Improvements in task performance after practice are associated with scale-free dynamics of brain activity

Kardan, Omid; Layden, Elliot A.; Choe, Kyoung Whan; Lyu, Muxuan; Zhang, Xihan; Beilock, Sian L.; Rosenberg, Monica D.; Berman, Marc G.

doi:10.1101/2020.05.25.114959

Cited by 17 publications

(15 citation statements)

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“…Thus, one possibility for this interaction of task difficulty and prefrontal activation is that this reflects individual differences in the learning and adoption of effective strategies during practice. Interestingly, recent neuroimaging work has shown that individuals whose brains are in a more scale-free or fractal state tend to reap the benefits of practice to a greater degree than do those starting in a less scale-free state ( Kardan et al, 2020 ). Though scale-free neural dynamics have been demonstrated in fMRI and EEG ( Churchill et al, 2016 ; Kardan et al, 2020 ), whether this signal can be extracted from fNIRS data remains an open question.…”

Section: Discussionmentioning

confidence: 99%

Load-dependent relationships between frontal fNIRS activity and performance: A data-driven PLS approach

et al. 2021

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Neuroimaging research frequently demonstrates load-dependent activation in prefrontal and parietal cortex during working memory tasks such as the N-back. Most of this work has been conducted in fMRI, but functional near-infrared spectroscopy (fNIRS) is gaining traction as a less invasive and more flexible alternative to measuring cortical hemodynamics. Few fNIRS studies, however, have examined how working memory load-dependent changes in brain hemodynamics relate to performance. The current study employs a newly developed and robust statistical analysis of task-based fNIRS data in a large sample, and demonstrates the utility of data-driven, multivariate analyses to link brain activation and behavior in this modality. Seventy participants completed a standard N-back task with three N-back levels ( N = 1, 2, 3) while fNIRS data were collected from frontal and parietal cortex. Overall, participants showed reliably greater fronto-parietal activation for the 2-back versus the 1-back task, suggesting fronto-parietal fNIRS measurements are sensitive to differences in cognitive load. The results for 3-back were much less consistent, potentially due to poor behavioral performance in the 3-back task. To address this, a multivariate analysis (behavioral partial least squares, PLS) was conducted to examine the interaction between fNIRS activation and performance at each N-back level. Results of the PLS analysis demonstrated differences in the relationship between accuracy and change in the deoxyhemoglobin fNIRS signal as a function of N-back level in eight mid-frontal channels. Specifically, greater reductions in deoxyhemoglobin (i.e., more activation) were positively related to performance on the 3-back task, unrelated to accuracy in the 2-back task, and negatively associated with accuracy in the 1-back task. This pattern of results suggests that the metabolic demands correlated with neural activity required for high levels of accuracy vary as a consequence of task difficulty/cognitive load, whereby more automaticity during the 1-back task (less mid-frontal activity) predicted superior performance on this relatively easy task, and successful engagement of this mid-frontal region was required for high accuracy on a more difficult and cognitively demanding 3-back task. In summary, we show that fNIRS activity can track working memory load and can uncover significant associations between brain activity and performance, thus opening the door for this modality to be used in more wide-spread applications.

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Section: Discussionmentioning

confidence: 99%

Load-dependent relationships between frontal fNIRS activity and performance: A data-driven PLS approach

et al. 2021

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“…Thus, one possibility for this interaction of task difficulty and prefrontal activation is that this reflects individual differences in the learning and adoption of effective strategies during practice. Interestingly, recent neuroimaging work has shown that individuals whose brains are in a more scale-free or fractal state tend to reap the benefits of practice to a greater degree than do those starting in a less scale-free state (Kardan, Layden, et al, 2020). Though scale-free neural dynamics have been demonstrated in fMRI and EEG (Churchill et al, 2016;Kardan, Adam, et al, 2020), whether this signal can be extracted from fNIRS data remains an open question.…”

Section: Discussionmentioning

confidence: 99%

Load-Dependent Relationships between Frontal fNIRS Activity and Performance: A Data-Driven PLS Approach

Meidenbauer¹,

Choe

Cardenas‐Iniguez³

et al. 2020

Preprint

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Neuroimaging research frequently demonstrates load-dependent activation in the prefrontal cortex during working memory tasks such as the N-back. Most of this work has been conducted in fMRI, but functional near-infrared spectroscopy (fNIRS) is gaining traction as a less invasive and more flexible alternative to measuring cortical hemodynamics. Few fNIRS studies, however, have examined how working memory load-dependent changes in brain hemodynamics relate to performance. The current study employs a newly developed and robust statistical analysis of task-based fNIRS data in a large sample, and demonstrates the utility of data-driven, multivariate analyses to link brain activation and behavior in this modality. Seventy participants completed a standard N-back task with three N-back levels (N = 1, 2, 3) while fNIRS data were collected from frontal and parietal cortex. Overall, participants showed reliably greater fronto-parietal activation for the 2-back versus the 1-back task, suggesting fronto-parietal fNIRS measurements are sensitive to differences in cognitive load. The results for 3-back were much less consistent, potentially due to poor behavioral performance in the 3-back task. To address this, a multivariate analysis (behavioral partial least squares, PLS) was conducted to examine the interaction between fNIRS activation and performance at each N-back level. Results of the PLS analysis demonstrated differences in the relationship between accuracy and change in the deoxyhemoglobin fNIRS signal as a function of N-back level in four mid-frontal channels. Specifically, greater reductions in deoxyhemoglobin (i.e., more activation) were positively related to performance on the 3-back task, unrelated to accuracy in the 2-back task, and negatively associated with accuracy in the 1-back task. This pattern of results suggests that the metabolic demands correlated with neural activity required for high levels of accuracy vary as a consequence of task difficulty/cognitive load, whereby more automaticity during the 1-back task (less mid-frontal activity) predicted superior performance on this relatively easy task, and successful engagement of this mid-frontal region was required for high accuracy on a more difficult and cognitively demanding 3-back task. In summary, we show that fNIRS activity can track working memory load and can uncover significant associations between brain activity and performance, thus opening the door for this modality to be used in more wide-spread applications.

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“…Based on previous findings in fMRI and EEG studies (Barnes, Bullmore, & Suckling, 2009;Churchill et al, 2016;He, 2011;Kardan et al, 2020aKardan et al, , 2020b, we hypothesize that increasing levels of cognitive load will be associated with suppression of H, indicating decreased scale invariance as measured by fNIRS signals. To test this in the current study, we examined H in an N-back working memory task while fNIRS data were recorded.…”

Section: Study Design and Hypothesismentioning

confidence: 86%

Scale Invariance in fNIRS as a Measurement of Cognitive Load

Zhuang

Meidenbauer

Kardan

et al. 2021

Preprint

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Cognitive load is an important psychological construct, closely related to performance and fatigue in daily cognitive tasks. 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 is the first to test whether scale invariance (as measured by the Hurst exponent, H) can be used with functional near-infrared spectroscopy (fNIRS) to quantify cognitive load. 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 Hurst 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 Hurst derived from HbR than from HbO. 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.

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Improvements in task performance after practice are associated with scale-free dynamics of brain activity

Cited by 17 publications

References 100 publications

Load-dependent relationships between frontal fNIRS activity and performance: A data-driven PLS approach

Load-dependent relationships between frontal fNIRS activity and performance: A data-driven PLS approach

Load-Dependent Relationships between Frontal fNIRS Activity and Performance: A Data-Driven PLS Approach

Scale Invariance in fNIRS as a Measurement of Cognitive Load

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