Replicability of time-varying connectivity patterns of the human brain at rest
Start Date
8-11-2017 1:30 PM
End Date
8-11-2017 5:30 PM
Abstract
In this study, we investigate the replicability of the human brain's inter-regional coupling dynamics during rest by evaluating two different dynamic (i.e. time-varying) functional network connectivity (dFNC) analysis frameworks using 7500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent inter-regional functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several statistical summary measures across multiple large, independent age-matched samples. Both approaches showed high consistency in the statistical summary measures for a range of model orders. Futhermore, application of the methods to conservatively configured surrogate datasets revealed that the studied measures were indeed statistically significant. This extensive testing of replicability of similarity statistics suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods.
Replicability of time-varying connectivity patterns of the human brain at rest
In this study, we investigate the replicability of the human brain's inter-regional coupling dynamics during rest by evaluating two different dynamic (i.e. time-varying) functional network connectivity (dFNC) analysis frameworks using 7500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent inter-regional functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several statistical summary measures across multiple large, independent age-matched samples. Both approaches showed high consistency in the statistical summary measures for a range of model orders. Futhermore, application of the methods to conservatively configured surrogate datasets revealed that the studied measures were indeed statistically significant. This extensive testing of replicability of similarity statistics suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods.
