Resting-state Co-activation Patterns as Accurate Predictors of Alzheimer’s Disease in Aged Mice

Abstract

AbstractAlzheimer’s disease (AD), a neurodegenerative disorder marked by accumulation of extracellular amyloid-beta (Aβ) plaques leads to progressive loss of memory and cognitive function. Resting state fMRI (RS-fMRI) studies have provided links between these two observations in terms of disruption of default mode and task positive resting state networks (RSNs). Important insights underlying these disruptions were recently obtained by investigating dynamic fluctuations in RS-fMRI signals in old TG2576 mice (mouse model of amyloidosis) using a set of quasi-periodic patterns (QPP). QPPs represent repeating spatiotemporal patterns of neural activity of predefined temporal length. In this article, we used an alternative methodology of co-activation patterns (CAPs) that represent instantaneous and transient brain configurations that are likely contributors to the emergence of commonly observed resting state networks (RSNs) and QPPs. We followed a recently published approach for obtaining CAPs that divided all time frames, instead of those corresponding to supra-threshold activations of a seed region as done traditionally, to extract CAPs from RS-fMRI recordings in 10 TG2576 female mice and 8 wild type littermates at 18 months of age. Subsequently we matched the CAPs from the two groups using the Hungarian method and compared the temporal (duration, occurrence rate) and the spatial (lateralization of significantly activated voxels) properties of matched CAPs. We found robust differences in the spatial components of matched CAPs. Finally, we used supervised learning to train a classifier using either the temporal or the spatial component of CAPs to distinguish the transgenic mice from the WT. We found that while duration and occurrence rates of all CAPs performed the classification with significantly higher accuracy than the chance-level, blood oxygen level dependent (BOLD) signals of significantly activated voxels from individual CAPs turned out to be a significantly better predictive feature demonstrating a near perfect classification accuracy. Our results demonstrate resting-state co-activation patterns are a promising candidate for a diagnostic, and potentially, prognostic biomarker of Alzheimer’s disease.