Diminished neural network dynamics in amnestic mild cognitive impairment

Abstract

Mild cognitive impairment (MCI) is widely regarded as an intermediate stage between typical aging and dementia, with nearly 50% of patients with amnestic MCI (aMCI) converting to Alzheimer's dementia (AD) within 30 months of follow-up (Fischer et al., 2007). The growing literature using resting-state functional magnetic resonance imaging reveals both increased and decreased connectivity in individuals with MCI and connectivity loss between the anterior and posterior components of the default mode network (DMN) throughout the course of the disease progression (Hillary et al., 2015; Sheline & Raichle, 2013; Tijms et al., 2013). In this paper, we use dynamic connectivity modeling and graph theory to identify unique brain "states," or temporal patterns of connectivity across distributed networks, to distinguish individuals with aMCI from healthy older adults (HOAs). We enrolled 44 individuals diagnosed with aMCI and 33 HOAs of comparable age and education. Our results indicated that individuals with aMCI spent significantly more time in one state in particular, whereas neural network analysis in the HOA sample revealed approximately equivalent representation across four distinct states. Among individuals with aMCI, spending a higher proportion of time in the dominant state relative to a state where participants exhibited high cost (a measure combining connectivity and distance), predicted better language performance and less perseveration. This is the first report to examine neural network dynamics in individuals with aMCI.

Keywords: Dynamic connectivity; Graph theory; Memory; fMRI; mild cognitive impairment.

Fig. I

This figure illustrates the output of the ICA, which resulted in 46 usable components. These components were sorted into the six networks shown in this figure. The bar to the right of each network illustrates different component numbers, which can also be examined in Figures II and III. “AUD” = Auditory Network, “CC” = Cognitive Control Network, “BG” = Basal Ganglia Network, “VIS” = Visual Network, “MOT” = Motor Network, “DMN” = Default Mode Network

Fig. II

This figure demonstrates the connectivity profile of each state, including a visualization of all components used. Component numbers are identical to those used in Figure I and Figure III. “AUD” = Auditory Network, “CC” = Cognitive Control Network, “BG” = Basal Ganglia Network, “VIS” = Visual Network, “MOT” = Motor Network, “DMN” = Default Mode Network

Fig. III

This figure depicts the correlation matrix of positive and negative connections for each state. The numbered components on the bottom of the bottom two squares are the component numbers, which correspond to those used in Figure I and Figure II. “AUD” = Auditory Network, “CC” = Cognitive Control Network, “BG” = Basal Ganglia Network, “VIS” = Visual Network, “MOT” = Motor Network, “DMN” = Default Mode Network

Fig IV:

This figure illustrates the proportion of time spent in the most dominant state, for each k-means cluster value, for each of the two groups.

Fig. V

Examining only components in the DMN and CC network, this figure shows average cost of posterior components.