Cognitive complaints in older adults at risk for Alzheimer's disease are associated with altered resting-state networks

Abstract

Introduction: Pathophysiological changes that accompany early clinical symptoms in prodromal Alzheimer's disease (AD) may have a disruptive influence on brain networks. We investigated resting-state functional magnetic resonance imaging (rsfMRI), combined with brain connectomics, to assess changes in whole-brain functional connectivity (FC) in relation to neurocognitive variables.

Methods: Participants included 58 older adults who underwent rsfMRI. Individual FC matrices were computed based on a 278-region parcellation. FastICA decomposition was performed on a matrix combining all subjects' FC. Each FC pattern was then used as a response in a multilinear regression model including neurocognitive variables associated with AD (cognitive complaint index [CCI] scores from self and informant, an episodic memory score, and an executive function score).

Results: Three connectivity independent component analysis (connICA) components (RSN, VIS, and FP-DMN FC patterns) associated with neurocognitive variables were identified based on prespecified criteria. RSN-pattern, characterized by increased FC within all resting-state networks, was negatively associated with self CCI. VIS-pattern, characterized by an increase in visual resting-state network, was negatively associated with CCI self or informant scores. FP-DMN-pattern, characterized by an increased interaction of frontoparietal and default mode networks (DMN), was positively associated with verbal episodic memory.

Discussion: Specific patterns of FC were differently associated with neurocognitive variables thought to change early in the course of AD. An integrative connectomics approach relating cognition to changes in FC may help identify preclinical and early prodromal stages of AD and help elucidate the complex relationship between subjective and objective indices of cognitive decline and differences in brain functional organization.

Keywords: Alzheimer's disease; Connectome; Functional connectivity; MRI; Memory; Mild cognitive impairment; Subjective cognitive decline.

Fig. S1

Schematic flow of how brain regions are organized into functional connectivity matrices and into canonical RSNs. (A) Brain regions are parcellated into 278 brain regions using a functional parcellation by Shen et al., 2013. (B) Brain regions are further organized into canonical RSNs according to Yeo et al., 2011. (C) Group average whole-brain functional connectivity (colorbar shown on the right, Pearson correlation coefficient) based on aforementioned gray-matter parcellation. Brain regions were ordered according to a RSN layout (as denoted by different colors on left and top). (D) Layout of resting-state networks within the functional connectivity matrix color coordinated by specific resting-state networks.

Fig. S2

Functional connectivity pattern selection criteria. The six robust patterns generated by connICA are illustrated by matrices of the loading values averaged within each block either within the described RSNs (diagonal) or between RSN pairs (“interaction”; off-diagonal). Green asterisk denotes significant blocks (only the upper triangle of these symmetric matrices is denoted). Only three FC patterns shown in the top row had significant blocks and were considered in subsequent analyses. See text for details.

Fig. 1

Connectivity independent component analysis (connICA) methodology. Individual functional connectivity (FC) matrices are concatenated into a group matrix where each row corresponds to one subject and columns are the functional connectivity entries in the FC matrix. FastICA extracts components (i.e., FC patterns) associated to the cohort and their relative weights across subjects. Color bars indicate positive (red) and negative (blue) values; Pearson correlation coefficient values for individual FC matrices (left side of figure) and unit-less connectivity weights for the FC patterns (right side of figure).

Fig. 2

Robust FC patterns and individual weights as obtained by connICA. (A) Visualization of the FC patterns sorted according to Yeo et al. (2011) functional RSNs. (B) Lines represent the quantified presence of each FC pattern on each individual's functional connectome (across all runs), termed as “weights.” All 58 subjects are represented along the x axis and ordered according to subjects with high presence of the corresponding FC pattern within their functional connectome to those with low presence of the FC pattern within their functional connectome. Additionally, each colored line represents a single ICA run.

Fig. 3

Relationship of FC patterns and neurocognitive variables of interest. Visualization of the three identified FC patterns (top row). The contributions of neurocognitive variables of interest showing significant increase of the baseline R² value in the multiple regression models are presented in the bar plots (middle row). A grouped bar plot where black bars indicate the baseline (only age, gender, and education) R² value, while the hatched patterned bars indicate when an individual neurocognitive variable of interest has been added. The standard error bars were calculated across the 100 ICA runs. R² value is shown above the cognitive variable that has the greatest increase from the baseline R² value. The scatter plots (bottom row) show actual versus model-predicted subject weights with different symbols indicating group membership. The multilinear regression models include age, gender, education, and one of the neurocognitive variables of interest. The three columns illustrate the relationships of (A) RSN-pattern and CCI-self score; (B) VIS-pattern and CCImax score, and (C) FP-DMN pattern and CVLT score. These relationships are further detailed in the Results section. Significance denoted by asterisk (P < .05).