Structural network efficiency is associated with cognitive impairment in small-vessel disease

Abstract

Objective: To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment.

Methods: A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested.

Results: Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed.

Conclusions: Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies.

Figure 1. Subnetwork identified as impaired in patients with small-vessel disease relative to controls

Projections of an example brain network taken from a randomly selected control subject (gray edges). (A) Whole-brain axial view. (B) Left hemisphere sagittal view. (C) Right hemisphere sagittal view. The network-based statistic significant subnetwork of impaired connections is overlaid in red (p < 0.001 adjusted, threshold of t = 3.4) (see appendix e-3; tables e-2, e-3, and e-4). Nodes are displayed as circles located at region of interest centers of gravity, with circle size scaled corresponding to degree. Node colors group Automated Anatomical Labeling regions according to brain macro-regions: light blue = frontal lobe cortex; blue-gray = subcortical regions; coral = limbic and paralimbic regions; dark red = temporal lobe cortex; yellow = parietal lobe cortex; cream = motor cortex; dark blue = occipital cortex. See appendix e-5 for key.

Figure 2. Diagrams showing statistical mediation of the relationship between diffusion tensor imaging measures and cognitive function by network efficiency in small-vessel disease

(A, B) Mediation models for the effect of fractional anisotropy (FA). (C, D) Mediation models for the effect of mean diffusivity (MD). These are then used to show models to predict processing speed (A and C) and models to predict executive function (B and D). Diagrams present the standardized regression coefficients controlling for confounders associated with each path in the model. Coefficients after the slash show path values adjusted for the mediation effect. The bootstrap statistical significance (p values) of the direct and indirect paths is presented in the center of each diagram.