ABSTRACT
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.
HIGHLIGHTS
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 model
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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
- Additional analyses to test whether simulated events exhibit a similar pattern of activity to that seen in BOLD events in Esfahlani et al 2020 (Fig. S4) - Additional analyses to further examine how temporal spacing relates to static FC estimations (Fig. S7) - Additional analyses to test the effects of low pass filtering on events (Fig. S2) - Additional analyses to investigate why the 100th bin is less similar to static FC than the 90th bin (Fig. S5) - Moved toy model from the supplement to main text (see Fig. 4) - Text revisions to discuss the relationship between events and movie watching and other tasks, to clarify why random points are closer to high cofluctuation points than low cofluctuation points, to discuss considerations in sampling points for the best estimation of static FC, and to place events in the context of previous methods like CAPS and PPA. - Expanded references and correction of typographical errors.