Over the last few decades, neuroimaging techniques have transformed our understanding of the brain and the effect of neurological conditions on brain function. More recently, light-based modalities such as functional near-infrared spectroscopy have gained popularity as tools to study brain function at the bedside. A recent application is to assess residual awareness in patients with disorders of consciousness, as some patients retain awareness albeit lacking all behavioural response to commands. Functional near-infrared spectroscopy can play a vital role in identifying these patients by assessing command-driven brain activity. The goal of this review is to summarise the studies reported on this topic, to discuss the technical and ethical challenges of working with patients with disorders of consciousness, and to outline promising future directions in this field.

Significance: Despite its advantages in terms of safety, low cost, and portability, functional nearinfrared spectroscopy applications can be challenging due to substantial signal contamination from hemodynamics in the extracerebral layer (ECL). Time-resolved near-infrared spectroscopy (tr NIRS) can improve sensitivity to brain activity but contamination from the ECL remains an issue. This study demonstrates how brain signal isolation can be further improved by applying regression analysis to tr data acquired at a single source-detector distance.Aim: To investigate if regression analysis can be applied to single-channel trNIRS data to further isolate the brain and reduce signal contamination from the ECL.Approach: Appropriate regressors for trNIRS were selected based on simulations, and performance was evaluated by applying the regression technique to oxygenation responses recording during hypercapnia and functional activation.Results: Compared to current methods of enhancing depth sensitivity for trNIRS (i.e., higher statistical moments and late gates), incorporating regression analysis using a signal sensitive to the ECL significantly improved the extraction of cerebral oxygenation signals. In addition, this study demonstrated that regression could be applied to trNIRS data from a single detector using the early arriving photons to capture hemodynamic changes in the ECL. Conclusion:Applying regression analysis to trNIRS metrics with different depth sensitivities improves the characterization of cerebral oxygenation signals.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical technique for detecting brain activity, which has been previously used during motor and motor executive tasks. There is an increasing interest in using fNIRS as a brain computer interface (BCI) for patients who lack the physical, but not the mental, ability to respond to commands. The goal of this study is to assess the feasibility of time-resolved fNIRS to detect brain activity during motor imagery. Stability tests were conducted to ensure the temporal stability of the signal, and motor imagery data were acquired on healthy subjects. The NIRS probes were placed on the scalp over the premotor cortex (PMC) and supplementary motor area (SMA), as these areas are responsible for motion planning. To confirm the fNIRS results, subjects underwent functional magnetic resonance imaging (fMRI) while performing the same task. Seven subjects have participated to date, and significant activation in the SMA and/or the PMC during motor imagery was detected by both fMRI and fNIRS in 4 of the 7 subjects. No activation was detected by either technique in the remaining three participants, which was not unexpected due to the nature of the task. The agreement between the two imaging modalities highlights the potential of fNIRS as a BCI, which could be adapted for bedside studies of patients with disorders of consciousness.