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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ResultInfluential
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Background
Subjective cognitive and non‐cognitive complaints and brain MRI biomarkers in the MEMENTO cohort
Dauphinot, V., Bouteloup, V., Mangin, J., Vellas, B. J., Pasquier, F., Blanc, F., Hanon, O., Gabelle, A., Annweiler, C., David, R., et al. (2020) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12051 · Cited by 20
Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline
Dong, Q., Zhang, W., Stonnington, C. M., Wu, J., Gutman, B. A., Chen, K., Su, Y., Baxter, L. C., Thompson, P. M., Reiman, E. M., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102338 · Cited by 23
“This knowledge will facilitate subject enrollment, in a timely fashion, in clinical trials aimed at prevention of AD process (Jack et al. 2004; Weiner 2008; Apostolova et al. 2012; Roussotte et al. 2014a; Madsen et al. 2015; Coupé et al. 2019).”
Background
Race, sex, and mid‐life changes in brain health: Cardia MRI substudy
Moonen, J. E. F., Nasrallah, I. M., Detre, J. A., Dolui, S., Erus, G., Davatzikos, C. A., Meirelles, O., Bryan, R. N., & Launer, L. J. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12560 · Cited by 23
Predicting progression from subjective cognitive decline to mild cognitive impairment or dementia based on brain atrophy patterns
Lerch, O., Ferreira, D., Stomrud, E., van Westen, D., Tideman, P., Palmqvist, S., Mattsson, N., Hort, J., Hansson, O., & Westman, E. (2024) · Alzheimer s Research & Therapy · doi:10.1186/s13195-024-01517-5 · Cited by 24
“This was expected, since overall levels of atrophy in AD dementia are higher than in aMCI [63], supposedly making classification of aMCI vs. CN individuals based on atrophy patterns more difficult than the classification of AD dementia vs. CN.”
Background
Subtotal corpus callosum agenesis with recurrent hyperhidrosis-hypothermia (Shapiro syndrome)
Tambasco, N., Corea, F., & Bocola, V. (2005) · Neurology · doi:10.1212/01.wnl.0000160423.17825.5e · Cited by 22
Diffusion tensor imaging measures of normal appearing white matter in patients who are aging, or have amnestic mild cognitive impairment, or Alzheimer’s disease
Wang, J., Lv, P., Wang, H., Li, Z., Li, N., Sun, Z., Zhao, B., & Huang, Y. M. (2013) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2012.09.025 · Cited by 23
Amyloid-Beta Deposition is Associated with Increased Medial Temporal Lobe Activation during Memory Encoding in the Cognitively Normal Elderly
Edelman, K. L., Tudorascu, D., Agudelo, C., Snitz, B. E., Karim, H. T., Cohen, A. D., Mathis, C. A., Price, J. C., Weissfeld, L. A., Klunk, W. E., et al. (2017) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2016.12.021 · Cited by 21
“In the third stage, decline from MCI to dementia is characterized by atrophy of the MTL (Figure 1c) (20-22) leading to the precipitous decline of MTL activity (23) (Figure 1b) and cognitive capacity (Figure 1a) characteristic of AD.”
Manganese Enhanced MRI for Use in Studying Neurodegenerative Diseases
Saar, G., & Koretsky, A. P. (2019) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00114 · Cited by 22
“This includes the diseases that are primarily neurodegenerative such as Alzheimer’s disease (AD), other forms of dementia such as frontal-temporal dementia (FTD) and mild cognitive impairment (MCI), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS).”
BackgroundInfluential
Predicting the progression of mild cognitive impairment to Alzheimer’s disease by longitudinal magnetic resonance imaging-based dictionary learning
Lin, Y., Huang, K., Xu, H., Qiao, Z., Cai, S., Wang, Y., & Huang, L. (2020) · Clinical Neurophysiology · doi:10.1016/j.clinph.2020.07.016 · Cited by 23
“The segmentation of ROIs is also observer-and algorithm-dependent and relies on a priori assumptions regarding the target structures between pMCI and sMCI(Chetelat and Baron, 2003), which may result in inconsistent results(Kaye et al., 1997; Jack et al., 2000; 2004).”
Longitudinal imaging: change and causality
Whitwell, J. L. (2008) · Current Opinion in Neurology · doi:10.1097/wco.0b013e32830719d4 · Cited by 23
Update on mild cognitive impairment
Bennett, D. A. (2003) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-003-0020-2 · Cited by 22
Predicting and Tracking Short Term Disease Progression in Amnestic Mild Cognitive Impairment Patients with Prodromal Alzheimer's Disease: Structural Brain Biomarkers
Marizzoni, M., Ferrari, C., Jovicich, J., Albani, D., Babiloni, C., Cavaliere, L., Didic, M., Forloni, G., Galluzzi, S., Hoffmann, K., et al. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-180152 · Cited by 20
“Moreover, a stronger correlation with changes on cognitive tests was reported for LVV enlargement compared with hippocampal atrophy rates [48].”
Lateral ventricle volume trajectories predict response inhibition in older age—A longitudinal brain imaging and machine learning approach
Lundervold, A. J., Vik, A., & Lundervold, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0207967 · Cited by 21
Accelerated Brain Atrophy on Serial Computed Tomography
Zahid, A. B., Mikheev, A. V., Srivatsa, N., Babb, J. S., Samadani, U., & Rusinek, H. (2016) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000000435 · Cited by 22
Longitudinal survival analysis and two-group comparison for predicting the progression of mild cognitive impairment to Alzheimer's disease
Platero, C. A., & Tobar, M. C. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108698 · Cited by 21
“For example, using two-group comparison approaches, greater rates of atrophy, as a biomarker, have been used in the hippocampus (Jack et al., 2004; Apostolova et al., 2010; Miller et al., 2013), entorhinal cortex (Jack et al., 2004; Miller et al., 2013) and the medial temporal lobe (Blasko et al.,…”
Effects of Supplementation with Folic Acid and Its Combinations with Other Nutrients on Cognitive Impairment and Alzheimer’s Disease: A Narrative Review
Puga, A. M., Ruperto, M., Samaniego-Vaesken, M. D. L., Montero-Bravo, A., Partearroyo, T., & Varela‐Moreiras, G. (2021) · Nutrients · doi:10.3390/nu13092966 · Cited by 23
“The identification of factors that reduce the rate of atrophy seems to be an interesting approach to slow down this progression, since to date, no specific cure is available for AD [14,15].”
Background
Applications of Neuroimaging to Disease-Modification Trials in Alzheimer’s Disease
Fleisher, A., Donohue, M., Chen, K., Brewer, J. B., Aisen, P. S. S., & Initiative, T. A. D. N. (2009) · Behavioural Neurology · doi:10.1155/2009/836437 · Cited by 21
Genetic Interactions within Inositol-Related Pathways are Associated with Longitudinal Changes in Ventricle Size
Koran, M. E. I., Initiative, A. D. N., Hohman, T. J., Meda, S. A., & Thornton‐Wells, T. A. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-130989 · Cited by 20
Genetic modification of the relationship between phosphorylated tau and neurodegeneration
Hohman, T. J., Koran, M. E. I., Thornton‐Wells, T. A., & Initiative, A. D. N. (2014) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2013.12.022 · Cited by 21
“The lateral ventricles have shown a strong relationship to AD onset and progression [19,20], and measures of ventricular dilation have been successfully applied as quantitative endophenotypes in genetic interaction analyses previously [21].”
ACR Appropriateness Criteria Dementia and Movement Disorders.
Wippold, F., Brown, D. C. S., Broderick, D., Burns, J., Corey, A., Deshmukh, T. K., Douglas, A. C., Holloway, K., Jagadeesan, B., Jurgens, J. S., et al. (2015) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2014.09.025 · Cited by 49
“For example, structural neuroimagingwithCT andMRI has defined patterns and rates of brain volume loss in AD [2-4].”
Background
MRI Volumetric Quantification in Persons with a History of Traumatic Brain Injury and Cognitive Impairment
Meysami, S., Raji, C. A., Merrill, D. A., Porter, V. R., & Mendez, M. F. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190708 · Cited by 20
“Quantitative volumetric MR brain imaging, which has been utilized for AD and cognitive disorders [8, 9], may be a potential tool for assessing the chronic, delayed effects of TBI.”
Method
Single Neuropsychological Test Scores Associated With Rate of Cognitive Decline in Early Alzheimer Disease
Parikh, M., Hynan, L. S., Weiner, M., Lacritz, L. H., Ringe, W. K., & Cullum, C. M. (2014) · The Clinical Neuropsychologist · doi:10.1080/13854046.2014.944937 · Cited by 19
“Inclusion of such measures (e.g., MRI volumetrics) might augment prediction equations, since they have shown independent predictive value in other investigations (e.g., Jack et al., 2004).”
Result
Maximizing the Alzheimer's Disease Neuroimaging Initiative II
Carrillo, M. C., Sanders, C. A., & Katz, R. G. (2009) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2009.02.005 · Cited by 20
Reduced parenchymal cerebral blood flow is associated with greater progression of brain atrophy: The SMART-MR study
Ghaznawi, R., Zwartbol, M. H. T., Zuithoff, N. P. A., de Bresser, J., Hendrikse, J., Geerlings, M. I., & on behalf of the UCC-SMART Study Group (2020) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x20948614 · Cited by 21
Effects of MRI scan acceleration on brain volume measurement consistency
Krüger, G., Granziera, C., Jack, C. R., Gunter, J. L., Littmann, A., Mortamet, B., Kannengießer, S., Sorensen, A. G., Ward, C. P., Reyes, D. A., et al. (2012) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.23694 · Cited by 19
Self-citation
ACE I/D polymorphism affects cognitive function and gray-matter volume in amnestic mild cognitive impairment
Zhang, Z., Deng, L., Bai, F., Shi, Y., Yu, H., Yuan, Y., Jiang, T., Jia, J., & Zhang, Z. (2010) · Behavioural Brain Research · doi:10.1016/j.bbr.2010.11.032 · Cited by 19
Structural correlates of mild cognitive impairment: A clinicovolumetric study
Menon, R. N., Sheelakumari, R., Kesavadas, C., Lekha, V., Justus, S., & Sarma, P. (2018) · Neurology India · doi:10.4103/0028-3886.227298 · Cited by 19
Multimodal neuroimaging study of cerebrovascular disease, amyloid deposition, and neurodegeneration in Alzheimer's disease progression
Lao, P. J., Brickman, A. M., & Initiative, A. D. N. (2018) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2018.08.007 · Cited by 18
“Further downstream, structural magnetic resonance imaging (MRI) has shown that cortical atrophy mirrors the distribution of tau pathology and is a ubiquitous feature of clinical AD [6,7].”
Teichmüller Shape Descriptor and Its Application to Alzheimer’s Disease Study
Initiative, A. D. N., Zeng, W., Shi, R., Wang, Y., Yau, S., & Gu, X. D. (2012) · International Journal of Computer Vision · doi:10.1007/s11263-012-0586-8 · Cited by 19
Annual Research Review: Progress in using brain morphometry as a clinical tool for diagnosing psychiatric disorders
Haubold, A., Peterson, B. S., & Bansal, R. (2012) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2012.02539.x · Cited by 20
Association of homocysteine with ventricular dilatation and brain atrophy in Parkinson's disease
Sapkota, S., Gee, M., Sabino, J., Emery, D. J., & Camicioli, R. M. (2014) · Movement Disorders · doi:10.1002/mds.25798 · Cited by 19
“development of sporadic AD, which is associated with an increased rate of brain atrophy.(31) In addition to a higher proportion with an APOE e4 genotype (60%”
Background
Automated Measurement of Hippocampal Atrophy Using Fluid-Registered Serial MRI in AD and Controls
Barnes, J., Lewis, E. B., Scahill, R. I., Bartlett, J. W., Frost, C. D., Schott, J. M., Rossor, M. N., & Fox, N. C. (2007) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0b013e31802f4139 · Cited by 19
“9% per year.(4,28,29) Other groups have reported atrophy rates in AD to be 5% to 6% per year (mean age, ~76 years), with 0.”
Background
The Correlation of Cognitive Decline with Frontotemporal Dementia Induced Annualized Gray Matter Loss Using Diffeomorphic Morphometry
Avants, B., Grossman, M., & Gee, J. C. (2005) · Alzheimer Disease & Associated Disorders · doi:10.1097/01.wad.0000183083.14939.82 · Cited by 19
Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry
Tsao, S., Gajawelli, N., Zhou, J., Shi, J., Ye, J., Wang, Y., & Leporé, N. (2017) · Brain and Behavior · doi:10.1002/brb3.733 · Cited by 18
Retinal Microvascular Alterations as the Biomarkers for Alzheimer Disease: Are We There Yet?
Jiang, H., Wang, J., Levin, B. E., Baumel, B. S., Camargo, C. J., Signorile, J. F., & Rundek, T. (2020) · Journal of Neuro-Ophthalmology · doi:10.1097/wno.0000000000001140 · Cited by 19
Perspectives in Molecular Imaging Using Staging Biomarkers and Immunotherapies in Alzheimer’s Disease
Leclerc, B., & Abulrob, A. (2013) · The Scientific World JOURNAL · doi:10.1155/2013/589308 · Cited by 18
Magnetic resonance spectroscopy and brain volumetry in mild cognitive impairment. A prospective study
Fayed, N. M., Modrego, P. J., García‐Martí, G., Sanz‐Requena, R., & Martí‐Bonmatí, L. (2016) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2016.12.010 · Cited by 19
“Whole brain, cortical, and ventricular volumes have also shown to be associated with progression to AD, although the volume measurements from MR images have relatively low accuracy in the prediction of AD[9-12] .”
Background
Mapping Dynamic Changes in Ventricular Volume onto Baseline Cortical Surfaces in Normal Aging, MCI, and Alzheimer’s Disease
Initiative, A. D. N., Madsen, S. K., Gutman, B. A., Joshi, S. H., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-02126-3_9 · Cited by 18
“VV [7], shape [2], and boundary shift integral [8] have been validated as highly sensitive biomarkers of AD and mild cognitive impairment (MCI), offering high classification accuracy and greater consistency than some cognitive tests [9], [10].”
Self-citation
Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing
Jiang, X., Zhou, Z., Ding, X., Deng, X., Zou, L., & Li, B. (2017) · Computational and Mathematical Methods in Medicine · doi:10.1155/2017/5256346 · Cited by 17
Characterization of Brain Iron Deposition Pattern and Its Association With Genetic Risk Factor in Alzheimer’s Disease Using Susceptibility-Weighted Imaging
You, P., Li, X., Wang, Z., Wang, H., Dong, B., & Li, Q. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.654381 · Cited by 17
Genetic evidence for the causal relationships between migraine, dementia, and longitudinal brain atrophy
Zhao, L., Tang, Y., Tu, Y., & Cao, J. (2024) · The Journal of Headache and Pain · doi:10.1186/s10194-024-01801-7 · Cited by 17
“Some longitudinal studies have shown that accelerated brain atrophy can precede the onset of dementia, [18, 19] suggesting a possible mediating role of accelerated brain atrophy in the association between migraine and the development of dementia in the future.”
Background
Brain change trajectories in healthy adults correlate with Alzheimer’s related genetic variation and memory decline across life
Roe, J. M., Dídac Vidal-piñeiro, A., Sørensen, Ø., Grydeland, H., Leonardsen, E. H., Iakunchykova, O. P., Pan, M., Mowinckel, A. M., Strømstad, M., Nawijn, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53548-z · Cited by 20
“Shrinkage of the hippocampus — a critical structure underpinning episodic memory and spatial navigation operations — is a well-known AD risk marker in patients 9,32,53 , with atrophy rates predicting clinical conversion 54 .”
Self-citation
Memory complaints and increased rates of brain atrophy: risk factors for mild cognitive impairment and Alzheimer's disease
Archer, H. A., Kennedy, J., Barnes, J., Pepple, T., Boyes, R. G., Randlesome, K., Clegg, S., Leung, K. K., Sebastien Ourselin, A., Frost, C. D., et al. (2010) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.2440 · Cited by 16
“Although there are many wellconducted studies that have shown a relationship between rates of WBA (Jack et al., 2004; Jack et al., 2005), HA (Jack et al., 2000; Jack et al., 2004), VE (Jack et al., 2005) and clinical progression, interestingly some groups, like us, have been able to use rates of…”
BackgroundResultInfluential
The validity of biomarkers as surrogate endpoints in Alzheimer's disease by means of the Quantitative Surrogate Validation Level of Evidence Scheme (QSVLES)
Wied, C. C. G., Kritsidima, M., & Elferink, A. J. A. (2009) · The journal of nutrition health & aging · doi:10.1007/s12603-009-0049-2 · Cited by 16
“Only four prospective observational studies were found that demonstrated statistically significant correlations between the rate of hippocampal atrophy and cognitive decline (12, 13, 48, 49).”
20 Brain Imaging in the Evaluation of Putative Alzheimer’s Disease-Slowing, Risk-Reducing and Prevention Therapies
Reiman, E. M., & Langbaum, J. B. S. (2009) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195328875.003.0020 · Cited by 16
Relationship of Hippocampal Volume to Amyloid Burden across Diagnostic Stages of Alzheimer's Disease
Trzepacz, P. T., Hochstetler, H. M., Yu, P., Castelluccio, P., Witte, M. M., Dell’Agnello, G., Degenhardt, E. K., & Initiative, A. D. N. (2015) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000441351 · Cited by 15

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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