Multi-dynamic Modelling Reveals Strongly Time-varying Resting fMRI Correlations

Abstract

The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variability Statement of SignificanceO_LIMAGE is multi-dynamic in that it models temporal fluctuations in FC independently from fluctuations in the mean of the activity. C_LIO_LIMAGE reveals stronger changes in FC over time than single-dynamic approaches, such as sliding window correlations. C_LIO_LIMulti-dynamic modelling provides an explanation and a solution as to why resting fMRI FC has previously looked so stable. C_LIO_LIMAGE models fMRI data as a set of reoccurring brain states, and importantly, these states do not have to be binary and mutually exclusive (e.g., multiple states can be active at one time-point). C_LIO_LIMAGE estimated time-varying FC is a better predictor of behavioural variability in the resting-state fMRI data than established methods. C_LI