Cited by 827Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsCancer-related cognitive impairment studies

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Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD

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Jack, C. R., Shiung, M. M., Gunter, J. L., O’Brien, P. C., Weigand, S. D., Knopman, D. S., Boeve, B. F., Ivnik, R. J., Smith, G. E., Cha, R. H., Tangalos, E. G., Petersen, R. C.·2004·Neurology

DOI 10.1212/01.wnl.0000110315.26026.efPMC PMC2730165

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Abstract

OBJECTIVE: To correlate different methods of measuring rates of brain atrophy from serial MRI with corresponding clinical change in normal elderly subjects, patients with mild cognitive impairment (MCI), and patients with probable Alzheimer disease (AD). METHODS: One hundred sixty subjects were recruited from the Mayo Clinic Alzheimer's Disease Research Center and Alzheimer's Disease Patient Registry Studies. At baseline, 55 subjects were cognitively normal, 41 met criteria for MCI, and 64 met criteria for AD. Each subject underwent an MRI examination of the brain at the time of the baseline clinical assessment and then again at the time of a follow-up clinical assessment, 1 to 5 years later. The annualized changes in volume of four structures were measured from the serial MRI studies: hippocampus, entorhinal cortex, whole brain, and ventricle. Rates of change on several cognitive tests/rating scales were also assessed. Subjects who were classified as normal or MCI at baseline could either remain stable or convert to a lower-functioning group. AD subjects were dichotomized into slow vs fast progressors. RESULTS: All four atrophy rates were greater among normal subjects who converted to MCI or AD than among those who remained stable, greater among MCI subjects who converted to AD than among those who remained stable, and greater among fast than slow AD progressors. In general, atrophy on MRI was detected more consistently than decline on specific cognitive tests/rating scales. With one exception, no differences were found among the four MRI rate measures in the strength of the correlation with clinical deterioration at different stages of the disease. CONCLUSIONS: These data support the use of rates of change from serial MRI studies in addition to standard clinical/psychometric measures as surrogate markers of disease progression in AD. Estimated sample sizes required to power a therapeutic trial in MCI were an order of magnitude less for MRI than for change measures based on cognitive tests/rating scales.

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Thompson, P. M., & Apostolova, L. G. (2007) · British Journal of Radiology · doi:10.1259/bjr/20005470 · Cited by 68
“When conventional volumetric measures of the hippocampus are used, Jack et al [82] estimated that in each arm of a therapeutic trial, only 21 subjects would be required to detect a 50% reduction in the rate of decline if hippocampal volumes were used as the outcome measure.”
Method
Ventricular maps in 804 ADNI subjects: correlations with CSF biomarkers and clinical decline
Chou, Y.-Y., Leporé, N., Saharan, P., Madsen, S. K., Hua, X., Jack, C. R., Shaw, L. M. J., Trojanowski, J. Q., Weiner, M. W., Toga, A. W., et al. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.05.001 · Cited by 66
“There is great interest in determining which MRI-based measures link best with standard cognitive assessments (Jack et al., 2004) and which sets of measures can optimally predict future clinical decline (Kohannim et al., 2010; Walhovd et al., 2010), often defined as conversion to AD over a specific…”
Self-citation
3D Mapping of Mini-mental State Examination Performance in Clinical and Preclinical Alzheimer Disease
Apostolova, L. G., Lu, P. H., Rogers, S., Dutton, R. A., Hayashi, K. M., Toga, A. W., Cummings, J. L., & Thompson, P. M. (2006) · Alzheimer Disease & Associated Disorders · doi:10.1097/01.wad.0000213857.89613.10 · Cited by 65
“We investigated the structural neuroimaging correlates of MMSE performance in patients with clinical and preclinical AD.”
MethodInfluential
High-field Magnetic Resonance Imaging of Brain Iron in Alzheimer Disease
Schenck, J. F., Zimmerman, E. A., Li, Z., Adak, S., Saha, A., Tandon, R., Fish, K. M., Belden, C. J., Gillen, R. W., Barba, A., et al. (2006) · Topics in Magnetic Resonance Imaging · doi:10.1097/01.rmr.0000245455.59912.40 · Cited by 65
Evaluation of treatment effects in Alzheimer's and other neurodegenerative diseases by MRI and MRS
Mueller, S. G., Schuff, N. W., & Weiner, M. W. (2006) · NMR in Biomedicine · doi:10.1002/nbm.1062 · Cited by 66
“hippocampus (51), would allow one to use volumetric measures even in the busy daily clinical routine.”
Background
White matter hyperintensities in autopsy-confirmed frontotemporal lobar degeneration and Alzheimer’s disease
Desmarais, P., Gao, A. F., Lanctôt, K. L., Rogaeva, E. A., Ramirez, J., Herrmann, N., Stuss, D. T., Black, S. E., Keith, J. L., & Masellis, M. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00869-6 · Cited by 63
Hippocampal Volume in Older Adults at Risk of Cognitive Decline: The Role of Sleep, Vascular Risk, and Depression
Elcombe, E. L., Lagopoulos, J., Duffy, S. L., Lewis, S. J. G., Norrie, L. M., Hickie, I. B., & Naismith, S. L. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-142016 · Cited by 66
The diffeomorphometry of regional shape change rates and its relevance to cognitive deterioration in mild cognitive impairment and A lzheimer's disease
Tang, X., Holland, D., Dale, A. M., Laurent Younes, B., Miller, M. I., & Initiative, A. D. N. (2015) · Human Brain Mapping · doi:10.1002/hbm.22758 · Cited by 67
“This observation is consistent with existing findings on the correlation between temporal horn change rates and rates of cognitive declines [Jack et al., 2004; Thompson et al., 2004] as well as our observation that the group differences in the ventricular expansion rates are much bigger than those…”
BackgroundResultInfluential
Can functional magnetic resonance imaging improve success rates in central nervous system drug discovery?
Borsook, D., Hargreaves, R. J., & Becerra, L. R. (2011) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2011.584529 · Cited by 63
“Brain atrophy Volumes of hippocampus entorhinal cortex, ventricle and whole brain [154]”
Background
Influence of APOE Genotype on Hippocampal Atrophy over Time - An N=1925 Surface-Based ADNI Study
Li, B., Shi, J., Gutman, B. A., Baxter, L. C., Thompson, P. M., Caselli, R. J., Wang, Y., & Initiative, A. D. N. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152901 · Cited by 67
Brain Mapping as a Tool to Study Neurodegeneration
Apostolova, L. G., & Thompson, P. M. (2007) · Neurotherapeutics · doi:10.1016/j.nurt.2007.05.009 · Cited by 63
“Ventricular enlargement appears to have a higher predictive value in the earlier transition stages as opposed to the later stage— that is, from normal cognition to MCI or AD and less so from MCI to AD.(13,42) VBM has also been used to reveal the spatial profile of aging and gender effects in normal subjects,(28,43,44)…”
Influential
Alzheimer’s disease biomarkers and their current use in clinical research and practice
Hunter, T., Santos, L. E., Tovar-Moll, F. F., & Felice, F. G. D. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02709-z · Cited by 78
“The rate of hippocampal atrophy accelerates at a greater rate than cortical atrophy and a deceleration of cortical atrophy has been observed in later AD stages [15, 16].”
Beta amyloid, tau, neuroimaging, and cognition: sequence modeling of biomarkers for Alzheimer’s Disease
Initiative, A. D. N., Han, S. D., Gruhl, J., Beckett, L. A., Dodge, H. H., Stricker, N. H., Farias, S. T., & Mungas, D. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9177-0 · Cited by 62
“Evidence also suggests that FDG-PET changes (de Leon et al., 2001) and even structural brain changes such as hippocampal atrophy and increases in ventricular size (Jack et al., 2004) may precede cognitive changes in AD.”
Background
Dementia Update 2005
Morris, J. C. (2005) · Alzheimer Disease & Associated Disorders · doi:10.1097/01.wad.0000167923.56275.d8 · Cited by 61
Ventricular and Periventricular Anomalies in the Aging and Cognitively Impaired Brain
Todd, K. L., Brighton, T., Norton, E. S., Schick, S., Elkins, W., Pletniková, O., Fortinsky, R. H., Troncoso, J. C., Molfese, P. J., Resnick, S. M., et al. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00445 · Cited by 62
“…correlate more strongly with cognitive decline and are more sensitive to varying degrees of cognitive impairment than medial temporal lobe (MTL) atrophy rates, which are currently being used as a diagnostic and monitoring tool (Bradley et al., 2002; Jack et al., 2004; Ferrarini et al., 2006).”
Background
Positron Emission Tomography Diagnosis of Alzheimer's Disease
Coleman, R. E. (2005) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2005.09.007 · Cited by 62
Performance reserves in brain-imaging-based phenotype prediction
Schulz, M.-A., Bzdok, D., Haufe, S., Haynes, J., & Ritter, K. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113597 · Cited by 62
“Significant progress has been made in some areas of application, for instance in the automated detection of brain atrophy 1–3 and the segmentation of brain lesions.”
Method
Cortical Thickness Analysis to Detect Progressive Mild Cognitive Impairment: A Reference to Alzheimer’s Disease
Julkunen, V., Niskanen, E. I., Muehlboeck, S., Pihlajamaki, M. M., Könönen, M., Hallikainen, M., Kivipelto, M., Tervo, S., Vanninen, R. L., Evans, A. C., et al. (2009) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000256274 · Cited by 65
Functional network resilience to pathology in presymptomatic genetic frontotemporal dementia
Rittman, T., Borchert, R. J., Jones, S., Swieten, J. C. V., Borroni, B., Galimberti, D., Masellis, M., Tartaglia, M. C., Graff, C., Tagliavini, F., et al. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.12.009 · Cited by 62
Reproducibility of brain tissue volumes in longitudinal studies: Effects of changes in signal-to-noise ratio and scanner software
Shuter, B., Yeh, I. B., Graham, S., Au, C., & Wang, S. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.02.003 · Cited by 59
Estimating sample sizes for predementia Alzheimer's trials based on the Alzheimer's Disease Neuroimaging Initiative
Grill, J. D., Di, L., Lu, P. H., Lee, C., Ringman, J., Apostolova, L. G., Chow, N., Kohannim, O., Cummings, J. L., Thompson, P. M., et al. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.03.006 · Cited by 58
“Using AD biomarkers as outcome measures in AD trials can similarly improve trial efficiency [9,27,28,31,35,57,59].”
Identification of Alzheimer’s Disease and Mild Cognitive Impairment Using Networks Constructed Based on Multiple Morphological Brain Features
Zheng, W., Yao, Z., Xie, Y., Fan, J., & Hu, B. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.06.004 · Cited by 63
Neurotoxic Saboteurs: Straws that Break the Hippo’s (Hippocampus) Back Drive Cognitive Impairment and Alzheimer’s Disease
Daulatzai, M. A. (2013) · Neurotoxicity Research · doi:10.1007/s12640-013-9407-2 · Cited by 56
Comparison study of amyloid PET and voxel-based morphometry analysis in mild cognitive impairment and Alzheimer's disease
Waragai, M., Okamura, N., Furukawa, K., Tashiro, M., Furumoto, S., Funaki, Y., Kato, M., Iwata, R., Yanai, K., Kudo, Y., et al. (2009) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2009.06.005 · Cited by 59
“It has been reported that the degree or rate of change of cerebral atrophy as measured by MRI analysis is closely related to the clinical progression of dementia [29,30].”
Background
Neuropsychological correlates of whole brain atrophy in Alzheimer's disease
Schott, J. M., Crutch, S. J., Frost, C. D., Warrington, E. K., Rossor, M. N., & Fox, N. C. (2008) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2008.02.015 · Cited by 60
Structural Neuroimaging in the Detection and Prognosis of Pre-Clinical and Early AD
Fennema‐Notestine, C., McEvoy, L. K., Hagler, D. J., Jacobson, M. W., Dale, A. M., & Initiative, T. A. D. N. (2009) · Behavioural Neurology · doi:10.1155/2009/698156 · Cited by 58
Variations in cortical thickness with dementia severity in Alzheimer's disease
Im, K., Lee, J. M., Seo, S. W., Yoon, U., Kim, S. T., Kim, Y., Kim, S. I., & Na, D. L. (2008) · Neuroscience Letters · doi:10.1016/j.neulet.2008.03.032 · Cited by 57
Relationships between retinal layer thickness and brain volumes in the UK Biobank cohort
Chua, S. Y. L., Lascaratos, G., Atan, D., Zhang, B., Reisman, C. A., Khaw, P. T., Smith, S. M., Matthews, P. M., Petzold, A., Strouthidis, N. G., et al. (2020) · European Journal of Neurology · doi:10.1111/ene.14706 · Cited by 56
Rates of brain atrophy over time in autopsy-proven frontotemporal dementia and Alzheimer disease
Whitwell, J. L., Jack, C. R., Pankratz, V. S., Parisi, J. E., Knopman, D. S., Boeve, B. F., Petersen, R. C., Dickson, D. W., & Josephs, K. A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.10.001 · Cited by 55
Self-citation
Comparison of phantom and registration scaling corrections using the ADNI cohort
Clarkson, M. J., Sebastien Ourselin, A., Nielsen, C. F., Leung, K. K., Barnes, J., Whitwell, J. L., Gunter, J. L., Hill, D., Weiner, M. W., Jack, C. R., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.045 · Cited by 54
“, 2006) although this can depend on the rate of progression of AD, where faster progressors (Jack et al., 2004) and more severely cognitively impaired individuals have higher brain atrophy rates (Mungas et al.”
Self-citation
Predicting Progression of Alzheimer’s Disease Using Ordinal Regression
Doyle, O., Westman, E., Marquand, A. F., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Soininen, H. S., Lovestone, S., Williams, S., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105542 · Cited by 56
Positron Emission Tomography Scans Obtained for the Evaluation of Cognitive Dysfunction
Silverman, D. H. S., Mosconi, L., Ercoli, L. M., Chen, W., & Small, G. W. (2008) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2008.02.006 · Cited by 55
Neuroimaging and its Relevance to Understanding Pathways Linking Diabetes and Cognitive Dysfunction
Moran, C., Beare, R. J., Phan, T. G., STARKSTEIN, S. E., Bruce, D. G., Mizrahi, R., & Srikanth, V. K. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-161166 · Cited by 53
“memory decline [7, 8] and conversion from MCI to”
Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns
Kwak, K., Giovanello, K. S., Bozoki, A., Styner, M., & Dayan, E. (2021) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100467 · Cited by 47
“cortex.(26,53,54) These findings are also consistent with recent findings on thepossible neuroprotective role of redundancy in the hippocampus(55) and other large-scale brain networks,(56) which may contribute to the observed differences between the subgroups.”
Result
Alzheimer's disease: rapid and slow progression
Thalhauser, C. J., & Komarova, N. L. (2011) · Journal of The Royal Society Interface · doi:10.1098/rsif.2011.0134 · Cited by 57
Neuroimaging biomarkers for clinical trials in atypical parkinsonian disorders: Proposal for a Neuroimaging Biomarker Utility System
van Eimeren, T., Antonini, A., Berg, D., Bohnen, N. I., Ceravolo, R., Drzezga, A. E., Höglinger, G. U., Higuchi, M., Lehéricy, S., Lewis, S. J. G., et al. (2019) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2019.01.011 · Cited by 53
Relationship Between CSF Biomarkers of Alzheimer's Disease and Rates of Regional Cortical Thinning in ADNI Data
Tosun, D., Initiative1, T. A. D. N., Schuff, N. W., Shaw, L. M. J., Trojanowski, J. Q., & Weiner, M. W. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-0006 · Cited by 51
Mild cognitive impairment in adult: A neuropsychological review
Chandra, M., Yanhong, O., & Venkatesh, D. (2013) · Annals of Indian Academy of Neurology · doi:10.4103/0972-2327.116907 · Cited by 57
“[76] In addition, the rate of reduction in the volume of HC and entorhinal cortex is more rapid in MCI than in NC. [77,78] Follow‑up studies using voxel‑based morphometry (VBM) measurement on serial MRI scans in subjects with MCI have revealed that loss of grey matter volumes is seen in anterior HC, amygdale, and ento…”
Normal Pressure Hydrocephalus
Graff‐Radford, N. R. (2007) · Neurologic Clinics · doi:10.1016/j.ncl.2007.03.004 · Cited by 52
MRI-based brain atrophy rates in ADNI phase 2: acceleration and enrichment considerations for clinical trials
Hua, X., Ching, C. R. K., Mezher, A. F., Gutman, B. A., Hibar, D. P., Bhatt, P. R., Leow, A., Jack, C. R., Bernstein, M. A., Weiner, M. W., et al. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.09.018 · Cited by 52
“…using positron emission tomography (PET) (Hardy and Selkoe, 2002; Jagust et al., 2010), tau imaging (Maruyama et al., 2013; Villemagne et al., 2014), and brain atrophy measured with structural magnetic resonance imaging (MRI) (Fox et al., 2000; Fox and Freeborough, 1997; Jack et al., 1998, 2004).”
MethodSelf-citation
Effects of aerobic exercise on cognition and hippocampal volume in Alzheimer’s disease: study protocol of a randomized controlled trial (The FIT-AD trial)
Yu, F., Bronas, U. G., Konety, S., Nelson, N. W., Dysken, M. W., Jack, C. R., Wyman, J. F., Vock, D. M., & Smith, G. E. (2014) · Trials · doi:10.1186/1745-6215-15-394 · Cited by 53
“BSI measures of brain and ventricle are global measures that do not require any a priori assumptions about where in the brain the effect of the intervention might be manifested [60,61].”
BackgroundSelf-citation
Fully-automated approach to hippocampus segmentation using a graph-cuts algorithm combined with atlas-based segmentation and morphological opening
Kwak, K., Yoon, U., Lee, D.-K., Kim, G. H., Seo, S. W., Na, D. L., Shim, H., & Lee, J. (2013) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2013.04.008 · Cited by 51
Usefulness of data from magnetic resonance imaging to improve prediction of dementia: population based cohort study
Stephan, B. C. M., Tzourio, C., Auriacombe, S., Amieva, H., Dufouil, C., Alpérovitch, A., & Kurth, T. (2015) · BMJ · doi:10.1136/bmj.h2863 · Cited by 53
Entorhinal and Transentorhinal Atrophy in Preclinical Alzheimer's Disease
Kulason, S., Xu, E., Tward, D. J., Bakker, A., Albert, M., Laurent Younes, B., & Miller, M. I. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00804 · Cited by 51
“More recent MRI studies have focused on evidence of atrophy that precede clinical symptoms (Jack et al., 2004; Csernansky et al., 2005; den Heijer et al., 2006; Apostolova et al., 2010; Dickerson et al., 2011; Miller et al., 2013; Soldan et al., 2015; Pettigrew et al., 2016), often detecting these…”
Background
The Value of Hippocampal and Temporal Horn Volumes and Rates of Change in Predicting Future Conversion to AD
Macdonald, K. E., Bartlett, J. W., Leung, K. K., Sebastien Ourselin, A., & Barnes, J. (2012) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0b013e318260a79a · Cited by 51
Characterization of Cerebral Damage in a Monkey Model of Alzheimer’s Disease Induced by Intracerebroventricular Injection of Streptozotocin
Yeo, H.-G., Lee, Y., Jeon, C., Jeong, K.-J., Jin, Y. B., Kang, P., Kim, S., Kim, J.-S., Huh, J., Kim, Y., et al. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-143222 · Cited by 50
“Significant ventricular enlargement has been clinically associated with AD as a strong biomarker of disease progression [35, 38, 39], due to AD subjects exhibiting significant ventricle enlargement progress much faster than normal elderly and mild cognitively impaired subjects [35, 40, 41].”
Background

Authors: Jack, C. R., Shiung, M. M., Gunter, J. L., O’Brien, P. C., Weigand, S. D., Knopman, D. S., Boeve, B. F., Ivnik, R. J., Smith, G. E., Cha, R. H., Tangalos, E. G., & Petersen, R. C.
Affiliations: Mayo Clinic (US)
ORCID: Jack, C. R.
Gunter, J. L.
O’Brien, P. C.
Weigand, S. D.
Knopman, D. S.
Boeve, B. F.
Smith, G. E.
Petersen, R. C.
Venue: Neurology
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2004-02-24
Volume: 62
Issue: 4
Pages: 591-600
ISSN: 0028-3878 (ISSN-L)
1526-632X
DOI: 10.1212/01.wnl.0000110315.26026.ef
PMC: PMC2730165
OpenAlex: W2005133023
Cited by: 827
References: 41
Citations by year: 2026: 17 · 2025: 19 · 2024: 21 · 2023: 25 · 2022: 20 · 2021: 36 · 2020: 27 · 2019: 38 · 2018: 36 · 2017: 37 · 2016: 33 · 2015: 39 · 2014: 41 · 2013: 48 · 2012: 38
FWCI: 28.59
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Neurology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute on Aging (AG-06786, AG 16574, U01AG006786, R01AG011378, AG-11378, U19AG010483, R37AG011378, P50AG016574)
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
Health Sciences › Medicine › Oncology › Cancer-related cognitive impairment studies
Keywords: Alzheimer's disease, brain atrophy rate, serial MRI, hippocampal atrophy, entorhinal cortex atrophy, whole brain atrophy, ventriculomegaly, mild cognitive impairment, disease progression, surrogate markers, cognitive decline, trial size, MCI conversion
MeSH terms: Aged, Aged, 80 and over, Alzheimer Disease, Apolipoproteins E, Atrophy, Brain, Cognition Disorders, Female, Hippocampus, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Cohort Studies, Disease Progression, Entorhinal Cortex, Apolipoprotein E4

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Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD

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