The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.
Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: A longitudinal MRI study
Abstract
Capturing the dynamics of gray matter (GM) atrophy in relation to the conversion from mild cognitive impairment (MCI) to clinically probable Alzheimer's disease (AD) would be of considerable interest. In this prospective study we have used a novel longitudinal voxel-based method to map the progression of GM loss in MCI patients over time and compared converters to non-converters. Eighteen amnestic MCI patients were followed-up for a predefined fixed period of 18 months and conversion was judged according to NINCDS-ADRDA criteria for probable AD. Each patient underwent a high-resolution T1-weighted volume MRI scan both at entry in the study and 18 months later. We used an optimal VBM protocol to compare baseline imaging data of converters to those of non-converters. Moreover, to map GM loss from baseline to follow-up assessment, we used a modified voxel-based morphometry (VBM) procedure specially designed for longitudinal studies. At the end of the follow-up period, seven patients had converted to probable AD. Areas of lower baseline GM value in converters mainly included the hippocampus, parahippocampal cortex, and lingual and fusiform gyri. Regions of significant GM loss over the 18-month follow-up period common to both converters and non-converters included the temporal neocortex, parahippocampal cortex, orbitofrontal and inferior parietal areas, and the left thalamus. However, there was significantly greater GM loss in converters relative to non-converters in the hippocampal area, inferior and middle temporal gyrus, posterior cingulate, and precuneus. This accelerated atrophy may result from both neurofibrillary tangles accumulation and parallel pathological processes such as functional alteration in the posterior cingulate. The ability to longitudinally assess GM changes in MCI offers new perspectives to better understand the pathological processes underlying AD and to monitor the effects of treatment on brain structure.
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“The role of thalamic structure and function in AD appears complex and perhaps nonlinear, as thalamic atrophy in MCI [48] and hypometabolism in APOE4 carriers [49] have been observed, whereas others have reported increased thalamic volume [50] and hypermetabolism with amyloid pathology [51] among APOE4 carriers.”
From Effects of APOE2 and APOE4 on brain microstructure in older adults: modification by age, sex, and cognitive status (2024) · cites this paper as [48]
Citations
Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282
The assessment and analysis of handedness: The Edinburgh inventory
Oldfield, R. C. (1971) · Neuropsychologia · doi:10.1016/0028-3932(71)90067-4 · Cited by 35,900
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
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002) · NeuroImage · doi:10.1006/nimg.2001.0978 · Cited by 16,319
Self-citationVoxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
Current concepts in mild cognitive impairment
Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., Ritchie, K., Rossor, M., Thal, L., & Winblad, B. (2001) · Archives of Neurology · doi:10.1001/archneur.58.12.1985 · Cited by 4,781
A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001) · NeuroImage · doi:10.1006/nimg.2001.0786 · Cited by 4,561
Valid conjunction inference with the minimum statistic
Nichols, T. E., Brett, M., Andersson, J., Wager, T. D., & Poline, J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.005 · Cited by 1,854
Stroop Color and Word Test: Manual for Clinical and Experimental Uses.
Golden, C. J., Golden, C. J., & Golden, C. (1978) · NSUWorks (Nova Southeastern University) · Cited by 1,814
Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease
Minoshima, S., Giordani, B., Berent, S., Frey, K. A., Foster, N. L., & Kuhl, D. E. (1997) · Annals of Neurology · doi:10.1002/ana.410420114 · Cited by 1,802
Research works printed but not linked: 1
- Yamada, 1996, Longitudinal neuropsychological and quantitative MRI assessments in early Alzheimer's disease, Alzheimer's Res., 2, 29
Cited by
Sorted by citations
Items 501–533 of 533
Saccades in Alzheimer’s disease: an MRI study.
Wright, S. L. (2011) · Otago University Research Archive (University of Otago)
Hartmann, F., Reinhardt, J., Stippich, C., & Krumm, S. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10214869
Ribas, R. H. (2012) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
Progression of Atrophy in Alzheimer’s Disease and Related Disorders
Whitwell, J. L. (2013) · Springer eBooks · doi:10.1007/978-1-4614-7264-3_17
Frisoni, G. B. (2008) · Springer eBooks · doi:10.1007/978-88-470-0391-0_13
Exploring the ATN classification system using Brain MorphologyPreprint
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2022) · medRxiv · doi:10.1101/2022.06.01.22275839
Population clustering of structural brain aging and its association with brain developmentPreprint
Duan, H., Shi, R., Kang, J., Banaschewski, T., Bokde, A. L. W., Büchel, C., Desrivières, S., Flor, H., Grigis, A., Garavan, H., et al. (2024) · eLife · doi:10.7554/elife.94970.1
Regional brain aging patterns reveal disease-specific pathways of neurodegenerationPreprint
Vakli, P., Weiss, B., Keresztes, A., Hermann, P., Initiative, A. D. N., Cole, J. H., & Vidnyánszky, Z. (2025) · medRxiv · doi:10.1101/2025.11.12.25339989
Other Neurodegenerative Conditions
Mascalchi, M. (2008) · Springer eBooks · doi:10.1007/978-88-470-0391-0_14
Additional file 4 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574865
Reas, E. T., Triebswetter, C., Banks, S. J., & McEvoy, L. K. (2024) · Figshare · doi:10.6084/m9.figshare.24987857
Additional file 5 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574868
Additional file 3 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maass, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2023) · Figshare · doi:10.6084/m9.figshare.26574862
Additional file 6 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574871
Additional file 10 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maass, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574883
Additional file 8 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574877
Additional file 1 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574856
Additional file 2 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maass, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574859
Additional file 9 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574880
Additional file 7 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574874
Contributions à l’apprentissage automatique pour l’analyse d’images cérébrales anatomiques
Cuingnet, R. (2011) · theses.fr (ABES) · doi:10.70675/3f4fc836zc450z4adezb36azc27b298384de
Dautricourt, S. (2021) · theses.fr (ABES) · doi:10.70675/63472a25z31eaz4ebczaebdzd8d2e061b4b8
Li, M., Guo, J., Wu, J., Deng, Z., Qi, W., Li, X., Zheng, Z., Lu, G., Qiu, Y., Huang, H., et al. (2026) · Journal of Alzheimer s Disease Reports · doi:10.1177/25424823261445998
Metadata
- Authors
- Chételat, G., Landeau, B., Eustache, F., Mézenge, F., Viader, F., de la Sayette, V., Desgranges, B., & Baron, J.-C.
- Affiliations
- Inserm (FR)
- Centre National de la Recherche Scientifique (FR)
- University of Cambridge (GB)
- Corresponding author
- Chételat, G.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2005-10
- Volume
- 27
- Issue
- 4
- Pages
- 934-946
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1970300450
- Cited by
- 526
- References
- 49
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 19 · 2023: 7 · 2022: 16 · 2021: 19 · 2020: 15 · 2019: 17 · 2018: 21 · 2017: 25 · 2016: 28 · 2015: 24 · 2014: 31 · 2013: 31 · 2012: 35
- FWCI
- 18.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Institut National de la Santé et de la Recherche Médicale
- Institut de Recherches Servier
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- mild cognitive impairment, conversion to Alzheimer's disease, gray matter atrophy, voxel-based morphometry, hippocampus, parahippocampal cortex, posterior cingulate cortex, precuneus, temporal lobe atrophy, thalamus, neurofibrillary tangles, treatment monitoring
- MeSH terms
- Aged, Alzheimer Disease, Amnesia, Atrophy, Brain, Brain Mapping, Cognition Disorders, Female, Humans, Image Interpretation, Computer-Assisted, Functional Laterality, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, Disease Progression
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