There are two major findings in this study: first, high amyloid-β levels, detected by 11C-PiB PET imaging, in mild cognitive impairment are associated with increased brain atrophy rates, detected by longitudinal MRI. The finding adds to the generally held view that patients with mild cognitive impairment who have amyloid-β pathology have high brain atrophy rates and are likely to progress to Alzheimer’s disease within a relatively short time, while subjects with mild cognitive impairment without amyloid-β pathology have much lower brain atrophy rates and may not develop Alzheimer’s disease. Second, the spatial distribution of increased amyloid-β and the associated spatial distribution of increased brain atrophy rates embrace a characteristic pattern of brain structures known for a high vulnerability to Alzheimer’s disease pathology.
The finding of an association between increased amyloid-β and increased brain atrophy rates in mild cognitive impairment implies that aggregated amyloid-β leads to a progressive loss of brain tissue over time as opposed to a static loss from random insults. Although a progressive tissue loss due to increased amyloid-β deposition is expected (Jack et al., 2009
), an interesting observation of our study is that the regional distribution of increased amyloid-β and that of increased rates of brain tissue loss are not necessarily overlapping in mild cognitive impairment. Specifically, the joint analysis of 11
C-PiB PET and structural MRI data identified two component pairs of spatially distributed relations between amyloid-β deposition and brain atrophy rates. In the first component pair, high amyloid-β burden in the left precuneus/cuneus and posterior cingulate regions was associated with increased rates of brain tissue loss primarily in the left medial temporal lobe, including the entorhinal cortex and parahippocampal gyrus. On the other hand, in the second component pair, high amyloid-β burden in bilateral precuneus/cuneus and posterior cingulate region was associated with increased rates of brain tissue loss primarily in the right medial temporal lobe regions. Taken together, these results may begin to shed light on the mechanisms by which regionally selective amyloid-β deposition in mild cognitive impairment leads to a spatial pattern of distributed neurodegeneration, which resembles that seen in Alzheimer’s disease. Potentially, these mechanisms are anterograde and selectively target the memory networks. The systematic patterns of increased amyloid-β burden and increased brain atrophy rates in the patients with mild cognitive impairment, strongly resembling the pattern typically seen in Alzheimer’s disease, further supports the view that these relations reflect brain alterations presymptomatic to Alzheimer’s disease and could be useful for staging disease severity and for monitoring treatment interventions in preclinical stages of Alzheimer’s disease.
Structural MRI studies in Alzheimer’s disease have consistently revealed a pattern of neuroanatomical abnormalities that predominantly involve structures in the medial temporal cortex (i.e. hippocampus and the entorhinal cortex) (Du et al., 2001
; deToledo-Morrell et al., 2004
; Thompson et al., 2004
; Hampel et al., 2005
; Stoub et al., 2005
; Morra et al., 2008
; Schroeter et al., 2009
) where the early pathological changes are seen, then gradually extends to temporoparietal cortical areas (Chetelat and Baron, 2003
; Whitwell et al., 2007
; Desikan et al., 2008
; Hua et al., 2008
) as severity of Alzheimer’s disease progresses (Jack et al., 2004
; DeCarli et al., 2007
; Whitwell et al., 2007
). Findings in both parallel independent component analysis component pairs point to a selective vulnerability of these regions to Alzheimer’s disease pathology, consistent with histopathological findings. The finding in mild cognitive impairment that a localized distribution of amyloid-β deposition is associated with a characteristic pattern of brain atrophy that resembles the atrophy pattern seen in Alzheimer’s disease is encouraging for the use of 11
C-PiB-PET measures as early indicators of Alzheimer’s disease. Most importantly, elucidating the detrimental relationship between the local amyloid-β burden and rates of brain atrophy is of great interest to enhance our understanding of the underlying mechanisms of disease and use of this knowledge in development of new anti-therapies for Alzheimer’s disease.
Another important observation is the prominence of the left hemisphere in the relationship between increased amyloid-β burden and increased rates of brain atrophy. This observation is consistent with previous reports of higher atrophy rates on the left hemisphere compared with the right hemisphere in patients with Alzheimer’s disease (Thompson et al., 2003
). It is also interesting in comparison to other MRI studies, which found a trend of higher right than left asymmetry of hippocampal atrophy rates in cognitively normal elderly subjects and some evidence that suggests there is a change in asymmetry during the progression toward Alzheimer’s disease (Barnes et al., 2005
). It is therefore possible that the laterality in brain atrophy rates that we observed in the first parallel independent component analysis component pair is another potentially useful index for staging Alzheimer’s disease severity as well as for assessing disease-altering interventions. Further prospective studies of joint 11
C-PiB PET and MRI evaluations including elderly individuals without cognitive deficits are warranted to determine the value of asymmetric findings for an early detection of Alzheimer’s disease pathology.
Another interesting observation in this study is a marked dissimilarity between the spatial distributions of increased amyloid-β deposition and that of increased brain atrophy rates in mild cognitive impairment. In particular, the spatial dissimilarity between increased amyloid-β deposition that included the precuneus and cingulate cortex whereas increased atrophy rates involved mainly medial temporal lobe structures but left precuneus and the cingulate cortex largely unaffected, is unexpected. It is possible that the brain regions with increased amyloid-β deposition in patients with mild cognitive impairment has already reached an equilibrium or plateau in terms of the rate of atrophy very early in the course of Alzheimer’s disease while atrophy rates in regions with low amyloid-β burden are further progressing. This interpretation is also consistent with the paradigm that amyloid-β is the first indication of disease and higher atrophy rates are a downstream process. From a functional anatomy point of view, the precuneus is implicated in the recollection of past episodes whereas posterior cingulate projecting from thalamus and neocortex to entorhinal cortex via cingulum fibres is characteristically active during recall and deactivated during encoding of episodic memory (Cabeza and Nyberg, 2000
). Cognitive decline in episodic memory is one of the earliest clinical syndromes of Alzheimer’s disease. A popular hypothesis on disease mechanism suggests that amyloid-β accumulation is responsible for brain atrophy and hence the cognitive decline (Jack et al., 2010
). According to this hypothesis, our findings together with the functional role of precuneus and posterior cingulate regions suggest that anti-amyloid-β therapy might be successful when administered very early in the disease evolution before the synaptic and neuronal loss in brain regions susceptible to early amyloid-β deposition reach a plateau. In a related investigation, Driscoll et al. (2010)
reported no association between amyloid-β burden and rates of brain atrophy in the preceding years in healthy elderly individuals. Their finding suggests that either ageing-related structural brain atrophy rates are independent of amyloid-β deposition, or amyloid-β deposition must reach a particular level and be present for an extended time period before causing extensive Alzheimer’s disease-related brain atrophy. To further elucidate these hypotheses future studies focusing on especially early mild cognitive impairment populations are needed. In addition, the use of a selective in vivo
marker of regional aggregated tau deposition in the form of neurofibrillary tangles would be useful to assess the linkage between regional amyloid-β, regional neurofibrillary tangle deposition and regional atrophy rates. To date, no useful neurofibrillary tangle-specific imaging agent has been conclusively demonstrated.
An important conceptual difference between our study and other imaging studies in mild cognitive impairment is the approach to jointly evaluate not only variations across the two imaging modalities, but also variations that the imaging modalities have in common across brain regions. This approach provided insight into the spatial distributions of amyloid-β and brain atrophy rate relations that conventional analysis methods, which test relationships by regions of interest or voxel-by-voxel, cannot provide. The difference in methods may also explain the differences in findings between our studies and others. For example, other 11
C-PiB-PET studies found higher amyloid-β depositions also in other brain regions, including the frontal, posterior cingulate, parietal, and lateral temporal cortices, as well as in the subcortical regions including caudate and putamen (Kemppainen et al., 2007
; Rowe et al., 2007
). Similarly, other MRI studies in mild cognitive impairment have also found significant atrophy in temporal lobe white matter, orbitofrontal and medial prefrontal grey matter, posterior cingulate, insula and uncus (Apostolova et al., 2006
; Becker et al., 2006
; Colliot et al., 2008
; Fan et al., 2008
). However, the variations in amyloid-β and brain atrophy rates in these regions might be independent of each other, which would explain our inability to detect significant variations in these regions with parallel independent component analysis. Although another study reported no significant relationship between increased amyloid-β and brain atrophy in mild cognitive impairment, the authors suggested a concordance between atrophy and amyloid-β deposition in the posterior cingulate, precuneus and lateral temporoparietal association cortices during early stages of the disease process (Chetelat et al., 2010
). Lack of an amyloid-β-brain atrophy rate association, especially in posterior cingulate and precuneus regions, is a surprising finding of our study. An explanation would be that reduced 11
C-PiB signal due to atrophy in these regions would affect our ability to detect a local relationship between amyloid-β deposition and atrophy progression, since we chose to analyse non-atrophy-corrected PiB-standardized uptake value ratio data.
Amyloid-β levels in CSF have also been proposed as a diagnostic biomarker for Alzheimer’s disease by various researchers (Andreasen et al., 1999
; Blennow and Hampel, 2003
; Clark et al., 2003
; Bouwman et al., 2007
; De Meyer et al., 2010
; Fjell et al., 2010
). Recently, we reported that lower CSF amyloid-β1–42
concentrations were associated with higher rates of brain volume loss in the left temporal and parietal cortices (Tosun et al., 2010
) in mild cognitive impairment, similar to our findings of higher amyloid-β levels in the brain from this study. Furthermore, significant correlation (r
−0.72) between the global 11
C-PiB binding load and the CSF amyloid-β1-42
concentrations were found across healthy elderly, mild cognitive impairment and Alzheimer’s disease populations (Jagust et al., 2009
; Tolboom et al.,
). Taken together, these findings also encourage the use of 11
C-PiB-PET imaging measures as early indicators of Alzheimer’s disease.
In Alzheimer’s disease, the default-mode network is markedly disconnected compared with healthy elderly controls, especially in the bilateral calcarine/cuneus, bilateral precuneus/post-cingulate, left lingual, left middle temporal gyrus, left parahippocampal, right angular, right dorsolateral prefrontal cortices as well as in the left hippocampus (Greicius et al., 2004
; Zhou et al., 2010
). It has been hypothesized that disruption of functional connectivity within default-mode network could be an alternative biomarker to structural alterations for early diagnosis of Alzheimer’s disease (Hedden et al., 2009
; Yvette et al., 2010
). Moreover, it has been suggested that destabilization of neuronal networks and compensatory responses contribute to cognitive impairments in Alzheimer’s disease and that such destabilization stems, at least in part, from aberrant increases in neuronal activity induced by high amyloid-β levels (Palop and Mucke, 2009
). Regions with high amyloid-β deposition and associated accelerated atrophy also mirror the regions affected in default-mode network. In particular, posterior components of the default-mode network, including the precuneus and posterior cingulate, are particularly vulnerable to early deposition of amyloid-β (Braak and Braak, 1991
) and the temporal components of the default-mode network are mentioned in regional atrophy early in the disease process. One interpretation of these observations is that amyloid-β pathology could be linked to neural dysfunction in a distributed network of brain regions linked to memory function (Sperling et al., 2009
). An important question is if default-mode network dysfunction is a functional consequence of amyloid-β deposition, especially since increased neuronal activity has been shown to increase the production, release and/or accumulation of amyloid-β, raising the possibility of a vicious cycle in which amyloid-β promotes its own production through alterations in neuronal network activity (Kamenetz et al., 2003
; Cirrito et al., 2005
). Further longitudinal studies are required to better understand such a causality between amyloid-β deposition and default-mode network dysfunction.
Several limitations of our study ought to be mentioned. First, mild cognitive impairment subjects are notoriously a very heterogeneous group and other pathologies, unrelated to Alzheimer’s disease, may have contributed to variations in both amyloid-β and rates of brain atrophy. In addition, we did not separate patients with mild cognitive impairment with high PiB burden and high atrophy rates from those with low PiB burden and low atrophy rates to maintain sufficient statistical power. By analysing the groups together, we may have underestimated the extent of joint variations in amyloid-β and atrophy rate in the high PiB burden/atrophy rate group of mild cognitive impairment and similarly, overestimated these variations in the low PiB burden/atrophy rate group. The value of our findings to predict Alzheimer’s disease remains uncertain because the number of patients with mild cognitive impairment who had 11C-PiB PET and converted to Alzheimer’s disease was small at the time of this study. In addition, the number of cognitively normal subjects in ADNI, who had both 11C-PiB PET and sequential MRI scans at the time of the study was too small for a reliable statistical analysis. Therefore, the extent to which our findings separate mild cognitive impairment from cognitively normal subjects or have value to predict who will develop mild cognitive impairment is unclear.
Our study also has several technical limitations. First, errors in image registrations between the image modalities as well as errors in spatial normalization may have increased variability and hence diminished statistical power to detect intrinsic relationships between atrophy rates and amyloid-β burden at a localized level. Second, the current framework of parallel independent component analysis assumes that measurements in each image voxel are independent and noise is identically distributed, which is likely not true and therefore statistical evaluations could be inflated.
In conclusion, our major finding links a greater amyloid-β deposition in the precuneus, a region generally known for early accumulation of amyloid plaques, to a unique pattern of increased brain atrophy within each hemisphere, resembling the pattern seen also in Alzheimer’s disease. These results may begin to shed light on the mechanisms by which amyloid-β deposition leads to neurodegeneration and cognitive decline and the development of a more specific Alzheimer’s disease-specific imaging signature for diagnosis.