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Randomized Controlled Trial
. 2013 Apr;273(4):396-409.
doi: 10.1111/joim.12028. Epub 2013 Jan 30.

An MRI-based index to measure the severity of Alzheimer's disease-like structural pattern in subjects with mild cognitive impairment

G Spulber  1 , A Simmons, J-S Muehlboeck, P Mecocci, B Vellas, M Tsolaki, I Kłoszewska, H Soininen, C Spenger, S Lovestone, L-O Wahlund, E Westman; dNeuroMed consortium and for the Alzheimer Disease Neuroimaging Initiative
Affiliations
Randomized Controlled Trial

An MRI-based index to measure the severity of Alzheimer's disease-like structural pattern in subjects with mild cognitive impairment

G Spulber et al. J Intern Med. 2013 Apr.

Abstract

Background: Structural magnetic resonance imaging (MRI) is sensitive to neurodegeneration and can be used to estimate the risk of converting to Alzheimer's disease (AD) in individuals with mild cognitive impairment (MCI). Brain changes in AD and prodromal AD involve a pattern of widespread atrophy. The use of multivariate analysis algorithms could enable the development of diagnostic tools based on structural MRI data. In this study, we investigated the possibility of combining multiple MRI features in the form of a severity index.

Methods: We used baseline MRI scans from two large multicentre cohorts (AddNeuroMed and ADNI). On the basis of volumetric and cortical thickness measures at baseline with AD cases and healthy control (CTL) subjects as training sets, we generated an MRI-based severity index using the method of orthogonal projection to latent structures (OPLS). The severity index tends to be close to 1 for AD patients and 0 for CTL subjects. Values above 0.5 indicate a more AD-like pattern. The index was then estimated for subjects with MCI, and the accuracy of classification was investigated.

Results: Based on the data at follow-up, 173 subjects converted to AD, of whom 112 (64.7%) were classified as AD-like and 61 (35.3%) as CTL-like.

Conclusion: We found that joint evaluation of multiple brain regions provided accurate discrimination between progressive and stable MCI, with better performance than hippocampal volume alone, or a limited set of features. A major challenge is still to determine optimal cut-off points for such parameters and to compare their relative reliability.

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Conflict of interest statement

Conflict of Interest Statement

No conflict of interest was declared.

Figures

Figure 1
Figure 1
Box plots of AD-specific brain structures (hippocampal volume and thickness of entorhinal cortex, medial and superior temporal gyruses) that showed significant differences between MCI-s and MCI-c groups using ANOVA with unequal n HSD post hoc test. Data are presented normalised as z-score. Median, percentiles 25–75 (box), range (whiskers). MCI-s, MCI stable; MCI-c, MCI converting to Alzheimer’s disease.
Figure 2
Figure 2
Box plots of the OPLS score for the study groups. Median, percentiles 25–75 (box), range (whiskers). Subjects with a score above 0.5 show a more AD-like pattern of atrophy and below 0.5 a more control-like pattern. MCI-s, MCI stable; MCI-c, MCI converting to AD.
Figure 3
Figure 3
Histograms of the severity index for the subjects with MCI (MCI-s, MCI stable; MCI-c, MCI converting to AD). Subjects with a score above 0.5 show a more AD-like pattern of atrophy and below 0.5 a more control-like pattern.
Figure 4
Figure 4
Survival curves for the AD-like and CTL-like MCI subgroups based on baseline MRI data for the ADNI cohort. The X axis represents the time in months since entry into the study and the Y axis represents percentage of MCI subjects.
See this image and copyright information in PMC

References

    1. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Carrillo MC, Thies B, Phelps CH. The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7:270–279. - PMC - PubMed
    1. Mariani E, Monastero R, Mecocci P. Mild cognitive impairment: a systematic review. J Alzheimers Dis. 2007;12:23–35. - PubMed
    1. Gauthier S, Reisberg B, Zaudig M, Petersen RC, Ritchie K, Broich K, Belleville S, Brodaty H, Bennett D, Chertkow H, Cummings JL, de Leon M, Feldman H, Ganguli M, Hampel H, Scheltens P, Tierney MC, Whitehouse P, Winblad B. Mild cognitive impairment. Lancet. 2006;367:1262–1270. - PubMed
    1. Ewers M, Sperling RA, Klunk WE, Weiner MW, Hampel H. Neuroimaging markers for the prediction and early diagnosis of Alzheimer's disease dementia. Trends Neurosci. 2011;34:430–442. - PMC - PubMed
    1. Fennema-Notestine C, Hagler DJJ, McEvoy LK, Fleisher AS, Wu EH, Karow DS, Dale AM. Structural MRI biomarkers for preclinical and mild Alzheimer's disease. Hum Brain Mapp. 2009;30:3238–3253. - PMC - PubMed

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