Functional network integrity presages cognitive decline in preclinical Alzheimer disease

Abstract

Objective: To examine the utility of resting-state functional connectivity MRI (rs-fcMRI) measurements of network integrity as a predictor of future cognitive decline in preclinical Alzheimer disease (AD).

Methods: A total of 237 clinically normal older adults (aged 63-90 years, Clinical Dementia Rating 0) underwent baseline β-amyloid (Aβ) imaging with Pittsburgh compound B PET and structural and rs-fcMRI. We identified 7 networks for analysis, including 4 cognitive networks (default, salience, dorsal attention, and frontoparietal control) and 3 noncognitive networks (primary visual, extrastriate visual, motor). Using linear and curvilinear mixed models, we used baseline connectivity in these networks to predict longitudinal changes in preclinical Alzheimer cognitive composite (PACC) performance, both alone and interacting with Aβ burden. Median neuropsychological follow-up was 3 years.

Results: Baseline connectivity in the default, salience, and control networks predicted longitudinal PACC decline, unlike connectivity in the dorsal attention and all noncognitive networks. Default, salience, and control network connectivity was also synergistic with Aβ burden in predicting decline, with combined higher Aβ and lower connectivity predicting the steepest curvilinear decline in PACC performance.

Conclusions: In clinically normal older adults, lower functional connectivity predicted more rapid decline in PACC scores over time, particularly when coupled with increased Aβ burden. Among examined networks, default, salience, and control networks were the strongest predictors of rate of change in PACC scores, with the inflection point of greatest decline beyond the fourth year of follow-up. These results suggest that rs-fcMRI may be a useful predictor of early, AD-related cognitive decline in clinical research settings.

Figure 1. Model estimates of Preclinical Alzheimer Cognitive Composite (PACC) decline according to resting-state functional connectivity networks

Slopes represent PACC trajectories according to connectivity in all functional networks. Blue line indicates baseline connectivity 1 SD above the group mean (i.e., high connectivity); purple line indicates mean baseline connectivity; red line indicates baseline connectivity 1 SD below the group mean (i.e., low connectivity).

Figure 2. Model estimates of Preclinical Alzheimer Cognitive Composite (PACC) slopes

Above the horizontal line: estimates of the linear slopes of cognitive decline according to each resting-state functional connectivity network. Below the horizontal line: estimates of the quadratic slopes of cognitive decline according to the interaction between β-amyloid (Aβ) and default mode/salience/frontoparietal networks. Estimate represents the change in PACC (by SD/year) according to a unit increase in network disconnectivity (above horizontal line) or a unit increase in the Aβ × network disconnectivity interaction (below the horizontal line).

Figure 3. 3D representation of the rate of change of Preclinical Alzheimer Cognitive Composite (PACC) over time according to default network connectivity and level of β-amyloid (Aβ) burden

Each bar represents rate of PACC change according to default network connectivity (x axis) and Aβ burden (z axis), with rates of change in PACC slope (on the y axis) represented by each bar in the figure. Darker blue bars indicate greater rates of PACC decline (evidenced in cases of highest Aβ burden and lowest default connectivity), while lighter yellow bars indicate practice effects. PiB DVR = Pittsburgh compound B distribution volume ratio.

Figure 4. Nonlinear model estimates of Preclinical Alzheimer Cognitive Composite (PACC) slopes according to default, salience, or control networks by β-amyloid (Aβ) interaction

Slopes represent nonlinear trajectories according to different levels of Aβ burden and network connectivity in (A) default, (B) salience, and (C) control networks; low Aβ burden (M_{PiB DVR} = 1.08), high Aβ burden (M_{PiB DVR} = 1.40), low default connectivity (M_{Below median split across networks} = −0.77), high default connectivity (M_{Above median split across networks} = 0.78). Error bars are 95% confidence interval. PiB DVR = Pittsburgh compound B distribution volume ratio.