Please wait a minute...
 Home  About the Journal Editorial Board Aims & Scope Peer Review Policy Subscription Contact us
Early Edition  //  Current Issue  //  Open Special Issues  //  Archives  //  Most Read  //  Most Downloaded  //  Most Cited
Aging and disease    2018, Vol. 9 Issue (4) : 755-760     DOI: 10.14336/AD.2017.0818
Short Communication |
Voxel-based Specific Regional Analysis System for Alzheimer’s Disease (VSRAD) on 3-tesla Normal Database: Diagnostic Accuracy in Two Independent Cohorts with Early Alzheimer’s Disease
Sone Daichi1,2, Imabayashi Etsuko2, Maikusa Norihide2, Ogawa Masayo2, Sato Noriko3, Matsuda Hiroshi2,*, Japanese-Alzheimer’s Disease Neuroimaging Initiative
1Department of Psychiatry, National Center of Neurology and Psychiatry, Tokyo, Japan
2Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
3Department of Radiology, National Center of Neurology and Psychiatry, Tokyo, Japan
Download: PDF(537 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    

Voxel-based specific regional analysis system for Alzheimer’s disease (VSRAD) software is widely used in clinical practice in Alzheimer’s disease (AD). The existing VSRAD is based on the normal database with 1.5-tesla MRI scans (VSRAD-1.5T), and its utility for patients have undergone 3-tesla MRI is still controversial. We recruited 19 patients with early AD and 28 healthy controls who had undergone 3-tesla MRI scans at our institute (Cohort 1). We also used the 3-tesla MRI data of 30 patients with early AD and 13 healthy controls from the Japanese Alzheimer’s Disease Neuroimaging Initiative (Cohort 2). We also created a new VSRAD based on 65 normal subjects’ 3-tesla MRI scans (VSRAD-3T), and compared the detectability of AD between VSRAD-1.5T and VSRAD-3T, using receiver operating characteristic curve and area under the curve (AUC) analyses. As a result, there were no significant differences in the detectability of AD between VSRAD-3T and VSRAD-1.5T, except for the whole white matter atrophy score, which showed significantly better AUC in VSRAD-3T than in VSRAD-1.5T in both Cohort 1 (p=0.04) and 2 (p<0.01). Generally, there were better diagnostic values in Cohort 2 than in Cohort 1. The optimal cutoff values varied but were generally lower than in the previously published data. In conclusion, for patients with 3-tesla MRI, the detectability of early AD by the existing VSRAD was not different from that by the new VSRAD based on 3-tesla database. We should exercise caution when using the existing VSRAD for 3-tesla white matter analyses or for setting cutoff values.

Keywords Alzheimer’s disease      VSRAD      voxel-based morphometry      3-tesla MRI     
Corresponding Authors: Matsuda Hiroshi   
About author:

These authors equally contributed to this work.

Issue Date: 01 August 2018
E-mail this article
E-mail Alert
Articles by authors
Sone Daichi
Imabayashi Etsuko
Maikusa Norihide
Ogawa Masayo
Sato Noriko
Matsuda Hiroshi
Japanese-Alzheimer’s Disease Neuroimaging Initiative
Cite this article:   
Sone Daichi,Imabayashi Etsuko,Maikusa Norihide, et al. Voxel-based Specific Regional Analysis System for Alzheimer’s Disease (VSRAD) on 3-tesla Normal Database: Diagnostic Accuracy in Two Independent Cohorts with Early Alzheimer’s Disease[J]. Aging and disease, 2018, 9(4): 755-760.
URL:     OR
Cohort 1 - Our InstituteCohort 2 - J-ADNINormal Databases

Early AD
Early AD
VSRAD-1.5T *
Age (mean ± SD)69.8 ± 8.666.9 ± 7.974.2 ± 6.868.2 ± 6.070.3 ± 8.670.4 ± 7.8
Age (range)53-8154-8661-8361-8054-8554-86
Gender (M:F)6:1315:1312:186:730:3537:43
Global CDR (range)0.5-1.000.5-1.00N/AN/A
MMSE (mean ± SD)21.9 ± 4.529.3 ± 1.024.8 ± 2.529.8 ± 0.6N/A29.1 ± 1.2
Table 1  The demographics of Cohorts 1 and 2 and the normal databases for this study.
Cohort 1 - Our Institute
Cohort 2 - J-ADNI
Whole GM0.7450.7720.630.8620.7970.12
Whole WM0.7230.5640.04*0.8650.662<0.01*
Table 2  AUC values for differentiation of early AD from healthy controls using both VSRADs of ROC analysis
Figure 1.  The beeswarm plots and diagnostic values at the optimal cutoff for each score on both VSRADs in Cohort 1.
Figure 2.  The beeswarm plots and diagnostic values at the optimal cutoff for each score on both VSRADs in Cohort 2.
[1] Matsuda H (2016). MRI morphometry in Alzheimer’s disease. Ageing Res Rev, 30: 17-24
[2] Matsuda H, Mizumura S, Nemoto K, Yamashita F, Imabayashi E, Sato N, et al. (2012). Automatic voxel-based morphometry of structural MRI by SPM8 plus diffeomorphic anatomic registration through exponentiated lie algebra improves the diagnosis of probable Alzheimer Disease. AJNR Am J Neuroradiol, 33: 1109-1114
[3] Matsuda H (2013). Voxel-based Morphometry of Brain MRI in Normal Aging and Alzheimer’s Disease. Aging Dis, 4: 29-37
[4] Goto M, Suzuki Y, Abe O, Hayashi N, Aoki S, Mori H, et al. (2008). Customization of normal data base specific for 3-tesla MRI is mandatory in VSRAD analysis. Radiol Phys Technol, 1: 196-200
[5] Briellmann RS, Syngeniotis A, Jackson GD (2001). Comparison of hippocampal volumetry at 1.5 tesla and at 3 tesla. Epilepsia, 42: 1021-1024
[6] Iwatsubo T (2010). Japanese Alzheimer’s Disease Neuroimaging Initiative: present status and future. Alzheimers Dement, 6: 297-299
[7] DeLong ER, DeLong DM, Clarke-Pearson DL (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 44: 837-845
[8] Zweig MH, Campbell G (1993). Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem, 39: 561-577
[9] Ho AJ, Hua X, Lee S, Leow AD, Yanovsky I, Gutman B, et al. (2010). Comparing 3 T and 1.5 T MRI for tracking Alzheimer’s disease progression with tensor-based morphometry. Hum Brain Mapp, 31: 499-514
[10] Moon CM, Shin IS, Jeong GW (2017). Alterations in white matter volume and its correlation with neuropsychological scales in patients with Alzheimer’s disease: a DARTEL-based voxel-based morphometry study. Acta Radiol, 58: 204-210
[11] Raz N, Lindenberger U, Rodrigue KM, Kennedy KM, Head D, Williamson A, et al. (2005). Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb Cortex, 15: 1676-1689
[12] Raji CA, Lopez OL, Kuller LH, Carmichael OT, Becker JT (2009). Age, Alzheimer disease, and brain structure. Neurology, 73: 1899-1905
[13] Niida R, Niida A, Motomura M, Uechi A (2011). Diagnosis of depression by MRI scans with the use of VSRAD - a promising auxiliary means of diagnosis: a report of 10 years research. Int J Gen Med, 4: 377-387
[14] Sugiyama A, Sato N, Kimura Y, Maekawa T, Wakasugi N, Sone D, et al. (2017). Thalamic involvement determined using VSRAD advance on MRI and easy Z-score analysis of 99mTc-ECD-SPECT in Gerstmann-Straussler-Scheinker syndrome with P102L mutation. J Neurol Sci, 373: 27-30
[15] Shimoda K, Kimura M, Yokota M, Okubo Y (2015). Comparison of regional gray matter volume abnormalities in Alzheimers disease and late life depression with hippocampal atrophy using VSRAD analysis: a voxel-based morphometry study. Psychiatry Res, 232: 71-75.
[1] Jong Bin Bae,Ji Won Han,Kyung Phil Kwak,Bong Jo Kim,Shin Gyeom Kim,Jeong Lan Kim,Tae Hui Kim,Seung-Ho Ryu,Seok Woo Moon,Joon Hyuk Park,Jong Chul Youn,Dong Young Lee,Dong Woo Lee,Seok Bum Lee,Jung Jae Lee,Jin Hyeong Jhoo,Ki Woong Kim. Is Dementia More Fatal Than Previously Estimated? A Population-based Prospective Cohort Study[J]. Aging and disease, 2019, 10(1): 1-11.
[2] Antonina Luca, Carmela Calandra, Maria Luca. Molecular Bases of Alzheimer’s Disease and Neurodegeneration: The Role of Neuroglia[J]. Aging and disease, 2018, 9(6): 1134-1152.
[3] Morroni Fabiana, Sita Giulia, Graziosi Agnese, Turrini Eleonora, Fimognari Carmela, Tarozzi Andrea, Hrelia Patrizia. Neuroprotective Effect of Caffeic Acid Phenethyl Ester in A Mouse Model of Alzheimer’s Disease Involves Nrf2/HO-1 Pathway[J]. Aging and disease, 2018, 9(4): 605-622.
[4] Xu Yangqi, Liu Xiaoli, Shen Junyi, Tian Wotu, Fang Rong, Li Binyin, Ma Jianfang, Cao Li, Chen Shengdi, Li Guanjun, Tang Huidong. The Whole Exome Sequencing Clarifies the Genotype- Phenotype Correlations in Patients with Early-Onset Dementia[J]. Aging and disease, 2018, 9(4): 696-705.
[5] Ding Qiong, Tanigawa Kitora, Kaneko Jun, Totsuka Mamoru, Katakura Yoshinori, Imabayashi Etsuko, Matsuda Hiroshi, Hisatsune Tatsuhiro. Anserine/Carnosine Supplementation Preserves Blood Flow in the Prefrontal Brain of Elderly People Carrying APOE e4[J]. Aging and disease, 2018, 9(3): 334-345.
[6] Shen Ting, You Yuyi, Joseph Chitra, Mirzaei Mehdi, Klistorner Alexander, Graham Stuart L., Gupta Vivek. BDNF Polymorphism: A Review of Its Diagnostic and Clinical Relevance in Neurodegenerative Disorders[J]. Aging and disease, 2018, 9(3): 523-536.
[7] Peng Fangyu, Xie Fang, Muzik Otto. Alteration of Copper Fluxes in Brain Aging: A Longitudinal Study in Rodent Using 64CuCl2-PET/CT[J]. Aging and disease, 2018, 9(1): 109-118.
[8] Farnaz Farokhian,Chunlan Yang,Iman Beheshti,Hiroshi Matsuda,Shuicai Wu. Age-Related Gray and White Matter Changes in Normal Adult Brains[J]. A&D, 2017, 8(6): 899-909.
[9] Diana L Castillo-Carranza,Ashley N Nilson,Candice E Van Skike,Jordan B Jahrling,Kishan Patel,Prajesh Garach,Julia E Gerson,Urmi Sengupta,Jose Abisambra,Peter Nelson,Juan Troncoso,Zoltan Ungvari,Veronica Galvan,Rakez Kayed. Cerebral Microvascular Accumulation of Tau Oligomers in Alzheimer’s Disease and Related Tauopathies[J]. A&D, 2017, 8(3): 257-266.
[10] Zohara Sternberg,Zihua Hu,Daniel Sternberg,Shayan Waseh,Joseph F. Quinn,Katharine Wild,Kaye Jeffrey,Lin Zhao,Michael Garrick. Serum Hepcidin Levels, Iron Dyshomeostasis and Cognitive Loss in Alzheimer’s Disease[J]. A&D, 2017, 8(2): 215-227.
[11] Annamaria Zaia,Pierluigi Maponi,Giuseppina Di Stefano,Tiziana Casoli. Biocomplexity and Fractality in the Search of Biomarkers of Aging and Pathology: Focus on Mitochondrial DNA and Alzheimer’s Disease[J]. A&D, 2017, 8(1): 44-56.
[12] Jianhui Wang,Xiaorui Cheng,Ju Zeng,Jiangbei Yuan,Zhongfu Wang,Wenxia Zhou,Yongxiang Zhang. LW-AFC Effects on N-glycan Profile in Senescence-Accelerated Mouse Prone 8 Strain, a Mouse Model of Alzheimer’s Disease[J]. A&D, 2017, 8(1): 101-114.
[13] Murat Serdar Gurses,Mustafa Numan Ural,Mehmet Akif Gulec,Omer Akyol,Sumeyya Akyol. Pathophysiological Function of ADAMTS Enzymes on Molecular Mechanism of Alzheimer’s Disease[J]. A&D, 2016, 7(4): 479-490.
[14] Ryan J. Day,Katie L. McCarty,Kayla E. Ockerse,Elizabeth Head,Troy T. Rohn. Proteolytic Cleavage of Apolipoprotein E in the Down Syndrome Brain[J]. A&D, 2016, 7(3): 267-277.
[15] Isaac G. Onyango,Jameel Dennis,Shaharyah M. Khan. Mitochondrial Dysfunction in Alzheimer’s Disease and the Rationale for Bioenergetics Based Therapies[J]. A&D, 2016, 7(2): 201-214.
Full text



Copyright © 2014 Aging and Disease, All Rights Reserved.
Address: Aging and Disease Editorial Office 3400 Camp Bowie Boulevard Fort Worth, TX76106 USA
Fax: (817) 735-0408 E-mail:
Powered by Beijing Magtech Co. Ltd