The ability to distinguish between high and low levels of task engagement is important for detecting and preventing performance decrements during safety-critical operational tasks in the real world. Examples of such tasks include commercial aviation, monitoring for air traffic control, executing spacewalks, performing surgery, and driving. Since accident-causing errors can be made even by skilled professionals, the ability to monitor cognitive state measures for low levels of task engagement in real time could be useful for developing an “early warning system” for detecting and preventing performance errors before they occur.
This work investigated whether functional Near Infrared Spectroscopy (fNIRS), a portable brain neuroimaging technique, can be used to distinguish between high and low levels of task engagement during the performance of a selective attention task. Since hemodynamic activity cannot be monitored with fMRI outside of a laboratory, fNIRS was used to determine whether monitoring brain activity is an effective method for monitoring cognitive state. Functional NIRS can be used to quantify hemodynamic activations, and is relatively low-cost, non-confining, non-invasive, and safe for longterm monitoring.
A group of participants alternated between resting and performing a multi-source interference task (MSIT) while brain activity was recorded with fNIRS from two brain regions. The MSIT is a selective attention task in which optimal performance requires participants to suppress multiple sources of interference. Thus, it reliably and robustly activates an attentional brain network that corresponds with task performance, even in individual task blocks for one participant. Eyes-open rest activates the Default Mode brain network, which deactivates during task performance.
One of the brain regions was a key region of the “task-positive” attentional network, which is associated with relatively high levels of task engagement. The second was a key region of the “task-negative” resting network, which is associated with relatively low levels of task engagement (e.g., resting and not performing a task). Using activity in these regions as inputs to a multivariate pattern classifier, predictions above chance levels were made as to whether participants were engaged in performing the MSIT or resting. Prior findings from functional magnetic resonance imaging (fMRI) were replicated, indicating that activity in task-positive and task-negative regions is negatively correlated during task performance. Finally, data from a companion fMRI study verified assumptions about the sources of brain activity in the fNIRS experiment, and established an upper bound on expectations for classification accuracy for the task.
The findings show, for the first time, that it is possible to detect negative correlations between activity in key regions of the task-positive and task-negative networks with fNIRS. Further, they show that including detection of activity in the task-negative network is useful for distinguishing between high and low levels of task engagement. This capability might prove useful in future applications of fNIRS that are aimed at discriminating between optimal behavioral performance (where a negative correlation is expected) and internally guided thought (where co-activation and, hence, a positive correlation is expected). Thus, it could function to improve the predictive power of a fNIRS-based cognitive state monitoring system. Finally, although the findings make a novel contribution to the field, it is important to note that they build on previous work showing that frontal oxygenation is sensitive to workload, and that fNIRS can reliably detect resting state networks.
In addition to supporting NASA mission goals, the implementation of network activity detection in a mobile fNIRS system presents new opportunities in clinical outpatient contexts. Default Mode Network activity has been implicated in clinical populations, especially including those characterized by cognitive deficit due to Alzheimer’s disease or attention-deficit/ hyperactivity disorder. Other areas of potential application include sleep deprivation countermeasures, depression treatment, meditation enhancement, and stress reduction. Using fNIRS to quantify functionally connected network activity may also be promising for motor or cognitive neurorehabilitation; for example, after stroke.