Subthreshold amyloid deposition, cerebral small vessel disease, and functional brain network disruption in delayed cognitive decline after stroke

Abstract

Background: Although its incidence is relatively low, delayed-onset post-stroke cognitive decline (PSCD) may offer valuable insights into the "vascular contributions to cognitive impairment and dementia," particularly concerning the roles of vascular and neurodegenerative mechanisms. We postulated that the functional segregation observed during post-stroke compensation could be disrupted by underlying amyloid pathology or cerebral small vessel disease (cSVD), leading to delayed-onset PSCD.

Methods: Using a prospective stroke registry, we identified patients who displayed normal cognitive function at baseline evaluation within a year post-stroke and received at least one subsequent assessment. Patients suspected of pre-stroke cognitive decline were excluded. Decliners [defined by a decrease of ≥3 Mini-Mental State Examination (MMSE) points annually or an absolute drop of ≥5 points between evaluations, confirmed with detailed neuropsychological tests] were compared with age- and stroke severity-matched non-decliners. Index-stroke MRI, resting-state functional MRI, and 18F-florbetaben PET were used to identify cSVD, functional network attributes, and amyloid deposits, respectively. PET data from age-, sex-, education-, and apolipoprotein E-matched stroke-free controls within a community-dwelling cohort were used to benchmark amyloid deposition.

Results: Among 208 eligible patients, 11 decliners and 10 matched non-decliners were identified over an average follow-up of 5.7 years. No significant differences in cSVD markers were noted between the groups, except for white matter hyperintensities (WMHs), which were strongly linked with MMSE scores among decliners (rho = -0.85, p < 0.01). Only one decliner was amyloid-positive, yet subthreshold PET standardized uptake value ratios (SUVR) in amyloid-negative decliners inversely correlated with final MMSE scores (rho = -0.67, p = 0.04). Decliners exhibited disrupted modular structures and more intermingled canonical networks compared to non-decliners. Notably, the somato-motor network's system segregation corresponded with the decliners' final MMSE (rho = 0.67, p = 0.03) and was associated with WMH volume and amyloid SUVR.

Conclusion: Disruptions in modular structures, system segregation, and inter-network communication in the brain may be the pathophysiological underpinnings of delayed-onset PSCD. WMHs and subthreshold amyloid deposition could contribute to these disruptions in functional brain networks. Given the limited number of patients and potential residual confounding, our results should be considered hypothesis-generating and need replication in larger cohorts in the future.

Keywords: amyloid deposition; connectome; neural network; small vessel disease; vascular cognitive impairment.

Figure 1

Enrollment flowchart.

Figure 2

Subthreshold amyloid in patients with amyloid PET-negative delayed-onset post-stroke cognitive impairment (n = 10). In amyloid-negative individuals, the standard uptake value ratios were negatively correlated with the last cognitive scores and cognitive changes during follow-up (A). T static map showing the difference in the topographical pattern of subthreshold amyloid deposition between the amyloid PET-negative decliners and the stroke-free, age-matched amyloid-negative controls (n = 21). The red color is the area where the standard uptake value ratios of the decliners increased compared to the control (thresholded by uncorrected p < 0.05) (B).

Figure 3

Spring-embedded graphs for the functional network compositions in the representative patients. The nodes correspond to different brain regions, with colors representing the corresponding canonical functional brain networks. The edges represent the functional connections between brain regions after thresholding. The spring-embedded graph plots present the functional brain network compositions for representative non-decliners (A) and decliners (B) at the threshold of network densities of 0.025. The plots show that the brain regions with the same canonical network were closely linked and arranged with each other (A). However, representative decliners (demonstrated disrupted and intermingled canonical brain modules) (B). The groups of strongly connected nodes are pulled together in the plot. Disconnected nodes are also visualized as dots without lines. Blue: visual, orange: somatomotor, yellow: dorsal attention, purple: ventral attention, green: limbic, light blue: frontoparietal, red: default mode, pink: subcortical, light purple: cerebellum, dark blue: brainstem.

Figure 4

(A) Network attributes at multiple thresholds were compared between decliners (cyan) and non-decliners (magenta). Decliners showed lower scores in all attributes than non-decliners, but the differences were not statistically significant. Characteristic path length was inverted so that the lower value represents poorer functions to make them align with other attributes and thus help comparisons. (B) System segregation for different functional brain networks was compared between decliners and non-decliners. VN, visual network; SMN, somatomotor network; DAN, dorsal attention network; VAN, ventral attention network; LN, limbic network; FPN, frontoparietal network; DMN (default mode network).

Figure 5

Associations between modularity, system segregation, and MMSE scores in delayed decliners. (A) Scatter plots for the associations between modularity averaged across all thresholds and the MMSE scores in delayed decliners, including the final MMSE score and the change in MMSE score over time. Modularity showed a significant association with the final MMSE score. (B) Scatter plots for the associations between system segregation across all networks, DMN, SMN, and the MMSE scores in delayed decliners, specifically the final MMSE score and the change in MMSE score over time. System segregation of the SMN showed a significant association with the final MMSE score.