Scale invariance in fNIRS as a measurement of cognitive load. (September 2022)

• Record Type: Journal Article
• Authors: Zhuang, Chu; Meidenbauer, Kimberly L.; Kardan, Omid; Stier, Andrew J.; Choe, Kyoung Whan; Cardenas-Iniguez, Carlos; Huppert, Theodore J.; Berman, Marc G.
• Is Part Of: Cortex. Volume 154(2022)
• Page Start: 62
• Page End: 76
• Publication Date: 2022-09
• Subjects: Functional near-infrared spectroscopy -- Hurst exponent -- N-back task -- Partial least squares -- Task difficulty; Neuropsychology -- Periodicals; Nervous system -- Periodicals; Neurology -- Periodicals; Psychophysiology -- Periodicals; Behavior -- Periodicals; Neurology -- Periodicals; 612.825
• DOI: 10.1016/j.cortex.2022.05.009
• Languages: English
• ISSNs: 0010-9452

Abstract

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.