Unveiling complex brain dynamics during movie viewing via deep recurrent autoencoder model

• Published in: NeuroImage (Orlando, Fla.) Vol. 310; p. 121177
• Main Authors: Wang, Kexin, Song, Limei, Li, Zhaowei, Wang, Liting, He, Xiaowei, Ren, Yudan, Lv, Jinglei
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.04.2025; Elsevier Limited; Elsevier
• Subjects: Adaptability; Adult; Arousal; Autoencoder; Brain - diagnostic imaging; Brain - physiology; Brain Mapping - methods; Brain research; Cognition & reasoning; Deep learning; Dynamic large-scale brain networks; Emotions; Emotions - physiology; Energy consumption; Female; Functional magnetic resonance imaging; Humans; Information processing; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Memory; Methods; Motion Pictures; Movie narrative structure; Naturalistic fMRI; Nerve Net - diagnostic imaging; Nerve Net - physiology; Neural networks; Neuroimaging; Recurrent autoencoder model; Sliding window strategy; Visual Perception - physiology; Young Adult; Naturalistic fMRI; Recurrent autoencoder model; Movie narrative structure; Sliding window strategy; Dynamic large-scale brain networks

•Leveraging naturalistic fMRI, we characterize the dynamic, complex interactions among large-scale brain networks during the viewing of emotionally charged movie.•The dynamic functional interactions covary with the development of the film's narrative, especially among the interactions of defaul...