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Scale invariance in fNIRS as a measurement of cognitive load

  • Chu Zhuang
  • , Kimberly L. Meidenbauer
  • , Omid Kardan
  • , Andrew J. Stier
  • , Kyoung Whan Choe
  • , Carlos Cardenas-Iniguez
  • , Theodore J. Huppert
  • , Marc G. Berman
  • The University of Chicago

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Scale invariant neural dynamics are a relatively new but effective means of measuring changes in brain states as a result of varied cognitive load and task difficulty. This study tests whether scale invariance (as measured by the Hurst exponent, H) can be used with functional near-infrared spectroscopy (fNIRS) to quantify cognitive load, paving the way for scale-invariance to be measured in a variety of real-world settings. We analyzed H extracted from the fNIRS time series while participants completed an N-back working memory task. Consistent with what has been demonstrated in fMRI, the current results showed that scale-invariance analysis significantly differentiated between task and rest periods as calculated from both oxy- (HbO) and deoxy-hemoglobin (HbR) concentration changes. Results from both channel-averaged H and a multivariate partial least squares approach (Task PLS) demonstrated higher H during the 1-back task than the 2-back task. These results were stronger for H derived from HbR than from HbO. This suggests that scale-free brain states are a robust signature of cognitive load and not limited by the specific neuroimaging modality employed. Further, as fNIRS is relatively portable and robust to motion-related artifacts, these preliminary results shed light on the promising future of measuring cognitive load in real life settings.

Original languageEnglish
Pages (from-to)62-76
Number of pages15
JournalCortex
Volume154
DOIs
StatePublished - Sep 2022

Keywords

  • Functional near-infrared spectroscopy
  • Hurst exponent
  • N-back task
  • Partial least squares
  • Task difficulty

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