Detecting neural state transitions underlying event segmentation

Neuroimage. 2021 Aug 1;236:118085. doi: 10.1016/j.neuroimage.2021.118085. Epub 2021 Apr 18.

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.

Keywords: Event segmentation; Greedy search; Hidden Markov model; Neural states; Timescales; fMRI.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Algorithms
  • Computer Simulation
  • Female
  • Functional Neuroimaging / methods*
  • Humans
  • Image Interpretation, Computer-Assisted
  • Image Processing, Computer-Assisted
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Prefrontal Cortex / diagnostic imaging
  • Prefrontal Cortex / physiology*
  • Visual Perception / physiology
  • Young Adult