Longitudinal investigation of changes in resting-state co-activation patterns and their predictive ability in the zQ175 DN mouse model of Huntington's disease

Abstract

Huntingtons disease (HD) is a neurodegenerative disorder caused by expanded ([≥]40) glutamine-encoding CAG repeats in the huntingtin gene, which leads to dysfunction and death of predominantly striatal and cortical neurons. While the genetic profile and behavioural signs of the disease are better known, changes in the functional architecture of the brain, especially before the behavioural symptoms become apparent, are not fully and consistently characterized. In this study, we sought markers at pre, early and late manifest states of phenotypic progression in the heterozygous (HET) zQ175 delta-neo (DN) mouse model, using resting-state functional magnetic resonance imaging (RS-fMRI). This mouse model shows molecular, cellular and circuitry alterations that resemble those seen in HD in humans. Specifically, we investigated, longitudinally, changes in co-activation patterns (CAPs) that are the transient states of brain activity constituting the resting-state networks (RSNs). Most robust changes in the temporal properties of CAPs occurred at the late manifest state; the durations of two anti-correlated CAPs, characterized by simultaneous co-activation of default-mode like network (DMLN) and co-deactivation of lateral-cortical network (LCN) and vice-versa, were reduced in the zQ175 DN HET animals compared to the wild-type mice. Changes in the spatial properties, measured in terms of activation levels of different brain regions, during CAPs were found at all three states and became progressively more pronounced at the manifest states. We then assessed the cross-validated predictive power of CAP metrics to distinguish HET animals from controls. Spatial properties of CAPs performed significantly better than the chance level at all three states with 80% classification accuracy at the early and late manifest states.