State-related neural influences on fMRI connectivity estimation

Abstract

The spatiotemporal structure of functional magnetic resonance imaging (fMRI) signals has provided a valuable window into the network underpinnings of human brain function and dysfunction. Although some cross-regional temporal correlation patterns (functional connectivity; FC) exhibit a high degree of stability across individuals and species, there is growing acknowledgment that measures of FC can exhibit marked changes over a range of temporal scales. Further, FC can co-vary with experimental task demands and ongoing neural processes linked to arousal, consciousness and perception, cognitive and affective state, and brain-body interactions. The increased recognition that such interrelated neural processes modulate FC measurements has raised both challenges and new opportunities in using FC to investigate brain function. Here, we review recent advances in the quantification of neural effects that shape fMRI FC and discuss the broad implications of these findings in the design and analysis of fMRI studies. We also discuss how a more complete understanding of the neural factors that shape FC measurements can resolve apparent inconsistencies in the literature and lead to more interpretable conclusions from fMRI studies.

Fig. 1.

Arousal states are accompanied by changes in fMRI signals and FC. (a) fMRI signals exhibit changes in fluctuation amplitude and FC during the transition from resting wakefulness to light sleep. Here, independent component analysis was used to derive a set of functional networks and their corresponding time courses. The lower panel shows the time series corresponding to a network encompassing auditory cortex, where increasing fluctuation amplitude is observed during the extended period of eyes-closed rest. The upper panel depicts changes in the auditory component across consecutive 320 s epochs. Adapted from Fukunaga et al. (2006). (b) Dynamic patterns of FC linked with drowsiness can be identified. Here, k-means clustering was applied to a series of sliding-window connectivity matrices, resulting in five clusters (“states”). State 4 (shown here) was one state that showed a linear trend in its expression over time, potentially reflecting changes in arousal. Adapted from Allen et al. (2018). (c) Global peaks of the fMRI signal are characterized by widespread cortical activity and opposing signal changes in subcortical (basal forebrain, thalamus, and midbrain) regions, suggesting their link with arousal fluctuation. The map was derived by averaging time-frames corresponding to peaks in the fMRI global signal. Adapted from Liu et al. (2018b).

Fig. 2.

Cognitive states shape fMRI functional connectivity. (a) Dynamic interactions between the posterior cingulate cortex (PCC) and the medial temporal lobe (MTL) subsystem of the DMN were found to be correlated with the degree of within-run mind-wandering. Adapted from Kucyi (2018). (b) Four different cognitive states can be detected from windowed FC using unsupervised learning, and (c) can be visualized in lower-dimensional space using 3D Laplacian Embedding. (d) Similar cognitive states demonstrate similar FC patterns in this low-dimensional (3D) space. Adapted from Gonzalez-Castillo et al. (2019).