Published Online:

Results of this study demonstrate that patient-specific estimates of the risk of conversion from mild cognitive impairment to Alzheimer disease can be derived from quantitative measures of brain atrophy obtained from single-time-point and serial MR imaging examinations.


To assess whether single-time-point and longitudinal volumetric magnetic resonance (MR) imaging measures provide predictive prognostic information in patients with amnestic mild cognitive impairment (MCI).

Materials and Methods

This study was conducted with institutional review board approval and in compliance with HIPAA regulations. Written informed consent was obtained from all participants or the participants’ legal guardians. Cross-validated discriminant analyses of MR imaging measures were performed to differentiate 164 Alzheimer disease (AD) cases from 203 healthy control cases. Separate analyses were performed by using data from MR images obtained at one time point or by combining single-time-point measures with 1-year change measures. Resulting discriminant functions were applied to 317 MCI cases to derive individual patient risk scores. Risk of conversion to AD was estimated as a continuous function of risk score percentile. Kaplan-Meier survival curves were computed for risk score quartiles. Odds ratios (ORs) for the conversion to AD were computed between the highest and lowest quartile scores.


Individualized risk estimates from baseline MR examinations indicated that the 1-year risk of conversion to AD ranged from 3% to 40% (average group risk, 17%; OR, 7.2 for highest vs lowest score quartiles). Including measures of 1-year change in global and regional volumes significantly improved risk estimates (P = 001), with the risk of conversion to AD in the subsequent year ranging from 3% to 69% (average group risk, 27%; OR, 12.0 for highest vs lowest score quartiles).


Relative to the risk of conversion to AD conferred by the clinical diagnosis of MCI alone, MR imaging measures yield substantially more informative patient-specific risk estimates. Such predictive prognostic information will be critical if disease-modifying therapies become available.

© RSNA, 2011

Supplemental material:


  • 1 Rafii MS, Aisen PS. Recent developments in Alzheimer’s disease therapeutics. BMC Med 2009;7:7. Crossref, MedlineGoogle Scholar
  • 2 Cummings JL, Doody R, Clark C. Disease-modifying therapies for Alzheimer disease: challenges to early intervention. Neurology 2007;69(16):1622–1634. Crossref, MedlineGoogle Scholar
  • 3 Aisen PS. Commentary on “a roadmap for the prevention of dementia II: Leon Thal Symposium 2008”—facilitating Alzheimer’s disease drug development in the United States. Alzheimers Dement 2009;5(2):125–127. Crossref, MedlineGoogle Scholar
  • 4 Black RS, Sperling RA, Safirstein B, et al.. A single ascending dose study of bapineuzumab in patients with Alzheimer disease. Alzheimer Dis Assoc Disord 2010;24(2):198–203. Crossref, MedlineGoogle Scholar
  • 5 McEvoy LK, Brewer JB. Quantitative structural MRI for early detection of Alzheimer’s disease. Expert Rev Neurother 2010;10(11):1675–1688. Crossref, MedlineGoogle Scholar
  • 6 Petersen RC, Thomas RG, Grundman M, et al.. Vitamin E and donepezil for the treatment of mild cognitive impairment. N Engl J Med 2005;352(23):2379–2388. Crossref, MedlineGoogle Scholar
  • 7 Petersen RC, Aisen PS, Beckett LA, et al.. Alzheimer’s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology 2010;74(3):201–209. Crossref, MedlineGoogle Scholar
  • 8 Larrieu S, Letenneur L, Orgogozo JM, et al.. Incidence and outcome of mild cognitive impairment in a population-based prospective cohort. Neurology 2002;59(10):1594–1599. Crossref, MedlineGoogle Scholar
  • 9 Jack CR, Petersen RC, Xu YC, et al.. Medial temporal atrophy on MRI in normal aging and very mild Alzheimer’s disease. Neurology 1997;49(3):786–794. Crossref, MedlineGoogle Scholar
  • 10 Dickerson BC, Feczko E, Augustinack JC, et al.. Differential effects of aging and Alzheimer’s disease on medial temporal lobe cortical thickness and surface area. Neurobiol Aging 2009;30(3):432–440. Crossref, MedlineGoogle Scholar
  • 11 Fennema-Notestine C, Hagler DJ, McEvoy LK, et al.. Structural MRI biomarkers for preclinical and mild Alzheimer’s disease. Hum Brain Mapp 2009;30(10):3238–3253. Crossref, MedlineGoogle Scholar
  • 12 Jack CR, Shiung MM, Weigand SD, et al.. Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI. Neurology 2005;65(8):1227–1231. Crossref, MedlineGoogle Scholar
  • 13 Whitwell JL, Shiung MM, Przybelski SA, et al.. MRI patterns of atrophy associated with progression to AD in amnestic mild cognitive impairment. Neurology 2008;70(7):512–520. Crossref, MedlineGoogle Scholar
  • 14 Bakkour A, Morris JC, Dickerson BC. The cortical signature of prodromal AD: regional thinning predicts mild AD dementia. Neurology 2009;72(12):1048–1055. Crossref, MedlineGoogle Scholar
  • 15 McEvoy LK, Fennema-Notestine C, Roddey JC, et al.. Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. Radiology 2009;251(1):195–205. LinkGoogle Scholar
  • 16 Vemuri P, Wiste HJ, Weigand SD, et al.. MRI and CSF biomarkers in normal, MCI, and AD subjects: predicting future clinical change. Neurology 2009;73(4):294–301. Crossref, MedlineGoogle Scholar
  • 17 Fox NC, Cousens S, Scahill R, Harvey RJ, Rossor MN. Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer disease: power calculations and estimates of sample size to detect treatment effects. Arch Neurol 2000;57(3):339–344. Crossref, MedlineGoogle Scholar
  • 18 Thompson PM, Hayashi KM, de Zubicaray G, et al.. Dynamics of gray matter loss in Alzheimer’s disease. J Neurosci 2003;23(3):994–1005. Crossref, MedlineGoogle Scholar
  • 19 Holland D, Brewer JB, Hagler DJ, Fennema-Notestine C, Dale AM; Alzheimer’s Disease Neuroimaging Initiative. Subregional neuroanatomical change as a biomarker for Alzheimer’s disease. Proc Natl Acad Sci U S A 2009;106(49):20954–20959. Crossref, MedlineGoogle Scholar
  • 20 McDonald CR, McEvoy LK, Gharapetian L, et al.. Regional rates of neocortical atrophy from normal aging to early Alzheimer disease. Neurology 2009;73(6):457–465. Crossref, MedlineGoogle Scholar
  • 21 McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 1984;34(7):939–944. Crossref, MedlineGoogle Scholar
  • 22 Jack CR, Bernstein MA, Fox NC, et al.. The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 2008;27(4):685–691. Crossref, MedlineGoogle Scholar
  • 23 Jovicich J, Czanner S, Greve D, et al.. Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 2006;30(2):436–443. Crossref, MedlineGoogle Scholar
  • 24 Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998;17(1):87–97. Crossref, MedlineGoogle Scholar
  • 25 Fischl B, Salat DH, Busa E, et al.. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33(3):341–355. Crossref, MedlineGoogle Scholar
  • 26 Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 1999;9(2):179–194. Crossref, MedlineGoogle Scholar
  • 27 Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II. Inflation, flattening, and a surface-based coordinate system. Neuroimage 1999;9(2):195–207. Crossref, MedlineGoogle Scholar
  • 28 Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 2000;97(20):11050–11055. Crossref, MedlineGoogle Scholar
  • 29 Fischl B, van der Kouwe A, Destrieux C, et al.. Automatically parcellating the human cerebral cortex. Cereb Cortex 2004;14(1):11–22. Crossref, MedlineGoogle Scholar
  • 30 Desikan RS, Ségonne F, Fischl B, et al.. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006;31(3):968–980. Crossref, MedlineGoogle Scholar
  • 31 Buckner RL, Head D, Parker J, et al.. A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage 2004;23(2):724–738. Crossref, MedlineGoogle Scholar
  • 32 Thompson WK, Holland D. Bias in tensor based morphometry stat-ROI measures may result in unrealistic power estimates. Neuroimage (in press). Google Scholar
  • 33 Duda RO, Hart PE, Strok DG. Pattern classification. New York, NY: Wiley, 2000. Google Scholar
  • 34 Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM. Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiol Aging 2008;29(4):514–523. Crossref, MedlineGoogle Scholar
  • 35 Fan Y, Batmanghelich N, Clark CM, Davatzikos C; Alzheimer’s Disease Neuroimaging Initiative. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 2008;39(4):1731–1743. Crossref, MedlineGoogle Scholar
  • 36 Klöppel S, Stonnington CM, Chu C, et al.. Automatic classification of MR scans in Alzheimer’s disease. Brain 2008;131(Pt 3):681–689. Crossref, MedlineGoogle Scholar
  • 37 Vemuri P, Gunter JL, Senjem ML, et al.. Alzheimer’s disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 2008;39(3):1186–1197. Crossref, MedlineGoogle Scholar
  • 38 Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983;148(3):839–843. LinkGoogle Scholar
  • 39 Gu C. Smoothing spline ANOVA models. New York, NY: Springer-Verlag, 2002. CrossrefGoogle Scholar
  • 40 Pencina MJ, D’Agostino RB, D’Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27(2):157–172; discussion 207–212. Crossref, MedlineGoogle Scholar
  • 41 Misra C, Fan Y, Davatzikos C. Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage 2009;44(4):1415–1422. Crossref, MedlineGoogle Scholar
  • 42 Rusinek H, De Santi S, Frid D, et al.. Regional brain atrophy rate predicts future cognitive decline: 6-year longitudinal MR imaging study of normal aging. Radiology 2003;229(3):691–696. LinkGoogle Scholar
  • 43 Risacher SL, Shen L, West JD, et al.. Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort. Neurobiol Aging 2010;31(8):1401–1418. Crossref, MedlineGoogle Scholar
  • 44 Mosconi L. Brain glucose metabolism in the early and specific diagnosis of Alzheimer’s disease: FDG-PET studies in MCI and AD. Eur J Nucl Med Mol Imaging 2005;32(4):486–510. Crossref, MedlineGoogle Scholar
  • 45 Herholz K, Carter SF, Jones M. Positron emission tomography imaging in dementia. Br J Radiol 2007;80(Spec No 2):S160–S167. Crossref, MedlineGoogle Scholar
  • 46 Landau SM, Harvey D, Madison CM, et al.. Comparing predictors of conversion and decline in mild cognitive impairment. Neurology 2010;75(3):230–238. Crossref, MedlineGoogle Scholar
  • 47 Karow DS, McEvoy LK, Fennema-Notestine C, et al.. Relative capability of MR imaging and FDG PET to depict changes associated with prodromal and early Alzheimer disease. Radiology 2010;256(3):932–942. LinkGoogle Scholar
  • 48 Villain N, Fouquet M, Baron JC, et al.. Sequential relationships between grey matter and white matter atrophy and brain metabolic abnormalities in early Alzheimer’s disease. Brain 2010;133(11):3301–3314. Crossref, MedlineGoogle Scholar
  • 49 Rabinovici GD, Jagust WJ. Amyloid imaging in aging and dementia: testing the amyloid hypothesis in vivo. Behav Neurol 2009;21(1):117–128. Crossref, MedlineGoogle Scholar
  • 50 Mattsson N, Zetterberg H, Hansson O, et al.. CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 2009;302(4):385–393. Crossref, MedlineGoogle Scholar
  • 51 Fjell AM, Walhovd KB, Fennema-Notestine C, et al.. CSF biomarkers in prediction of cerebral and clinical change in mild cognitive impairment and Alzheimer’s disease. J Neurosci 2010;30(6):2088–2101. Crossref, MedlineGoogle Scholar
  • 52 Vemuri P, Wiste HJ, Weigand SD, et al.. MRI and CSF biomarkers in normal, MCI, and AD subjects: diagnostic discrimination and cognitive correlations. Neurology 2009;73(4):287–293. Crossref, MedlineGoogle Scholar
  • 53 Brewer JB. Fully-automated volumetric MRI with normative ranges: translation to clinical practice. Behav Neurol 2009;21(1):21–28. Crossref, MedlineGoogle Scholar

Article History

Received September 30, 1010; revision requested November 22; revision received January 5, 2011; accepted January 19; final version accepted January 27.
Published online: June 2011
Published in print: June 2011