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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Neuroimage. Author manuscript; available in PMC Jun 16, 2009.
Published in final edited form as:
Neuroimage. Apr 15, 2009; 45(3): 645–655.
doi:  10.1016/j.neuroimage.2009.01.004
PMCID: PMC2696624
NIHMSID: NIHMS115619
Alzheimer’s Disease Neuroimaging Initiative: A one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition
Alex D. Leow,ab* Igor Yanovsky,c Neelroop Parikshak,a Xue Hua,a Suh Lee,a Arthur W. Toga,a Clifford R. Jack, Jr.,d Matt A. Bernstein,d Paula J. Britson,d Jeffrey L. Gunter,d Chadwick P. Ward,d Bret Borowski,d Leslie M. Shaw,e John Q. Trojanowski,e Adam S. Fleisher,f Danielle Harvey,g John Kornak,h Norbert Schuff,i Gene E. Alexander,k Michael W. Weiner,ij and Paul M. Thompsona, The Alzheimer’s Disease Neuroimaging Initiative
a Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
b Resnick Neuropsychiatric Hospital at UCLA, Los Angeles, CA, USA
c Department of Mathematics, University of California, Los Angeles, CA, USA
d Mayo Clinic, Rochester, MN, USA
e Department Pathology and Laboratory Medicine, and Institute on Aging, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
f Department of Neurosciences, UC San Diego, La Jolla, CA, USA
g Department of Public Health Sciences, UC Davis School of Medicine, Davis, CA, USA
h Department of Radiology and Biomedical Imaging and Department of Epidemiology and Biostatistics, UC San Francisco, San Francisco, CA, USA
i Veterans Affairs Medical Center, and Department Radiology, UC San Francisco, San Francisco, CA, USA
j Department Medicine and Psychiatry, UC San Francisco, San Francisco, CA, USA
k Department Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
* Corresponding author. Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA. Fax: +1 310 206-5518. E-mail address: feuillet/at/ucla.edu (A.D. Leow)
Tensor-based morphometry can recover three-dimensional longitudinal brain changes over time by nonlinearly registering baseline to follow-up MRI scans of the same subject. Here, we compared the anatomical distribution of longitudinal brain structural changes, over 12 months, using a subset of the ADNI dataset consisting of 20 patients with Alzheimer’s disease (AD), 40 healthy elderly controls, and 40 individuals with mild cognitive impairment (MCI). Each individual longitudinal change map (Jacobian map) was created using an unbiased registration technique, and spatially normalized to a geometrically-centered average image based on healthy controls. Voxelwise statistical analyses revealed regional differences in atrophy rates, and these differences were correlated with clinical measures and biomarkers. Consistent with prior studies, we detected widespread cerebral atrophy in AD, and a more restricted atrophic pattern in MCI. In MCI, temporal lobe atrophy rates were correlated with changes in mini-mental state exam (MMSE) scores, clinical dementia rating (CDR), and logical/verbal learning memory scores. In AD, temporal atrophy rates were correlated with several biomarker indices, including a higher CSF level of p-tau protein, and a greater CSF tau/beta amyloid 1-42 (ABeta42) ratio. Temporal lobe atrophy was significantly faster in MCI subjects who converted to AD than in non-converters. Serial MRI scans can therefore be analyzed with nonlinear image registration to relate ongoing neurodegeneration to a variety of pathological biomarkers, cognitive changes, and conversion from MCI to AD, tracking disease progression in 3-dimensional detail.
Alzheimer’s disease is the most common form of dementia, afflicting over 24 million people worldwide. In early AD, short-term memory function is typically among the first to be impaired, followed by a progressive decline in other cognitive functions (such as language, attention, orientation, visuospatial skills, and executive function) along with emotional/behavioral disturbances. At present, there is no cure for AD, whose natural course is insidious yet gradually debilitating, and is typically fatal at its most advanced stage, usually due to medical complications. In recent years, scientific interest has also focused on mild cognitive impairment (MCI), a pre-dementia stage that carries a 4–6-fold increased risk of future diagnosis of dementia, relative to the general population (Petersen et al., 1999, 2001; Petersen, 2000).
Many investigators have used MRI and PET imaging to measure longitudinal progression of brain changes in normal aging, MCI and AD, with varying results. As drug candidates that might slow the progression of Alzheimer’s pathology began to be developed, the need to develop robust and sensitive imaging methods to quantify progression of Alzheimer’s disease has become increasingly important. To this end, the National Institute of Aging and pharmaceutical industry funded the Alzheimer’s Disease Neuroimaging Initiative, with the goal of developing improved methods based on imaging and other biomarkers, for AD treatment trials.
A variety of methods have been used to quantify the longitudinal changes in structural brain MRI including: region-of-interest measurements (especially of the hippocampus (Frisoni et al., 1999), the “boundary shift integral” technique which quantifies differences between two successive co-registered 3D MRIs (Fox et al., 2000), voxel-based morphometry (Good et al., 2001; Whitwell et al., 2007), and tensor-based morphometry (Studholme et al., 2004, 2006; Leow et al., 2007).
Tensor-based morphometry (TBM) is a relatively new image analysis technique that identifies regional structural differences in the brain, across groups or over time, from the gradients of the deformation fields that align, or ‘warp,’ images to a common anatomical template (reviewed in Ashburner and Friston, 2003). Highly automated methods such as TBM are being tested to examine their utility in large-scale clinical trials, and in studies to identify factors that influence disease onset and progression (Leow et al., 2005b; Cardenas et al., 2007), or normal development (Thompson et al., 2000a; Chung et al., 2001; Hua et al., 2007).
In this paper, TBM is applied to a longitudinal ADNI dataset by using a nonlinear registration algorithm to match 3D baseline structural MR images with follow-up images acquired 1 year later (for related approaches, see Leow et al. 2005a; Studholme et al., 2006; Studholme and Cardenas, 2007; van de Pol et al., 2007; Barnes et al., 2008; Ridha et al., 2008).
Color-coded Jacobian maps — which show the local expansion or compression factor at each point in the image — can be used to indicate local volume loss or gain relative to the baseline image (Freeborough and Fox, 1998; Chung et al., 2001; Fox et al., 2001; Ashburner and Friston, 2003; Riddle et al., 2004). Here we examined longitudinal brain changes, using these Jacobian maps, in groups of AD and MCI subjects relative to controls. We also investigated, at a voxelwise level, how ongoing brain atrophy correlated with clinical measures including MMSE scores, and the global Clinical Dementia Rating (CDR), as well as biomarkers of AD pathology including CSF levels of tau protein, 181-phosphorylated tau protein (p-tau), beta amyloid (ABeta 42), and tau/Abeta42 ratio (Andreasen et al. 2001; Itoh et al., 2001; Verbeek et al., 2003; Clark et al., 2003; Hampel et al., 2004; Lee and Trojanowski, 2006).
Our hypotheses were as follows: (1) brain atrophic rates would be greater in AD and MCI than in controls, with MCI-control differences restricted primarily to the temporal lobe; (2) MCI converters (who transitioned to AD during the one-year follow-up interval) would have faster atrophic rates than non-converters, but slower atrophic rates than those with AD; (3) longitudinal temporal lobe atrophy would be significantly correlated with progression of cognitive impairment in AD and MCI; and (4) higher CSF tau protein level, lower CSF ABeta42 level, and higher p-tau/ABeta42 ratio would be significantly associated with higher rates of temporal lobe atrophy. In further tests that were considered exploratory rather than hypothesis-based, for purposes of statistical inference, we also determined which additional CSF biomarkers might correlate best with atrophic rates in each diagnostic group, and in all subjects combined.
Subjects
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Mueller et al., 2005a,b) is a large multi-center longitudinal MRI and FDG-PET (fluorodeoxyglucose positron emission tomography) study of 800 adults, ages 55 to 90, including 200 elderly controls, 400 MCI subjects, and 200 AD patients. The ADNI was launched in 2003 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, 5-year 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 in a multi-site manner mirroring enrollment methods used in clinical trials, 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 develop new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical trials.
At the time of writing this report, data collection for the ADNI project was in progress. In this paper, we studied longitudinal brain structural changes in 100 subjects, divided into 3 groups: 40 healthy elderly individuals, 40 individuals with amnestic MCI, and 20 individuals with probable AD. All groups were well matched for gender and age: each group included 50% men and 50% women; mean ages for the control, MCI and AD groups were, respectively, 75.27 years (standard deviation (SD)=5.33 years), 75.43 years (SD=7.02), and 75.70 years (SD=7.36), with no significant age differences among the three groups. We included twice as many subjects in the MCI and control groups versus the AD group, based on the availability of one-year follow-up scans at the time of writing this paper.
All subjects underwent thorough clinical/cognitive assessment at the time of both the baseline and the follow-up scan acquisitions. As part of each subject’s cognitive evaluation, the Mini-Mental State Examination (MMSE) was administered to provide a global measure of mental status based on evaluation of five cognitive domains (Folstein et al., 1975; Cockrell and Folstein, 1988); scores of 24 or less (out of a maximum of 30) are generally consistent with dementia. The Clinical Dementia Rating (CDR) was also assessed as a measure of dementia severity (Hughes et al., 1982; Morris, 1993). A global CDR of 0, 0.5, 1, 2 and 3, respectively, indicate no dementia, very mild, mild, moderate, and severe dementia. The elderly normal subjects had MMSE scores between 28 and 30 (inclusive), all had a global CDR of 0, and no symptoms of depression, MCI, or other forms of dementia. The MCI subjects had MMSE scores in the range of 24 to 30, all had a global CDR of 0.5, and mild memory complaints, with memory impairment assessed via education-adjusted scores on the Wechsler Memory Scale–Logical Memory II (Wechsler, 1987). All AD patients met NINCDS/ADRDA criteria for probable AD (McKhann et al., 1984) with baseline MMSE scores as high as 26, and a lower limit of 20. In this study, 16 AD patients had a CDR of 0.5, and the remainder had a CDR of 1. Detailed exclusion criteria, e.g., regarding concurrent use of psychotropic medications, can be found in the ADNI protocol (Mueller et al., 2005a,b). Briefly, subjects were excluded if they had any serious neurological disease other than incipient AD, any history of brain lesions or head trauma, or psychotropic medication use (including antidepressants, neuroleptics, chronic anxiolytics or sedative hypnotics, etc.).
MRI acquisition and image correction
All subjects were scanned with a standardized MRI protocol, developed after a substantial effort evaluating and comparing 3D T1-weighted sequences for morphometric analyses (Leow et al., 2006; Jack et al., 2008). High-resolution structural brain MRI scans were acquired at multiple ADNI sites using 1.5 Tesla MRI scanners from General Electric Healthcare and Siemens Medical Solutions (ADNI also collects a smaller subset of data at 3 Tesla but it was not analyzed here to avoid the additional complications of combining data across scanner field strengths). All scans were collected according to the standard ADNI MRI protocol. For each subject, two T1-weighted MRI scans were collected using a sagittal 3D MP-RAGE sequence. As described in Jack et al. (2008), typical 1.5 T acquisition parameters are repetition time (TR) of 2400 ms, minimum full TE, inversion time (TI) of 1000 ms, flip angle of 8°, 24 cm field of view, with 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. In-plane, zero-filled reconstruction yielded a 256×256 matrix for a reconstructed voxel size of 0.9375× 0.9375×1.2 mm3.
Additional image corrections were also applied, using a processing pipeline at the Mayo Clinic, consisting of: (1) a procedure termed GradWarp for correction of geometric distortion due to gradient non-linearity (Jovicich et al., 2006), (2) a “B1-correction,” to adjust for image intensity inhomogeneity due to B1 non-uniformity using calibration scans (Jack et al., 2008), (3) “N3” bias field correction, for reducing residual intensity inhomogeneity (Sled et al., 1998), and (4) geometrical scaling, according to a phantom scan acquired for each subject (Jack et al., 2008), to adjust for scanner- and session-specific calibration errors. In addition to the original uncorrected image files, images with all of these corrections already applied (GradWarp, B1, phantom scaling, and N3) are available to the general scientific community (at www.loni.ucla.edu/ADNI).
Image pre-processing
To adjust for global differences in brain positioning and scale across individuals, all scans were linearly registered to the stereotactic space defined by the International Consortium for Brain Mapping (ICBM-53) (Mazziotta et al., 2001) with a 9-parameter (9P) transformation (3 translations, 3 rotations, 3 scales) using the Minctracc algorithm (Collins et al., 1994). Globally aligned images were resampled in an isotropic space of 220 voxels along each axis (x, y, and z) with a final voxel size of 1 mm3.
Specifically, baseline scans were first linearly normalized with a 9-parameter registration to the ICBM space. Follow-up scans, on the other hand, were 9-parameter registered to their corresponding baseline scans, followed by normalization to the ICBM space using the same baseline transformation that carries baseline scans to the ICBM space (for discussions on the effect of global scaling in longitudinal studies, see Paling et al., 2004; Whitwell et al., 2004).
Three-dimensional Jacobian maps quantifying structural changes over time
To quantify 3D patterns of volumetric brain atrophy over time for each subject, an individual change map, or Jacobian map, was computed by non-linearly registering the follow-up scan to the baseline scan with an unbiased registration algorithm we developed (Leow et al., 2007). The unbiased image registration technique computes deformation fields by penalizing statistical bias in the resulting Jacobian maps, thus eliminating skew from the distribution of Jacobian determinants, and has been shown to perform favorably in recovering true physiological changes in serial MRI data (Yanovsky et al., 2007). Non-positive Jacobians are prevented when using unbiased registration, as a regularization term is penalized based on the logarithmic transform of the local Jacobian values. Moreover, unbiased registration is inherently symmetric (i.e., inverse-consistent), so there is no methodologically-induced bias towards detecting gain versus loss (see Leow et al., 2005, Leow et al., 2007, for examination of the Jacobian statistical distributions).
Unbiased group average template — Minimal Deformation Target (MDT)
To facilitate voxelwise comparisons between groups, we followed our previous approaches and further nonlinearly registered all individual brains and their corresponding Jacobian maps to the Minimal Deformation Target (MDT) created in Hua et al. (2008), an unbiased average template image in the ICBM space representing common anatomical features on a voxel level for the group of control ADNI subjects (Good et al., 2001; Kochunov et al., 2002; Joshi et al., 2004; Studholme and Cardenas, 2004; Kovacevic et al., 2005; Christensen et al., 2006; Lorenzen et al., 2006; Lepore et al., 2008).
After this final step, these spatially normalized Jacobian maps now share a common anatomical coordinate as defined by the MDT. Thus, statistical analyses may be conducted at each voxel to assess the magnitude and significance of deficits in MCI and AD versus the healthy controls.
Statistical tests
Using these spatially normalized Jacobian maps that encode longitudinal brain changes, we carried out voxel-wise statistical tests between the Jacobian maps in each group. The Jacobian maps in MCI and AD were compared to those from normal controls, using both a spatial average of the Jacobian values within specific regions of interest (ROIs; defined below), and voxel-wise tests controlled for multiple comparisons. In the latter, at each voxel, we evaluated the significance level of group differences using a two-sample t test with unequal variance. The resulting p-values were displayed as maps to visualize patterns of significant differences throughout the brain.
To correct for multiple comparisons, we used permutation testing to assess the overall significance of group differences (see, e.g., Bullmore et al., 1999; Nichols and Holmes 2002; Thompson et al. 2003a,b; Chiang et al., 2007a,b). A null distribution for the group differences in Jacobian at each voxel was constructed using 10,000 random permutations of the data. The number of permutations N was chosen to be 10,000, to control the standard error SEp of the omnibus probability p, which follows a binomial distribution B(N, p) with known standard error (Edgington, 1995). When N=10,000, the approximate margin of error (95% confidence interval) for p is approximately 5% of p. For each test, the subjects’ diagnosis was randomly permuted and voxel-wise t tests were conducted to identify voxels more significant than p=0.05. The volume of voxels in the brain more significant than p=0.05 was computed for the real experiment and for the random assignments. Finally, a ratio, describing the fraction of the time the suprathreshold volume was greater in the randomized maps than the real effect (the original labeling), was calculated to give an overall p-value for the significance of the map.
CDF plots
Cumulative distribution function (CDF) plots based on the above two-sample t-tests were used to compare the effect sizes of group differences and effects of covariates of interest in all three groups. These CDF plots are commonly generated when using false discovery rate methods to assign overall significance values to statistical maps (Benjamini and Hochberg, 1995; Genovese et al., 2002; Storey, 2002); they may also be used to compare effect sizes of different methods, subject to certain caveats (Lepore et al., 2008; Hua et al., 2008; Morra et al., 2008a,b), as they show the proportion of supra-threshold voxels in a statistical map, for a range of thresholds.
Regions of interest (ROIs)
Regions of interest, including frontal, parietal, temporal, and occipital lobes, were defined by manually labeling the normal group MDT. The MDT was traced by a trained anatomist to generate binary masks for each lobe, which were subsequently used to summarize brain atrophy at a regional level in each group. Within each lobe, tissue types were distinguished by creating maps of gray and white matter, CSF, and non-brain tissues using the partial volume classification (PVC) algorithm from the BrainSuite software package (Shattuck and Leahy, 2002). One single voxel was eroded from the boundary of each tissue class to avoid the inclusion of partial volumed voxels at tissue interfaces (e.g., CSF, gray and white matter mixtures) where the Jacobian values may not be representative of the rest of the region. To avoid confounding the average values in regions where tissue atrophy was assessed, CSF was excluded from the masks for the ROI-based Jacobian averages, but was included for voxel-wise comparisons (i.e., maps of group differences).
Correlations of structural brain differences (Jacobian values) with clinical measurements and biomarkers
At each voxel, correlations were assessed for each group, using the general linear model, between the Jacobian values and several clinical measures at baseline — MMSE scores (Folstein et al., 1975), the geriatric depression scale (GDS), Clinical Dementia Rating summary scores (Morris, 1993), and CDR sum-of-boxes scores. The CDR assesses a patient’s cognitive and functional performance in six areas on a scale of 0 (no impairment) to 3 (impaired): memory, orientation, judgment and problem solving, community affairs, home and hobbies, and personal care. As there is a significant range restriction with global CDR scores, we also assessed correlations with the CDR ‘sum-of-boxes’ scores, which has a greater dynamic range (0–18), and may provide more useful information than the CDR global score, especially in mild cases (Lynch et al., 2006). In addition, several biomarkers obtained from CSF were also included for assessing correlations, including beta amyloid 1-42 (ABeta42), tau protein, phosphorylated-tau protein 181 (p-tau), the tau and ABeta42 ratio (tau/ABeta42), and p-tau ABeta42 ratio (p-tau/ABeta42). Biomarker measurements were performed by Drs. Leslie Shaw and John Trojanowski of the ADNI Biomarker Core at the University of Pennsylvania School of Medicine, which collects and banks biological samples (DNA, blood, urine and CSF) from all participating sites, and conducts studies of selected AD biomarkers, including apolipoprotein E (ApoE) genotype, isoprostanes, tau, ABeta, and homocysteine levels (Shaw et al., 2007).
As individual Jacobian maps encode structural differences between baseline and follow-up scans, correlations were also assessed between the Jacobian values and the differences in clinical measures between the two time points. All correlations were corrected for multiple comparisons as described above.
3D maps of brain atrophy in MCI and AD
We first visualized the mean brain structural change map for each group (AD, MCI, and CTL) by averaging individual Jacobian maps within each group. These maps may be considered as showing the mean percent tissue loss over the one-year interval, or, if changes are assumed to be approximately linear, they provide a regional estimate of the atrophy rate. The resulting statistical maps, shown in Fig. 1, suggest widespread progressive atrophy throughout the entire brain and expansion of ventricular and CSF spaces in AD (compared to controls), and a more restricted pattern of atrophy in MCI. To test whether there were statistically significant volume changes, over this 12-month time period, for each group, and whether these changes differ between groups, we conducted regional (ROI) analyses as well as voxel-wise tests controlled for multiple comparisons as follows (Figs. 2 and and33).
Fig. 1
Fig. 1
Unbiased registration was performed on 100 pairs of serial MR images, acquired 12 months apart, from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The selected sample consisted of 20 patients with Alzheimer’s disease (more ...)
Fig. 2
Fig. 2
Voxel-wise Z-statistics (top row) comparing mean Jacobian values for AD (N =20) versus Controls (N=40) on the top, and MCI (N=40) versus Controls (N=40) on the bottom. Corresponding color-coded P maps also show the local significance of these differences (more ...)
Fig. 3
Fig. 3
Percent brain tissue loss from baseline to follow-up as determined by the average Jacobian value within each lobe (with CSF excluded). The diagnosis for the AD, MCI, and CTL groups was determined at baseline. Here, MCI at 12Mo denotes subjects diagnosed (more ...)
Mean Jacobian values within each ROI, normalized to indicate annualized rates of atrophy (in percent per year) were computed to represent overall differences in the rates of atrophy for each region (Fig. 3). Here, we also separated MCI converters (N=7) from MCI non-converters (N=32). (These MCI subgroups excluded the one MCI subject whose diagnosis was changed to control at follow-up). In general, a consistent trend was observed for atrophy rates, with AD >MCI converters >MCI non-converters. MCI non-converters showed comparable rates to controls (see bars colored camel and white, respectively, in Fig. 3). The AD group (N=40) showed significant progressive atrophy (p values, based on 1-sample t-test, <0.01 for all ROIs) in the frontal lobes (annual atrophy rate in AD: 2.41%), occipital lobes (2.07%), parietal lobes (2.40%), and temporal lobes (2.22%). The corresponding mean atrophy rates were 0.37%, 0.63%, 0.22%, and 0.74% for the control group, with corresponding P-values exhibiting significance for the occipital lobes (p=0.02) and the temporal lobes (p=0.05) (the MCI non-converter group showed a similar atrophy profile; see Fig. 3 for more explanations). The small group of MCI converters (N=7) showed an atrophy profile resembling AD (no statistical tests were conducted in this sub-group due to a smaller sample size, although the MCI converters/AD combined group, with a sample size of 27, also exhibited significant volume loss for all ROIs). These data are consistent with earlier reports assessing atrophy rates using 2 MRI brain scans separated by a 12-month interval: using the boundary shift integral method, Fox et al. (2000) found that mean (SD) rate of brain atrophy for the patients with mild to moderate AD was 2.37% (1.11%) per year, while in the control group it was 0.41% (0.47%) per year.
Permutation tests, using suprathreshold percentages, were conducted to assess the presence of statistically significant local volume change differences between groups, based on the voxel-wise two-sample t-test on the rates of change (Fig. 2). This step provides a detailed 3-D visualization of the local atrophy profile, in terms of voxel-wise mean change rates and their significance level). Here, significantly greater atrophy rates in AD versus controls were confirmed for all ROIs, including left/right/both temporal lobes (p=0.0021/0.0015/0.0015, corrected), occipital lobes (p=0.0014, corrected), parietal lobes (p =0.0015, corrected), and frontal lobes (p=0.005, corrected). Also, significantly greater atrophy rates in MCI versus controls were confirmed in the left/right/total temporal lobe (p=0.03/0.03/0.03, corrected), and parietal lobe (p=0.046, corrected) but not in the occipital or frontal lobes (non-converters were not separated in this permutation testing, due to a relatively small sample size of 7). This is consistent with previous findings that parietal and temporal lobes are among the first to be involved in AD. Although mean Jacobian values (Fig. 3) — when spatially averaged across the temporal lobe — were similar in MCI (both converters and non-converters) and controls (annual atrophy rate 0.73% and 0.74%), locally faster atrophy was detected in the MCI group versus controls using a voxelwise permutation test. This is partly due to contributions from the converters, who have smaller Jacobian values in general —denoting faster atrophy — compared to non-converters. This also shows that permutation testing, on the suprathreshold volume of statistics in a map may be more powerful for detecting group differences than performing univariate tests on regional averages (numeric summaries) derived from the maps.
Correlations of Jacobian values with clinical measures and biomarkers
Any quantitative measure of brain atrophy has greater value if it can be shown to correlate with established measures of cognitive or clinical decline, or with future outcome measures. Here, we investigated whether longitudinal temporal lobe atrophy over this 12-month period correlates with cognitive decline over the same interval. Our results in Tables 1 and and22 show corrected p-values for these correlations (although all markers and clinical measures were correlated for each diagnostic group, only significant results are reported here). To avoid reductions in power due to restricting the range to the AD or MCI groups separately, correlations were also examined across the entire sample of 100 subjects (which we will refer to as the “pooled” group). In this case, the normal subjects, who tend to score in the normal range for all clinical measures, drive these associations to some extent. Diagnostic groups were not pooled together for these cognitive tests since the groups are stratified according to their test scores.
Table 1
Table 1
Biomarker information and voxel-wise correlation results
Table 2
Table 2
Baseline cognitive scores (a), changes in cognitive scores (b), and conversion from MCI to AD (c) and voxel-wise correlation results
Our results in Table 1 and Fig. 4a indicate that higher CSF tau protein level, lower CSF ABeta42 level, and higher p-tau/ABeta42 ratio are significantly associated with higher rates of temporal lobe atrophy only in the pooled group (p=0.003, 0.001 and 0.002, corrected), but not in any of the 3 individual diagnostic groups. On the other hand, correlations between temporal lobe atrophy rates and the CSF p-tau level and tau/Abeta42 ratio were significant for both the pooled data (p=0.01 and 0.0003) and within the AD group (p=0.02 and 0.02).
Fig. 4
Fig. 4
(a) CDF plots for voxel-wise correlation of progressive temporal lobe tissue loss in MCI, AD, and pooled groups (ALL, N=100) with (a) various biomarker indices including ABeta42 (AB142), tau protein (TAU), phosphorylated-tau 181 (PTAU), tau/ABeta42 ratio (more ...)
For clinical measures (Table 2; Fig. 4b), longitudinal temporal lobe atrophy was significantly correlated with progression of cognitive impairment in the MCI group, including an increase over time in CDR score (p=0.03), a decrease over time in MMSE (p=0.02), a worsening in immediate logical memory performance (p=0.04), and worsening scores on the 30-minute delayed auditory verbal learning test (AVLT) (p=0.04). Moreover, a higher baseline Geriatric Depression Score (i.e., more severe depression) and a lower baseline score on the delayed logical memory test also correlated with a greater ongoing rate of temporal lobe atrophy (p=0.02 and 0.04). Lastly, converters exhibited significantly faster rates of temporal lobe atrophy (p=0.03) than non-converters (i.e., atrophy rates differentiated the group of converters from non-converters).
Interestingly, in our analysis, longitudinal temporal lobe atrophy was not correlated with baseline MMSE, CDR, or sum-of boxes scores. Nor does it correlate with the change in sum-of-boxes scores over the one-year interval. However, in the AD group, a higher baseline sum-of-boxes score, i.e., greater cognitive impairment at baseline, correlated with less ongoing atrophy (i.e., larger Jacobian values) (p=0.02). In AD, the sum-of-boxes score may therefore better reflect the disease severity in advanced AD, at a point when advanced temporal atrophy has already occurred, and progressive atrophy has slowed.
CDF curves
The cumulative distribution curves in Fig. 4 show relative effect sizes for the associations between rates of brain atrophy and different pathological markers and clinical correlates. In general, curves that rise more sharply at the origin denote statistical maps with greater effect sizes, and those curves that intersect the line y=20x at points other than the origin, pass the conventional criterion of controlling the false discovery rate at an expected rate of 5%, and are regarded as significant after multiple comparisons correction. This approach allows an approximate ranking of the effect sizes for different correlates: higher tau/ABeta42 ratio was the pathological biomarker most highly correlated with higher rates of temporal lobe atrophy when all subjects were combined; other biomarkers showed comparable but slightly lower effect sizes. The clinical score that correlated most with higher rates of temporal lobe atrophy was one-year change in MMSE in MCI, with many other cognitive measures showing comparable but slightly lower effect sizes.
In this paper, our hypotheses were largely confirmed regarding longitudinal brain structural changes in three groups of subjects including normal controls, those with MCI, and patients with Alzheimer’s disease. Alzheimer’s disease was associated with significantly faster ongoing atrophy in the temporal and parietal lobes, relative to matched healthy controls. There was also significantly faster expansion in the CSF spaces, consistent with previous studies (e.g., Boyes et al., 2006), and significant progressive tissue loss in frontal and occipital lobes, indicating that ongoing atrophy is widely distributed in AD.
Prior studies of the disease trajectory in AD (e.g., Scahill et al., 2002; Thompson et al., 2003a,b), show a shift in the distribution of atrophy with advancing disease. In line with the trajectory of neurofibrillary pathology (Braak and Braak, 1991), the entorhinal and medial temporal lobes show the earliest signs of atrophy in MCI, with frontal atrophy typically occurring later, and primary sensory and motor cortices spared until late in the illness (see Thompson and Apostolova, 2007, for a review of this trajectory mapped with different imaging modalities). Consistent with this, in our MCI group, progressive atrophy was detected only in the temporal and parietal lobes, in line with evidence that ongoing changes are more anatomically restricted at this pre-dementia stage.
One notable aspect of the topography of brain matter loss shown in Figs. 1 and and22 is that the greatest proportion of brain matter loss appears to lie in the white matter rather than at the voxels on the cortical surface. There are two reasons for this, both technical: (1) the deformations are spatially smooth and partial volume averaging effects occur and diminish the signal somewhat at tissue boundaries, such as the cortex/CSF interface, and (2) the registration accuracy of TBM is poorer at the cortical surface, at least relative to some approaches that explicitly model the cortical surface. To clarify this, note that Figs. 1 and and22 visualize group differences by averaging rates of volumetric changes (i.e., Jacobian maps), after nonlinearly aligning individual maps of change to the minimal deformation template (MDT). This deformation field is spatially smooth, so some partial volume effects between cortex and CSF are inevitable and are more pronounced along the MDT boundary. As a result, some signal spillover from outside of the brain tissue may be present, explaining the reduction in the atrophy signal along the boundary. This effect has been noted in our prior work (Hua et al., 2008), where the disease-related expansion in the ventricles spills over into the subcortical white matter by about 1–2 mm, in the average maps. Similarly, the cortical atrophy signal is partially canceled by the signal in the CSF outside the brain, which may not show the same level of atrophy, and if anything, may show slight expansion over time. Second, and perhaps more importantly, the deformation fields are based on automated matching of intensities in the images, and the spatial smoothness of the fields makes it difficult to register the entire cortical mantle within subjects from one time-point to the next, as would be required to gauge the atrophy of cortical gray matter. Alternative approaches may be used that compute thickness at each point, but these are typically more time-consuming as they generally require extraction of explicit models of the cortical surface as geometric meshes, prior to computing the cortical thickness either directly from the meshes (Lerch and Evans, 2005), or by tissue classification of the images and voxel coding (Thompson et al., 2004; Aganj et al., 2008).
There are at least two possible solutions to better sensitizing our TBM approach for detecting cortical gray matter loss. The first is to use a method termed voxel-based morphometry (VBM; Ashburner and Friston, 2000) or a related approach termed RAVENS (Davatzikos et al., 2001). In VBM, the deformation-based compression signal at each point is multiplied by maps of gray matter density, which are based on smoothing maps of gray matter voxels derived from an explicit tissue classification into gray and white matter and CSF. An additional modulation step is also included that preserves information on the volume of gray matter in the baseline images after warping. When gray matter density and deformation signals are multiplied together in this way, VBM maps in AD do typically show progressive cortical gray matter atrophy in a temporal-to-frontal pattern that matches the spread of neurofibrillary tangle pathology (Baron et al., 2001). A second approach to identifying cortical gray matter atrophy with TBM was developed by (Studholme et al., 2003), in which deformation-based compression signals at each point are smoothed adaptively depending on the amount of gray matter lying under the filter kernel. This is a way to avoid some of the signal depletion that occurs when atrophying gray matter is partial-volumed with CSF. A third solution is to run the deformation maps at a very high spatial resolution and with less spatial regularization, or with a regularization term that enforces continuity but not smoothness. Because of the complexity of differentiating cortical gray matter changes from underlying white matter changes, we seek to assign signals to the cortex without surface-based modeling, which can be time consuming for larger analyses (Thompson et al., 2004), Thus, in this paper, we decided to combine both gray and white matter for each region of interest (ROI) in our analyses, instead of separating them.
Although much of the literature has suggested that gray matter loss is the primary change in AD that is observable on MRI (see Thompson and Apostolova, 2007, for a review), there has been substantial theoretical and empirical evidence supporting white matter pathology in AD (Bartzokis et al., 2003). For example, in Rose et al. (2000), the authors summarized recent DTI studies in AD, and many have reported reduced FA (fractional anisotropy) in temporal, frontal, and parietal lobes, especially in the internal capsule and limbic association fibers, the corticothalamic pathway, superior longitudinal fasciculus, and posterior cingulate bundle (Yoshiura et al., 2002). White matter degeneration in AD has also been detected with MR relaxometry (Bartzokis et al., 2003) and myelin and oligodendrocyte reductions have been detected in neuropathological studies of AD. Future MRI studies, using state-of-the-art techniques such as diffusion-weighted MRI (Rose et al., 2000; Choi et al., 2005; Medina et al., 2006) or High Angular Resolution Diffusion Imaging (HARDI), are likely to further elucidate white matter pathology in AD.
A further notable feature, which requires some explanation, is that a related technique (the voxel-compression method) has shown unequivocally temporal gray matter loss and ventricular expansion, but no change in the white matter (Fox et al., 2001) in serial MRI studies of AD. As already noted, we did indeed detect subtle and diffuse changes in the subcortical white matter, but the ability to detect them depends to some degree on the level of regularization used in TBM. In TBM, there is a smoothness term, which causes correlations in the deformation signals at neighboring voxels. In general, for simplicity and practicality, an elastic (Leow et al., 2005a) or fluid (Fox et al., 2001) model of registration is used, in which the Green’s function of the governing operator is spatially uniform and fixed. If the correlations are assumed to be long-range (i.e., the deformations are spatially quite smooth), there is more signal enhancement in large homogeneous regions such as the white matter, whereas if the correlations are assumed to be short-range (i.e., the deformations are spatially quite rough, as in the fluid registration model of Fox et al., 2001), there is typically more sensitivity to finer-scale differences (as found in the gray matter in the Fox et al. study), while sacrificing some power to detect broader-scale differences (e.g., the failure to detect white matter atrophy in Fox et al., 2001). In future, the differential sensitivity of both approaches could be combined by estimating these spatially varying correlations empirically from anatomical landmarks using 6-dimensional covariance tensors (Fillard et al., 2008) and incorporating them into a statistically-based adaptive registration model as we have begun to do (Brun et al., 2007 and 2008).
In this paper, the correlations between atrophy and CSF biomarkers are also of significant interest. Prior literature has indicated that there is lower ABeta42, but higher tau and p-tau protein, in the CSF of AD patients versus those with other dementia subtypes or normal subjects (Andreasen et al. 2001; Itoh et al., 2001; Verbeek et al., 2003; Clark et al., 2003; Hampel et al., 2004). More recently, researchers have also investigated the utility of using these markers for predicting conversion from MCI to AD (Fagan et al., 2007; Li et al., 2007). In our results, progressive temporal lobe atrophy was highly correlated with baseline p-tau, tau/ABeta42 ratio (for both AD groups and all subjects pooled), ABeta42, and tau (the latter two only for data pooled across all diagnostic groups). This suggests that p-tau and tau/Abeta42 may be more clinically useful than Abeta42 or tau in predicting ongoing atrophy (in Fig. 4, CDF curves rise more rapidly for the correlations with p-tau and with the tau/ABeta42 ratio).
We could not demonstrate significant correlations between biomarkers and ongoing temporal lobe atrophy in the MCI group. This is perhaps not surprising due to the heterogeneous nature of MCI, and the relatively small sample of 40 subjects.
Clinical measures correlated more strongly with atrophy rates in MCI than in AD, supporting the use of serial neuropsychiatric testing in monitoring disease progression in MCI. In AD, atrophy rates exceeded those in MCI, but did not correlate so strongly with interval changes in neuropsychiatric test scores. This may suggest that (1) decline in cognition is more tightly linked with atrophy rates early in the illness, or (2) in late AD, atrophy rates may eventually plateau or slow down, which may disrupt any correlation between the absolute rate of tissue loss and further changes in cognition, or (3) correlations may only be detectable in samples that are larger and/or have a broader range of disease severity. Our AD sample was only half the size of our MCI sample, and was somewhat restricted in disease severity to reflect relatively mild AD; by contrast, in recent study of 52 subjects with mild-to-moderate AD (Ridha et al., 2008), there was a strong association between brain atrophy rate and MMSE decline (r=0.59, p<0.0001). In addition, there is some evidence that atrophy rates do not slow down as AD progresses; Chan et al. (2003) found that in 12 patients with mild dementia (MMSE=23), scanned from a presymptomatic stage through to moderately severe dementia, mean yearly loss of brain volume was 2.8% (95% CI: 2.3–3.3), but rose by 0.32% per year (0.15–0.50). In 39 healthy control subjects, Scahill et al. (2003) also found rates of atrophy accelerated nonlinearly with increasing age, with the most marked changes occurring after the age of 70.
To summarize, our results further support the value of serial MRI scanning, combined with quantitative nonlinear registration, for tracking disease progression in Alzheimer’s and MCI. Our detailed 3D Jacobian maps, reflecting regional brain atrophy, correlated well with disease progression and conversion to AD, as well as with various biomarkers and clinical measures. Moreover, groups of MCI converters and non-converters were differentiated by measures of temporal lobe atrophy over time.
Lastly, instead of separating hippocampus in our analysis, it was included as part of the temporal lobe. Any TBM study is limited by the accuracy with which deformable registration can match anatomical boundaries between individual brains and corresponding regions on the template. Our mean deformation template (MDT) was created after rigorous nonlinear registration, and geometric centering. Most anatomical features and boundaries are well-preserved in the MDT, and the hippocampus is sufficiently discernible to be labeled by hand. Even so, it may not always be possible to achieve accurate regional measurements of atrophy in small regions such as the hippocampus, since that would require a locally highly accurate registration. Some research groups have successfully computed hippocampal atrophy rates from fluid registration methods (e.g., Crum et al., 2001), and found that they can be superior to manual delineations in separating AD from controls (p<0.0001; Barnes et al., 2007a,b) and more reliable (van de Pol et al., 2007).
To detect more subtle effects, direct modeling of brain structures, e.g., using surface-based geometrical methods (e.g., Morra et al., 2008a,b), or using a template in conjunction with boundary shift integral measures (Barnes et al., 2007a,b), may offer additional statistical power to detect subregional differences. We are currently investigating longitudinal hippocampal changes using the ADNI dataset with a range of different methods, which we plan to report in the future.
Acknowledgments
The data used in preparing this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative database (www.loni.ucla.edu/ADNI). Many ADNI investigators therefore contributed to the design and implementation of ADNI or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators is available at www.loni.ucla.edu/ADNI/Collaboration/ADNI_Citation.shtml. This work was primarily funded by the ADNI (principal investigator: Michael Weiner; NIH grant number U01 AG024904). ADNI is funded by the National Institute of Aging, the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and the Foundation for the National Institutes of Health, through generous contributions from the following companies and organizations: Pfizer Inc., Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Company, GlaxoSmithKline, Merck & Co. Inc., AstraZeneca AB, Novartis Pharmaceuticals Corporation, the Alzheimer’s Association, Eisai Global Clinical Development, Elan Corporation plc, Forest Laboratories, and the Institute for the Study of Aging (ISOA), with participation from the U.S. Food and Drug Administration. 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. Algorithm development for this study was also funded by the NIA, NIBIB, the National Library of Medicine, and the National Center for Research Resources (AG016570, EB01651, LM05639, RR019771 to PT). Author contributions were as follows: AL, IY, NP, XH, SL, AT, NS, and PT performed the image analyses; CJ, MAB, PB, JG, CW, BB, LS, JT, AF, DH, JK, GA, 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.
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