Functional connectivity and graph theory in preclinical Alzheimer's disease

Abstract

Alzheimer's disease (AD) has a long preclinical phase in which amyloid and tau cerebral pathology accumulate without producing cognitive symptoms. Resting state functional connectivity magnetic resonance imaging has demonstrated that brain networks degrade during symptomatic AD. It is unclear to what extent these degradations exist before symptomatic onset. In this study, we investigated graph theory metrics of functional integration (path length), functional segregation (clustering coefficient), and functional distinctness (modularity) as a function of disease severity. Further, we assessed whether these graph metrics were affected in cognitively normal participants with cerebrospinal fluid evidence of preclinical AD. Clustering coefficient and modularity, but not path length, were reduced in AD. Cognitively normal participants who harbored AD biomarker pathology also showed reduced values in these graph measures, demonstrating brain changes similar to, but smaller than, symptomatic AD. Only modularity was significantly affected by age. We also demonstrate that AD has a particular effect on hub-like regions in the brain. We conclude that AD causes large-scale disconnection that is present before onset of symptoms.

Keywords: Alzheimer's disease; Biomarker; Functional connectivity; Graph theory; Resting-state.

Figure 1

Map of ROI locations colored by network membership. Cerebellar network is red, cingulo-opercular network is green, default mode network is blue, fronto-parietal network is teal, occipital network is purple, and sensori-motor network is yellow.

Figure 2

Path length (L; left), clustering coefficient (C; middle left), small-worldness (S; middle right) and modularity (Q; right) for CDR 0 (blue), CDR 0.5 (green) and CDR 1 (red) groups as a function of K. The false discovery rate (FDR) corrected p-values (lower frames) for these effects are shown for the one-way ANOVA (black crosses), and for each of the contrasts [CDR 0 vs. CDR 0.5 (cyan), CDR 0.5 vs. CDR 1 (magenta), and CDR 0 vs. CDR 1 (purple)]. We investigated only the individual contrasts where the ANOVA showed a significant CDR effect. The black line indicates FDR corrected p = 0.05. The figure shows that there was a significant effect of CDR on C, S, and Q over certain ranges of K.

Figure 3

Scatter plots of path length (L), clustering coefficient (C), and modularity (Q) at a representative threshold (K= 30). No age effect was observed for path length or clustering coefficient. However, a significant age effect was seen for modularity.

Figure 4

ROI maps of participation coefficient and betweenness. A: Participation coefficient at each vertex grouped by CDR status (first row). Areas with high participation coefficients are potential hubs. Difference images (second row) demonstrate where advancing CDR impacts participation coefficient. B: Betweenness maps for each of the CDR groups. Because the large range over which betweenness varies, the values displayed here are log₁₀ transformed for display purposes only. The difference maps show the difference in log values.

Figure 5

Bar plots of participation coefficient and betweenness in the three hub regions identified as hubs. Overarching bars indicate significance of one-way ANOVA and pair-wise bars indicate significant contrasts. * indicates p<0.05, ** indicates p<0.01. There was no significant effect of CDR on betweenness (p > 0.10).

Figure 6

Path length (left), clustering coefficient (middle left), small-worldness (middle right) and modularity (right) for the NIA stage 0 (cyan), NIA stage 1&2 (purple) and CDR 0.5 (green) groups as a function of K. The FDR corrected p-values (lower frames) for these effects are shown for the one-way ANOVA (black crosses), and for each of the contrasts [NIA 0 vs. NIA 1&2 (red), NIA 1&2 vs. CDR 0.5 (green), and NIA 0 vs. CDR 0.5 (blue)]. We investigated only the individual contrasts where the ANOVA showed a significant CDR effect. The black line indicates FDR corrected p = 0.05.