Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: Age, sex, and behavioral associations

Abstract

Spatially remote brain regions exhibit dynamic functional interactions across various task conditions. While time-varying functional connectivity during movie watching shows sensitivity to movie content, stationary functional connectivity remains relatively stable across videos. These findings suggest that dynamic and stationary functional interactions may represent different aspects of brain function. However, the relationship between individual differences in time-varying and stationary connectivity and behavioral phenotypes remains elusive. To address this gap, we analyzed an open-access functional MRI dataset comprising participants aged 5-22 years, who watched two cartoon movie clips. We calculated regional brain activity, time-varying connectivity, and stationary connectivity, examining associations with age, sex, and behavioral assessments. Model comparison revealed that time-varying connectivity was more sensitive to age and sex effects compared with stationary connectivity. The preferred age models exhibited quadratic log age or quadratic age effects, indicative of inverted-U shaped developmental patterns. In addition, females showed higher consistency in regional brain activity and time-varying connectivity than males. However, in terms of behavioral predictions, only stationary connectivity demonstrated the ability to predict full-scale intelligence quotient. These findings suggest that individual differences in time-varying and stationary connectivity may capture distinct aspects of behavioral phenotypes.

Keywords: Brain connectivity; Brain development; Model comparison; Movie watching; Time-varying connectivity.

Fig. 1

Summary of main data analysis steps in the current study. After preprocessing independent component analysis (ICA) was applied to the fMRI data to reduce the data dimension to 18 spatial networks and the associated time series for each participant and movie clip. Stationary and time-varying connectivity matrices were calculated for each individual and movie clip. Age and sex effects on connectivity measures were then studied using a model comparison approach. Lastly, machine learning regression was used to study behavioral associations with the different connectivity measures.

Fig. 2

A, Maps of eighteen independent components that were included in the current analysis. The maps were thresholded at z > 3 after z transformations of the original IC maps, and were shown in a winner-take-all manner when overlapping. The arrows indicate the networks of interest for movie watching, IC3, dorsal visual; IC4, temporoparietal junction; IC7 supramarginal; IC9, secondary somatosensory; and IC16, posterior cingulate. BrainNet Viewer was used for visualization (Xia et al., 2013). B, inter-individual consistency of regional activity (percent variance explained by the first principal component) for the two video clips.

Fig. 3

Akaike weights for different age models for regional activity in the 18 networks (independent components) for the video clips “The Present” (A) and “Despicable Me” (B). C shows the model evidence for the sex effects. D and F show fitted age effects in networks that exhibited Akaike weights greater than 0.6 in a specific model. E and G show the fitted age curves and individual scatter plot in two representative networks, corresponding to the top curves in D and F, respectively.

Fig. 4

A and E, group averaged stationary connectivity among 18 independent component (IC) networks for the two video clips. B and F, connectivity with winning age models with model evidence greater than 0.6. C and G, fitted curves with corresponding color representing the specific age models. D and H, connectivity with evidence of sex effects greater than 0.8. The bottom row shows the representative maps of the 18 networks.

Fig. 5

A and E, the inter-individual consistency of time-varying connectivity (percent variance explained by the first principal component) among 18 networks (independent components, ICs) for the two video clips. B and F, connectivity with winning age models with model evidence greater than 0.6. C and G, fitted curves with corresponding color representing the specific age models. D and H, connectivity with evidence of sex effects greater than 0.8. The bottom row shows the representative maps of the 18 networks.

Fig. 6

Results of full-scale intelligence quotient (FSIQ) predictions using stationary connectivity for the two video clips. Top row, each dot represents a predicted value using leave-one-out cross validation and its corresponding actual value. The red line indicates y = x. Bottom row, averaged weights of the prediction model across all the LOO models. The maps of the corresponding independent components (ICs) are shown on the right.

Fig. 7

Replication analysis of age and sex effects on stationary connectivity and time-varying connectivity using independent templates from Di et al., (2022).