Journal of Alzheimer’s Disease 40 (2014) 953–966 DOI 10.3233/JAD-131517 IOS Press
953
1
H-Proton Magnetic Resonance Spectroscopy Differentiates Dementia with Lewy Bodies from Alzheimer’s Disease Xiaomei Zhonga , Haishan Shia , Zhiwei Shenb , Le Houa , Xinni Luoa , Xinru Chena , Sha Liuc , Yuefeng Zhanga , Dong Zhenga , Yan Tana , Guimao Huangc , Yaxiu Fanga , Hui Zhanga , Nan Mud , Jianping Chend , Renhua Wub,∗ and Yuping Ninga,∗ a Department
of Neurology, Guangzhou Brain Hospital, Affiliated Hospital of Guangzhou Medical University, Guangzhou, China b Department of Medical Imaging, The Second Affiliated Hospital, Medical College of Shantou University, Shantou, China c Department of Medical Imaging, Guangzhou Brain Hospital, Affiliated Hospital of Guangzhou Medical University, Guangzhou, China d Department of Geriatric Psychiatry, Guangzhou Brain Hospital, Affiliated Hospital of Guangzhou Medical University, Guangzhou, China Handling Associate Editor: Kuncheng Li
Accepted 31 December 2013
Abstract. FDG-PET and SPECT studies suggest that hypometabolism and hypoperfusion in occipital lobe and posterior cingulate gyrus (PCG) are prominent features of dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD), respectively. Cerebral blood flow and glucose metabolism are tightly linked to brain energy metabolism. 1 H-MRS is a useful tool to directly detect energy metabolism. We aimed to use 1 H-MRS to characterize metabolite concentrations in the occipital lobe and PCG in DLB and AD patients, and estimate their usefulness in the diagnosis of DLB. Nineteen DLB, 21 AD, and 18 normal control (NC) subjects underwent 1 H-MRS with the voxels placed in bilateral occipital lobes and left PCG. The N-acetylaspartate (NAA) and glutamate (Glu) concentrations in occipital lobe in DLB were lower than those in AD and NC. Concentrations of these two metabolites in PCG in DLB were lower than those in NC, and were the same as those in AD. These results remained robust after correcting for relative gray matter volume in the region of interest. The NAA and Glu concentrations in occipital lobe in DLB were found correlated with global cognitive function. From the ROC curves with Glu concentrations in occipital lobe, the mean areas under the curve were 0.845 for the DLB/control (with sensitivity 83.3% and specificity 84.2%) and 0.773 for the DLB/AD (with sensitivity 66.7% and specificity 84.2%). Our study suggests that 1 H-MRS investigation is valuable to detect the characteristic patterns of metabolite concentrations and is helpful in the diagnostic process and assessing dementia severity in DLB. Keywords: Dementia with Lewy bodies, Alzheimer’s disease, diagnosis, differential diagnosis, hydrogen proton magnetic resonance spectroscopy, occipital lobe, posterior cingulate gyrus
∗ Correspondence
to: Yuping Ning, Department of Neurology, Guangzhou Brain Hospital, Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. Tel.: +86 20 8189 1425; Fax: +86 20 8189 1391; E-mail: [email protected]; Renhua Wu, Department of Medical Imaging, The Second Affiliated Hospital, Medical College of Shantou University, Shantou, China. Tel.: +86 0754 88915930; E-mail: [email protected].
ISSN 1387-2877/14/$27.50 © 2014 – IOS Press and the authors. All rights reserved
954
X. Zhong et al. / 1 H-MRS is Useful for Diagnosis of DLB
INTRODUCTION Dementia with Lewy bodies (DLB) is the second most common form of late-life neurodegenerative dementia after Alzheimer’s disease (AD) [1, 2]. The main clinical symptoms of DLB include recurrent visual hallucination, fluctuating cognitive impairment, and spontaneous motor parkinsonism. Because of their severe sensitivity reaction to antipsychotics [3] and favorable response to cholinesterase inhibitors [4, 5], accurate clinical diagnosis of DLB is important for optimizing patient therapy. However, it is sometimes challenging to differentiate DLB from AD owing to overlapping pathological features and symptom profiles [6–9]. Data from studies in DLB patients using fluorodeoxyglucose (FDG) positron emission tomography (PET) and single photon emission computed tomography (SPECT) consistently show reduction of cerebral glucose metabolism or blood flow in the occipital cortex, particularly in the primary visual cortex [10–18]. Occipital hypoperfusion/hypometabolism has been proven to be useful in discriminating between DLB and AD [10–14]. Reduced occipital activity has been proposed as a supportive feature for the clinical diagnosis of DLB by the consortium on DLB international workshop [3]. Hydrogen proton magnetic resonance spectroscopy (1 H-MRS) is a method used to directly determine energy metabolism non-invasively and nondisruptively in the human brain. There is a large literature looking at metabolite alterations in various cerebral regions including occipital lobe by using 1 HMRS in patients with AD [19–25]. However, we are not aware of any studies exploring the metabolite concentrations in the occipital lobe of DLB patients. Cerebral blood flow and glucose metabolism are two major parameters linked to brain energy metabolism [26]. We speculated that 1 H-MRS may be a useful and practical approach for characterizing the feature of reduced occipital activity in DLB patients. Aside from the occipital lobe, we measured the metabolite concentrations in the posterior cingulate gyrus (PCG) for the following reasons: 1) FDG-PET, SPECT, and arterial spin labeling perfusion magnetic resonance imaging studies suggest that decreased metabolism or perfusion of the PCG is an early sign of AD [18, 27–35]; and 2) Lower concentrations of N-acetylaspartate (NAA) in PCG of AD patients were consistently found [19–23], while concentrations of NAA were not changed from normal controls in PCG of DLB patients [19]. Thus, we had two a priori hypotheses for our study: (a) that 1 H-
MRS would detect metabolite changes in the occipital lobe of patients with DLB, relative to those of patients with AD and normal control (NC) subjects, and (b) that metabolite concentrations of occipital lobe and PCG would be useful for the clinical diagnosis and differential diagnosis of DLB. METHODS Subjects All patients were recruited from the wards and the outpatient clinics of the Neurology Department and Geriatric Psychiatry Department of Guangzhou Brain Hospital. The study was approved by the ethics committees at the Guangzhou Brain Hospital. Written informed consent was obtained from all the participants or their nearest relatives. All the subjects underwent comprehensive multidisciplinary assessment, including interview, neuropsychological assessment, appropriate laboratory tests, as well as brain MRI. Clinical diagnosis was made by a neurologist and a psychiatrist. All the patients fulfilled the Diagnostic and Statistical Manual of Mental Disorders-IV (DSMIV) criteria for dementia. The DLB diagnosis was made according to the McKeith criteria for the clinical diagnosis of probable DLB [3], and the AD diagnosis was made according to the NINCDS-ADRDA criteria for the clinical diagnosis of probable AD [36]. The NC subjects were community volunteers with no neurological or psychiatric conditions who were matched with patients for gender, age, and education. Global cognitive function was assessed with the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog). The severity of dementia was assessed with the Clinical Dementia Rating (CDR). Function was measured using the 14-item Activities of Daily Living (ADL). Neuropsychiatric features were assessed using the Neuropsychiatric Inventory (NPI). In patients with DLB, we regarded visual hallucination as positive if a patient had a history of repetitive involuntary images of people, animals, or objects that were experienced as real during the waking state, but for which there was no objective reality in the past month [37]. Visual hallucination was further estimated using the hallucinations subscale of the NPI. MRI data acquisition The 1 H-MRS as well as the structural MRI examinations were performed on a Philips 3T Achieva
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Fig. 1. Locations of regions of interest for 1 H-MRS. A) Region of interest was located in the left posterior cingulate gyrus. B) Region of interest was located in bilateral occipital lobes.
MR scanner (Philips Medical Systems, Best, The Netherlands) with an 8 channel SENSE head coil for signal reception. Point-resolved spectroscopy sequence (PRESS; TR = 2000 ms, TE = 35 ms, NEX = 128) was used to acquire single voxel 1 H-MRS. We defined the anatomy of the regions of interest (ROIs) in the axial, sagittal, and coronal planes. As shown in Fig. 1, a square volume of interest of 2 cm × 2 cm × 2 cm and a rectangular volume of interest of 2 cm × 3 cm × 2 cm were located in the left PCG and bilateral occipital lobes, respectively. A sagittal three-dimensional, T1-weighted, gradientecho sequence (188 slices, 256 × 256 matrix, field of view 256 × 256 cm2 , voxel size = 1 × 1 × 1 mm3 , TR/TE/NEX/FA = shortest/shortest/1/7◦ ) was performed to obtain the structural MRI data. Image processing Metabolite concentrations The absolute metabolite concentrations in the ROIs were measured using the linear combination of model (LCModel) (http://s-provencher.com/pages/lcmodel. shtml) (version 6.2-0), applying an eddy current correction and using the unsuppressed water signal as
an internal reference. The values of NAA, choline (Cho), inositol (Ins), creatine (Cr), glutamate (Glu), and the summed concentrations of Glu + glutamine (Gln), referred to as Glx, were reported automatically. Absolute metabolite values were only included if the Cram´er-Rao lower bounds were 20% or less. Examples of proton spectra obtained from PCG and occipital lobe of DLB and AD patients are shown in Fig. 2. Gray matter volume in selected ROIs Structural MRI data were first analyzed by using VBM8 implemented in SPM8 (http://www.fil.ion.ucl. ac.uk/spm/). The standard preprocessing steps, including normalization, segmentation, modulation, and smoothing were performed. Then we used the Extract ROI Signals program in the REST toolkit (http:// sourceforge.net/projects/resting-fmri) to extract the values of relative gray matter volume in the ROIs placed on the PCG and occipital lobe. Statistical analysis Statistical analyses were performed by using the Statistical Package for Social Sciences (SPSS, version 13). Group comparisons were performed by the Chi-
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Fig. 2. Examples of proton spectra obtained from posterior cingulate gyrus and occipital lobe. These figures depict the proton spectra obtained from the posterior cingulate gyrus (A) and occipital lobe (B) in a DLB patient, an AD patient, and a NC subject. When compared to NC, the DLB patient showed lower NAA, Glu, and sum of Glu and Gln concentrations in the PCG, and lower NAA, Glu, Cr, and sum of Glu and Gln concentrations in the occipital lobe. When compared to AD, the DLB patient showed lower NAA, Cr, and Glu concentrations in the occipital lobe.
square test and one-way analysis of variance (ANOVA), followed by post hoc least significant difference test. When comparing metabolite concentrations, analysis of covariance (ANCOVA) was further used to adjust for relative gray matter volume in selected ROI. The correlations of 1 H-MRS data with scale scores were assessed with Pearson and Spearman correlations. We used the logistic regression analyses to construct predicted probabilities which combined the metabolites that showed different concentrations among DLB, AD, and NC. Receiver operating characteristic (ROC) analysis was employed to assess the diagnostic values of isolated metabolite and predicted probability. A p value of less than 0.05 was considered statistically significant. RESULTS Demographic and clinical characteristics We recruited 19 patients with DLB, 21 patients with AD, and 18 NC subjects. In the DLB patients, 15 had extrapyramidal symptom, 6 had fluctuating cognition, and 13 had visual hallucinations. None of the AD patients had extrapyramidal symptom, fluctuating cognition, or visual hallucinations. Due to subject-related
reasons, not all structural MRI or 1 H-MRS data were obtained in all subjects. Four DLB, 7 AD, and 1 NC subjects’ structural MRI data and 2 DLB subjects’ 1 H-MRS data in the PCG were lacking. The demographic data and clinical characteristics are presented in Table 1. All the three groups were matched for age (F = 2.150, p = 0.126), gender (χ2 = 3.681, p = 0.159), and education (F = 0.514, p = 0.601). As expected, the DLB and AD groups had significantly lower scores on the MMSE and MoCA and higher scores on the ADAS-cog and CDR Sum of Boxes (CDR-SB) compared to the NC group (all p < 0.001). There was a trend toward higher NPI total scores in patients with DLB (p = 0.09), while compared with the AD group. No significant differences in MMSE, MoCA, ADAS-cog, ADL, CDR-SB, or CDR staging were found between the DLB and AD groups, suggesting a similar level of dementia severity. Relative gray matter volume in ROIs We compared the relative grey matter volume in ROIs located in the PCG and occipital lobe. No significant differences were observed among DLB, AD, and NC groups (Fig. 3).
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Table 1 Demographic and clinical characteristics of dementia with Lewy bodies (DLB), Alzheimer’s disease (AD), and normal control (NC) subjects n Women/men Age (years) Education (years) Mini-Mental State Examination Montreal Cognitive Assessment ADAS-cog Neuropsychiatric Inventory total Activities of Daily Living CDR-SB CDR Staging (0 / 1 / 2 / 3)
DLB
AD
19
21
9/10 69.8 ± 9.1 9.0 ± 4.0 15.7 ± 5.2∗∗∗ 10.1 ± 2.6∗∗∗ 30.2 ± 10.3∗∗∗ 25.0 ± 10.8 27.8 ± 5.0∗∗∗ 9.9 ± 3.7∗∗∗ 0 / 7 / 12 / 0
16/5 69.5 ± 10.1 8.2 ± 4.2 14.9 ± 4.9∗∗∗ 11.2 ± 5.3∗∗∗ 32.0 ± 12.6∗∗∗ 17.4 ± 16.2 25.2 ± 9.3∗∗∗ 8.6 ± 3.9∗∗∗ 0 / 12 /9 / 0
NC 18 12/6 64.5 ± 6.4 9.6 ± 3.9 27.8 ± 2.0 25.8 ± 1.3 8.3 ± 3.2 na 14.0 ± 0 0.1 ± 0.2 18 / 0 / 0 / 0
∗∗∗ p < 0.001 compared with NC. na, not applicable. ADAS-cog, Alzheimer’s Disease Assessment Scale-cognitive subscale; CDR, Clinical Dementia Rating; CDR-SB, Clinical Dementia Rating-Sum of Boxes.
in these two ROIs, no differences were found among the three groups. The differences in these six kinds of metabolites between DLB and AD patients were that the NAA, Cr, and Glu concentrations in the occipital lobe of DLB patients were significant lower than those of AD patients (all p < 0.05) (Fig. 4 and Table 2). Differences in concentrations of NAA and Glu between DLB and AD patients remained robust when adjusted for relative grey matter volume in the ROI placed on the occipital lobe (Table 2). Associations of metabolite concentrations and neuropsychological measures
Fig. 3. Relative grey matter volume. There were no significant differences in relative grey matter volume in ROIs located in the PCG (A) or occipital lobe (B) among DLB, AD, and NC subjects.
The PCG and occipital lobe metabolite concentrations The absolute concentrations in PCG and occipital lobe are presented in Fig. 4. The AD patients showed significantly lower concentrations of NAA (p < 0.01), Glu (p < 0.05), and Glx (p < 0.05) in the PCG compared to the NC subjects. Decreases in NAA (p < 0.05), Glu (p < 0.01), and Glx (p < 0.05) concentrations in the PCG were also detected in patients with DLB compared to the NC subjects. With regard to the occipital lobe, we observed lower concentrations of NAA (p < 0.001), Cr (p < 0.01), Glu (p < 0.001), and Glx (p < 0.001) in patients with DLB compared to NC subjects. The AD group displayed lower concentrations of Glu (p < 0.01) and Glx (p < 0.05) in occipital lobe, with normal concentrations of NAA and Cr, while compared with the NC subjects. As to Cho and Ins concentrations
To explore the relationships between the clinical characteristics and 1 H-MRS data, we correlated the metabolite concentrations with MMSE, MoCA, ADAS-cog, and CDR-SB scores in patients with DLB and AD. In the AD group, we found that the NAA concentrations in the PCG were positively correlated with MMSE scores (r = 0.456, p = 0.038) and negatively correlated with ADAS-cog scores (r = −0.407, p = 0.031), with a trend toward a negative correlation with CDR-SB scores (r = −0.378, p = 0.091). As to the DLB group, we observed that the Glu concentrations in the PCG were positively correlated with MoCA scores (r = 0.487, p = 0.047) and negatively correlated with CDR-SB scores (r = −0.554, p = 0.021), with a trend toward a positive correlation with MMSE scores (r = 0.457, p = 0.065). With regard to the correlations of occipital lobe metabolite concentrations and neuropsychological measures, we detected that the NAA and Glu concentrations in the occipital lobe of DLB patients were significantly correlated with MMSE (r = 0.540, p = 0.017, and r = 0.642, p = 0.003, respectively), MoCA (r = 0.519, p = 0.023, and r = 0.626, p = 0.004, respectively), ADAS-cog(r = −0.649,
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Fig. 4. The average absolute metabolite concentrations in DLB, AD, and NC subjects. A) The NAA, Glu, and Glx concentrations in the PCG of DLB and AD patients were significantly lower than those of NC subjects. B) The NAA, Cr, Glu, and Glx concentrations in the occipital lobe of DLB patients were significantly lower than those of NC subjects. The Glu and Glx concentrations in the occipital lobe of AD patients were significantly lower than those of NC subjects. The NAA, Cr, and Glu concentrations in the occipital lobe of DLB patients were significantly lower than those of AD patients. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. All the results are relative to the NC group unless otherwise indicated. Table 2 Comparisons of metabolite concentrations among dementia with Lewy bodies (DLB), Alzheimer’s disease (AD), and normal control (NC) subjects
Posterior cingulate gyrus ANOVA LSD test DLB versus NC AD versus NC DLB versus AD ANCOVA a Compare main effects DLB versus NC AD versus NC DLB versus AD Occipital lobe ANOVA LSD test DLB versus NC AD versus NC DLB versus AD ANCOVAa Compare main effects DLB versus NC AD versus NC DLB versus AD
NAA p value
Glu p value
Glx p value
Ins p value
Cr p value
Cho p value
0.015
0.014
0.015
0.332
0.086
0.403
0.030 0.006 0.613 0.047
0.004 0.050 0.272 0.033
0.011 0.012 0.886 0.020
0.139 0.473 0.402 0.649
0.035 0.096 0.570 0.083
0.185 0.388 0.595 0.387
0.051 0.025 0.833
0.017 0.042 0.642
0.022 0.013 0.924
0.357 0.637 0.643
0.034 0.113 0.534
0.320 0.196 0.810
0.001
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H-Proton Magnetic Resonance Spectroscopy Differentiates Dementia with Lewy Bodies from Alzheimer’s Disease Xiaomei Zhonga , Haishan Shia , Zhiwei Shenb , Le Houa , Xinni Luoa , Xinru Chena , Sha Liuc , Yuefeng Zhanga , Dong Zhenga , Yan Tana , Guimao Huangc , Yaxiu Fanga , Hui Zhanga , Nan Mud , Jianping Chend , Renhua Wub,∗ and Yuping Ninga,∗ a Department
of Neurology, Guangzhou Brain Hospital, Affiliated Hospital of Guangzhou Medical University, Guangzhou, China b Department of Medical Imaging, The Second Affiliated Hospital, Medical College of Shantou University, Shantou, China c Department of Medical Imaging, Guangzhou Brain Hospital, Affiliated Hospital of Guangzhou Medical University, Guangzhou, China d Department of Geriatric Psychiatry, Guangzhou Brain Hospital, Affiliated Hospital of Guangzhou Medical University, Guangzhou, China Handling Associate Editor: Kuncheng Li
Accepted 31 December 2013
Abstract. FDG-PET and SPECT studies suggest that hypometabolism and hypoperfusion in occipital lobe and posterior cingulate gyrus (PCG) are prominent features of dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD), respectively. Cerebral blood flow and glucose metabolism are tightly linked to brain energy metabolism. 1 H-MRS is a useful tool to directly detect energy metabolism. We aimed to use 1 H-MRS to characterize metabolite concentrations in the occipital lobe and PCG in DLB and AD patients, and estimate their usefulness in the diagnosis of DLB. Nineteen DLB, 21 AD, and 18 normal control (NC) subjects underwent 1 H-MRS with the voxels placed in bilateral occipital lobes and left PCG. The N-acetylaspartate (NAA) and glutamate (Glu) concentrations in occipital lobe in DLB were lower than those in AD and NC. Concentrations of these two metabolites in PCG in DLB were lower than those in NC, and were the same as those in AD. These results remained robust after correcting for relative gray matter volume in the region of interest. The NAA and Glu concentrations in occipital lobe in DLB were found correlated with global cognitive function. From the ROC curves with Glu concentrations in occipital lobe, the mean areas under the curve were 0.845 for the DLB/control (with sensitivity 83.3% and specificity 84.2%) and 0.773 for the DLB/AD (with sensitivity 66.7% and specificity 84.2%). Our study suggests that 1 H-MRS investigation is valuable to detect the characteristic patterns of metabolite concentrations and is helpful in the diagnostic process and assessing dementia severity in DLB. Keywords: Dementia with Lewy bodies, Alzheimer’s disease, diagnosis, differential diagnosis, hydrogen proton magnetic resonance spectroscopy, occipital lobe, posterior cingulate gyrus
∗ Correspondence
to: Yuping Ning, Department of Neurology, Guangzhou Brain Hospital, Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. Tel.: +86 20 8189 1425; Fax: +86 20 8189 1391; E-mail: [email protected]; Renhua Wu, Department of Medical Imaging, The Second Affiliated Hospital, Medical College of Shantou University, Shantou, China. Tel.: +86 0754 88915930; E-mail: [email protected].
ISSN 1387-2877/14/$27.50 © 2014 – IOS Press and the authors. All rights reserved
954
X. Zhong et al. / 1 H-MRS is Useful for Diagnosis of DLB
INTRODUCTION Dementia with Lewy bodies (DLB) is the second most common form of late-life neurodegenerative dementia after Alzheimer’s disease (AD) [1, 2]. The main clinical symptoms of DLB include recurrent visual hallucination, fluctuating cognitive impairment, and spontaneous motor parkinsonism. Because of their severe sensitivity reaction to antipsychotics [3] and favorable response to cholinesterase inhibitors [4, 5], accurate clinical diagnosis of DLB is important for optimizing patient therapy. However, it is sometimes challenging to differentiate DLB from AD owing to overlapping pathological features and symptom profiles [6–9]. Data from studies in DLB patients using fluorodeoxyglucose (FDG) positron emission tomography (PET) and single photon emission computed tomography (SPECT) consistently show reduction of cerebral glucose metabolism or blood flow in the occipital cortex, particularly in the primary visual cortex [10–18]. Occipital hypoperfusion/hypometabolism has been proven to be useful in discriminating between DLB and AD [10–14]. Reduced occipital activity has been proposed as a supportive feature for the clinical diagnosis of DLB by the consortium on DLB international workshop [3]. Hydrogen proton magnetic resonance spectroscopy (1 H-MRS) is a method used to directly determine energy metabolism non-invasively and nondisruptively in the human brain. There is a large literature looking at metabolite alterations in various cerebral regions including occipital lobe by using 1 HMRS in patients with AD [19–25]. However, we are not aware of any studies exploring the metabolite concentrations in the occipital lobe of DLB patients. Cerebral blood flow and glucose metabolism are two major parameters linked to brain energy metabolism [26]. We speculated that 1 H-MRS may be a useful and practical approach for characterizing the feature of reduced occipital activity in DLB patients. Aside from the occipital lobe, we measured the metabolite concentrations in the posterior cingulate gyrus (PCG) for the following reasons: 1) FDG-PET, SPECT, and arterial spin labeling perfusion magnetic resonance imaging studies suggest that decreased metabolism or perfusion of the PCG is an early sign of AD [18, 27–35]; and 2) Lower concentrations of N-acetylaspartate (NAA) in PCG of AD patients were consistently found [19–23], while concentrations of NAA were not changed from normal controls in PCG of DLB patients [19]. Thus, we had two a priori hypotheses for our study: (a) that 1 H-
MRS would detect metabolite changes in the occipital lobe of patients with DLB, relative to those of patients with AD and normal control (NC) subjects, and (b) that metabolite concentrations of occipital lobe and PCG would be useful for the clinical diagnosis and differential diagnosis of DLB. METHODS Subjects All patients were recruited from the wards and the outpatient clinics of the Neurology Department and Geriatric Psychiatry Department of Guangzhou Brain Hospital. The study was approved by the ethics committees at the Guangzhou Brain Hospital. Written informed consent was obtained from all the participants or their nearest relatives. All the subjects underwent comprehensive multidisciplinary assessment, including interview, neuropsychological assessment, appropriate laboratory tests, as well as brain MRI. Clinical diagnosis was made by a neurologist and a psychiatrist. All the patients fulfilled the Diagnostic and Statistical Manual of Mental Disorders-IV (DSMIV) criteria for dementia. The DLB diagnosis was made according to the McKeith criteria for the clinical diagnosis of probable DLB [3], and the AD diagnosis was made according to the NINCDS-ADRDA criteria for the clinical diagnosis of probable AD [36]. The NC subjects were community volunteers with no neurological or psychiatric conditions who were matched with patients for gender, age, and education. Global cognitive function was assessed with the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog). The severity of dementia was assessed with the Clinical Dementia Rating (CDR). Function was measured using the 14-item Activities of Daily Living (ADL). Neuropsychiatric features were assessed using the Neuropsychiatric Inventory (NPI). In patients with DLB, we regarded visual hallucination as positive if a patient had a history of repetitive involuntary images of people, animals, or objects that were experienced as real during the waking state, but for which there was no objective reality in the past month [37]. Visual hallucination was further estimated using the hallucinations subscale of the NPI. MRI data acquisition The 1 H-MRS as well as the structural MRI examinations were performed on a Philips 3T Achieva
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Fig. 1. Locations of regions of interest for 1 H-MRS. A) Region of interest was located in the left posterior cingulate gyrus. B) Region of interest was located in bilateral occipital lobes.
MR scanner (Philips Medical Systems, Best, The Netherlands) with an 8 channel SENSE head coil for signal reception. Point-resolved spectroscopy sequence (PRESS; TR = 2000 ms, TE = 35 ms, NEX = 128) was used to acquire single voxel 1 H-MRS. We defined the anatomy of the regions of interest (ROIs) in the axial, sagittal, and coronal planes. As shown in Fig. 1, a square volume of interest of 2 cm × 2 cm × 2 cm and a rectangular volume of interest of 2 cm × 3 cm × 2 cm were located in the left PCG and bilateral occipital lobes, respectively. A sagittal three-dimensional, T1-weighted, gradientecho sequence (188 slices, 256 × 256 matrix, field of view 256 × 256 cm2 , voxel size = 1 × 1 × 1 mm3 , TR/TE/NEX/FA = shortest/shortest/1/7◦ ) was performed to obtain the structural MRI data. Image processing Metabolite concentrations The absolute metabolite concentrations in the ROIs were measured using the linear combination of model (LCModel) (http://s-provencher.com/pages/lcmodel. shtml) (version 6.2-0), applying an eddy current correction and using the unsuppressed water signal as
an internal reference. The values of NAA, choline (Cho), inositol (Ins), creatine (Cr), glutamate (Glu), and the summed concentrations of Glu + glutamine (Gln), referred to as Glx, were reported automatically. Absolute metabolite values were only included if the Cram´er-Rao lower bounds were 20% or less. Examples of proton spectra obtained from PCG and occipital lobe of DLB and AD patients are shown in Fig. 2. Gray matter volume in selected ROIs Structural MRI data were first analyzed by using VBM8 implemented in SPM8 (http://www.fil.ion.ucl. ac.uk/spm/). The standard preprocessing steps, including normalization, segmentation, modulation, and smoothing were performed. Then we used the Extract ROI Signals program in the REST toolkit (http:// sourceforge.net/projects/resting-fmri) to extract the values of relative gray matter volume in the ROIs placed on the PCG and occipital lobe. Statistical analysis Statistical analyses were performed by using the Statistical Package for Social Sciences (SPSS, version 13). Group comparisons were performed by the Chi-
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Fig. 2. Examples of proton spectra obtained from posterior cingulate gyrus and occipital lobe. These figures depict the proton spectra obtained from the posterior cingulate gyrus (A) and occipital lobe (B) in a DLB patient, an AD patient, and a NC subject. When compared to NC, the DLB patient showed lower NAA, Glu, and sum of Glu and Gln concentrations in the PCG, and lower NAA, Glu, Cr, and sum of Glu and Gln concentrations in the occipital lobe. When compared to AD, the DLB patient showed lower NAA, Cr, and Glu concentrations in the occipital lobe.
square test and one-way analysis of variance (ANOVA), followed by post hoc least significant difference test. When comparing metabolite concentrations, analysis of covariance (ANCOVA) was further used to adjust for relative gray matter volume in selected ROI. The correlations of 1 H-MRS data with scale scores were assessed with Pearson and Spearman correlations. We used the logistic regression analyses to construct predicted probabilities which combined the metabolites that showed different concentrations among DLB, AD, and NC. Receiver operating characteristic (ROC) analysis was employed to assess the diagnostic values of isolated metabolite and predicted probability. A p value of less than 0.05 was considered statistically significant. RESULTS Demographic and clinical characteristics We recruited 19 patients with DLB, 21 patients with AD, and 18 NC subjects. In the DLB patients, 15 had extrapyramidal symptom, 6 had fluctuating cognition, and 13 had visual hallucinations. None of the AD patients had extrapyramidal symptom, fluctuating cognition, or visual hallucinations. Due to subject-related
reasons, not all structural MRI or 1 H-MRS data were obtained in all subjects. Four DLB, 7 AD, and 1 NC subjects’ structural MRI data and 2 DLB subjects’ 1 H-MRS data in the PCG were lacking. The demographic data and clinical characteristics are presented in Table 1. All the three groups were matched for age (F = 2.150, p = 0.126), gender (χ2 = 3.681, p = 0.159), and education (F = 0.514, p = 0.601). As expected, the DLB and AD groups had significantly lower scores on the MMSE and MoCA and higher scores on the ADAS-cog and CDR Sum of Boxes (CDR-SB) compared to the NC group (all p < 0.001). There was a trend toward higher NPI total scores in patients with DLB (p = 0.09), while compared with the AD group. No significant differences in MMSE, MoCA, ADAS-cog, ADL, CDR-SB, or CDR staging were found between the DLB and AD groups, suggesting a similar level of dementia severity. Relative gray matter volume in ROIs We compared the relative grey matter volume in ROIs located in the PCG and occipital lobe. No significant differences were observed among DLB, AD, and NC groups (Fig. 3).
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Table 1 Demographic and clinical characteristics of dementia with Lewy bodies (DLB), Alzheimer’s disease (AD), and normal control (NC) subjects n Women/men Age (years) Education (years) Mini-Mental State Examination Montreal Cognitive Assessment ADAS-cog Neuropsychiatric Inventory total Activities of Daily Living CDR-SB CDR Staging (0 / 1 / 2 / 3)
DLB
AD
19
21
9/10 69.8 ± 9.1 9.0 ± 4.0 15.7 ± 5.2∗∗∗ 10.1 ± 2.6∗∗∗ 30.2 ± 10.3∗∗∗ 25.0 ± 10.8 27.8 ± 5.0∗∗∗ 9.9 ± 3.7∗∗∗ 0 / 7 / 12 / 0
16/5 69.5 ± 10.1 8.2 ± 4.2 14.9 ± 4.9∗∗∗ 11.2 ± 5.3∗∗∗ 32.0 ± 12.6∗∗∗ 17.4 ± 16.2 25.2 ± 9.3∗∗∗ 8.6 ± 3.9∗∗∗ 0 / 12 /9 / 0
NC 18 12/6 64.5 ± 6.4 9.6 ± 3.9 27.8 ± 2.0 25.8 ± 1.3 8.3 ± 3.2 na 14.0 ± 0 0.1 ± 0.2 18 / 0 / 0 / 0
∗∗∗ p < 0.001 compared with NC. na, not applicable. ADAS-cog, Alzheimer’s Disease Assessment Scale-cognitive subscale; CDR, Clinical Dementia Rating; CDR-SB, Clinical Dementia Rating-Sum of Boxes.
in these two ROIs, no differences were found among the three groups. The differences in these six kinds of metabolites between DLB and AD patients were that the NAA, Cr, and Glu concentrations in the occipital lobe of DLB patients were significant lower than those of AD patients (all p < 0.05) (Fig. 4 and Table 2). Differences in concentrations of NAA and Glu between DLB and AD patients remained robust when adjusted for relative grey matter volume in the ROI placed on the occipital lobe (Table 2). Associations of metabolite concentrations and neuropsychological measures
Fig. 3. Relative grey matter volume. There were no significant differences in relative grey matter volume in ROIs located in the PCG (A) or occipital lobe (B) among DLB, AD, and NC subjects.
The PCG and occipital lobe metabolite concentrations The absolute concentrations in PCG and occipital lobe are presented in Fig. 4. The AD patients showed significantly lower concentrations of NAA (p < 0.01), Glu (p < 0.05), and Glx (p < 0.05) in the PCG compared to the NC subjects. Decreases in NAA (p < 0.05), Glu (p < 0.01), and Glx (p < 0.05) concentrations in the PCG were also detected in patients with DLB compared to the NC subjects. With regard to the occipital lobe, we observed lower concentrations of NAA (p < 0.001), Cr (p < 0.01), Glu (p < 0.001), and Glx (p < 0.001) in patients with DLB compared to NC subjects. The AD group displayed lower concentrations of Glu (p < 0.01) and Glx (p < 0.05) in occipital lobe, with normal concentrations of NAA and Cr, while compared with the NC subjects. As to Cho and Ins concentrations
To explore the relationships between the clinical characteristics and 1 H-MRS data, we correlated the metabolite concentrations with MMSE, MoCA, ADAS-cog, and CDR-SB scores in patients with DLB and AD. In the AD group, we found that the NAA concentrations in the PCG were positively correlated with MMSE scores (r = 0.456, p = 0.038) and negatively correlated with ADAS-cog scores (r = −0.407, p = 0.031), with a trend toward a negative correlation with CDR-SB scores (r = −0.378, p = 0.091). As to the DLB group, we observed that the Glu concentrations in the PCG were positively correlated with MoCA scores (r = 0.487, p = 0.047) and negatively correlated with CDR-SB scores (r = −0.554, p = 0.021), with a trend toward a positive correlation with MMSE scores (r = 0.457, p = 0.065). With regard to the correlations of occipital lobe metabolite concentrations and neuropsychological measures, we detected that the NAA and Glu concentrations in the occipital lobe of DLB patients were significantly correlated with MMSE (r = 0.540, p = 0.017, and r = 0.642, p = 0.003, respectively), MoCA (r = 0.519, p = 0.023, and r = 0.626, p = 0.004, respectively), ADAS-cog(r = −0.649,
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X. Zhong et al. / 1 H-MRS is Useful for Diagnosis of DLB
Fig. 4. The average absolute metabolite concentrations in DLB, AD, and NC subjects. A) The NAA, Glu, and Glx concentrations in the PCG of DLB and AD patients were significantly lower than those of NC subjects. B) The NAA, Cr, Glu, and Glx concentrations in the occipital lobe of DLB patients were significantly lower than those of NC subjects. The Glu and Glx concentrations in the occipital lobe of AD patients were significantly lower than those of NC subjects. The NAA, Cr, and Glu concentrations in the occipital lobe of DLB patients were significantly lower than those of AD patients. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. All the results are relative to the NC group unless otherwise indicated. Table 2 Comparisons of metabolite concentrations among dementia with Lewy bodies (DLB), Alzheimer’s disease (AD), and normal control (NC) subjects
Posterior cingulate gyrus ANOVA LSD test DLB versus NC AD versus NC DLB versus AD ANCOVA a Compare main effects DLB versus NC AD versus NC DLB versus AD Occipital lobe ANOVA LSD test DLB versus NC AD versus NC DLB versus AD ANCOVAa Compare main effects DLB versus NC AD versus NC DLB versus AD
NAA p value
Glu p value
Glx p value
Ins p value
Cr p value
Cho p value
0.015
0.014
0.015
0.332
0.086
0.403
0.030 0.006 0.613 0.047
0.004 0.050 0.272 0.033
0.011 0.012 0.886 0.020
0.139 0.473 0.402 0.649
0.035 0.096 0.570 0.083
0.185 0.388 0.595 0.387
0.051 0.025 0.833
0.017 0.042 0.642
0.022 0.013 0.924
0.357 0.637 0.643
0.034 0.113 0.534
0.320 0.196 0.810
0.001
