The psychological correlates of distinct neural states occurring during wakeful rest

Theodoros Karapanagiotidis*, Diego Vidaurre, Andrew J. Quinn, Deniz Vatansever, Giulia L. Poerio, Adam Turnbull, Nerissa Siu Ping Ho, Robert Leech, Boris C. Bernhardt, Elizabeth Jefferies, Daniel S. Margulies, Thomas E. Nichols, Mark W. Woolrich, Jonathan Smallwood

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


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
Issue number1
Publication statusPublished - Dec 2020

Bibliographical note

Funding Information:
JS was supported by European Research Council (WANDERINGMINDS - 646927). RL received support from the Wellcome/EPSRC Centre for Medical Engineering (Ref: WT 203148/Z/16/Z) and would also like to acknowledge support from the Data to Early Diagnosis and Precision Medicine Industrial Strategy Challenge Fund, UK Research and Innovation (UKRI). The work was also part-funded by a European Research Council grant to EJ (FLEXSEM - 771863). The authors would like to thank Mladen Sormaz, Charlotte Murphy and Hao-Ting Wang for their contribution to data acquisition.

Publisher Copyright:
© 2020, The Author(s).

ASJC Scopus subject areas

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