. Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nat Commun. 2017 Oct 31;8(1):1214. PubMed.


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  1. This study by Palmqvist et al. shows in a rigorous way that when amyloid values in CSF are abnormal, it is possible to identify early amyloid increases in a supposedly normal PET image when focusing on specific anatomical areas. A particularly innovative aspect of this study is the in-depth investigation of the relationship between early amyloid accumulation and its effects on functional connectivity patterns within the same individual. Together, these results provide support for the idea that synaptic dysfunction caused by amyloid aggregation is a very early event in Alzheimer’s disease.

    The hypothesised link between amyloid aggregation and brain activation is appealing, as it provides an elegant explanation as to why seemingly remote areas may share a vulnerability to amyloid pathology, while atrophy occurs much later in other distant brain areas. Rodent studies have provided the most convincing support that brain areas showing higher levels of neuronal activation and functional connectivity seem to show increased Aβ secretion, and at later points during development, are more prone to amyloid plaque deposition (Bero et al., 2012; 2011). The present and other studies (e.g., Villain et al., 2012) identified the precuneus amongst brain areas showing the earliest signs of amyloid accumulation. The precuneus is an associative brain area that connects to many other areas in the brain either anatomically and/or functionally. This puts the precuneus in an ideal position to link early pathological alterations in terms of amyloid deposition and later atrophy in other distant areas, like the medial temporal lobe. Potentially, atrophy might result from the loss of connections through a mechanism related to Hebbian learning: Areas that fire together wire together, and potentially in case of neurodegenerative diseases, might also die together.

    Using structural MRI, we previously observed in non-demented individuals that loss of gray matter connectivity of the precuneus (and other brain areas) was associated with lower Aβ values in CSF that were mostly within the normal range (Tijms et al., 2016), and associated with clinical progression (Tijms et al., 2017). The results from Palmqvist et al. further encourage that measures of brain connectivity seem to be especially sensitive to pick up subtle brain alterations associated with amyloid accumulation.

    The results imply an inverted U-curve relationship, with connectivity values first increasing for normal amyloid levels near the cut point and decreasing after abnormality is reached, possibly because at that point synapses become affected. However, maximum connectivity values for normal but near abnormal amyloid values were similar to those in the most abnormal range. The peak of functional connectivity at the cut point was about five times higher, and so this might be a peculiarity for that subset of individuals. Further longitudinal functional MRI in combination with amyloid imaging would be necessary to study the temporal dynamics of functional connectivity and amyloid alterations in more detail.


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    . Gray matter network disruptions and amyloid beta in cognitively normal adults. Neurobiol Aging. 2016 Jan;37:154-60. Epub 2015 Oct 22 PubMed.

    . Gray matter networks and clinical progression in subjects with predementia Alzheimer's disease. Neurobiol Aging. 2018 Jan;61:75-81. Epub 2017 Sep 20 PubMed.

    . Regional dynamics of amyloid-β deposition in healthy elderly, mild cognitive impairment and Alzheimer's disease: a voxelwise PiB-PET longitudinal study. Brain. 2012 Jul;135(Pt 7):2126-39. Epub 2012 May 23 PubMed.

    View all comments by Betty Tijms
  2. The approach of using CSF and PET to identify earlier stages of β-amyloidosis is quite interesting. Understanding where individuals are in the time course of disease is very difficult, particularly in preclinical stages, and the dual CSF/PET Aβ approach to help identify individuals in very early stage of Aβ accumulation looks quite promising. Using this group of early accumulators to identify regions of measureable accumulation over a few years is another nice addition that can help support primary prevention initiatives. Whether or not these regions represent an insight into disease biology, as opposed to a reflection of the sensitivity limitations of Aβ-PET tracers, remains an open question, and will be an interesting area of future research.

    The authors also report default mode hyper-connectivity in CSF-low/PET-negative individuals and hypoconnectivity in CSF-positive/PET-negative individuals. A period of hyperactivity and hyperconnectivity has been implicated in several prior publications, but none have demonstrated the effect this early in the AD cascade. Further investigation of this phenomena is clearly warranted and may have important implications for understanding both the consequences of Aβ pathology, as well as the drivers of Aβ pathology.

    View all comments by Aaron Schultz
  3. Although our methodological approaches were very different, there is a striking agreement between our finding that amyloid-PET scans considered to be "amyloid-negative" according to commonly used criteria can harbor notable regional amyloid signals in specific neocortical association areas. A distinct strength of the study by Palmqvist et al. is the use of longitudinal PET imaging and CSF biomarker data, which allows the authors to determine regional (i.e. voxel-wise) rates of amyloid accumulation in individuals with CSF evidence of cerebral amyloidosis but before conventional global PET thresholds are reached. Moreover, they combine their data with several other imaging markers of brain dysfunction and degeneration. The demonstration that these early, regionally restricted amyloid deposits have a measurable effect on brain function as measured by rs-fMRI connectivity, which is not paralleled by effects on imaging markers of overt neurodegeneration, is particularly intriguing. A major difference to our in vivo staging analyses is that the early amyloid regions are identified by group-level statistics, and thus the degree to which all individuals adhere to this early accumulation pattern remains unknown—a limitation also acknowledged by the authors in the discussion section.

    The areas identified by Palmqvist et al. overlap to a great extent with the areas that define the earliest amyloid stages according to our in vivo staging scheme. However, some differences are also evident, such as relatively less prominent involvement of the inferior temporal lobe and more prominent involvement of the posterior medial cortex (posterior cingulate/precuneus). These differences may be accounted for by several methodological factors related to image processing and analysis (such as use and choice of partial volume correction method, reference region, region-wise versus voxel-wise analyses, etc.). However, given the markedly different analytic approaches used to determine these regions, the convergence on a distinct set of heteromodal association areas is still striking. Palmqvist et al. also use quantitative methods to demonstrate that these early affected areas coincide with a well-described functional brain network known as the default mode network (DMN). This nicely aligns with the previous observation by our group that although advanced amyloid deposition is widespread throughout the entire cortex, the DMN stands out as the most severely affected of all functional brain networks (Grothe and Teipel, 2016). 

    The study by Palmqvist et al. represents an important step toward a better characterization of the earliest amyloid accumulating regions in the human brain. An exciting avenue for future research that emerges from these findings is the question of the underlying mechanisms for this regionally selective vulnerability to amyloid pathology. Why does amyloid preferentially accumulate in these areas? Which functional, structural, or molecular characteristics distinguish these vulnerable areas from those that are more resistant to the accumulation of pathology? Answering these questions may provide interesting new insights into the complex pathophysiologic mechanisms of Alzheimer's disease (Sep 2016 conference news). 

    View all comments by Michel Grothe
  4. I just recently commented on the recent Grothe staging paper and now would like to comment on this Palmqvist-Schöll staging paper (which is also a very nice paper!). As I said in my previous remarks, a very interesting aspect of this work is the goal of identifying very early amyloid accumulation with which to apply to individuals without clinical symptoms who may represent an ideal group for prevention trials. We know from postmortem work that traditional cut offs and global reads of amyloid PET data reflect high levels of plaque pathology, emphasizing that global PET measures are likely missing lower and intermediate levels of plaque pathology. There is also strong evidence to suggest that CSF amyloid changes before global elevations in PET. Thus, approaches that interrogate regional PET values during the earliest stages of accumulation may be insightful for understanding where and when signal emerges with amyloid PET during the preclinical stage of Alzheimer’s disease. 

    Interestingly, in looking at these papers side by side, there is little overlap in the early amyloid pattern described by Grothe (Figure 1, second column) versus Palmqvist-Schöll (Figure 1a). There seems to be overlap in the cingulate, but Grothe Stage 1 highlights the inferior temporal cortex whereas Palmqvist-Schöll Stage 1 highlights the default mode network. Although this may simply be due to methodological differences (Grothe used a cut-off-based approach on cross-sectional data; Palmqvist-Schöll used CSF-defined groups and examined change in amyloid PET over time), theses inconsistencies may represent an opportunity to further explore very early amyloid accumulation.

    One possibility is that there is heterogeneity in the regions that are involved early in the disease across individuals, and that these two distinct approaches have different sensitivities for detecting these different patterns. Heterogeneity in the clinical presentation of Alzheimer’s disease patients has long been established, even among typical presentations, and although spatial patterns of amyloid deposition do not seem to correspond with this heterogeneity during the later stages of AD, distinct patterns of regional involvement may begin decades before symptoms. Thus, some individuals may accumulate preferentially in the inferior temporal cortex, and others in the precuneus, rather than all individuals undergoing the same pattern of accumulation over time. Therefore it may be worth deriving maps capturing “spread” over time at the individual subject level across all amyloid PET negative cases to understand whether regional diversity exists across individuals. Another important analysis will be to understand when these spatial patterns emerge, which may require examination of earlier ages (middle age).

    Finally, now that these spatial maps have been derived across these two separate groups, the authors may consider sharing these masks and working together to understand this signal a little better. For instance, what is the correlation between the amyloid PET values extracted from Grothe Stage 1 and Palmqvist-Schöll Stage 1 maps among amyloid normal? Do extracted values from each of these maps provide independent information in predicting future cognitive decline? Overall, the approaches implemented across these two independent studies represent an important step toward establishing whether we have enough signal in amyloid PET to confidentially and reliably capture very early amyloid pathology before the familiar global pattern of accumulation is evident by PET. 

    View all comments by Elizabeth Mormino
  5. This is a very nice study that points ever more explicitly to the fact that selective vulnerability among brain networks is an important feature of AD. The brain’s default mode network (DMN) stands out in this regard and forces us to consider what it is about this network that underlies this vulnerability. Several things come to mind, but at the moment there is no definitive answer. Work by others (e.g., Margulies et al., 2016) places the DMN at the top of a hierarchy of brain networks conferring on this network a major responsibility for the overall functional organization of the brain. Genetically, it remains “youthful,” harboring genes concerned with synaptic plasticity (Goyal et al., 2014). Do the underlying mechanisms of such large-scale functions harbor the secret to the true cause of AD? We don’t know, but they certainly point to directions of inquiry that are likely to contain the answer.


    . Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci U S A. 2016 Nov 1;113(44):12574-12579. Epub 2016 Oct 18 PubMed.

    . Aerobic glycolysis in the human brain is associated with development and neotenous gene expression. Cell Metab. 2014 Jan 7;19(1):49-57. PubMed.

    View all comments by Marcus Raichle

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