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. 2022 Jul 12:14:951076.
doi: 10.3389/fnagi.2022.951076. eCollection 2022.

Structural and Functional Brain Connectivity Uniquely Contribute to Episodic Memory Performance in Older Adults

Affiliations

Structural and Functional Brain Connectivity Uniquely Contribute to Episodic Memory Performance in Older Adults

Kylie H Alm et al. Front Aging Neurosci. .

Abstract

In this study, we examined the independent contributions of structural and functional connectivity markers to individual differences in episodic memory performance in 107 cognitively normal older adults from the BIOCARD study. Structural connectivity, defined by the diffusion tensor imaging (DTI) measure of radial diffusivity (RD), was obtained from two medial temporal lobe white matter tracts: the fornix and hippocampal cingulum, while functional connectivity markers were derived from network-based resting state functional magnetic resonance imaging (rsfMRI) of five large-scale brain networks: the control, default, limbic, dorsal attention, and salience/ventral attention networks. Hierarchical and stepwise linear regression methods were utilized to directly compare the relative contributions of the connectivity modalities to individual variability in a composite delayed episodic memory score, while also accounting for age, sex, cerebrospinal fluid (CSF) biomarkers of amyloid and tau pathology (i.e., Aβ42/Aβ40 and p-tau181), and gray matter volumes of the entorhinal cortex and hippocampus. Results revealed that fornix RD, hippocampal cingulum RD, and salience network functional connectivity were each significant independent predictors of memory performance, while CSF markers and gray matter volumes were not. Moreover, in the stepwise model, the addition of sex, fornix RD, hippocampal cingulum RD, and salience network functional connectivity each significantly improved the overall predictive value of the model. These findings demonstrate that both DTI and rsfMRI connectivity measures uniquely contributed to the model and that the combination of structural and functional connectivity markers best accounted for individual variability in episodic memory function in cognitively normal older adults.

Keywords: diffusion tensor imaging; episodic memory; individual differences; multimodal neuroimaging; resting state functional connectivity.

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Conflict of interest statement

AM receives research support from Fujirebio Diagnostics Ltd. SM is part owner and CEO of “AnatomyWorks.” This arrangement is being managed by Johns Hopkins University in accordance with its conflict of interest policies. AB is an inventor on Johns Hopkins University intellectual property with patents pending and licensed to AgeneBio. AB’s role in the current study was in compliance with the conflict of interest policies of the Johns Hopkins University School of Medicine. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Regression coefficient betas (absolute values) from the hierarchical linear regression plotted for variables of interest color coded based on entry into the regression. Error bars are 95% confidence intervals. Demographic variables were entered in Step 1, CSF measures were entered in Step 2, gray matter volumes were entered in Step 3, DTI measures were entered in Step 4, and functional connectivity measures were entered in Step 5. Fornix RD, hippocampal cingulum RD, and salience network resting state connectivity were significant predictors of delayed episodic memory performance. *p < 0.05; **p < 0.01; ***p < 0.001.
FIGURE 2
FIGURE 2
Visualization of the fornix [restricted to the body and column due to resolution constraints], hippocampal cingulum, and salience network with corresponding partial regression plots from the stepwise linear regression with standardized residuals depicting the relationships between the structural and functional connectivity markers with delayed episodic memory performance. Shaded 95% confidence interval bands.

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