Regular articleMemory decline shows stronger associations with estimated spatial patterns of amyloid deposition progression than total amyloid burden
Introduction
The hallmark pathologies of Alzheimer's disease are histology-confirmed amyloid plaques and neurofibrillary tangles. Currently, disease progression must usually be estimated from cross-sectional data and is defined as lower-to-higher total pathology. As shown in a large series of post-mortem brains studied by Braak and Braak (Braak and Braak, 1997b), the progression of amyloid plaque deposition (both diffuse and neuritic) follows characteristic spatially unique stages rather than uniform deposition throughout the brain. There are substantial interindividual variations in spatial patterns of amyloid deposition, but frontal, lateral temporal, and parietal regions are affected early, with relative sparing of the occipital lobe and motor cortices until later in disease progression.
Spatial heterogeneity of patterns of amyloid deposition have also been found using recently developed in vivo amyloid tracers (Lockhart et al., 2007). Across studies, elevated amyloid deposition has been found in frontal cortex, lateral temporal cortex, and precuneus, especially in subjects with dementia of the Alzheimer type (DAT), with less consistent binding in the occipital cortex (Klunk et al., 2004, Lopresti et al., 2005, Rowe et al., 2008, Wong et al., 2010). A significant proportion of cognitively normal subjects also display elevated cortical amyloid burden (Jack et al., 2008, Pike et al., 2007), with some likely in the preclinical stages of DAT. In addition, patients with mild cognitive impairment (MCI) show a bimodal distribution, such that some subjects exhibit amyloid deposition similar to that of DAT subjects while other subjects exhibit deposition similar to healthy controls (Mintun et al., 2006, Villemagne et al., 2008, Villemagne et al., 2011). Furthermore, MCI subjects who exhibit amyloid burden similar to DAT subjects are more likely to progress to DAT, compared to subjects who exhibit levels of amyloid burden similar to that in healthy controls (Forsberg et al., 2008).
Mean or regional cortical amyloid burden are the typical biomarkers measured in in vivo amyloid imaging studies. Advances in in vivo imaging technique methods now allow us to investigate temporal changes in amyloid deposition and relation to other imaging modalities in greater detail (Shoghi-Jadid et al., 2002, Sojkova et al., 2011). Moreover, the spatiotemporal dynamics of amyloid deposition have not been studied in detail, in part due to the relatively recent availability of amyloid imaging radiotracers and the time lag in acquiring sufficient longitudinal data. In the current study, we use a pseudo-dynamic image analysis method, similar to that used by Braak and Braak (1997b), estimating the progression of amyloid deposition from cross-sectional images of older individuals. In particular, nonlinear regional fits are used to determine regional amyloid burden as a function of total amyloid load, thereby generating maps that indicate how much amyloid must be accumulated globally in the brain before a given brain region is affected. This is accomplished by making an assumption that total amyloid burden is related to temporal dynamics, that is, a cortex will have lower total amyloid load earlier in the disease progression, and progressively higher amyloid load later in the disease progression, although a spline smoothing process is used to handle deviations from this assumption. Pseudo-temporal maps can then be generated from cross-sectional data. These maps are herein found to provide insights into the dynamics of amyloid spread throughout the brain that are not evident in conventional group comparisons.
Although cross-sectional studies of relationships between amyloid burden and cognition have yielded mixed results, higher amyloid burden has been associated with greater longitudinal memory decline in a number of studies (reviewed by Resnick and Sojkova, 2011). In prior work, we have demonstrated associations between rates of longitudinal change in verbal episodic memory performance and structural/functional changes in the cortex (Clark et al., 2012). Thus, in the present study, we aimed to use rates of change in California Verbal Learning Test (CVLT) scores to group participants for comparison of amyloid progression patterns. Decline in verbal episodic memory is typically the earliest change during the prodromal phase of DAT (Grober et al., 2008).
Analyses of amyloid deposition patterns in relation to cognitive performance that have been based on region-of-interest or voxel-wise approaches have resulted in heterogeneous findings. Generally, the regions that appear to be more involved in episodic memory changes include the precuneus, also the frontal, posterior cingulate, and lateral parietal cortices (Rentz et al., 2011), and the (frontal and lateral) temporal regions (Chetelat et al., 2011, Resnick et al., 2010). Although these analyses begin to address differential associations between cognition and amyloid deposition across the cortex, they are confounded by high interindividual variability introduced by the fact that different individuals enrolled in a study are generally at different stages of amyloid progression, which may obscure relationships between the spatial distribution and progression of amyloid and cognition. In other words, the same value of amyloid burden at a given spatial location might relate differently to cognitive decline, depending on the overall spatial pattern of deposition and the stage of the disease. Moreover, amyloid imaging, based on conventional measurements obtained from cross-sectional snapshots, is not very informative of the dynamics of disease progression. To mitigate this problem, we undertook to investigate spatial patterns of amyloid deposition as a function of total amyloid burden throughout the brain, such that total amyloid burden is used as a proxy for the underlying stage of disease progression in the absence of a more precise measure. Furthermore, we sought to determine whether the spatial patterns of amyloid deposition between subgroups would show a striking and significant divergence when individuals were classified according to longitudinal change in cognitive performance. A varying spatial pattern may indicate an earlier involvement of many specific brain regions in cognitively declining (CD) individuals compared with a relatively more constrained amyloid spread in cognitively stable (CS) individuals.
Section snippets
Participants
A series of 64 participants (35 men and 29 women; mean age [SD] = 76.61 years [6.89 years]; cortical distributed volume ratio [SD] 1.16 [0.26]) from the Baltimore Longitudinal Study of Aging (BLSA) neuroimaging substudy were included. Additional participants were evaluated but excluded because of clinical stroke (n = 2), brain injury (n = 1), and intolerance of magnetic resonance imaging (MRI) (n = 1). At baseline, BLSA neuroimaging participants were excluded for the following conditions:
Results
To investigate amyloid distribution as a function of total amyloid burden—our proxy to amyloid deposition dynamics—we analyzed voxel-wise 11C-PiB PET scans from 64 subjects using the aforementioned spline fitting method. By spatially mapping the inflection points and accumulation, patterns of relative sparing of some regions compared to other regions may be observed. In other words, much higher total amyloid burden must be reached before relatively spared regions become affected, and this is
Spatial patterns of amyloid deposition
We obtained estimates of the spatial progression of amyloid deposition in older adults using 11C-PiB PET imaging and an image analysis method that assumed that the total amyloid burden would be a reasonable approximation of the stage of the pathology. This assumption allowed the approximation of spatiotemporal patterns from cross-sectional data, leading to an estimation of relative sparing of certain brain regions, as reflected by relatively later inflection points in regional amyloid signal.
Disclosure statement
The authors have nothing to disclose.
Acknowledgements
This research was supported in part by National Institutes of Health (NIH) grant NIA-R01-14971, by the Intramural Research Program of the NIH, National Institute on Aging (NIA), and by NIA Research and Development Contract HHSN-260-2004-00012C. R.A.Y. received support from an NIH T-32 Training Grant. We thank the staff of the Johns Hopkins PET facility for their efforts and the BLSA participants for their participation.
References (44)
- et al.
Frequency of stages of Alzheimer-related lesions in different age categories
Neurobiol. Aging
(1997) - et al.
Longitudinal imaging pattern analysis (SPARE-CD index) detects early structural and functional changes prior to cognitive decline in healthy older adults
Neurobiol. Aging
(2012) - et al.
PET imaging of amyloid deposition in patients with mild cognitive impairment
Neurobiol. Aging
(2008) - et al.
Clinical severity of Alzheimer's disease is associated with PIB uptake in PET
Neurobiol. Aging
(2009) - et al.
Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade
Lancet Neurol.
(2010) - et al.
Face-name associative memory performance is related to amyloid burden in normal elderly
Neuropsychologia
(2011) - et al.
Imaging of amyloid β in Alzheimer's disease with 18F-BAY94-9172, a novel PET tracer: proof of mechanism
Lancet Neurol.
(2008) - et al.
Aβ deposits in older non-demented individuals with cognitive decline are indicative of preclinical Alzheimer's disease
Neuropsychologia
(2008) - et al.
Linear regression with spatial constraint to generate parametric images of ligand-receptor dynamic PET studies with a simplified reference tissue model
NeuroImage
(2003) - et al.
Using a reference tissue model with spatial constraint to quantify [11C]Pittsburgh compound B PET for early diagnosis of Alzheimer's disease
NeuroImage
(2007)
Controlling the false discovery rate: a practical and powerful approach to multiple testing
J. R. Stat. Soc. Series. B Methodol.
The association between quantitative measures of dementia and of senile change in the cerebral grey matter of elderly subjects
Br. J. Psychiatry
Neuropathological stageing of Alzheimer-related changes
Acta Neuropathol.
Staging of Alzheimer-related cortical destruction
Int. Psychogeriatr.
Independent contribution of temporal beta-amyloid deposition to memory decline in the pre-dementia phase of Alzheimer's disease
Brain
California Verbal Learning Test—Research Edition
MRI cortical thickness biomarker predicts AD-like CSF and cognitive decline in normal adults
Neurology
Two-year follow-up of amyloid deposition in patients with Alzheimer's disease
Brain
High PIB retention in Alzheimers disease is an early event with complex relationship with CSF biomarkers and functional parameters
Curr. Alzheimer Res.
Memory impairment, executive dysfunction, and intellectual decline in preclinical Alzheimer's disease
J. Int. Neuropsychol. Soc.
The amyloid hypothesis for Alzheimer's disease: a critical reappraisal
J. Neurochem.
11C PiB and structural MRI provide complementary information in imaging of Alzheimer's disease and amnestic mild cognitive impairment
Brain
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