Psychiatry Research: Neuroimaging 231 (2015) 346–352

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Detection of early changes in the parahippocampal and posterior cingulum bundles during mild cognitive impairment by using high-resolution multi-parametric diffusion tensor imaging Kenji Ito a,n, Makoto Sasaki a, Junko Takahashi b, Ikuko Uwano a, Fumio Yamashita a, Satomi Higuchi a, Jonathan Goodwin a, Taisuke Harada a,c, Kohsuke Kudo a,c, Yasuo Terayama b a b c

Division of Ultra-high Field MRI, Institute for Biomedical Sciences, Iwate Medical University, 2-1-1 Nishitokuta, Yahaba, Iwate 028-3694, Japan Department of Neurology and Gerontology, Iwate Medical University, 19-1 Uchimaru, Morioka, Iwate 020-8505, Japan Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5 Kita-ku, Sapporo, Hokkaido 060-8648, Japan

art ic l e i nf o

a b s t r a c t

Article history: Received 3 May 2014 Received in revised form 11 June 2014 Accepted 18 January 2015 Available online 30 January 2015

We aimed to determine alterations occurring in the parahippocampal cingulum bundle (PhC) and posterior cingulum bundle (PoC) in patients with mild cognitive impairment (MCI) through analysis of high-resolution multi-parametric diffusion tensor imaging (DTI). Participants comprised 41 patients with MCI (21 AD converters [MCI-C] and 20 non-converters [MCI-NC]), 20 patients with Alzheimer's disease (AD), and 26 healthy elderly subjects who underwent prospective examination with highresolution DTI. An atlas-based regions-of-interest (ROIs) method calculated fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (DA), and radial diffusivity (DR) in the PhC and PoC. For the PhC, FA values showed significant decreases, and MD and DR values showed significantly increases, in the MCI-C and AD groups compared with the healthy controls, although the MCI-C and MCI-NC groups did not differ significantly in these metrics. Conversely, none of the diffusion metrics for the PoC showed a significant difference among the MCI groups and the control groups, although there were significant differences between the AD group and control groups. High-resolution multi-parametric DTI analysis was able to detect substantial changes in diffusion anisotropy and diffusivity in the PhC of patients with MCI who were destined to convert to AD. & 2015 Elsevier Ireland Ltd. All rights reserved.

Keywords: Diffusion tensor imaging Mild cognitive impairment Alzheimer's disease Parahippocampal cingulum bundle Posterior cingulum bundle

1. Introduction Mild cognitive impairment (MCI) is a neurological condition that may include the prodromal stage of Alzheimer's disease (AD) (Petersen et al., 2001). Approximately 50% of all MCI cases have been reported to convert to AD within 5 years (Gauthier et al., 2006), which is nine times higher than the conversion rate for healthy elderly individuals (Mitchell and Shiri-Feshki, 2009). Moreover, although MCI is considered a cardinal stage for targeting therapeutics for the early intervention in AD, additive noninvasive biomarkers are still needed to improve the diagnostic accuracy of the prodromal stage of AD and to achieve successful disease modification therapies in the future. Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that is widely used to quantify the anisotropy of water diffusion in normal and abnormal brain tissues, particularly

n

Corresponding author. Tel.: þ 81 19 651 5111; fax: þ81 19 908 8021. E-mail address: [email protected] (K. Ito).

http://dx.doi.org/10.1016/j.pscychresns.2015.01.020 0925-4927/& 2015 Elsevier Ireland Ltd. All rights reserved.

in cerebral white matter (WM) (Le Bihan et al., 2001). In DTI analysis, fractional anisotropy (FA) is usually used as a quantitative metric for diffusion anisotropy, which can be reduced in various pathological conditions of WM such as subtle demyelination and axonal degeneration (Beaulieu, 2002). DTI has been applied for the early diagnosis of AD and has revealed that changes in diffusion anisotropy and/or diffusivity of various WM structures can occur even in early AD and MCI (Sexton et al., 2011; Clerx et al., 2012), which may reflect Wallerian degeneration and other axonal alterations secondary to neuronal loss in cortical areas (Pierpaoli et al., 2001; Coleman, 2005). The parahippocampal cingulum bundle (PhC) and the posterior cingulum bundle (PoC) are nerve tracts that connect the medial parietal lobe, including the posterior cingulate cortex and the precuneus, to the medial temporal lobe, including the entorhinal cortex, and they are considered to play an important role in memory processing and storage. The entorhinal cortex is well known as one of the earliest areas affected in AD in terms of neuronal degeneration and volume loss (Killiany et al., 2002), while the posterior cingulate cortex and the precuneus are reported to be

K. Ito et al. / Psychiatry Research: Neuroimaging 231 (2015) 346–352

the earliest affected areas to show hypoperfusion and hypometabolism (Herholz et al., 2007; Morbelli et al., 2010). A recent study that combined volumetric MRI and positron emission tomography (PET) demonstrated that medial temporal atrophy preceded hypoperfusion in the posterior cingulate cortex and precuneus, suggesting that the latter change may be caused by retrograde transneuronal effects through the PhC and PoC (Villain et al., 2010). Therefore, functional or pathological changes in the PhC and PoC, which the DTI technique may detect, are considered to be additive biomarkers for the early diagnosis of AD. Several DTI studies have reported reduced anisotropy of the PhC and PoC in patients with AD (Zhang et al., 2007; Choo et al., 2010; Agosta et al., 2011; O’Dwyer et al., 2011). However, only two studies have revealed substantial differences in diffusion anisotropy of these tracts in patients with MCI (Zhang et al., 2007; Choo et al., 2010). The paucity of positive results is presumably due to the relatively low spatial resolution of DTI, particularly in the superior–inferior direction, which can interfere with the imaging of small limbic fibers, particularly those along the x–y plane such as the PhC. Compounding this problem is a lack of additive diffusion metrics such as axial diffusivity (DA) and radial diffusivity (DR), which can enable the detection of minute diffusion changes in WM tracts as compared with measures such as FA and mean diffusivity (MD) (Acosta-Cabronero et al., 2010; Boespflug et al., 2014). Therefore, we investigated if the high-resolution multiparametric DTI technique with 1.6-mm thickness can detect subtle changes that may occur in the PhC and PoC of patients with MCI and if it can determine which patients with MCI are destined to convert to AD by using an automated atlas-based region-of-interest (ROI) analysis.

2. Methods 2.1. Subjects For the MRI examination that included DTI, 46 consecutive patients who had amnestic MCI, fulfilled Petersen's criteria (Petersen et al., 2001; Winblad et al., 2004), and were at Functional Assessment Staging (FAST) Stage 3 were recruited between 2006 and 2011 (19 men and 27 women; age, 59–92 years [median, 78.5 years]; duration of disease, 0.25–8 years [median, 2 years]). For a disease control, 20 patients who had probable AD met the NINCDS–ADRDA criteria (McKhann et al., 1984; Dubois et al., 2007) and were at FAST Stage 4 were also recruited (11 men and nine women; age, 64–89 years [median, 79.5 years]; duration of disease, 0.5–10 years [median, 5 years]). In addition, 26 age-matched healthy subjects with no neurological or psychiatric disorders participated in this study (eight men and 18 women; age, 55–90 years [median, 78.5 years]). Cholinesterase inhibitors were being taken by 29 out of the 46 patients with MCI, 18 out of the 20 patients with AD, and none of the healthy subjects. The scores on a Mini-Mental State Examination (MMSE) performed within 1 week of the MRI examination were 19–30 (median, 25.5) in patients with MCI and 16–26 (median, 22) in patients with AD. The MMSE was not performed in the healthy subjects because of ethical issues.

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After at least a 2-year follow-up of patients with MCI (2–6 years [median, 4 years]), 21 of them met criteria for a diagnosis of probable AD. These patients were then included in the AD-converted MCI (MCI-C) group (eight men and 13 women; age, 67–92 years [median, 80 years]). Another 20 patients who did not fulfill the criteria for a diagnosis of probable AD were included in the nonconverted MCI (MCI-NC) group (eight men and 12 women; age, 63–92 years [median, 78 years]). The remaining five MCI patients were excluded from further analyses for the following reasons: one patient showed reversion of symptoms, and four patients stopped visiting the hospital. We performed all of the examinations after obtaining approval from the institutional review board and written informed consent from each subject.

2.2. MRI acquisition All of the 87 participants underwent an MRI examination that included a whole-brain high-resolution DTI acquisition using a 3 T MRI scanner (Signa Excite HD; GE Healthcare, Milwaukee, WI, USA) with an eight-channel head coil. As previously reported (Fujiwara et al., 2008), the following pulse sequence parameters were used for the high-resolution DTI acquisition: single-shot spin-echo EPI (echoplanar imaging); repetition time/echo time, 17,000/61.9; images with sixdirection motion-probing gradients (MPGs) with a b value of 1000 s/mm2 and an image with a b value of 0 s/mm2; matrix size, 128  128; field of view, 22 cm; slice thickness, 1.6 mm with no interslice gaps; number of slices, 80; number of repetitions for averaging, four; parallel imaging with a reduction factor of 2. Total acquisition time was 8 min 30 s. The data, with four acquisitions and in-plane zerofill interpolation, were automatically averaged and performed by the scanner, respectively, yielding an apparent voxel size of 0.86  0.86  1.6 mm3. Axial T1-and T2-weighted images were also obtained in order to exclude other neurological disorders and any coexisting lesions that could interfere with further analyses.

2.3. DTI processing One of the authors (K.I.), who was unaware of the subject information, performed DTI processing in a blinded fashion by using a brain image analysis package, FMRIB Software Library (FSL) [http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/] v5.0, which includes various tools, including the Brain Extraction Tool (BET), FMRIB's Diffusion Toolbox (FDT), and FMRIB's Nonlinear Image Registration Tool (FNIRT). DTI data were first filtered using a Gaussian operator (full width at halfmaximum of 2.35 mm) to remove noise because of having a relatively low signalto-noise ratio, and then corrected for motion and eddy-current distortion by using FSL's eddy correct tool. In addition, head motion during image acquisition was quantified in terms of the rotation and translation according to the method introduced by Ling et al. (2012). Next, the brain structure was extracted from the images after an interpolation along the z-axis by using BET. From the preprocessed DTI data, maps of FA, MD, DA, and DR were generated through FDT. Then, the FA maps were realigned to the FA template (JHU ICBM FA 1 mm) by using FNIRT, and the corresponding MD, DA, and DR maps were subsequently realigned to the transformation information obtained from the FA maps. Further, as in the previous study (Sexton et al., 2010), atlas-based ROIs of the WM for the PhC and PoC were generated by using the JHU ICBM DTI 81 WM labels atlas of the FSL atlas tool (Fig. 1) (Mori et al., 2008). The anterior border of the PoC was placed at the anterior border of the splenium of the corpus callosum. The ROI of the posterior limb of internal capsule (pIC) served as a control ROI. Mean FA, MD, DA, and DR values within each ROI were calculated after applying a binary mask with FA Z 0.2 to remove partial volume effects from adjacent gray matter structures.

Fig. 1. Atlas-based regions-of-interest of the left parahippocampal and posterior cingulum bundles. The regions-of-interest of the parahippocampal cingulate bundle (red area) and of the posterior cingulate bundle (blue area) are shown on the three-dimensional images of the JHU ICBM FA template ((a) oblique view; (b) lateral view).

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2.4. Statistical analysis All statistical analyses were performed using JMP 9.0.3 (SAS Institute, Cary, NC, USA). Differences in the clinical characteristics among the groups were compared using the Steel–Dwass test or the chi-square test. The FA, MD, DA, and DR values of the PhC, PoC, and pIC were compared among the groups with the Steel–Dwass test. Statistical significance was set at P o 0.05. To determine the sensitivity and specificity of the DTI metrics for discriminating patients who had MCI-C or AD from healthy subjects, receiver-operating characteristic (ROC) analyses were performed, in which cut-off values were determined by using Youden's index. Differences in the area under the ROC curves (AUCs) were examined using the method described by DeLong et al. (1988).

3. Results All participants were examined with MRI, but 10 participants were excluded for the following reasons: head motion more than two degrees of rotation and/or 2 mm of translation in any direction according to the previous reports (Knaus et al., 2010; Ding et al., 2013; Yendiki et al., 2013) in two patients with MCI-C, three patients with MCI-NC and four controls; and marked distortion from metallic dental prostheses in one control. Table 1 summarizes the demographic characteristics of the remaining subjects who were eligible for further quantitative analyses. There were no significant differences in age, sex, and administration of cholinesterase inhibitors among the four groups; however, MMSE scores were significantly lower in the AD group than in the MCI-NC group (Po0.01, Steel–Dwass test) and disease durations were significantly longer in the former than the latter (Po0.01, Steel–Dwass test). Fig. 2 shows the FA, MD, DA, and DR values of the PhC, PoC, and pIC in the four groups. For the pIC, these values were not significantly different among the groups (P ¼0.49–1.00, Steel– Dwass test). For the PhC, in contrast, the FA values in the AD group were significantly lower than those in the control group (P ¼0.004/0.006 [left/right], Steel–Dwass test) and the MD and DR values in the former were significantly higher than corresponding values in the latter (P ¼0.002/0.002 [left/right] and o0.001/0.001 [left/right], respectively). The FA values were also significantly lower in the MCI-C group (P ¼0.037/0.029 [left/right]), and the MD and DR values were significantly higher on the left side (P¼ 0.048 and 0.006, respectively) compared with corresponding values in the controls; however, the DA values showed no significant difference between the two groups (P¼ 0.64–0.72). In contrast to the differences found compared with the control group, no significant differences emerged in any of the DTI metrics among the AD, MCI-C, and MCI-NC groups (AD vs. MCI-C, P¼ 0.42–0.90; AD vs. MCI-NC, P ¼0.051–0.60; MCI-C vs. MCI-NC, P ¼0.44–0.99). Moreover, there were no differences between the MCI-NC and control groups (P ¼0.17–0.95). The only exception was for the MD values of the left PhC between the AD and MCI-NC groups (P ¼0.049). Nevertheless, there were tendencies toward a gradual decrement in the FA values and a gradual increment in the MD, DA, and DR values moving along the spectrum of groups, from the

control group to the MCI-NC group, to MCI-C group, and finally to the AD group. For the PoC, the FA, MD, and DR values differed significantly on the right side between the AD and control groups (P¼ 0.047, 0.049, and 0.009, respectively). However, there were no significant differences in any of the metrics between the AD and MCI-C groups or between the AD and MCI-NC groups (P¼ 0.34–0.99 and 0.065–0.89, respectively), nor among the MCI-C, MCI-NC, and control groups (MCI-C vs. MCI-NC, P ¼0.83–1.00; MCI-C vs. control, P¼ 0.61–1.00; MCI-NC vs. control, P ¼0.94–1.00). ROC analyses of the FA, MD, and DR values of the PhC showed significant differences between the MCI-C or AD and control groups. The AUCs for the FA, MD, and DR values to discriminate the MCI-C group from the controls were 0.75/0.76, 0.74/0.70, and 0.80/0.73 (left/right), respectively; whereas those to discriminate AD from the controls were 0.81/0.80, 0.84/0.83, and 0.86/0.85 (left/right), respectively (Table 2, Fig. 3). There were no significant differences in the AUC values between the DTI metrics (P¼0.35–0.94, DeLong's test) or between the MCI-C and AD groups (P¼0.24–0.67, DeLong's test). The sensitivity for discriminating the MCI-C group from the control groups was relatively high in FA (89.5/79.0% [left/right]), whereas the specificity was relatively high in MD and DR (90.5/90.5% and 90.5/80.9% [left/right], respectively). The range of sensitivities and specificities between the AD and the control groups was 70.0–90.0 and 71.4–90.5, respectively (Table 2). Between the MCI-C and MCINC groups or between the MCI-NC and control groups, ROC analyses were not applicable because there were no significant differences between the groups.

4. Discussion In this study, we successfully detected substantial changes in the DTI metrics of the PhC in patients with MCI-C as well as in patients with AD by using a high-resolution multi-parametric DTI technique, while there were no changes in those of the pIC. This finding indicates that pathological and/or functional alterations occur in the nerve fibers within the PhC even during the prodromal stage of AD. These changes were much more evident than changes in the PoC, suggesting that changes in the PhC precede those in the PoC in patients with MCI and AD. Therefore, the DTI analysis of the PhC may have potential advantages for the diagnosis of cases of MCI that in time will convert to AD. The PhC has been one of the targets of DTI studies for the early diagnosis of AD because the PhC consists of abundant afferent fibers from the posterior cingulate cortex and the precuneus to the entorhinal cortex, the region in which neuronal degeneration occurs earliest. Previous studies have already revealed decreased FA values of the PhC in patients with MCI (Zhang et al., 2007; Choo et al., 2010; Wang et al., 2012). In these studies, however, it remained unclear if this change is a characteristic of the MCI subgroup that will convert to AD. In this study, we found that FA reduction of the PhC was significant in patients with MCI-C but not

Table 1 Demographic characteristics.

Age (years) Sex Disease duration (years) MMSE CE inhibitors

Range (median) Men (%) Range (median) Range (median) Number (%)

Control (n¼ 21)

MCI-NC (n ¼17)

MCI-C (n ¼19)

AD (n¼20)

P-value

55–90 (75) 7 (33) NA NA NA

63–92 (76) 8 (47) 0.25–5 (2) 19–30 (26) 13 (76)

68–92 (80) 7 (37) 0.5–8 (2) 19–28 (23) 13 (68)

64–89 (79.5) 11 (55) 0.5–10 (5) 16–26 (22) 18 (90)

NS NS o 0.01a (MCI-NC vs. AD) o 0.01a (MCI-NC vs. AD) NS

CE, cholinesterase; AD, Alzheimer disease; MCI, mild cognitive impairment; MCI-NC, MCI non-converter; MCI-C, MCI converter; MMSE, Mini-Mental State Examination; NA, not assessed; NS, not significant. a

Steel–Dwass test.

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Fig. 2. Diffusion tensor metrics of the parahippocampal, posterior cingulum bundles in mild cognitive impairment (MCI) and Alzheimer's disease (AD). For the parahippocampal cingulum bundle (PhC), values of fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (DR) were significantly altered in patients with MCI converted (MCI-C) and those who had AD when compared with the healthy controls. However, there are no significant differences in these values between the patients with MCI non-converted (MCI-NC) and the controls as well as among the MCI-NC, MCI-C, and AD groups. Regarding the posterior cingulum bundle (PoC), only the AD and control groups show significant differences. There are no significant differences in the diffusion metrics of the posterior limb of the internal capsule (pIC) between the groups. *Po 0.05, **Po 0.01, †Po 0.001 (Steel–Dwass test). Table 2 ROC analysis of multiparametric DTI for discriminating patients with MCI-C or AD from healthy subjects. DTI metrics

FA MD DR

PhC

Left Right Left Right Left Right

MCI-C vs. Control

AD vs. Control

Area under the curve

Cutoff value

Sensitivity (%)

Specificity (%)

Area under the curve

Cutoff value

Sensitivity (%)

Specificity (%)

0.75 0.76 0.74 0.70 0.80 0.73

0.354 0.359 0.810 0.851 0.648 0.668

89.5 79.0 63.2 47.4 63.2 57.9

57.1 71.4 90.5 90.5 90.5 80.9

0.81 0.80 0.84 0.83 0.86 0.85

0.341 0.355 0.815 0.819 0.658 0.672

70.0 90.0 70.0 90.0 70.0 80.0

85.7 71.4 90.5 71.4 90.5 80.9

AD, Alzheimer's disease; DR, radial diffusivity; DTI, diffusion tensor imaging; FA, fractional anisotropy; MCI-C, mild cognitive impairment converted; MD, mean diffusivity; PhC, parahippocampal cingulum bundle; ROC, receiver operating characteristic.

in patients with MCI-NC. These findings suggest that a loss of diffusion anisotropy in the PhC, which may reflect WM changes that are secondary to neuronal degeneration of the entorhinal cortex, occurs during the prodromal stage of AD. Changes in DTI metrics of the PhC were detected in this study, possibly because of the high-resolution DTI acquisition with 1.6-mm thickness. In general, the PhC runs almost parallel to the x–y plane because elderly patients usually cannot pull their chin during scanning, resulting in a substantial posterior tilt of the AC–PC line. Hence, improvement of spatial resolution in the z-direction we used can help to avoid contamination of surrounding structures in the PhC. Although there are no previous studies to which this study can be

directly compared in terms of sensitivity and specificity, FA values of the PhC in AD and MCI groups in the previous study in which 3.5-mm-thick sections were used (Choo et al., 2010) were lower and the standard deviations were larger than those in this study, suggesting that contamination of surrounding brain structures by partial volume effects can be minimized when using thin slice sections. In addition to the FA reduction, we found augmented MD and DR values, but not DA values, for the PhC in patients with MCI-C as well as in patients with AD, which suggests increased diffusivity, particularly perpendicular to the axonal direction. These tendencies corresponded well to those previously reported (Agosta et al.,

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Fig. 3. Receiver-operating characteristic (ROC) curves of DTI metrics in the parahippocampal cingulum bundle (PhC) for differentiating patients with mild cognitive impairment (MCI) or Alzheimer's disease (AD) from healthy subjects. The areas under the ROC curves (AUC) of the fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (DR) in the PhC for discriminating patients with MCI converted (MCI-C) ranged from 0.70 to 0.80, whereas those for discriminating AD ranged from 0.80 to 0.86. There were no significant differences in the AUC values between DTI metrics and between MCI-C and AD.

2011; O’Dwyer et al., 2011; Wang et al., 2012). Increased DR may reflect mainly demyelination or a loss of myelin integrity, while alteration of DA mainly stems from axonal loss or degeneration (Song et al., 2003). Axonal damage can lead to an initial decrease in DA, presumably because of axial/myelin fragments, followed by a subsequent increase in DA because of the clearance of the fragments by microglia (O’Dwyer et al., 2011). Therefore, decreased FA in patients with MCI-C probably is mainly due to increased DR resulting from the mild pathological changes of nerve tracts in the PhC. Moreover, in this study, we unexpectedly found that the statistical significance of MD and DR values for discriminating patients with MCI-C from the controls was equivalent and higher, respectively, when compared with that of FA values. Further, the specificity of MD and DR values for discriminating the patients with MCI-C from the controls was higher than that of FA, whereas the sensitivity was higher in FA than in MD and DR, although we found no significant difference in the AUC values between the metrics. Thus, combined assessment of diffusion anisotropy and diffusivity, as measured by FA and DR (or MD), may improve the ability to detect patients with MCI-C who ultimately convert to AD, although these metrics failed to differentiate the patients with MCI-C from those with MCI-NC because of the substantial overlaps. The PoC is considered another target of DTI analyses for the early diagnosis of AD, because the PoC includes both the afferent and efferent fibers of the posterior cingulate cortex and the precuneus in which hypoperfusion and hypometabolism are observed beginning from the prodromal stages of AD (Herholz et al., 2007; Morbelli et al., 2010). Previous studies have shown alterations of diffusion anisotropy of the PoC in patients with MCI (Zhang et al., 2007; Kiuchi et al., 2009) as well as in patients with AD (Zhang et al., 2007; Kiuchi et al., 2009; Choo et al., 2010; Agosta et al., 2011; O’Dwyer et al., 2011). However, no earlier study has directly compared changes of the PoC with those in the PhC in the MCI-NC, MCIC, and AD groups by using multiple DTI metrics. This study revealed that changes in diffusion anisotropy and diffusivities of the MCI and AD groups were more evident in the PhC than in the PoC and that only the PhC showed significant differences in DTI metrics in patients with MCI-C. These results suggest that changes in the PhC may precede those in the PoC in MCI-C and AD, although

inherently heterogeneous fiber directions within the voxels of the PoC may affect the precision of DTI metrics. This issue could potentially be solved by using diffusion kurtosis imaging or q-space imaging techniques, which are beyond the scope of this study. In this study, differences in DTI metrics among the groups were more obvious in the left PhC than in the contralateral one. This tendency corresponds well to the findings in previous studies using structural MRI (Thompson et al., 2003) or PET (Loewenstein et al., 1989) and may indicate that the left PhC is more susceptible in MCI and PD patients. In contrast, the DTI metrics were more different among the groups in the right PoC when compared with those in the left side. At present, the reason for this laterality remains unclear, but it may partly reflect the inherent complexity of the fiber directions within the PoC. Discrimination of patients with MCI-C from patients with MCI-NC, ideally at the single subject level, is one of the important issues of DTI analyses to establish this technique as a biomarker for determining strategies of disease modification therapies. A previous study using a manual ROI method showed that FA values from the temporal white matter better distinguished between the MCI-C and MCI-NC groups than those from the other structures (Scola et al., 2010). This study also showed similar tendencies between the groups, although we failed to determine significant differences. There are some discrepancies between the two studies in terms of the imaging parameters and analysis techniques for DTI. We used 1.6 mm as slice thickness for the DTI data, whereas the previous study used 6 mm slice thickness. Hence, the former can remove partial volume effects but can diminish the signal-to-noise ratio, whereas the latter can improve the signal-tonoise ratio but can be limited by partial volume effects. We also adopted atlas-based ROI analysis, whereas the previous study used a manual ROI analysis. To detect changes in the WM structures of patients with AD and MCI, various DTI analysis methods have been employed, such as manual ROI analysis, atlas-based ROI analysis, tractspecific analysis, voxel-based analysis, and tract-based spatial statistics, and these all have their own different strengths and weaknesses (Chua et al., 2008; Nakata et al., 2010). Among these, the manual ROI method generally yields accurate delineation of cerebral structures but is operator-dependent, less reproducible, time-consuming, and labor-intensive. In contrast, the atlas-based ROI method we used is

K. Ito et al. / Psychiatry Research: Neuroimaging 231 (2015) 346–352

operator-independent, highly reproducible, time-efficient, and easyto-use. However, one concern for this approach is potential errors that can occur during the normalization process. Because of substantial differences in individual brain morphology, re-alignment of various brain structures across many subjects may be imperfect, particularly in small structures such as the PhC, although a Gaussian filter and thresholding are usually applied to minimize this issue. In this study, misregistration of the PhC during the atlas-based ROI analyses could have diminished the accuracy of the DTI metrics to some extent. Further technical advancements, as well as more sophisticated approaches to data acquisition and post-processing, are needed to improve the accuracy of differential diagnosis between MCI-C and MCI-NC groups. The present study has several limitations. First, the number of subjects was relatively small and may have lacked sufficient statistical power in the DTI metrics among the groups because of the singlecenter study. Second, we did not examine relationships between DTI changes in the PhC/PoC and impairments of cognitive functions because of a paucity of data from cognitive batteries, particularly in the control group. Third, we performed no comparisons between different DTI analytic methods because the atlas-based ROI method appeared to be the most appropriate for the purpose of this study. We also performed no comparisons between DTI and other neuroimaging techniques such as structural MRI and PET. There are many reports indicating that hippocampal volume, accumulation of amyloid tracers, or glucose metabolism can differentiate among AD, MCI-C, MCI-NC, and healthy elderly groups (Yang et al., 2012; Daniela et al., 2014). Therefore, it remains unclear whether the measurement of DTI metrics in the PhC adds anything beyond what can be determined by these standard biomarkers. However, previous studies suggest that a combination of DTI and structural MRI measurements improved the accuracy of the differentiation (Zhang et al., 2007, 2013; Wang et al., 2009), presumably because this method can evaluate changes in both white matter and gray matter structures. Hence, we assume that a combination of our DTI approach and hippocampal volumetry can improve accuracy of the differentiation, particularly between MCI-NC and MCI-C groups, an assumption to be tested in the near future. Another limitation is a technical issue. We applied only six MPG directions for the DTI protocol because we had to compromise on the number of MPG directions to maintain an adequate signal-to-noise ratio and to limit acquisition time in the high-resolution acquisition with 1.6-mm thickness and four averages. Therefore, values of FA, DA, and DR, but not of MD, may be somewhat affected because accuracies of these parameters are known to depend on the number of MPG directions (Giannelli et al., 2010). However, a recent study revealed that DTI data using six MPG directions with five or 10 averages are comparable to those using 30 or 60 directions with one average (Lebel et al., 2012). Therefore, the DTI metrics we obtained are roughly comparable to those that have 12 directions and two averages or those that have 24 directions and one average. In this study, we used a Gaussian filter to improve the signal-to-noise ratio of thin-slice DTI data for accurate calculation of DTI metrics. However, this technique could diminish spatial resolution, which might influence the results to some extent. Finally, because this is a cross-sectional study, it remains unknown whether longitudinal changes in DTI metrics of the PhC and PoC can detect patients with MCI and can discriminate patients with MCI-C. A future longitudinal prospective study with larger cohorts is needed to investigate this question.

5. Conclusion Atlas-based ROI analysis of high-resolution DTI data found significant changes in FA, MD, and DR in the PhC of patients with MCI-C as well as in patients with AD when compared with healthy subjects. These changes in the PhC were more evident than those

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in the PoC, although there were no significant differences in these metrics between the MCI-C and MCI-NC groups. This method could be effective for the early diagnosis of patients with MCI who will convert to AD.

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