BOLD cofluctuation ‘events’ are predicted from static functional connectivity

• Published in: NeuroImage (Orlando, Fla.) Vol. 260; p. 119476
• Main Authors: Ladwig, Zach, Seitzman, Benjamin A., Dworetsky, Ally, Yu, Yuhua, Adeyemo, Babatunde, Smith, Derek M., Petersen, Steven E., Gratton, Caterina
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.10.2022; Elsevier Limited; Elsevier
• Subjects: Brain - diagnostic imaging; Brain mapping; Brain Mapping - methods; Cofluctuations; Datasets; Events; Functional magnetic resonance imaging; Humans; Magnetic Resonance Imaging - methods; Networks; Neural networks; Neural Pathways; Neuroimaging; Physiology; Respiration; Resting-state fMRI; RSFC; Sampling; Simulations; Resting-state fMRI; RSFC; Simulations; Networks; Events; Cofluctuations
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.119476

•Past results suggested high cofluctuation BOLD “events” drive fMRI functional connectivity, FC.•Here, events were examined in both real fMRI data and a stationary null model to test this idea.•In real data, >50% of BOLD timepoints show high modularity and similarity to time-averaged FC.•Stationary null models identified events with similar behavior to real data.•Events may not be a transient driver of static FC, but rather an expected outcome of it. Recent work identified single time points (“events”) of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low cofluctuation time points. This suggested that events might be a discrete, temporally sparse signal which drives functional connectivity (FC) over the timeseries. However, a different, not yet explored possibility is that network information differences between time points are driven by sampling variability on a constant, static, noisy signal. Using a combination of real and simulated data, we examined the relationship between cofluctuation and network structure and asked if this relationship was unique, or if it could arise from sampling variability alone. First, we show that events are not discrete – there is a gradually increasing relationship between network structure and cofluctuation; ∼50% of samples show very strong network structure. Second, using simulations we show that this relationship is predicted from sampling variability on static FC. Finally, we show that randomly selected points can capture network structure about as well as events, largely because of their temporal spacing. Together, these results suggest that, while events exhibit particularly strong representations of static FC, there is little evidence that events are unique timepoints that drive FC structure. Instead, a parsimonious explanation for the data is that events arise from a single static, but noisy, FC structure.