Interrelating differences in structural and functional connectivity in the older adult's brain

Abstract

In the normal aging process, the functional connectome restructures and shows a shift from more segregated to more integrated brain networks, which manifests itself in highly different cognitive performances in older adults. Underpinnings of this reorganization are not fully understood, but may be related to age-related differences in structural connectivity, the underlying scaffold for information exchange between regions. The structure-function relationship might be a promising factor to understand the neurobiological sources of interindividual cognitive variability, but remain unclear in older adults. Here, we used diffusion weighted and resting-state functional magnetic resonance imaging as well as cognitive performance data of 573 older subjects from the 1000BRAINS cohort (55-85 years, 287 males) and performed a partial least square regression on 400 regional functional and structural connectivity (FC and SC, respectively) estimates comprising seven resting-state networks. Our aim was to identify FC and SC patterns that are, together with cognitive performance, characteristic of the older adults aging process. Results revealed three different aging profiles prevalent in older adults. FC was found to behave differently depending on the severity of age-related SC deteriorations. A functionally highly interconnected system is associated with a structural connectome that shows only minor age-related decreases. Because this connectivity profile was associated with the most severe age-related cognitive decline, a more interconnected FC system in older adults points to a process of dedifferentiation. Thus, functional network integration appears to increase primarily when SC begins to decline, but this does not appear to mitigate the decline in cognitive performance.

Keywords: aging; cognitive performance; functional connectivity; multivariate analyses; structural connectivity.

FIGURE 1

PCA derived factor loadings for the cognitive performance, ordered descendent according to the strength of loading. STM, short‐term memory; WM, working memory

FIGURE 2

PLSR model description. (a) Model performance across 1000 real models (green) or null models (grey): RMSEP (SD) as bars and explained variance in age (%, R ²) as lines including up to 10 components. Dashed line indicating the utilized model in the current study. (b) Prediction accuracies derived from applying the PLSR model on 80% of the sample to unseen test datasets (20% of the sample): Correlation between predicted and chronological age including the information of only the first component, the first and second component, or all three components. Individual score values depict the mean scores across 1000 permutations

FIGURE 3

(a) Model derived individual score values for the first, second, and third components in relation to the participant's chronological age. (b) Loading values for cognitive performance in the first, second, and third components: Higher loadings indicate lower cognitive performance at higher ages. (c) Region‐specific loading values for the first (A, B, C), second (D, E, F), and third component (G, H, I): Intra‐ and inter‐network SC (A, D, G), FC_pos (B, E, H), and FC_neg (C, F, I) plotted onto the brain surface. Blue colors indicate lower and red colors higher connectivity values being characteristic for higher ages

FIGURE 4

Network‐wise mean loading values (SD) for the first, second, and third components visualized as bar plots: Inter‐ and inter‐network SC, FC_pos, and FC_neg (colored according to their respective network, from top to bottom: Brown = DMN, orange = FPN, grey = LN, pink = VAN, green = DAN, blue = SMN, violet = VN)