Kexin Wang , Limei Song , Zhaowei Li , Liting Wang , Xiaowei He , Yudan Ren , Jinglei Lv
{"title":"Unveiling complex brain dynamics during movie viewing via deep recurrent autoencoder model","authors":"Kexin Wang , Limei Song , Zhaowei Li , Liting Wang , Xiaowei He , Yudan Ren , Jinglei Lv","doi":"10.1016/j.neuroimage.2025.121177","DOIUrl":null,"url":null,"abstract":"<div><div>Naturalistic stimuli have become an effective tool to uncover the dynamic functional brain networks triggered by cognitive and emotional real-life experiences through multimodal and dynamic stimuli. However, current research predominantly focused on exploring dynamic functional connectivity generated via chosen templates under resting-state paradigm, with relatively limited investigation into the dynamic functional interactions among large-scale brain networks. Moreover, these studies might overlook the longer time-scale adaptability and information transmission that occur over extended periods during naturalistic stimuli. In this study, we introduced an unsupervised deep recurrent autoencoder (DRAE) model combined with a sliding window approach, effectively capturing the brain's long-term temporal dependencies, as measured in functional magnetic resonance imaging (fMRI), when subjects viewing a long-duration and emotional film. The experimental results revealed that naturalistic stimuli can induce dynamic large-scale brain networks, of which functional interactions covary with the development of the film's narrative. Furthermore, the dynamic interactions among brain networks were temporally synchronized with specific features of the movie, especially with the emotional arousal and valence. Our study provided novel insight to the underlying neural mechanisms of dynamic functional interactions among brain regions in an ecologically valid sensory experience.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"310 ","pages":"Article 121177"},"PeriodicalIF":4.7000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NeuroImage","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S105381192500179X","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROIMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Naturalistic stimuli have become an effective tool to uncover the dynamic functional brain networks triggered by cognitive and emotional real-life experiences through multimodal and dynamic stimuli. However, current research predominantly focused on exploring dynamic functional connectivity generated via chosen templates under resting-state paradigm, with relatively limited investigation into the dynamic functional interactions among large-scale brain networks. Moreover, these studies might overlook the longer time-scale adaptability and information transmission that occur over extended periods during naturalistic stimuli. In this study, we introduced an unsupervised deep recurrent autoencoder (DRAE) model combined with a sliding window approach, effectively capturing the brain's long-term temporal dependencies, as measured in functional magnetic resonance imaging (fMRI), when subjects viewing a long-duration and emotional film. The experimental results revealed that naturalistic stimuli can induce dynamic large-scale brain networks, of which functional interactions covary with the development of the film's narrative. Furthermore, the dynamic interactions among brain networks were temporally synchronized with specific features of the movie, especially with the emotional arousal and valence. Our study provided novel insight to the underlying neural mechanisms of dynamic functional interactions among brain regions in an ecologically valid sensory experience.
期刊介绍:
NeuroImage, a Journal of Brain Function provides a vehicle for communicating important advances in acquiring, analyzing, and modelling neuroimaging data and in applying these techniques to the study of structure-function and brain-behavior relationships. Though the emphasis is on the macroscopic level of human brain organization, meso-and microscopic neuroimaging across all species will be considered if informative for understanding the aforementioned relationships.