Fig. 3: Arousal fluctuation modulated functional gradient dynamics in marmoset and human.
From: Multimodal analysis demonstrating the shaping of functional gradients in the marmoset brain
a Pipeline for estimating arousal relevant dynamic function gradients. For each scan, we obtained dynamic connectivity matrices using the dynamic conditional correlation strategy35. After binned by the arousal index, connectivity matrices were averaged and projected into dynamic function gradients, respectively. b Inverted U-shape relationship between arousal level and explained variance across gradients. Details were shown in Supplementary Fig. 14. c Arousal relevant flow of each network in gradient space. Arrow reflected the direction of the shift along with arousal dynamics for both ION (upper panel) and NIH (lower panel) datasets. Statistical evaluation of these regional dynamics was shown Supplementary Fig. 15. d Inverted U-shape relationship between the arousal index and structure-function gradient similarities. C.C., Pearson’s correlation coefficients. e, f As in (b, c) but for human functional gradients. The gradient values were parceled based on the Yeo et al.’s seven networks2. Source data are provided as a Source Data file.
