TY - JOUR
T1 - BOLD cofluctuation ‘events’ are predicted from static functional connectivity
AU - Ladwig, Zach
AU - Seitzman, Benjamin A.
AU - Dworetsky, Ally
AU - Yu, Yuhua
AU - Adeyemo, Babatunde
AU - Smith, Derek M.
AU - Petersen, Steven E.
AU - Gratton, Caterina
N1 - Publisher Copyright:
© 2022 The Author(s)
PY - 2022/10/15
Y1 - 2022/10/15
N2 - 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.
AB - 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.
KW - Cofluctuations
KW - Events
KW - Networks
KW - RSFC
KW - Resting-state fMRI
KW - Simulations
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U2 - 10.1016/j.neuroimage.2022.119476
DO - 10.1016/j.neuroimage.2022.119476
M3 - Article
C2 - 35842100
AN - SCOPUS:85134694047
SN - 1053-8119
VL - 260
JO - NeuroImage
JF - NeuroImage
M1 - 119476
ER -