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Detecting neural state transitions underlying event segmentation

View ORCID ProfileLinda Geerligs, View ORCID ProfileMarcel van Gerven, Umut Güçlü
doi: https://doi.org/10.1101/2020.04.30.069989
Linda Geerligs
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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  • For correspondence: l.geerligs@donders.ru.nl
Marcel van Gerven
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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Umut Güçlü
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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Abstract

Segmenting perceptual experience into meaningful events is a key cognitive process that helps us make sense of what is happening around us in the moment, as well as helping us recall past events. Nevertheless, little is known about the underlying neural mechanisms of the event segmentation process. Recent work has suggested that event segmentation can be linked to regional changes in neural activity patterns. Accurate methods for identifying such activity changes are important to allow further investigation of the neural basis of event segmentation and its link to the temporal processing hierarchy of the brain. In this study, we introduce a new set of elegant and simple methods to study these mechanisms. We introduce a method for identifying the boundaries between neural states in a brain area and a complementary one for identifying the number of neural states. Furthermore, we present the results of a comprehensive set of simulations and analyses of empirical fMRI data to provide guidelines for reliable estimation of neural states and show that our proposed methods outperform the current state-of-the-art in the literature. This methodological innovation will allow researchers to make headway in investigating the neural basis of event segmentation and information processing during naturalistic stimulation.

Highlights

  • Boundaries between meaningful events are related to neural state transitions.

  • Neural states are temporarily stable regional brain activity patterns.

  • We introduce novel methods for data-driven detection of neural state boundaries.

  • These methods can identify the location and the number of neural state boundaries.

  • Simulations and empirical data support the reliability and validity of our methods.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/lgeerligs/State-segmentation-GSBS

  • https://lindageerligs.files.wordpress.com/2020/11/data.zip

  • https://pypi.org/project/statesegmentation/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 05, 2021.
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Detecting neural state transitions underlying event segmentation
Linda Geerligs, Marcel van Gerven, Umut Güçlü
bioRxiv 2020.04.30.069989; doi: https://doi.org/10.1101/2020.04.30.069989
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Detecting neural state transitions underlying event segmentation
Linda Geerligs, Marcel van Gerven, Umut Güçlü
bioRxiv 2020.04.30.069989; doi: https://doi.org/10.1101/2020.04.30.069989

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