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, 10 (1), 2353

Aβ-induced Vulnerability Propagates via the Brain's Default Mode Network

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Aβ-induced Vulnerability Propagates via the Brain's Default Mode Network

Tharick A Pascoal et al. Nat Commun.

Abstract

The link between brain amyloid-β (Aβ), metabolism, and dementia symptoms remains a pressing question in Alzheimer's disease. Here, using positron emission tomography ([18F]florbetapir tracer for Aβ and [18F]FDG tracer for glucose metabolism) with a novel analytical framework, we found that Aβ aggregation within the brain's default mode network leads to regional hypometabolism in distant but functionally connected brain regions. Moreover, we found that an interaction between this hypometabolism with overlapping Aβ aggregation is associated with subsequent cognitive decline. These results were also observed in transgenic Aβ rats that do not form neurofibrillary tangles, which support these findings as an independent mechanism of cognitive deterioration. These results suggest a model in which distant Aβ induces regional metabolic vulnerability, whereas the interaction between local Aβ with a vulnerable environment drives the clinical progression of dementia.

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Global rather than local amyloid-β (Aβ) is associated with DMN hypometabolism. Voxel-wise models, taking voxel and global Aβ values into consideration, showed that global Aβ is associated with hypometabolism in (a) CN Aβ positive (n = 53) and in (b) MCI Aβ-positive (n = 170) individuals in the posterior cingulate, precuneus, lateral temporal, and inferior parietal cortices, and in (c) transgenic Aβ rats (n = 10) in the retrosplenial (which corresponds to the posterior cingulate in humans), medial and lateral temporal, and inferior parietal cortices. The effects of regional Aβ values on its overlapping hypometabolism were negligible in the voxel-wise models. Regression models performed in (d) CN Aβ-positive and in (e) MCI Aβ-positive individuals within anatomically segregated regions further supported that the effects of local Aβ on hypometabolism (i.e., in the same region) were negligible. Similarly, inside segregated clusters, local Aβ effects on hypometabolism were negligible in (f) transgenic Aβ rats. In panels df, the dots and bars represent β estimates and standard error, respectively, of the independent variables used in the models. Parametric images were FWER corrected at P < 0.05 and adjusted for age, gender, education, APOE ε4 status, p-tau, and gray matter (GM) density in humans, and age, gender, and gray matter density in rats
Fig. 2
Fig. 2
Amyloid-β (Aβ) is associated with hypometabolism in distant, but functionally connected brain regions. Metabolic connectivity analysis between eight regions-of-interest used to compose the global Aβ value in the precuneus (Pre), posterior (PCC) and anterior (ACC) cingulate, inferior parietal (IP), paracentral (Par), medial prefrontal (MPF), lateral temporal (LT), and orbitofrontal (OF), as well as two additional regions in the insular (Ins) and occipital pole (OP) cortices demonstrated that regions comprising the DMN were highly correlated with each other in (a) CN Aβ negative (n = 99), (b) CN Aβ positive (n = 53), and (c) MCI Aβ positive (n = 170) individuals. Partial correlation analysis showed that Aβ within the DMN was associated with distant but within-network hypometabolism in (d) CN Aβ-positive and (e) MCI Aβ-positive individuals. Note that Aβ and its overlapping metabolism showed positive or non-correlation in these individuals. Correlation maps displayed in 3D brain surfaces show the representations of (f) the DMN and posterior DMN and correlations of glucose–glucose (left side) and Aβ-glucose (right side) in the (g) Pre, (h) PCC, (i) LT, (j) IP, and (l) MPF cortices in MCIs Aβ positive (see Supplementary Movie 1). The matrices are presented with Pearson partial correlation coefficients (r) controlled for age, gender, education, APOE ε4 status, p-tau, gray matter density, and Bonferroni-corrected at P < 0.05
Fig. 3
Fig. 3
Amyloid-β (Aβ) in DMN is predominantly associated with distant within-network hypometabolism in CN Aβ positive. a In the 3D brain, the dots represent the regions-of-interest in which Aβ values were obtained. The regions in which Aβ values were obtained are also shown in white, superimposed on the structural MRI templates in panels be. Statistical parametric maps, overlaid on a structural MRI template, show the brains regions where voxel-wise glucose metabolism was negatively associated with Aβ load in the (b) precuneus (green), (c) posterior cingulate (yellow), (d) lateral temporal (red), and (e) inferior parietal (blue) cortices in CN Aβ positive (n = 53). The bar graphs show the distribution of the voxels in the aforementioned statistical parametric maps across seven functional brain networks (DM default mode, FP frontoparietal, DA dorsal attention, VA ventral attention, limbic visual, SM somatomotor). Thus, the sum of the seven bars in each graph is 100%. Aβ in the medial prefrontal, anterior cingulate, orbitofrontal, paracentral, insular, and occipital pole cortices (gray dots) did not significantly associate with hypometabolism. Parametric images were FWER corrected at P < 0.05 and adjusted for age, gender, education, APOE ε4 status, p-tau, and gray matter density
Fig. 4
Fig. 4
Amyloid-β (Aβ) in DMN is predominantly associated with distant within-network hypometabolism in MCI Aβ positive. a In the 3D brain, the dots represent the regions-of-interest in which Aβ values were obtained. The regions in which Aβ values were obtained are also shown in white, superimposed on the structural MRI templates in panels bh. Statistical parametric maps, overlaid on a structural MRI template, show the brains regions where voxel-wise glucose metabolism was negatively associated with Aβ load in the (b) precuneus (green), (c) posterior cingulate (yellow), (d) lateral temporal (red), (e) inferior parietal (blue), (f) medial prefrontal (pink), (g) anterior cingulate (light blue), and (h) paracentral (orange) cortices in MCI Aβ positive (n = 170). The bar graphs show the distribution of the significant voxels in the aforementioned statistical parametric maps across seven functional brain networks (DM default mode, FP frontoparietal, DA dorsal attention, VA ventral attention, limbic visual, SM somatomotor). Thus, the sum of the seven bars in each graph is 100%. Aβ in the orbitofrontal, insular, and occipital pole cortices (gray dots) did not significantly associate with hypometabolism. Parametric images were FWER corrected at P < 0.05 and adjusted for age, gender, education, APOE ε4 status, p-tau, and gray matter density
Fig. 5
Fig. 5
Amyloid-β (Aβ) is associated with distant within-network hypometabolism in transgenic Aβ rats. a 3D brain representation of the regions-of-interest in which the Aβ values were obtained. Statistical parametric maps, overlaid on a structural MRI template, show the brains regions where voxel-wise glucose metabolism was negatively associated with Aβ load in the (b) retrosplenial (yellow), (c) medial temporal (green), (d) lateral temporal (red), (e) inferior parietal (blue), (f) frontoparietal (light blue), (g) olfactory bulb (pink), and (h) cerebellar (orange) cortices in transgenic Aβ rats (n = 10). Aβ in the somatosensory cortex (gray dot) did not significantly associate with hypometabolism. Parametric images were FWER corrected at P < 0.05 and adjusted for age, gender, and gray matter density
Fig. 6
Fig. 6
The synergy of amyloid-β (Aβ) with overlapping hypometabolism drives cognitive decline. a The parametric map shows significant interactive effects at a voxel level between Aβ and glucose uptake on MMSE worsening over up to 5.6 years in the precuneus, posterior cingulate, inferior parietal, and lateral temporal cortices in MCIs Aβ positive (n = 170). The aforementioned interaction was not significantly associated with cognitive decline in CN Aβ positive. b In transgenic Aβ rats, significant voxel-wise interactive effects between Aβ and glucose uptake on MWM worsening over 8 months were found in the retrosplenial cortex, which corresponds to the posterior cingulate in humans, inferior parietal, and mediobasal and lateral temporal cortices. The plots show the graphical representation of the interaction in (c) MCIs Aβ positive (see Supplementary Movie 2) and in (d) transgenic Aβ rats, where each parallel line represents a single subject. For longitudinal changes in MMSE and MWM, lower values indicate greater impairment. Parametric images were FWER corrected at P < 0.05 and adjusted for global Aβ, age, gender, education, APOE ε4 status, p-tau, gray matter density, and follow-up duration in humans, and adjusted for global Aβ, age, gender, and gray matter density in rats
Fig. 7
Fig. 7
Structured equation modeling showed that the distant and local amyloid-β (Aβ) effects on hypometabolism well describe AD progression. This model represents the hypothesis that distant and local Aβ effects (yellow) on posterior DMN hypometabolism (red) are associated with cognitive decline. The model used CN Aβ-positive and MCI Aβ-positive individuals, and the associations were adjusted for age, gender, education, APOE ε4 status, p-tau, and gray matter density. Negative associations are shown in solid lines, whereas dashed lines show positive associations. Notably, Aβ showed positive association with its overlapping metabolism but negative associations with distant metabolism. The hypothesized model fitted the data well (n = 223, X2 = 81, degrees of freedom = 21, P < 0.01, standardized root mean square residual (SRMR) = 0.092, and Comparative Fit Index (CFI) = 0.966)
Fig. 8
Fig. 8
Schematic representation of the distant and local amyloid-β (Aβ) effects on metabolism. Aβ from distant brain regions leads to regional metabolic vulnerability, whereas the synergy between this vulnerability with local Aβ effects drives the clinical progression of dementia. Importantly, either Aβ or metabolic vulnerability as a single abnormality is insufficient to determine dementia
Fig. 9
Fig. 9
Multimodal analytical operations performed at every brain voxel. The illustration shows the analytical model developed to perform statistical operations on multiple scalar variables and with multiple imaging modalities at every brain voxel in humans and rats. Briefly, (a) the brain image data were retrieved from a 3D image space and converted to a 2D matrix in the image space for each subject. b Then, the image data were transformed into the processing space using artificial parcellation. c In the computational phase, the statistical modeling was performed in every brain voxel accounting for voxel and global PET uptake values, as well as voxel gray matter density and covariates. d Subsequently, statistical matrices were generated from the results and (e) transformed back to the 3D image space. f Finally, 3D parametric maps displaying the results of the regression models were generated. k = subjects, u = image slice, v = slice elements, m = artificial parcellation, n = elements in each parcellation

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