The DOI matches its Crossref record; 50 of the 51 references it cites were found in a registry or an index.
18FDG PET in Vascular Dementia: Differentiation from Alzheimer's Disease Using Voxel-Based Multivariate Analysis
Abstract
The brain metabolic pattern of vascular dementia (VaD) remains poorly characterized. Univariate voxel-based analysis ignores the functional correlations among structures and may lack sensitivity and specificity. Here, we applied a novel voxel-based multivariate technique to a large (18 F)2-fluoro-2-deoxy-D-glucose positron emission tomography data set. The sample consisted of 153 subjects, one-third each being probable subcortical VaD, probable Alzheimer disease (AD) (matched for Mini-Mental-State examination (MMSE) and age), and normal controls (NCs). We first applied principal component (PC) analysis and removed PCs significantly correlated to age. The remainders were used as feature vectors in a canonical variate analysis to generate canonical variates (CVs), that is, linear combinations of PC-scores. The first two CVs efficiently separated the groups. CV 1 separated VaD from AD with 100% accuracy, whereas CV2 separated NC from demented subjects with 72% sensitivity and 96% specificity. Images depicting CV 1 and CV 2 showed that lower metabolism differentiating VaD from AD mainly concerned the deep gray nuclei, cerebellum, primary cortices, middle temporal gyrus, and anterior cingulate gyrus, whereas lower metabolism in AD versus VaD concerned mainly the hippocampal region and orbitofrontal, posterior cingulate, and posterior parietal cortices. The hypometabolic pattern common to VaD and AD mainly concerned the posterior parietal, precuneus, posterior cingulate, prefrontal, and anterior hippocampal regions, and linearly correlated with the MMSE. This study shows the potential of voxel-based multivariate methods to highlight independent functional networks in dementing diseases. By maximizing the separation between groups, this method extracted a metabolic pattern that efficiently differentiated VaD and AD.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“FDG-PET in VaD showed reduced uptake in deep grey structures, the cerebellum, the middle temporal gyrus and the anterior cingulate compared to AD [125].”
From The use of neuroimaging techniques in the early and differential diagnosis of dementia (2023) · cites this paper as [125]
Citations
Cites
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
Román, G. C., Tatemichi, T. K., Erkinjuntti, T., Cummings, J. L., Masdeu, J. C., Garcia, J. H., Amaducci, L., Orgogozo, J.-M., Brun, A., Hofman, A., et al. (1993) · Neurology · doi:10.1212/wnl.43.2.250 · Cited by 4,921
A User's Guide to Principal Components
Leigh, S. D., & Jackson, J. E. (1993) · Technometrics · doi:10.2307/1269292 · Cited by 3,346
A Use's Guide to Principal Components
Jackson, J. E. (1991) · Wiley series in probability and statistics · doi:10.1002/0471725331 · Cited by 2,523
Brain Infarction and the Clinical Expression of Alzheimer Disease<subtitle>The Nun Study</subtitle>
Snowdon, D. A. (1997) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1997.03540340047031 · Cited by 1,590
The Generalization of Student's Ratio
Hotelling, H. (1931) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177732979 · Cited by 1,561
de Leeuw, F.-E. (2001) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.70.1.9 · Cited by 1,291
The prevalence of frontotemporal dementia
Ratnavalli, E., Brayne, C., Dawson, K. E., & Hodges, J. R. (2002) · Neurology · doi:10.1212/wnl.58.11.1615 · Cited by 1,182
O'Brien, J. T., Erkinjuntti, T., Reisberg, ., Román, G. C., Pantoni, L., Bowler, J., Ballard, C., DeCarli, C., Gorelick, P., Rockwood, K., et al. (2014) · International Journal of Stroke · doi:10.1111/ijs.12374_18 · Cited by 1,165
O'Brien, J. T., Erkinjuntti, T., Reisberg, B., Roman, G., Sawada, T., Pantoni, L., Bowler, J. V., Ballard, C., DeCarli, C., Gorelick, P. B., et al. (2003) · The Lancet Neurology · doi:10.1016/s1474-4422(03)00305-3 · Cited by 1,139
Research works printed but not linked: 4
- Baron JC, 1998, Rev Neurol, 154, 122
- Graham NL, 2004, J Neurol Neurosurg Psychiatry, 75, 61
- Herholz K, 1990, Dementia, 1, 156
- Salmon E, 1994, J Nucl Med, 35, 391
Cited by
Sorted by citations
Brain PET in Suspected Dementia: Patterns of Altered FDG Metabolism
Brown, R. K. J., Bohnen, N. I., Wong, K. K., Minoshima, S., & Frey, K. A. (2014) · RadioGraphics · doi:10.1148/rg.343135065 · Cited by 256
Brain FDG PET and the Diagnosis of Dementia
Shivamurthy, V. K. N., Tahari, A. K., Marcus, C., & Subramaniam, R. M. (2015) · American Journal of Roentgenology · doi:10.2214/ajr.13.12363 · Cited by 227
Nobili, F. M., Arbizu, J., Bouwman, F. H., Drzezga, A. E., Agosta, F., Nestor, P. J., Walker, Z., Boccardi, M., & the EANM‐EAN Task Force for the Prescription of FDG‐PET for Dementing Neurodegenerative Disorders (2018) · European Journal of Neurology · doi:10.1111/ene.13728 · Cited by 220
Cognitive and Behavioral Neurology
Finger, E. C., & Daffner, K. R. (2011) · Springer eBooks · doi:10.1007/978-0-387-88555-1_8 · Cited by 201
The use of neuroimaging techniques in the early and differential diagnosis of dementia
Chouliaras, L., & O’Brien, J. T. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02215-8 · Cited by 221
“FDG-PET in VaD showed reduced uptake in deep grey structures, the cerebellum, the middle temporal gyrus and the anterior cingulate compared to AD [125].”
ResultMultivariate and univariate neuroimaging biomarkers of Alzheimer's disease
Habeck, C. G., Foster, N. L., Perneczky, R., Kurz, A., Alexopoulos, P., Koeppe, R. A., Drzezga, A. E., & Stern, Y. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.01.056 · Cited by 173
Cortical Thinning in Vascular Mild Cognitive Impairment and Vascular Dementia of Subcortical Type
Seo, S. W., Ahn, J., Yoon, U., Im, K., Lee, J., Kim, S. T., Ahn, H., Chin, J., Jeong, Y., & Na, D. L. (2009) · Journal of Neuroimaging · doi:10.1111/j.1552-6569.2008.00293.x · Cited by 144
Update on Vascular Cognitive Impairment Associated with Subcortical Small-Vessel Disease
Wallin, A., Román, G. C., Esiri, M. M., Kettunen, P., Svensson, J., Paraskevas, G. P., & Kapaki, E. N. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-170803 · Cited by 133
Yakushev, I. Y., Landvogt, C., Buchholz, H., Fellgiebel, A., Hammers, A., Scheurich, A., Schmidtmann, I. M., Gerhard, A., Schreckenberger, M., & Bartenstein, P. (2008) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2007.11.004 · Cited by 118
A life course of adiposity and dementia
Gustafson, D. (2008) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2008.01.052 · Cited by 120
Differential diagnosis of parkinsonian syndromes using PCA-based functional imaging features
Spetsieris, P. G., Ma, Y., DHAWAN, V. M., & Eidelberg, D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.063 · Cited by 112
Clinical utility of FDG-PET for the differential diagnosis among the main forms of dementia
for the EANM-EAN Task Force for the Prescription of FDG-PET for Dementing Neurodegenerative Disorders, Nestor, P. J., Altomare, D., Festari, C., Drzezga, A. E., Rivolta, J., Walker, Z., Bouwman, F. H., Orini, S., Law, I. G., et al. (2018) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-018-4035-y · Cited by 115
Yi, L., Wang, J., Jia, L., Zhao, Z., Lu, J., Li, K., Jia, J., Yong He, A., Jiang, C., & Han, Y. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044758 · Cited by 113
Toussaint, P., Perlbarg, V., Bellec, P., Désarnaud, S., Lacomblez, L., Doyon, J., Habert, M., & Benali, H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.091 · Cited by 112
Sanabria-Díaz, G., Martínez‐Montes, E., Melie‐García, L., & Initiative, T. A. D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068860 · Cited by 104
Tang, L., Wang, Y., Gong, X., Xiang, J., Zhang, Y., Qin, X., & Li, J. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1182803 · Cited by 100
Seo, S. W., Cho, S. S., Park, A., Chin, J., & Na, D. L. (2009) · Journal of Neuroimaging · doi:10.1111/j.1552-6569.2008.00292.x · Cited by 98
Brain Glucose Metabolism in Vascular White Matter Disease With Dementia
Pascual, B., Prieto, E., Arbizu, J., Martí-Climent, J. M., Olier, J., & Masdeu, J. C. (2010) · Stroke · doi:10.1161/strokeaha.110.591552 · Cited by 92
Identifying patients with obsessive–compulsive disorder using whole-brain anatomy
Soriano‐Mas, C., Pujol, J., Alonso, P., Cardoner, N., Menchón, J. M., Harrison, B. J., Deus, J., Vallejo, J., & Gaser, C. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.01.011 · Cited by 86
Fluorodeoxyglucose PET in Neurology and Psychiatry
Schöll, M., Damián, A., & Engler, H. (2014) · PET Clinics · doi:10.1016/j.cpet.2014.07.005 · Cited by 83
Panegyres, P. K., Rogers, J. M., McCarthy, M., Campbell, A., & Wu, J. S. (2009) · BMC Neurology · doi:10.1186/1471-2377-9-41 · Cited by 81
An Overview of PET Neuroimaging
Nasrallah, I. M., & Dubroff, J. G. (2013) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2013.06.003 · Cited by 84
Neuroimaging in Vascular Cognitive Impairment and Dementia: A Systematic Review
Frantellizzi, V., Pani, A., Ricci, M., Locuratolo, N., Fattapposta, F., & Vincentis, G. D. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-191046 · Cited by 70
Ma, Y., & Eidelberg, D. (2007) · Molecular Imaging and Biology · doi:10.1007/s11307-007-0085-4 · Cited by 62
Rutkowsky, J. M., Lee, L. L., Puchowicz, M., Golub, M. S., Befroy, D. E., Wilson, D. W., Anderson, S., Cline, G. W., Bini, J., Borkowski, K., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191909 · Cited by 62
Cerebral blood flow by arterial spin labeling in poststroke dementia
Firbank, M., He, J., Blamire, A. M., Singh, B., Danson, P., Kalaria, R. N., & O'Brien, J. T. (2011) · Neurology · doi:10.1212/wnl.0b013e318217e76a · Cited by 59
PET imaging in the differential diagnosis of vascular dementia
Heiss, W., & Zimmermann‐Meinzingen, S. (2012) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2012.09.023 · Cited by 59
On the multivariate nature of brain metabolic impairment in Alzheimer's disease
Salmon, É., Kerrouche, N., Perani, D., Lekeu, F., Holthoff‐Detto, V., Beuthien‐Baumann, B., Sorbi, S., Lemaire, C. F., Collette, F., & Herholz, K. (2007) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2007.06.010 · Cited by 58
Self-citationScherder, E., Eggermont, L. H. P., Visscher, C., Scheltens, P., & Swaab, D. F. (2010) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2010.08.009 · Cited by 62
Neuroimaging in aging and neurologic diseases
Risacher, S. L., & Saykin, A. J. (2019) · Handbook of clinical neurology · doi:10.1016/b978-0-12-804766-8.00012-1 · Cited by 56
Peng, S., Ma, Y., Spetsieris, P. G., Mattis, P. J., Feigin, A., DHAWAN, V. M., & Eidelberg, D. (2013) · Human Brain Mapping · doi:10.1002/hbm.22295 · Cited by 46
Regional cerebral blood flow reductions, heart failure and Alzheimer's disease
Alves, T. C. T. F., & Busatto, G. F. (2006) · Neurological Research · doi:10.1179/016164106x130416 · Cited by 45
Regularized 3D functional regression for brain image data via Haar wavelets
Wang, X., Nan, B., Zhu, J., & Koeppe, R. (2014) · The Annals of Applied Statistics · doi:10.1214/14-aoas736 · Cited by 44
Sun, Y., Ge, X., Han, X., Cao, W., Wang, Y., Ding, W., Cao, M., Zhang, Y., Xu, Q., Zhou, Y., et al. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00081 · Cited by 44
Yassine, H. N., Solomon, V. A., Thakral, A., Sheikh‐Bahaei, N., Chui, H. C., Braskie, M. N., Schneider, L. S. S., & Talbot, K. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.12474 · Cited by 42
Pagani, M., Salmaso, D., Rodriguez, G., Nardo, D., & Nobili, F. M. (2009) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2008.07.016 · Cited by 42
Abnormal Intrinsic Brain Activity Patterns in Patients with Subcortical Ischemic Vascular Dementia
Liu, C., Li, C., Yin, X., Yang, J., Zhou, D., Gui, L., & Wang, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087880 · Cited by 42
Markiewicz, P. J., Matthews, J. C., Declerck, J. M., & Herholz, K. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.020 · Cited by 40
Self-citationFunctional imaging of cognition in Alzheimer's disease using positron emission tomography
Salmon, É., Lekeu, F., Bastin, C., Garraux, G., & Collette, F. (2007) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2007.11.022 · Cited by 38
Li, C., Liu, C., Yin, X., Yang, J., Gui, L., Wei, L., & Wang, J. (2014) · Behavioural Brain Research · doi:10.1016/j.bbr.2014.08.019 · Cited by 36
Zhang, H., Sachdev, P. S., Wen, W., Crawford, J. D., Brodaty, H., Baune, B. T., Kochan, N. A., Slavin, M. J., Reppermund, S., Kang, K., et al. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.10.008 · Cited by 32
Wang, J., Chen, H., Liang, H., Wang, W., Liang, Y., Liang, Y., & Zhang, Y. (2019) · Medical Science Monitor · doi:10.12659/msm.915528 · Cited by 32
Loftus, J. R., Puri, S., & Meyers, S. P. (2023) · Insights into Imaging · doi:10.1186/s13244-022-01358-6 · Cited by 30
Su, J., Huang, Q., Ren, S., Xie, F., Zhai, Y., Guan, Y., Liu, J., & Hua, F. (2019) · Neuroscience · doi:10.1016/j.neuroscience.2019.07.048 · Cited by 27
Nuclear Medicine in Cerebrovascular Disease
Lewis, D. H., Toney, L. K., & Baron, J. (2012) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2012.06.002 · Cited by 25
Self-citationHierarchical Multivariate Covariance Analysis of Metabolic Connectivity
Carbonell, F., Charil, A., Zijdenbos, A. P., Evans, A. C., Bedell, B. J., & Initiative, A. D. N. (2014) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2014.165 · Cited by 24
Advances in PET Imaging of Degenerative, Cerebrovascular, and Traumatic Causes of Dementia
Eisenmenger, L. B., Huo, E. J., Hoffman, J. M., Minoshima, S., Matesan, M. C., Lewis, D. H., Lopresti, B. J., Mathis, C. A., Okonkwo, D. O., & Mountz, J. M. (2015) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2015.09.003 · Cited by 21
AutoEncoder-based feature ranking for Alzheimer Disease classification using PET image
Tuấn, P. M., Phan, T.-L., Adel, M., Guedj, É., & Trung, N. L. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100184 · Cited by 23
Whitwell, J. L., & Jack, C. R. (2007) · PET Clinics · doi:10.1016/j.cpet.2007.09.002 · Cited by 22
EEG in Silent Small Vessel Disease
Sheorajpanday, R. V. A., Mariën, P., Weeren, A. J. T. M., Nagels, G., Saerens, J., van Putten, M. J. A. M., & Deyn, P. P. D. (2013) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0b013e3182767d15 · Cited by 26
Abnormal intrinsic brain activity patterns in leukoaraiosis with and without cognitive impairment
Li, C., Yang, J., Yin, X., Liu, C., Zhang, L., Zhang, X., Gui, L., & Wang, J. (2015) · Behavioural Brain Research · doi:10.1016/j.bbr.2015.06.033 · Cited by 21
Tukacs, V., Mittli, D., Györffy, B. A., Hunyady-Gulyás, É., Hlatky, D., Tóth, V., Ravasz, L., Medzihradszky, F. K., Nyitrai, G., Czurkó, A., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-72868-w · Cited by 19
Whitwell, J. L., & Jack, C. R. (2007) · Neurologic Clinics · doi:10.1016/j.ncl.2007.03.003 · Cited by 20
A robust volumetric feature extraction approach for 3D neuroimaging retrieval
Liu, S., Cai, W., Wen, L., Eberl, S., FULHAM, M. J., & Feng, D. D. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5627900 · Cited by 18
Vollhardt, A., Frölich, L., Stockbauer, A., Danek, A., Schmitz, C., & Wahl, A.-S. (2025) · Neurobiology of Disease · doi:10.1016/j.nbd.2025.106845 · Cited by 18
An Overview of ICA/BSS-Based Application to Alzheimer’s Brain Signal Processing
Yang, W., Pilozzi, A. R., & Huang, X. (2021) · Biomedicines · doi:10.3390/biomedicines9040386 · Cited by 17
Vascular cognitive impairment: Advances in clinical research and management
You, T., Wang, Y., Chen, S., Dong, Q., Yu, J., & Cui, M. (2024) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003220 · Cited by 18
Philbert, S. A., Xu, J., Scholefield, M., Church, S. J., Unwin, R. D., & Cooper, G. J. S. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.822787 · Cited by 13
Albin, R. L., Koeppe, R. A., Burke, J. F., Giordani, B., Kilbourn, M. R., Gilman, S., & Frey, K. A. (2010) · Archives of Neurology · doi:10.1001/archneurol.2010.34 · Cited by 12
Colloby, S. J., Firbank, M., Pakrasi, S., Perry, E. K., Pimlott, S. L., Wyper, D., McKeith, I. G., Williams, E. D., & O'Brien, J. T. (2009) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2009.02.005 · Cited by 12
Autonomic responses to pain in aging and dementia
Plooij, B., Swaab, D. F., & Scherder, E. (2011) · Reviews in the Neurosciences · doi:10.1515/rns.2011.045 · Cited by 12
FDG PET in dementia multicenter studies and clinical trials
Herholz, K., Boecker, H., Németh, I., & Dunn, G. (2013) · Clinical and Translational Imaging · doi:10.1007/s40336-013-0018-y · Cited by 11
Self-citationBrain Structures Inoalved in Dementia
Brand, M., & Markowitsch, H. J. (2007) · Springer eBooks · doi:10.1007/978-3-211-72369-2_3 · Cited by 11
Evaluation of in vivo staging of amyloid deposition in cognitively unimpaired elderly aged 78–94
Michalowska, M. M., Herholz, K., Hinz, R., Amadi, C., McInnes, L., Anton-Rodriguez, J. M., Karikari, T. K., Blennow, K., Zetterberg, H., Ashton, N. J., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01685-6 · Cited by 11
Self-citationShepherd, T. M., & Nayak, G. K. (2019) · Topics in Magnetic Resonance Imaging · doi:10.1097/rmr.0000000000000225 · Cited by 11
Mise au point sur les démences vasculaires
Auriacombe, S., Amarenco, P., Baron, J., Ceccaldi, M. P., Dartigues, J., Lehéricy, S., Hénon, H., Hinaut, P., & Orgogozo, J. (2008) · Revue Neurologique · doi:10.1016/j.neurol.2007.06.001 · Cited by 10
Self-citationBalogiannis, S. (2007) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2007.03.003 · Cited by 10
Neuroimaging Characteristics of Hearing Loss in the Mayo Clinic Study of Aging
Khandalavala, K. R., Marinelli, J. P., Lohse, C. M., Przybelski, S. A., Petersen, R. C., Vassilaki, M. E., Vemuri, P., & Carlson, M. L. (2023) · Otolaryngology · doi:10.1002/ohn.583 · Cited by 9
Structure-Function–Based Quantitative Brain Image Analysis
Zaidi, H., Montandon, M., & Assal, F. (2010) · PET Clinics · doi:10.1016/j.cpet.2010.02.003 · Cited by 9
Neuroimaging Characteristics of Frailty Status in Patients with Alzheimer’s Disease
Hirose, D., Shimizu, S., Hirao, K., Ogawa, Y., Sato, T., Kaneko, Y., Takenoshita, N., Namioka, N., Fukasawa, R., Umahara, T., et al. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-180701 · Cited by 8
Park, S. Y., Yoon, H., Lee, N., Oh, J. K., Yoo, I. R., Kim, S. H., & Chung, Y. A. (2014) · Nuclear Medicine and Molecular Imaging · doi:10.1007/s13139-014-0287-z · Cited by 8
Burneo, J. G., Jenkins, M. E., & Demaerschalk, B. M. (2011) · Springer eBooks · doi:10.1007/978-0-387-88555-1 · Cited by 8
Bousser, M., & Chabriat, H. (2012) · Bulletin de l Académie Nationale de Médecine · doi:10.1016/s0001-4079(19)31836-9 · Cited by 6
Molecular Imaging with PET in the Assessment of Vascular Dementia and Cerebrovascular Disease
Patil, S., Patel, D., Kata, R., Teichner, E. M., Subtirelu, R. C., Ayubcha, C., Werner, T. J., & Alavi, A. A. (2024) · PET Clinics · doi:10.1016/j.cpet.2024.09.001 · Cited by 6
Principal Component Analysis Applied to SPECT and PET Data of Dementia Patients – A Review
Stuhler, E., & Merhof, D. (2012) · InTech eBooks · doi:10.5772/38010 · Cited by 5
Current role of 18F-FDG-PET in the differential diagnosis of the main forms of dementia
Guillén, E. F., Rosales, J. J., Coscia, D. L., Grisanti, F., Riverol, M., & Arbizu, J. (2020) · Clinical and Translational Imaging · doi:10.1007/s40336-020-00366-0 · Cited by 6
Global brain glucose uptake on 18F-FDG-PET/CT is influenced by chronic cardiovascular risk
Borja, A. J., Hancin, E. C., Zhang, V., Koa, B., Bhattaru, A., Rojulpote, C., Detchou, D. K. E. D., Aly, M., Kaghazchi, F., Gerke, O., et al. (2020) · Nuclear Medicine Communications · doi:10.1097/mnm.0000000000001349 · Cited by 6
Metabolic connectivity in Alzheimer’s diseases
Rahmani, F., Moghaddam, H. S., Rahmani, M., & Aarabi, M. H. (2020) · Clinical and Translational Imaging · doi:10.1007/s40336-020-00371-3 · Cited by 6
Distinct changes in brain metabolism in patients with dementia and hearing loss
Han, J. H., Lee, S., Bae, S. H., Yun, M., Ye, B. S., & Jung, J. (2024) · Brain and Behavior · doi:10.1002/brb3.3374 · Cited by 4
Chapter 4 Neuroimaging of Cognitive Impairments in Vascular Disease
Perri, C. D., Dalaker, T. O., Beyer, M. K., & Zivadinov, R. (2009) · International review of neurobiology · doi:10.1016/s0074-7742(09)00404-8 · Cited by 4
SPECT and PET in Vascular Dementia
Zheng, Y., & Zhou, Z. (2020) · Springer eBooks · doi:10.1007/978-3-030-53168-3_18 · Cited by 4
Parcellation of fMRI Datasets with ICA and PLS - A Data Driven ApproachPreprint
Ji, Y., Hervé, P., Aickelin, U., & Pitiot, A. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2830354 · Cited by 4
The enigma of vascular dementia: current state and emerging perspectives
Jellinger, K. A. (2026) · Journal of Neural Transmission · doi:10.1007/s00702-026-03104-4 · Cited by 3
Pan-cerebral sodium elevations in vascular dementia: Evidence for disturbed brain-sodium homeostasis
Philbert, S. A., Xu, J., Church, S. J., Unwin, R. D., Roncaroli, F., & Cooper, G. J. S. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.926463 · Cited by 4
Ding, J., Zhang, H., Hua, B., Feng, C., Yang, M., Ding, X., & Yang, C. (2023) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2023.05.011 · Cited by 3
Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach
Ji, Y., Hervé, P., Aickelin, U., & Pitiot, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04268-3_121 · Cited by 3
Changing landscapes in the neuroimaging of dementia
Ranganathan, L. N., Guhan, R., Shivaraman, M. A., LeninSankar, P., Srinivasan, A. V., Suriyakumar, G., & Periakaruppan, A. L. (2018) · Annals of Indian Academy of Neurology · doi:10.4103/aian.aian_48_18 · Cited by 3
15 Differential Diagnosis of Dementia Using Functional Neuroimaging
Salmon, É., Collette, F., & Garraux, G. (2009) · doi:10.1093/acprof:oso/9780195328875.003.0015 · Cited by 3
Whitwell, J. L., & Jack, C. R. (2007) · CONTINUUM Lifelong Learning in Neurology · doi:10.1212/01.con.0000267241.37992.30 · Cited by 3
Early diagnosis of Alzheimer’s disease: potential of 18F-FDG PET as a biomarker of neurodegeneration
Emelin, А. Y., Litvinenko, I., Lobzin, V. Y., Lupanov, I. A., Kolmakova, K. A., Дынин, П. С., & Boykov, I. V. (2024) · Russian Military Medical Academy Reports · doi:10.17816/rmmar636520 · Cited by 2
Oveisgharan, S., & Hachinski, V. (2015) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/1533317515619480 · Cited by 2
Donepezil for severe Alzheimer's disease
Kida, Y., & Sato, T. (2006) · The Lancet · doi:10.1016/s0140-6736(06)69096-x · Cited by 2
Tripathi, S. M., Shiells, H., Waymont, J. M. J., Staff, R. T., Schelter, B. O., Bentham, P., Wischik, C. M., & Murray, A. D. (2026) · Current Alzheimer Research · doi:10.2174/0115672050399596251106033110 · Cited by 2
Tuấn, P. M., Le, Q. A., Thanh, L. T., Phan, T.-L., Adel, M., Bourennane, S., Guedj, É., Parlak, İ. B., Nagels, G., & Trung, N. L. (2026) · APSIPA Transactions on Signal and Information Processing · doi:10.1108/atsip-05-2026-0037 · Cited by 1
Functional Imaging Studies of Human Cognition Using Positron Emission Tomography
Misciagna, S. (2013) · InTech eBooks · doi:10.5772/57124 · Cited by 2
Kim, T., Chung, Y., Song, I., & Lee, K. (2014) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22107 · Cited by 1
Emelin, А. Y., Boykov, I. V., Lobzin, V. Y., Kolmakova, K. A., Naumov, K. M., Dynin, P. S., & Lupanov, I. A. (2023) · Russian Military Medical Academy Reports · doi:10.17816/rmmar501788 · Cited by 1
Emelin, А. Y., Odinak, M. M., Lobzin, V. Y., & Kolmakova, K. A. (2024) · S S Korsakov Journal of Neurology and Psychiatry · doi:10.17116/jnevro202412404217 · Cited by 1
Aiello, M., Cavaliere, C., Inglese, M., Monti, S., & Salvatore, M. (2016) · Springer eBooks · doi:10.1007/978-3-319-31614-7_6 · Cited by 1
18F-FDG-PET/CT (FDG-PET) in Neurodegenerative Disease
Capitanio, S., Bauckneht, M., Arnaldi, D., Massa, F., Meli, R., Ceriani, V., Nobili, F. M., & Morbelli, S. D. (2019) · Springer eBooks · doi:10.1007/978-3-030-01523-7_5 · Cited by 1
Metadata
- Authors
- Kerrouche, N., Herholz, K., Mielke, R., Holthoff, V., & Baron, J.-C.
- Affiliations
- Inserm (FR)
- University of Cologne (DE)
- Department of Psychiatry and Psychotherapy, Dresden University of Technology, Dresden, Germany
- Corresponding author
- Baron, Jean-Claude
- Venue
- Journal of Cerebral Blood Flow & Metabolism
- Publisher
- SAGE Publications
- Published
- 2006-09
- Volume
- 26
- Issue
- 9
- Pages
- 1213-1221
- ISSN
- 0271-678X (ISSN-L)
- 1559-7016
- OpenAlex
- W2101670636
- Cited by
- 120
- References
- 51
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 8 · 2023: 6 · 2022: 3 · 2021: 4 · 2020: 7 · 2019: 6 · 2018: 5 · 2017: 3 · 2016: 3 · 2015: 4 · 2014: 9 · 2013: 7 · 2012: 10
- FWCI
- 5.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Cerebral Blood Flow & Metabolism (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- Kölner Universitäts PublikationsServer (Universität zu Köln) (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Neurology › Neurological Disease Mechanisms and Treatments
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- vascular dementia, Alzheimer's disease, 18F-FDG PET, canonical variate analysis, brain glucose metabolism, differential diagnosis of dementia, functional brain networks, cognitive impairment
- MeSH terms
- Aged, Alzheimer Disease, Basal Ganglia, Cerebellum, Cerebral Cortex, Data Interpretation, Statistical, Diagnosis, Differential, Female, Gyrus Cinguli, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Neuropsychological Tests, Dementia, Vascular, Multivariate Analysis, Radiopharmaceuticals, Fluorodeoxyglucose F18, Principal Component Analysis, Positron-Emission Tomography
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
