A whole-brain computational modeling approach to explain the alterations in resting-state functional connectivity during progression of Alzheimer's disease

Abstract

Alzheimer's disease (AD) is the most common dementia with dramatic consequences. The research in structural and functional neuroimaging showed altered brain connectivity in AD. In this study, we investigated the whole-brain resting state functional connectivity (FC) of the subjects with preclinical Alzheimer's disease (PAD), mild cognitive impairment due to AD (MCI) and mild dementia due to Alzheimer's disease (AD), the impact of APOE4 carriership, as well as in relation to variations in core AD CSF biomarkers. The synchronization in the whole-brain was monotonously decreasing during the course of the disease progression. Furthermore, in AD patients we found widespread significant decreases in functional connectivity (FC) strengths particularly in the brain regions with high global connectivity. We employed a whole-brain computational modeling approach to study the mechanisms underlying these alterations. To characterize the causal interactions between brain regions, we estimated the effective connectivity (EC) in the model. We found that the significant EC differences in AD were primarily located in left temporal lobe. Then, we systematically manipulated the underlying dynamics of the model to investigate simulated changes in FC based on the healthy control subjects. Furthermore, we found distinct patterns involving CSF biomarkers of amyloid-beta (Aβ1 - 42) total tau (t-tau) and phosphorylated tau (p-tau). CSF Aβ1 - 42 was associated to the contrast between healthy control subjects and clinical groups. Nevertheless, tau CSF biomarkers were associated to the variability in whole-brain synchronization and sensory integration regions. These associations were robust across clinical groups, unlike the associations that were found for CSF Aβ1 - 42. APOE4 carriership showed no significant correlations with the connectivity measures.

Keywords: Alzheimer's disease; Biomarkers; Computational modeling; Dynamic functional connectivity; Resting state fMRI.

Fig. 1

Overview of the model. Top panel illustrates the optimization of the effective connectivity (EC). Middle panel illustrates Hopf normal model. Bottom panel illustrates the effect of local bifurcation parameter (a) and the overview of the computational experiment.

Fig. 2

A Group differences in coherence (average Kuramoto order parameter). B Group differences in metastability (standard deviation of Kuramoto order parameter). The results are shown for 0.06–0.09 Hz narrowband signal. The comparisons were done using permutation t-test (10,000 permuations; ^⁎p < 0.05, ^⁎⁎p < 0.01) after regressing out age as a confounding variable. C The correspondence between empirical and simulated coherence. D The correspondence between empirical and simulated metastability.

Fig. 3

Group differences in FC and EC strengths. A FC strength of healthy control group vs. Alzheimer's disease (AD) group. B EC strength of healthy control group vs. Alzheimer's disease (AD) group. C EC strength of healthy control group vs. preclinical Alzheimer's disease subjects (PAD). The comparisons were done using permutation t-test (10,000 permuations) after regressing out age as a confounding variable. Colorbars indicate T-statistic, where hot colors indicate higher values in healthy control group. Only the regions that showed significant differences after FDR correction (adjusted p-value < 0.05) were shown.

Fig. 4

CSF biomarker partial correlation maps across all subjects. Upper row shows the relationship between FC strength and each CSF biomarker. Bottom row shows the relationship between FC strength and each CSF biomarker. Colorbars indicate the partial correlation coefficient (rho) between Aβ-42 and FC (A), between Aβ-42 and EC (B), between p-tau and FC (C), between p-tau and EC (D), between t-tau and FC (E), between t-tau and EC (F) that were calculated across all subjects controlled for age, gender and education level. Only significant correlations were colored on cortical surface plots. We found similar trends in regional connectivity measures (FC and EC strengths) (this figure and Fig. 5) (Supplementary Tables 3–6). We found no significant correlations between APOE4 allele carrier status and FC strengths (across all subjects and across clinical groups) and EC strengths (across all subjects). Across clinical groups, left superior frontal EC strength showed a significant correlation with APOE-4 allele carrier status (rho = − 0.30, p-value < 0.05) (Supplementary Tables 3–6).

Fig. 5

CSF biomarker partial correlation maps across clinical groups. Upper row shows the relationship between FC strength and each CSF biomarker. Bottom row shows the relationship between FC strength and each CSF biomarker. Colorbars indicate the partial correlation coefficient (rho) between Aβ-42 and FC (A), between Aβ-42 and EC (B), between p-tau and FC (C), between p-tau and EC (D), between t-tau and FC (E), between t-tau and EC (F) that were calculated across PC, MCI and AD groups controlled for age, gender and education level. Only significant correlations were colored on cortical surface plots.

Fig. 6

Computational experiment. FC strengths were calculated after performing simulations with manipulated local bifurcation parameters (within range − 0.05 and − 0.15) based on healthy control subjects. A The Euclidean distance between simulated FC strengths and group averaged empirical FC strengths of PAD (green), MCI (blue) and AD (red) subjects. Dashed lines indicate the minimum distance between simulated FC strengths and group averaged empirical FC strengths of each group, colored accordingly. B The simulated coherence with respect to manipulated local bifurcation parameters. C The simulated metastability with respect to manipulated local bifurcation parameters. Gray shadings show the standard deviations of each parameter across subjects. Colored dots indicate the average empirical coherence and metastability of PAD (green), MCI (blue) and AD (red) groups. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 7

Principal Component Analysis (PCA) of the simulated FC strengths across manipulated local bifurcation parameters. A The spatial map of the first principal component scores. The spatial map indicates the most and the least affected regions due to local bifurcation parameter manipulation in hot and cold colors, respectively. B The first principal component reflecting the FC alterations due to the divergence from optimal bifurcation parameter, explaining 85% of the total variance. C The correlation coefficient between the spatial map of first principal component and that of the relationship between FC strength and APOE-4 allelle carier status, CSF biomarkers and behavioral performance measures (i.e. partial correlation coefficients between FC strength and each measure). Black and gray bars indicate across all subject and across clinical group spatial maps, respectively.