Do Alzheimer-specific microstructural changes in mild cognitive impairment predict conversion?

Psychiatry Research: Neuroimaging 203 (2012) 184–193

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Do Alzheimer-specific microstructural changes in mild cognitive impairment predict conversion? Thomas van Bruggen a, Bram Stieltjes b, Philipp A. Thomann c, d, Peter Parzer e, Hans-Peter Meinzer a, Klaus H. Fritzsche a, b,⁎ a

Division of Medical and Biological Informatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany Division of Quantitative Imaging-based Disease Characterization, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany Structural Neuroimaging Group, Department of General Psychiatry, University of Heidelberg, Voss-Str. 4, 69115 Heidelberg, Germany d Center for Psychosocial Medicine, Department of General Psychiatry, University of Heidelberg, Voss-Str. 4, 69115 Heidelberg, Germany e Psychosocial Medicine, Department of Child and Adolescent Psychiatry, University of Heidelberg, Blumenstr. 8, 69115 Heidelberg, Germany b c

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Article history: Received 12 September 2011 Received in revised form 24 November 2011 Accepted 8 December 2011 Keywords: Tract-based spatial statistics Diffusion anisotropy indices White matter

a b s t r a c t Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that provides information on the fiber architecture of the brain by measuring water diffusion. Prior work has shown that neuronal degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI) alters this architecture. Since the conversion rate to AD is much higher for MCI patients than for normal healthy people, it is important to identify biomarkers with a predictive value on this conversion. In this study, we applied tract-based spatial statistics (TBSS) on datasets of 15 healthy controls, 15 AD patients, and 17 MCI patients. Of these MCI patients eight remained stable, whereas nine developed AD within the first 12–18 months of follow-up investigations. Analysis using TBSS combined with a maximum likelihood regression with random effects of the fornix, the corpus callosum, and the cingulum identified significant differences between these two types of MCI patients in fractional anisotropy (FA) and radial diffusivity (DR). Thus, DTI reveals Alzheimer-specific changes in those MCI subjects that later convert, although they were clinically identical to the other MCI-patients at the time the data were acquired. This finding could lead to early identification of AD and thereby aid early clinical intervention. © 2012 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Alzheimer's disease (AD) is the most frequently occurring form of dementia beyond the age of 65. Research has focused on the transition between normal aging and Alzheimer's disease, a state that is called mild cognitive impairment (MCI) (Petersen et al., 2001; Petersen, 2004). The rate of conversion to AD is about nine times higher for MCI patients than for healthy people of the same age (Mitchell and Shiri-Feshki, 2009), which makes it important to identify markers with a predictive value on this conversion. Such markers would allow early and more successful medical treatment, and would increase our understanding of the early pathology in AD. Using magnetic resonance imaging (MRI), it is possible to detect structural atrophic changes in the brain non-invasively. Previous studies were successful in detecting and classifying changes in patients with AD when compared to healthy controls, but the differentiation of MCI patients from AD patients or healthy controls has ⁎ Corresponding author at: Division of Medical and Biological Informatics (E130), German Cancer Research Center, Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany. Tel.: + 49 6221 423545; fax: + 49 6221 422345. E-mail address: [email protected] (K.H. Fritzsche). 0925-4927/$ – see front matter © 2012 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.pscychresns.2011.12.003

proved to be more difficult. T1-weighted structural MRI studies have shown gray matter (GM) atrophy primarily in the medial temporal lobe structures, including the hippocampus, amygdala, and entorhinal, perirhinal, and parahippocampal cortices in MCI and AD patients (Frisoni et al., 2002; Apostolova et al., 2006; Teipel et al., 2006; Fritzsche et al., 2008; Fritzsche et al., 2010). Volumetric reduction of the hippocampus and the entorhinal cortex was also used as a biomarker to diagnose MCI and early AD and to predict the progression from MCI to AD (Laakso et al., 1995, 1998; Frisoni et al., 1999; Pennanen et al., 2004; Teipel et al., 2006; Devanand et al., 2007). In addition to atrophic changes of GM, the integrity of brain white matter (WM) has been studied using diffusion tensor imaging (DTI). DTI is an MRI technique that provides microstructural information about the fiber architecture of the brain by measuring the diffusion of water (Stejskal and Tanner, 1965; Basser et al., 1994). In white matter, diffusion perpendicular to the fiber direction is lower than diffusion parallel to the fiber direction (Moseley et al., 1990; Pierpaoli et al., 1996; Bihan et al., 2001). The amount of anisotropy can be related to the tract integrity and is often quantified as fractional anisotropy (FA) (Bihan et al., 2001; Hagmann et al., 2006). Using this measure, studies applied conventional region of interest (ROI) analysis

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or voxel-based morphometry (VBM) (Ashburner and Friston, 2000) to demonstrate a decrease of white matter integrity in MCI and AD patients compared to healthy controls (Bozzali et al., 2002; Fellgiebel et al., 2004; Zhuang et al., 2010) and between amnesic and nonamnesic MCI patients (Petersen, 2004; Zhuang et al., 2010). Recently, several studies appeared that address the problem of predicting conversion from MCI to AD using markers such as neuropsychological tests, MRI, or cerebrospinal fluid (CSF) biomarkers (Buerger et al., 2011; Heister et al., 2011; McEvoy et al., 2011; Modrego et al., 2011). Some studies also combined different markers with techniques from machine learning (Cui et al., 2011; Duchesne and Mouiha, 2011; Zhang and Shen, 2012). Basically, these studies use similar approaches as the studies that attempt to distinguish between patients and healthy controls. However, additional longitudinal data are necessary to verify later conversion of the MCI subjects. Tract-based spatial statistics (TBSS) is a recently developed technique for automated analysis of whole brain DTI data that addresses the problems of arbitrarily placed ROIs or registration errors typically associated with ROI-based methods or VBM (Ashburner and Friston, 2001; Bookstein, 2001; Jones et al., 2005; Smith et al., 2006; Liu et al., 2011). The approach includes a projection step of locally maximal individual FA values onto a white matter skeleton for group comparison of corresponding tract positions in atlas space. In this way it attempts to reduce the impact of registration errors and eliminates, as far as possible, voxels containing partial volume (Smith et al., 2006). A review of this projection algorithm discusses this methodology (Zalesky, 2011). The TBSS method has already been used to evaluate white matter (WM) changes in MCI and AD in previous studies (Damoiseaux et al., 2009; Acosta-Cabronero et al., 2010; Bosch et al., 2010; Salat et al., 2010; Zhuang et al., 2010) and is part of the FMRIB Software Library (FSL) toolkit (Smith et al., 2004). The statistical analysis in a typical TBSS study identifies voxels showing significant group differences using a permutation-based statistical test and correction for multiple comparisons (Nichols and Holmes, 2001). This can be combined with a method called threshold-free cluster enhancement (TFCE), which enhances the significance values in voxels that are supported by their neighborhood (Smith and Nichols, 2009). TBSS studies in the context of AD partly show conflicting results; for example, a study of subjects with amnesic MCI (Damoiseaux et al., 2009) reported no significant FA reductions, whereas Liu et al. (2011) showed significant FA reductions in the parahippocampal WM, uncinate fasciculus and WM tracts of the brain. Possible reasons for these discrepancies are the inclusion or exclusion of TFCE as a preprocessing step before analysis, group sizes, or other different criteria for MCI and AD. Another recently reported issue is that studies mostly use the FA as a measure for anisotropy, which is influenced by all three eigenvalues of the diffusion tensor (Acosta-Cabronero et al., 2010). Studies suggest that different directions of diffusion, e.g., axial diffusion (DA) and radial diffusion (DR), provide more specific information about the underlying neuropathology. It has been proposed that axonal damage has an effect on the axial (parallel) diffusivity, whereas an increase in radial diffusivity is the effect of demyelination (Song et al., 2002, 2003). However, other factors might play an equally important role. A study of correlations between human in vivo DTI and histology can be found in Concha et al. (2010). In this study, DTI data from MCI patients that were involved in a longitudinal study were examined retrospectively in order to identify differences between MCI patients that developed AD within a time frame of 12–18 months and MCI patients that remained stable (Fritzsche et al., 2010). To our knowledge, this is the first study that uses TBSS for this task. The MCI patients involved scored similarly on neuropsychological tests at the time of their inclusion in this study, but a subgroup of them developed AD, whereas the rest remained stable. The patients that developed AD are referred to as the converting MCI patients (MCI-c) and the stable patients are

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referred to as non-converting MCI patients (MCI-nc). For comparison, a group of healthy controls and a group of AD patients were included. Since axial and radial diffusivities measure different aspects of diffusion, these measures were included in the analysis and compared to the FA. Since we have four groups and three measurements, we chose to exclude a whole-brain analysis because this would greatly increase the amount of data and the statistical complexity involved. Instead, analysis was restricted to three structures: the corpus callosum as a large bundle that allows for robust quantification, and the fornix and cingulum as fiber bundles directly related to AD. On these structures a voxel-wise analysis as well as group comparisons were performed using a maximum likelihood regression with random effects. Although parahippocampal white matter is also known to be affected by Alzheimer's disease (Salat et al., 2010), we did not include this in our study because it is a white matter region that consists of parts of the cingulum and the fornix. In this analysis, we focused on well-defined fiber tracts with a clearly traceable pathway. 2. Materials and methods 2.1. Subjects Subjects were recruited from the Department of Geriatric Psychiatry at the University of Heidelberg and from participants of a population-based longitudinal study of aging in the Heidelberg area. The group of participants consisted of 15 healthy controls, 15 AD patients, and 17 MCI patients with a mean age of 66 (±7) years, 72 (±7) years, and 70 (±5) years, respectively. The clinical evaluation of all subjects included ascertainment of personal and family history and physical, neurological, and neuropsychological examination. Those with a history of ischemic heart disease, cancer, and cerebrovascular risk factors were excluded. Neuropsychological performance was assessed using an extensive test battery. As a part of this assessment, declarative memory was measured using a declarative reminding test (Morris et al., 1989). The results were the basis of the clinical diagnosis. MCI was defined by Levy's criteria of aging-associated cognitive decline (Levy, 1994). Mild to moderate Alzheimer's disease was defined by the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADDRA) criteria (McKhann et al., 1984). After follow-up nine MCI patients converted to AD and eight remained stable. At the time of inclusion, all groups were evaluated with the Mini Mental State Examination (MMSE) (Folstein et al., 1975), the results of which were compared for significance using a Mann–Whitney test. After 12–18 months, the MMSE was used again to assess the participants for a second time. 2.2. MRI data acquisition Diffusion-weighted imaging was performed on a 1.5 T whole body clinical scanner and a quadrature head coil (Magnetom Symphony, Siemens Medical Solutions, Erlangen, Germany) with a gradient strength of 40 mT/m. A single shot echo planar imaging technique with a twice refocused spin echo diffusion preparation was employed using the following parameters: repetition time (TR)/echo time (TE) 4700/78 ms, field of view 240 mm, data matrix of 96× 96 yielding an in plane resolution of 2.5 mm, 50 axial slices with a thickness of 2.5 mm and no gap, N=6 gradient directions, and two b-values (0 and 1000 s/mm2). In order to increase the measurement stability, 10 subsequent DTI datasets were acquired, spatially matched, and averaged. 2.3. Preprocessing The diffusion-weighted images were corrected for eddy currents using FSL, diffusion tensor images were estimated from the diffusion-weighted images using a linear least squares fit (Westin et al., 2002), and FA, DA and, DR images were calculated for all subjects. Brain masks were extracted from the non-diffusion-weighted T2 scans using the Brain Extraction Tool (BET) (Smith, 2002) that is included in the FSL package (Smith et al., 2004). The resulting brain masks were used to mask the brains in the FA, DA, and DR images. 2.4. Registration The FA images were registered to the FMRIB58_FA template using FSLs non-linear image registration tool (FNIRT) (Andersson et al., 2007a, 2007b), which uses a b-spline representation of the registration warp field (Rueckert et al., 1999). The resulting transformations from the registration of the FA images were also applied to the DR and DA images. 2.5. Skeletonization and projection A mean FA image was created by averaging all the spatially normalized FA images. In order to obtain a binary mask of voxels that are positioned in the tract centers of the

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averaged FA-image. The mean FA image was thinned using the TBSS skeletonization algorithm. The resulting FA skeleton was thresholded at FA = 0.2 in order to discard voxels containing partial volume of gray matter or cerebrospinal fluid. Each subject's aligned FA data were then projected onto this skeleton by filling the skeleton with FA values from the nearest relevant tract center. This is achieved for each skeleton voxel by searching perpendicular to the local skeleton structure for the maximum value in the subject's FA image (Smith et al., 2006). 2.6. Statistics and classification The resulting individual measures were used to perform voxel-wise group statistics on three major white matter structures that have previously been identified as relevant structures in AD. • The corpus callosum, since it is a very strong and robustly quantifiable structure that plays an important role in many neurodegenerative diseases. • The fornix, since it is directly connected to the hippocampus, which is a structure known to be very prominently affected by AD (Fritzsche et al., 2010) and since the fornix was shown to be affected by AD in previous studies (Mielke et al., 2009). • The cingulum, since it is a clearly identifiable structure that lies around the corpus callosum and is also known to be affected in AD (Zhang et al., 2007). These structures were segmented manually from the FA skeleton using the Medical Imaging Interaction Toolkit (MITK) (Wolf et al., 2005; Maleike et al., 2009). Fig. 1 shows the ROIs in a 3D rendering of the mean FA images that was acquired by averaging all registered FA images. FA values along the tracts were read out in the anterior to posterior direction and voxel-wise group statistics were performed by means of t-tests using a significance level of 0.05. Single voxel occurrence of P-values smaller than 0.05 should not be interpreted as proof for a significant difference due to multiple comparison issues; however, they can be interpreted as a descriptive statistic. In order to test whether or not there are significant differences between the groups, we performed a maximum likelihood regression with random effects with a correction for multiple comparisons (O'Dwyer et al., 2011) over all regions. For each measurement, the following hypotheses were tested: 1. MCI-c = MCI-nc 2. MCI-c = AD 3. MCI-nc = Controls These three comparisons were made because they compare adjacent groups when ordering them by disease severity (healthy, MCI-nc, MCI-c and AD).

3. Results At the time of inclusion, the healthy volunteer group had an MMSE score of 29.3, the MCI group a score of 26.4, and the AD group a score of 19.2. The MCI-nc and the MCI-c groups showed comparable MMSE scores of 26.8 versus 26.2. The MMSE score of the MCI group was significantly lower than the MMSE score of the healthy controls (Mann–Whitney: Pb 0.0001) and the MMSE score of the AD group was significantly lower than that of the MCI group (Mann–Whitney: Pb 0.0001). At the second assessment, 12–18 months after the first assessment, the mean MMSE score in the MCI-nc group was 26.9 and showed no

significant difference from the initial MMSE score (Mann–Whitney: P = 0.564). The mean MMSE in the MCI-c group at the second assessment was 23.3, which was a significant drop compared to the initial MMSE (Mann–Whitney: P = 0.006). For every structure and for every measurement, we created graphs (profiles) showing the averages of the measured values on the respective structures. In these profiles the x-axis represents the position on the structure of interest and the y-axis the anisotropy or diffusion measure. For all groups the FA profiles are shown and additionally the FA, DR, and DA profiles are shown for both MCI groups. Fig. 2a shows the FA profiles of the corpus callosum for the four groups under study, and Fig. 2b,c, and d shows the FA, DR, and DA profiles for the MCI-nc group and the MCI-c group with error bars indicating the standard deviations and indicators of positions where the difference was significant (uncorrected for multiple comparisons). The healthy control group and the MCI-nc group had higher FA and lower DR and DA values than the MCI-c and the AD groups, although the difference was not as pronounced for DA as it was for FA and DR. Since the fornix cannot be described by a single path, but rather consists of a body in the center of the brain (in the sagittal direction) and splits into two branches, the profiles in Fig. 3 are divided into three segments. The first segment describes the body (center part) and the other two describe both respective branches. Fig. 3a shows the FA profiles of all four groups and Fig. 3b, c, and d shows the FA, DR, and DA profiles only for the two MCI groups with standard deviations and indicators of significant differences between MCI-nc and MCI-c (uncorrected for multiple comparisons). Here, the groups can be ordered by FA value from low to high in the following order: AD, MCI-c, MCI-nc, healthy controls. Furthermore, the MCI-nc group showed lower DR and DA values than the MCI-c group. Figs. 4 and 5 show the same information for the left and the right cingulum, respectively. On both the left and the right cingulum the FA was lower for the AD group and the MCI-c group than for the MCI-nc group and the healthy control group. The MCI-nc group had lower DR values than the MCI-c group, whereas the DA values of the MCI-nc group were higher than those of the MCI-c group. The results of the regression analysis are shown in Table 1. No significant differences were found between the healthy controls and the MCI-nc group or between the MCI-c and the AD groups. Significant differences were found between the MCI-nc group and the MCI-c group for the FA and the DR, but not for the DA. In Fig. 6, the averages of all structures are shown per group with 95% confidence intervals. Fig. 6a shows the FA averages, Fig. 6b shows the DR averages, and Fig. 6c shows the DA averages. In these figures the ordering from low to high of FA values was: AD, MCI-c,

Fig. 1. A coronal view (a), a sagittal view (b), and an axial view (c) of a 3D volume rendering of the mean FA volume from all the subjects' registered FA volumes with the ROIs. The fornix is drawn in red, the corpus callosum in green, the right cingulum in yellow and the left cingulum in blue.

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Fig. 2. Profiles showing the group-averaged FA and diffusion measures on the corpus callosum. The x-axis corresponds to the position in the anterior–posterior direction. Panel (a) shows the FA for the healthy controls, the MCI-nc patients, the MCI-c patients and the AD patients. Panels (b), (c), and (d) show the FA, DR, and DA values for the MCI-nc group and the MCI-c group with error bars showing the standard deviations and indicators of positions where the difference was significant (P b 0.05, uncorrected for multiple comparisons).

MCI-nc, healthy controls, and was inverse for the DR and DA values, except on the left and the right cingulum, where the DA was lower for MCI-c patients than for MCI-nc patients. Fig. 7 shows receiver operator curves (ROC) (Sing et al., 2005) for the separation of MCI-nc and MCI-c patients. To summarize the performance, the area under curve (AUC) is also reported for each curve in the graphs. Fig. 7a shows the curves for FA, DR, and DA for the corpus callosum. Here, the FA and the DR (both with an AUC of 0.94) perform much better than the DA (AUC = 0.72). Fig. 7b shows the curves for these three measurements for the body of the fornix, where the DR and the DA (AUC = 0.78 and AUC = 0.79, respectively) perform better than the FA (AUC = 0.71). Fig. 7c–d shows these curves for the left and the right cingulum, respectively. For the cingulum the FA and the DR perform better than DA with AUCs of 0.94, 0.94, and 0.74 for the left cingulum and 0.85, 0.78, and 0.67 for the right cingulum. Furthermore, the AUCs for the left cingulum are higher than for the right cingulum.

4. Discussion In this study, a group analysis of diffusion in three major tracts of the brain's white matter was performed using TBSS. In addition to other studies that focus on the differences between AD patients and healthy controls, this study retrospectively looked at MCI patients in order to find early predictors for a later development of AD. A voxel-wise analysis of these structures was performed, as well as a comparison of mean diffusion measurements and diffusion anisotropy. Significant differences between MCI-nc and MCI-c were found using a maximum likelihood regression model with random effects. On the corpus callosum the FA profiles corresponding to the healthy control group and the MCI-nc group show a high degree of similarity and the same can be seen when comparing the MCI-c group and the Alzheimer group, which suggests that the MCI-c group has similar pathology as the AD group whereas the MCI-nc group still resembles the healthy control group. The corpus callosum

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Fig. 3. Profiles showing the group-averaged FA and diffusion measures on the fornix. The x-axis corresponds to the position in the anterior-posterior direction. Since the fornix consists of three segments (see description in text), the profiles consist of three disconnected parts. Panel (a) shows the FA for the healthy controls, the MCI-nc patients, the MCI-c patients and the AD patients. Panels (b), (c), and (d) show the FA, DR, and DA values for the MCI-nc group and the MCI-c group with error bars showing the standard deviations and indicators of positions where the difference was significant (P b 0.05, uncorrected for multiple comparisons).

exhibits large clusters of voxels with significant differences (uncorrected for multiple comparisons), especially of the FA and the DR profiles between both MCI groups. For DA these differences are much less apparent and the number of voxels exhibiting a significant difference is strongly reduced. In Fig. 6a, the 95% confidence intervals of the mean FA and RD on the corpus callosum of these groups do not overlap. On the body of the fornix, differences between the healthy controls and the MCI-nc group and between the MCI-c and the AD groups can be seen. For the left and the right crura of the fornix there is mainly a difference between healthy controls and the other three groups. There were more positions with significant differences (uncorrected for multiple comparisons) between MCI-nc and MCI-c groups in DR and DA than for FA. This is an effect that has been described in literature, where it was reported that axonal damage, like Wallerian degeneration (Pierpaoli et al., 2001), had an effect on the DA diffusivity, whereas an increase in radial diffusivity has been shown to be the

effect of demyelination (Song et al., 2002, 2003). Here it seems that the increased diffusion in all directions of the diffusion tensor leaves FA relatively unchanged, a problem with the FA that has been recently described (Acosta-Cabronero et al., 2010). One must also consider that there are still a lot of partial volume effects when the structure is thinner than the voxel size despite reduction of the data to a skeleton of WM. This could well be the case here with the fornix and could therefore disturb the measurements (Smith et al., 2006). In Fig. 6, for all three parts of the fornix, there is considerable overlap of the confidence intervals of both MCI groups for FA and also for DA; however, for DR on the body of the fornix there is a clear separation between the confidence intervals of the MCI groups. Some remarkable observations were made with regard to the cingulum. When compared to the corpus callosum and the fornix, the FA profiles created for the left cingulum do not seem to differ much on first sight, but there are still a considerable number of positions with a significant difference (uncorrected for multiple comparisons)

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Fig. 4. Profiles showing the group-averaged FA and diffusion measures on the left cingulum. The x-axis corresponds to the position in the anterior–posterior direction. Panel (a) shows the FA for the healthy controls, the MCI-nc patients, the MCI-c patients and the AD patients. Panels (b), (c), and (d) show the FA, DR, and DA values for the MCI-nc group and the MCI-c group with error bars showing the standard deviations and indicators of positions where the difference was significant (P b 0.05, uncorrected for multiple comparisons).

between both MCI groups. Another observation is that the differences between both MCI groups are larger for the left cingulum than for the right. Although significance was not tested for in this study, Figs. 4 and 5 show more positions with a significant difference (uncorrected for multiple comparisons) for the left cingulum than for the right. This can also be seen in Fig. 6, where for all three measurements the 95% confidence intervals of the MCI groups overlap more for the right cingulum than for the left. If in future studies this difference proved to be significant, it would be consistent with other studies reporting a left over right prevalence of neuronal degeneration in MCI patients on the cingulum, e.g., Zhang et al. (2007), or other structures, e.g., the entorhinal cortex (Rose et al., 2006) and in the parahippocampal WM (Salat et al., 2010). Furthermore, the DA in MCI-c patients appears to be lower than in MCI-nc patients. One explanation might lie in the chain of events of Wallerian degeneration (Pierpaoli et al., 2001) where the damage to the WM first leads to a temporary decrease of DA followed by a subsequent increase when axonal fragments are cleared by microglia, enabling the water molecules to diffuse in the

axial direction again (Thomalla et al., 2005; Thomas et al., 2005; O'Dwyer et al., 2011). In other studies, measurements in the temporal lobe of AD and MCI patients demonstrated significant reductions in FA for both groups (Huang and Auchus, 2007; Huang et al., 2007). In Huang et al. (2007), the MCI group only showed a decreased DA, whereas the AD group showed decreases in DA and increases in DR. These effects should be investigated further in longitudinal studies of MCI groups. The measurement averages of the structures show that generally the FA values of the healthy and the MCI-nc groups are higher than the FA values of the MCI-c and the AD groups (see Fig. 6). For the DR averages there are not many surprises either. The general trend is that RD averages of the MCI-c group and the AD group are higher than those of the healthy control group and the MCI-nc group; however, for the left and the right part of the fornix, the healthy group has the lowest value, whereas the other three groups were comparable. The DA averages of the fornix and the corpus callosum are lower for the healthy control group and the MCI-nc group than for the

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Fig. 5. Profiles showing the group-averaged FA and diffusion measures on the right cingulum. The x-axis corresponds to the position in the anterior–posterior direction. Panel (a) shows the FA for the healthy controls, the MCI-nc patients, the MCI-c patients and the AD patients. Panels (b), (c), and (d) show the FA, DR, and DA values for the MCI-nc group and the MCI-c group with error bars showing the standard deviations and indicators of positions where the difference was significant (P b 0.05, uncorrected for multiple comparisons).

MCI-c group and the AD group, whereas for the left and the right cingulum a different effect can be seen. Here, the MCI-c group has lower DA averages than the MCI-nc group and the AD group and approximately the same as the healthy control group. As already mentioned, this might be due to the different effects of Wallerian degeneration; however, the effects of the different types of pathology in diffusion tensor data are not well understood yet and should be investigated further in the future (Acosta-Cabronero et al., 2010). Since the early prediction of the conversion from MCI to AD is an important aspect for future research, it is important to identify indices

Table 1 Sidak corrected P-values for the group comparison over all structures. The * indicates significance (P b 0.05).

Controls vs. MCI-nc MCI-nc vs. MCI-c MCI-c vs. AD

FA

DR

DA

P = 0.9386 P = 0.0040* P = 0.9947

P = 0.3354 P = 0.0142* P = 0.9999

P = 0.1501 P = 0.85 P = 0.8052

that could perform well on this task. For this, receiver operator curves are very useful. The AUC is a commonly used summary of the ROC. An AUC of 0.5–0.6 is considered a failure; the results are merely coincidences. An AUC of 0.6–0.7 is considered poor, 0.70–0.80 is considered fair, 0.8–0.9 is considered good, and 0.9–1 is considered excellent. Fig. 7 shows the ROCs for this study. On the corpus callosum for FA and DR, the AUC is 0.94, whereas the DA performs much less well with an AUC of 0.72. On the body of the fornix DR and DA have higher AUCs (0.78 and 0.79) than the FA (0.71). This is consistent with the previously discussed results that gave the impression of increases in both DR and DA that suppressed the effect of the changed diffusivity on FA. For both the left and the right cingulum the FA (AUC = 0.85) and RD (AUC = 0.78) performed better than the DA (AUC = 0.67). Most of these results, like the FA and RD on the corpus callosum and the cingulum and the DR and DA on the body of the fornix, have good and, in some cases, excellent performance. The modest sample size and the retrospective design are major limitations to our study. Also, when comparing the present results to those of previous longitudinal MRI studies, it is important to bear in mind that our MCI sample was characterized by a relatively high

0.2

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Healthy MCInc MCIc AD

body fornix

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corpus callosum

body fornix

left fornix

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right fornix

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0.020

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0.010

Mean Axial Diffusivity (mm2s−1)

c

0.000

Mean Radial Diffusivity (mm2s−1)

b

0.000 0.005 0.010 0.015 0.020 0.025

0.0

Mean Fractional Anisotropy

a

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Fig. 6. The average FA (a), DR (b), and DA (c) of all structures used in this study for all four study groups. The error bars indicate the 95% confidence intervals.

conversion rate of approximately 50%, indicating that the respective subjects might have been in an already advanced stage of MCI at baseline. Early pathologic hallmarks of AD that are clearly manifested in the diffusion images of the MCI-c patients were identified. This is an important result, since current clinical tests and diagnostic tools fail to separate the converting and non-converting subjects at time of the MCI diagnosis. Although differences between MCI-c and MCI-nc were found and the results presented here are promising, improvement is necessary in order to be able to successfully classify, and thereby predict, conversion from MCI to AD. TBSS reduces the data to a fiber skeleton and thus reduces the dimensionality of the data and at the same time eliminates partial volume effects. For each individual it finds locations for its measurements where the FA is locally maximal. A problem here is that the maximum as a statistical measurement is typically sensitive to noise and thus can impair the quality of the measurements. Furthermore, partial volume with cerebrospinal fluid (CSF) can bias the measurement, especially if the size of a structure is smaller than the voxel size. Future work should concentrate on methods for acquiring more robust and distinguishing measurements,

for example, by acquiring datasets of regions like the limbic system with a higher resolution. Partial volume effects could be handled by using a CSF-suppression technique such as fluid attenuation inversion recovery (FLAIR), which has been shown to ameliorate the effects of partial volume at the cost of a lower signal-to-noise ratio (SNR) (Concha et al., 2005). Pasternak et al. (2009) have proposed a method to eliminate partial volume in a post-hoc analysis, during tensor estimation. Furthermore, it seems that the performance of diffusion indices is not equally good in different structures. Future work is therefore needed to further identify combinations of brain structures and diffusion indices with a high predictive power on the conversion to AD. In summary, this study used TBSS to analyze the diffusion and fractional anisotropy indices on three important structures of the brain's white matter with respect to AD and MCI in a retrospective manner on data of MCI patients. Differences were shown between MCI patients that remained stable and MCI patients that developed Alzheimer's disease within a time frame of 12–18 months. MCI-c patients already exhibited Alzheimer-specific pathology to a similar extent compared to AD patients. This is an important result, since the early detection of AD is such an important aspect of applying

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a

b 0.8 0.6 0.4

True positive rate

FA. AUC= 0.71 DR. AUC= 0.78 DA. AUC= 0.79

0.2

0.8 0.6 0.4 0.2

FA. AUC= 0.94 DR. AUC= 0.94 DA. AUC= 0.72

0.0

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True positive rate

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FA. AUC= 0.85 DR. AUC= 0.78 DA. AUC= 0.67

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True positive rate

0.8

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1.0 0.8 0.6 0.4

FA. AUC= 0.94 DR. AUC= 0.94 DA. AUC= 0.74

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Fig. 7. Receiver operator curves for the separation of the MCI-nc group from the MCI-c group. Each respective figure shows the curve for the FA, DR, and DA figures. ROCs were created for the corpus callosum (a), the body of the fornix (b), the left cingulum (c) and the right cingulum (d). The area under curve (AUC) for each curve is given in the graphs as well.

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