Abstract
.Monitoring speech tasks with functional near-infrared spectroscopy (fNIRS) enables investigation of speech production mechanisms and informs treatment strategies for speech-related disorders such as stuttering. Unfortunately, due to movement of the temporalis muscle, speech production can induce relative movement between probe optodes and skin. These movements generate motion artifacts during speech tasks. In practice, spurious hemodynamic responses in functional activation signals arise from lack of information about the consequences of speech-related motion artifacts, as well as from lack of standardized processing procedures for fNIRS signals during speech tasks. To this end, we characterize the effects of speech production on fNIRS signals, and we introduce a systematic analysis to ameliorate motion artifacts. The study measured 50 healthy subjects performing jaw movement (JM) tasks and found that JM produces two different patterns of motion artifacts in fNIRS. To remove these unwanted contributions, we validate a hybrid motion-correction algorithm based sequentially on spline interpolation and then wavelet filtering. We compared performance of the hybrid algorithm with standard algorithms based on spline interpolation only and wavelet decomposition only. The hybrid algorithm corrected 94% of the artifacts produced by JM, and it did not lead to spurious responses in the data. We also validated the hybrid algorithm during a reading task performed under two different conditions: reading aloud and reading silently. For both conditions, we observed significant cortical activation in brain regions related to reading. Moreover, when comparing the two conditions, good agreement of spatial and temporal activation patterns was found only when data were analyzed using the hybrid approach. Overall, the study demonstrates a standardized processing scheme for fNIRS data during speech protocols. The scheme decreases spurious responses and intersubject variability due to motion artifacts.
Highlights
Functional near-infrared spectroscopy is a robust tool for measuring brain function.[1,2,3,4,5,6,7,8] It probes the brain using the differential absorption of near-infrared (NIR) light by hemoglobin
We found that movement of the jaw produces strong changes at both wavelengths that are temporally correlated with task duration
These changes are easy to be incorrectly assigned as brain activation in any functional speech task
Summary
Functional near-infrared spectroscopy (fNIRS) is a robust tool for measuring brain function.[1,2,3,4,5,6,7,8] It probes the brain using the differential absorption of near-infrared (NIR) light by hemoglobin. Hemodynamic changes in superficial layers and/or global systemic changes in the brain can affect fNIRS measurements and can produce misleading results and interpretation.[38,39,40,41,42,43,44,45] One approach to address this problem, which has been partially successful, is to add detectors close to the light sources (typically source–detector separations less than 1 cm) These source–detector pairs are predominantly sensitive to the layers above the cortex, and one can use their information to account for extracortical signal contributions.[40,46] In speech protocols, for example, it is known that partial pressure of arterial CO2 (PaCO2) varies during inner and outer speech tasks,[47,48,49] and the data in the short-separation channels can help remove systemic physiological contributions that are simultaneously present in both extracortical and cortical tissues.[46]
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