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Original Contribution |

Comparison of Imaging Biomarkers in the Alzheimer Disease Neuroimaging Initiative and the Mayo Clinic Study of Aging FREE

Jennifer L. Whitwell, PhD; Heather J. Wiste, BA; Stephen D. Weigand, MS; Walter A. Rocca, MD, MPH; David S. Knopman, MD; Rosebud O. Roberts, MB, ChB; Bradley F. Boeve, MD; Ronald C. Petersen, MD, PhD; Clifford R. Jack Jr, MD; for the Alzheimer Disease Neuroimaging Initiative
[+] Author Affiliations

Author Affiliations: Department of Radiology (Drs Whitwell and Jack), Divisions of Biomedical Statistics and Informatics (Ms Wiste and Mr Weigand) and Epidemiology (Drs Rocca and Roberts), Department of Health Sciences Research, and Department of Neurology (Drs Rocca, Knopman, Boeve, and Petersen), College of Medicine, Mayo Clinic, Rochester, Minnesota.

Group Information: A list of the Alzheimer Disease Neuroimaging Initiative Investigators appears athttp://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Authorship_List.pdf.


Arch Neurol. 2012;69(5):614-622. doi:10.1001/archneurol.2011.3029.
Text Size: A A A
Published online

Objective To determine whether magnetic resonance imaging measurements observed in the Alzheimer Disease Neuroimaging Initiative (ADNI) convenience sample differ from those observed in the Mayo Clinic Study of Aging (MCSA) population-based sample.

Design Comparison of 2 samples.

Setting Fifty-nine recruiting sites for the ADNI in the United States and Canada and the MCSA, a population-based cohort in Olmsted County, Minnesota.

Patients Cognitively normal subjects and amnestic subjects with mild cognitive impairment were selected from the ADNI convenience cohort and MCSA population-based cohort. A simple random sample of subjects from both cohorts in the same age range was selected, and a second sample applied matching for age, sex, educational level, apolipoprotein E genotype, and Mini-Mental State Examination score.

Main Outcome Measures Baseline hippocampal volumes and annual percentage of decline in hippocampal volume.

Results In the population-based sample, MCSA subjects were older, had less education, performed worse on the Mini-Mental State Examination, and had a family history of Alzheimer disease less often than did ADNI subjects. Baseline hippocampal volumes were larger in ADNI compared with MCSA cognitively normal subjects in the random sample, although no differences were observed after matching. Rates of decline in hippocampal volume were greater in the ADNI compared with the MCSA for cognitively normal subjects and those with amnestic mild cognitive impairment, even after matching.

Conclusions Rates of decline in hippocampal volume suggest that ADNI subjects have a more aggressive brain pathologic process than MCSA subjects and hence may not be representative of the general population. These findings have implications for treatment trials that use ADNI-like recruitment mechanisms and for studies validating new diagnostic criteria for Alzheimer disease in its various stages.

Figures in this Article

Imaging plays an important role in the study of Alzheimer disease (AD). Imaging biomarkers can track disease progression,1 detect changes early in the phase of mild cognitive impairment (MCI),2,3 and help predict which subjects may later develop AD.4,5 Imaging measures have become common outcome measures in clinical treatment trials because they may reduce sample size.6,7 Increasing interest in the use of imaging in clinical trials led to the development of the Alzheimer Disease Neuroimaging Initiative (ADNI), which aimed to improve methods for clinical trials and validate imaging and other biomarkers.8,9 The ADNI is an observational study of MCI and AD that used identical recruitment mechanisms as typical trials, including advertising and recruitment from memory clinics. Therefore, the ADNI is based on a highly selected convenience sample. Because ADNI data are freely available, a large number of studies are published each year using these data. However, it is unclear to what extent subjects recruited through these mechanisms are representative of the general population and hence whether results are generalizable.

We aimed to determine whether imaging measures would differ in ADNI participants compared with the population-based cohort of the Mayo Clinic Study of Aging (MCSA). We assessed hippocampal volume and rates of decline in hippocampal volume because they are established and widely studied biomarkers of AD.6,10,11 Because results could be influenced by differences in inclusion characteristics and demographics, we compared the cohorts before and after matching for specific demographic and cognitive features.

SOURCES OF SUBJECTS AND DIAGNOSTIC CRITERIA

Subjects with a clinical diagnosis of amnestic MCI (aMCI) and cognitively normal (CN) subjects who had been recruited into the MCSA (and had agreed to undergo magnetic resonance imaging [MRI]) or the ADNI underwent analysis.

The MCSA is a longitudinal epidemiologic study of normal aging and MCI in Olmsted County, Minnesota. The recruitment mechanisms have been reported in detail previously.12 Briefly, all Olmsted County residents aged 70 to 89 years on October 1, 2004, were identified using the medical records linkage system of the Rochester Epidemiology Project.13,14 The population was also resampled in 2008 and 2009 to replenish the cohort. Subjects were randomly selected from this enumeration. Subjects received a letter of invitation giving them the opportunity to refuse participation by returning a letter of refusal. Subjects who did not return the letter then received a follow-up telephone call inviting them to participate. Magnetic resonance imaging was performed in all subjects who agreed to participate and did not have any contraindications to MRI. S ubjects who underwent imaging in the MCSA have demographic characteristics very similar to those who did not undergo imaging (Table 1). Subjects were characterized as CN by consensus12,15 and when their age-adjusted neuropsychological test scores were consistent with normative data developed in this community.16 Diagnostic criteria for MCI were as follows17: (1) cognitive concern by the subject, an informant, a nurse, or a physician; (2) impairment in 1 or more of the 4 cognitive domains (from the cognitive battery); (3) essentially normal functional activities (using the Clinical Dementia Rating Scale18 and Functional Activities Questionnaire); and (4) absence of dementia (defined by the DSM-IV).19 Subjects were categorized as having aMCI if their memory was impaired. The diagnosis of aMCI was made on clinical grounds without the use of rigid cutoffs on psychometric scores.

Table Graphic Jump LocationTable 1. Representativeness of the MCSA Imaging Sample

The ADNI is a longitudinal multisite observational study of CN subjects and subjects with aMCI and AD (http:// www.ADNI-info.org).8 Subjects were recruited using local AD research centers, memory clinics, newspaper advertisements, radio, and other public media campaigns. Diagnostic criteria for the ADNI were largely the same as for the MCSA. Criteria for CN subjects included (1) Mini-Mental State Examination (MMSE)20 score between 24 and 30 inclusive; (2) no memory complaints; (3) objective memory performance in the normal range; and (4) a Clinical Dementia Rating Scale score of 0 and memory box score of 0. Diagnostic criteria for aMCI included (1) memory complaint verified by an informant; (2) objective memory impairment measured by the educational level–adjusted score on the Wechsler Memory Scale–Revised, Logical Memory II; (3) MMSE scores between 24 and 30 inclusive; (4) a Clinical Dementia Rating Scale score of 0.5 and memory box score of at least 0.5; and (5) preservation of general cognition and functional activities of daily living. Subjects enrolled in the ADNI were aged 55 to 90 years. The ADNI participants with AD were not included in our analysis because the MCSA does not follow up participants with AD.

Informed consent was obtained from all subjects. The MCSA was approved by the Mayo Clinic institutional review board, and the ADNI was approved by the institutional review board at each site.

SUBJECT SELECTION

We selected 2 samples of subjects. The first was a random sample of all available MCSA and ADNI subjects, and the second sample applied matching for demographic and cognitive variables. Cross-sectional and longitudinal samples were selected. The first available MRI was used for the cross-sectional analysis and as baseline for the longitudinal analysis. Two serial MRIs were used for the longitudinal analysis for each subject. Scan interval was approximately 12 months for the ADNI and 15 months for the MCSA (the routine follow-up interval in the MCSA).

SAMPLE 1: SIMPLE RANDOM SAMPLE OF EACH COHORT

For the cross-sectional analysis, the total number of available CN subjects was 229 in the ADNI and 1283 in the MCSA. The total number of aMCI subjects available was 397 in the ADNI and 179 in the MCSA. To obtain comparable sample sizes between the ADNI and MCSA, we took a simple random sample of the MCSA CN subjects, resulting in 229 subjects. Similarly, we took a simple random sample of the ADNI aMCI subjects, resulting in 179 subjects. Because of the random subsampling strategy, the samples used for our analyses were representative (within sampling error) of the parent cohorts from which they were drawn (eTable). For the longitudinal analysis, 206 ADNI CN subjects, 686 MCSA CN subjects, 347 ADNI aMCI subjects, and 92 MCSA aMCI subjects had serial scans available for analysis. Again, to obtain comparable group sizes, we took a random sample of the MCSA CN subjects and ADNI aMCI subjects, resulting in 206 MCSA CN subjects and 92 ADNI aMCI subjects.

SAMPLE 2: AGE-, SEX-, EDUCATIONAL LEVEL–, APOLIPOPROTEIN GENOTYPE–, AND MMSE-MATCHED SAMPLES

In sample 2, the ADNI and MCSA subjects were frequency matched by age, sex, educational level, apolipoprotein E (APOE) genotype, and MMSE score. All variables were dichotomized into strata by age (70-79 and 80-90 years), sex (men and women), educational level (6-13 and 14-20 years), and MMSE score (24-28 and 29-30 for CN subjects; 22-25 and 26-30 for aMCI subjects). The ADNI and MCSA subjects were matched with a one-to-one frequency by taking a random sample within each of the 32 strata of the larger study group to match the number of subjects in the smaller study group. We matched the CN and aMCI subjects separately. Subjects who could not be matched were excluded. For the cross-sectional analysis, 212 CN subjects and 97 aMCI subjects were selected for the ADNI and the MCSA. For the longitudinal analysis, 191 CN subjects and 65 aMCI subjects were selected for the ADNI and the MCSA.

Subject demographics for the 2 cross-sectional and longitudinal samples are shown in Tables 2, 3, 4, and 5. The samples used for analysis differ slightly from those reported in the preceding paragraph because some subjects were excluded owing to poor quality of the imaging.

Table Graphic Jump LocationTable 2. Descriptive Characteristics of Sample 1 Used for Cross-sectional Comparisonsa
Table Graphic Jump LocationTable 3. Descriptive Characteristics of Sample 2 Used for Cross-sectional Comparisonsa
Table Graphic Jump LocationTable 4. Descriptive Characteristics of Sample 1 Used for Longitudinal Comparisonsa
Table Graphic Jump LocationTable 5. Descriptive Characteristics of Sample 2 Used for Longitudinal Comparisonsa
IMAGE ANALYSIS

Protocols for MRI acquisition were very similar for MCSA and ADNI subjects, although MCSA subjects underwent 3.0-T and ADNI subjects underwent 1.5-T MRI. The ADNI collects 1.5-T MRIs in all subjects and 3.0-T images in only 25% of the sample; therefore, ADNI 1.5-T MRI scans were used for this study. To ensure that field strength did not bias our results, we compared hippocampal volumes at 1.5 T and 3.0 T in ADNI subjects who underwent scanning at both field strengths. Similar to findings of a previous study,21 hippocampal measurements were comparable across field strengths (Figure 1).

Place holder to copy figure label and caption
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Figure 1. A comparison of hippocampal volume measured from scans performed at 1.5-T and 3.0-T. The comparison was performed using 91 subjects from the Alzheimer Disease Neuroimaging Initiative who had undergone a 1.5-T and 3.0-T scan at the same visit (32 cognitively normal [CN] subjects, 39 with amnestic mild cognitive impairment [aMCI], and 20 with Alzheimer disease [AD]). Scatterplots show the 3.0-T vs 1.5-T hippocampal volume (A) and total intracranial volume (TIV) (B). The identity line indicating perfect agreement is shown as a solid black line. The Spearman correlation (r) and Lin's concordance correlation coefficient (CCC), a measure of intraclass correlation, are shown at the top of each plot. The data demonstrate an excellent agreement between 1.5-T and 3.0-T hippocampal volumes and TIV.

The MCSA subjects underwent imaging with a 3-dimensional, magnetization-prepared, rapid-acquisition gradient echo sequence developed at the Mayo Clinic for the ADNI.9 The sequence was acquired in the sagittal plane with a repetition time of 2300 milliseconds, echo time of 3 milliseconds, inversion time of 900 milliseconds, flip angle of 8°, 26-cm field of view, and a 256 × 256 in-plane matrix with a phase field of view of 0.94 and section thickness of 1.2 mm. The ADNI is a multisite study, and minor variations in the MRI protocol are based on the specific hardware/software configuration on each scanner. The nominal variables in the sagittal plane of the ADNI magnetization-prepared rapid-acquisition gradient echo included repetition time of 2400 milliseconds, echo time of 3 milliseconds, inversion time of 1000 milliseconds, flip angle of 8°, 24-cm field of view, a 192 × 192 in-plane matrix, and a section thickness of 1.2 mm.9

All images were corrected for gradient nonlinearity22 and intensity inhomogeneity.23 Hippocampal volumes were measured using Freesurfer software, version 4.5.0 (Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital).24 The cross-sectional analysis pipeline was used to calculate hippocampal volumes for the cross-sectional sample, and the longitudinal analysis pipeline was used to assess rates of hippocampal change for the longitudinal sample. Hippocampal measurements calculated using Freesurfer software have been previously validated against manual measurements.25 Total intracranial volumes (TIVs) were measured using an algorithm developed in-house.26

STATISTICAL ANALYSIS

Statistical analyses were performed in R software, version 2.11.0 (http://www.r-project.org), and tests of statistical significance were conducted at the 2-sided α level of .05. For the cross-sectional analysis, we used hippocampal volume (in cubic centimeters) adjusted for TIV (in cubic centimeters). We fit a linear regression model of hippocampal volume (y) vs TIV (x) in all ADNI and MCSA CN subjects with available data (n = 1480) and then used the intercept (b0) and slope (b1) estimates from the model to calculate hippocampal volume adjusted for TIV (represented as HVa) as a residual using the following equation:

HVa = Hippocampal Volume − (b0 + b1 × TIV).

For the longitudinal analysis, the annual percentage of decline in hippocampal volume was calculated using unadjusted hippocampal volumes (represented as HV) in the following equation:

(Follow-up HV − Baseline HV)/(Baseline HV × Years Between Scans) × 100.

Wilcoxon rank sum and Mann-Whitney tests were used to test differences in continuous measures between the ADNI and MCSA groups, and χ2 tests with continuity correction or Fisher exact test were used to test differences in categorical variables. We summarized group differences in imaging measures using the probabilistic index (corresponding to the area under the receiver operating characteristic curve).27 The probabilistic index is a nonparametric estimate of groupwise differences or discrimination that measures the probability that the value from a randomly selected subject in one group is higher than the value from a randomly selected subject in the other group. A probabilistic index of 0.50 (or 50%) indicates no difference across groups.

SUBJECT DEMOGRAPHICS

Differences in demographic features across the MCSA and ADNI were similar for cross-sectional and longitudinal cohorts (Tables 2, 3, 4, and 5). In sample 1, MCSA subjects (aMCI and CN) were older and less educated and had worse performance on the MMSE than did ADNI subjects. The MCSA aMCI subjects included a lower proportion of APOE ϵ4 carriers than did ADNI subjects. No differences were observed in sex, educational level, or APOE genotype between the MCSA and ADNI subjects in sample 2. Despite frequency matching, age (cross-sectional sample only) and MMSE in the CN subjects still differed across the cohorts, although the median and interquartile ranges were similar. The ADNI CN subjects had a greater proportion of family history of AD and of racial and ethnic minorities across all samples, with a similar trend for aMCI in the cross-sectional sample.

CROSS-SECTIONAL RESULTS

In sample 1, hippocampal volume adjusted for TIV was significantly smaller in the MCSA CN subjects compared with the ADNI CN subjects, with no differences between the groups for the aMCI subjects (Figure 2A). After matching for age, sex, educational level, APOE genotype, and MMSE score in sample 2, no differences in hippocampal volume adjusted for TIV were observed between the MCSA and ADNI in the CN or aMCI subjects (Figure 2B).

Place holder to copy figure label and caption
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Figure 2. Box plots of adjusted hippocampal volumes (HVa) in cognitively normal (CN) subjects and those with amnestic mild cognitive impairment (aMCI). The graphs contrast findings in the Alzheimer Disease Neuroimaging Initiative (ADNI) with findings in the Mayo Clinic Study of Aging (MCSA). Results are shown from 2 simple random samples (A) and 2 samples frequency matched by age, sex, educational level, apolipoprotein E genotype, and Mini-Mental State Examination score (B). The boxes indicate the median and interquartile range (IQR) of the distributions, whereas the vertical lines extending from the boxes stop at the most extreme data points within 1.5 IQRs. Because all individual points are shown, the points have been shifted randomly in the horizontal direction to avoid overlap and to improve the visual display. We summarize groupwise difference using the probabilistic index (PI) and Wilcoxon rank sum P values. A PI of 0.50 indicates no difference across groups, whereas a PI of 0.60 indicates that 60% of the time the hippocampal volume from a random subject in the ADNI is higher than the corresponding value in a random subject from the MCSA.

LONGITUDINAL RESULTS

In sample 1, the annual percentage of decline in hippocampal volume was greater in the ADNI compared with the MCSA for aMCI and CN subjects (Figure 3A). After matching for age, sex, educational level, APOE genotype, and MMSE score in sample 2, these differences across the ADNI and MCSA were still observed (Figure 3B).

Place holder to copy figure label and caption
Graphic Jump Location

Figure 3. Box plots of the annual percentage of decline in hippocampal volume in cognitively normal (CN) subjects and those with amnestic mild cognitive impairment (aMCI). The graphs contrast findings in the Alzheimer Disease Neuroimaging Initiative (ADNI) with findings in the Mayo Clinic Study of Aging (MCSA). Results are shown from 2 simple random samples (A) and 2 samples frequency matched by age, sex, educational level, apolipoprotein E genotype, and Mini-Mental State Examination score (B). Negative values represent a decline in hippocampal volume over time. The boxes indicate the median and interquartile range (IQR) of the distributions, whereas the vertical lines extending from the boxes stop at the most extreme data points within 1.5 IQRs. Because all individual points are shown, the points have been shifted randomly in the horizontal direction to avoid overlap and to improve the visual display. We summarize groupwise difference using the probabilistic index (PI) and Wilcoxon rank sum P values. A PI of 0.50 indicates no difference across groups, whereas a PI of 0.60 indicates that 60% of the time the annual percentage of decline in hippocampal volume from a random subject in the ADNI is greater than the corresponding value in a random subject from the MCSA.

This study highlights demographic differences in subjects recruited into the convenience-sample ADNI cohort compared with subjects recruited into the population-based MCSA cohort and demonstrates that imaging biomarkers from these 2 different recruitment mechanisms differ.

The most striking difference was that rates of decline in hippocampal volume were greater in the ADNI compared with the MCSA for both CN and aMCI subjects. This difference was observed even after matching for key demographic and cognitive variables. Increased rates of decline in hippocampal volume in CN subjects predict a faster rate of progression to dementia,28 suggesting that the ADNI CN population includes a larger proportion of subjects on the path to AD dementia. Although it was somewhat unexpected that the proportion of APOE ϵ4 carriers was not higher among the ADNI CN subjects, our findings are consistent with the unusually high proportion (50%) of ADNI control subjects who showed amyloid pathologic changes as measured by Pittsburgh Compound B.29 By contrast, the proportion of MCSA controls with positive findings for Pittsburgh Compound B was only 30%.30 The pathologic diagnosis of AD was also more common in controls from a clinic vs a community setting in a previous study.31 The ADNI CN subjects were more highly educated than the MCSA CN subjects; therefore, cognitive reserve mechanisms may have protected them from clinical decline even though they are on a steeper downward trajectory of brain atrophy. Similarly, the higher rates of atrophy suggest that the ADNI aMCI group consists of a higher proportion of subjects with more aggressive disease than the MCSA aMCI group. Indeed, the ADNI aMCI subjects had a higher proportion of APOE ϵ4 carriers than those in the MCSA in sample 1. Again, the ADNI aMCI subjects had more education than MCSA aMCI subjects, suggesting that cognitive reserve mechanisms may have protected them from decline on the MMSE and progression to a clinical diagnosis of AD.

We hypothesize that this bias in the ADNI is a result of the recruitment mechanism. We can speculate that CN subjects who are worried about their cognition would be more likely to attend memory clinics and be more motivated to answer advertisements for the study. The CN and aMCI subjects with higher levels of education are also more likely to seek medical help at a memory clinic and become involved in observational studies. These highly educated subjects could have a more aggressive underlying disease but are able to compensate cognitively. Amnestic MCI subjects recruited through a population-based study are less likely to have sought medical care at a memory clinic and may have a broader spectrum of cognitive function. In addition, an important motivator for participation in the ADNI and other convenience studies could be the presence of a family history of dementia. Indeed, ADNI subjects had a higher proportion of family history compared with MCSA subjects. Although one may assume that similar biases would be observed in the MCSA subjects who agreed to undergo imaging, we have demonstrated that this is not the case, likely because less effort was required to agree to undergo imaging than to seek out participation in the ADNI.

The clinical inclusion criteria for CN and aMCI subjects differed slightly across the 2 cohorts. A diagnosis of CN in the MCSA was made by multidisciplinary consensus, which may be more conservative than the method used in the ADNI. Similarly, the diagnosis of aMCI in the MCSA is based on clinical grounds, whereas the ADNI relied more on a specific cutoff point on a memory test. The ADNI approach is likely to result in the recruitment of more impaired subjects. The reason this is not reflected in the MMSE scores could be that having a higher educational level provides a cognitive reserve, and the MMSE may be insensitive to subtle cognitive impairment. The ADNI also recruited younger subjects than the MCSA, which could also have resulted in the recruitment of subjects with more aggressive disease. Rates of atrophy have been found to be greater in younger aMCI subjects,32 possibly because they have purer and hence more aggressive AD pathologic changes compared with older subjects. Older subjects are more likely to have a mixture of pathologic findings,33 including cerebrovascular disease.34,35 However, the trend for greater APOE ϵ4 carrier frequency, younger age, and higher educational levels in convenience samples compared with population-based samples has been observed in other cohorts,3639 suggesting that this bias may be due to the general recruitment mechanism rather than the specific inclusion criteria used in the ADNI. Our findings suggest that CN and aMCI subjects in the ADNI are not representative of the general population and that subjects included in future preclinical prevention trials using the same recruitment mechanisms will also not be representative of the population. Finally, our results indicate that even rigorous demographic matching efforts are insufficient to correct for the selection bias.

The only difference observed in baseline hippocampal volumes between the ADNI and MCSA was in the CN subjects in sample 1, with larger hippocampal volumes observed in the ADNI. This difference is likely being driven by the younger age of the ADNI cohort because hippocampal volume has been shown to decrease with age.40 After matching for demographic features, no differences in hippocampal volume were observed across cohorts. Cross-sectional hippocampal volumes also did not differ across the ADNI and MCSA within the aMCI subjects in sample 1 despite the observed differences in age, educational level, APOE genotype, and MMSE score. This finding could suggest that rates of decline in hippocampal volume are more sensitive markers of incident AD than cross-sectional hippocampal volume, perhaps because of the large degree of intersubject variability in hippocampal volume. Total intracranial volume also differed between the MCSA and ADNI. We suspect that MCSA subjects have larger TIVs because of the northern European heritage of many Minnesotan residents and the link between these nationalities and tall height.41

The strengths of this study include the large numbers of subjects and the use of 2 samples with and without restrictive correction for major demographic or cognitive confounders. A limitation, however, is that, although matching was performed on the major demographic factors, it may not eliminate other potential differences, such as other comorbidities, medication use, family history, and race and ethnicity, that may influence the imaging findings. The ADNI included a higher proportion of ethnic and racial minorities than did the MCSA. The MCSA and ADNI cohorts underwent imaging at different field strengths; however, we demonstrated excellent agreement between hippocampal volumes measured across field strengths (Figure 1). Scan intervals also differed between the ADNI and MCSA, although we adjusted for these differences. Although atrophy rates have been shown to accelerate over time in AD,32 the trajectory of change is likely to be approximately linear during these relatively short intervals. Finally, although the MCSA is a population-based study, some inherent participation biases may exist,12 as is the case with any survey. However, the MCSA is representative of Olmsted County in Minnesota and of white individuals in the United States in general. The incidence of MCI and the demographic predictors of incident MCI in the MCSA are also similar to those reported in other population-based studies,4244 including studies that have assessed other racial groups.45

Overall, our findings show that subjects recruited into the ADNI are not representative of the general population and instead more closely resemble clinical populations. The imaging findings all point toward the ADNI including more CN subjects who are on the path to AD dementia and more aMCI subjects who have a pure and aggressive disease phenotype. Therefore, convenience clinical series may be limited by selection biases. These findings have important implications for the design of future treatment trials. If studies that assess power calculations and sample size estimates are performed in biased convenience samples, the high rates of atrophy will lead to smaller-than-appropriate sample size estimates, and therefore trials could be underpowered to detect treatment effects in the population. In addition, treatment trials that use a convenience sample will include a higher proportion of subjects with a pure and aggressive disease and hence are more likely to detect a treatment effect. However, the magnitude of the treatment effect is likely to be less than expected when the treatment is applied to an unbiased population, in which subjects are less likely to have pure AD. Care should also be taken when interpreting imaging studies from convenience samples such as the ADNI. Biomarkers identified from these highly selected convenience samples may not perfectly translate to the general population, and the findings will need to be validated in a population-based sample. This implication will be particularly important for studies seeking to validate new diagnostic criteria for AD in its various stages, in which imaging biomarkers play an important role.

Correspondence: Jennifer L. Whitwell, PhD, Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (whitwell.jennifer@mayo.edu).

Accepted for Publication: November 1, 2011.

Author Contributions:Study concept and design: Whitwell, Weigand, and Jack. Acquisition of data: Roberts and Jack. Analysis and interpretation of data: Whitwell, Wiste, Weigand, Rocca, Knopman, Boeve, Petersen, and Jack. Drafting of the manuscript: Whitwell and Jack. Critical revision of the manuscript for important intellectual content: Wiste, Weigand, Rocca, Knopman, Roberts, Boeve, Petersen, and Jack. Statistical analysis: Wiste, Weigand, Rocca, and Jack. Obtained funding: Roberts and Jack. Administrative, technical, and material support: Roberts, Boeve, and Jack. Study supervision: Jack.

Financial Disclosure: None reported.

Funding/Support: This study was supported by grants U01-AG024904-01, R01-AG11378, P50-AG16574, U01-AG06786, R21-AG38736, R01-DC010367, R01-AG037491, K01-AG028573, U24-AG026395, R01-AG15866, R01-AG034676, R01-AG023195, and R01-HL70825 from the National Institutes of Health (NIH); grant 90BC0009 from the Department of Health and Human Services/Office of the Secretary; the Dana Foundation; the Alexander Family Alzheimer's Disease Research Professorship of the Mayo Foundation; and the Robert H. and Clarice Smith and Abigail Van Buren Alzheimer's Disease Research Program of the Mayo Foundation. Data collection and sharing for this project was funded by grant U01-AG024904 from the NIH (ADNI). The ADNI is funded by the National Institute on Aging; the National Institute of Biomedical Imaging and Bioengineering; and through generous contributions from Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co, Medpace, Inc, Merck and Co, Inc, Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc, and nonprofit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the US Food and Drug Administration. Private sector contributions to the ADNI are facilitated by the Foundation for the NIH (http:// www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. The ADNI data are disseminated by the Laboratory for Neuroimaging at the University of California, Los Angeles.

Additional Contributions: Data used in preparation of this article were obtained from the ADNI database (http://adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of the ADNI and/or provided data but did not participate in data analysis or in the writing of this report.

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PubMed
Harris ME, Ivnik RJ, Smith GE. Mayo's Older Americans Normative Studies: expanded AVLT Recognition Trial norms for ages 57 to 98.  J Clin Exp Neuropsychol. 2002;24(2):214-220
PubMed
Petersen RC. Mild cognitive impairment as a diagnostic entity.  J Intern Med. 2004;256(3):183-194
PubMed
Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules.  Neurology. 1993;43(11):2412-2414
PubMed
American Psychiatric Association.  Diagnostic and Statistical Manual of Mental Disordersed 4. Washington, DC: American Psychiatric Association; 1994
Folstein MF, Folstein SE, McHugh PR. “Mini-Mental State:” a practical method for grading the cognitive state of patients for the clinician.  J Psychiatr Res. 1975;12(3):189-198
PubMed
Briellmann RS, Syngeniotis A, Jackson GD. Comparison of hippocampal volumetry at 1.5 Tesla and at 3 Tesla.  Epilepsia. 2001;42(8):1021-1024
PubMed
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
PubMed
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
PubMed
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
PubMed
Morey RA, Petty CM, Xu Y,  et al.  A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes.  Neuroimage. 2009;45(3):855-866
PubMed
Whitwell JL, Jack CR Jr, Boeve BF,  et al.  Imaging correlates of pathology in corticobasal syndrome.  Neurology. 2010;75(21):1879-1887
PubMed
Acion L, Peterson JJ, Temple S, Arndt S. Probabilistic index: an intuitive non-parametric approach to measuring the size of treatment effects.  Stat Med. 2006;25(4):591-602
PubMed
den Heijer T, van der Lijn F, Koudstaal PJ,  et al.  A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline.  Brain. 2010;133(pt 4):1163-1172
PubMed
Jagust WJ, Landau SM, Shaw LM,  et al; Alzheimer's Disease Neuroimaging Initiative.  Relationships between biomarkers in aging and dementia.  Neurology. 2009;73(15):1193-1199
PubMed
Jack CR Jr, Lowe VJ, Senjem ML,  et al.  11C PiB and structural MRI provide complementary information in imaging of Alzheimer's disease and amnestic mild cognitive impairment.  Brain. 2008;131(pt 3):665-680
PubMed
Schneider JA, Aggarwal NT, Barnes L, Boyle P, Bennett DA. The neuropathology of older persons with and without dementia from community versus clinic cohorts.  J Alzheimers Dis. 2009;18(3):691-701
PubMed
Jack CR Jr, Weigand SD, Shiung MM,  et al.  Atrophy rates accelerate in amnestic mild cognitive impairment.  Neurology. 2008;70(19, pt 2):1740-1752
PubMed
Jellinger KA, Attems J. Prevalence of dementia disorders in the oldest-old: an autopsy study.  Acta Neuropathol. 2010;119(4):421-433
PubMed
Jicha GA, Parisi JE, Dickson DW,  et al.  Neuropathologic outcome of mild cognitive impairment following progression to clinical dementia.  Arch Neurol. 2006;63(5):674-681
PubMed
Schneider JA, Arvanitakis Z, Leurgans SE, Bennett DA. The neuropathology of probable Alzheimer disease and mild cognitive impairment.  Ann Neurol. 2009;66(2):200-208
PubMed
Barnhart RL, van Belle G, Edland SD,  et al.  Geographically overlapping Alzheimer's disease registries: comparisons and implications.  J Geriatr Psychiatry Neurol. 1995;8(4):203-208
PubMed
Kokmen E, Ozsarfati Y, Beard CM, O’Brien PC, Rocca WA. Impact of referral bias on clinical and epidemiological studies of Alzheimer's disease.  J Clin Epidemiol. 1996;49(1):79-83
PubMed
Knopman DS, Petersen RC, Rocca WA, Larson EB, Ganguli M. Passive case-finding for Alzheimer's disease and dementia in two U.S. communities.  Alzheimers Dement. 2011;7(1):53-60
PubMed
Tsuang D, Kukull W, Sheppard L,  et al.  Impact of sample selection on APOE epsilon 4 allele frequency: a comparison of two Alzheimer's disease samples.  J Am Geriatr Soc. 1996;44(6):704-707
PubMed
Mu Q, Xie J, Wen Z, Weng Y, Shuyun Z. A quantitative MR study of the hippocampal formation, the amygdala, and the temporal horn of the lateral ventricle in healthy subjects 40 to 90 years of age.  AJNR Am J Neuroradiol. 1999;20(2):207-211
PubMed
Komlos J, Breitfelder A. Are Americans shorter (partly) because they are fatter? a comparison of US and Dutch children's height and BMI values.  Ann Hum Biol. 2007;34(6):593-606
PubMed
Caracciolo B, Palmer K, Monastero R, Winblad B, Bäckman L, Fratiglioni L. Occurrence of cognitive impairment and dementia in the community: a 9-year-long prospective study.  Neurology. 2008;70(19, pt 2):1778-1785
PubMed
Luck T, Luppa M, Briel S,  et al.  Mild cognitive impairment: incidence and risk factors: results of the Leipzig Longitudinal Study of the Aged.  J Am Geriatr Soc. 2010;58(10):1903-1910
PubMed
Roberts RO, Geda YE, Knopman DS,  et al.  The incidence of MCI differs by subtype and is higher in men: the Mayo Clinic Study of Aging.  Neurology. 2012;78(5):342-351
PubMed
Unverzagt FW, Ogunniyi A, Taler V,  et al.  Incidence and risk factors for cognitive impairment no dementia and mild cognitive impairment in African Americans.  Alzheimer Dis Assoc Disord. 2011;25(1):4-10
PubMed

Figures

Place holder to copy figure label and caption
Graphic Jump Location

Figure 1. A comparison of hippocampal volume measured from scans performed at 1.5-T and 3.0-T. The comparison was performed using 91 subjects from the Alzheimer Disease Neuroimaging Initiative who had undergone a 1.5-T and 3.0-T scan at the same visit (32 cognitively normal [CN] subjects, 39 with amnestic mild cognitive impairment [aMCI], and 20 with Alzheimer disease [AD]). Scatterplots show the 3.0-T vs 1.5-T hippocampal volume (A) and total intracranial volume (TIV) (B). The identity line indicating perfect agreement is shown as a solid black line. The Spearman correlation (r) and Lin's concordance correlation coefficient (CCC), a measure of intraclass correlation, are shown at the top of each plot. The data demonstrate an excellent agreement between 1.5-T and 3.0-T hippocampal volumes and TIV.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 2. Box plots of adjusted hippocampal volumes (HVa) in cognitively normal (CN) subjects and those with amnestic mild cognitive impairment (aMCI). The graphs contrast findings in the Alzheimer Disease Neuroimaging Initiative (ADNI) with findings in the Mayo Clinic Study of Aging (MCSA). Results are shown from 2 simple random samples (A) and 2 samples frequency matched by age, sex, educational level, apolipoprotein E genotype, and Mini-Mental State Examination score (B). The boxes indicate the median and interquartile range (IQR) of the distributions, whereas the vertical lines extending from the boxes stop at the most extreme data points within 1.5 IQRs. Because all individual points are shown, the points have been shifted randomly in the horizontal direction to avoid overlap and to improve the visual display. We summarize groupwise difference using the probabilistic index (PI) and Wilcoxon rank sum P values. A PI of 0.50 indicates no difference across groups, whereas a PI of 0.60 indicates that 60% of the time the hippocampal volume from a random subject in the ADNI is higher than the corresponding value in a random subject from the MCSA.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 3. Box plots of the annual percentage of decline in hippocampal volume in cognitively normal (CN) subjects and those with amnestic mild cognitive impairment (aMCI). The graphs contrast findings in the Alzheimer Disease Neuroimaging Initiative (ADNI) with findings in the Mayo Clinic Study of Aging (MCSA). Results are shown from 2 simple random samples (A) and 2 samples frequency matched by age, sex, educational level, apolipoprotein E genotype, and Mini-Mental State Examination score (B). Negative values represent a decline in hippocampal volume over time. The boxes indicate the median and interquartile range (IQR) of the distributions, whereas the vertical lines extending from the boxes stop at the most extreme data points within 1.5 IQRs. Because all individual points are shown, the points have been shifted randomly in the horizontal direction to avoid overlap and to improve the visual display. We summarize groupwise difference using the probabilistic index (PI) and Wilcoxon rank sum P values. A PI of 0.50 indicates no difference across groups, whereas a PI of 0.60 indicates that 60% of the time the annual percentage of decline in hippocampal volume from a random subject in the ADNI is greater than the corresponding value in a random subject from the MCSA.

Tables

Table Graphic Jump LocationTable 1. Representativeness of the MCSA Imaging Sample
Table Graphic Jump LocationTable 2. Descriptive Characteristics of Sample 1 Used for Cross-sectional Comparisonsa
Table Graphic Jump LocationTable 3. Descriptive Characteristics of Sample 2 Used for Cross-sectional Comparisonsa
Table Graphic Jump LocationTable 4. Descriptive Characteristics of Sample 1 Used for Longitudinal Comparisonsa
Table Graphic Jump LocationTable 5. Descriptive Characteristics of Sample 2 Used for Longitudinal Comparisonsa

References

Whitwell JL, Przybelski SA, Weigand SD,  et al.  3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer's disease.  Brain. 2007;130(pt 7):1777-1786
PubMed   |  Link to Article
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
PubMed
Chételat G, Desgranges B, De La Sayette V, Viader F, Eustache F, Baron JC. Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment.  Neuroreport. 2002;13(15):1939-1943
PubMed
Jack CR Jr, Petersen RC, Xu YC,  et al.  Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment.  Neurology. 1999;52(7):1397-1403
PubMed
Ferreira LK, Diniz BS, Forlenza OV, Busatto GF, Zanetti MV. Neurostructural predictors of Alzheimer's disease: a meta-analysis of VBM studies [published online December 11, 2009].  Neurobiol Agingdoi:10.1016/j.neurobiolaging.2009.11.008
Jack CR Jr, Slomkowski M, Gracon S,  et al.  MRI as a biomarker of disease progression in a therapeutic trial of milameline for AD.  Neurology. 2003;60(2):253-260
PubMed
Schott JM, Frost C, Whitwell JL,  et al.  Combining short interval MRI in Alzheimer's disease: implications for therapeutic trials.  J Neurol. 2006;253(9):1147-1153
PubMed
Mueller SG, Weiner MW, Thal LJ,  et al.  The Alzheimer's Disease Neuroimaging Initiative.  Neuroimaging Clin N Am. 2005;15(4):869-877, xi-xii
PubMed
Jack CR Jr, Bernstein MA, Fox NC,  et al.  The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods.  J Magn Reson Imaging. 2008;27(4):685-691
PubMed
Jack CR Jr, Dickson DW, Parisi JE,  et al.  Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia.  Neurology. 2002;58(5):750-757
PubMed
Fox NC, Warrington EK, Freeborough PA,  et al.  Presymptomatic hippocampal atrophy in Alzheimer's disease: a longitudinal MRI study.  Brain. 1996;119(pt 6):2001-2007
PubMed
Roberts RO, Geda YE, Knopman DS,  et al.  The Mayo Clinic Study of Aging: design and sampling, participation, baseline measures and sample characteristics.  Neuroepidemiology. 2008;30(1):58-69
PubMed
Melton LJ III. History of the Rochester Epidemiology Project.  Mayo Clin Proc. 1996;71(3):266-274
PubMed
St Sauver JL, Grossardt BR, Yawn BP, Melton LJ III, Rocca WA. Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester Epidemiology Project.  Am J Epidemiol. 2011;173(9):1059-1068
PubMed
Petersen RC, Roberts RO, Knopman DS,  et al; Mayo Clinic Study of Aging.  Prevalence of mild cognitive impairment is higher in men.  Neurology. 2010;75(10):889-897
PubMed
Harris ME, Ivnik RJ, Smith GE. Mayo's Older Americans Normative Studies: expanded AVLT Recognition Trial norms for ages 57 to 98.  J Clin Exp Neuropsychol. 2002;24(2):214-220
PubMed
Petersen RC. Mild cognitive impairment as a diagnostic entity.  J Intern Med. 2004;256(3):183-194
PubMed
Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules.  Neurology. 1993;43(11):2412-2414
PubMed
American Psychiatric Association.  Diagnostic and Statistical Manual of Mental Disordersed 4. Washington, DC: American Psychiatric Association; 1994
Folstein MF, Folstein SE, McHugh PR. “Mini-Mental State:” a practical method for grading the cognitive state of patients for the clinician.  J Psychiatr Res. 1975;12(3):189-198
PubMed
Briellmann RS, Syngeniotis A, Jackson GD. Comparison of hippocampal volumetry at 1.5 Tesla and at 3 Tesla.  Epilepsia. 2001;42(8):1021-1024
PubMed
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
PubMed
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
PubMed
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
PubMed
Morey RA, Petty CM, Xu Y,  et al.  A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes.  Neuroimage. 2009;45(3):855-866
PubMed
Whitwell JL, Jack CR Jr, Boeve BF,  et al.  Imaging correlates of pathology in corticobasal syndrome.  Neurology. 2010;75(21):1879-1887
PubMed
Acion L, Peterson JJ, Temple S, Arndt S. Probabilistic index: an intuitive non-parametric approach to measuring the size of treatment effects.  Stat Med. 2006;25(4):591-602
PubMed
den Heijer T, van der Lijn F, Koudstaal PJ,  et al.  A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline.  Brain. 2010;133(pt 4):1163-1172
PubMed
Jagust WJ, Landau SM, Shaw LM,  et al; Alzheimer's Disease Neuroimaging Initiative.  Relationships between biomarkers in aging and dementia.  Neurology. 2009;73(15):1193-1199
PubMed
Jack CR Jr, Lowe VJ, Senjem ML,  et al.  11C PiB and structural MRI provide complementary information in imaging of Alzheimer's disease and amnestic mild cognitive impairment.  Brain. 2008;131(pt 3):665-680
PubMed
Schneider JA, Aggarwal NT, Barnes L, Boyle P, Bennett DA. The neuropathology of older persons with and without dementia from community versus clinic cohorts.  J Alzheimers Dis. 2009;18(3):691-701
PubMed
Jack CR Jr, Weigand SD, Shiung MM,  et al.  Atrophy rates accelerate in amnestic mild cognitive impairment.  Neurology. 2008;70(19, pt 2):1740-1752
PubMed
Jellinger KA, Attems J. Prevalence of dementia disorders in the oldest-old: an autopsy study.  Acta Neuropathol. 2010;119(4):421-433
PubMed
Jicha GA, Parisi JE, Dickson DW,  et al.  Neuropathologic outcome of mild cognitive impairment following progression to clinical dementia.  Arch Neurol. 2006;63(5):674-681
PubMed
Schneider JA, Arvanitakis Z, Leurgans SE, Bennett DA. The neuropathology of probable Alzheimer disease and mild cognitive impairment.  Ann Neurol. 2009;66(2):200-208
PubMed
Barnhart RL, van Belle G, Edland SD,  et al.  Geographically overlapping Alzheimer's disease registries: comparisons and implications.  J Geriatr Psychiatry Neurol. 1995;8(4):203-208
PubMed
Kokmen E, Ozsarfati Y, Beard CM, O’Brien PC, Rocca WA. Impact of referral bias on clinical and epidemiological studies of Alzheimer's disease.  J Clin Epidemiol. 1996;49(1):79-83
PubMed
Knopman DS, Petersen RC, Rocca WA, Larson EB, Ganguli M. Passive case-finding for Alzheimer's disease and dementia in two U.S. communities.  Alzheimers Dement. 2011;7(1):53-60
PubMed
Tsuang D, Kukull W, Sheppard L,  et al.  Impact of sample selection on APOE epsilon 4 allele frequency: a comparison of two Alzheimer's disease samples.  J Am Geriatr Soc. 1996;44(6):704-707
PubMed
Mu Q, Xie J, Wen Z, Weng Y, Shuyun Z. A quantitative MR study of the hippocampal formation, the amygdala, and the temporal horn of the lateral ventricle in healthy subjects 40 to 90 years of age.  AJNR Am J Neuroradiol. 1999;20(2):207-211
PubMed
Komlos J, Breitfelder A. Are Americans shorter (partly) because they are fatter? a comparison of US and Dutch children's height and BMI values.  Ann Hum Biol. 2007;34(6):593-606
PubMed
Caracciolo B, Palmer K, Monastero R, Winblad B, Bäckman L, Fratiglioni L. Occurrence of cognitive impairment and dementia in the community: a 9-year-long prospective study.  Neurology. 2008;70(19, pt 2):1778-1785
PubMed
Luck T, Luppa M, Briel S,  et al.  Mild cognitive impairment: incidence and risk factors: results of the Leipzig Longitudinal Study of the Aged.  J Am Geriatr Soc. 2010;58(10):1903-1910
PubMed
Roberts RO, Geda YE, Knopman DS,  et al.  The incidence of MCI differs by subtype and is higher in men: the Mayo Clinic Study of Aging.  Neurology. 2012;78(5):342-351
PubMed
Unverzagt FW, Ogunniyi A, Taler V,  et al.  Incidence and risk factors for cognitive impairment no dementia and mild cognitive impairment in African Americans.  Alzheimer Dis Assoc Disord. 2011;25(1):4-10
PubMed

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Supplemental Content

Whitwell JL, Wiste HJ, Weigand SD, et al. Comparison of imaging biomarkers in the Alzheimer Disease Neuroimaging Initiative and the Mayo Clinic Study of Aging. Arch Neurol. 2012;69(5):614-622..

eTable 1. Descriptive characteristics of all available subjects compared with subjects included in the simple random sample (sample 1)

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