Mapping the time-varying functional brain networks in response to naturalistic movie stimuli

Abstract

One of human brain's remarkable traits lies in its capacity to dynamically coordinate the activities of multiple brain regions or networks, adapting to an externally changing environment. Studying the dynamic functional brain networks (DFNs) and their role in perception, assessment, and action can significantly advance our comprehension of how the brain responds to patterns of sensory input. Movies provide a valuable tool for studying DFNs, as they offer a naturalistic paradigm that can evoke complex cognitive and emotional experiences through rich multimodal and dynamic stimuli. However, most previous research on DFNs have predominantly concentrated on the resting-state paradigm, investigating the topological structure of temporal dynamic brain networks generated via chosen templates. The dynamic spatial configurations of the functional networks elicited by naturalistic stimuli demand further exploration. In this study, we employed an unsupervised dictionary learning and sparse coding method combing with a sliding window strategy to map and quantify the dynamic spatial patterns of functional brain networks (FBNs) present in naturalistic functional magnetic resonance imaging (NfMRI) data, and further evaluated whether the temporal dynamics of distinct FBNs are aligned to the sensory, cognitive, and affective processes involved in the subjective perception of the movie. The results revealed that movie viewing can evoke complex FBNs, and these FBNs were time-varying with the movie storylines and were correlated with the movie annotations and the subjective ratings of viewing experience. The reliability of DFNs was also validated by assessing the Intra-class coefficient (ICC) among two scanning sessions under the same naturalistic paradigm with a three-month interval. Our findings offer novel insight into comprehending the dynamic properties of FBNs in response to naturalistic stimuli, which could potentially deepen our understanding of the neural mechanisms underlying the brain's dynamic changes during the processing of visual and auditory stimuli.

Keywords: dictionary learning and sparse coding; dynamic functional brain network; fMRI; naturalistic stimuli; time-varying.

Figure 1

Group-wise static functional brain networks (FBNs) of session A, including (A) visual network, (B) auditory network, (C) auditory and cerebellar network (AC), (D) audiovisual and sensorimotor network (VAS), (E) partial default mode network (DMN), salience, and cerebellar network (pDSC), (F) DMN and cerebellar network (DC), (G) dorsal attention network (DAN).

Figure 2

Dynamic evolution of the number of activated voxels (NAV) of seven brain function networks (FBNs) (session A). The corresponding FBNs of the first window among every 50 windows are displayed at the bottom.

Figure 3

Dynamic evolution of the intensity of activated voxels (IAV) of seven FBNs (session A). The corresponding FBNs of the first window among every 50 windows are displayed at the bottom.

Figure 4

Dynamic inter-subject correlation (ISC) (session A): group-wise and individual dynamic ISC. The thick blue line represents the group-wise dynamic ISC, and the thin colorful lines depict the dynamic ISC of 16 different individuals.

Figure 5

Correlation between the movie ratings and difference of individual dynamic ISC. (A) The inter-subject distances of the movie ratings were mapped onto a two-dimensional plane, with movie ratings shown in the inset and coded accordingly. The arrangement of movie ratings from left to right signifies participants’ engagement with the movie, as those who were more engaged reported higher levels of enjoyment, emotion, and audio quality and lower levels of boredom. The top-to-bottom scale reflects the participants’ ratings of evoked emotions. (B) Inter-subject distance matrix of the movie ratings. (C) Distance matrices of dynamic ISC for AC, pDSC, and DC networks. (D) The correlation between the movie rating distances and the inter-subject distances of dynamic ISC.

Figure 6

Intra-group correlation coefficient (ICC) for seven representative FBNs. (A) Dynamic ICC. The red line represents the group-wise dynamic ICC. (B) Static ICC.

Figure 7

The overview of the proposed framework. (A) Using group-wise dictionary learning and sparse coding to represent static FBNs. (B) Sliding-window method applying for the representation of dynamic individual FBNs. p, the subject number; n, the number of voxels in the brain; K, the number of atoms in the dictionary; t, the time points; L, the length of each window; w, the total number of windows generated by the sliding-window method.