Contributors: Combinatorics, Optimization and Algorithms for Telecommunications (COATI); Inria Sophia Antipolis - Méditerranée (CRISAM); Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-COMmunications, Réseaux, systèmes Embarqués et Distribués (Laboratoire I3S - COMRED); Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S); Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S); Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA); Modélisation des résaux dynamiques cérébraux (CRONOS); Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria); Université Côte d'Azur (UniCA); Cognition, Action, et Plasticité Sensorimotrice Dijon - U1093 (CAPS); Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM); ANR-17-EURE-0004,UCA DS4H,UCA Systèmes Numériques pour l'Homme(2017)
نبذة مختصرة : Functional connectivity derived from functional Magnetic Resonance Imaging(fMRI) data has been increasingly used to study brain activity. In this study, wemodel brain dynamic functional connectivity during narrative tasks as a temporalbrain network and employ a machine learning model to classify in a supervisedsetting the modality (audio, movie), the content (airport, restaurant situations) ofnarratives, and both combined. Leveraging Shapley values, we analyze subnetworkcontributions within Yeo parcellations (7- and 17-subnetworks) to explore theirinvolvement in narrative modality and comprehension. This work represents thefirst application of this approach to functional aspects of the brain, validated byexisting literature, and provides novel insights at the whole-brain level. Our findingssuggest that schematic representations in narratives may not depend solely on pre-existing knowledge of the top-down process to guide perception and understanding,but may also emerge from a bottom-up process driven by the ventral attentionsubnetwork.
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