Can time-resolved NIRS provide the sensitivity to detect brain activity during motor imagery consistently?

Abstract

Previous functional magnetic resonance imaging (fMRI) studies have shown that a subgroup of patients diagnosed as being in a vegetative state are aware and able to communicate by performing a motor imagery task in response to commands. Due to the fMRI's cost and accessibility, there is a need for exploring different imaging modalities that can be used at the bedside. A promising technique is functional near infrared spectroscopy (fNIRS) that has been successfully applied to measure brain oxygenation in humans. Due to the limited depth sensitivity of continuous-wave NIRS, time-resolved (TR) detection has been proposed as a way of enhancing the sensitivity to the brain, since late arriving photons have a higher probability of reaching the brain. The goal of this study was to assess the feasibility and sensitivity of TR fNIRS in detecting brain activity during motor imagery. Fifteen healthy subjects were recruited in this study, and the fNIRS results were validated using fMRI. The change in the statistical moments of the distribution of times of flight (number of photons, mean time of flight and variance) were calculated for each channel to determine the presence of brain activity. The results indicate up to an 86% agreement between fMRI and TR-fNIRS and the sensitivity ranging from 64 to 93% with the highest value determined for the mean time of flight. These promising results highlight the potential of TR-fNIRS as a portable brain computer interface for patients with disorder of consciousness.

Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication

“…The study focused on a A B MI paradigm (i.e., imagine playing tennis) that has been used previously with fMRI to assess residual brain function in DOC patients and to provide rudimentary mental communication (Monti et al, 2010). Furthermore, the detection sensitivity of TR-fNIRS for this tennis imagery task was found to be comparable to fMRI in a cohort of healthy participants (Abdalmalak et al, 2017a). Based on these promising results, the motivation for this study was to evaluate the combination of TR-NIRS and MI for mental communication involving multiple closed-ended questions.…”

“…Data were analyzed in MATLAB (MathWorks Inc., United States) using the following processing steps. First, <t> was calculated for every DTOF in a time series after truncating each DTOF at 10% of the ascending side and 1% of the descending side to reduce noise (Liebert et al, 2004).<t> was chosen since previous work has shown that it provided a good compromise between activation sensitivity and signal-to-noise ratio (Abdalmalak et al, 2017a). The change in mean time-of-flight ( <t>) relative to the initial values was calculated, and these time series were corrected for motion artifacts using an algorithm based on a moving standard deviation and spline interpolation (Scholkmann et al, 2010;Metz et al, 2015).…”

“…To improve the sensitivity of fNIRS to MI, time-resolved (TR) fNIRS has been investigated (Abdalmalak et al, 2017a(Abdalmalak et al, , 2020. TR detection involves recording the arrival times of single photons, which can be used to enhance depth sensitivity since photons that interrogate superficial tissue are detected earlier than photons that travel farther (i.e., deeper).…”

“…This can be achieved by calculating the statistical moments of the recorded distribution of arrival times since higher moments are weighted toward late-arriving light (Liebert et al, 2004;Milej et al, 2015). Previous work has shown that the first moment (i.e., the mean time-offlight, <t>) provided a good compromise between depth sensitivity and signal-to-noise for detecting MI activation from probes interrogating the SMA and premotor cortex (PMC) (Abdalmalak et al, 2017a). Using fMRI as a benchmark, the classification accuracy of TR NIRS based on <t> analysis was 93% (Abdalmalak et al, 2017a).…”

“…Previous work has shown that the first moment (i.e., the mean time-offlight, <t>) provided a good compromise between depth sensitivity and signal-to-noise for detecting MI activation from probes interrogating the SMA and premotor cortex (PMC) (Abdalmalak et al, 2017a). Using fMRI as a benchmark, the classification accuracy of TR NIRS based on <t> analysis was 93% (Abdalmalak et al, 2017a). In a follow-up study, rudimentary communication was established with a locked-in patient who was instructed to use tennis imagery as affirmation to a series of questions (Abdalmalak et al, 2017b).…”