum of boxes [SB], MMSE and cognitive test scores) were tested in each AD group. The analyses were adjusted for age and results were assessed at p < 0.05 FWE-corrected. To reduce the number of cognitive variables included in the analysis, cognitive composite scores were computed for each subject by transforming scores into standardized Z scores (i.e., computing the difference between each subject score and the average score, divided by the standard deviation of the score). Composite scores were obtained for memory (by averaging the Z scores of verbal and spatial span and Rey’s Figure delayed recall), language (by averaging Z scores of phonemic and semantic fluency tests), and executive functions (by averaging Z scores of Raven’s Coloured Progressive and Attentive Matrices). Scores for visuospatial abilities were obtained by converting the Rey’s Figure Copy test into Z scores. Using regression models in FSL, the correlations between age of onset and the DT MRI metrics (FA and MD) were tested in all AD patients, adjusting for years of education and WM lesion load. 3. Results 3.1. Demographic, clinical, and cognitive features Each AD group was similar to the corresponding control group in terms of age, gender, and WMH load (Table 1). AD patients had a lower education level than controls. LOAD and EOAD patients were similar in terms of gender, disease duration, and disease severity (Table 1). LOAD patients had a higher WMH load and a lower level of education compared with EOAD patients.

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Table 2 Neuropsychological features of patients classified by age at onset pa

pb

19.5  3.9 (12e27; 83%)

18.6  4.8 (8e26; 90%)

0.73

0.70

4.4  0.8 (3e6; 10%) 3.4  1.2 (0e6; 50%) 2.8  3.2 (0e11; 81%)

4.3  0.75 (3e6; 17%) 2.5  0.9 (0e4; 93%) 1.8  2.2 (0e6; 94%)

0.55 0.01 0.37

0.66 0.01 0.13

25.5  5.7 (5e33; 39%) 15.9  6.0 (7e32; 28%) 17.4  5.6 (4e29; 47%)

22.2  7.0 (4e30; 76%) 16.8  9.3 (2e39; 42%) 19.7  8.8 (6e38; 68%)

0.04 0.84 0.68

0.03 0.36 0.16

0.001

0.03

0.001 0.12

0.001 0.01

LOAD patients MMSE (c-o: 24) Memory Digit span (c-o: 3.75) Spatial span (c-o: 3.75) Rey’s Figure Recall (c-o: 9.47) Language Token Test (c-o: 26.5) Phonemic fluency (c-o: 17) Semantic fluency (c-o: 25) Visuo-spatial abilities Rey’s Figure Copy (c-o: 28.88) Reasoning and attention Raven Coloured Progressive Matrices (c-o: 18) Attentive matrices (c-o: 31)

EOAD patients

20.1  9.1 (1e36; 75%) 18.5  6.9 (4e31; 14%) 32.0  11.4 (5e55; 34%)

9.1  7.4 (1e26; 100%) 11.0  5.7 (0e22; 63%) 27.8  11.4 (0e49; 78%)

Values are mean  standard deviation (ranges); percentages of patients with an abnormal score compared with normative data. Key: c-o, normal/abnormal cut-off; EOAD, early-onset Alzheimer’s disease; LOAD, late-onset Alzheimer’s disease; MMSE, Mini-Mental State Examination. a p Values refer to the ManneWhitney U test between patient groups. b p Values refer to the Fisher’s exact test between the percentages of patients with an abnormal score in the 2 patient groups. Table shows unadjusted raw scores. Individual normal or abnormal scores were defined compared with age-, gender-, and education-adjusted scores of the normative data. Cut-off values are the fifth percentile of the distribution in the normative population and reflects an equivalent score of 0 when comparing performances of patients with matched normal subjects.

Compared with LOAD, EOAD patients performed worse at tests investigating short-term verbal memory, verbal comprehension, visuo-spatial abilities, and reasoning functions (Table 2). In these cognitive domains and in selective attention, a higher number of EOAD than LOAD patients had data indicating an abnormal performance compared with corresponding age- and education-matched normative data (Table 2). There was no test at which LOAD patients had a worse performance compared with EOAD cases. 3.2. WM microstructural damage 3.2.1. EOAD versus younger healthy controls Compared with controls, EOAD patients showed decreased FA and increased MD and radD along the entire corpus callosum, cingulum, fornix, superior longitudinal, uncinate, inferior longitudinal and inferior fronto-occipital fasciculi, anterior limb of internal capsule, and thalamic radiations, bilaterally (p < 0.05, FWE corrected; Fig. 1A). EOAD patients also showed an increased axD in the corpus callosum, internal and external capsule, and some regions of the temporal and parietal lobes (p < 0.05, FWE corrected). AxD and radD findings are shown in Online Fig. 1. 3.2.2. LOAD versus older healthy controls Compared with controls, LOAD patients showed a decreased FA in the genu and splenium of the corpus callosum and posterior cingulum, bilaterally (p < 0.05, FWE-corrected; Fig. 1A). Increased radD was observed in the genu and splenium of the corpus callosum (p < 0.05, FWE-corrected; Online Fig. 1). No regions of increased MD and axD were found in LOAD. 3.2.3. EOAD versus LOAD 3.2.3.1. Whole brain. The interaction analysis showed that compared with LOAD, EOAD patients had a decreased FA and an increased MD in the posterior cingulum bilaterally, corpus callosum fibers connecting the superior parietal and the occipital lobes bilaterally, and left superior longitudinal fasciculus (p < 0.05, uncorrected; Fig. 1B). Decreased FA involved also the parahippocampal portion of the left cingulum. There was no region of decreased FA and increased MD in LOAD compared with EOAD patients. 3.2.3.2. Cingulum (“tract-restricted” TBSS). Compared with LOAD, EOAD patients had a decreased FA and increased MD in the posterior cingulum bilaterally (p < 0.05, uncorrected; Fig. 1C). The

greater involvement of the bilateral posterior cingulum in EOAD patients survived the FWE correction for multiple comparisons. There was no region of decreased FA and increased MD in LOAD compared with EOAD patients. 3.2.3.3. Corpus callosum (“tract-restricted” TBSS). Compared with LOAD, EOAD patients had a decreased FA along the entire corpus callosum, particularly in the fibers connecting the parietal lobes (p < 0.05, uncorrected; Fig. 1C). An increased MD was observed in the bilateral splenium, mainly on the left side (p < 0.05, uncorrected; Fig. 1C). There was no region of decreased FA and increased MD in LOAD compared with EOAD patients. 3.2.4. Older versus younger healthy controls Older healthy controls compared with younger healthy subjects showed a decreased FA and an increased MD in the genu and anterior body of the corpus callosum, external capsule, orbitofrontal, inferior frontal, and anterior temporal regions, and posterior cingulum bilaterally (p < 0.05, FWE-corrected; Online Fig. 2). A decreased FA was also found in the temporo-occipital connections bilaterally (p < 0.05, FWE corrected; Online Fig. 2). Online Fig. 2 also shows regions of increased axD and radD in older relative to younger healthy subjects. No region of decreased FA and increased MD, axD, and radD was found in younger compared with older healthy subjects. 3.3. GM atrophy Online Tables 1 and 2 and Fig. 2 show VBM results in EOAD and LOAD patients. The severity and regional distribution of GM loss in each patient group versus age-matched controls were consistent with those reported in the literature (Canu et al., 2012; Frisoni et al., 2007; Ishii et al., 2005; Migliaccio et al., 2009). The interaction analysis showed that, compared with LOAD patients, EOAD patients had greater GM volume loss in the precuneus, superior and middle temporal regions, superior and inferior frontal gyri, and occipital cortex (p < 0.001, uncorrected; Online Table 3, Fig. 2). There was no region in which LOAD patients showed greater GM atrophy compared with EOAD patients. Online Table 4 reports the volumes of selected GM regions in AD patients and healthy controls. Older healthy controls compared with younger healthy subjects showed GM volume loss in the hippocampus bilaterally, right PCC

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Fig. 1. Tract-Based Spatial Statistics (TBSS) results in early-onset Alzheimer’s disease (EOAD) patients and late-onset Alzheimer’s disease (LOAD) patients versus age-matched healthy controls (A), and each other (B, interaction analysis). Voxelwise group differences are shown in red (fractional anisotropy [FA]) and blue (mean diffusivity [MD]). Results are overlaid on the WM skeleton (light green) and displayed on the sagittal and axial sections of the Montreal Neurological Institute (MNI) standard brain in neurological convention (right is right) at p < 0.05 corrected for multiple comparisons (Family Wise Error) for the comparison between patients and controls, and at p < 0.05 uncorrected for the interaction analysis. (C) Tract-restricted TBSS results showing the more severe damage of the cingulum and corpus callosum in EOAD versus LOAD patients (interaction analysis). Voxelwise group differences are shown in red (FA) and blue (MD). Results are overlaid on the cingulum (light green) or corpus callosum (yellow) tractography maps and displayed on the 3-dimensional sections of the MNI standard brain at p < 0.05 uncorrected.

and precuneus, left lateral temporal region, and left head of caudate nucleus (p < 0.05, FWE corrected; Online Fig. 2). 3.4. Relationships between WM microstructural damage and GM volumes 3.4.1. EOAD patients In EOAD patients, the hippocampal and ACC volumes correlated with the FA of the posterior and middle cingulum bilaterally (p < 0.05, FWE corrected; Fig. 3), and the left and right hippocampal, ACC and precuneus volumes were associated with FA of the entire corpus callosum (p < 0.05, FWE corrected; Fig. 4). No relationship was found with MD values. 3.4.2. LOAD patients In LOAD patients, the left and right hippocampal volumes correlated with the FA of the left and right posterior, middle, and parahippocampal portions of the cingulum (p < 0.05, FWE corrected; Fig. 3). The ACC and PCC volumes were associated with FA of the posterior and middle portions of the cingulum, bilaterally (p < 0.05, FWE corrected; Fig. 3). The ACC volume was also related with FA of the right anterior and parahippocampal tract portions of the cingulum (p < 0.05, FWE corrected; Fig. 3). No relationship was found with MD values.

Hippocampal, PCC, ACC, and precuneus volumes were associated with FA of the entire corpus callosum (p < 0.05, FWE corrected; Fig. 4). The right inferior parietal cortical volume was related with FA of the splenium and genu of the corpus callosum (p < 0.05, FWE corrected; Fig. 4). No relationship was found with MD values. 3.4.3. Interaction with age at onset No effect of age at onset on the associations between GM volumes and DT MRI variables in AD patients was found. 3.5. Relationships between WM microstructural damage and clinical features In EOAD patients, the CDR-SB correlated with MD values in the corpus callosum, PCC, frontal and parietal parts of the superior longitudinal fasciculus bilaterally, and left temporal regions (p < 0.05 FWE corrected; Fig. 5). In LOAD patients, a significant correlation was found between the MMSE score and FA values in the corpus callosum (p < 0.05 FWE corrected; Fig. 5). No correlation was found between DT MRI variables and cognitive test scores in EOAD and LOAD patients. Online Fig. 3 shows the correlation between age of onset and the DT MRI metrics (FA and MD) in all AD patients.

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Fig. 2. Voxel-based morphometry (VBM) results showing cortical atrophy in early-onset Alzheimer’s disease (EOAD) and late-onset Alzheimer’s disease (LOAD) patients versus matched healthy controls (A), and each other (B, interaction analysis). Results are overlaid on the coronal, sagittal, and axial sections of the Montreal Neurological Institute standard brain in neurological convention (right is right), and displayed at p < 0.05 corrected for multiple comparisons (Family Wise Error) for EOAD versus younger healthy controls and at p < 0.001 uncorrected for multiple comparisons within 20 contiguous voxels for the other contrasts.

4. Discussion In this study, we used DT MRI voxelwise and tractography-based approaches to quantify WM regional microstructural damage in EOAD compared with LOAD patients, and showed that EOAD is associated with more severe and distributed WM abnormalities, although both patient groups had similar dementia severity and disease duration. In EOAD, WM microstructural damage involved the interhemispheric connections, limbic network and major associative tracts, and spared the corticospinal tracts, and brainstem and cerebellar WM. On the contrary, LOAD patients showed a more restricted pattern of WM damage, which involved mainly the corpus callosum. EOAD patients also experienced a marked parietal WM microstructural damage, as shown by the observation that they harbored more severe diffusion abnormalities in the posterior cingulum and splenium of the corpus callosum connecting the parietal cortices than patients with LOAD. Of note, DT MRI alterations in EOAD patients were much more diffuse than the WM atrophy detected by previous studies in these patients (Canu et al., 2012; Migliaccio et al., 2012), suggesting that WM diffusivity abnormalities may precede the development of detectable WM atrophy. Along with parietotemporal cortical atrophy, WM damage is likely to contribute to the clinical dementia severity and the multidomain cognitive impairment typically seen in EOAD. We showed a direct association between WM damage to the corpus callosum, PCC, superior longitudinal fasciculus, and temporal regions and disease severity in EOAD. The lack of correlations between WM damage and cognitive test scores is likely to be related to the “floor effect” of cognitive data in our patients. In LOAD, a significant correlation was found between the MMSE score and FA values in the corpus callosum, which fits with the predominant memory deficits seen in these patients (Smits et al., 2012). The more severe microstructural WM abnormalities in EOAD relative to LOAD patients could be secondary to their greater cortical tissue loss, as shown by VBM. To investigate this aspect further, unlike previous studies on WM damage in EOAD (Canu et al., 2012; Migliaccio et al., 2012), we investigated the relationships between

atrophy of “critical” GM areas (i.e., the hippocampi, PCC, ACC, precuneus, and inferior parietal cortices) and microstructral damage to the cingulum and corpus callosum, which are the main fiber tracts that are known to connect these cortical structures, in EOAD and LOAD patients separately. Consistent with the hypothesis that links WM degeneration to cortical damage (Medina and Gaviria, 2008) and results of previous imaging studies (Agosta et al., 2011; Bosch et al., 2012; Bozzali et al., 2012; Villain et al., 2008; Xie et al., 2005), we found that cingulum and corpus callosum FA values were correlated with hippocampi and medial/dorsal parietal cortical volumes in patients with LOAD. By contrast, since damage to the cingulum and corpus callosum was only partially explained by cortical volume loss in EOAD patients, our findings might suggest that, in addition to secondary degeneration, other processes are likely to be associated with microstructural WM damage in these patients. However, the results of the interaction analysis that directly tested the effect of age at onset on the relationship between GM volumes and WM damage were not significant. As a consequence, we cannot exclude the possibility that the different patterns of GM/WM association that we observed in EOAD and LOAD patients is just a reflection of the different samples. There are several, not mutually exclusive, explanations for the discrepancy between WM and cortical abnormalities in EOAD. First, we cannot rule out vascular lesions in the genesis of WM abnormalities (Sjobeck et al., 2006). However, we excluded patients with significant WMHs; the analyses were corrected for WM lesion load; the 2 groups of patients had similar WMH burdens compared with age-matched controls; and LOAD patients had a higher WMH load compared with EOAD cases. All of these factors suggest that the effect of WMHs on diffusivity differences between EOAD and LOAD patients might not have influenced our results a great deal. Second, WM microstructural damage could be secondary to Ab deposition. In AD brains, Ab deposits have been observed in close proximity to damaged WM (Wisniewski et al., 1989). In vitro studies showed that Ab oligomerization can cause direct damage to oligodendrocytes, and thereby myelin, in a dose-dependent manner, which is accompanied by nuclear DNA fragmentation, mitochondrial dysfunction, and cytoskeletal disintegration (Xu et al., 2001). The investigation of

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greater structural brain damage in EOAD relative to LOAD may reflect a different severity of the underlying neuropathology. This hypothesis builds on post-mortem studies almost invariably reporting higher burdens of Ab plaques, and neurofibrillary tangles, in patients with EOAD (Bigio et al., 2002; Ho et al., 2002; Marshall et al., 2007; Nochlin et al., 1993), particularly in the parietal lobe (Bigio et al., 2002). These findings have been recently confirmed by an in vivo 11C-PIB PET study (Ossenkoppele et al., 2012). In light of this theory, the greater microstructural WM abnormalities that we detected in EOAD might be interpreted as a distinctive distribution of early upstream events (i.e., Ab deposition). The second theory hinges on different findings and especially on results from another 11C-PIB-PET study, which showed no global and regional differences in Ab burden between EOAD and LOAD (Rabinovici et al., 2010). Furthermore, Ab1e42 and hyperphosphorylated tau levels in CSF were found not to differ in AD patients according to age at onset (Bouwman et al., 2009). These observations suggest that an increased Ab deposition in EOAD cannot explain the detected functional and structural abnormalities (including WM damage). Instead, these results are consistent with a model in which an increased susceptibility to the neurotoxic effects of early Ab deposition and/or more severe downstream neurodegenerative processes (including tau

Fig. 3. Relationships between fractional anisotropy (FA) of the bilateral cingulum and gray matter (GM) volumes (light blue; left and right hippocampus, anterior cingulate cortex [ACC], and posterior cingulate cortex [PCC]) in early-onset Alzheimer’s disease (EOAD; A) and late-onset Alzheimer’s disease (LOAD; B) patients. Regions of decreased FA associated with reduced GM volumes (red) are overlaid on the cingulum tractography map (light green) and displayed on the 3- and 2-dimensional sections of the Montreal Neurological Institute standard brain at p < 0.05 corrected for multiple comparisons (Family Wise Error).

the Ab toxicity to oligodendrocytes and myelin is still in its infancy, and no study has assessed yet whether it follows different patterns according to age at AD onset. MD and FA alterations could be secondary to changes of diffusion either parallel (axD) or perpendicular (radD) to the principal direction of the tensor (Pierpaoli et al., 2001). In EOAD patients, a prominent tensor variations in all 3 spatial dimensions was observed, determining the widespread increase of MD values. RadD clusters of significance, however, were more extensive than those of axD. Such a partial topographical mismatch in the tensor variations is the reason for the FA reductions seen in these patients. Conversely, FA (but not MD) alterations in LOAD were driven by the radD increase, with no axD abnormalities. The study of axD and radD helps us to interpret MD and FA changes and suggests possible different pathological processes in the EOAD and LOAD patients, although radio-pathological correlations based on the investigation of these metrics remain controversial (Wheeler-Kingshott and Cercignani, 2009). Our DT MRI findings add an intriguing piece of information to the current understanding of the pathophysiology of EOAD. To date, 2 major theories have emerged from postmortem and 11C-Pittsburgh compound B (11C-PIB) PET studies. The first theory argues that the

Fig. 4. Relationships between fractional anisotropy (FA) of the corpus callosum and gray matter (GM) volumes (light blue; left and right hippocampus, anterior cingulate cortex [ACC], and posterior cingulate cortex [PCC], precuneus, and right inferior parietal cortex) in early-onset Alzheimer’s disease (EOAD; A) and late-onset Alzheimer’s disease (LOAD; B) patients. Regions of decreased FA associated with reduced GM volumes (red) are overlaid on the corpus callosum tractography map (yellow) and displayed on the 3- and 2-dimensional sections of the Montreal Neurological Institute standard brain at p < 0.05 corrected for multiple comparisons (Family Wise Error).

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Fig. 5. Relationships of mean diffusivity (MD) and fractional anisotropy (FA) with clinical/cognitive variables (i.e., Clinical Dementia RatingeSum of boxes [CDR-SB] and Mini-Mental State Examination [MMSE]) in early-onset Alzheimer’s disease (EOAD) and late-onset Alzheimer’s disease (LOAD) patients. Regions of increased MD (blue) associated with increased CDR-SB in EOAD and regions of decreased FA (red) associated with reduced MMSE scores in LOAD are overlaid on the WM skeleton (light green) and displayed on the sagittal and axial sections of the Montreal Neurological Institute (MNI) standard brain in neurological convention (right is right) at p < 0.05 corrected for multiple comparisons (Family Wise Error).

pathology) may play a central role in the pathogenesis of EOAD (Rabinovici et al., 2010). In such a complex framework, it is tempting to speculate that the greater and more widely distributed WM involvement that we found in EOAD patients is likely to be associated with both a more severe upstream process (i.e., early, and possibly higher, Ab accumulation) and downstream process (i.e., more severe cortical atrophy). To our knowledge, no pathological studies so far have investigated the WM damage in AD at different ages of onset. The study is not without limitations. To test in vivo the hypothesis that greater WM injury in EOAD may, in part, be related to greater Ab burden, the association between DT MRI variables and CSF Ab1e42 levels could be assessed. However, CSF analysis was performed only in a subgroup of patients, which prevented us from running such an analysis. In addition, no Apolipoprotein E genotype was available for our patients. Future studies in larger samples are warranted to investigate in vivo the biological and genetic correlates of our findings. Furthermore, the lack of a verbal episodic memory test in the neuropsychological battery is another weakness of the study, as this is a domain more affected in LOAD than in EOAD. Finally, although our approach has been commonly used in previous published papers in the field (Canu et al., 2012; Frisoni et al., 2005; Frisoni et al., 2007; Kim et al., 2005; Migliaccio et al., 2009), to set at 65 years the boundary between early and late onset of AD is arbitrary. Disclosure statement No author’s institution has contracts relating to our research through which it or any other organization may stand to gain financially now or in the future. Acknowledgements Federica Agosta has received research support from the Italian Ministry of Health, funding for travel from Teva Pharmaceutical Industries Ltd; has received speaker honoraria from Bayer Schering

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Pharma, Biogen Idec, Sanofi Aventis, and Serono Symposia International Foundation. Giancarlo Comi has received personal compensation for activities with Teva Neuroscience, Merck Serono, Bayer-Schering, Novartis, Sanofi-Aventis Pharmaceuticals, and Biogen Idec as a consultant, speaker, or scientific advisory board member. Massimo Filippi serves on scientific advisory boards for Teva Pharmaceutical Industries Ltd and Genmab A/S; has received funding for travel from Bayer Schering Pharma, Biogen Idec, Genmab A/S, Merck Serono, and Teva Pharmaceutical Industries Ltd; serves as a consultant to Bayer Schering Pharma, Biogen Idec, Genmab A/S, Merck Serono, Pepgen Corporation, and Teva Pharmaceutical Industries Ltd; serves on speakers’ bureaus for Bayer Schering Pharma, Biogen Idec, Genmab A/S, Merck Serono, and Teva Pharmaceutical Industries Ltd; receives research support from Bayer Schering Pharma, Biogen Idec, Genmab A/S, Merck Serono, Teva Pharmaceutical Industries Ltd, Fondazione Italiana Sclerosi Multipla, the Italian Ministry of Health, and CurePSP; is Editor-inChief of the Journal of Neurology and serves on editorial boards of the American Journal of Neuroradiology, BMC Musculoskeletal Disorders, Clinical Neurology and Neurosurgery, Erciyes Medical Journal, Journal of Neuroimaging, Journal of Neurovirology, Magnetic Resonance Imaging, Multiple Sclerosis, Neurological Sciences, and Lancet Neurology. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.neurobiolaging. 2013.03.026. References Agosta, F., Pievani, M., Sala, S., Geroldi, C., Galluzzi, S., Frisoni, G.B., Filippi, M., 2011. White matter damage in Alzheimer disease and its relationship to gray matter atrophy. Radiology 258, 853e863. Ashburner, J., 2007. A fast diffeomorphic image registration algorithm. NeuroImage 38, 95e113. Basso, A., Capitani, E., Laiacona, M., 1987. Raven’s coloured progressive matrices: normative values on 305 adult normal controls. Funct. Neurol. 2, 189e194. Behrens, T.E., Berg, H.J., Jbabdi, S., Rushworth, M.F., Woolrich, M.W., 2007. Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage 34, 144e155. Benedetti, B., Charil, A., Rovaris, M., Judica, E., Valsasina, P., Sormani, M.P., Filippi, M., 2006. Influence of aging on brain gray and white matter changes assessed by conventional, MT, and DT MRI. Neurology 66, 535e539. Bigio, E.H., Hynan, L.S., Sontag, E., Satumtira, S., White, C.L., 2002. Synapse loss is greater in presenile than senile onset Alzheimer disease: implications for the cognitive reserve hypothesis. Neuropathol. Appl. Neurobiol. 28, 218e227. Bosch, B., Arenaza-Urquijo, E.M., Rami, L., Sala-Llonch, R., Junque, C., SolePadulles, C., Pena-Gomez, C., Bargallo, N., Molinuevo, J.L., Bartres-Faz, D., 2012. Multiple DTI index analysis in normal aging, amnestic MCI and AD. Relationship with neuropsychological performance. Neurobiol. Aging 33, 61e74. Bouwman, F.H., Schoonenboom, N.S., Verwey, N.A., van Elk, E.J., Kok, A., Blankenstein, M.A., Scheltens, P., van der Flier, W.M., 2009. CSF biomarker levels in early and late onset Alzheimer’s disease. Neurobiol. Aging 30, 1895e1901. Bozzali, M., Giulietti, G., Basile, B., Serra, L., Spano, B., Perri, R., Giubilei, F., Marra, C., Caltagirone, C., Cercignani, M., 2012. Damage to the cingulum contributes to Alzheimer’s disease pathophysiology by deafferentation mechanism. Hum. Brain Mapp. 33, 1295e1308. Braak, H., Braak, E., 1991. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239e259. Caffarra, P., Vezzadini, G., Dieci, F., Zonato, F., Venneri, A., 2002. Rey-Osterrieth complex figure: normative values in an Italian population sample. Neurol. Sci. 22, 443e447. Canu, E., Frisoni, G.B., Agosta, F., Pievani, M., Bonetti, M., Filippi, M., 2012. Early and late onset Alzheimer’s disease patients have distinct patterns of white matter damage. Neurobiol. Aging 33, 1023e1033. De Renzi, E., Faglioni, P., 1978. Normative data and screening power of a shortened version of the Token Test. Cortex 14, 41e49. Folstein, M.F., Folstein, S.E., McHugh, P.R., 1975. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189e198. Frisoni, G.B., Pievani, M., Testa, C., Sabattoli, F., Bresciani, L., Bonetti, M., Beltramello, A., Hayashi, K.M., Toga, A.W., Thompson, P.M., 2007. The topography of grey matter involvement in early and late onset Alzheimer’s disease. Brain 130, 720e730.

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