Performance comparison of systemic activity correction in functional near-infrared spectroscopy for methods with and without short distance channels

Abstract

Significance: Functional Near Infrared Spectroscopy (fNIRS) is a promising tool for neurofeedback (NFB) or brain computer interfaces (BCIs). However, fNIRS signals are typically highly contaminated by systemic activity (SA) artifacts and, if not properly corrected, NFB or BCIs run the risk of being based on noise instead of brain activity. This risk can likely be reduced by correcting for SA, in particular when short distance channels (SDCs) are available. Literature comparing correction methods with and without SDCs is still sparse, specifically comparisons considering single trials are lacking. Aim: This study aimed at comparing the performance of SA correction methods with and without SDCs. Approach: Semi-simulated and real motor task data of healthy elderly individuals were used. Correction methods without SDCs included a simple and a more advanced spatial filter. Correction methods with SDCs included a regression approach considering only the closest SDC and two GLM-based methods, one including all eight SDCs and one using only two a priori selected SDCs as regressors. All methods were compared to data uncorrected for SA and correction performance was assessed with quality measures quantifying signal improvement and spatial specificity at single trial level. Results: All correction methods were found to improve signal quality and to enhance spatial specificity as compared to the uncorrected data. Methods with SDCs usually outperformed methods without SDCs. Correction methods without SDCs tended to overcorrect the data. However, the exact pattern of results and the degree of differences observable between correction methods varied between semi-simulated and real data, and also between quality measures. Conclusions: Overall, results confirmed that both Δ[HbO] and Δ[HbR] are affected by SA and that correction methods with SDCs outperform methods without SDCs. Nonetheless, improvements in signal quality can also be achieved without SDCs and should therefore be given priority over not correcting for SA.

Lights on music cognition: a systematic and critical review of fNIRS applications and future perspectives

“…When designing an fNIRS experiment, numerous methodological issues need to be considered; some being similar to constraints in fMRI. Repeating the stimulus multiple times allows reducing the probability to embed physiological confounds in the recorded signal (Klein et al, 2022) and increases the probability to effectively isolate the neural response to a stimulus (Amaro et al, 2006). To avoid a confounding effect that may be caused by the alignment to physiological pattern frequencies (e.g., respiration, heart rate, "Meyer waves" originating from blood pressure; Kirlilna et al, 2013), it is necessary to introduce pseudo-random, jittered intervals between successively presented stimuli.…”

“…4) The extracerebral and physiological noise (e.g., cardiac pulsation, respiration frequency) should then be removed via the regression of the signal recorded with shortseparation channels (Brigadoi et al, 2015;Klein et al, 2022) and digital filtering of the signal, respectively.…”

Towards fNIRS Hyperfeedback: A Feasibility Study on Real-Time Interbrain Synchrony

“…This means that, in particular, the bandpass filter used here needs to be exchanged by a causal filter. Also, artifact control is essential and should be improved, e.g., by utilizing short-distance channels to measure physiological artifacts online and filter them out (Godet et al, 2023;Klein et al, 2022). Note that there are already different approaches for real-time preprocessing in practice (Kohl et al 2020).…”

Using preregistration as a tool for transparent fNIRS study design

“…So far, the most accurate way of removing the extracerebral part of such artifacts is by using the hardware-based solution: shortdistance channels (SDCs). SDCs are generated by placing the source and detector at a distance of < 1 cm, and ideally at ~0.8 cm for adults 72 , to capture the hemodynamic activity from extracerebral tissue only 33,100,[114][115][116][117][118] . SDC signals can be used for correcting the regular distance channels, for instance, by applying a regression-based approach.…”

“…SDC signals can be used for correcting the regular distance channels, for instance, by applying a regression-based approach. If no SDCs are available, it is recommended to apply an alternative method to reduce the extracerebral systemic activity 8,18,102,116,117 .…”