Abstract
Introduction
The regional cerebral blood flow (rCBF) distribution can affect brain functioning, leading to amnestic mild cognitive impairment (aMCI) and mild Alzheimer disease (AD). This study aimed to clarify the detailed characteristics of rCBF distribution in patients with mild AD and aMCI.
Methods
This cross-sectional study from April 2015 to March 2018 included 103 older adults (mean age 78.9 years; 60% females), out of a total of 302 adults, and categorized them into 3 groups according to cognitive symptoms. The normal control (NC), aMCI, and mild AD groups included 20, 50, and 33 participants, respectively. The primary outcome was rCBF, which was compared among the 3 groups using a 2-sample t test without correction for multiple comparisons.
Results
In the aMCI group, the rCBF decreased in the bilateral parietal and left frontal association cortex and the bilateral premotor cortex (p < 0.01) but increased in the bilateral cerebellum (p < 0.01). In the mild AD group, the rCBF decreased in the bilateral parietal and occipital association cortex, the bilateral premotor cortex, the left temporal and frontal association cortex, and the left limbic lobe (p < 0.01). Conversely, the rCBF increased in some parts of the cerebellum, the bilateral frontal and temporal association cortex, the left occipital association cortex, and the right premotor cortex (p < 0.01).
Conclusion
Based on the analysis of the values obtained, it was inferred that the rCBF undergoes reduction and elevation in aMCI and AD patients.
Keywords: Alzheimer dementia, Regional cerebral blood flow, Mild cognitive impairment, Single-photon emission computerized tomography, Cross-sectional study
Introduction
Single-photon emission computerized tomography (SPECT) has been increasingly employed for the diagnosis of functional alterations in the brain, resulting in mild cognitive impairment (MCI) or Alzheimer disease (AD) [1, 2, 3]; the extent of hypoperfusion on SPECT is greater than the atrophy on magnetic resonance imaging (MRI) [4]. Studies with SPECT have indicated the characteristics of the regional cerebral blood flow (rCBF) in older adults with MCI [5, 6, 7, 8, 9] or mild AD [9, 10, 11, 12, 13, 14, 15, 16, 17]. Consequently, specific areas, such as the posterior cingulate gyrus, the parietotemporal association cortex, and the medial temporal lobe, are regarded as the primary affected regions; however, the primary motor, sensory, and visual cortices are reported to be typically spared until the extremely severe stages [5]. However, these findings are yet to be established; the majority of such studies have been conducted in a relatively minor group of subjects [9, 12, 13, 15], with some analyzing exclusively those regions of the brain with a decreased rCBF [8, 9, 11, 15, 17].
The aim of the present study was to specify the characteristics of rCBF distribution in patients who have progressed to mild AD and amnestic MCI (aMCI) compared to typical older adults.
Materials and Methods
Study Design and Participants
The present cross-sectional study included 302 Japanese older adults who consulted the memory clinic of the Tokyo Saiseikai Central Hospital for a cognitive function examination from April 2015 to March 2018; cerebral blood flow was examined by SPECT. Out of 302 patients, 199 were excluded due to: age <65 years (n = 10), stroke comorbidity (n = 6), tracers other than 99mTc-ECD (n = 74), a non-AD dementia diagnosis based on National Institute of Neurological and Communicative Disorders and Stroke and Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria [18] or the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [19] (n = 55), a Mini-Mental State Examination (MMSE) [20] score <20 (n = 24), non-aMCI (n = 1), and missing data (n = 27); the present study analyzed 103 older adults. The study participants were divided into the following 3 groups: normal control (NC), aMCI, and mild AD. The NC category included those diagnosed with age-related cognitive decline or impairment. The aMCI class included those diagnosed according to MCI criteria [21], brain imaging, and medical examination via an interview. The mild AD group included those diagnosed based on NINCDS-ADRDA criteria [18] or the DSM-5 [19]. The NC, aMCI, and mild AD groups included 20, 50, and 33 participants, individually.
Study Measurements
Patient demographic and clinical characteristics were retrospectively extracted from medical records. These included age, sex, years of education, handedness, number of household members, dementia medication after diagnosis, prevalence of risk factors and comorbidities, z-scores based on the voxel-based specific regional analysis system for AD (VSRAD) to assess the degree of hippocampal atrophy [7, 22], and MMSE scores. rCBF, as the primary outcome, was examined by SPECT.
SPECT Evaluation
All of the participants underwent SPECT imaging with radiopharmaceutical technetium-99 m ethyl cysteinate dimer (99 mTc-ECD; Neurolite, Fujifilm RI Pharma, Tokyo, Japan) for measurement of the rCBF, injected with 600 MBq of 99 mTc-ECD in a supine resting state. After 10 min, neurological scanning was performed using an integrated SPECT/CT system (Discovery NM/CT 670 QSuite; GE Healthcare, Tokyo, Japan). The matrix and pixel sizes of the images were 64 × 64 and 5.89 × 5.89 mm, respectively. Tomographic 3-D reconstruction was performed using a filtered back projection algorithm (Butterworth filter order 10 with a cut-off frequency of 0.5 cycles/cm) and Chang attenuation correction [23]. A voxel-by-voxel group study was performed using Statistical Parametric Mapping version 8 (SPM8; Welcome Department of Cognitive Neurology, University College London, London, UK), running on R2013a (MATLAB 8.1; Mathworks, Inc., Sherborn, MA, USA). The images were spatially normalized by SPM8 to an original template for 99 mTc-ECD [24]. The data were standardized with the Montreal Neurological Institute atlas using a 12-parameter affine transformation, followed by nonlinear transfiguration and a trilinear interpolation. The dimensions of the resulting voxels were 2 × 2 × 2 mm. The images were smoothened with a gaussian kernel as full width at half maximum of 12 mm. The Montreal Neurological Institute coordinates were finally converted into Talairach values using the Talairach Daemon database.
Statistical Analysis
Statistical analysis was performed using SPSS version 21 (IBM Japan, Tokyo, Japan). The Shapiro-Wilk test assessed the normality of the clinical and demographic data. For group comparisons, continuous variables with a normal distribution were analyzed by ANOVA. Analysis of variables with a nonnormal distribution was performed using the Kruskal-Wallis test; categorical variables were analyzed using a χ2 test. The Bonferroni-adjusted p value was used to assess differences in significance among groups. To compare rCBF in the SPM8, aMCI NC, and mild AD groups, an analysis was performed using the 2-sample t test (the general linear model at each voxel). Factors displaying significant differences between clinical and demographic characteristics were considered nuisance covariates. The significance of the rCBF was estimated at p < 0.01, without correction for multiple comparisons (T > 2.38).
Results
The clinical and demographic characteristics are described in Table 1. The mean (±SD) age was 78.9 ± 6.4 years and the median MMSE score was 26 (IQR 23–28) points. Age and antidementia medications were significantly higher in the mild AD group compared to the NC and aMCI groups (p < 0.05); contrastingly, the VSRAD z-score was significantly lower for in the mild AD group (p < 0.05). Furthermore, significant differences in MMSE scores were observed (p < 0.05).
Table 1.
Clinical and demographic characteristics
| Overall | NC | aMCI | Mild AD | p valuea | Post hoc testb | |
|---|---|---|---|---|---|---|
| Subjects | 103 | 20 | 50 | 33 | ||
| Age, years | 78.9±6.4 | 74.9±6.0 | 77.9±5.7 | 82.8±5.8 | <0.001 | a, b |
| Females | 60 (58.3) | 15 (75.0) | 24 (48.0) | 21 (63.6) | 0.088 | |
| Education, years | 12 (12–16) | 12.5 (12–15.5) | 12 (12–16) | 12 (9–14) | 0.099 | |
| Handedness | ||||||
| Right | 98 (95.1) | 18 (90.0) | 48 (96.0) | 32 (97.0) | 0.361 | |
| Left | 1 (1.0) | 1 (5.0) | 0 (0.0) | 0 (0.0) | ||
| Ambidextrous | 4 (3.9) | 1 (5.0) | 2 (4.0) | 1 (3.0) | ||
| Household members (persons) | 2 (1–2) | 2 (1–2.8) | 2 (1.8–2) | 2 (1–2.5) | 0.758 | |
| Living alone | 30 (29.1) | 8 (40.0) | 12 (24.0) | 10 (30.3) | 0.406 | |
| Risk factors | ||||||
| Hypertension | 53 (51.5) | 7 (35.0) | 25 (50.0) | 21 (63.6) | 0.124 | |
| Dyslipidemia | 36 (35.0) | 9 (45.0) | 17 (34.0) | 10 (30.3) | 0.543 | |
| Diabetes mellitus | 31 (30.1) | 4 (20.0) | 16 (32.0) | 11 (33.3) | 0.543 | |
| Atrial fibrillation | 10 (9.7) | 1 (5.0) | 7 (14.0) | 2 (6.1) | 0.358 | |
| Comorbidities | ||||||
| Heart disease | 14 (13.6) | 2 (10.0) | 7 (14.0) | 5 (15.2) | 0.863 | |
| Respiratory disease | 10 (9.7) | 1 (5.0) | 5 (10.0) | 4 (12.1) | 0.694 | |
| Orthopedic disease | 15 (14.6) | 1 (5.0) | 9 (18.0) | 5 (15.2) | 0.376 | |
| Kidney disease | 9 (8.7) | 2 (10.0) | 3 (6.0) | 4 (12.1) | 0.612 | |
| Mental illnesses | 5 (4.9) | 3 (15.0) | 1 (2.0) | 1 (3.0) | 0.062 | |
| VSRAD z-score | 1.34 (0.93–2.13) 1.17 (0.82–1.37) 1.16 (0.81–1.70) 2.15 (1.17–3.21)<0.001 | a, b | ||||
| MMSE points | 26 (23–28) | 29 (27–29.8) | 27 (26–28) | 22 (21–24) | <0.001 | a, b, c |
| Anti-dementia medication (yes) | 34 (33.0) | 0 (0.0) | 4 (8.0) | 30 (90.9) | <0.001 | a, b |
| Donepezil | 17 (16.5) | 0 (0.0) | 0 (0.0) | 17 (51.5) | <0.001 | a, b |
| Galantamine | 7 (6.8) | 0 (0.0) | 3 (6.0) | 4 (12.1) | 0.225 | |
| Rivastigmine | 9 (8.7) | 0 (0.0) | 1 (2.0) | 8 (24.2) | <0.001 | a, b |
| Memantine | 1 (1.0) | 0 (0.0) | 0 (0.0) | 1 (3.0) | 0.343 |
Values are presented as means ± SD, medians (IQR), or numbers (%).
ANOVA, Kruskal-Wallis test, or χ2 test.
Bonferroni test, p < 0.05. a, NC vs. mild AD; b, aMCI vs. mild AD; c, NC vs. aMCI.
The brain regions with a decreased rCBF are shown in Table 2 and Figure 1.
Table 2.
Brain regions showing a decreased rCBF
| Association cortex | Regions | Side | BA | MNI coordinates (x, y, z) | t value | p value |
|---|---|---|---|---|---|---|
| In the aMCI group compared to the NC group | ||||||
| Frontal association cortex | Inferior frontal gyrus | L | 47 | −50, 22, −8 | 3.05 | 0.002 |
| Premotor cortex | Superior frontal gyrus | R | 6 | 2, 26, 66 | 3.63 | <0.001 |
| Middle frontal gyrus | L | 6 | −26, 26, 62 | 3.28 | 0.001 | |
| Parietal association cortex | Precuneus | R | 7 | 28, −48, 46 | 3.10 | 0.001 |
| Precuneus | L | 7 | 0, −34, 46 | 2.54 | 0.007 | |
| Inferior parietal lobule | R | 40 | 68, −26, 32 | 2.47 | 0.008 | |
| Inferior parietal lobule | L | 40 | −38, −40, 42 | 2.61 | 0.006 | |
| In the mild AD group compared to the aMCI group | ||||||
| Premotor cortex | Precentral gyrus | R | 6 | 38, −12, 62 | 2.79 | 0.003 |
| Superior frontal gyrus | R | 6 | 16, 20, 64 | 2.51 | 0.007 | |
| Middle frontal gyrus | L | 6 | −32, −2, 58 | 2.68 | 0.004 | |
| Parietal association cortex | Precuneus | R | 7 | 12, −82, 48 | 3.67 | <0.001 |
| Superior parietal lobule | R | 7 | 40, −54, 58 | 2.51 | 0.007 | |
| Precuneus | R | 7 | 10, −50, 46 | 2.48 | 0.008 | |
| Inferior parietal lobule | L | 40 | −40, −48, 54 | 3.20 | 0.001 | |
| Postcentral cyrus | L | 40 | −40, −30, 54 | 3.05 | 0.002 | |
| Temporal association cortex | Inferior temporal gyrus | L | 37 | −66, −54, −12 | 3.24 | 0.001 |
| Middle temporal gyrus | L | 37 | −56, −50, −10 | 2.72 | 0.004 | |
| Middle temporal gyrus | L | 21 | −68, −50, −4 | 2.89 | 0.002 | |
| Occipital association cortex | Cuneus | R | 18 | 18, −100, 12 | 2.61 | 0.005 |
| Middle occipital gyrus | L | 19 | −46, −84, 12 | 3.17 | 0.001 | |
| Limbic lobe | Parahippocampal gyrus | L | 35 | −28, −14, −22 | 2.81 | 0.003 |
| Others | Cuneus | L | − | −20, −94, 2 | 2.43 | 0.009 |
| In the mild AD group compared to the NC group | ||||||
| Frontal association cortex | Middle frontal gyrus | L | 46 | −42, 34, 18 | 2.77 | 0.004 |
| Premotor cortex | Superior frontal gyrus | R | 6 | 12, 26, 64 | 3.41 | 0.001 |
| Middle frontal gyrus | L | 6 | −26, 24, 62 | 3.39 | 0.001 | |
| Parietal association cortex | Inferior parietal lobule | R | 40 | 42, −50, 58 | 3.01 | 0.002 |
| Inferior parietal lobule | L | 40 | −40, −46, 48 | 3.56 | <0.001 | |
| Superior parietal lobule | R | 7 | 34, −58, 50 | 2.94 | 0.002 | |
| Superior parietal lobule | L | 7 | −34, −68, 48 | 3.19 | 0.001 | |
| Precuneus | L | 7 | −6, −68, 36 | 3.65 | <0.001 | |
MNI, Montreal Neurological Institute; L, left; R, right. Unpaired t test in SPM8, uncorrected, covariate for age, p < 0.01.
Fig. 1.
Brain regions mapping a decreased rCBF in SPM8. SPM t mapping was thresholded at an uncorrected p < 0.01 at the voxel level. A voxel-based unpaired t test covariated for age was used to identify voxels that differed significantly in the aMCI group compared to the NC group (a), the mild AD group compared to the aMCI group (b), and the mild AD group compared to the NC group (c), respectively. The most significant area was the right superior frontal gyrus (BA 6) in a, the right precuneus (BA 7) in b, and the left precuneus (BA 7) in c, respectively. R, right; L, left.
The aMCI group showed a reduction in the biparietal parietal and premotor cortex and the left frontal association cortices (p < 0.01, uncorrected) compared to NC; the most significant one was in the right superior frontal gyrus (Brodman area [BA] 6; shown in Fig. 1a).
The mild AD group demonstrated a decrease in the left temporal, bilateral parietal, occipital association, and premotor cortices, as well as the left limbic lobe (p < 0.01, uncorrected), compared to the aMCI group; the most important one was in the right precuneus (BA 7; shown in Fig. 1b).
Compared to NC, the mild AD group indicated a decline in bilateral parietal association and the premotor cortex, as well as the left frontal association cortices (p < 0.01, uncorrected); the most critical one was in the left precuneus (BA 7; shown in Fig. 1c).
The brain regions that showed an increased rCBF are indicated in Table 3 and Figure 2.
Table 3.
Brain regions showing an increased rCBF
| Association cortex | Regions | Side | BA | MNI coordinates (x, y, z) | t value | p value |
|---|---|---|---|---|---|---|
| In the aMCI group compared to the NC group | ||||||
| Temporal association cortex | Inferior temporal gyrus | L | 37 | −64, −60, −8 | 3.25 | 0.001 |
| Occipital association cortex | Cuneus | R | 18 | 28, −100, 0 | 3.00 | 0.002 |
| Fusiform gyrus | R | 19 | 36, −72, −8 | 2.73 | 0.004 | |
| Others | Uvula | R | − | 18, −74, −32 | 3.88 | <0.001 |
| Thalamus | R | − | 26, −22, 6 | 2.92 | 0.002 | |
| Culmen | R | − | 44, −34, −26 | 2.64 | 0.005 | |
| Culmen | L | − | −46, −38, −24 | 3.83 | <0.001 | |
| Inferior semi-lunar lobule | L | − | −18, −68, −36 | 3.73 | <0.001 | |
| In the mild AD group compared to the aMCI group | ||||||
| Frontal association cortex | Inferior frontal gyrus | R | 47 | 56, 20, −4 | 3.19 | 0.001 |
| Inferior frontal gyrus | L | 47 | −52, 24, −8 | 2.56 | 0.006 | |
| Middle frontal gyrus | L | 11 | −28, 36, −2 | 2.40 | 0.009 | |
| Temporal association cortex | Superior temporal gyrus | R | 22 | 64, 0, 4 | 2.61 | 0.005 |
| In the mild AD group compared to the NC group | ||||||
| Frontal association cortex | Inferior frontal gyrus | R | 47 | 48, 40, −18 | 2.68 | 0.005 |
| Premotor cortex | Middle frontal gyrus | R | 6 | 36, −4, 44 | 2.64 | 0.005 |
| Temporal association cortex | Middle temporal gyrus | R | 37 | 62, −64, −2 | 2.82 | 0.003 |
| Fusiform gyrus | R | 20 | 46, −34, −24 | 2.86 | 0.003 | |
| Fusiform gyrus | L | 20 | −50, −32, −30 | 3.51 | <0.001 | |
| Occipital association cortex | Middle occipital gyrus | L | 19 | −58, −70, −4 | 2.72 | 0.004 |
| Others | Cerebellar lingual | R | − | 0, −48, −18 | 3.56 | <0.001 |
| Inferior semi-lunar lobule | L | − | −10, −68, −40 | 3.60 | <0.001 | |
Unpaired t test in SPM8, uncorrected, covariate for age, p < 0.01. MNI, Montreal Neurological Institute; L, left; R, right.
Fig. 2.
Brain regions mapping an increased rCBF in SPM8. SPM t mapping was thresholded at an uncorrected p < 0.01 at the voxel level. A voxel-based unpaired t test covariated for age was used to identify voxels that differed significantly in the aMCI group compared to the NC group (a), the mild AD group compared to the aMCI group (b), and the mild AD group compared to the NC group (c), respectively. The most significant area was the right uvula at the cerebellum in a, right inferior frontal gyrus (BA 47) in b, and the left inferior semi-lunar lobule at the cerebellum in c, respectively. R, right; L, left.
Compared to the NC group, the aMCI group displayed elevation in certain areas in the bilateral cerebellum, as well as the left and the right occipital association cortices (p < 0.01, uncorrected); the most significant one was in the right uvula of the cerebellum (shown in Fig. 2a).
In the mild AD group, rising rCBF levels were observed in specific areas of the bilateral frontal or right temporal association cortex (p < 0.01, uncorrected) compared to MCI; the most critical one was in the right inferior frontal gyrus (BA 47; shown in Fig. 2b).
Compared to NC, the mild AD group displayed an increase in some of the cerebellum, the bilateral temporal cortex, the left occipital association cortex, the right frontal association, and the right premotor cortices (p < 0.01, uncorrected); the most important one was in the left inferior semi-lunar lobule of the cerebellum (shown in Fig. 2c).
Discussion
The present retrospective cross-sectional study investigated the characteristics of rCBF in patients with aMCI and mild AD. This was detected to be lower in the bilateral premotor, parietal, and left frontal association cortices in the aMCI group compared to NC; higher values were observed in the bilateral cerebellum. Second, for mild AD, a reduction was seen in the bilateral parietal, premotor, left temporal, occipital, and frontal association cortices, as well as the left limbic lobe, compared to the aMCI and NC groups. Contrastingly, an elevation was seen in specific portions of the cerebellum and the bilateral frontal, right premotor, temporal, and left occipital association cortices.
The present investigation uncovered varying findings compared to previous studies. The present study showed decreased rCBF regions in the frontal lobe, such as the dorsal premotor and supplementary motor areas, in MCI patients; these are practically consistent with characteristics of AD patients [25]. Interestingly, attention deficit, especially executive control, may significantly contribute to the behavioral and cognitive deficits of aMCI [26], which are indicative of a decrease in rCBF. This is consistent with the hypotheses of previous studies.
Some findings in the mild AD group and others were consistent with previous studies; however, others were not. Regions with a reduced rCBF, such as the temporal, occipital, and frontal association cortices, in addition to the parietal cortex, extended to the entire brain [5]. Similarly, mild AD displayed increased rCBF regions in the cerebellum and prefrontal cortex; this was consistent with previous studies comparing values to those of NC [16, 27, 28, 29, 30]. However, the integrated elevation and reduction in rCBF, including the cerebral hemisphere, lacked uniformity with previous studies [31, 32]. A significant increase in the cerebellum may cause a vanishing phenomenon, compensating for rCBF in the cerebral cortex [10] and possibly affecting aMCI or AD.
The present study has strengths and limitations. The recruited patients were classified as mild AD, aMCI, or NC; both the decreased and elevated regions were considered for rCBF. The findings of the present study suggest detailed characteristics in mild AD and aMCI, providing evidence for the establishment of an effective intervention method. Contrastingly, its limitation is its cross-sectional design; important indicators of rCBF, such as memory and executive function, were not considered in the present study. Therefore, the pathway of changes and the relationship with multiple cognitive functions are not precise. Further studies with a longitudinal cohort or a clinical trial design in multiple facilities are required to overcome these limitations.
Conclusion
We observed the characteristics of changes in rCBF for patients with aMCI and AD. The specified regions of reduced and elevated rCBF are novel findings of the present study. Furthermore, it is necessary to clarify the background of the participants, including the cause and clinical symptoms of MCI or AD, and subsequently conduct a longitudinal clinical trial.
Statement of Ethics
The present study conformed to the ethical guidelines of the Declaration of Helsinki. The study protocol was reviewed and approved by the Ethics Committee of the Tokyo Saiseikai Central Hospital (approval No. 30-72).
Conflict of Interest Statement
The authors have no conflict of interests to declare.
Funding Sources
No specific grant from any funding agency or the commercial or not-for-profit sector was received for this study.
Author Contributions
Y. Kunieda, C. Arakawa, T. Yamada, M. Suzuki, S. Koyama, Y. Kimura, T. Ichikawa, S. Shino, and M. Yamada contributed to the study concept and design, analysis and interpretation of data, and elaboration of this paper. R. Hirokawa contributed to the acquisition of clinical and imaging data. T. Matsuda participated in the acquisition and analysis of data. T. Takakura, T. Adachi, and H. Hoshino verified many aspects of this study and assisted in the preparation of this report. All of the authors approved the final version of this report.
Acknowledgement
The authors thank the staff of the Tokyo Saiseikai Central Hospital and all of the participants for their cooperation with the present study. We are grateful to Mr. Naoharu Takemura and Mr. Tetsushi Miki of Fujifilm Toyama Chemical Co., Ltd. for their cooperation with image processing in brain SPECT. We would like to thank Editage (www.editage.com) for English language editing.
References
- 1.Jack CR. Jr Alzheimer disease: new concepts on its neurobiology and the clinical role imaging will play. Radiology. 2012 May;263((2)):344–61. doi: 10.1148/radiol.12110433. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Jack CR, Jr, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, et al. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013 Feb;12((2)):207–16. doi: 10.1016/S1474-4422(12)70291-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Morinaga A, Ono K, Ikeda T, Ikeda Y, Shima K, Noguchi-Shinohara M, et al. A comparison of the diagnostic sensitivity of MRI, CBF-SPECT, FDG-PET and cerebrospinal fluid biomarkers for detecting Alzheimer's disease in a memory clinic. Dement Geriatr Cogn Disord. 2010;30((4)):285–92. doi: 10.1159/000320265. [DOI] [PubMed] [Google Scholar]
- 4.Murray AD. Imaging approaches for dementia. AJNR Am J Neuroradiol. 2012 Nov;33((10)):1836–44. doi: 10.3174/ajnr.A2782. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Matsuda H. Role of neuroimaging in Alzheimer's disease, with emphasis on brain perfusion SPECT. J Nucl Med. 2007 Aug;48((8)):1289–300. doi: 10.2967/jnumed.106.037218. [DOI] [PubMed] [Google Scholar]
- 6.Imabayashi E, Matsuda H, Asada T, Ohnishi T, Sakamoto S, Nakano S, et al. Superiority of 3-dimensional stereotactic surface projection analysis over visual inspection in discrimination of patients with very early Alzheimer's disease from controls using brain perfusion SPECT. J Nucl Med. 2004 Sep;45((9)):1450–7. [PubMed] [Google Scholar]
- 7.Matsuda H. The role of neuroimaging in mild cognitive impairment. Neuropathology. 2007 Dec;27((6)):570–7. doi: 10.1111/j.1440-1789.2007.00794.x. [DOI] [PubMed] [Google Scholar]
- 8.Niwa F, Kondo M, Sakurada K, Nakagawa M, Imanishi J, Mizuno T. Regional Cerebral Blood Flow in [123]I-IMP Single-photon Emission Computed Tomography and the Wechsler Memory Scale-revised in Nondemented Elderly Subjects with Subjective Cognitive Impairment. Intern Med. 2016;55((24)):3571–8. doi: 10.2169/internalmedicine.55.6725. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Alegret M, Cuberas-Borrós G, Espinosa A, Valero S, Hernández I, Ruíz A, et al. Cognitive, genetic, and brain perfusion factors associated with four year incidence of Alzheimer's disease from mild cognitive impairment. J Alzheimers Dis. 2014;41((3)):739–48. doi: 10.3233/JAD-132516. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Kogure D, Matsuda H, Ohnishi T, Asada T, Uno M, Kunihiro T, et al. Longitudinal evaluation of early Alzheimer's disease using brain perfusion SPECT. J Nucl Med. 2000 Jul;41((7)):1155–62. [PubMed] [Google Scholar]
- 11.Hirao K, Ohnishi T, Hirata Y, Yamashita F, Mori T, Moriguchi Y, et al. The prediction of rapid conversion to Alzheimer's disease in mild cognitive impairment using regional cerebral blood flow SPECT. Neuroimage. 2005 Dec;28((4)):1014–21. doi: 10.1016/j.neuroimage.2005.06.066. [DOI] [PubMed] [Google Scholar]
- 12.Takahashi M, Tada T, Nakamura T, Koyama K, Momose T. Efficacy and Limitations of rCBF-SPECT in the Diagnosis of Alzheimer's Disease With Amyloid-PET. Am J Alzheimers Dis Other Demen. 2019 Aug;34((5)):314–21. doi: 10.1177/1533317519841192. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Bonte FJ, Tintner R, Weiner MF, Bigio EH, White CL. 3rd Brain blood flow in the dementias: SPECT with histopathologic correlation. Radiology. 1993 Feb;186((2)):361–5. doi: 10.1148/radiology.186.2.8421735. [DOI] [PubMed] [Google Scholar]
- 14.Okamura N, Arai H, Maruyama M, Higuchi M, Matsui T, Tanji H, et al. Combined analysis of CSF tau levels and [123I]Iodoamphetamine SPECT in mild cognitive impairment: implications for a novel predictor of alzheimer's disease. Am J Psychiatry. 2002 Mar;159((3)):474–6. doi: 10.1176/appi.ajp.159.3.474. [DOI] [PubMed] [Google Scholar]
- 15.Alegret M, Cuberas-Borrós G, Vinyes-Junqué G, Espinosa A, Valero S, Hernández I, et al. A two-year follow-up of cognitive deficits and brain perfusion in mild cognitive impairment and mild Alzheimer's disease. J Alzheimers Dis. 2012;30((1)):109–20. doi: 10.3233/JAD-2012-111850. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Johnson KA, Moran EK, Becker JA, Blacker D, Fischman AJ, Albert MS. Single photon emission computed tomography perfusion differences in mild cognitive impairment. J Neurol Neurosurg Psychiatry. 2007 Mar;78((3)):240–7. doi: 10.1136/jnnp.2006.096800. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kikukawa T, Abe T, Ataka S, Saito H, Hasegawa I, Mino T, et al. Amyloid deposition and CBF patterns predict conversion of mild cognitive impairment to dementia. Neurol Sci. 2018 Sep;39((9)):1597–602. doi: 10.1007/s10072-018-3477-0. [DOI] [PubMed] [Google Scholar]
- 18.McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology. 1984 Jul;34((7)):939–44. doi: 10.1212/wnl.34.7.939. [DOI] [PubMed] [Google Scholar]
- 19.Sachdev PS, Blacker D, Blazer DG, Ganguli M, Jeste DV, Paulsen JS, et al. Classifying neurocognitive disorders: the DSM-5 approach. Nat Rev Neurol. 2014 Nov;10((11)):634–42. doi: 10.1038/nrneurol.2014.181. [DOI] [PubMed] [Google Scholar]
- 20.Tombaugh TN, McIntyre NJ. The mini-mental state examination: a comprehensive review. J Am Geriatr Soc. 1992 Sep;40((9)):922–35. doi: 10.1111/j.1532-5415.1992.tb01992.x. [DOI] [PubMed] [Google Scholar]
- 21.Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med. 2004 Sep;256((3)):183–94. doi: 10.1111/j.1365-2796.2004.01388.x. [DOI] [PubMed] [Google Scholar]
- 22.Hirata Y, Matsuda H, Nemoto K, Ohnishi T, Hirao K, Yamashita F, et al. Voxel-based morphometry to discriminate early Alzheimer's disease from controls. Neurosci Lett. 2005 Jul;382((3)):269–74. doi: 10.1016/j.neulet.2005.03.038. [DOI] [PubMed] [Google Scholar]
- 23.Chang LT. A method for attenuation correction in radionuclide computed tomography. IEEE Trans Nucl Sci. 1978 Feb;25((1)):638–43. [Google Scholar]
- 24.Ohnishi T, Matsuda H, Hashimoto T, Kunihiro T, Nishikawa M, Uema T, et al. Abnormal regional cerebral blood flow in childhood autism. Brain. 2000 Sep;123((Pt 9)):1838–44. doi: 10.1093/brain/123.9.1838. [DOI] [PubMed] [Google Scholar]
- 25.Li R, Wu X, Fleisher AS, Reiman EM, Chen K, Yao L. Attention-related networks in Alzheimer's disease: a resting functional MRI study. Hum Brain Mapp. 2012 May;33((5)):1076–88. doi: 10.1002/hbm.21269. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Van Dam NT, Sano M, Mitsis EM, Grossman HT, Gu X, Park Y, et al. Functional neural correlates of attentional deficits in amnestic mild cognitive impairment. PLoS One. 2013;8((1)):e54035. doi: 10.1371/journal.pone.0054035. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82((4)):239–59. doi: 10.1007/BF00308809. [DOI] [PubMed] [Google Scholar]
- 28.Thal DR, Rüb U, Orantes M, Braak H. Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology. 2002 Jun;58((12)):1791–800. doi: 10.1212/wnl.58.12.1791. [DOI] [PubMed] [Google Scholar]
- 29.Thal DR, Griffin WS, Braak H. Parenchymal and vascular Abeta-deposition and its effects on the degeneration of neurons and cognition in Alzheimer's disease. J Cell Mol Med. 2008 Oct;12((5b 5B)):1848–62. doi: 10.1111/j.1582-4934.2008.00411.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Huang C, Wahlund LO, Almkvist O, Elehu D, Svensson L, Jonsson T, et al. Voxel- and VOI-based analysis of SPECT CBF in relation to clinical and psychological heterogeneity of mild cognitive impairment. Neuroimage. 2003 Jul;19((3)):1137–44. doi: 10.1016/s1053-8119(03)00168-x. [DOI] [PubMed] [Google Scholar]
- 31.Reesink FE, García DV, Sánchez-Catasús CA, Peretti DE, Willemsen AT, Boellaard R, et al. Crossed Cerebellar Diaschisis in Alzheimer's Disease. Curr Alzheimer Res. 2018;15((13)):1267–75. doi: 10.2174/1567205015666180913102615. [DOI] [PubMed] [Google Scholar]
- 32.Sagiuchi T, Ishii K, Asano Y, Aoki Y, Kikuchi K, Jinguuji K, et al. Interictal crossed cerebellar hyperperfusion on Tc-99m ECD SPECT. Ann Nucl Med. 2001 Aug;15((4)):369–72. doi: 10.1007/BF02988245. [DOI] [PubMed] [Google Scholar]