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Dynamic reconfiguration of the default mode network during narrative comprehension

Author

Listed:
  • Erez Simony

    (Princeton University
    Princeton Neuroscience Institute, Princeton University)

  • Christopher J Honey

    (University of Toronto)

  • Janice Chen

    (Princeton University
    Princeton Neuroscience Institute, Princeton University)

  • Olga Lositsky

    (Princeton Neuroscience Institute, Princeton University)

  • Yaara Yeshurun

    (Princeton University)

  • Ami Wiesel

    (School of Computer Science and Engineering, Faculty of Science, The Hebrew University of Jerusalem)

  • Uri Hasson

    (Princeton University
    Princeton Neuroscience Institute, Princeton University)

Abstract

Does the default mode network (DMN) reconfigure to encode information about the changing environment? This question has proven difficult, because patterns of functional connectivity reflect a mixture of stimulus-induced neural processes, intrinsic neural processes and non-neuronal noise. Here we introduce inter-subject functional correlation (ISFC), which isolates stimulus-dependent inter-regional correlations between brains exposed to the same stimulus. During fMRI, we had subjects listen to a real-life auditory narrative and to temporally scrambled versions of the narrative. We used ISFC to isolate correlation patterns within the DMN that were locked to the processing of each narrative segment and specific to its meaning within the narrative context. The momentary configurations of DMN ISFC were highly replicable across groups. Moreover, DMN coupling strength predicted memory of narrative segments. Thus, ISFC opens new avenues for linking brain network dynamics to stimulus features and behaviour.

Suggested Citation

  • Erez Simony & Christopher J Honey & Janice Chen & Olga Lositsky & Yaara Yeshurun & Ami Wiesel & Uri Hasson, 2016. "Dynamic reconfiguration of the default mode network during narrative comprehension," Nature Communications, Nature, vol. 7(1), pages 1-13, November.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms12141
    DOI: 10.1038/ncomms12141
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    Cited by:

    1. Hongmi Lee & Janice Chen, 2022. "Predicting memory from the network structure of naturalistic events," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Manoj Kumar & Cameron T Ellis & Qihong Lu & Hejia Zhang & Mihai Capotă & Theodore L Willke & Peter J Ramadge & Nicholas B Turk-Browne & Kenneth A Norman, 2020. "BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-12, January.
    3. Kean Ming Tan & Junwei Lu & Tong Zhang & Han Liu, 2021. "Estimating and inferring the maximum degree of stimulus‐locked time‐varying brain connectivity networks," Biometrics, The International Biometric Society, vol. 77(2), pages 379-390, June.

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