Effects of Beta-Amyloid on Resting State Functional Connectivity Within and Between Networks Reflect Known Patterns of Regional Vulnerability

Abstract

Beta-amyloid (Aβ) deposition is one of the hallmarks of Alzheimer's disease (AD). However, it is also present in some cognitively normal elderly adults and may represent a preclinical disease state. While AD patients exhibit disrupted functional connectivity (FC) both within and between resting-state networks, studies of preclinical cases have focused primarily on the default mode network (DMN). The extent to which Aβ-related effects occur outside of the DMN and between networks remains unclear. In the present study, we examine how within- and between-network FC are related to both global and regional Aβ deposition as measured by [(11)C]PIB-PET in 92 cognitively normal older people. We found that within-network FC changes occurred in multiple networks, including the DMN. Changes of between-network FC were also apparent, suggesting that regions maintaining connections to multiple networks may be particularly susceptible to Aβ-induced alterations. Cortical regions showing altered FC clustered in parietal and temporal cortex, areas known to be susceptible to AD pathology. These results likely represent a mix of local network disruption, compensatory reorganization, and impaired control network function. They indicate the presence of Aβ-related dysfunction of neural systems in cognitively normal people well before these areas become hypometabolic with the onset of cognitive decline.

Keywords: PIB-PET; aging; beta-amyloid; functional connectivity; resting-state fMRI.

Figure 1.

Flow chart of analysis. After preprocessing, fMRI data from 15 young subjects were included in a group ICA and 30 components were generated. A dual-regression procedure was employed to generate subject-specific time courses and spatial maps of each component for 92 older subjects. The first stage produces a mean network time course for each component, and the second stage produces spatial maps in which voxel values represent the degree to which that voxel's time course is correlated with the mean network time course. A template-matching procedure was used to identify 7 networks of interest for subsequent group analysis. Changes of network connectivity were tested against both global and regional measures of Aβ deposition.

Figure 2.

Global PIB results. Networks showing significant changes in FC related to PIB Index are displayed on inflated surfaces. PIB Index was derived from the mean DVR value across an averaged group of ROIs. Statistical maps representing how strongly each voxel's time course was associated with the mean time course of a given network were regressed against PIB Index using a robust regression with the bisquare weight function. Regressors controlling for age, scanner, motion, and voxelwise gray matter were included. Results were cluster-corrected to P < 0.05 using a voxel threshold of P < 0.01. Each panel displays Aβ-related changes in FC related to a different network time course (identified by the label under each set of brains). Warm colors indicate a positive relationship between PIB Index and the degree of coactivation with the mean network time course; cool colors indicate a negative relationship. See the text for information on directionality relative to baseline. The overlay colors on the brain show the group component with the highest z-score for each voxel to demonstrate the network most strongly associated with each region.

Figure 3.

Global PIB ROI examples. Group effects may appear as positive or negative changes in FC related to PIB. However, these effects may be driven by different patterns of connectivity relative to baseline. Parameter estimates generated by dual-regression stage 2 were extracted from significant clusters and plotted against PIB Index (log). Y-axis represents the degree to which the time course of a given region (identified in plot title) is associated with the overall time course of a network (identified in y-axis label). Four examples are shown above: (A) The right inferior parietal lobe (IPL) shows a decrease of positive within-network connectivity with the mean network time course of the DMN as PIB increases. (B) An increase of between-network connectivity related to PIB that is driven by reduced anti-correlation in the right IPL with the left FPCN. (C) An increase in positive connectivity between the left frontal pole and precuneus/DMN time course, which are not strongly coactive at low levels of PIB. (D) A flip from anti-correlation between the precuneus and the mean network time course of the anterior–ventral SN at low levels of PIB that becomes positively correlated at high levels.

Figure 4.

Regional PIB results. Networks showing significant changes in FC related to voxelwise PIB are displayed on inflated surfaces. Statistical maps representing how strongly each voxel's time course was associated with the mean time course of a given network were regressed against each subject's DVR map using a robust regression with the bisquare weight function. Regressors controlling for age, scanner, motion, and voxelwise gray matter were included. Results were cluster-corrected to P < 0.05 using a voxel threshold of P < 0.01. Each panel displays Aβ-related changes in FC related to a different network time course (identified by the label under each set of brains). Warm colors indicate a positive relationship between voxelwise PIB and the degree of coactivation with the mean network time course; cool colors indicate a negative relationship. See the text for information on directionality relative to baseline. The overlay colors on the brain show the group component with the highest z-score for each voxel to demonstrate the network most strongly associated with each region.

Figure 5.

Regions of PIB-related changes in FC. Regions demonstrating a change in FC (either positive or negative) with any network are displayed on inflated brain surfaces. The overlay colors on the brain show the group component with the highest z-score for each voxel to demonstrate the network most strongly associated with each region. The majority of effects occur in medial and lateral parietal cortices and the PFC. The analyses are largely consistent, with a greater extent of changes detected in the regional PIB analysis. However, changes of connectivity in the OFC were only found in the global analysis. These alterations in FC primarily occur in regions where multiple networks converge, and the effects extend across network borders.