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New MovieSTAGE framework improves ADHD classification using fMRI data

Researchers have developed MovieSTAGE, a novel framework designed to improve the classification of attention deficit hyperactivity disorder (ADHD) using fMRI data collected during movie viewing. This multiscale approach integrates hypergraph-structured functional connectivity within narrative scenes, differences in connectivity across transitions between scenes, and overall movie connectivity. In evaluations using the CMI-HBN Despicable Me cohort, MovieSTAGE demonstrated superior performance in ADHD classification tasks compared to existing methods. AI

IMPACT Introduces a novel AI-driven approach for medical classification using fMRI data, potentially improving diagnostic accuracy for neurological conditions.

RANK_REASON The item is a research paper published on arXiv detailing a new methodology for ADHD classification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New MovieSTAGE framework improves ADHD classification using fMRI data

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The item is a research paper published on arXiv detailing a new methodology for ADHD classification. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Boseong Kim, Haejun Chung, Ikbeom Jang ·

    MovieSTAGE: Scene, Transition, and Global Encoding for Movie-fMRI ADHD Classification

    arXiv:2610.09306v1 Announce Type: new Abstract: 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 …