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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Neurobiol Aging. Author manuscript; available in PMC 2011 August 1.
Published in final edited form as:
PMCID: PMC2927200
NIHMSID: NIHMS214423

Sex and age differences in atrophic rates: an ADNI study with N=1368 MRI scans

Xue Hua, PhD,a Derrek P. Hibar, BS,a Suh Lee, BS,a Arthur W. Toga, PhD,a Clifford R Jack, Jr, MD,b Michael W. Weiner, MD,c,d,e Paul M. Thompson, PhD,a and Alzheimer's Disease Neuroimaging Initiative*

Abstract

We set out to determine factors that influence the rate of brain atrophy in 1-year longitudinal MRI data. With tensor-based morphometry (TBM), we mapped the 3D profile of progressive atrophy in 144 subjects with probable AD (age: 76.5±7.4 years), 338 with amnestic mild cognitive impairment (MCI; 76.0±7.2), and 202 healthy controls (77.0±5.1), scanned twice 1-year apart. Statistical maps revealed significant age and sex differences in atrophic rates. Brain atrophic rates were about 1– 1.5% faster in women than men. Atrophy was faster in younger than older subjects, most prominently in MCI, with a 1% increase in the rates of atrophy and 2% in ventricular expansion, for every 10-year decrease in age. TBM-derived atrophic rates correlated with reduced beta-amyloid and elevated tau levels (N=363) at baseline, baseline and progressive deterioration in clinical measures, and increasing numbers of risk alleles for the ApoE4 gene. TBM is a sensitive, high-throughput biomarker for tracking disease progression in large imaging studies; sub-analyses focusing on women or younger subjects gave improved sample size requirements for clinical trials.

Keywords: Alzheimer’s disease, Mild cognitive impairment, MRI, Longitudinal, Tensor-based morphometry, Age, Sex effect, Atrophy rate, Neuroimaging, Biomarker, Drug trial enrichment

1. Introduction

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by pathological accumulation of misfolded beta-amyloid (Aβ) peptides in the neuropil, and hyperphosphorylated tau (p-tau) proteins in neurons (Selkoe, 2004, Skovronsky, et al., 2006). The macroscopic effects of neuronal atrophy, cell death and myelin impairment are detectable on high-resolution structural magnetic resonance imaging (MRI), offering an in vivo index of progressive brain deterioration. AD pathology accumulates up to two decades before overt cognitive decline, and minimally symptomatic subjects, with mild cognitive impairment (MCI) (Petersen, et al., 2001, Petersen, 2003), are a key target in clinical trials (Grundman, et al., 2004). Various imaging measures have been proposed as biomarkers of the disease, reflecting different aspects of AD pathology. Efforts are underway to assess their power for diagnosis, predicting future decline, and sensitivity to the effects of potential disease-modifying treatments (Shaw, et al., 2007, Frisoni, et al., 2009, Jagust, et al., 2009).

Longitudinal brain MRI can be used to track disease progression with high precision and statistical power (Leow, et al., 2006, Hua, et al., 2009). Brain MRI scans can be analyzed with automated or semi-automated methods to measure hippocampal atrophy (Jack, et al., 2004, Thompson, et al., 2004, Chetelat, et al., 2008, Morra, et al., 2009a, Morra, et al., 2009b, Schuff, et al., 2009), ventricular enlargement (Jack, et al., 2003, Thompson, et al., 2004, Carmichael, et al., 2006, Chou, et al., 2008, Nestor, et al., 2008, Chou, et al., 2009a, Chou, et al., 2009b), or whole brain atrophy (Fox, et al., 1999, Fox, et al., 2000, Smith, et al., 2002, Smith, et al., 2004, Sluimer, et al., 2008). The trajectory of brain atrophy on structural MRI largely mirrors the anatomical pattern and trajectory of neurofibrillary tangle deposition (Chetelat, et al., 2002, Thompson, et al., 2003, Vemuri, et al., 2008, Whitwell, et al., 2008, Vemuri, et al., 2009), correlates with clinical decline (Fox, et al., 1999, Thompson, et al., 2004, Hua, et al., 2008b, Evans, et al., 2009, Jack, et al., 2009, Leow, et al., 2009), and predicts future conversion from preclinical to symptomatic AD (Jack, et al., 1999, Apostolova, et al., 2006, Chetelat, et al., 2008, Hua, et al., 2008b, Misra, et al., 2009, Risacher, et al., 2009, Vemuri, et al., 2009), suggesting that MRI measures are useful outcome measures for early diagnosis (Chetelat and Baron, 2003) and clinical trials (Mueller, et al., 2005b, Mueller, et al., 2006, Shaw, et al., 2007, Halperin, et al., 2009, Frisoni, et al., 2010, Hill, 2010).

As AD progresses slowly, drug trials are usually under-powered to detect subtle therapeutic effects in a reasonable time interval, given the high cost of scanning large numbers of subjects. Several sample “enrichment” strategies have been proposed to selectively target subjects most likely to decline based on their genotypes (e.g., ApoE4 carriers, those with abnormal Aβ precursor protein genes, presenilin 1 and 2) (Saunders, et al., 1993, 1998), MRI markers of early AD (e.g., hippocampal or entorhinal atrophy) (Frisoni, et al., 1999, Du, et al., 2001, Jack, et al., 2004, Devanand, et al., 2007, Morra, et al., 2009b), or cerebrospinal fluid (CSF) biomarker profiles (e.g. Aβ, tau, p-tau) (Clark, et al., 2003, de Leon, et al., 2006, Hansson, et al., 2006, Ibach, et al., 2006), to reduce patient heterogeneity and improve statistical power in trials (Frank, et al., 2003, Thal, et al., 2006, Shaw, et al., 2007, Clark, et al., 2008). If factors influencing atrophic rates were better understood, they could be used, in principle, to stratify cohorts into subgroups of subjects most likely to decline. Sex and age differences in atrophic rates are still poorly understood: atrophic rates may be faster in young versus older MCI subjects (Jack, et al., 2008c), and greater atrophy is seen in early- versus late-onset AD (Frisoni, et al., 2007). Women may have higher risk of developing AD than men (Gao, et al., 1998) and, relative to men, women with AD may suffer from greater cognitive impairments (Henderson and Buckwalter, 1994, Fleisher, et al., 2005, Moreno-Martinez, et al., 2008, Bai, et al., 2009), greater functional disability (Dodge, et al., 2003), and more frontal metabolic impairment (Herholz, et al., 2002). Even so, MRI evidence of a “sexual dimorphism” in AD is still lacking. Most of the studies to date are underpowered, i.e. do not have a large enough sample size to detect a subtle sex effect on atrophic rates.

Here we assessed how brain atrophic rates depend on age and sex, in one of the largest MRI studies to date, in the hope that adjusting for these factors might enhance the power to track brain atrophy and factors that influence it. We related atrophic rates to other AD biomarkers, including Aβ, tau, and hyperphosphorylated tau (p-tau) levels in the CSF. We correlated atrophic rates with well known and candidate risk genes (ApoE and GRIN2b). We hypothesized that there would be age and sex differences in atrophy rates, in a diffuse pattern through the brain. We also attempted to rank the clinical variables in terms of their strength of association with rates of atrophy. We hypothesized that atrophic rates might correlate more strongly with cognitive scores, both at baseline and their rates of decline, than with changes in CSF biomarkers, which have poorer temporal reproducibility. We also explored some implications of these correlations for boosting power in clinical trials.

2. Methods

2.1. Subjects

Baseline and 1-year follow-up brain MRI scans were downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public database (http://www.loni.ucla.edu/ADNI/Data/) on or before June 1, 2009, and reflect the status of the database at that point; as data collection is ongoing, we focused on analyzing all available baseline and 1-year follow-up scans, together with the associated demographic information, ApoE genotypes, CSF biomarker measures (for Aβ, tau, p-tau), and clinical and cognitive data based on functional and behavioral assessments.. ADNI is a large five-year study launched in 2004 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a $60 million public-private partnership. The primary goal of ADNI has been to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessments acquired at multiple sites (as in a typical clinical trial), can replicate results from smaller single site studies measuring the progression of MCI and early AD. Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to monitor the effectiveness of new treatments, and lessen the time and cost of clinical trials. The Principal Investigator of this initiative is Michael W. Weiner, M.D., VA Medical Center and University of California, San Francisco.

We analyzed 1,368 brain MRI scans, from 144 probable AD patients (age at baseline: 76.5±7.4 years), 338 individuals with amnestic mild cognitive impairment (MCI; 76.0±7.2), and 202 healthy elderly controls (CTL; 77.0±5.1), each scanned twice one year apart. ADNI patients are scanned at other intervals, but here were focused on the one-year follow-up data, as such an interval is common in clinical trials, and we wanted to focus on an interval over which changes would be readily detectable. All AD patients met NINCDS/ADRDA criteria for probable AD (McKhann, et al., 1984). ADNI inclusion and exclusion criteria (Mueller, et al., 2005a, Mueller, et al., 2005b), are detailed online at http://www.alzheimers.org/clinicaltrials/fullrec.asp?PrimaryKey=208.

All subjects (N=684, consisting of 144 ADs, 338 MCIs, and 202 controls) completed thorough clinical and cognitive assessments at the time of baseline scan. During the one year follow-up, 660 (122 ADs, 336 MCIs, and 202 controls) of them completed an additional set of clinical and cognitive tests. Cognitive tests examined here included the Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog), a 70-point scale designed to measure the severity of cognitive impairment; this is currently the most widely used cognitive measure in AD trials (Rosen, et al., 1984, Mohs, 1994). It consists of 11 tasks assessing learning and memory, language production and comprehension, constructional and ideational praxis, and orientation. The Mini-Mental State Examination (MMSE) provides a global measure of mental status, evaluating five cognitive domains: orientation, registration, attention and calculation, recall, and language (Folstein, et al., 1975, Cockrell and Folstein, 1988). The Rey Auditory Verbal Learning Test (AVLT) evaluates learning and memory functions by assessing the ability to recall a list of 15 words, both immediately after each of the five learning trials (AVLT-5), and after a 30-minute delay (AVLT-del) (Rey, 1964). The Logical Memory (LM) test is a modified version of the episodic memory assessment from the Wechsler Memory Scale-Revised (WMS-R) (Wechsler, 1987). Subjects were asked to recall a short story consisted of 25 pieces of information, both immediately after it was read to the subject (LM-im), and after a 30 minute delay (LM-del). Functional and behavioral assessments, analyzed here, included the sum-of-boxes Clinical Dementia Rating (CDR-SB), ranging from 0 to 18. The CDR-SB measures dementia severity by evaluating patients’ performance in six domains: memory, orientation, judgment and problem solving, community affairs, home and hobbies, and personal care (Hughes, et al., 1982, Berg, 1988, Morris, 1993). Finally, the Functional Assessment Questionnaire (FAR) summarizes the functional activities of daily living (Pfeffer, et al., 1982). Medical histories of cardiovascular, endocrine-metabolic, gastrointestinal disorders, alcohol abuse, drug abuse, and smoking were obtained at the screening visit from the participant and the study partner. Complete details of the ADNI assessments are found in the ADNI Procedures Manual (http://adni-info.org/images/stories/adniproceduresmanual12.pdf) and www.ADNI-info.org.

The study was conducted according to the Good Clinical Practice guidelines, the Declaration of Helsinki and U.S. 21 CFR Part 50-Protection of Human Subjects, and Part 56-Institutional Review Boards. Written informed consent was obtained from all participants.

2.2. CSF biomarkers

CSF samples were obtained from a subset of the ADNI subjects through lumbar puncture, after an overnight fast. Samples collected at various sites were transferred, on dry ice, to the ADNI Biomarker Core Laboratory at the University of Pennsylvania Medical Center. Levels of Aβ 1–42 peptide, total tau, and tau phosphorylated at the threonine 181 (p-tau) were measured in 363 subjects at baseline (83 AD, 173 MCI, and 107 CTL), and in 251 subjects at 1-year follow-up (50 AD, 122 MCI, and 79 CTL).

2.3. Genotyping

ApoE and genome-wide genotyping were performed on DNA samples obtained from subjects’ blood. Genomic DNA samples were analyzed on the Human610-Quad BeadChip (Illumina, Inc. San Diego, CA) at the University of Pennsylvania (see www.adni-info.org for detailed information on blood sample collection, DNA preparation, and single nucleotide polymorphism (SNP) genotyping methods). We also assessed the effect of a common genetic variant in the GRIN2B gene, a subunit of the NMDA-type glutamate receptor, at SNP rs-10845840, which we previously found was associated with bilateral temporal lobe volume in a genome-wide study of the ADNI data (Stein, et al., 2010) using the Plink software (Purcell, et al., 2007). This SNP encodes a polymorphism in the glutamate receptor, and is over-represented in AD versus controls and is associated with cognitive decline (Stein, et al., 2010).

2.4. MRI acquisition and image correction

Scans were acquired on 1.5T MR scanners at 60 sites across the United States and Canada. Although different type of scanners (GE, Siemens, or Philips) and various software platforms were used, a standardized MRI protocol ensured cross-site comparability (Jack, et al., 2008a). A typical 1.5T MR protocol involved a 3D sagittal MP-RAGE scan with repetition time (TR): 2400 ms, minimum full TE, inversion time (TI): 1000 ms, flip angle: 8°, 24 cm field of view, and a 192×192×166 acquisition matrix in the x-, y-, and z- dimensions, yielding a voxel size of 1.25×1.25×1.2 mm3, later reconstructed to 1 mm isotropic voxels.

Image corrections were applied using a processing pipeline at the Mayo Clinic, consisting of: (1) correction of geometric distortion due to gradient non-linearity (Jovicich, et al., 2006), i.e. "gradwarp" (2) “B1-correction” to adjust for image intensity inhomogeneity due to B1 non-uniformity (Jack, et al., 2008a), (3) “N3” bias field correction for reducing residual intensity inhomogeneity (Sled, et al., 1998), and (4) geometrical scaling to remove scanner- and session-specific calibration errors using a phantom scan acquired for each subject (Gunter, et al., 2006). All original image files as well as all corrected images are available at http://www.loni.ucla.edu/ADNI/Data/.

2.5. Image pre-processing

First, each subject’s follow-up scan was linearly registered to their baseline scan, with a 9-parameter (9P) transformation driven by a mutual information (MI) cost function (Collins, et al., 1994), to adjust for linear differences in position and scale across time. 9P registration can correct for scanner voxel size variations in large longitudinal studies involving multiple sites, scanners and acquisition sequences (Clarkson, et al., 2009), consistently outperforming 6P registration in terms of statistical power (Paling, et al., 2004, Hua, et al., 2009). Second, to account for global differences in brain scale across subjects, the mutually aligned scan pairs were then linearly registered to the International Consortium for Brain Mapping template (ICBM-53) (Mazziotta, et al., 2001), applying the same 9P transformation to both mutually aligned scans. Globally aligned images were re-sampled in an isotropic space of 220 voxels along x-, y- and z-dimensions with a final voxel size of 1 mm3.

2.6. Tensor-based morphometry (TBM) and 3D maps of atrophic rates

Individual maps of atrophic rates (also known as “Jacobian maps”) were derived from a TBM analysis of MRI scans acquired one year apart. These maps represent the rates of tissue shrinkage (or CSF space expansion) at each voxel location in the brain. A Jacobian map was created by nonlinearly warping the 1-year follow-up scan to match the baseline scan of the same individual, driven by a mutual information cost function, and a regularizing term called the symmetrized Kullback-Leibler (sKL-MI) distance (Yanovsky, et al., 2009). Registration parameters (sigma=6 and lambda=8) were chosen based on our earlier optimization study (Hua, et al., 2009). A color-coded map of the Jacobian determinants was computed from the gradient of the deformation field to illustrate regions of volume expansion (i.e., with det J (r) >1 ), or contraction (i.e., with det J (r) <1) (Freeborough and Fox, 1998, Toga, 1999, Thompson, et al., 2000, Chung, et al., 2001, Ashburner and Friston, 2003, Riddle, et al., 2004) over the 1-year interval, yielding a map that estimates tissue change rates. Jacobian maps were also spatially normalized across subjects by nonlinearly aligning all individual maps to a minimal deformation template (MDT), for regional comparisons and group statistical analysis. The MDT represented the average shape of 40 healthy elderly controls; the procedure to construct the MDT is detailed in (Hua, et al., 2008a, Hua, et al., 2008b). Average maps were computed by taking the mean at each voxel of the Jacobian maps across subjects.

2.7. Statistical analyses

We performed several statistical analyses to assess factors influencing or related to brain atrophic rates in Alzheimer’s disease and normal aging. First, general linear regressions were used to investigate the relations between TBM-derived brain atrophic rates and demographic variables, CSF biomarkers, clinical and neuropsychological measures, known risk genes, imminent conversion to AD, and other risk factors. These correlations were subsequently evaluated by cumulative distribution functions (CDF) to determine if they were significant after controlling for multiple comparisons using conventional criteria, inside the whole brain or within the temporal lobes. The CDFs were also used to rank the strengths of correlations within each category, to find out which factors are most strongly associated with the rates of structural brain atrophy. Second, the 3D map was reduced to a single numerical score, representing the overall atrophic rate for each individual within an ROI. Third, based on these numerical scores, a power analysis was used to estimate the patient recruitment size for a hypothetical clinical trial of a disease-modifying drug, using structural imaging or other biomarkers as surrogate measures of disease progression.

2.7.1. General linear correlations and cumulative distribution functions (CDF) computed to assess false discovery rates (FDR)

At each voxel within the brain, correlations were assessed, using the general linear model, between atrophy rates and (1) demographic variables: age, sex, and education; (2) baseline and 1-year changes in CSF biomarker levels: Aβ, tau, p-tau, and the ratio of tau to Aβ; (3) baseline and 1-year changes in clinical and behavioral measures: ADAS-cog, MMSE, AVLT, LM, CDR-SB, and FAQ; (4) medical histories of cardiovascular, endocrine-metabolic, and gastrointestinal disorders, as well as information on alcohol abuse, drug abuse, and smoking; (5) body mass index (BMI); (6) AD risk genes: ApoE4, and a newly discovered candidate risk gene, GRIN2b (Stein et al., 2010). Correlations were assessed within each diagnostic group independently, and in the combined group (of all AD, MCI, CTL subjects), where appropriate. Binary categorical (or indicator) variables were used to code sex (female sex as 0; male as 1), medical histories (no medical history as 0; present as 1), and conversion to AD (non-converters as 0; converters as 1). Risk genes were coded as 0, 1, and 2 for zero, one, and two risk alleles, respectively, to represent an additive model assuming an equal contribution of each risk allele to brain atrophy. All other covariates were represented as continuous variables. Multiple regressions allowed the fitting of a number of predictor variables simultaneously. We first examined age and sex effects (independent variables) on atrophic rates (dependent variables), and age and sex were fitted as covariates to adjust the rest of the correlations for these effects.

CDF plots of the regression p-values were used to determine the significance and compare the strengths of association (effect sizes) for the various factors that correlated with atrophic rates, inside a pre-defined region-of-interest (e.g., the temporal lobes or whole brain). CDF plots are commonly used by false discovery rate methods to assign overall significance values to statistical maps (Benjamini and Hochberg, 1995, Genovese, et al., 2002, Storey, 2002). A significant correlation is declared if the CDF intersects the y = 20x line (other than at the origin), i.e., critical P>0, as this shows that the volume of supra-threshold statistics is more than 20 times that expected under null-hypothesis (Hua et al., 2008a, Chou et al., 2009b, Hua, et al., 2009, Morra, et al., 2009b). The critical P-value refers to the point at where CDF plot intersects with the line y = 20x, and this represents the highest statistical threshold for which at most 5% false positives are expected in the map. This value is generally higher for stronger effect sizes in the maps, but is not defined if no effect is present (i.e. the false discovery rate in the map cannot be controlled). CDFs may also be used to compare effect sizes for different clinical correlations: CDF curves show increasing statistical correlations in rank order from bottom to top, in each graph.

2.7.2. Numerical summaries of atrophy rates derived from a statistically-defined region-of-interest (Stat-ROI)

A statistically-defined region of interest (stat-ROI), based on voxels with significant atrophic rates over time (p < 0.001) within a pre-defined anatomical ROI, was established in a non-overlapping training set of 20 AD patients (age at baseline: 74.8±6.3 years; 7 men and 13 women) scanned at baseline and 12-month. The anatomical ROIs included the whole brain gray matter and temporal lobes, two of the best search regions giving the highest statistical power in tracking AD progress (Hua, et al., 2010). This procedure is detailed in (Chen, et al., 2009, Hua, et al., 2009, Ho, et al., 2009, Hua, et al., 2010). A numerical summary of the atrophic rate in the whole brain gray matter, or temporal lobe, was computed by taking the arithmetic mean of Jacobian values within the corresponding stat-ROI (Hua, et al., 2009, Ho, et al., 2009, Hua, et al., 2010), giving a single rate-of-atrophy score for each individual.

2.7.3. Power analysis and sample size estimates

A power analysis was defined by the ADNI Biostatistics Core to estimate the sample size required to detect, with 80% power, a 25% reduction in the mean annual change, as captured by imaging, clinical or CSF biomarker measures, using a two-sided test and standard significance level (α=0.05) for a hypothetical two-arm study (treatment versus placebo). The estimated minimum sample size for each arm was computed with the formula below. Briefly, β denotes the estimated annual change (average of the group) and σD refers to the standard deviation of the rate of atrophy across subjects.

equation M1

Here zα is the value of the standard normal distribution for which P[Z < zα]= α (Rosner, 1990). The sample size required to achieve 80% power was computed, denoted by n80. The 95% confidence interval (c) for the n80 statistic was computed based on 10,000 bootstrapped resamplings, with a bias corrected and accelerated percentile method (Efron and Tibshirani, 1993, Davison and Hinkley, 1997).

3. Results

3.1. Age and sex effects in atrophy rates

The rates of atrophy (Jacobian values) at each location inside the brain were tested for correlations with age and sex in AD, MCI, and CTL groups independently, as well as in the combined group (ALL). The CDF plots (Figure 1a, 1b) show that age and sex correlate with atrophic rates, especially in the MCI group, and when all subjects were combined. There was no systematic age difference between the 3 diagnostic groups (mean age was 76.5, 76.0, and 77.0 for AD/MCI/CTL), so these effects are driven by differences in age within the diagnostic groups, not between them. Comparing CDF curves of the same color - for the whole brain versus temporal lobes – gives a clear impression of the power gained by restricting analyses to regions that are known to change the most. For example, the black curves show that age and sex effects are detected with greater effect sizes when focusing on the temporal lobes, as the CDF curves have a steeper gradient at the origin. They also cross the reference line y=20x at a higher point, which means that a higher threshold (critical P-value or C.P.) can be applied to the statistical maps while keeping the false discovery rate to 5% of the voxels shown.

Figure 1
Age and sex differences in atrophic rates are shown across the entire brain and also in an analysis restricted to changes within the temporal lobes. CDF plots for the effects on atrophic rates of age (a) and sex (b) show the statistical significance of ...

The sign of the correlations with age—positive inside tissues and negative in the CSF—indicates faster brain degeneration in younger MCI subjects (Figure 1c), about 1% increase in atrophic rates and 2% increase in ventricular expansion rates for every 10-year decrease in age; AD patients showed a similar but lesser age effect. Healthy controls showed a small but significant age effect in the opposite direction: a few voxels in the CSF and at the boundary of GM/CSF showed positive correlations, i.e. younger age is associated with less ventricular expansion. Atrophic rates were faster in women than men by about 1–1.5%/year, signified by positive correlations between the atrophic rates and sex (female sex was coded arbitrarily as 0; male as 1; Figure 1d). As expected, the regression coefficient maps, using thresholds derived within the temporal lobes or across the entire brain, are generally consistent in their spatial distributions. However, a broader area reaches significance if restricting the search region to the temporal lobes, as the critical P-values are higher within the temporal lobes than those from the whole brain (results not shown).

When we added education and BMI into this regression model, they did not show significant correlations in any group so were not pursued further as confounds. To better illustrate the age and sex differences in atrophic rates, the MCI group was divided into six sub-groups (in age brackets: 60–<70, 70–<80, and 80–<90 years; further split by sex into female and male). Figure 2 shows the age and sex effects in a straightforward fashion, as group average maps. The rest of correlations tested in this paper were all statistically adjusted for these effects of age and sex.

Figure 2
Average maps of atrophic rates in MCI subjects, subdivided by age and sex. Female MCI subjects (top) are divided into three age groups, 60–70 (N=24), 70–80 (N=59), and 80–90 years (N=37). Male MCI subjects (bottom) are divided ...

As a related question, one might also wonder if age and sex differences were present in the baseline MRI measures. In fact, there were significant age and sex differences in baseline temporal lobe atrophy, within each group independently and in the combined group.

3.2. Correlations between atrophic rates and clinical (cognitive/behavioral) measures

Temporal lobe atrophy rates were correlated with baseline clinical measures (Figure 3) and with their rates of decline (Figure 4). In AD and MCI, atrophic rates were most strongly correlated with the ADAS-cog, LM-im, and AVLT-5 scores at baseline (Figure 3a, 3b). Baseline LM-del, AVLT-del, FAQ, and MMSE also showed significant correlations in MCI (Figure 3b). Anatomical changes over time were also highly correlated with ongoing changes in LM-del, ADAS-cog, CDR-SB, in AD, and CDR-SB, FAQ, LM-im, ADAS-cog, LM-del, in MCI (Figure 4). The rank order - from highest to lowest effect sizes – is shown for these correlations, with baseline ADAS-cog showing the highest correlations with future atrophic rates. The highest curves show the covariates that are most strongly correlated with the measured atrophic rate.

Figure 3
Whole brain and temporal lobe atrophic rates are correlated with baseline clinical measures in AD (a) and MCI (b). Significant correlations are marked with a critical P value greater than 0.01 or 0.0001. Interestingly, the ADAS-Cog, perhaps the most widely ...
Figure 4
Whole brain and temporal lobe atrophic rates correlated with rates of clinical decline, for various different clinical measures, in AD (a) and MCI (b) groups separately. Significant correlations are marked with critical P>0.01 or >0.0001. ...

Similar but weaker effect sizes (lower CDF curves and critical P-values) were obtained when expanding the search region to the entire brain, relative to restricting to the temporal lobes, comparing curves of the same color on each side of the plot (Figure 3, ,4).4). Using the whole brain ROI, atrophic rates were only significantly correlated with the ADAS-cog at baseline in AD, and baseline measures of ADAS-cog, AVLT-5, LM-del, LM-im and MMSE in MCI (Figure 3). Likewise, with the whole brain ROI, atrophic rates were only linked to LM-del decline over a year in AD, while the effect sizes were substantially reduced in MCI (Figure 4). These “butterfly plots” show that there is a clear boosting of power for detecting statistical effects on atrophy when focusing on the regions where greatest changes are expected (i.e., the temporal lobes).

3.3. Correlating atrophic rates with CSF biomarkers

Rates of brain atrophy were significantly correlated with CSF biomarker levels—Aβ, tau, p-tau, and tau/Aβ—at baseline in the combined group of all subjects (blue CDF curves in Figure 5). These correlations did not reach statistical significance within each diagnostic group independently, except that the level of CSF Aβ showed weak but significant correlations (critical P=0.004 in the temporal lobes and 0.001 in the whole brain) in MCI (cyan CDF curves in Figure 5).Also, there were no detectable correlations between rates of tissue atrophy and the rates of change in the CSF biomarkers within the individual groups, with the exception of tau/Aβ in the whole brain in AD (critical P=0.003). The ratio of tau to Aβ also showed some weak correlations with atrophic rates in the combined group (critical P=0.0004 in the temporal lobes and 0.001 in the whole brain). In the common sample, clinical correlations were compared with the results from CSF biomarkers. Baseline ADAS-cog and CDR-SB rates of decline were more strongly correlated with structural brain atrophy, as indicated by higher CDF curves and higher critical P values, with significant correlations also found in the separate diagnostic groups. Again, the effect sizes are substantially boosted by focusing on a temporal lobe region of interest, rather than including all the voxels in the brain; this is clearly evident as the curves on the right of each plot tend to rise more steeply at the original and intersect the FDR reference line (y=20x) at a higher intersection point, whose x-value denotes the highest P-value threshold that can be applied to the statistical maps while preserving the expected false discovery rate at the conventional level of 5%.

Figure 5
Correlations between atrophic rates and CSF biomarker levels (biomarker and clinical labels are with and without borders, respectively). Whole brain and temporal lobe atrophic rates were correlated with biomarker levels in the following rank order, from ...

3.4. Temporal lobe atrophy rates linked to AD risk genes

Carriers of the E4 allele of the ApoE (apolipoprotein E) gene, a commonly carried risk gene for late-onset AD (Saunders, et al., 1993, Roses and Saunders, 1994), showed faster atrophic rates in the temporal lobes overall. Associations were weak but significant within each diagnostic group individually only inside the temporal lobes, but strong when all groups were combined (Figure 6). The newly discovered risk allele (rs-10845840, which codes for GRIN2b, a glutamate receptor subunit; Stein et al., 2010) was associated with atrophic rates in the combined group, but more weakly than ApoE was (Figure 6; higher curves denote stronger effects). When ApoE4 was added to the statistical model that estimated the age and sex effects on the rates of atrophy, the sex effect turned out to be stronger (AD: critical P=0.001; MCI: 0.02; CTL: n.s.; ALL: 0.02) but the age effect was slightly attenuated (AD: n.s.; MCI: critical P=0.007; CTL: 0.0008; ALL: 0.01) inside the temporal lobes.

Figure 6
Genetic influences on brain atrophy. The presence of the ApoE4 (marked by solid lines) and the GRIN2b risk gene (also known as SNP rs-10845840; dotted lines; Stein et al., 2010) were associated with faster rates of atrophy in the temporal lobes, with ...

When expanding the search region to the whole brain, the presence of the ApoE4 risk allele was no longer associated with higher atrophic rates in individual diagnostic groups, but the effect remained significant in the combined group.

3.5. Faster temporal lobe atrophy in converters to AD within one year

MCI subjects who converted to AD within a year (13% of the total MCI group) showed faster atrophic rates than nonconverters, as seen in the contrast map and the significance map (Figure 7). Converters, on average, displayed 2–3% faster atrophic rates than non-converters in the temporal lobes. A similar test in the whole brain did not reach statistical significance (critical P=n.s.).

Figure 7
MCI converters showed faster rates of brain atrophy in temporal lobes than MCI non-converters. The mean difference map shows regions where atrophy rates are faster in converters than non-converters (left panel; blue colors: 3% faster). Red colors show ...

3.6. Correlations between atrophic rates and other risk factors

We evaluated correlations between atrophic rates and histories of cardiovascular, endocrine-metabolic, gastrointestinal disorders, alcohol abuse, drug abuse, and smoking. A medical history of drug abuse was weakly associated with a faster rate of tissue atrophy (critical P=0.0001) in the AD group only, while the other factors had no detectable effect.

3.7. Using covariates to boost power in clinical trials

Given the age and sex effects in atrophic rates, we broke down the MCI groups into six age- and sex-divided sub-groups. The n80s (sample size estimates) and 95% confidence intervals are shown in Table 1. In this table, lower numbers are considered better as they imply that smaller sample sizes would be required to detect a 25% change in the rate of disease progress, measured by a specific AD biomarker, in response to a potentially disease-modifying drug. Younger men gave smaller n80s than older men, as expected from the age effects in MCI, where younger MCI subjects showed faster atrophy. For the sample size to be smaller, the atrophic rate may be higher and/or its standard deviation smaller. Women aged 60–70 or 70–80 had smaller n80s than men at similar ages. This is also consistent with the earlier finding that women had marginally faster atrophic rates in MCI (by ~0.5–1.5%/year locally). In other words, trials focusing on younger subjects, or with sub-analyses focusing on women versus men, would be better powered with these measures.

Table 1
The sample sizes (n80s) and 95% confidence intervals (in square brackets) for groups of MCI subjects subdivided by age and sex, and in the combined group with all MCI subjects included. Sub-analyses focusing on women or younger subjects led to smaller ...

3.8. n80 for the CSF biomarkers

To compare structural MRI versus CSF biomarkers, we computed the n80s based on 1-year changes in CSF biomarker levels. Given their poorer reproducibility than MRI, the n80s were much larger than those from neuroimaging measures (Table 2). Although clearly not their intended use, tens of thousands to millions of subjects would need to be recruited to detect a potential drug effect using CSF biomarkers as surrogate markers measuring the rate of disease progression.

Table 2
The n80s for AD and MCI using CSF biomarkers versus MRI measures of whole-brain gray matter atrophy and temporal lobe atrophy, with a common sample consisting of 50 AD patients and 122 MCI subjects. The numerical summaries of imaging measures were generated ...

4. Discussion

In one of the largest ADNI 1-year follow-up studies, we applied TBM to map the rates of atrophy throughout the brain. Atrophic rates were shown to be correlated with some demographic factors (age and sex), but not education or BMI (although BMI has been associated with baseline levels of atrophy in an independent sample normal subjects; (Raji, et al., 2009)). Atrophic rates were also associated with CSF biomarker levels (Aβ, tau, p-tau, tau/Aβ), cognitive performance, behavioral assessments, and risk genes (ApoE, GRIN2b).

In this study, greatest atrophy was primarily localized to the temporal lobes and several broadly distributed gray and white matter regions, and was evidenced by ventricular expansions (Figure 2); largely the same regions showed ongoing tissue loss in MCI and AD. This pattern of localization of atrophy agrees with many prior papers using voxel-based morphometry, tensor-based morphometry, and cortical thickness maps (Smith and Jobst, 1996, Baron, et al., 2001, Chetelat et al., 2002, Scahill et al., 2002, Smith, 2002, Karas et al., 2004, Whitwell et al., 2007, Frisoni et al., 2009, Pievani et al., 2009), based on cross-sectional data or smaller longitudinal studies.

This study was preceded by a smaller pilot study (20 AD, 40 MCI, 40 CTL) with a similar design, in which temporal lobe atrophy rates were correlated with clinical measures and biomarkers (Leow et al., 2009). The current study substantially extended the earlier study by expanding the search region to the whole brain, and by investigating age and sex effects as well as correlations with many newly added biomarkers and risk factors in a sample size almost seven times larger. We confirmed earlier findings that temporal lobe atrophy rates were faster in MCI converters than non-converters, and were correlated with baseline CSF biomarker levels (Aβ, tau, p-tau, tau/Aβ) in the combined group, with baseline LM-del in MCI, and with changes of CDR-SB and LM-im in MCI; however, rate of atrophy, in the current study, was not shown to correlate with baseline level of p-tau, change in MMSE, and change in AVLT-del in MCI. The discrepancy might be due to the sample composition (although sample selection was unbiased) but is more likely due to the sample size difference, which is 7 times larger here. Additionally, we identified significant age and sex differences in atrophic rates; temporal lobe atrophy rates correlated with Aβ in MCI, baseline ADAS-cog, LM-im, and AVLT-5 in AD, baseline ADAS-cog, AVLT-5, AVLT-del, LM-im, FAQ, and MMSE in MCI, changes in LM-del, ADAS-cog, and CDR-SB in AD, changes in FAQ, ADAS-cog, and LM-del in MCI. In the current study, we were also able to detect the associations between common variants in the ApoE and GRIN2b genes and brain atrophic rates; we also explored the implications of drug trial enrichment by performing sub-analyses based on this information.

4.1. Age and sex effects

Age effects

The age effects on atrophic rates in our study are based on comparing atrophic rates in individuals, which is not to be confused with mapping disease acceleration or deceleration within individual subjects scanned more than twice (Sluimer, et al., 2009). A recent non-ADNI study of individuals with 3 or more serial MRI scans (46 amnestic MCI subjects who later converted to AD, 46 healthy controls, and 23 stable MCI subjects) found that the rates of atrophy do tend to accelerate as individuals progress from amnestic MCI to typical late-onset AD; and the rates of atrophy were greater in younger than older MCI subjects (Jack, et al., 2008c). Our study, in a much larger sample of 684 ADNI subjects (114 AD, 338 MCI, and 202 CTL), confirmed the trend for faster degeneration in younger amnestic MCI subjects versus older subjects. The most plausible explanation is that younger MCI subjects have a more biologically aggressive disease course than older subjects (Jack, et al., 2008c). There is substantial clinical and neuroimaging evidence that early-onset AD (onset before age 65 and typically in the 40’s and 50’s) generally represents a more aggressive form of disease than late-onset AD (onset after age 65) (Frisoni, et al., 2007). A second possibility is that younger MCI subjects may have a larger cognitive reserve than older subjects; under this theory, young people may have greater ability to compensate for the brain deficits so that symptoms may not be evident until brain atrophy has progressed to a greater degree, and is proceeding faster (see, e.g., Mortimer et al., (2005); but see also Christensen et al., (2007) for an opposing view). Finally, some very old subjects were assessed (80–90 years of age), so one has to keep in mind the possibility of a selection bias. Very old people in the study might tend to be more well (well enough to participate in a neuroimaging study requiring multiple follow-ups), and have lower atrophic rates; even though when those same people were younger (long before ADNI) they may have had even slower atrophy rates. In other words, early mortality may prevent people from enrolling in ADNI if they die earlier due to very fast atrophy, so the oldest subjects in ADNI, as a survivor effect, may have slower atrophic rates for this reason. This attrition effect could explain the paradoxical “adverse” effect of young age in a cross-sectional study (faster atrophy in younger people), even when people’s atrophic rates may speed up as the disease progresses (within an individual); this has been demonstrated in early-onset AD (Chan, et al., 2003, Ridha, et al., 2006) and late-onset AD (Jack, et al., 2008c). In a normal aging study, Scahill et al. (2003) found evidence that atrophic rates accelerated with increasing age; our study also showed a small age effect in the control group, with a similar direction of correlation.

Sex effects

We provided the first structural MRI evidence, to our knowledge, of sexual dimorphism in atrophic rates, although several studies have found worse cognitive and behavioral deficits in women versus men with AD. Most early MRI studies failed to detect a sex difference in atrophic rates, but were limited by small sample sizes and limited statistical power. Sex differences in brain structure are found naturally and well-studied (see, e.g., Brun el al. (2009) for a TBM study) but sex differences in the rates of brain change over time are less commonly reported, except in studies of childhood brain development where they occur around puberty (Giedd, et al., 1999). Why atrophic was faster in women is not clear. Numerous demographic studies provide evidence for a “male-female health-survival paradox.” According to this, older men are generally in better health and are less limited in their daily activities than women of the same age, but mortality rates are higher in men than women at all ages (Christensen, 2008). Genetic variation in the sex chromosomes may contribute to sex differences in the incidence of some comorbid disorders. Men may have earlier and higher incidence of hypertension and cardiovascular diseases (high mortality risk diseases) while women suffer more from migraine, arthritis, and musculoskeletal diseases (low mortality risk diseases) (Macintyre, et al., 1996); this may be related to the cohort effect discussed earlier. Sex hormones may also influence the expression of genes that affect lifespan and longevity (Tower, 2006, Tower and Arbeitman, 2009).

Baseline differences

We identified significant age and sex differences in baseline measures of brain atrophy, within each group independently and in the combined group. These baseline effects may reflect a combination of (1) the cumulative influence of age and sex throughout life, and (2) naturally occurring sex differences in brain structure, as different structures tend to occupy different proportions of the total brain volume in men versus women (i.e., allometry; Brun, et al., 2009).

Lower education levels are also linked to a higher risk of developing AD and faster rate of progression when compared to more highly educated people (Scarmeas, et al., 2006, Ngandu, et al., 2007). Higher BMI, an index of obesity, is associated with greater brain atrophy in elderly normal subjects (Raji, et al., 2009). We therefore added education and BMI to the statistical models of age and sex, but the conclusions remained the same even after adjusting for these additional factors. BMI was associated with baseline atrophy but not with atrophic rates.

4.2. Structural MRI, clinical, and CSF biomarkers

Different biomarkers provide complementary information at different stages of AD (Jack, et al., 2008b, Jagust, et al., 2009). In particular, structural MRI measures tend to correlate better with cognitive test scores than with CSF biomarker levels. This may be because (1) CSF biomarker changes tend to precede the gross anatomical changes on MRI, and (2) because CSF measures are primarily intended to help with diagnosis rather than resolve subtle changes over time within diagnostic categories. We note that CSF measures were not used to assist diagnosis in the ADNI study. However, at least part of the difference in statistical power is due to the different sample sizes of subjects who had available cognitive measures versus CSF biomarker measures. We tested a common set of subjects who had both cognitive and CSF measures (Figure 5). By reducing the full sample (N=684 at baseline and N=660 with 1-year follow-up) to the common set (N=363 at baseline and N=251 with 1-year follow-up), the clinical correlations all became weaker; however, their statistical effects remained higher than those of CSF biomarkers – for example, there were significant correlations even within the separate diagnostic groups, while only a couple of CSF biomarkers (baseline level of Aβ in MCI and rate of tau/Aβ decline in AD) survived statistical testing within the separate diagnostic groups.

4.3. AD risk genes

ApoE4 is a well known AD risk gene (Corder, et al., 1993, Saunders, et al., 1993, Roses and Saunders, 1994, Roses, et al., 1995, Roses, 1996), and in our earlier cross-sectional study of 676 ADNI subjects, ApoE2 (the “protective” allele) was associated with reduced CSF volume (an index of lesser brain atrophy) and ApoE4 was associated with greater temporal lobe atrophy (Hua, et al., 2008b). In this longitudinal analysis, ApoE4 and GRIN2b were linked to faster rates of temporal lobe atrophy, in a dose-dependent fashion. GRIN2b is a newly identified risk SNP that predicts temporal lobe volumes in ADNI at baseline (Stein, et al., 2010), but its association with AD is not as strong as ApoE, so requires replication. As well as its use for measuring disease progression, structural MRI measures can also be used to identify genes that influence brain volumes in genome-wide association studies (GWAS) (Joyner, et al., 2009, Potkin, et al., 2009, Stein, et al., 2010).

4.4. Statistical analyses

We applied stratified analyses and ran separate regressions independently in each diagnostic group, to ensure that the observed statistical effects were not driven by diagnosis. Alternatively, the analysis could be carried out by pooling all subjects, by applying indicator variables to encode diagnostic groups and interaction terms to quantify inter-group differences on the main effects. However, this increases the computational burden, and each analysis already involves ~2,000,000 correlations. Because of the very large number of possible interactions, and the likelihood of not being able to fit them all stably, we did not test for interactions between diagnostic groups and predictor variables. We also did not attempt to quantify inter-group differences in the main effects, which requires a second order analysis and has still greater statistical power requirements. Instead, we treated the three diagnostic groups independently, merely to ensure that the observed statistical effects were not driven by diagnosis. Correlations were later assessed in the combined group (AD+MCI+CTL), after stratified analyses.

In all analyses, we first ran correlations in the separate clinical groups, and then we ran another correlation in the combined group, where appropriate. This is the most agnostic approach as it allows the correlations to differ, in principle, in the different diagnostic categories, avoiding the risk that the detected correlations may be shadowing diagnosis. We did not perform correlations with clinical measures in the combined group. As clinical measures are used to determine diagnosis, a correlation in the combined group will be significant by construction. The CSF biomarkers, however, were not used to define diagnosis so it is reasonable to correlate them with levels of atrophy across the combined group. Even so, correlations detected in the combined group may not even apply within some of the groups, either indicating a lack of association or, more likely, limited power to track subtle disease progression within the reduced samples of subjects in individual diagnostic groups. This effect likely explains the lack of correlations with CSF biomarkers within groups. The correlations between CSF biomarkers and MRI changes tend to break down as the disease progresses, as changes in CSF biomarker levels may primarily occur prior to the MRI changes. A similar pattern has been noted in studies of amyloid PET (Braskie et al., 2009), where cortical thinning may not correlate with amyloid deposition if the two processes occur or saturate at different times. In a recent study using serial imaging, the rate of neurodegeneration was shown to associate with clinical symptoms but dissociate from amyloid deposition measured by (11)C Pittsburgh compound B (PIB) positron emission tomography (Jack, et al., 2009).

We used categorical variables or indicator variables to encode binary predictors, such as sex, medical history, and conversion to AD, each of these variables only has two distinct classes, i.e. male vs. female, those with or without a medical history, and converters vs. non-converters. If a simple linear regression only includes a two-class categorical variable as the independent variable, the regression acts as a two-sample Student’s t test. An added benefit of using regressions over t tests is that regression allowed us to control for effects of several covariates simultaneously. For example, by fitting both age and sex in the regression model, the sex effect was controlled for when assessing any age effects on atrophic rates.

4.5. Choices of search region

The results in the whole brain ROI are generally consistent with those derived from the temporal lobes, but are weaker in statistical power. This is expected as brain degeneration is not uniformly distributed across the brain, nor does it progress uniformly. The volume loss pattern from mild to moderate AD spreads over time from temporal and limbic cortices into frontal and occipital brain regions, largely sparing primary sensorimotor cortices (Braak and Braak, 1991; Thompson et al., 2003). One advantage of focusing on the temporal lobes is the improved statistical power by restricting the search region to the area most affected in MCI and early AD. In examining genes influencing brain atrophy (Figure 6) and comparing differences between groups of MCI-converters and non-converters (Figure 7), the statistical effects were only significant in the temporal lobes – which makes sense as these are the regions with greatest pathological burden in MCI. The inclusion of many voxels with much slower atrophic rates and with lower effect sizes tends to inflate the number of voxels assessed to the point where no FDR-controlling threshold can be found. Nevertheless, it is also important to examine the results across the entire brain to have a better understand factors influencing brain atrophy in normal aging and AD.

4.6. Conclusions and Limitations

Our study is one of many that support the use of structural MRI for providing valid surrogate markers in clinical drug trials. MRI is also useful for detecting factors that affect structural changes in anatomical regions involved in AD. CSF biomarkers, despite their value for early diagnosis, might not be so effective for tracking disease progression over time or even for evaluating therapeutic interventions in MCI and AD. For example, their n80s – measures of sample size requirements to detect a fixed percent reduction in the rate of progression - are 1,000 to 10,000 times larger than those from structural MRI (Table 2).

TBM-derived maps of atrophic rates, coupled with voxel-based statistics, offer an easy-to-implement process to investigate factors that exert negative and positive influences on aging and AD. Full 3D maps are used in these correlations, as opposed to only one biomarker measure per individual. This type of map-based method may offer more information and spatial detail on the profile of effects, and may offer better statistical power if effect sizes are not constant across the brain.

Each AD biomarker, derived from structural MRI, clinical, or CSF measures, can be used independently to evaluate drug treatment effects, providing a surrogate outcome measure to track the rate of disease progress. As a result of using different biomarkers, the sample size estimates (n80) should be interpreted with care. For example, a 25% reduction in the atrophic rate (measured by MRI) may have a different functional significance for a patient than a 25% reduction in the rate of decline for clinical or cognitive test scores; similarly, it may also have a different biological significance than a 25% reduction in the rate of change in CSF biomarkers. For example, there may be important and relevant biological events that do not have an immediate imaging correlate. Future efforts will focus on combining multiple biomarkers that measure different aspects of disease progress to reduce the sample size even further.

This study has some limitations. The age and sex effects on atrophic rates, which were still significant here after controlling for education, BMI, and ApoE4, need to be replicated in future, independent studies. A more complete dataset from a large number of subjects with MRI, PIB-PET, [18F]fluorodeoxyglucose (FDG)-PET, diffusion tensor imaging (DTI), resting-state functional MRI, and arterial spin labeling is now being collected to explore the complementary value of each of these neuroimaging markers. Future longitudinal ADNI studies will make use of more than two serial scans, allowing acceleration hypotheses regarding age effects to be tested in the same subjects. More advanced statistical designs, such as random effects or mixed effects models, may then be used to estimate intra-subject variance and group effects with repeated measures (Fitzmaurice, et al., 2004, Frost, et al., 2004, Schuff, et al., 2009).

Acknowledgments

Acknowledgments and Author ContributionsData collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: 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 Wyeth, as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org <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. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation. Algorithm development and image analysis for this study was funded by grants to P.T. from the NIBIB (R01 EB007813, R01 EB008281, R01 EB008432), NICHD (R01 HD050735), and NIA (R01 AG020098), and National Institutes of Health through the NIH Roadmap for Medical Research, Grants U54-RR021813 (CCB) (to AWT and PT). Author contributions were as follows: XH, DH, SL, AT, and PT performed the image analyses; CJ and MW contributed substantially to the image and data acquisition, study design, quality control, calibration and pre-processing, databasing and image analysis. We thank Anders Dale for his contributions to the image pre-processing and the ADNI project.

Glossary

AD
Alzheimer’s disease
Beta-amyloid peptides
CSF
Cerebrospinal fluid
CTL
Healthy elderly controls
MCI
Mild cognitive impairment
TBM
Tensor-based morphometry
p-tau
Phosphorylated tau proteins

Clinical measures

ADAS-Cog
Alzheimer's Disease Assessment Scale-cognitive subscale
AVLT
Rey Auditory Verbal Learning Test
AVLT-5
AVLT conducted immediately after each of the five learning trials
AVLT-del
AVLT conducted after a 30-minute delay
CDR-SB
Sum-of-boxes Clinical Dementia Rating
FAQ
Functional Assessment Questionnaire
LM
Logical Memory test
LM-im
LM test conducted immediately after information was read to the subject
LM-del
LM test conducted after a 30 minute delay
MMSE
Mini-Mental State Examination

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

*Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. ADNI investigators include (complete listing available at: http://www.loni.ucla.edu/ADNI/Collaboration/ADNI_Manuscript_Citations.pdf).

References

  • Consensus report of the Working Group on: "Molecular and Biochemical Markers of Alzheimer's Disease". The Ronald and Nancy Reagan Research Institute of the Alzheimer's Association and the National Institute on Aging Working Group. Neurobiol Aging. 1998;19(2):109–116. [PubMed]
  • Apostolova LG, Dutton RA, Dinov ID, Hayashi KM, Toga AW, Cummings JL, Thompson PM. Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch Neurol. 2006;63(5):693–699. [PubMed]
  • Ashburner J, Friston KJ. Human Brain Function. Academic Press; 2003. Morphometry.
  • Bai F, Zhang Z, Watson DR, Yu H, Shi Y, Zhu W, Wang L, Yuan Y, Qian Y. Absent gender differences of hippocampal atrophy in amnestic type mild cognitive impairment. Neurosci Lett. 2009;450(2):85–89. [PubMed]
  • Baron JC, Chetelat G, Desgranges B, Perchey G, Landeau B, de la Sayette V, Eustache F. In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease. Neuroimage. 2001;14(2):298–309. [PubMed]
  • Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B. 1995;57(1):289–300.
  • Berg L. Clinical Dementia Rating (CDR) Psychopharmacol Bull. 1988;24(4):637–639. [PubMed]
  • Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol (Berl) 1991;82(4):239–259. [PubMed]
  • Braskie MN, Klunder AD, Hayashi KM, Protas H, Kepe V, Miller KJ, Huang SC, Barrio JR, Ercoli LM, Siddarth P, Satyamurthy N, Liu J, Toga AW, Bookheimer SY, Small GW, Thompson PM. Plaque and tangle imaging and cognition in normal aging and Alzheimer's disease. Neurobiol Aging. 2008 Nov;10 [Epub ahead of print2008] [PMC free article] [PubMed]
  • Brun CC, Lepore N, Luders E, Chou YY, Madsen SK, Toga AW, Thompson PM. Sex differences in brain structure in auditory and cingulate regions. Neuroreport. 2009;20(10):930–935. [PMC free article] [PubMed]
  • Carmichael OT, Thompson PM, Dutton RA, Lu A, Lee SE, Lee JY, Kuller LH, Lopez OL, Aizenstein HJ, Meltzer CC, Liu Y, Toga AW, Becker JT. Mapping ventricular changes related to dementia and mild cognitive impairment in a large community-based cohort. IEEE ISBI. 2006:315–318.
  • Chan D, Janssen JC, Whitwell JL, Watt HC, Jenkins R, Frost C, Rossor MN, Fox NC. Change in rates of cerebral atrophy over time in early-onset Alzheimer's disease: longitudinal MRI study. Lancet. 2003;362(9390):1121–1122. [PubMed]
  • Chen K, Reschke C, Lee W, Napatkamon A, Liu X, Bandy D, Langbaum J, Alexander GE, Foster NL, Koeppe RA, Jagust WJ, Weiner MW, Reiman EM. Cross-sectional and longitudinal analyses of fluorodeoxyglucose positron emission tomography images from the Alzheimer’s disease neuroimaging initiative. ADNI Data Presentations Meeting; Seattle, WA. 2009. [PMC free article] [PubMed]
  • Chetelat 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]
  • Chetelat G, Baron JC. Early diagnosis of Alzheimer's disease: contribution of structural neuroimaging. Neuroimage. 2003;18(2):525–541. [PubMed]
  • Chetelat G, Fouquet M, Kalpouzos G, Denghien I, De la Sayette V, Viader F, Mezenge F, Landeau B, Baron JC, Eustache F, Desgranges B. Three-dimensional surface mapping of hippocampal atrophy progression from MCI to AD and over normal aging as assessed using voxel-based morphometry. Neuropsychologia. 2008;46(6):1721–1731. [PubMed]
  • Chou YY, Leporé N, Avedissian C, Madsen SK, Hua X, Jack CR, Jr, Weiner MW, Toga AW, Thompson PM. SPIE Medical Imaging. Florida: Lake Buena Vista; 2009a. Feb 7 – 12, Mapping ventricular expansion and its clinical correlates in Alzheimer's disease and mild cognitive impairment using multi-atlas fluid image alignment. 2009.
  • Chou YY, Lepore N, Avedissian C, Madsen SK, Parikshak N, Hua X, Shaw LM, Trojanowski JQ, Weiner MW, Toga AW, Thompson PM. Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 subjects with Alzheimer's disease, mild cognitive impairment and elderly controls. Neuroimage. 2009b;46(2):394–410. [PMC free article] [PubMed]
  • Chou YY, Lepore N, de Zubicaray GI, Carmichael OT, Becker JT, Toga AW, Thompson PM. Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease. Neuroimage. 2008;40(2):615–630. [PMC free article] [PubMed]
  • Christensen H, Anstey KJ, Parslow RA, Maller J, Mackinnon A, Sachdev P. The brain reserve hypothesis, brain atrophy and aging. Gerontology. 2007;53(2):82–95. [PubMed]
  • Christensen K. Human biodemography: Some challenges and possibilities for aging research. Demographic Research. 2008;19:1575–1586.
  • Chung MK, Worsley KJ, Paus T, Cherif C, Collins DL, Giedd JN, Rapoport JL, Evans AC. A unified statistical approach to deformation-based morphometry. Neuroimage. 2001;14(3):595–606. [PubMed]
  • Clark CM, Davatzikos C, Borthakur A, Newberg A, Leight S, Lee VM, Trojanowski JQ. Biomarkers for early detection of Alzheimer pathology. Neuro-Signals. 2008;16(1):11–18. [PMC free article] [PubMed]
  • Clark CM, Xie S, Chittams J, Ewbank D, Peskind E, Galasko D, Morris JC, McKeel DW, Jr, Farlow M, Weitlauf SL, Quinn J, Kaye J, Knopman D, Arai H, Doody RS, DeCarli C, Leight S, Lee VM, Trojanowski JQ. Cerebrospinal fluid tau and beta-amyloid: how well do these biomarkers reflect autopsy-confirmed dementia diagnoses? Arch Neurol. 2003;60(12):1696–1702. [PubMed]
  • Clarkson MJ, Ourselin S, Nielsen C, Leung KK, Barnes J, Whitwell JL, Gunter JL, Hill DL, Weiner MW, Jack CR, Jr, Fox NC. Comparison of phantom and registration scaling corrections using the ADNI cohort. Neuroimage. 2009;47(4):1506–1513. [PMC free article] [PubMed]
  • Cockrell JR, Folstein MF. Mini-Mental State Examination (MMSE) Psychopharmacol Bull. 1988;24(4):689–692. [PubMed]
  • Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr. 1994;18(2):192–205. [PubMed]
  • Corder EH, Saunders AM, Strittmatter WJ, Schmechel DE, Gaskell PC, Small GW, Roses AD, Haines JL, Pericak-Vance MA. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science. 1993;261(5123):921–923. [PubMed]
  • Davison AC, Hinkley DV. Bootstrap methods and their application. Cambridge University Press; 1997.
  • de Leon MJ, DeSanti S, Zinkowski R, Mehta PD, Pratico D, Segal S, Rusinek H, Li J, Tsui W, Saint Louis LA, Clark CM, Tarshish C, Li Y, Lair L, Javier E, Rich K, Lesbre P, Mosconi L, Reisberg B, Sadowski M, DeBernadis JF, Kerkman DJ, Hampel H, Wahlund LO, Davies P. Longitudinal CSF and MRI biomarkers improve the diagnosis of mild cognitive impairment. Neurobiol Aging. 2006;27(3):394–401. [PubMed]
  • Devanand DP, Pradhaban G, Liu X, Khandji A, De Santi S, Segal S, Rusinek H, Pelton GH, Honig LS, Mayeux R, Stern Y, Tabert MH, de Leon MJ. Hippocampal and entorhinal atrophy in mild cognitive impairment: prediction of Alzheimer disease. Neurology. 2007;68(11):828–836. [PubMed]
  • Dodge HH, Shen C, Pandav R, DeKosky ST, Ganguli M. Functional transitions and active life expectancy associated with Alzheimer disease. Arch Neurol. 2003;60(2):253–259. [PubMed]
  • Du AT, Schuff N, Amend D, Laakso MP, Hsu YY, Jagust WJ, Yaffe K, Kramer JH, Reed B, Norman D, Chui HC, Weiner MW. Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer's disease. J Neurol Neurosurg Psychiatry. 2001;71(4):441–447. [PMC free article] [PubMed]
  • Efron B, Tibshirani RJ. An introduction to the bootstrap. New York: Chapman & Hall; 1993.
  • Evans MC, Barnes J, Nielsen C, Kim LG, Clegg SL, Blair M, Leung KK, Douiri A, Boyes RG, Ourselin S, Fox NC. Volume changes in Alzheimer's disease and mild cognitive impairment: cognitive associations. European radiology. 2009 [PubMed]
  • Fitzmaurice GM, Laird NM, Ware JH. Applied Longitudinal Analysis. Wiley-Interscience; 2004.
  • Fleisher A, Grundman M, Jack CR, Jr, Petersen RC, Taylor C, Kim HT, Schiller DH, Bagwell V, Sencakova D, Weiner MF, DeCarli C, DeKosky ST, van Dyck CH, Thal LJ. Sex, apolipoprotein E epsilon 4 status, and hippocampal volume in mild cognitive impairment. Arch Neurol. 2005;62(6):953–957. [PubMed]
  • 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]
  • 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. [PubMed]
  • Fox NC, Scahill RI, Crum WR, Rossor MN. Correlation between rates of brain atrophy and cognitive decline in AD. Neurology. 1999;52(8):1687–1689. [PubMed]
  • Frank RA, Galasko D, Hampel H, Hardy J, de Leon MJ, Mehta PD, Rogers J, Siemers E, Trojanowski JQ. Biological markers for therapeutic trials in Alzheimer's disease. Proceedings of the biological markers working group; NIA initiative on neuroimaging in Alzheimer's disease. Neurobiol Aging. 2003;24(4):521–536. [PubMed]
  • Freeborough PA, Fox NC. Modeling brain deformations in Alzheimer disease by fluid registration of serial 3D MR images. J Comput Assist Tomogr. 1998;22(5):838–843. [PubMed]
  • Frisoni GB, Laakso MP, Beltramello A, Geroldi C, Bianchetti A, Soininen H, Trabucchi M. Hippocampal and entorhinal cortex atrophy in frontotemporal dementia and Alzheimer's disease. Neurology. 1999;52(1):91–100. [PubMed]
  • Frisoni GB, Pievani M, Testa C, Sabattoli F, Bresciani L, Bonetti M, Beltramello A, Hayashi KM, Toga AW, Thompson PM. The topography of grey matter involvement in early and late onset Alzheimer's disease. Brain. 2007;130(Pt 3):720–730. [PubMed]
  • Frisoni GB, Prestia A, Rasser PE, Bonetti M, Thompson PM. In vivo mapping of incremental cortical atrophy from incipient to overt Alzheimer’s disease. Journal of Neurology. 2009;256(6):916–924. [PubMed]
  • Frisoni GB, Fox NC, Jack CR, Jr, Scheltens P, Thompson PM. The clinical use of structural MRI in Alzheimer disease. Nature Reviews | Neurology. 2010;6:1–11. [PMC free article] [PubMed]
  • Frost C, Kenward MG, Fox NC. The analysis of repeated 'direct' measures of change illustrated with an application in longitudinal imaging. Statistics in medicine. 2004;23(21):3275–3286. [PubMed]
  • Gao S, Hendrie HC, Hall KS, Hui S. The relationships between age, sex, and the incidence of dementia and Alzheimer disease: a meta-analysis. Archives of general psychiatry. 1998;55(9):809–815. [PubMed]
  • Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage. 2002;15(4):870–878. [PubMed]
  • Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H, Zijdenbos A, Paus T, Evans AC, Rapoport JL. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci. 1999;2(10):861–863. [PubMed]
  • Grundman M, Petersen RC, Ferris SH, Thomas RG, Aisen PS, Bennett DA, Foster NL, Jack CR, Jr, Galasko DR, Doody R, Kaye J, Sano M, Mohs R, Gauthier S, Kim HT, Jin S, Schultz AN, Schafer K, Mulnard R, van Dyck CH, Mintzer J, Zamrini EY, Cahn-Weiner D, Thal LJ. Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. Arch Neurol. 2004;61(1):59–66. [PubMed]
  • Gunter J, Bernstein M, Borowski B, Felmlee J, Blezek D, Mallozzi R. Validation testing of the MRI calibration phantom for the Alzheimer's Disease Neuroimaging Initiative Study. ISMRM 14th Scientific Meeting and Exhibition; 2006.
  • Halperin I, Morelli M, Korczyn AD, Youdim MB, Mandel SA. Biomarkers for evaluation of clinical efficacy of multipotential neuroprotective drugs for Alzheimer's and Parkinson's diseases. Neurotherapeutics. 2009;6(1):128–140. [PubMed]
  • Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L. Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet neurology. 2006;5(3):228–234. [PubMed]
  • Henderson VW, Buckwalter JG. Cognitive deficits of men and women with Alzheimer's disease. Neurology. 1994;44(1):90–96. [PubMed]
  • Herholz K, Salmon E, Perani D, Baron JC, Holthoff V, Frolich L, Schonknecht P, Ito K, Mielke R, Kalbe E, Zundorf G, Delbeuck X, Pelati O, Anchisi D, Fazio F, Kerrouche N, Desgranges B, Eustache F, Beuthien-Baumann B, Menzel C, Schroder J, Kato T, Arahata Y, Henze M, Heiss WD. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage. 2002;17(1):302–316. [PubMed]
  • Hill D. Neuroimaging to assess safety and efficacy of AD therapies. Expert opinion on investigational drugs. 19(1):23–26. [PubMed]
  • Ho AJ, Hua X, Lee S, Yanovsky I, Leow AD, Gutman B, Dinov ID, Toga AW, Jack CR, Jr, Bernstein MA, Reiman EM, Harvey D, Kornak J, Schuff N, Alexander GE, Weiner MW, Thompson PM. Comparing 3T and 1.5T MRI for tracking AD progression with tensor-based morphometry. NeuroImage. 2009 in press. [PMC free article] [PubMed]
  • Hua X, Lee S, Hibar DP, Yanovsky I, Leow AD, Toga AW, Jack CR, Jr, Bernstein MA, Reiman EM, Harvey DJ, Kornak J, Schuff N, Alexander GE, Weiner MW, Thompson PM. Mapping Alzheimer’s disease progression in 1309 MRI scans: power estimates for different inter-scan intervals. 2010 submitted. [PMC free article] [PubMed]
  • Hua X, Lee S, Yanovsky I, Leow AD, Chou YY, Ho AJ, Gutman B, Toga AW, Jack CR, Jr, Bernstein MA, Reiman EM, Harvey DJ, Kornak J, Schuff N, Alexander GE, Weiner MW, Thompson PM. Optimizing power to track brain degeneration in Alzheimer's disease and mild cognitive impairment with tensor-based morphometry: an ADNI study of 515 subjects. Neuroimage. 2009;48(4):668–681. [PMC free article] [PubMed]
  • Hua X, Leow AD, Lee S, Klunder AD, Toga AW, Lepore N, Chou YY, Brun C, Chiang MC, Barysheva M, Jack CR, Jr, Bernstein MA, Britson PJ, Ward CP, Whitwell JL, Borowski B, Fleisher AS, Fox NC, Boyes RG, Barnes J, Harvey D, Kornak J, Schuff N, Boreta L, Alexander GE, Weiner MW, Thompson PM. Alzheimer's Disease Neuroimaging, I. 3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry. Neuroimage. 2008a;41(1):19–34. [PMC free article] [PubMed]
  • Hua X, Leow AD, Parikshak N, Lee S, Chiang MC, Toga AW, Jack CR, Jr, Weiner MW, Thompson PM. Tensor-based morphometry as a neuroimaging biomarker for Alzheimer's disease: an MRI study of 676 AD, MCI, and normal subjects. Neuroimage. 2008b;43(3):458–469. [PMC free article] [PubMed]
  • Hughes CP, Berg L, Danziger WL, Coben LA, Martin RL. A new clinical scale for the staging of dementia. Br J Psychiatry. 1982;140:566–572. [PubMed]
  • Ibach B, Binder H, Dragon M, Poljansky S, Haen E, Schmitz E, Koch H, Putzhammer A, Kluenemann H, Wieland W, Hajak G. Cerebrospinal fluid tau and beta-amyloid in Alzheimer patients, disease controls and an age-matched random sample. Neurobiol Aging. 2006;27(9):1202–1211. [PubMed]
  • Jack CR, Jr, Petersen RC, Xu Y, O'Brien PC, Smith GE, Ivnik RJ, Tangalos EG, Kokmen E. Rate of medial temporal lobe atrophy in typical aging and Alzheimer's disease. Neurology. 1998;51:993–999. [PMC free article] [PubMed]
  • Jack CR, Jr, Petersen RC, Xu YC, Waring SC, O'Brien PC, Tangalos EG, Smith GE, Ivnik RJ, Kokmen E. Medial temporal atrophy on MRI in normal aging and very mild Alzheimer's disease. Neurology. 1997;49:786–794. [PMC free article] [PubMed]
  • Jack CR, Jr, Petersen RC, Xu YC, O'Brien PC, Smith GE, Ivnik RJ, Boeve BF, Waring SC, Tangalos EG, Kokmen E. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology. 1999;52(7):1397–1403. [PMC free article] [PubMed]
  • Jack CR, Jr, Slomkowski M, Gracon S, Hoover TM, Felmlee JP, Stewart K, Xu Y, Shiung M, O'Brien PC, Cha R, Knopman D, Petersen RC. MRI as a biomarker of disease progression in a therapeutic trial of milameline for AD. Neurology. 2003;60(2):253–260. [PMC free article] [PubMed]
  • Jack CR, Jr, Shiung MM, Gunter JL, O'Brien PC, Weigand SD, Knopman DS, Boeve BF, Ivnik RJ, Smith GE, Cha RH, Tangalos EG, Petersen RC. Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology. 2004;62(4):591–600. [PMC free article] [PubMed]
  • Jack CR, Jr, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, Borowski B, Britson PJ, J LW, Ward C, Dale AM, Felmlee JP, Gunter JL, Hill DL, Killiany R, Schuff N, Fox-Bosetti S, Lin C, Studholme C, DeCarli CS, Krueger G, Ward HA, Metzger GJ, Scott KT, Mallozzi R, Blezek D, Levy J, Debbins JP, Fleisher AS, Albert M, Green R, Bartzokis G, Glover G, Mugler J, Weiner MW. The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008a;27(4):685–691. [PMC free article] [PubMed]
  • Jack CR, Jr, Lowe VJ, Senjem ML, Weigand SD, Kemp BJ, Shiung MM, Knopman DS, Boeve BF, Klunk WE, Mathis CA, Petersen RC. 11C PiB and structural MRI provide complementary information in imaging of Alzheimer's disease and amnestic mild cognitive impairment. Brain. 2008b;131(Pt 3):665–680. [PMC free article] [PubMed]
  • Jack CR, Jr, Weigand SD, Shiung MM, Przybelski SA, O'Brien PC, Gunter JL, Knopman DS, Boeve BF, Smith GE, Petersen RC. Atrophy rates accelerate in amnestic mild cognitive impairment. Neurology. 2008c;70(19 Pt 2):1740–1752. [PMC free article] [PubMed]
  • Jack CR, Jr, Lowe VJ, Weigand SD, Wiste HJ, Senjem ML, Knopman DS, Shiung MM, Gunter JL, Boeve BF, Kemp BJ, Weiner M, Petersen RC. Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer's disease: implications for sequence of pathological events in Alzheimer's disease. Brain. 2009;132(Pt 5):1355–1365. [PMC free article] [PubMed]
  • Jagust WJ, Landau SM, Shaw LM, Trojanowski JQ, Koeppe RA, Reiman EM, Foster NL, Petersen RC, Weiner MW, Price JC, Mathis CA. Relationships between biomarkers in aging and dementia. Neurology. 2009;73(15):1193–1199. [PMC free article] [PubMed]
  • Jovicich J, Czanner S, Greve D, Haley E, van der Kouwe A, Gollub R, Kennedy D, Schmitt F, Brown G, Macfall J, Fischl B, Dale A. 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]
  • Joyner AH, J CR, Bloss CS, Bakken TE, Rimol LM, Melle I, Agartz I, Djurovic S, Topol EJ, Schork NJ, Andreassen OA, Dale AM. A common MECP2 haplotype associates with reduced cortical surface area in humans in two independent populations. Proc Natl Acad Sci U S A. 2009;106(36):15483–15488. [PubMed]
  • Karas GB, Scheltens P, Rombouts SA, Visser PJ, van Schijndel RA, Fox NC, Barkhof F. Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease. Neuroimage. 2004;23(2):708–716. [PubMed]
  • Killiany RJ, Moss MB, Albert MS, Sandor T, Tieman J, Jolesz F. Temporal lobe regions on magnetic resonance imaging identify patients with early Alzheimer's disease. Arch Neurol. 1993;50:949–954. [PubMed]
  • Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, Bergstrom M, Savitcheva I, Huang GF, Estrada S, Ausen B, Debnath ML, Barletta J, Price JC, Sandell J, Lopresti BJ, Wall A, Koivisto P, Antoni G, Mathis CA, Langstrom B. Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann Neurol. 2004;55(3):306–319. [PubMed]
  • Leow AD, Klunder AD, Jack CR, Jr, Toga AW, Dale AM, Bernstein MA, Britson PJ, Gunter JL, Ward CP, Whitwell JL, Borowski BJ, Fleisher AS, Fox NC, Harvey D, Kornak J, Schuff N, Studholme C, Alexander GE, Weiner MW, Thompson PM. Longitudinal stability of MRI for mapping brain change using tensor-based morphometry. Neuroimage. 2006;31(2):627–640. [PMC free article] [PubMed]
  • Leow AD, Yanovsky I, Parikshak N, Hua X, Lee S, Toga AW, Jack CR, Jr, Bernstein MA, Britson PJ, Gunter JL, Ward CP, Borowski B, Shaw LM, Trojanowski JQ, Fleisher AS, Harvey D, Kornak J, Schuff N, Alexander GE, Weiner MW, Thompson PM. Alzheimer's disease neuroimaging initiative: a one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition. Neuroimage. 2009;45(3):645–655. [PMC free article] [PubMed]
  • Macintyre S, Hunt K, Sweeting H. Gender differences in health: are things really as simple as they seem? Social science & medicine (1982) 1996;42(4):617–624. [PubMed]
  • Mathis CA, Wang Y, Holt DP, Huang GF, Debnath ML, Klunk WE. Synthesis and evaluation of 11C-labeled 6-substituted 2-arylbenzothiazoles as amyloid imaging agents. Journal of medicinal chemistry. 2003;46(13):2740–2754. [PubMed]
  • Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, Woods R, Paus T, Simpson G, Pike B, Holmes C, Collins L, Thompson P, MacDonald D, Iacoboni M, Schormann T, Amunts K, Palomero-Gallagher N, Geyer S, Parsons L, Narr K, Kabani N, Le Goualher G, Boomsma D, Cannon T, Kawashima R, Mazoyer B. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM) Philos Trans R Soc Lond B Biol Sci. 2001;356(1412):1293–1322. [PMC free article] [PubMed]
  • 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. [PubMed]
  • 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. [PMC free article] [PubMed]
  • Mohs RC. Administration and scoring manual for the Alzheimer’s disease assessment scale. 1994 Copyright © 1994 by The Mount Sinai School of Medicine.
  • Moreno-Martinez FJ, Laws KR, Schulz J. The impact of dementia, age and sex on category fluency: greater deficits in women with Alzheimer's disease. Cortex; a journal devoted to the study of the nervous system and behavior. 2008;44(9):1256–1264. [PubMed]
  • Morra JH, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, Parikshak N, Hua X, Toga AW, Jack CR, Jr, Schuff N, Weiner MW, Thompson PM. Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls. Hum Brain Mapp. 2009a [PMC free article] [PubMed]
  • Morra JH, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, Parikshak N, Toga AW, Jack CR, Jr, Schuff N, Weiner MW, Thompson PM. Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls. Neuroimage. 2009b;45 1 Suppl:S3–S15. [PMC free article] [PubMed]
  • Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology. 1993;43(11):2412–2414. [PubMed]
  • Mortimer JA, Borenstein AR, Gosche KM, Snowdon DA. Very early detection of Alzheimer neuropathology and the role of brain reserve in modifying its clinical expression. Journal of geriatric psychiatry and neurology. 2005;18(4):218–223. [PMC free article] [PubMed]
  • Mueller SG, Schuff N, Weiner MW. Evaluation of treatment effects in Alzheimer's and other neurodegenerative diseases by MRI and MRS. NMR in biomedicine. 2006;19(6):655–668. [PMC free article] [PubMed]
  • Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack C, Jagust W, Trojanowski JQ, Toga AW, Beckett L. The Alzheimer's disease neuroimaging initiative. Neuroimaging Clin N Am. 2005a;15(4):869–877. xi–xii. [PMC free article] [PubMed]
  • Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack CR, Jagust W, Trojanowski JQ, Toga AW, Beckett L. Ways toward an early diagnosis in Alzheimer's disease: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Alzheimers Dement. 2005b;1(1):55–66. [PMC free article] [PubMed]
  • Nestor SM, Rupsingh R, Borrie M, Smith M, Accomazzi V, Wells JL, Fogarty J, Bartha R. Ventricular enlargement as a possible measure of Alzheimer's disease progression validated using the Alzheimer's disease neuroimaging initiative database. Brain. 2008;131(Pt 9):2443–2454. [PMC free article] [PubMed]
  • Ngandu T, von Strauss E, Helkala EL, Winblad B, Nissinen A, Tuomilehto J, Soininen H, Kivipelto M. Education and dementia: what lies behind the association? Neurology. 2007;69(14):1442–1450. [PubMed]
  • Paling SM, Williams ED, Barber R, Burton EJ, Crum WR, Fox NC, O'Brien JT. The application of serial MRI analysis techniques to the study of cerebral atrophy in late-onset dementia. Med Image Anal. 2004;8(1):69–79. [PubMed]
  • Petersen RC. Mild Cognitive Impairment: Aging to Alzheimer's Disease. New York: Oxford University Press; 2003.
  • Petersen RC, Doody R, Kurz A, Mohs RC, Morris JC, Rabins PV, Ritchie K, Rossor M, Thal L, Winblad B. Current concepts in mild cognitive impairment. Arch Neurol. 2001;58(12):1985–1992. [PubMed]
  • Pfeffer RI, Kurosaki TT, Harrah CH, Jr, Chance JM, Filos S. Measurement of functional activities in older adults in the community. Journal of gerontology. 1982;37(3):323–329. [PubMed]
  • Pievani M, Rasser PE, Galluzzi S, Benussi L, Ghidoni R, Sabattoli F, Bonetti M, Binetti G, Thompson PM, Frisoni GB. Mapping the effect of APOE epsilon4 on gray matter loss in Alzheimer's disease in vivo. Neuroimage. 2009;45(4):1090–1098. [PMC free article] [PubMed]
  • Potkin SG, Guffanti G, Lakatos A, Turner JA, Kruggel F, Fallon JH, Saykin AJ, Orro A, Lupoli S, Salvi E, Weiner M, Macciardi F. Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer's disease. PloS one. 2009;4(8):e6501. [PMC free article] [PubMed]
  • Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. American journal of human genetics. 2007;81(3):559–575. [PubMed]
  • Raji CA, Ho AJ, Parikshak NN, Becker JT, Lopez OL, Kuller LH, Hua X, Leow AD, Toga AW, Thompson PM. Brain structure and obesity. Hum Brain Mapp. 2009 [PMC free article] [PubMed]
  • Rey A. L’examen clinique en psychologie. Paris: Presses Universitaires de France; 1964.
  • Riddle WR, Li R, Fitzpatrick JM, DonLevy SC, Dawant BM, Price RR. Characterizing changes in MR images with color-coded Jacobians. Magn Reson Imaging. 2004;22(6):769–777. [PubMed]
  • Ridha BH, Barnes J, Bartlett JW, Godbolt A, Pepple T, Rossor MN, Fox NC. Tracking atrophy progression in familial Alzheimer's disease: a serial MRI study. Lancet neurology. 2006;5(10):828–834. [PubMed]
  • Risacher SL, Saykin AJ, West JD, Shen L, Firpi HA, McDonald BC. Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Current Alzheimer research. 2009;6(4):347–361. [PMC free article] [PubMed]
  • Rosen WG, Mohs RC, Davis KL. A new rating scale for Alzheimer's disease. Am J Psychiatry. 1984;141(11):1356–1364. [PubMed]
  • Roses AD. Apolipoprotein E alleles as risk factors in Alzheimer's disease. Annu Rev Med. 1996;47:387–400. [PubMed]
  • Roses AD, Saunders AM. APOE is a major susceptibility gene for Alzheimer's disease. Curr Opin Biotechnol. 1994;5(6):663–667. [PubMed]
  • Roses AD, Saunders AM, Alberts MA, Strittmatter WJ, Schmechel D, Gorder E, Pericak-Vance MA. Apolipoprotein E E4 allele and risk of dementia. Jama. 1995;273(5):374–375. author reply 5–6. [PubMed]
  • Rosner B. Fundamentals of Biostatistics. Boston: PWS-Kent Publishing Company; 1990.
  • Saunders AM, Strittmatter WJ, Schmechel D, George-Hyslop PH, Pericak-Vance MA, Joo SH, Rosi BL, Gusella JF, Crapper-MacLachlan DR, Alberts MJ, et al. Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer's disease. Neurology. 1993;43(8):1467–1472. [PubMed]
  • Scahill RI, Schott JM, Stevens JM, Rossor MN, Fox NC. Mapping the evolution of regional atrophy in Alzheimer's disease: unbiased analysis of fluid-registered serial MRI. Proc Natl Acad Sci U S A. 2002;99(7):4703–4707. [PubMed]
  • Scahill RI, Frost C, Jenkins R, Whitwell JL, Rossor MN, Fox NC. A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. Arch Neurol. 2003;60(7):989–994. [PubMed]
  • Scarmeas N, Albert SM, Manly JJ, Stern Y. Education and rates of cognitive decline in incident Alzheimer's disease. J Neurol Neurosurg Psychiatry. 2006;77(3):308–316. [PMC free article] [PubMed]
  • Schuff N, Woerner N, Boreta L, Kornfield T, Shaw LM, Trojanowski JQ, Thompson PM, Jack CR, Jr, Weiner MW. MRI of hippocampal volume loss in early Alzheimer's disease in relation to ApoE genotype and biomarkers. Brain. 2009;132(4):1067–1077. [PMC free article] [PubMed]
  • Selkoe DJ. Cell biology of protein misfolding: the examples of Alzheimer's and Parkinson's diseases. Nature cell biology. 2004;6(11):1054–1061. [PubMed]
  • Shaw LM, Korecka M, Clark CM, Lee VM, Trojanowski JQ. Biomarkers of neurodegeneration for diagnosis and monitoring therapeutics. Nature reviews. 2007;6(4):295–303. [PubMed]
  • Skovronsky DM, Lee VM, Trojanowski JQ. Neurodegenerative diseases: new concepts of pathogenesis and their therapeutic implications. Annual review of pathology. 2006;1:151–170. [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]
  • Sluimer JD, van der Flier WM, Karas GB, Fox NC, Scheltens P, Barkhof F, Vrenken H. Whole-brain atrophy rate and cognitive decline: longitudinal MR study of memory clinic patients. Radiology. 2008;248(2):590–598. [PubMed]
  • Sluimer JD, van der Flier WM, Karas GB, van Schijndel R, Barnes J, Boyes RG, Cover KS, Olabarriaga SD, Fox NC, Scheltens P, Vrenken H, Barkhof F. Accelerating regional atrophy rates in the progression from normal aging to Alzheimer's disease. European radiology. 2009;19(12):2826–2833. [PMC free article] [PubMed]
  • Smith AD, Jobst KA. Use of structural imaging to study the progression of Alzheimer's disease. British Medical Bulletin. 1996;52(3):575–586. [PubMed]
  • Smith AD. Imaging the progression of Alzheimer pathology through the brain. Proc Natl Acad Sci U S A. 2002;99(7):4135–4137. [PubMed]
  • Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23 Suppl 1:S208–S219. [PubMed]
  • Smith SM, Zhang Y, Jenkinson M, Chen J, Matthews PM, Federico A, De Stefano N. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage. 2002;17(1):479–489. [PubMed]
  • Stein JL, Hua X, Morra JH, Lee S, Hibar DP, Ho AJ, Leow AD, Toga AW, Sul JH, Kang H, Eskin E, Saykin AJ, Shen L, Foroud T, Pankratz N, Huentelman MJ, Craig DW, Gerber JD, Allen AN, Corneveaux JJ, Stephan DA, Webster J, DeChairo BM, Potkin SG, Jack CR, Weiner MW, Thompson PM. Genome-wide analysis reveals novel genes influencing temporal lobe structure with relevance to neurodegeneration in Alzheimer's disease. 2010 submitted. [PMC free article] [PubMed]
  • Storey JD. A direct approach to false discovery rates. J R Statist Soc B. 2002;64(Pt. 3):479–498.
  • Thal LJ, Kantarci K, Reiman EM, Klunk WE, Weiner MW, Zetterberg H, Galasko D, Pratico D, Griffin S, Schenk D, Siemers E. The role of biomarkers in clinical trials for Alzheimer disease. Alzheimer Dis Assoc Disord. 2006;20(1):6–15. [PMC free article] [PubMed]
  • Thompson PM, Giedd JN, Woods RP, MacDonald D, Evans AC, Toga AW. Growth patterns in the developing brain detected by using continuum mechanical tensor maps. Nature. 2000;404(6774):190–193. [PubMed]
  • Thompson PM, Hayashi KM, de Zubicaray G, Janke AL, Rose SE, Semple J, Herman D, Hong MS, Dittmer SS, Doddrell DM, Toga AW. Dynamics of gray matter loss in Alzheimer's disease. J Neurosci. 2003;23(3):994–1005. [PubMed]
  • Thompson PM, Hayashi KM, De Zubicaray GI, Janke AL, Rose SE, Semple J, Hong MS, Herman DH, Gravano D, Doddrell DM, Toga AW. Mapping hippocampal and ventricular change in Alzheimer disease. Neuroimage. 2004;22(4):1754–1766. [PubMed]
  • Toga AW. Brain Warping. 1st ed. San Diego: Academic Press; 1999.
  • Tower J. Sex-specific regulation of aging and apoptosis. Mechanisms of ageing and development. 2006;127(9):705–718. [PubMed]
  • Tower J, Arbeitman M. The genetics of gender and life span. Journal of biology. 2009;8(4):38. [PMC free article] [PubMed]
  • Vemuri P, Whitwell JL, Kantarci K, Josephs KA, Parisi JE, Shiung MS, Knopman DS, Boeve BF, Petersen RC, Dickson DW, Jack CR., Jr Antemortem MRI based STructural Abnormality iNDex (STAND)-scores correlate with postmortem Braak neurofibrillary tangle stage. Neuroimage. 2008;42(2):559–567. [PMC free article] [PubMed]
  • Vemuri P, Wiste HJ, Weigand SD, Shaw LM, Trojanowski JQ, Weiner MW, Knopman DS, Petersen RC, Jack CR., Jr MRI and CSF biomarkers in normal, MCI, and AD subjects: predicting future clinical change. Neurology. 2009;73(4):294–301. [PMC free article] [PubMed]
  • Wechsler D. WMS-R Wechsler Memory Scale - Revised Manual. New York: The Psychological Corporation, Harcourt Brace Jovanovich, Inc.; 1987.
  • Whitwell JL, Przybelski SA, Weigand SD, Knopman DS, Boeve BF, Petersen RC, Jack CR., Jr 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. [PMC free article] [PubMed]
  • Whitwell JL, Josephs KA, Murray ME, Kantarci K, Przybelski SA, Weigand SD, Vemuri P, Senjem ML, Parisi JE, Knopman DS, Boeve BF, Petersen RC, Dickson DW, Jack CR., Jr MRI correlates of neurofibrillary tangle pathology at autopsy: a voxel-based morphometry study. Neurology. 2008;71(10):743–749. [PMC free article] [PubMed]
  • Yanovsky I, Leow AD, Lee S, Osher SJ, Thompson PM. Comparing registration methods for mapping brain change using tensor-based morphometry. Medical Image Analysis. 2009;13(5):679–700. [PMC free article] [PubMed]