Structural brain network efficiency and cognitive processing speed in healthy aging: Neuroimaging / Normal brain aging. (7th December 2020)

• Record Type: Journal Article
• Authors: Seiler, Stephan; Fletcher, Evan; Beiser, Alexa S; Himali, Jayandra J; Satizabal, Claudia L; Seshadri, Sudha; Maillard, Pauline; DeCarli, Charles
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.044563
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: To test the hypothesis that structural brain network efficiency relates to cognitive processing speed and executive function, we computed structural brain networks of 2, 278 healthy adults and conducted graph theoretical analyses. Method: 2, 278 healthy adults from the cross‐sectional Framingham Heart Study, aged 26‐91 years, were included. We used the trail making test (TMT) parts A and B to assess processing speed. To assess executive function, we subtracted the TMT A from the TMT B score to obtain the TMT difference score. We computed structural brain networks from MRI, using diffusion tensor imaging (DTI) probabilistic tractography. Graph theory was then applied to assess global network efficiency (GE) and nodal efficiency (NE) of 72 nodes (gray matter regions). Global‐ and nodal relationships between efficiency measures and age, white matter hyperintensities (WMH), and processing speed scores were tested using linear models. All linear models were adjusted for confounding variables. Node‐wise p‐values <0.05, corrected for multiple comparisons, were considered statistically significant. Result: Higher age was significantly associated with lower GE (β=‐0.143, p<0.001, figure 1.1). Higher WMH volumes related to lower GE, independent of age (β=‐0.069, p<0.001, figure 1.2). Node‐wise regression analysis revealed that associations of age and WMH with NE were differentially distributed across the brain. Efficiencies of widespread cortical and subcortical nodes correlated negatively with age and WMH (figure 2), while nodes including the posterior and caudal cingulate cortex and precuneus correlated positively with age (figure 3), but not WMH (p<0.05, corrected). GE related positively to TMT B (β=0.046, p=0.006, figure 4.1) and the TMT difference score (β=0.039, p=0.012, figure 4.2). Efficiencies of mainly fronto‐temporal, but also subcortical nodes were associated with TMT B and the TMT difference score (p<0.05, corrected, figure 5). Conclusion: Aging and WMH negatively impact efficiency of the structural brain network. Regional efficiency losses, mainly in fronto‐temporal, but also subcortical nodes, relate to reduced processing speed and executive function. Positive nodal relationships with age might represent a compensatory effect, but this hypothesis needs further exploration.