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Beta amyloid (Aβ)-plaque deposition and neurodegeneration within temporoparietal and hippocampal regions may indicate increased risk of Alzheimer’s disease (AD). This study examined relationships between AD biomarkers of Aβ and neurodegeneration as well as cognitive performance in cognitively normal older individuals. Aβ burden was quantified in 72 normal older human subjects from the Berkeley Aging Cohort (BAC) using [11C] Pittsburgh compound B (PIB) PET. In the same individuals, we measured hippocampal volume, as well as glucose metabolism and cortical thickness, which were extracted from a template of cortical AD-affected regions. The three functional and structural biomarkers were merged into a highly AD-sensitive multi-modality biomarker reflecting neural integrity. In the normal older individuals, there was no association between Aβ burden and either the single-modality or the multi-modality neurodegenerative biomarkers. While lower neural integrity within the AD-affected regions and a control area (the visual cortex) was related to lower scores on memory and executive function tests, the same association was not found with PIB retention. The relationship between cognition and the multi-modality AD biomarker was stronger in individuals with the highest PIB uptake. The findings indicate that neurodegeneration occurs within AD regions irrespective of Aβ deposition and accounts for worse cognition in cognitively normal older people. The impact of neural integrity on cognitive functions is enhanced in the presence of high Aβ burden for regions that are vulnerable to AD pathology.
There is great interest in detecting and characterizing cognitively normal older individuals with an increased risk of Alzheimer’s disease (AD). Both the hallmark AD pathology of beta amyloid (Aβ)-plaque burden and neurodegeneration are seen in a substantial proportion of cognitively normal older people (Morris et al., 2009; Dickerson et al., 2011), suggesting a preclinical stage of AD. Cortical Aβ deposition has been proposed as the initiating factor in AD progression (Jack et al., 2010), however, this view is hypothetical and other models have been suggested (Herrup, 2010). Although preclinical AD criteria include biomarkers of Aβ burden and neurodegeneration (Sperling et al., 2011), the relationships between Aβ, neuronal damage and cognition in the normal elderly remain unclear.
Imaged in-vivo with [11C] Pittsburgh compound B (PIB) Positron Emission Tomography PET) (Klunk et al., 2004), Aβ deposition is found in 20% – 30% of cognitively normal older people (Mintun et al., 2006; Quigley et al., 2011). Neurodegenerative AD pathology is known to target circumscribed posterior cortical and hippocampal regions (Perrin et al., 2009). There it can be detected using biomarkers of neuronal function and structure such as glucose metabolism measured by [18F] Fluorodeoxyglucose [FDG] PET (Landau et al., 2011), as well as cortical thickness (Dickerson et al., 2009) and hippocampal volume, both delineated from structural magnetic resonance images (MRIs).
In cognitively normal older individuals, the relationship between Aβ and cognitive functioning is inconsistent and weak (Aizenstein et al., 2008; Mormino et al., 2009). By contrast, neurodegeneration (as reflected in MRI, FDG PET and tau biomarkers) seems to be more closely tied to cognitive ability (den Heijer et al., 2006; Dickerson et al., 2011; Desikan et al., 2012). While atrophy within the hippocampus (Storandt et al., 2009; Rowe et al., 2010) and cortical AD-affected regions (Becker et al., 2011; Chetelat et al., 2012) may be related to fibrillar Aβ plaques, other reports have failed to show such associations (Storandt et al., 2012). In previous studies, AD-like neurodegeneration was found in individuals without evidence of cortical Aβ deposition (Dickerson and Wolk, 2012; Jack et al., 2012). These results imply that neurodegenerative abnormalities in AD-affected regions may not be invariably associated with Aβ.
Reductions in neural integrity may also be attributed to age-associated degeneration (Raz and Rodrigue, 2006). This is because AD-related and age-related gray matter atrophy converges on hetero-modal regions (Raz et al., 2004; Fjell et al., 2009). One approach to examine the AD biomarker model in cognitively normal older adults is therefore the definition of biomarkers that have a high enough power to discriminate abnormal (AD-related) from normal (age-related) neuronal properties on an individual basis.
The present study quantified AD-like neurodegeneration in cognitively normal older adults employing highly sensitive AD biomarkers. Using a sample of AD patients and Aβ-negative healthy controls, a multi-modality parameter (reflecting neural integrity) was created from single-modality biomarkers (cortical thickness, glucose metabolism and hippocampal volume) as extracted within AD-affected regions. We assessed whether neurodegeneration within the AD regions would be associated with both poorer cognitive functions and cortical Aβ deposition.
The design of the study entailed deriving AD neurodegenerative biomarkers in one group of subjects, and validating them in another. The sample used for derivation came from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and included a group of Aβ-negative cognitively normal controls (ADNI NC) and a group of patients with mild AD (ADNI AD). The validation group was comprised of a group of normal older controls from the Berkeley Aging Cohort (BAC NC) and AD patients from the University of California, San Francisco (UCSF AD). Relationships between the AD neurodegeneration biomarkers, PET measures of Aβ and cognition were then tested in the BAC NC group.
Written informed consent was obtained from each participant in the study prior to enrollment in accordance with the local Institutional Review Boards (IRBs) for the ADNI sites, and the University of California, Berkeley and Lawrence Berkeley National Laboratory (LBNL) for BAC and UCSF subjects. Subject characteristics are provided in Table 1 and Table 2.
The ADNI is a multi-site, longitudinal, prospective, natural-history study that was launched in 2003 by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, the Food and Drug Administration, private pharmaceutical companies and non-profit organizations as a public–private partnership. ADNI evaluates serial MRI, PET and other biomarkers as well as clinical and neuropsychological markers for the onset and progression of mild cognitive impairment (MCI) and early AD.
Full inclusion/exclusion criteria can be found at www.adni-info.org. In brief, ADNI subjects are between 55 – 90 (inclusive) years old. ADNI NC have MMSE scores 24 – 30 (inclusive), a clinical dementia rating (CDR, Morris, 1993) of 0, no signs of depression, no memory complaints and normal memory functions as determined via the Logical Memory II subscale from the Wechsler Memory Scaled – Revised (Wechsler, 1987). Mild AD patients demonstrate a CDR of 0.5 or 1.0, MMSE scores between 20 – 26 (inclusive) and are required to meet NINCDS/ADRDA criteria for probable AD (McKhann et al., 1984).
For the present study, a sample of 39 (17 females, 22 males) Aβ-negative NC and 50 (20 women) mild AD were selected from the ADNI database. ADNI subjects were included, if they had completed structural 1.5T MRI and FDG PET imaging and cognitive testing. We included only Aβ-negative ADNI NC, as defined by florbetapir-PET or (when florbetapir was not available) CSF Aβ using previously published thresholds (Shaw, 2008). ADNI NC and AD groups were matched for age, sex, education as well as the time interval between FDG and MRI measurements (Table 1).
The current sample of the BAC NC included a total of 72 (48 females, 24 males) community-dwelling cognitively intact elderly individuals. Eligibility criteria were set to a Geriatric depression scale (GDS) (Yesavage et al., 1982) score ≤ 10, Mini mental status examination (MMSE) (Folstein et al., 1975) score ≥ 25, no current neurological and psychiatric illness, normal functions on verbal and visual memory tests (all scores ≥-1.5 standard deviations of age-, gender- and education-adjusted norms) and age of ≥ 60 years. All subjects underwent a detailed standardized neuropsychological test session and neuroimaging measurements, all of which were obtained in close temporal proximity (Table 1).
The sample included 19 (9 females, 10 males) patients with mild to severe AD evaluated at the UCSF Memory and Aging Center. Subjects were required to have FDG and PIB PET images and structural MRI scans obtained on a 1.5T MR system. AD patients met the NINCDS/ADRDA criteria for probable AD (McKhann et al., 1984) and were free of significant co-morbid medical, neurologic or psychiatric illnesses. AD diagnosis was based on a multi-disciplinary evaluation.
In the BAC NC sample, the dimensionality of the behavioral data was reduced by constructing memory and executive functions composite measures using a 2-step procedure. In step 1 domain-sensitive cognitive tests were defined, which were averaged using z scores in step 2.
The domain-sensitive tests of memory and executive functions were selected in a procedure that optimized conceptual relevance and suitable measurement characteristics (minimum data loss and mid-range test scores). Domain sensitivity of the cognitive tests was corroborated using a principal component analysis (PCA) with orthogonal (varimax) rotation. The analysis was performed for the test scores of the first neuropsychological test session using a larger sample of the cognitively normal older BAC participants (N = 303 at the time of analysis, mean age = 71.5 (9.0) years, age span 50 – 96 years, N of women= 205 (68%), education = 16.9 (2.1) years, education span 12 – 20 years, MMSE = 28.8 (1.3), MMSE span 25 – 30). Bartlett's test of sphericity indicated that the inter-correlations between cognitive tests were sufficiently large for the PCA. Two factor components were confirmed using Kaiser’s criterion of an eigenvalue greater than 1 and explained 60.3% of the variance in combination.
The memory factor was composed of episodic memory tests, specifically the free recall trials 1 – 5 of the California Verbal Learning Test (CVLT) II (Delis, 2000), the Logical Memory recall of story A and B (Wechsler, 1997a) and the Visual Reproduction Delayed Recall as well as Recognition (Wechsler, 1997a). The factor measuring executive functions consisted of the Stroop Test (correct naming of printed colors) (Trenerry et al., 1989), the Controlled Oral Word Association Test (FAS) (Benton, 1983), the Trail Making Test Part B (Reitan, 1958) and the Digit Symbol-Coding Test (Wechsler, 1997b). The cognitive tests were combined into the composite measures by converting each test to z scores using the mean and standard deviation of the overall BAC cohort (see above) and averaging them.
Cognitive data used to describe the ADNI participants and the UCSF AD patients were limited to the MMSE and CDR (Table 1).
The high-resolution T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) were obtained at multiple ADNI sites using a standardized acquisition protocol (http://adni.loni.ucla.edu/research/protocols/mri-protocols/) on General Electric (GE), Siemens, or Philips 1.5T systems. For the present study, we used pre-processed MPRAGE scans that had undergone a full set of image correction steps at the time of analysis, including gradient warping (except Philips Scanner), intensity correction and scaling (available in 33/39 cases) (Jack et al., 2008).
MRI scans were acquired at the Lawrence Berkeley National Laboratory (LBNL) on a 1.5 Tesla Magnetom Avanto system (Siemens Medical Systems, Iselin, NJ) using a 12 channel head coil run in triple mode. Each subject’s MPRAGE scans was collected axially with the following measurement parameters: TR = 2110 ms, TE = 3.58 ms, flip angle = 15°, field of view = 256 × 256 mm, number of slices = 160 with a 50% gap, voxel size = 1 × 1 × 1 mm3.
MPRAGE scans were collected coronally via a 1.5T Magnetom Vision System (Siemens Medical Systems, Erlangen, Germany) with a quadrature head coil and following acquisition parameters: TR = 10 ms, TE = 7 ms, flip angle=15°, voxel size = 1 × 1 × 1.5 mm3.
Multi-site FDG PET scans were acquired using standardized FDG acquisition protocols described in detail elsewhere (http://adni.loni.ucla.edu/research/protocols/pet-protocols/). The FDG PET images were collected with the same protocol used for BAC and UCSF subjects. The “raw” FDG PET frames were averaged and processed to yield images with standard orientation, voxel size, and 8 mm isotropic FWHM resolution (Joshi et al., 2009; Jagust et al., 2010). The pre-processed FDG PET images (standardized orientation, intensity correction and resolution) were normalized using the pons as the reference region.
FDG PET imaging was performed at LBNL with a Siemens ECAT EXACT HR PET scanner (Siemens Medical Systems, Erlangen Germany), approximately two hours after PIB tracer injection. Following 6 – 10 mCi of tracer injection, 6 × 5 min frames of emission data were collected starting 30 min post-injection. All FDG PET data were reconstructed using an ordered subset expectation maximization algorithm with weighted attenuation. Images were smoothed applying a 4 × 4 × 4 mm Gaussian kernel with scatter correction.
The six “raw” FDG PET frames were aligned to the first frame and averaged using Statistical Parametric Mapping version 8 (SPM8; http://www.fil.ion.ucl.ac.uk/spm). Individual FDG frames were then realigned to the resultant average image and combined to create a single-frame average image. To enhance comparability with the ADNI FDG scans, each subject’s average FDG PET image was smoothed to a resolution of 8 FWHM using a Gaussian smoothing kernel of 5.5 mm in plane and 4.0 mm in the slice direction. The kernel was determined with the same protocol used to create standard resolution images in ADNI (Joshi et al., 2009). The smoothed FDG PET scans were intensity normalized to the pons, as edited from the subcortical FreeSurfer 5.1. parcellation of the native-space MRI scans. The choice of reference region was based on evidence indicating preservation of pontine glucose metabolism in AD patients (Minoshima et al., 1995). There was no partial volume correction performed.
PIB PET scans were collected at LBNL. After approximately 15 mCi tracer injection into an antecubital vein, dynamic acquisition frames were obtained in the 3D acquisition mode over a 90 min measurement interval (4 × 15 sec-frames, 8 × 30 sec-frames, 9 × 60 sec-frames, 2 × 180 sec-frames, 8 × 300 sec-frames and 3 × 600 sec-frames) after a 10 min transmission scan. Frames 6–34 were aligned to frame 17 using a two-pass algorithm. Frames 1–5 were and registered to the mean of frames 6–34, with the alignment parameters applied to each individual frame. The realigned frames corresponding to the first 20 min of acquisition were averaged and used to co-register the structural MRI to the native-space PIB PET image. Distribution volume ratios (DVRs) were generated with Logan graphical analysis on the aligned PIB frames using the native-space gray matter cerebellum as a reference region (Logan et al., 1996; Klunk et al., 2004; Price et al., 2005), fitting 35 – 90 min after injection.
For each subject, a global cortical PIB index was derived from the native-space DVR image over frontal (cortical regions anterior to the precentral sulcus), temporal (middle and superior temporal regions), parietal (supramarginal gyrus, inferior/superior parietal lobules and precuneus) and anterior/posterior cingulate Regions-of-Interest (ROIs), previously demonstrated to exhibit higher PIB retention in AD subjects compared to cognitively normal controls (Price et al., 2005). ROI–specific values were extracted from the automated FreeSurfer 5.1. anatomical parcellation using the Desikan-Killiany atlas (Fischl et al., 2002; Desikan et al., 2006) and combined as a weighted average. There was no partial volume correction performed.
The BAC NC subjects were dichotomized into a high (BAC NC PIB+) and low (BAC NC PIB-) PIB binding status, indicating the presence or absence of abnormal PIB retention, respectively. The specific cutoff score for PIB positivity was two standard deviations above the mean of the PIB index estimated in an independent group of healthy young adults (Oh et al., 2011; Mormino et al., 2012), yielding a value of 1.08. Demographic information for PIB+ and PIB- subgroups of the BAC NC are provided in Table 2.
Surface-based cortical thickness maps were estimated from the native-space MRI scans for each individual using the FreeSurfer 5.1. software package (http://surfer.nmr.mgh.harvard.edu/). The processing pipeline is described elsewhere and will only be explained in brief (Dale et al., 1999; Fischl et al., 1999a). In an automated procedure each MPRAGE scan was bias field corrected, intensity normalized, and skull stripped using a watershed algorithm (Dale et al., 1999; Segonne et al., 2004). The resulting image was subjected to a white matter segmentation algorithm to define the white/gray matter boundary. After topology correction of the reconstructed white/gray matter border surfaces (Dale et al., 1999; Fischl et al., 2002; Segonne et al., 2004), the pial surfaces were estimated (Fischl and Dale, 2000). A triangular mesh represented the structures of white and gray matter (or pial) surfaces, such that image properties could be mapped to each node (or vertex) of the triangular mesh. Cortical thickness surface maps were derived by calculating the distance between the two surfaces at each vertex across the entire cortex (Fischl and Dale, 2000). The corresponding values were projected onto each subject‘s reconstructed cortical surface (Fischl et al., 1999a).
We adopted a procedure previously described (Park et al., 2006). Each subject’s pre-processed FDG PET image was co-registered to the MRI scan using an SPM-based co-registration algorithm (Ashburner and Friston, 1997). The co-registered volumetric FDG image was then sampled onto the cortical surface by means of maximum projection. Namely, an intensity profile was created by sampling ten equal proportions of the FDG PET intensity values between white matter and pial surface at each vertex. The FDG surface maps were created by mapping the maximum intensity value derived from the vertex-wise intensity profile to the reconstructed surface. Maximum projection was chosen because it is more robust to minor registration and MRI segmentation errors (Park et al., 2006).
We began by defining the single-modality biomarkers of cortical thickness, glucose metabolism (FDG PET) and hippocampal volume (HV) using the ADNI NC and AD groups within brain regions most affected by AD. The single-modality biomarkers were then combined into an AD-sensitive multi-modality biomarker. In order to examine whether biomarker effects were stronger within AD-affected regions, a non-AD “control” region was chosen. Similar to a prior study (Dickerson et al., 2009), the bilateral primary visual cortex of the automated FreeSurfer 5.1. Desikan-Killany parcellation was used.
The individual thickness surface maps were registered to a template surface using a spherical morphing procedure that aligns cortical folding patterns (Fischl et al., 199b). The resulting registration also mapped the FDG surface maps into a common surface space. All surface maps were smoothed by an iterative nearest-neighbor averaging procedure with a full-width/half-maximum (FWHM) Gaussian smoothing kernel of 15mm for cortical thickness and 10mm for FDG PET(Fischl et al., 1999b). To validate data quality, we first generated statistical surface maps for FDG PET and cortical thickness using general linear models (GLMs) with the predictors of diagnosis (ADNI NC, ADNI AD) and demeaned covariates of no interest (education and age). The single-modality group contrasts (ADNI NC > ADNI AD) produced maps of AD-related cortical thinning and hypometabolism.
In order to limit neurodegenerative biomarkers to regions that were affected by both AD-related cortical thinning and hypometabolism, we defined the convergence of cortical thinning and hypometabolism maps. This was done, because our preliminary data analyses indicated that the convergence map comprised a minimum set of cortical regions without reductions in AD-sensitivity and specificity compared to the single-modality (FDG PET or cortical thickness) statistical surface maps. To extract the convergence map, a binary mask was created from the statistical hypometabolic surface map to spatially restrict the computation of the statistical thinning map using an equivalent GLM. Within each hemisphere, ROIs with an area larger than 300 mm2 were defined in the statistical thinning map and converted to an ROI template of cortical AD regions (or cortical AD template). For all analyses, the statistical threshold was set to p < 0.00005 (uncorrected); the equivalent False discovery rate (FDR) threshold is provided.
Using the cortical AD and control templates, cortical thickness and FDG PET were derived for each subject of each sample (ADNI NC, ADNI AD, BAC NC, UCSF AD). The templates were mapped onto each subject’s native-space cortical thickness and FDG PET surface image using the spherical registration of the MRI scan to the standard brain. For each ROI, mean cortical thickness and FDG PET were estimated. ROI-specific values were averaged and weighted by the number of vertices.
Hippocampal volume was obtained within each hemisphere of the native-space MRI scan using the automated subcortical FreeSurfer 5.1. parcellation and averaged across hemispheres. The HV was adjusted for head size (abbreviated as HVicv) via a regression model including all available subjects. The method removed shared variance with total intracranial volume (ICV); i.e., an estimate that includes brain tissue, cerebrospinal fluid- and blood-filled spaces (Mathalon et al., 1993).
Finally, each biomarker was age-adjusted using the ADNI NC sample. Age was regressed on the biomarker of interest within the ADNI NC reference population. The unstandardized regression coefficients estimated age-adjusted residuals for each individual of each sample. The age-adjusted values were z transformed using mean and standard deviation of the ADNI NC sample.
Using the derivation sample (ADNI NC, ADNI AD) and discriminant function analysis (DFA) the single-modality biomarkers were merged into a continuous multi-modality parameter. The DFA determined a linear combination (or function) that best separated (or discriminates) target groups using several predictors (Field, 2005). Diagnostic group (ADNI NC, ADNI AD) was entered as the dependent variable of interest; the three single-mode age-adjusted and z transformed biomarkers (cortical thickness, FDG uptake and HVicv) were fitted as independent variables. The DFA identified one discriminant function (canonial R2 = 0.66) that significantly discriminated the diagnostic groups of the derivation sample (Wilk’s Lamda (Λ) criterion: Λ = 0.34, χ2 (3) = 91.95, p < 0.001). This function correctly classified 90% of the ADNI AD patients. Discriminant function scores were estimated for every subject of each sample (ADNI NC, ADNI AD, UCSF AD, BAC NC); scores of less than 0 indicate that the individual would be classified as AD, above 0 implies normal control classification. The multi-modality biomarker of the control region was created by averaging the z transformed and age-adjusted mean cortical thickness and FDG PET values of the visual cortex.
The multi-modality and single-modality biomarkers were compared between AD patients and NC individuals of the derivation and validation samples using the 95% confidence interval (CI) of the difference. If this CI did not include 0, the group differences were significant.
The discriminatory power of the multi-modality (discriminant scores) and single-modality (cortical thickness, FDG uptake and HVicv) biomarkers within the AD-affected and control regions was validated using the BAC NC and UCSF AD groups and the area under the curve (AUC) of the receiver operating characteristic (ROC). The AUC is a measure that represents the ability of a classifier variable to correctly discriminate individuals with and without the disease (Metz, 1978). AUC values may vary between 0.5 (indicating random classification) and 1 (indicating perfect classification).
Following development and validation of the biomarkers, they were related to PIB binding status and cross-sectional cognitive performance in cognitively normal older individuals of the BAC NC sample.
For the multi-modality biomarker, a univariate Analysis of Covariance (ANCOVA) was conducted with PIB binding status (BAC NC PIB+, BAC NC PIB-) as predictor. A multivariate ANCOVA was carried out to evaluate effects of each single-modality biomarker (cortical thickness, FDG uptake and HVicv) as dependent variables and dichotomous PIB uptake as independent predictors. In addition, the statistical analyses were restricted to PIB+ subjects, since findings suggest relationships between neurodegenerative biomarkers and Aβ presence, particularly for individuals with lower levels of CSF Aβ (Fjell et al., 2010c). In the ANCOVA model, the PIB index was fitted as a continuous predictor and the neurodegenerative biomarkers as dependent variables.
In order to explore the possibility that our results may be impacted by our choice of PIB uptake threshold, we repeated the analysis using a more conservative threshold. Fourteen individuals with highest Aβ burden (high BAC NC PIB+) were isolated from the BAC NC sample using the iterative outlier approach (Aizenstein et al., 2008). Cases with slight or ambiguous PIB elevation were excluded in these analyses (Mormino et al., 2012).
Unless otherwise stated, model-specific assumptions of homogeneity of covariance matrices and/or regression slopes were confirmed for all ANCOVA models. The statistical threshold was set to p < 0.05, two-tailed. Pillai’s trace values (V) were chosen to evaluate predictor effects. Covariates were only included, when a considerable relationship (here p < 0.1) with the dependent variable of interest was empirically indicated.
To evaluate relationships between the AD neurodegenerative biomarker and cognition, omnibus multivariate ANCOVA models were performed with cognitive (memory and executive function) performance of the BAC NC participants as dependent variables and the multi-modality biomarker as well as PIB uptake status (BAC NC PIB+, BAC NC PIB-) as independent predictors. Education and age were fitted as covariates of no interest. Post-hoc univariate ANOCA models evaluated predictor effects on either memory performance or executive functions.
For the multi-modality biomarkers of the AD-affected and control regions, omnibus multivariate ANCOVAs were carried out with memory performance and executive functions as dependent variables. The models assessed main and interactive effects between dichotomous PIB uptake status (high BAC NC PIB+, BAC NC PIB-) and the multi-modality biomarker. Age and education were fitted as categorical covariates (defined by median split) of no interest. In the statistical models with interaction, the multi-modality biomarker was z transformed. The procedure provided a meaningful zero-point, since the main effect of one predictor represents a “conditional” effect at the value of 0 of the other predictor, when an interaction term is fitted.
The following post-hoc analysis was carried out to examine whether the interaction effect between PIB uptake and the neurodegeneration biomarkers on cognition were enhanced for the AD biomarker compared to the control region biomarker. Two partial correlation coefficients were obtained for each PIB uptake group (PIB+, PIB-) from within-group-regression models, one for the AD-affected (r1) and one for the control region (r2). Within each group, the correlation coefficients (r1, r2) were compared by applying the formula for dependent correlations (Cohen and Cohen, 1975). The significance level was set to p < 0.05, one-tailed.
Single-modality comparisons of surface-based FDG PET and cortical thickness maps for the derivation sample (ADNI NC, ADNI AD) replicated well-known regional patterns of hypometabolism and thinning in AD patients. AD-related hypometabolism (p < 0.00005 uncorrected, equivalent to FDR < 0.0005, Figure 1A) was mainly present in bilateral temporal (inferior, middle, medial temporal), inferior parietal (angular gyrus) regions, frontal (superior) cortex and the precuneus. AD-related cortical thinning was detected in highly comparable, though somewhat smaller regions (p < 0.00005 uncorrected, equivalent to FDR < 0.001, Figure 1B).
The statistical maps reflecting convergence of AD-related hypometabolism and cortical thinning were extracted in the derivation sample as a set of posterior cortical ROIs (p < 0.00005 uncorrected, equivalent to FDR < 0.005, cs ≥ 300 mm2, Figure 1C). These cortical regions included temporal areas (mostly middle and medial temporal), inferior parietal cortex (angular gyrus) and the posterior cingulate to precuneus regions. The topographical distribution of the AD-affected ROIs was similar across hemispheres, although somewhat different in size.
There was a clear reduction in the multi-modality neurodegenerative biomarker of the AD-affected regions between the AD patients compared to NC individuals of the derivation sample (ADNI NC > ADNI AD, 95% confidence interval [CI] of the difference [2.34, 3.19]) and the validation sample (BAC NC > UCSF AD, 95% CI of the difference [2.36, 3.84]) (Figure 2A). Similar effects were measured for the single-modality biomarkers of cortical thickness, FDG uptake and HVicv (all 95% CI of the difference excluded 0, data displayed in Figure 2B–D).
Classification accuracy of the single- and multi-modality biomarkers within the AD regions was excellent (all p’s < 0.001) and similar across the derivation and validation samples. Highest accuracy was measured for the multi-modality biomarker (derivation sample: AUC = 0.98, validation sample: AUC = 0.97). For the single-modality biomarkers, highest accuracy was achieved for HVicv (derivation sample: AUC = 0.95, validation sample: AUC = 0.92), followed by cortical thickness (derivation sample: AUC = 0.94, validation sample: AUC = 0.90,) and FDG PET (derivation sample: AUC = 0.87, validation sample: AUC = 0.92).
For the multi-modality biomarker of the control region (visual cortex, data not depicted), there was no significant difference between the AD patients and the NC individuals of the derivation sample (ADNI NC ADNI AD, 95% CI of the difference [−0.10, 0.50]). There was a difference, however, in the validation sample (BAC NC > UCSF AD, 95% CI [0.23, 1.04]), reflecting what is likely more extensive AD pathology in the UCSF AD patients. The biomarker classification accuracy was random for the derivation sample (AUC = 0.58, p = 0.2), and poor, although significant for the validation sample (AUC = 0.70, p < 0.01).
In the BAC NC sample, the multi-modality biomarker values of AD-affected regions were similar (F (1, 70) = 0.08, p = 0.8) for PIB+ and PIB- individuals (Figure 3A), as estimated using a univariate ANCOVA. The single-modality biomarkers were also comparable for high and low PIB uptake status (V = 0.01, F (3, 67) = 0.24, p = 0.9) as confirmed using a multivariate ANCOVA controlling for gender (V = 0.18, F (3, 67) = 4.8, p < 0.01) (Figure 3B–D). Likewise, within PIB+ individuals there were no significant relationships of the PIB binding index (evaluated as continuous rank transformed and original values) and the AD-sensitive neurodegenerative biomarkers (all p’s ≥ 0.1, data not shown). For the biomarker of the control region, no significant effects of PIB uptake status on the neurodegenerative biomarker were obtained (all p’s ≥ 0.2, data not shown).
In the BAC NC sample, the omnibus multivariate ANCOVA demonstrated that the multi-modality neurodegenerative biomarker of the AD regions was significantly associated with poorer cognition (V = 0.22, F (2, 66) = 9.21, p < 0.001, η2 = 0.22) accounting for age (V = 0.20, F (2, 66) = 7.99, p < 0.01) and education (V = 0.10, F (2, 66) = 3.79, p < 0.05). Post-hoc univariate ANCOVAs corroborated that lower neural integrity explained reductions in memory (F (1, 67) = 5.06, p < 0.05, η2 = 0.07) and executive (F (1, 67) = 18.26, p < 0.001, η2 = 0.21) functions (Figure 4).
For the multi-modality biomarker of the control region (data not depicted), the ANCOVA model demonstrated that lower values were significantly related to poorer cognition (V = 0.23, F (2, 66) = 10.04, p < 0.01, η2 = 0.23) accounting for age (V = 0.15, F (2, 66) = 5.74, p < 0.05) and education (V = 0.18, F (2, 66) = 7.35, p < 0.01). Post-hoc univariate ANCOVAs corroborated that lower biomarker values accounted for reduced memory (F (1, 67) = 5.63, p < 0.05, η2 = 0.08) and executive (F (1, 67) = 19.82, p < 0.01, η2 = 0.23) functions.
There were also no effects of dichotomous PIB uptake (all p’s ≥ 0.5) on the cognitive measures in any of the statistical models for the AD-affected or the control region.
The multivariate ANCOVA confirmed the significant main effect of the multi-modality biomarker of the AD regions on cognition (V = 0.39, F (2, 54) = 17.10, p < 0.001, η2 = 0.39). Importantly though, the model also detected a significant interaction between PIB uptake status (high PIB+ BAC NC, PIB- BAC NC) and the multi-modality biomarker (V = 0.16, F (2, 54) = 5.22, p < 0.05, η2 = 0.16), controlling for age and education (Figure 5, upper panels). Post-hoc univariate ANCOVA models indicated that the interaction effect was significant for the memory domain (F (1, 55) = 5.07, p < 0.05, η2 = 0.08) and executive functions (F (1, 55) = 9.30, p < 0.01, η2 = 0.15).
For the visual region, the multivariate ANCOVA confirmed the significant main effect of the multi-modality biomarker on cognition (V = 0.27, F (2, 54) = 9.09, p < 0.001, η2 = 0.27), controlling for age and education (Figure 5, lower panels). Importantly though, there was no significant interaction between the PIB uptake status and the neurodegenerative biomarker (V = 0.02, F (2, 54) = 0.65, p = 0.5).
Post-hoc comparisons of the dependent partial correlation coefficients from the neurodegenerative biomarker – cognition relationships within each PIB group (BAC NC PIB +, BAC NC PIB-) specified the following: For the high PIB+ individuals, the partial correlation coefficient obtained for the AD-affected region was significantly higher compared to the correlation coefficient of the control region (visual cortex) with regard to memory (rAD = 0.90, rcontrol = 0.72, t (11) = 2.03, p < 0.05) and executive functions (rAD = 0.82, rcontrol = 0.55, t (11) = 2.53, p < 0.05). For the PIB- group, the partial correlation coefficients, as computed for the AD and control regions, were comparable (all p’s ≥ 0.4) for memory (rAD = 0.22, rcontrol = 0.27) and executive functions (rAD = 0.48, rcontrol = 0.48).
It is increasingly important to identifiy older individuals who experience cognitive decline and may be in the process of developing AD. Current criteria for preclinical AD propose that cognitively normal older people, who harbor both amyloid burden and associated neuronal injury, are at an advanced preclinical stage (Jack et al., 2010; Sperling et al., 2011). The present study therefore assessed relationships between AD biomarkers of Aβ and neurodegeneration and their relation to cross-sectional cognitive performance in cognitively normal older adults.
There were four important findings: Using an optimized combination of cortical thickness, FDG PET and hippocampal volume, we created a multi-modality biomarker with high power to differentiate AD patients from healthy (Aβ-negative) older individuals. This AD-sensitive biomarker can be interpreted to capture the presence or loss of neural integrity within regions that are severely affected by AD pathology. Sampled from cognitively normal older individuals (BAC NC sample), there was no association between the biomarkers of Aβ burden (measured by the PIB uptake status) and neurodegeneration (measured by the multi-modality biomarker). Lower levels of neural integrity within the AD-affected and the control region (visual cortex) were related to poorer cognitive abilities. For the AD regions, this biomarker-cognition-relationship was stronger for individuals with the highest Aβ burden.
The amyloid cascade model postulates that abnormal Aβ-plaque accumulation is a necessary trigger of AD-typical neurodegeneration (Jack et al., 2010). One can therefore reason that individuals with abnormal neurodegenerative biomarkers would be Aβ-positive. Using a hypothesis-driven approach that defined neural properties within AD-affected regions, we found considerable variability in neural integrity among our normal older individuals, irrespective of Aβ burden. The finding conflicts with the biomarker model, but is consistent with emerging data. A recent study classified the Aβ and the neurodegenerative biomarker status of normal older people according to criteria for preclinical AD (Jack et al., 2012). In this sample, neurodegenerative AD biomarker abnormalities were found in a substantial proportion (23%) of Aβ-negative individuals. In another study, the presence of cortical thinning in AD signature regions was associated with AD-like CSF Aβ in 60% of subjects, leaving the high proportion of 40% of such individuals exhibiting normal CSF Aβ (Dickerson and Wolk, 2012).
Our data and others (Fjell et al., 2010a; Jack et al., 2012) thus converge on the fact that neuronal damage within AD target regions is non-specific for Aβ pathology in normal older adults. This suggests that biomarker models and preclinical AD stages proposing Aβ-plaque burden as the initiating factor of AD-typical neurodegeneration could be incorrect (Jack et al., 2010; Sperling et al., 2011). In older people, reductions of neural integrity may be the result of non-AD (pathological) factors that modulate neuronal structure and function over the lifespan. Further, recent demonstrations of trans-neuronal propagation of tau pathology (de Calignon et al., 2012) raise the possibility that the early appearance of entorhinal cortical neurofibrillary tangles could be associated with alterations of function in neocortical neurons projecting to medial temporal lobe. Thus, in an alternative model to that which proposes Aβ as the initiating event, tau-related neurodegeneration may arise independently from Aβ deposition (Mesulam, 1999; Small and Duff, 2008). Scenarios where neurodegenerative abnormalities emerge prior to or in parallel with Aβ may converge with theoretical models that posit an initiating role of neuronal injury in the development of AD (Herrup, 2010).
In our study, lower neural integrity within (but not limited to) AD-affected regions was a close predictor of decreased cognitive abilities. The observation mirrors earlier findings that documented a positive relationship between cognitive abilities and cortical thickness in distributed brain areas (Fjell et al., 2006). Aβ burden itself was a poor predictor of cognitive performance in the same individuals corroborating previous inconsistent effects in cross-sectional data (e.g., Pike et al., 2007; Aizenstein et al., 2008; Mormino et al., 2009; Oh et al., 2011, 2012). Other studies have highlighted the notion that neuronal injury within AD-affected regions is not benign and may increase the risk for time-dependent cognitive decline and AD conversion in moral older adults (den Heijer et al., 2006; Dickerson et al., 2011). Moreover, in patients with mild cognitive impairment (MCI), medial temporal lobe degeneration without Aβ abnormalities yielded almost as high risk of AD conversion than having both Aβ and neurodegenerative abnormalities (Heister et al., 2011). Our results contribute to these findings by showing that neurodegeneration is associated with poorer cognition regardless of Aβ burden in cognitively intact older adults.
Neuronal injury in AD-affected regions seemed to increase the vulnerability of the brain to late-life Aβ pathology. This was supported by the interaction between the biomarkers of Aβ deposition and neurodegeneration. Specifically, although Aβ burden itself was unrelated to the neurodegenerative biomarker and cognitive abilities, worse cognition occurred when Aβ deposits coincided with lower neural integrity, specifically within AD regions. The observation agrees with theoretical conceptualizations (Jack et al., 2010; Sperling et al., 2011) and empirical data (Knopman et al., 2012) that denote heightened risk for poor cognitive functions in those individuals that harbor Aβ burden and neurodegeneration. However, the presence of neurodegeneration without Aβ, and the potentiation of neurodegenerative effects by Aβ, are not consistent with these models. The observation that neurodegeneration and Aβ are not simply additive suggests that these two processes interact through mechanisms that remain unclear. Similar to our present findings, deleterious Aβ effects on longitudinal cognition change were previously moderated by the presence of tau (Desikan et al., 2012) and signs of neuronal damage within the hippocampus as well as cortical AD regions (Wirth et al., in press). At the same time, cognitive functioning was maintained in our high Aβ burden individuals with normal neural integrity, suggesting that Aβ burden per se might not be detrimental (Desikan et al., 2012). The data convey that AD-like neurodegeneration, as emerging from Aβ-independent pathways, could act as a catalyst of Aβ-associated effects.
In a broader view, the present findings imply that different (other than Aβ) neurodestructive and/or neuroprotective factors cause inter-subject variability in neural structure and function that is relevant to cognitive abilities in advanced age. In particular, age (or aging) itself is associated with cognitive worsening (Hedden and Gabrieli, 2004) and gray matter shrinkage within heteromodal cortices (Raz et al., 2004; Fjell et al., 2009; 2010b). While the present neurodegenerative biomarkers were adjusted for chronological age, it could be possible that there are residual effects of age that remained. Alternatively, non-AD vascular pathology, even subclinical cerebrovascular risk factors, may cause brain atrophy in normal elderly adults in distributed brain areas in older individuals (Raz et al., 2004; Leritz et al., 2011). In addition, tau pathology could arise independently from Aβ in advanced ages, affect neural integrity and interact with Aβ in producing cognitive decline (Small and Duff, 2008). Finally, as a developmental modifier of brain properties a person’s intellectual” baseline” needs to be considered. Intellectual ability in old age is related to intelligence in early life stages (Deary, 2000), where it is associated with enhanced gray matter structure (Hulshoff Pol et al., 2006; Karama et al., 2009; Luders et al., 2009). Thus, a form of brain reserve could be reflected in these measures of brain volume and metabolism.
Our study has several strengths. We created highly sensitive neurodegenerative biomarkers using an independent sample of AD patients and Aβ-negative well-screened cognitively normal older individuals. We further combined single- into multi-modality biomarkers using a linear function. Although we had only a small and variable clinical validation sample, the results of the validation process coincided with previous observations that multi-modality biomarker fusion can increase diagnostic accuracy (Oishi et al., 2011; Zhang et al., 2011). An optimized combination of functional and structural biomarkers has been suggested to increase the power to capture different neuro-pathological processes in different brain areas. This can be used to improve the differentiation of normal and pathological neuronal properties (Knopman et al., 2012). However, it is important to note that the cross-sectional nature of our study limits interpretation regarding biomarker causality as well as AD progression.
Taken together, our data concur with some aspects of the amyloid hypothesis of AD but conflict with others. In agreement with the amyloid hypothesis, we find that neurodegeneration is more strongly associated with cognition than is Aβ. However, the interaction between the two processes suggests that neurodegeneration does not simply mediate the effects of Aβ on the brain. The data further conflict with the amyloid hypothesis by showing that Aβ is not required to develop neurodegeneration within AD-affected regions. The present findings have major bearings on current conceptualizations of aging and preclinical AD.
Data 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; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences Ltd.; AstraZeneca; Bayer HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and ist affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (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, AG034570, K23-AG031861, P01-AG1972403, P50-AG023501, the Alzheimer’s Association grant NIRG-07-59422, the John Douglas French Alzheimer’s Foundation, the State of California Department of Health Services Alzheimer’s Disease Research Center of California grant 04-33516 and the Swiss National Science Foundation grant PA00P1_131515. We sincerely thank Suzanne Baker, Grace Tang and Pia Ghosh for their help in data processing as well as Sylvia Villeneuve and Rik Ossenkoppele for their knowledgeable comments in data discussion. Further, we express our gratitude to Tad Haight for his statistical counsel, Bruce Miller of the UCSF Memory & Aging Center for patient referrals and Michael W. Weiner of the VA Medical Center and University of California, San Francisco for the MRI scanning of the UCSF AD patients.
Conflict of interest: The authors declare no competing financial interests.