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The psychological correlates of distinct neural states occurring during wakeful rest

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  • Theodoros Karapanagiotidis, University of York
  • ,
  • Diego Vidaurre
  • Andrew J. Quinn, University of Oxford
  • ,
  • Deniz Vatansever, Fudan University
  • ,
  • Giulia L. Poerio, University of Essex
  • ,
  • Adam Turnbull, University of York
  • ,
  • Nerissa Siu Ping Ho, University of York
  • ,
  • Robert Leech, King's College London
  • ,
  • Boris C. Bernhardt, McGill University
  • ,
  • Elizabeth Jefferies, University of York
  • ,
  • Daniel S. Margulies, CNRS
  • ,
  • Thomas E. Nichols, University of Oxford
  • ,
  • Mark W. Woolrich, University of Oxford
  • ,
  • Jonathan Smallwood, University of York

When unoccupied by an explicit external task, humans engage in a wide range of different types of self-generated thinking. These are often unrelated to the immediate environment and have unique psychological features. Although contemporary perspectives on ongoing thought recognise the heterogeneity of these self-generated states, we lack both a clear understanding of how to classify the specific states, and how they can be mapped empirically. In the current study, we capitalise on advances in machine learning that allow continuous neural data to be divided into a set of distinct temporally re-occurring patterns, or states. We applied this technique to a large set of resting state data in which we also acquired retrospective descriptions of the participants’ experiences during the scan. We found that two of the identified states were predictive of patterns of thinking at rest. One state highlighted a pattern of neural activity commonly seen during demanding tasks, and the time individuals spent in this state was associated with descriptions of experience focused on problem solving in the future. A second state was associated with patterns of activity that are commonly seen under less demanding conditions, and the time spent in it was linked to reports of intrusive thoughts about the past. Finally, we found that these two neural states tended to fall at either end of a neural hierarchy that is thought to reflect the brain’s response to cognitive demands. Together, these results demonstrate that approaches which take advantage of time-varying changes in neural function can play an important role in understanding the repertoire of self-generated states. Moreover, they establish that important features of self-generated ongoing experience are related to variation along a similar vein to those seen when the brain responds to cognitive task demands.

Original languageEnglish
Article number21121
JournalScientific Reports
Number of pages11
Publication statusPublished - Dec 2020

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