White matter integrity in mild cognitive impairment: A tract-based spatial statistics study

NeuroImage 53 (2010) 16–25

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White matter integrity in mild cognitive impairment: A tract-based spatial statistics study Lin Zhuang a, Wei Wen a,b,⁎, Wanlin Zhu a, Julian Trollor a, Nicole Kochan a,b, John Crawford a, Simone Reppermund a, Henry Brodaty a,c,d, Perminder Sachdev a,b a

Brain & Ageing Research Program, School of Psychiatry, University of New South Wales, Sydney, Australia Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW, Australia Academic Department for Old Age Psychiatry, Prince of Wales Hospital, Randwick, NSW, Australia d Primary Dementia Collaborative Research Centre, School of Psychiatry, University of New South Wales, Sydney, Australia b c

a r t i c l e

i n f o

Article history: Received 17 December 2009 Revised 23 April 2010 Accepted 26 May 2010 Available online 2 June 2010 Keywords: Diffusion tensor imaging Amnestic MCI Non-amnestic MCI

a b s t r a c t Mild cognitive impairment (MCI) as a clinical diagnosis has limited specificity, and identifying imaging biomarkers may improve its predictive validity as a pre-dementia syndrome. This study used diffusion tensor imaging (DTI) to detect white matter (WM) structural alterations in MCI and its subtypes, and aimed to examine if DTI can serve as a potential imaging marker of MCI. We studied 96 amnestic MCI (aMCI), 69 nonamnestic MCI (naMCI), and 252 cognitively normal (CN) controls. DTI was performed to measure fractional anisotropy (FA), and tract-based spatial statistics (TBSS) were applied to investigate the characteristics of WM changes in aMCI and naMCI. The diagnostic utility of DTI in distinguishing MCI from CN was further evaluated by using a binary logistic regression model. We found that FA was significantly reduced in aMCI and naMCI when compared with CN. For aMCI subjects, decreased FA was seen in the frontal, temporal, parietal, and occipital WM, together with several commissural, association, and projection fibres. The best discrimination between aMCI and controls was achieved by combining FA measures of the splenium of corpus callosum and crus of fornix, with accuracy of 74.8% (sensitivity 71.0%, specificity 76.2%). For naMCI subjects, WM abnormality was more anatomically widespread, but the temporal lobe WM was relatively spared. These results suggest that aMCI is best characterized by pathology consistent with early Alzheimer's disease, whereas underlying pathology in naMCI is more heterogeneous, and DTI analysis of white matter structural integrity can serve as a potential biomarker of MCI and its subtypes. © 2010 Elsevier Inc. All rights reserved.

Introduction Mild cognitive impairment (MCI) is a clinical syndrome characterized as a transition stage between normal ageing and dementia (Petersen, 2001; Petersen et al., 1999). MCI has been classified into subtypes including amnestic MCI (aMCI) and non-amnestic MCI (naMCI) (Petersen, 2004) depending upon the presence or absence of memory impairment as a distinguishing feature. Patients with aMCI are considered to be at high risk of developing Alzheimer's disease, whereas naMCI, characterized by non-memory cognitive dysfunction is more likely to progress to frontotemporal dementia, vascular or other types of dementias (Mariani et al., 2007; Rountree et al., 2007). The prevalence of MCI in individuals aged over 65 is reported to be 3% to 19% in different studies (Ritchie, 2004). Compared with agematched healthy elderly, the likelihood of developing dementia is 7 ⁎ Corresponding author. School of Psychiatry, University of New South Wales, NPI, Euroa Centre, Prince of Wales Hospital, Barker Street, Randwick, NSW 2031, Australia. Fax: +61 2 9382 3774. E-mail address: [email protected] (W. Wen). 1053-8119/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2010.05.068

times higher in MCI patients (Boyle et al., 2006). Therefore, accurate diagnosis of MCI and prediction of its conversion to dementia would be of special importance for the early diagnosis and prevention of dementia if and when effective preventative strategies become available. Magnetic resonance imaging (MRI) is a non-invasive neuroimaging tool capable of detecting brain structural changes and informing the structural basis of MCI. Several studies have examined gray matter (GM) abnormalities in MCI and early Alzheimer's disease (AD). T1weighted structural MRI studies have reported that GM atrophy occurs primarily in the medial temporal lobe structures, including the hippocampus, amygdala, and entorhinal, perirhinal, and parahippocampal cortices in MCI and AD patients (Bottino, 2002; Chetelat and Baron, 2003; Dickerson, 2009; Du, 2001; Pennanen, 2004; Tapiola et al., 2008; Teipel et al., 2006). Volumetric reductions of hippocampus and entorhinal cortex have been consistently regarded as clinically useful biomarkers for the accurate diagnosis of MCI or early AD, and for the prediction of progression of MCI to AD (Bottino, 2002; Chetelat and Baron, 2003; Du, 2001; Pennanen, 2004; Tapiola et al., 2008). In addition to the investigation of gray matter structural changes in MCI,

L. Zhuang et al. / NeuroImage 53 (2010) 16–25

the integrity of white matter (WM) has been assessed by diffusion tensor imaging (DTI). DTI provides microstructural information about white matter integrity and coherence by measuring fractional anisotropy (FA) values (Le Bihan et al., 2001; Mori and Zhang, 2006). DTI studies using conventional regions of interest (ROI) and voxel-based morphometry (VBM) analyses have reported WM alterations in MCI individuals in the frontal, parietal and temporal lobes (Medina et al., 2006; Rose et al., 2006), posterior cingulum (Chua et al., 2009; Fellgiebel et al., 2005; Zhang et al., 2007; Zhou et al., 2008), splenium of corpus callosum (Cho et al., 2008; Chua et al., 2009; Parente et al., 2008), long association fascicles (Bai et al., 2009), and internal capsule (Chua et al., 2009; Medina et al., 2006). However, the above findings vary considerably on the anatomical locations of reduced FA measures. For instance, using ROI analysis, Chua et al. found significant FA reduction in the splenium of corpus callosum in MCI (Chua et al., 2009), but another study reported significant FA decrease in the genu of corpus callosum (Bai et al., 2009). The application of ROI and VBM methods for DTI analysis has limitations. The placement of ROIs is a subjective process associated with only modest reliability and reproducibility. In addition, the partial volume effect can confound the results by including different tissue types (CSF or GM) in the same ROI, resulting in spurious DTI measurements. Although the observer-independent VBM-style DTI analysis circumvents the problems of ROI analyses, it also has limitations because of imperfect image registration, and random selection of smoothing factors (Smith et al., 2006). Tract-based spatial statistics (TBSS) which retains the strength of VBM analysis while addressing the registration and smoothing issues, has been recently developed for automated whole brain DTI analysis (Smith et al., 2006). The TBSS method has been used to evaluate WM changes of aMCI and AD in previous studies (Damoiseaux et al., 2009; Liu et al., 2009; Salat et al., 2010; Serra et al., 2010; Stricker et al., 2009). However, conflicting findings have been reported across these studies. In subjects with aMCI, Damoiseaux et al. (2009) reported no significant FA reduction, whereas another study (Liu et al., 2009) showed significant FA decrease in the parahippocampal WM, uncinate fasciculus, and WM tracts of the brain stem and cerebellum. However this discrepancy may be due to limited sample size in both prior studies. Therefore, the effect of MCI pathology on the WM microstructural integrity should be further investigated in the population with large sample size. In this study, we apply TBSS to investigate the white matter microstructural changes in MCI from a large population-based sample. Our previous study using a ROI approach in a subsample of this cohort reported white matter alterations in the frontal white matter, parahippocampal white matter, posterior cingulate, and splenium of corpus callosum in aMCI, while naMCI had abnormal white matter located in the frontal and occipital lobes, anterior limb of internal capsule, and posterior cingulate (Chua et al., 2009). To the best of our knowledge, most white matter studies using DTI are focused on aMCI (Bai et al., 2009; Fellgiebel et al., 2004; Huang et al., 2007; Medina et al., 2006; Rose et al., 2006). Only two studies have investigated white matter structural integrity in naMCI (Chua et al., 2009; Kantarci et al., 2009), but the ROI approach of these studies is incapable of revealing the global pattern of white matter damage. We hope to address the above limitations in the current study. In addition, white matter hyperintensity (WMH) on T2-weighted fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging has been consistently thought to represent white matter abnormalities on the macrostructural level. However, it is still not well understood that whether disrupted white matter integrity measured by reduced FA values and WM lesions detected by T2 FLAIR image provide distinct information in terms of white matter abnormalities in MCI. Therefore, we also aim to investigate the relationship between DTI-based FA measure and WM lesions. Furthermore, the diagnostic utility of DTI in distinguishing MCI from cognitively normal subjects will be evaluat-

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ed. We hypothesize that white matter alterations in aMCI typically occur in the memory network, whereas naMCI shows more diffuse white matter degradation, and DTI can provide extra information when compared to conventional WMH measure. Materials and methods Subjects Participants were recruited from wave 1 of the Sydney Memory and Aging Study (MAS), a longitudinal study of a population-based sample of older non-demented people living in the Eastern suburbs of Sydney, Australia. The community dwelling individuals were approached randomly using the electoral roll from two electorates, and those willing to participate were contacted by telephone for eligibility. Those who had a previous diagnosis of dementia, schizophrenia, bipolar disorder, multiple sclerosis, motor neuron disease, developmental disability, progressive malignancy were excluded as were participants suspected of having dementia based on an adjusted mini-mental status examination (MMSE) score of less than 24 (Folstein et al., 1975) at initial evaluation or if they received a diagnosis of dementia after comprehensive assessment. In addition, participants were required to have sufficient English language abilities to complete the assessment. Informed written consent was provided by each participant. The study was approved by the Human Research Ethics Committees of the University of New South Wales and the South Eastern Sydney and Illawarra Area Health Service. The total sample comprised 1037 subjects aged between 70 and 90 years, of whom 542 underwent MRI scans. The current report utilized a subsample of 417 participants with MRI scans who had an English-speaking background and complete neuropsychological test scores. The subjects included 252 cognitively normal individuals, 96 diagnosed as aMCI and 69 diagnosed as naMCI based on a consensus conference. Participants were classified as MCI if they met the most recent international consensus criteria (Winblad, 2004), that is: (1) complaint of decline in memory and/or other cognitive functions by the participant or a knowledgeable informant; (2) preserved instrumental activities of daily living (IADL), defined here as a total average score b 3.0 on the Bayer ADL Scale (Hindmarch, 1998); (3) objective cognitive impairment operationalized as at least 1.5 SD below the normative data of any one neuropsychological test. Entry requirement of MMSE ≥ 24 satisfied the other MCI criterion of generally normal cognitive function. For aMCI diagnosis, impairment on any one neuropsychological assessment in the memory domain was necessary, and if this was absent and yet MCI criteria were met, naMCI was diagnosed. Demographic profiles of each group and between-group comparisons are presented in Table 1. Briefly, in comparison with controls, aMCI subjects were significantly older, more depressed, with poorer cognitive functioning, and more likely to be male, while naMCI individuals only had significantly lower cognitive test scores than cognitive normal subjects. Compared with naMCI, aMCI had a significantly greater proportion of males, were older and more educated. In addition, since we did not exclude subjects who were on psychotropic medications, we performed a statistical analysis to investigate the potential bias due to psychotropic medications, and found that the proportions of subjects in the three groups taking psychotropic medications were not significantly different, nor was their demographic profile (see Supplementary Table 1). Magnetic resonance imaging MRI scans were acquired from two scanners, both 3 T Phillips, at the Clinical and Research Imaging Centre at Prince of Wales Medical Research Institute, Sydney, Australia. Scanner 1 (138 controls, 48 aMCI, and 43 naMCI) was a Philips 3 T Intera MR scanner (Philips

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Table 1 Demographic characteristics and some clinical features of the sample.

Age Sex (M/F) Education (years) MMSE GDS

F/χ2 (P)

CN (n = 252)

aMCI (96)

naMCI(69)

Mean ± SD

Mean ± SD

Mean ± SD

aMCI vs. naMCI

77.62 ± 4.49 21/48 10.85 ± 3.55 28.29 ± 1.31 2.21 ± 2.14

0.021⁎ b 0.001⁎ 0.031⁎

77.87 ± 4.52 106/146 11.85 ± 3.57 28.74 ± 1.13 1.95 ± 1.80

79.57 ± 4.71 57/39 12.31 ± 3.76 28.05 ± 1.45 2.52 ± 2.18

5.533 14.735 3.389 11.997 3.118

(0.004) (0.001) (0.035) (b 0.001) (0.045)

P value (post-hoc)

0.687 0.921

aMCI vs. CN 0.006⁎ 0.004⁎ 0.852 b0.001⁎ 0.042⁎

naMCI vs. CN 1.000 0.80 0.126 0.023⁎ 0.971

Abbreviations used in the Table: CN—cognitively normal; aMCI—amnestic mild cognitive impairment; naMCI—non-amnestic mild cognitive impairment; MMSE—Mini-mental status examination; SD—standard deviation; GDS—geriatric depression scale; M—male; and F—female. ⁎ Significant at pb=0.05 after Bonferroni adjustment.

Medical Systems, Best, the Netherlands), and scanner 2 (114 controls, 48 aMCI, and 26 naMCI) was a Philips 3 T Achieva Quasar Dual MR scanner (Philips Medical Systems, Best, The Netherlands). Each subject had T1-weighted imaging, T2-weighted fluid attenuated inversion recovery (FLAIR) imaging, and DTI. A single-shot spinecho echo-planar imaging (EPI) sequence was used for the acquisition of DTI data in these two scanners. The scanning parameters were identical for the two scanners. DTI protocol: six directional diffusionsensitizing gradients (b = 1000 s/mm2) with one no-diffusionweighted b = 0 pulse; echo time (TE) = 68; flip angle = 90°; number of excitation (NEX) = 1; 38 axial slices with 3.5 mm thickness and

no gap; field of view (FOV) = 250 × 133 × 250 mm, matrix size = 128 × 128, in-plane image resolution = 1.953 × 1.953 mm. The FLAIR sequence was acquired with TR = 10000 ms, TE = 110 ms, TI = 2800; matrix size = 512 × 512; slice thickness = 3.5 mm with no gap between slices, yielding spatial resolution of 0.488 × 0.488 × 3.5 mm3/ voxel. Scanner change occurred midway through the study for reasons outside the investigators' control, and since recruitment occurred randomly, a systematic scanner bias was unlikely. Further, we scanned 5 healthy subjects in both scanners within 2 months, and the results indicated no significant effect of scanner change (See below: TBSS analysis).

Fig. 1. Significant FA reduction in aMCI (amnestic mild cognitive impairment) subjects compared with controls is highlighted in red–yellow and superimposed on the MNI152 template (p b 0.05, corrected for multiple comparisons). The left side of the image corresponds to the right hemisphere of the brain.

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subject image misalignment. Following the thresholding of the mean FA skeleton, each individual's transformed FA map was projected to the mean FA skeleton to create a skeletonized FA map which is the input image in the later voxel-wise statistics across participants, by assigning the maximum FA value in the tract-perpendicular direction to the corresponding skeletal point. The averaged values of the skeletonised FA maps from 5 participants who were scanned on both scanners were calculated and correlated across scanners to assess the effect of scanner change on the FA measure. The resulting high correlation coefficient (r = 0.976, p = 0.004) indicated that the systematic error from different scanners was almost linear and could be controlled for in the general linear model. Therefore, DTI images from the two scanners were combined to increase the statistical power of our TBSS analysis by adding a binary categorical variable in the general linear model.

Image analysis Diffusion tensor image processing All DTI data were preprocessed offline on a Linux system-based workstation using the FMRIB's Diffusion Toolbox (FDT), which is a part of the FMRIB's Software Library (FSL) program (http://www. fmrib.ox.ac.uk/fsl, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Oxford University, UK). The raw DTI images were visually inspected, and then corrected for eddy current and head motion by registering each participant's 6 diffusion weighted images to their own T2-weighted (b = 0) image using an affine image registration (FLIRT), which is part of FSL (Jenkinson et al., 2002). Brain extraction was performed to remove non-brain structures, using brain extraction tool (BET) implemented in FSL (Smith, 2002). Thereafter, diffusion tensor was reconstructed by fitting a diffusion tensor model to each image voxel of the preprocessed DTI data, using the DTIfit program included in FSL. Following this, the fractional anisotropy (FA) map was calculated. The resulting FA images were fed into TBSS, which is also a part of FSL, to carry out the voxel-wise statistical analysis (Smith et al., 2006). In brief, the FA maps of all participants were spatially transformed to a 1 × 1 × 1 mm MNI (Montreal Neurological Institute) standard space by the nonlinear registration method FNIRT. The nonlinearly registered FA images were further averaged to generate a mean FA image. A nonmaximum suppression algorithm was applied afterwards to search the image voxels with highest FA value along the direction perpendicular to the local tract surface to create a mean FA skeleton. The mean FA skeleton was further thresholded by a FA value of 0.2 to exclude the skeleton voxels which may contain gray matter or cross-

Measurement of white matter hyperintensity (WMH) volume The total volume of WMH on T2-weighted FLAIR images was automatically delineated and calculated using our in-house developed software in Matlab (version 7.1 The Math Works) (Wen and Sachdev, 2004; Wen et al., 2009). This automated measurement of WMH volume has shown a high reliability in our previous studies. Briefly, the major steps involved in the automatic detection and measurement of WMH volume are as follows: (1) coregistration of each participant's FLAIR images to their corresponding T1-weighted structural images; (2) spatial transformation of T2 FLIAR and T1 images into MNI space; (3) generation of a brain mask to remove non-brain tissue by segmenting both T1-weighted and FLAIR images into GM, WM and CSF;

Table 2 Neuroanatomical regions with reduced FA values in aMCI group compared with cognitively normal subjects. Anatomical region

Frontal lobe

Parietal lobe

Temporal lobe

Occipital lobe

Association fibre

Commissural fibre

Projection fibre

Superior frontal WM L Postcentral WM R Postcentral WM L Superior parietal WM R Superior parietal WM L Precuneus WM R Precuneus WM L Angular WM L Superior temporal WM L Middle temporal WM L Inferior temporal WM L Crus of fornix R Crus of fornix L Middle occipital WM R Middle occipital WM L Lingual WM L Posterior cingulum R Posterior cingulum L Sagittal stratum L Inferior frontoccipital fasciculus L Superior longitudinal fasciculus R Genu of corpus callosum R Genu of corpus callosum L Splenium of corpus callosum R Splenium of corpus callosum L Body of corpus callosum R Body of corpus callosum L Posterior thalamic radiation R Posterior thalamic radiation L Posterior corona radiata R Posterior corona radiata L Anterior corona radiata L Superior corona radiata L Anterior limb of internal capsule L Retrolenticular part of internal capsule R

MNI coordinates (mm) x

y

z

− 14 21 − 22 22 − 22 13 − 23 − 31 − 43 − 41 − 32 29 − 29 29 − 29 − 26 10 −9 − 40 − 19 35 13 − 10 25 − 20 8 − 10 27 − 27 29 − 29 − 12 − 17 − 13 35

27 − 37 − 39 − 48 − 44 − 56 − 55 − 55 − 23 − 41 − 11 − 23 − 23 − 67 − 66 − 62 − 26 − 28 − 40 22 − 28 33 23 − 52 − 49 − 24 − 25 − 63 − 62 − 48 − 50 30 10 13 − 24

41 39 47 38 45 25 24 30 −9 −5 − 13 −9 −8 18 20 −1 36 35 −9 − 12 36 −2 −8 12 10 25 27 83 15 22 20 −9 32 −8 −2

The overlapping regions between significant FA reduction and WM lesions were highlighted in bold. Abbreviation: FA: fractional anisotropy; aMCI: amnestic mild cognitive impairment; WM: white matter, R: right, L: left.

P value (minimum)

Cluster size (mm3)

0.042 0.046 0.046 0.032 0.046 0.046 0.03 0.03 0.046 0.044 0.046 0.046 0.046 0.02 0.028 0.048 0.01 0.01 0.042 0.042 0.046 0.046 0.042 0.008 0.008 0.014 0.014 0.01 0.01 0.028 0.046 0.042 0.042 0.046 0.046

451 224 128 477 137 16 10 80 50 150 33 93 101 39 170 34 82 110 158 72 53 319 114 1358 1153 381 671 238 406 230 12 196 177 147 68

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(4) detection and grading of WMH from T2 FLAIR images; and (5) visual inspection of the extracted WMH map and manual removal of voxels misclassified as WMH. WMH probability distribution maps were then generated for both aMCI and naMCI groups respectively by applying the transformation matrix obtained in the above step to each individual's corresponding binarized WMH image. The spatially normalized WMH maps were added together and then divided by the number of WMH images to finally create the spatial probability distribution of WM lesions (see Supplementary Figs. 1 and 2). In order to account for the variation in the intracranial volume (ICV), an index of WMH burden (a ratio of the total WMH volume against the ICV of the same subject) was considered in the statistical analysis. Statistical analysis One-way analysis of variance (ANOVA) with post-hoc analyses were used for the comparisons of continuous variables of the demographic profile, while categorical variables were compared using the Pearson Chi-square test. Standardized z-scores of the FA values for each white matter region of aMCI showing significant difference in FA measure compared with controls (see voxel-wise analysis of group comparisons in FA measures below) were calculated. The z-scores were first entered separately into a series of logistic regression models, together with age, sex, years of education and scanner as control variables, and with diagnostic group as the dependent variable. Those WM regions exhibiting an area under curve (AUC) of 0.65 or greater were retained for further logistic regression analysis.

The retained ROIs were entered together into a logistic regression model to examine whether the discrimination accuracy would improve when used in combination. In addition, analysis of covariance (ANCOVA) was performed to assess the group difference in WMH burden, while controlling for age, sex, and years of education. The relationship between WMH burden and the average skeleton FA values was further evaluated controlling for age, sex, and years of education, using Pearson partial correlation. All these statistical analyses were performed using SPSS software package version 17 (SPSS, Inc., Chicago, IL, USA). For the voxel-wise analysis of group differences in FA between aMCI, naMCI and normal controls, a nonparametric permutation test with 5000 random permutations was performed by using the FSL randomise program. Contrasts of aMCI versus controls, naMCI versus controls, and aMCI versus naMCI, were set up to detect the FA differences, while regressing out the linear effect of age, sex, years of education, and different scanners as covariates of no interest. As an alternative to conventional cluster-based thresholding which is normally compromised by the arbitrary definition of the clusterforming threshold, a recently developed algorithm, known as threshold-free cluster enhancement (TFCE) (Smith, 2009) was used to obtain the skeletal voxels significantly different between groups at p b 0.05, after accounting for multiple comparisons by controlling for family-wise error (FWE) rates. From the results of voxel-wise group comparisons, the skeletal regions showing significant between-group differences in FA were located and labeled anatomically by mapping the FWE-corrected statistical map of p b 0.05 to the JHU DTI WM atlas

Fig. 2. Significant FA reduction in naMCI (non-amnestic mild cognitive impairment) subjects compared with controls is highlighted in red–yellow and superimposed on the MNI152 template (p b 0.05, corrected for multiple comparisons). The left side of the image corresponds to the right hemisphere of the brain.

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(Wakana, 2004) in MNI space, and the mean FA values within each of those regions in MCI and controls were calculated for the logistic regression analysis. Results Group comparison of FA between aMCI and Controls Compared to control subjects, aMCI showed reduced FA in the following regions: the left superior frontal WM; the left lateral temporal lobe WM and medial temporal fornix; the parietal lobe consisting of the bilateral superior parietal WM, precuneus and postcentral gyrus WM, and the left angular WM; the occipital lobe including the middle occipital and lingual WM; the corpus callosum; the association fibres with the involvement of the left sagittal stratum and inferior frontoccipital fasciculus, and the right superior longitudinal fasciculus; the projection fibre consisting of the bilateral posterior thalamic radiation and bilateral posterior corona radiata, the left anterior and superior corona radiata, the left anterior limb and retrolenticular part of the internal capsule (Fig. 1 and Table 2). In addition, some WM regions with significant FA reduction were found to partially overlap with the distribution of WM lesions in the frontal, parietal, occipital lobes, corpus callosum, corona radiata and posterior thalamic radiation (Fig. 3 and Table 2). There were no WM regions of increased FA in aMCI compared with normal controls. Group comparison of FA between naMCI and controls The spatial distribution of reduced FA in naMCI group is presented in Fig. 2 and Table 3. Compared with controls, naMCI patients demonstrated FA reduction in the following locations: the corpus callosum; association fibre including the bilateral superior longitudinal fascicle and posterior cingulum, and the left inferior frontoccipital fasciculus; projection fibre consisting of the bilateral anterior, superior and posterior corona radiata, posterior thalamic radiation, and internal capsule, the left external capsule and cerebral peduncle; the frontal lobe region involving the right middle frontal lobe WM, the superior frontal lobe WM bilaterally, the bilateral precentral and postcentral gyri WM; the parietal lobe including the bilateral precuneus WM, angular WM, supramarginal WM, and superior parietal WM; the occipital lobe including the bilateral superior and middle occipital WM. There was also an overlap between the location of significant FA decrease and that of white matter lesions (Fig. 4 and Table 3). No significant FA increase in naMCI compared with controls was evident.

Fig. 3. Significant FA reduction in aMCI (amnestic mild cognitive impairment) subjects compared with controls is superimposed on the MNI152 template (p b 0.05, corrected for multiple comparisons). Light-blue represents white matter regions with significant FA reduction overlapping with WM lesions. Areas of significant FA decrease without the involvement of WM lesions are marked in red–yellow. Black arrows show the location of the crus of fornix. 1st row: axial view, 2nd row: coronal view, 3rd row: left sagittal view, 4th row: right sagittal view. The left side of the image corresponds to the right hemisphere of the brain for the coronal and axial views.

Classification of MCI and cognitively normal subjects

Direct comparison between aMCI and naMCI did not reveal any significant difference in FA measures.

In the classification between aMCI and controls, FA values of the bilateral splenium of the corpus callosum and the crus of fornix were sensitive discriminators between the groups (Table 4). Of these WM regions, FA values of the right crus of fornix discriminated aMCI from healthy subjects with an overall classification accuracy of 67.2% (sensitivity 62.5%, specificity 69.0%). FA of the left crus of fornix showed an overall diagnostic accuracy of 66.5% (sensitivity 60.6%, specificity 68.7%). The left splenium of corpus callosum yielded an overall correct classification rate of 66.3% (sensitivity 57.9%, specificity 69.5%) and the right splenium of corpus callosum demonstrated an overall diagnostic accuracy of 66.8% (sensitivity 59.4%, specificity 69.6%). When used in combination to distinguish aMCI from controls, the total diagnostic accuracy increased to 74.8%, with sensitivity and specificity of 71.0% and 76.2% respectively. In the classification between naMCI and controls, there was no significant predictor discriminating the two categories.

WMH burden and its association with global white matter integrity

Discussion

As shown in Fig. 5, the mean values (SD) of WMH burden increase from 0.0052 (0.0079) in controls to 0.0078 (0.0132) in naMCI and 0.0081 (0.0124) in aMCI. Using ANCOVA analysis, it was found that aMCI, naMCI, and controls differed significantly in WMH burden (F(2.409) = 3.909, p = 0.021), after controlling for age, sex, and years of education. Post-hoc analysis with Bonferroni adjustment showed that aMCI subjects had significantly more extensive WMH than the controls (p = 0.038). No significant difference in WMH load was observed for the comparison of naMCI versus aMCI, and naMCI versus controls. In addition, we found that there was a significant negative correlation (r = −0.244, p b 0.001) between the average skeleton FA values and WMH burden, while treating age, sex, and years of education as covariates of no interest.

In this study, we have characterized in vivo WM alterations in aMCI and naMCI. Two recent TBSS (Damoiseaux et al., 2009, Liu et al., 2009) studies have performed preliminary investigation of WM changes in aMCI, but discrepant findings were reported. One study (Damoiseaux et al., 2009) did not reveal any significant FA reduction in aMCI compared with controls, even though their aMCI subjects had more severe cognitive impairment than ours (mean MMSE score 25.9 compared to 28.05 in our aMCI subjects). This is possibly due to their smaller sample size (8 aMCI subjects). By contrast, the other TBSS study (Liu et al., 2009) using 27 aMCI subjects found a significant FA decrease in the right parahippocampal WM, bilateral uncinate fasciculus, and WM in the brain stem and cerebellum, but this finding could not reach a significance level when multiple comparison correction was applied.

Group comparison of FA between aMCI and naMCI

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Table 3 Neuroanatomical regions with reduced fractional anisotropy values in naMCI group compared with cognitively normal subjects. Anatomical region

Frontal lobe

Parietal lobe

Occipital lobe

Association fibre

Commissural fibre

Projection fibre

Middle frontal WM R Superior frontal WM R Superior frontal WM L Precentral WM R Precentral WM L Postcentral WM R Postcentral WM L Superior parietal WM R Superior parietal WM L Precuneus WM R Precuneus WM L Angular WM R Angular WM L Supramarginal WM R Supramarginal WM L Superior occipital WM R Superior occipital WM L Middle occipital WM R Middle occipital WM L Posterior cingulum R Posterior cingulum L Superior longitudinal fasciculus R Superior longitudinal fasciculus L Inferior fronto-occipital fasciculus L Genu of corpus callosum R Genu of corpus callosum L Splenium of corpus callosum R Splenium of corpus callosum L Body of corpus callosum R Body of corpus callosum L Posterior thalamic radiation R Posterior thalamic radiation L Anterior corona radiata R Anterior corona radiata L Posterior corona radiata R Posterior corona radiata L Superior corona radiata R Superior corona radiata L External capsule L Anterior limb of internal capsule L Posterior limb of internal capsule L Retrolenticular part of internal capsule R Retrolenticular part of internal capsule L Cerebral peduncle L

MNI coordinates (mm) x

y

z

22 21 − 18 − 20 18 21 − 22 23 − 22 13 − 22 38 − 33 40 − 44 25 − 24 30 − 33 13 − 12 28 − 22 − 21 13 − 15 8 −6 10 − 10 28 − 30 17 − 23 33 − 23 17 − 26 − 26 − 21 − 19 29 − 27 −9

44 46 38 − 26 − 27 − 37 − 38 − 51 − 60 − 58 − 55 − 53 − 52 − 40 − 25 − 53 − 56 − 62 − 58 − 29 − 28 − 39 − 49 13 30 36 − 33 − 29 − 25 − 25 − 61 − 43 31 29 − 46 − 41 2 16 19 20 −6 − 32 − 32 −8

7 −2 24 46 52 39 43 38 32 32 24 29 24 20 30 26 23 20 24 29 29 37 37 − 14 14 5 22 23 26 26 13 19 − 14 3 20 35 35 24 −1 6 9 20 20 − 10

P value (minimum)

Cluster size (mm3)

0.019 0.019 0.018 0.012 0.027 0.006 0.006 0.005 0.005 0.006 0.006 0.037 0.006 0.037 0.025 0.006 0.006 0.006 0.006 0.005 0.006 0.007 0.006 0.023 0.008 0.016 0.005 0.006 0.005 0.006 0.006 0.007 0.013 0.015 0.007 0.006 0.028 0.016 0.016 0.016 0.018 0.009 0.016 0.027

100 701 630 56 57 219 308 661 1008 168 199 251 117 157 36 52 119 41 192 223 198 208 533 75 499 345 1267 1119 1009 750 368 231 456 860 398 410 124 224 96 291 155 39 64 98

The overlapping regions between significant FA reduction and WM lesions were highlighted in bold. Abbreviations: naMCI: non-amnestic mild cognitive impairment; WM: white matter; L: left; R: right.

By using a large sample size of MCI and controls recruited from a community-dwelling non-demented cohort and more stringent statistical analysis, we showed that aMCI subjects had reduced FA in the frontal, parietal, temporal and occipital lobes WM, together with association fibres including the posterior cingulum, sagittal stratum, superior longitudinal fasciculus, inferior frontoccipital fasciculus, commissural fibre of the corpus callosum, and projection fibres comprising the corona radiata and thalamic radiation. The anatomical locations of FA reductions in aMCI in our study are mostly consistent with a number of previous DTI studies using ROI and VBM methods (Bai et al., 2009; Cho et al., 2008; Chua et al., 2009; Fellgiebel et al., 2005; Fellgiebel et al., 2004; Huang et al., 2007; Lövblad et al., 2004; Medina et al., 2006; Parente et al., 2008; Rose et al., 2006; Wang et al., 2009; Zhang et al., 2007; Zhou et al., 2008). In the naMCI group, the location of FA reduction overlapped that of the aMCI group to some extent with the additional involvement of the middle frontal WM, precentral WM, supramarginal WM, posterior limb of the internal capsule, and cerebral peduncle. However, significant temporal lobe FA reduction was absent in naMCI. These different patterns of WM deficits in temporal regions are consistent with our understanding of the important role of temporal WM in memory function, which by definition is impaired in aMCI but not

naMCI. Therefore, this finding is consistent with the suggestion that aMCI is more likely to represent early AD, whereas naMCI is more heterogeneous in pathology other than AD. In the aMCI group, it was of particular interest to find decreased FA in the medial temporal fornix and posterior cingulum, given the medial temporal lobes and posterior cingulate are two pivotal regions supporting memory function (Dickerson, 2004; Zhou et al., 2008). The fornix is a WM tract connecting the hippocampus with the septal region, mammillary bodies, and prefrontal cortex. Previous DTI studies have found reduced fornix integrity and its association with hippocampal atrophy in Alzheimer's disease (DeCarli et al., 2008; Liu et al., 2009; Teipel, 2007). Therefore, the presence of fornix degradation in our aMCI subjects probably stems from Wallerian degeneration secondary to hippocampal atrophy, thus indirectly reflecting hippocampal damage which is a hallmark of AD pathology. More importantly, the disrupted connectivity between hippocampus and prefrontal cortex via fornix can cause memory dysfunction, such as abnormal memory organization and reduced overall episodic memory (Nestor, 2007; Takei et al., 2008). In addition, in animal experiments, it was demonstrated that transection of the fornix in the macaque severely impaired memory acquisition of spatio-temporal information (Buckley, 2008). Therefore, the disruption of fornix found

L. Zhuang et al. / NeuroImage 53 (2010) 16–25

Fig. 4. Significant FA reduction in naMCI (non-amnestic mild cognitive impairment) subjects compared with controls is superimposed on the MNI152 template (p b 0.05, corrected for multiple comparisons). Light-blue represents white matter regions with significant FA reduction overlapping WM lesions. Areas of significant FA decrease without the involvement of WM lesions are marked in red–yellow. 1st row: axial view, 2nd row: coronal view, 3rd row: left sagittal view, 4th row: right sagittal view. The left side of the image corresponds to the right hemisphere of the brain in the coronal and axial views.

in our study further supports the well-established notion that medial temporal memory system is affected early in the preclinical AD stage (Dickerson, 2004). The posterior cingulum is a WM tract linking the posterior cingulate to the entorhinal cortex which in turn has reciprocal connections with the hippocampal formation. Functional MRI (fMRI) studies have shown functional disruption in the posterior cingulate either during the memory cognitive task or in the resting state in aMCI and AD patients (Rombouts, 2005; Yetkin, 2006). Consistent with fMRI findings, a positron emission tomography (PET) study has reported abnormal posterior cingulate hypometabolism by measuring glucose consumption in MCI and AD (Mosconi, 2008). These functional and metabolic abnormalities probably result from the vulnerability of the posterior cingulate to the amyloid deposition (Forsberg, 2008), whose toxicity could cause posterior cingulum degeneration found in the present study. However, it is intriguing that naMCI also demonstrated posterior cingulum abnormality, but without clinical symptom of memory impairment. The abnormalities of the posterior cingulate region in naMCI have been consistently reported by structural and metabolic studies (Chua et al., 2009; Kantarci et al., 2009; Wolk, 2009). One possible explanation for the reduced FA in the posterior cingulate in the absence of clinical manifestation of memory decline is that the compensatory recruitment process may be involved to maintain the memory performance in naMCI (Dickerson and Sperling, 2008). A recent ROI study investigating DTI characteristics of aMCI and naMCI showed a disrupted posterior cingulum in both MCI subtypes, but the hippocampal alteration was only evident in aMCI (Kantarci et al., 2009). Our results are consistent with this finding and showed compromised posterior cingulum in aMCI and naMCI but abnormal fornix specific to aMCI. In addition, a recent meta-analysis has reported that hippocampal atrophy, but not posterior cingulate alteration is predictive of conversion of MCI to AD (Schroeter, 2009). Taken together, it is indicated that clinically-evident memory

23

Fig. 5. Top panel: Box plots of WMH burden across three groups. Bottom panel: Scatter plots and regression line for the significant negative correlation (r = −0.244, p b 0.001) between WMH burden and global average skeleton FA values.

dysfunction and further decline into AD is more likely to arise from the disruption to the medial temporal memory system. It is noteworthy that we did not find significant differences in FA between aMCI and naMCI. One possible explanation is that some individuals in our naMCI subgroup may represent early AD, thus limiting the statistical power to detect WM structural difference between aMCI and naMCI, as a number of epidemiologic studies have demonstrated that not only the aMCI subtype but also the naMCI subtype could progress to AD dementia (Busse, 2006; Fischer, 2007; Rountree et al., 2007). In addition, one recent PET study demonstrated increased level of amyloid-β deposition in naMCI patients, a typical feature of AD (Wolk, 2009). It is also possible that our DTI images were only six directional with low signal-to-noise ratio and may be less sensitive to subtle local differences between these two subtypes of MCI. One of the major manifestations of vascular pathology is white matter hyperintensity which is frequently observed on T2-weighted FLAIR images in MCI and AD (Bigler, 2002) as well as normal ageing (Wen and Sachdev, 2004; Wen et al., 2009). In the present study, the spatial distribution of significant FA reduction to some extent overlapped with the regions of WMH in the frontal, parietal, occipital WM, corona radiata, posterior thalamic radiation, some of which are localised in the territories supplied by small vessels known as lenticulostriate branches of the cerebral artery (Wen and Sachdev, 2004). The white matter in these arterial territories would be susceptible to insufficient blood supply due to hypoperfusion in the arterioles. Furthermore, WMH burden was found to be significantly increased in the aMCI subjects and inversely correlated with the mean skeleton FA values. This finding is compatible with a previous study demonstrating the association between WMH and decrease of FA values (Jones et al., 1999). Taken together, it is suggested that vascular pathology could lead to loss of white matter integrity.

24

L. Zhuang et al. / NeuroImage 53 (2010) 16–25

Table 4 Neuroanatomical regions distinguishing aMCI and controls using logistic regression analysis, while age, sex, years of education, and scanner difference were included as covariates. Groups

Anatomical regions

B

P

Wald

Odds ratio

Sensitivity (%)

Specificity (%)

Accuracy (%)

aMCI vs. CN

Splenium L Splenium R Crus of Fornix L Crus of Fornix R

− 0.781 − 0.663 − 0.609 − 0.674

b 0.001 b 0.001 b 0.001 b 0.001

25.373 19.612 16.340 20.024

0.458 0.515 0.544 0.510

57.9 59.4 60.6 62.5

69.5 69.6 68.7 69.0

66.3 66.8 66.5 67.2

Abbreviations: CN: cognitively normal; aMCI: amnestic mild cognitive impairment; L: left hemisphere; R: right hemisphere.

However, it should be noted that some WM areas with reduced FA did not overlap with WM lesions, such as the crus of fornix and temporal white matter in aMCI, implying that other pathological mechanisms may account for compromised WM integrity as measured by the decreased FA value in MCI. Apart from Wallerian degeneration mentioned previously, histological studies have shown that WM deterioration is associated with dementia, by disrupting myelin integrity, reducing axonal and glial cell density, and loss of oligodendrocytes in WM (Bronge, 2002; Englund, 1998; Sjobeck, 2003). In a postmortem study of AD patients, using optical density measurement, Sjobeck and colleagues reported reduced myelin density localised in the frontal, temporal, and parietal areas of the brain (Sjöbeck et al., 2006). Differential myelin characteristics may make the late-myelinating WM areas, like frontal, temporal, temporoparietal cortical WM, and long association fibre more vulnerable to the demyelination process than early-myelinating regions, such as primary motor and occipital cortical WM and projection fibres (Bartzokis, 2004; Stricker et al., 2009). With regard to the diagnostic utility of DTI measures in aMCI, the splenium of corpus callosum and crus of fornix were sensitive discriminators between aMCI and cognitively normal individuals. This finding is partly consistent with our previous research using ROI approach (Chua et al., 2009) which showed that the splenium of corpus callosum and posterior cingulum were the best predictors of aMCI in a subgroup of subjects used in our present study. Unlike our previous report, however, we failed to find any diagnostic value in the posterior cingulum, and instead found the crus of fornix as being discriminating. The discrepancies could be due to different methodologies of DTI analysis. We believe that in the previous report by Chua et al. the posterior cingulate ROI might have included some adjacent WM from the splenium of corpus callosum. In addition, ROI of the fornix was not included in the previous study. The present study extends the gray matter (e.g. hippocampus and entorhinal cortex) morphometry found in previous studies (Bottino, 2002; Chetelat and Baron, 2003; Du, 2001; Pennanen, 2004; Tapiola et al., 2008) to the DTI measure of WM integrity in the diagnosis of MCI. However, we did not find any region which can effectively predict naMCI from controls. This finding was not unexpected, as naMCI is characterized by decline in one of more non-memory cognitive functions (e.g. executive function, visual–spatial function, language), with diverse neuroanatomical underpinnings. Whereas aMCI consistently includes memory dysfunction and the finding of medial temporal lobe abnormalities is thereby understandable, no white matter structure is likely to be consistently abnormal in all naMCI patients. There are some limitations to our study. First, the cross-sectional design cannot determine the potential role of the DTI measure in the prediction of dementia in MCI subjects. Second, DTI performed in our study is only six directional, resulting in low signal-to-noise ratio. Third, DTI images were collected from two scanners, but these were both 3 T Philips scanners and the same protocol was used; we combined the data from the scanners only after demonstrating the comparability of the data from the two scanners. In spite of these limitations, we have detected WM abnormalities of MCI subjects as compared with cognitively normal subjects in a population-based cohort with large sample size, which mostly conform to our hypotheses.

In summary, to our knowledge, this is the first study employing tractbased spatial statistics to investigate the global white matter alterations in amnestic MCI and non-amnestic MCI in a large population-based sample. The results provide insight into the similarities and differences of spatial patterns of WM damage in aMCI and naMCI subtypes. The medial temporal WM alteration underlying memory dysfunction was evident in aMCI but not naMCI, supporting the notion that aMCI is more likely to represent early AD dementia. Loss of white matter integrity in MCI could be attributed to either vascular pathology or degenerative process. In addition, DTI measures of the splenium of corpus callosum and fornix are useful biomarker to distinguish aMCI from cognitively normal individuals. Future longitudinal follow-up should examine whether DTI measure of WM integrity can effectively predict MCI patients at high risk of progressing to dementia. Acknowledgments This research was supported by the National Health and Medical Research Council (NHMRC) Program Grant ID 350833, the NHMRC Project Grant ID 510175, and an Australian Research Council Discovery Grant (ARC DP-0774213). We thank the study participants and interviewers, as well the large Sydney Memory and Ageing Study team. We also thank Angie Russell for her assistance with manuscript preparation. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2010.05.068. References Bai, F., Zhang, Z., Watson, D.R., Yu, H., Shi, Y., Yuan, Y., Qian, Y., Jia, J., 2009. Abnormal integrity of association fiber tracts in amnestic mild cognitive impairment. J. Neurol. Sci. 278, 102–106. Bartzokis, G., 2004. Heterogeneous age-related breakdown of white matter structural integrity: implications for cortical “disconnection” in aging and Alzheimer's diesase. Neurobiol. Aging 25, 843–851. Bigler, E.D., 2002. White matter lesions, quantitative magnetic resonance imaging, and dementia. Alzheimer Dis. Assoc. Disord. 16, 161–170. Bottino, C.M.C., 2002. Volumetric MRI measurements can differentiate Alzheimer's disease, mild cognitive impairment, and normal aging. Int. Psychogeriatr. 14, 59–72. Boyle, P.A., Wilson, R.S., Aggarwal, N.T., Tang, Y., Bennett, D.A., 2006. Mild cognitive impairment: risk of Alzheimer disease and rate of cognitive decline. Neurology 67, 441–445. Bronge, L., 2002. Postmortem MRI and histopathology of white matter changes in Alzheimer brains—a quantitative, comparative study. Dement. Geriatr. Cogn. Disord. 13, 205–212. Buckley, M.J., 2008. Fornix transection impairs visuospatial memory acquisition more than retrieval. Behav. Neurosci. 122, 44–53. Busse, A., 2006. Mild cognitive impairment—long-term course of four clinical subtypes. Neurology 67, 2176–2185. Chetelat, G., Baron, J.C., 2003. Early diagnosis of Alzheimer's disease: contribution of structural neuroimaging. Neuroimage 18, 525–541. Cho, H., Yang, D.W., Shon, Y.M., Kim, B.S., Kim, Y.I., Choi, Y.B., Lee, K.S., Shim, Y.S., Yoon, B., Kim, W., Ahn, K.J., 2008. Abnormal integrity of corticocortical tracts in mild cognitive impairment: a diffusion tensor imaging study. Korean J. Lab. Med. 28, 477–483. Chua, T.C., Wen, W., Chen, X., Kochan, N., Slavin, M.J., Trollor, J.N., Brodaty, H., Sachdev, P.S., 2009. Diffusion tensor imaging of the posterior cingulate is a useful biomarker of mild cognitive impairment. Am. J. Geriatr. Psychiatry 17, 602–613. Damoiseaux, J.S., Smith, S.M., Witter, M.P., Sanz-Arigita, E.J., Barkhof, F., Scheltens, P., Stam, C.J., Zarei, M., Rombouts, S.A.R.B., 2009. White matter tract integrity in aging and Alzheimer's disease. Hum. Brain Mapp. 30, 1051–1059.

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