Abstract
How spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods to Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.
Highlights
The emergence of large-scale distributed networks in spontaneous brain activity as measured by functional magnetic resonance imaging is a widely-studied phenomenon (Biswal et al, 1995; Fox and Raichle, 2007)
In resting state functional magnetic resonance imaging (fMRI), the quantification of time-varying functional connectivity (FC) has elicited considerable interest and controversy: that is, to what extent can we measure and interpret within-session changes in the patterns of FC between areas? Whereas many studies rely on the average magnitude of activation that is evoked by a task or stimulus, FC is a second-order statistic and is harder to estimate accurately
It is unclear whether FC can reflect changing patterns of communication between distant neuronal populations, and be meaningful for investigating cognition
Summary
The emergence of large-scale distributed networks in spontaneous brain activity as measured by functional magnetic resonance imaging (fMRI) is a widely-studied phenomenon (Biswal et al, 1995; Fox and Raichle, 2007) These networks have been consistently identified using cross-regional temporal correlations – referred to as functional connectivity (FC) (Damoiseaux et al, 2006; Smith et al, 2013; Hipp and Siegel, 2015). FC is estimated by averaging over several minutes of data (e.g. across a scanning session, for each pair of regions) to provide a stable representation of the functional network architecture for an individual (Finn et al, 2015) This time-averaged FC has previously been associated with mental performance (Hampson et al, 2006; Hasson et al, 2009) and, more generally, to widespread behavioural phenotypes (Smith et al, 2015). This would provide evidence that time-varying FC from fMRI can reflect momentary neuronal communication fluctuating around a stable functional architecture, and might be related to dynamic elements of cognition such as attention and thinking (Smallwood and Schooler, 2015; Kucyi, 2017)
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