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

Abstract

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.

Scale invariance in fNIRS as a measurement of cognitive load

“…Results comparing task and rest conditions show that, as expected, H values averaged across all channels were higher during rest than during task, and higher in the 1-back task than the 2-back task. Contrary to our hypotheses, there were no significant differences between 3-back and other N-back levels, though this is consistent with the task-activation results examined in this same dataset reported in Meidenbauer et al [29] and are likely due to participants disengaging with the task as indicated by poor task performance on the 3-back. These effects were somewhat larger for H calculated from the deoxyhemoglobin signal (HbR) than the oxyhemoglobin signal (HbO), which may be due to the tighter coupling between HbR and the BOLD response observed in fMRI relative to HbO [1].…”

“…In contrast, the 2-back vs. 3-back comparison was marginally significant for H by HbR (not with HbO), and all the other t-test results for 3-back condition (3-back vs. 1-back and 3-back vs. 2-back) were not significant. However, as observed in Meidenbauer et al (2021) which used this same dataset, performance in 3-back condition was generally low and was highly variable.…”

Shedding Light on functional Near Infrared Spectroscopy and Open Science Practices

“…Additionally, sharing materials promotes replicability and aids advanced researchers to refine or build new methods inspired by others in the fNIRS community. Sharing such tools and materials (see Mazziotti et al, 2022; Meidenbauer, Choe, Cardenas-Iniguez, Huppert, & Berman, 2021 for examples of sharing fNIRS presentation paradigms) has become easier across all fields with code sharing databases such as GitHub, and fNIRS specific code sharing databases (e.g., https://fnirs.org/resources/data-analysis/software/). In addition, our fNIRS journal, Neurophotonics , already promotes code sharing through its acceptance of tutorial manuscripts and also provides suggestion for code sharing on its website (see https://codeocean.com/signup/spie).…”

The Impact of Tenderness on Prefrontal Cortical Hemodynamics: An Objective Measurement of Wrist-ankle Acupuncture Analgesic Bracelet

“…fNIRS can be used for monitoring cortical activation in response to noxious stimuli/pain of new-born infants [2], healthy adults [3,4], and patients undergoing chronic pain [5]. The measurement function of fNIRS in cerebral cortical activity is equivalent to functional magnetic resonance imaging (fMRI), but it is superior to blood oxygen level-dependent signal in temporal resolution and superior to electroencephalography in spatial resolution [6]. fNIRS is relatively portable and also has lower equipment costs, requires short testing sessions, and safe.…”

“…Fig 6. Cerebral cortex activation map obtained by group analysis of three tenderness experiments (Unit:…”