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. 2015 Apr 8;10(4):e0120315.
doi: 10.1371/journal.pone.0120315. eCollection 2015.

White matter integrity supports BOLD signal variability and cognitive performance in the aging human brain

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White matter integrity supports BOLD signal variability and cognitive performance in the aging human brain

Agnieszka Z Burzynska et al. PLoS One. .

Abstract

Decline in cognitive performance in old age is linked to both suboptimal neural processing in grey matter (GM) and reduced integrity of white matter (WM), but the whole-brain structure-function-cognition associations remain poorly understood. Here we apply a novel measure of GM processing-moment-to-moment variability in the blood oxygenation level-dependent signal (SDBOLD)-to study the associations between GM function during resting state, performance on four main cognitive domains (i.e., fluid intelligence, perceptual speed, episodic memory, vocabulary), and WM microstructural integrity in 91 healthy older adults (aged 60-80 years). We modeled the relations between whole-GM SDBOLD with cognitive performance using multivariate partial least squares analysis. We found that greater SDBOLD was associated with better fluid abilities and memory. Most of regions showing behaviorally relevant SDBOLD (e.g., precuneus and insula) were localized to inter- or intra-network "hubs" that connect and integrate segregated functional domains in the brain. Our results suggest that optimal dynamic range of neural processing in hub regions may support cognitive operations that specifically rely on the most flexible neural processing and complex cross-talk between different brain networks. Finally, we demonstrated that older adults with greater WM integrity in all major WM tracts had also greater SDBOLD and better performance on tests of memory and fluid abilities. We conclude that SDBOLD is a promising functional neural correlate of individual differences in cognition in healthy older adults and is supported by overall WM integrity.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Multivariate relationships between cognitive performance and SDBOLD.
A: PLS spatial pattern. Blue regions indicate greater and yellow/red regions indicate lesser SDBOLD with better performance on fluid and memory, and worse performance on vocabulary. Significant regions: bootstrap ratio > ±3. M1: primary motor, PMC: premotor cortex, MFG: middle frontal gyrus, SFG: superior frontal gyrus, SMA: supplementary motor area, PCC: posterior cingulate gyrus, PCUN: precuneus, ACC: anterior cingulate cortex, PCC: posterior parietal cortex, SMG: supramarginal gyrus, INS: insula, OCCIP: occipital cortex, STG: superior temporal gyrus, TP: temporal pole, MTG: middle temporal gyrus, MTL: medial temporal lobe, IFG: interior temporal gyrus, TF: temporal fusiform, CEREB: cerebellum, TH: thalamus, B: Correlation magnitudes (Pearson r) between 4 cognitive constructs and SDBOLD during rest (permuted p < 0.001, error bars represent bootstrapped 95% confidence intervals). The speed construct did not contribute to the LV as its error bars cross the zero. C: Scatterplot showing the relationship between global FA (WM integrity) and cognition–SDBOLD relationship.

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