MovieSTAGE: Scene, Transition, and Global Encoding for Movie-fMRI ADHD Classification
Naturalistic movie-fMRI provides a shared, temporally structured probe of brain dynamics, yet predictive models commonly rely on whole-run functional connectivity (FC) or temporally generic representations that are not aligned with narrative events.
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Key points
- We introduce MovieSTAGE (Scene, Transition, and Global Encoding), a multiscale framework that combines hypergraph-structured FC-profile organization within scenes, unsigned FC-profile differences across adjacent scenes, and whole-movie FC.
- We evaluated 260 participants from the CMI-HBN Despicable Me cohort on case-control, ADHD-subtype, and three-class classification using 10 repetitions of stratified five-fold cross-validation, complete out-of-fold (OOF) predictions, and paired subject-cluster bootstrap and permutation tests.
- These controlled results support incremental predictive value from event-aligned scene and transition representations when combined with whole-movie FC in this cohort.
- Post-hoc model-derived analyses generated network-level hypotheses involving frontoparietal and default-mode systems.
Sources (1)
- [1]MovieSTAGE: Scene, Transition, and Global Encoding for Movie-fMRI ADHD ClassificationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 02:04 AM
Naturalistic movie-fMRI provides a shared, temporally structured probe of brain dynamics, yet predictive models commonly rely on whole-run functional connectivity (FC) or temporally generic representations that are not aligned with narrative events.
We introduce MovieSTAGE (Scene, Transition, and Global Encoding), a multiscale framework that combines hypergraph-structured FC-profile organization within scenes, unsigned FC-profile differences across adjacent scenes, and whole-movie FC.
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