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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Alzheimers Dis. Author manuscript; available in PMC 2011 January 1.
Published in final edited form as:
PMCID: PMC2889147
NIHMSID: NIHMS172022

Microstructural diffusion changes are independent of macrostructural volume loss in moderate to severe Alzheimer's disease

Abstract

Although it is established that Alzheimer's disease (AD) leads to cerebral macrostructural atrophy, microstructural diffusion changes have also been observed, but it is not yet known whether these changes offer unique information about the disease pathology. Thus, a multimodal imaging study was conducted to determine the independent contribution of each modality in moderate to severe AD.

17 patients with moderate-severe AD and 13 healthy volunteers underwent diffusion-weighted and T1-weighted MR scanning. Images were processed to obtain measures of macrostructural atrophy (GM and WM volumes) and microstructural damage (fractional anisotropy and mean diffusivity). Microstructural diffusion changes independent of macrostructural loss were investigated using an ANCOVA where macrostructural maps were used as voxel-wise covariates. The reverse ANCOVA model was also assessed, where macrostructural loss was the dependent variable and microstructural DTI maps were the imaging covariates.

Diffusion differences between patients and controls were observed after controlling for volumetric differences in medial temporal, retrosplenial regions, anterior commissure, corona radiata, internal capsule, thalamus, corticopontine tracts, cerebral peduncle, striatum and precentral gyrus. Independent volumetric differences were observed in the entorhinal cortex, inferior temporal lobe, posterior cingulate cortex, splenium and cerebellum.

While it is well known that AD is associated with pronounced volumetric change, this study suggests that measures of microstructure provide unique information not obtainable with volumetric mapping in regions known to be pivotal in AD and in those thought to be spared. As such this work provides great understanding of the topography of pathological changes in AD that can be captured with imaging.

Keywords: Diffusion Weighted Imaging (DWI), Alzheimer's disease (AD), Microstructure, Fractional Anisotropy (FA), Mean Diffusivity (MD)

Introduction

Although Alzheimer's Disease (AD) is often viewed as a gray matter (GM) disease, white matter (WM) alterations are also commonly observed [1-4]. Whether these alterations are explained by a primary neurodegenerative process, or are secondary to GM loss (or both) is currently unknown and requires further research. Microstructural investigations with diffusion tensor imaging (DTI) offer a promising approach to improve our understanding of WM, but also GM, changes in disease progression [5, 6]. At the microscopic level, brain parenchymal structures have distinct boundaries, including axons membranes and myelin sheaths, which constrain the diffusion of water molecules along boundaries rather than across them [7]. Fractional anisotropy (FA) and mean diffusivity (MD) are two quantitative indices of diffusion reflecting the integrity of the brain tissue [8, 9]. Alterations in the microstructure environment, as happens in neurodegenerative processes, such as small vessel alterations, demyelination of axonal structures, degradation of microtubules, loss of axonal structure and possibly gliosis reduce directional diffusion and thus reduce FA [10-12]. Increased MD results from decreased tissue density reflecting cell loss of both neurons and glia [10, 13].

In AD, a number of studies using diffusivity indices have been carried out using specific region-of-interest (ROI) analyses [14-20] or whole brain voxel-wise methods [21-23]. However, the meaning of changes in FA or MD must be interpreted cautiously because diffusivity changes may be influenced by GM and WM atrophy [24]. For instance, although these indices are considered to be measures of tissue microstructure, it is currently unknown how they are related to WM volume [24]. Moreover, the relationship between FA and volume may not be consistent across the lifespan [25, 26], due to changes in cell and tissue complexity at different stages of development and aging [27], and this relationship may be altered further by neurodegenerative disease. The situation is more complicated in the presence of tissue atrophy. A study by Hugenschmidt and colleagues [28] used a recently published method [29] to detect FA changes over WM and GM volume loss in 66 healthy adults across a broad age span. They observed that regions exhibiting decrease in FA in middle age were the same areas that exhibit volume loss in older age, suggesting that microstructural FA changes may precede and predict volume loss. They also found age-related decreases in FA in atrophy-spared regions, suggesting FA provides unique information.

Isolating the contribution of microstructural damage in AD will likely improve models of disease progression, be important for early diagnosis and for monitoring the efficacy of treatments. The purpose of the present study is to determine whether AD patients show microstructural changes that are not explained by GM and WM volume loss in areas specific to AD. In the light of previous data on healthy people [28], we hypothesized that AD patients compared to controls would exhibit separable microstructural (FA and MD indices) and macrostructural or volumetric changes that each provide unique information about the disease.

Methods

Participants

Seventeen patients with moderate to severe probable AD, diagnosed according to NINCDS-ADRDA criteria [30] at the IRCCS Centro S. Giovanni di Dio Fatebenefratelli (Brescia, Italy) and 13 healthy volunteers participated in the study. The participant or primary caregiver provided written informed consent. All patients with a Clinical Dementia Rating of 2 or greater [31], were included in this study. Additionally, disease severity was assessed with the Mini Mental State Examination (MMSE) [32]. AD patients were recruited as part of a functional MRI study of memantine; data reported here are those acquired at baseline. Healthy volunteers were mostly patients' non-consanguineous relatives of similar age. Exclusion criteria included history of transient ischemic attack (TIA) or stroke, WM hyperintensities and lesions, head trauma, alcohol or substance abuse, corticosteroid therapy, recent weight loss, or a modified Hachinski ischemic scale score greater than 4 [33]. Standardized history taking, behavioral and functional assessments, physical and neurological examinations, and a comprehensive neuropsychological battery were carried out for all the participants. There was no difference in age between groups, however they differed in gender, years of education and MMSE score (Table 1). The study was approved by the Ethics Committee of the Centre in accordance with the Declaration of Helsinki.

Table 1
Demographic characteristics of AD patients and healthy controls

MRI acquisition

Images were acquired on a Siemens (Erlangen, Germany) 3 Telsa Allegra scanner at the Neuroradiology Unit of the Ospedale Maggiore Borgo Trento, Verona, Italy, with a standard head coil. High resolution T1-weighted scans were acquired with a 3D sagittal magnetization prepared rapid gradient echo (3D MP-RAGE) sequence: repetition time (TR) = 2300 ms, echo time (TE) = 3.93 ms, inversion time (TI) = 1100 ms, flip angle = 12°, acquisition matrix = 256×256, field of view (FOV) = 265mm, slice thickness = 1 mm, total number of slices = 160. The scan duration was 8 minutes and 37 seconds. Diffusion-weighted images were acquired in 30 directions with the following echo-planar acquisition protocol: TR = 5000 ms, TE = 118 ms, b value = 1000 s/mm2, acquisition matrix = 128×128, FOV = 220mm, slice thickness = 5 mm, slice gap = 1mm. Twenty-three slices were acquired in 3 minutes.

Voxel-based Morphometry (VBM)

T1-weighted MR scans were processed with Statistical Parametric Mapping software http://www.fil.ion.ucl.ac.uk/spm (SPM5, University College London, London, UK) using the following supervised protocol (see also Figure 1, beginning at the top left): (i) T1-weighted images were segmented using the VBM5.1 toolbox [a modified version of unified VBM, http://dbm.neuro.uni-jena.de/vbm/vbm5-for-spm5/; 34] to produce tissue probability maps in MNI space; this first segmentation brings all images in the same common space (MNI) and from each image generates GM and WM segments (ii) tissue segmentations were averaged across participants and smoothed with an 8mm FWHM Gaussian filter to create customized prior probability maps; (iii) a customized T1-weighted template was created by applying the transformations produced in step (i) to the native space T1-weighted images, averaged across participants, and smoothed with an 8 mm FWHM Gaussian filter; (iv) Original T1-weighted images were segmented a second time with VBM5.1 using the custom priors to obtain new segmentation and normalization parameters. Segmenting the images one more time with the customized T1-weight template created at step (iii) was important because this template better represents the sample compared to the default SPM5 template. The remaining process stream uses the fast diffeomorphic image registration algorithm developed by Ashburner and colleagues as implemented in the Diffeomorphic Anatomical Registration using Exponentiated Lie Algebra, DARTEL toolbox with the default settings [35]. For using DARTEL it is necessary to work with at least the version 7 of Matlab (http://www.mathworks.com/) and spm5 (http://www.fil.ion.ucl.ac.uk/spm/software/spm5/). The DARTEL procedure is briefly described here: (v) T1-weighted images were rigidly aligned (using the rigid –body component of the normalization parameters from step (iv)), segmented into GM and WM (using the segmentation parameters from step (iv)) and resampled to 1.5 mm isotropic voxels; (vi) GM and WM segments were simultaneously coregistered using the fast diffeomorphic image registration algorithm [35]; (vii) the flow fields were then applied to the rigidly-aligned segments to warp them to the common DARTEL space and then modulated using the Jacobian determinants; this step generates the “modulated” warped GM and WM images in DARTEL space. Modulation scales the final GM and WM images by the amount of contraction required to warp the images to the template. The final result is GM and WM volume maps for each participant, where the total amount of GM and WM remains the same as in the original images.

Figure 1
A Diagrammatic Representation of the Image Processing Streams. The text provides a full description of the image processing stream with Roman Numerals detailing T1-weighted processing and letters detailing Diffusion Tensor Imaging processing.

Note that the parameters created in DARTEL provided a more accurate spatial normalization: they improve the alignment within subjects and benefit the alignment between modalities once the DTI images are included in the procedure. Since the DARTEL process warps to a common space that is smaller than MNI space, we performed an additional transformations as follows: (viii) the modulated images from DARTEL were normalized to the Montreal Neurological Institute (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). Prior to the statistical computations, the images were smoothed with an 8 mm FWHM Gaussian filter.

Fractional Anisotropy and Mean Diffusivity

Diffusion-weighted DICOM images were converted to ANALYZE format (Mayo Clinic, Rochester, MN) using MRI converter (http://lcni.uoregon.edu/~jolinda/MRIConvert/). FA and MD measures were calculated with the following procedures in FSL [http://www.fmrib.ox.ac.uk/fsl/fdt/index.html, 36]: (1) image distortions in the DTI data caused by eddy currents were corrected; (2) estimation of diffusion tensors was achieved using DTIFIT; (3) three-dimensional maps of FA and MD images were computed from the tensors from step (2).

In order to employ GM and WM volume as covariates in the analyses of the DTI measurements, it was necessary to align the FA and MD maps with the T1-weighted images normalized using DARTEL. To do this we used the following procedure (see also Figure 1, beginning at the top right): (a) the non-diffusion-weighted (S0) images were normalized to the MNI T2 template in SPM5; (b) a custom FA template was created by normalizing the FA images using the normalization parameters from step (a), averaging them and smoothing them with an 8mm FWHM Gaussian filter; (c) the custom FA template was normalized to the custom T1-weighted template (VBM step (iii)) using 12 parameters in FLIRT [http://www.fmrib.ox.ac.uk/fsl/flirt/index.html, 37]; (d) native space FA and MD images were normalized to the custom FA template (from step (c)); (e) the normalization parameters from step (d) were multiplied by the inverse transformation parameters for the second T1-weighted segmentation (VBM step (iv)) and applied to both the FA and MD images; (f) FA and MD images were rigidly aligned using the rigid-body component of the transformations produced in VBM step (iv) and resampled to 1.5 mm isotropic voxels; (g) warped using flow fields from DARTEL (VBM step (vi)), but not modulated; (h) the DARTEL FA and MD images were normalized to MNI space using the affine transformation estimated from the DARTEL GM template and the a priori GM probability map in SPM5 (VBM step (viii) without resampling- http://brainmap.wisc.edu/normalizeDARTELtoMNI). As with the VBM, the FA and MD images were smoothed with an 8 mm FWHM Gaussian filter similar to the strategy employed by Hugenschmidt and colleagues [28].

Masking to exclude the CSF

To exclude the CSF contamination from the results, a mask was created based on previously published CSF levels for MD [9]. For each participant's normalized and unsmoothed MD image, a mask of voxels with values less than 0.00283 mm2/sec (2 standard deviations below the CSF mean) was created. A group mask was computed as the intersection of all participant masks. This mask has been applied after the smoothing and used through all statistical analyses.

Statistical analysis

Voxel-wise analyses were performed in order to test for group differences on GM, WM, FA and MD regardless of the effect of the other imaging modalities. Next, voxel-wise analyses were performed using microstructural variables as voxel-wise covariates for macrostructural (GM and WM volume) group differences. Similarly, macrostructural variables (GM and WM volume) were used as voxel-wise covariates for microstructural (FA and MD) group differences. The Biological Parametric Mapping (BPM) toolbox implemented in SPM5 [29] was used. For all analyses CSF voxels were excluded by using the aforementioned mask.

Macrostructural analyses (GM and WM)

VBM analyses without imaging covariates

Two analyses of covariance (ANCOVAs) were computed in SPM5 to assess GM and WM differences between AD patients and controls. Smoothed GM and WM images were added in two separated models as factors. Age, gender, and Total Intracranial Volume (TIcV) were included as covariates in both models. Statistical maps were threshold at p<.001 uncorrected in at least 50 edge connected voxels. TIcV was obtained by manual tracing of the entire intracranial cavity (the lower boundary being the foramen magnum) on coronal MRIs (voxel size 1×1×7mm) using the software Display 1.3 (McConnel Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada, http://www.bic.mni.mcgill.ca/software). The images were transferred to a SUN Workstation and transformed from Digital Imaging and Communication in Medicine – DICOM to mnc format. A resampling algorithm, included in the MINC package developed at the McConnel Brain Imaging Centre, was used to set the voxel dimension to 1×1×7mm for all images. The manual TicVs correlated with those obtained automatically by SPM5 (r=0.95) and SIENAX in FSL http://www.fmrib.ox.ac.uk/fsl/siena/index.html (r=0.96).

VBM analyses with imaging covariates (using BPM)

Two ANCOVAs, one for GM and the other for WM, were computed using the BPM toolbox to assess macrostructural differences between AD patients and controls. Smoothed and modulated GM and WM images were entered as dependent variables in two separate models. Smoothed FA and MD maps were included in both models as imaging covariates. Age, gender and TIcV were included as non-imaging covariates. This approach tests for locations where volume loss explains unique variance not accounted for FA and MD microstructure.

Microstructural analyses (FA and MD)

DTI analyses without imaging covariates

We performed two ANCOVAs to assess the differences in FA and MD between AD patients and controls without using the imaging covariates. Smoothed FA and MD images were examined in two separate models. Age and gender were included as covariates in both models. All statistical maps were threshold at p<.001 uncorrected in at least 50 edge connected voxels.

DTI analyses with imaging covariates (using BPM)

Two ANCOVAs, one for MD and the other for FA, were computed using the BPM toolbox to assess MD and FA differences between AD patients and controls. Smoothed FA and MD images were entered as dependent variables in two separate models. Smoothed and modulated GM and WM segmented maps were included in both models as imaging covariates. Age and gender were included as non-imaging covariates. This approach tests for locations where DTI explains unique variance not accounted for GM and WM macrostructure.

VBM/DTI Combination

To assess the overlap in VBM and DTI group differences, we identified the total volume loss (derived from the combination of the T-maps of both GM and WM analysis without imaging covariates) and microstructural damage (derived from the combination of the T-maps of both FA and MD analysis without imaging covariates) using the logical AND between the thresholded statistical images. Thus, we created the combination map included areas with either volume loss or microstructural damage. From this map, we observed regions that had volume loss only, microstructural damage only, and volume loss with microstructural damage.

Sociodemographic analyses

ANOVA and chi-squared tests were performed to analyze the sociodemographic and clinical features (Table 1).

Results

Macrostructural analyses (GM and WM)

VBM analyses without imaging covariates

The AD group showed less GM than controls in the medial temporal lobe including the hippocampi, prefrontal cortex, frontal lobe, posterior cingulate cortex and cerebellum bilaterally, and in the right parietal lobe and insula (Fig. 2A). Additionally, the AD group showed less WM than controls in the body, genu and splenium of the corpus callosum, in the left retrosplenial region and right superior parietal lobe, posterior cingulate and prefrontal region, in the cingulum, frontal lobe, occipital and temporal lobe, and cerebellar hemispheres (Fig. 2B). No regions showed less GM or WM in the controls compared to AD patients.

Figure 2
Coronal slices from Y=-5 to Y=-42 showing regional decreases of GM (A) and WM (B) loss in AD compared to controls (VBM) overlaid on the GM (A) and WM (B) normalized DARTEL templates. All contrast are shown using p<0.001 uncorrected in at least ...

VBM analyses with imaging covariates (using BPM)

After controlling for microstructural changes with BPM we found GM atrophy in AD compared to controls in the inferior temporal lobe bilaterally and in left posterior cingulate; in WM in the splenium of corpus callosum, in the right inferior temporal lobe, in the entorhinal region and cerebellum bilaterally (Data not shown).

Microstructural analysis (MD and FA)

DTI analyses with images covariates (using BPM)

Using an ANCOVA with GM and WM volume as imaging covariates, we found that AD brains had significantly higher MD, indicating damage, in the parahippocampal regions, parietal lobe and precentral gyrus, internal capsule and caudate bilaterally, in the right thalamus, occipital lobe, in the body of cingulum and in the left medial frontal lobe including white matter tracts such as fornix, corona radiata, thalamic, corticopontine and corticospinal radiation (Table 2; Fig. 3A).

Figure 3
Coronal slices from Y=-5 to Y=-42 showing regions with higher MD values (‘warm’ colors) and lower FA values (‘cold’ colors) in AD compared to controls. Row A: BPM ANCOVAs using GM and WM images as covariates. Row B: ANCOVAs ...
Table 2
BPM: Regions with higher MD in AD compared to controls accounting for GM and WM volume loss

Reduced FA values, indicating damage, were found in the right cerebral peduncle and posterior limb of the internal capsule, in the thalamus and parietal lobe bilaterally, and in the left medial temporal lobe and striatum. The following tracts pass through those regions: the anterior commissure, superior corona radiata, thalamic, cortico-pontine and corticospinal radiation (Table 3, Fig. 3A). No regions showed a significant decrease in MD or increase in FA in AD patients compared to controls.

Table 3
BPM: Regions with lower FA in AD compared to controls accounting for GM and WM volume loss

DTI analyses without images covariates

Using an ANCOVA without GM and WM as imaging covariates, we found that AD brains had significant higher MD compared to controls in the hippocampal and parahippocampal regions, in temporal, superior parietal and medial frontal lobe bilaterally, in the right insula, internal capsule, thalamus, posterior cingulate cortex, occipital lobe, prefrontal and frontal lobe, inferior frontal gyrus and precentral gyrus, cingulum and corpus callosum, in the left caudate nucleus and anterior internal capsule (Table S1, Fig. 3B).

A significant reduction in FA in AD brains was found in the anterior cingulate, caudate and parietal lobe bilaterally, in right hippocampus, cerebral peduncle, thalamus and occipital lobe, in the left putamen and medial temporal lobe (Table S2; Fig. 3B).

VBM/DTI Combination

The combination of the VBM and the DTI results reveals regions that show both atrophy and microstructrual damage; however, there are regions that show microstructural damage but not significant atrophy; and, finally, there are regions with atrophy but not significant microstructural changes (Fig. 4).

Figure 4
Coronal slices from Y=-5 to Y=-42 showing areas with macrostructural volume loss only (GM or WM, red); microstructural damage only (MD and FA, blue); and the combination of macrostructural and microstructural changes (green).

Discussion

In the present work we differentiated microstructural damage from GM and WM volume loss in patients with moderate to severe AD. We observed atrophy-independent group differences in both MD and FA, indicative of microstructural damage beyond that explained by gross GM and WM volume loss in AD. Regions affected included the medial temporal and retrosplenial regions as well as the anterior commissure, corona radiata, internal capsule, thalamus and thalamo-cortical projections, corticopontine tracts, cerebral peduncle, striatum and precentral gyrus. Finally, volumetric differences independent of microstructural changes were observed in the entorhinal cortex, inferior temporal lobe, posterior cingulate, splenium and cerebellum.

Diffusion changes in late stage AD

In the present work, without controlling for GM and WM volume loss, we observed that our results would confirm previous findings on altered diffusion changes in AD. The comparison of FA and MD between patients and controls without GM and WM maps as imaging covariates, produced results that closely resembled other DTI studies in AD. Specifically, we observed higher MD in AD group in the hippocampi and parahippocampal regions [5, 17, 20, 22], internal capsule [22, 23], cingulum and posterior cingulate cortex [5, 22, 38], medial temporal lobe and temporal pole [15, 17, 20, 22], parietal [5, 15, 20, 22] and frontal lobe [15, 22], including the inferior and medial frontal gyrus [22]. Confirming previous studies we observed lower anisotropy in AD compared to controls in parahippocampal regions [39, 40], putamen [41], thalamus [22, 39], cerebral peduncle [23], cingulum [21, 38, 42, 43], temporal [15, 17, 20, 23, 39, 43], frontal lobe [15, 21, 22, 39, 43] and occipital lobe [39, 43].

Using the same BPM method in a normal sample with a broad age range, a previous study [28] showed that regions with decreased FA in the middle age were the same regions that exhibit volume loss in old age. In the light of those findings, regions in our sample showing microstructural alterations in AD may be predictive of future atrophy with further disease progression. While this hypothesis can only be evaluated with longitudinal follow-up, several recent studies provide supporting evidence. First, some of the regions where we found microstructural alterations independent of macrostructural volume loss, such as internal capsule, middle cerebellar peduncle, striatum, thalamus, motor cortex (precentral region), corticopontine and talamo-cortical projections, are involved in movement control. This is consistent with the notion that advanced AD patients are more likely to show motor deficits in disease stages more severe than that of the patients in the present study [23, 44]. Second, the presence of amyloid depositions and neurofibrillar tangles has been found in the striatum of moderate AD [45], in the primary motor cortex of severe AD [46] and in early onset AD [47, 48] which may cause white matter changes in input and output projection fibers. Third, higher iron levels, known to be associated to neurodegeneration [49, 50], have been found in the striatum of moderate AD compared to controls [51]. Moreover, pathological changes in the motor system regions have been reported in AD mainly related to the severity of the disease. The primary sensorimotor cortex for instance undergo significant incremental atrophy at the stage of moderate AD [52]. The volumes of thalamus and putamen have been found positively correlated with impaired cognitive functioning in patients with probable AD [53]. In another work Ferrarini and colleagues [54] observed that the severity of cognitive impairment, based on MMSE of a range from normal to severe AD, correlated with increased severity of atrophy in periventricular structures as corona radiata, caudate nuclei and thalamus. Given that the MMSE score of the AD patients in the present study is 14 (±3.7), it is not unlikely that the FA and MD changes in those areas could be expression of future atrophy due to any or both reasons: motor system involvement and/or cognitive decline associated to those regions.

In the light of the mentioned literature, this study shows that changes in AD, in terms of low FA and high MD values, may suggest where the atrophy could occur during the late stages of the disease. If this is true, knowledge of regions at-risk for atrophy could provide valuable insight into the optimal treatment of patients when appropriate drugs are available and for monitoring the efficacy of the treatment itself.

Regions with volume loss but not microstructural changes: Why?

Without controlling for FA and MD changes, as we expected based on several previous VBM studies (for a detailed review see [55]), we found GM volume loss in regions known to be pivotal in AD such as the hippocampus, medial temporal lobe, posterior cingulate cortex and parietal lobe. Recently, using VBM, WM volume loss has been observed in AD in regions relevant for the anatomical connection through the brain as the corpus callosum [56], the parahipppocampal areas [57] and the cingulum bundle [58], which are the same regions we observed in the present study.

Moreover, we found some regions of atrophy without significant microstrucural differences, a finding that has several possible explanations. First, the relation between gross volume and DTI indices may not be linear [59, 60]. The relationship between FA and WM differs over the life-span [25, 26]. FA is related to the microstructure of WM and only some properties may affect volume such as the degree of myelination and axonal degeneration [61]. Moreover, FA reflects a complex and dense cytoskeleton of axons composed of microtubules containing cellular organelles (mitochondria and vesicles); in general the cellular microstructure of tissue influences the overall mobility of diffusing molecules by providing different barriers (intracellular and extracellular) [7], which are in turn affected by age and AD [27]. Second, the image modalities used in this study differed in voxel resolution and this may have affected sensitivity to group differences in favor of the higher resolution T1w modality. Third, morphometry findings included regions where DTI acquistion was poor such as in the inferior temporal lobe and cerebellum where inhomogeneity artifact can result in a significant signal dropout with an echo-planar acquisition such as that used to collect the diffusion-weighted images. Future studies will benefit from the use of less suceptibility-prone sequences and higher resolution DTI acquisition protocols. The latter will also assist in detecting microstructural alterations in regions where the number of crossing fiber tracts is high, such as the centrum semiovale.

The pathophysiology of alterations of diffusion in AD

It is well known that diffusion changes in the WM could reflect Wallerian degeneration and be secondary to cortical pathology [62]. However, amyloid β (Aβ) deposits have been found near the vessels in the WM in AD [63] and recently it has been demonstrated that Aβ peptides are cytotoxic to oligodendrocytes in vitro [64]. A neuropathologic study [65] established the phases of Aβ deposition in AD cases. They also observed that the Aβ deposition correlated with the presence and the phases of the neurofibrillar tangles. Thus, tau may also provide a mechanism pathogenesis of white matter in AD. The role of tau has recently gained credibility for the apparently successful phase II trial of an anti-tau drug (Claude M. Wischik. “Tau aggregation inhibitor (TAI) therapy with remberTM arrests disease progression in mild and moderate Alzheimer's disease over 50 weeks”, ICAD, Chicago 2008). Tau protein binds to the microtubules for the integrity and stabilization of the cytoskeleton and for axonal transport [66]. With the disease, the axonal cytoskeleton can be perturbated and axonal transport becomes disrupted [67]. Thus, the functional failure of tau due to hyperphosphorylation may be result in changes in FA. This hypothesis is supported by data from the present study, indicating that the largest areas of alteration of diffusion map to regions known to be affected by tangle pathology [68].

Limitations

Some limitations of the present study need to be discussed. First, the two imaging modalities we used differ in resolution. Although, the images are rigidly aligned, resolution differences may impact the results. Future studies should taking into account this possible issue. Second, years of education differed between groups. It is unlikely that this accounted for the microstructural differences, as adding education to the models did not change the findings. Third, the sample size was small and a replication of the method in a larger sample is needed. Fourth, groups of MCI and mild AD could add information on the progression of these microstructural alterations. Finally, the impact of these alterations requires longitudinal follow-up. The longitudinal component is critical in evaluating the hypothesis that future volume loss occurs in regions with altered diffusion which was generated by the present study.

Supplementary Material

S-Table 1. Regions with higher MD in AD compared to controls not accounting for GM and WM loss

S-Table 2. Regions with lower FA in AD compared to controls not accounting for GM and WM loss

Acknowledgments

This work has been supported by research grant 125/2004 of the Italian Ministry of Health, Ricerca Finalizzata “Malattie neurodegenerative legate all'invecchiamento: dalla patogenesi alle prospettive terapeutiche per un progetto traslazionale” and by Lundbeck Italia SpA. This study was also supported by National Institutes of Health grants AG021155 and AG000213, and by the facilities and resources at the William S. Middleton Memorial Veterans Hospital. All authors report no conflicts of interest.

The authors are very grateful to Melissa Romano for her precious organizational contribution on the realization and progression of the study. Finally, we wish to thank the patients and their families for their persisting and admirable collaboration.

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