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. 2020 May 25:14:26.
doi: 10.3389/fninf.2020.00026. eCollection 2020.

NICA: A Novel Toolbox for Near-Infrared Spectroscopy Calculations and Analyses

Philipp Raggam  1 , Günther Bauernfeind  2 , Selina C Wriessnegger  1   3
Affiliations

NICA: A Novel Toolbox for Near-Infrared Spectroscopy Calculations and Analyses

Philipp Raggam et al. Front Neuroinform. .

Abstract

Functional near-infrared spectroscopy (fNIRS) measures the functional activity of the cerebral cortex. The concentration changes of oxygenated (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) can be detected and associated with activation of the cortex in the investigated area (neurovascular coupling). Recorded signals of hemodynamic responses may contain influences from physiological signals (systemic influences, physiological artifacts) which do not originate from the cerebral cortex activity. The physiological artifacts contain the blood pressure (BP), respiratory patterns, and the pulsation of the heart. In order to perform a comprehensive analysis of recorded fNIRS data, a proper correction of these physiological artifacts is necessary. This article introduces NICA - a novel toolbox for near-infrared spectroscopy calculations and analyses based on MATLAB. With NICA it is possible to process and visualize fNIRS data, including different signal processing methods for physiological artifact correction. The artifact correction methods used in this toolbox are common average reference (CAR), independent component analysis (ICA), and transfer function (TF) models. A practical example provides results from a study, where NICA was used for analyzing the measurement data, in order to demonstrate the signal processing steps and the physiological artifact correction. The toolbox was developed for fNIRS data recorded with the NIRScout 1624 measurement device and the corresponding recording software NIRStar.

Keywords: MATLAB toolbox; NIRScout; functional near-infrared spectroscopy; graphical user interface; hemodynamic responses; physiological artifact correction; signal processing.

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Figures

FIGURE 1
FIGURE 1
The surface of the GUI is divided into different panels: Information about the measurement data (A); settings for the pre-processing of the measurement data, correction methods for physiological artifacts, excluding noisy channels or trials, and options for visualizing the results of the analysis (B); information about the current analysis status (C); a push button to start the analysis (D); information about the individual analysis and signal processing steps (E); and menu bar items allow the user to load the measurement data and save the analysis settings (F).
FIGURE 2
FIGURE 2
Different probe sets, which can be selected with the corresponding number and positions of the channels.
FIGURE 3
FIGURE 3
Error handling: The source of the error is displayed at the panel Analysis Status; more detailed information is displayed at the panel Output Text and at the popup window Error Dialog.
FIGURE 4
FIGURE 4
Physiological signals: BP waves with systolic (red line, BPsys) and diastolic (blue line, BPdia) values (A); movement of the thorax due to respiration (B); HR values (C); and peaks of the ECG signal (D).
FIGURE 5
FIGURE 5
Power of the frequency spectrum of oxy-Hb (red line) and deoxy-Hb (blue line) signals, before (thin line, raw) and after (thick line, clean) artifact correction, averaged over all channels. The corrected frequency bands are highlighted in green.
FIGURE 6
FIGURE 6
Concentration change of oxy-Hb (red) and deoxy-Hb (blue) at channel 30 (motor cortex, left hemisphere), averaged over all trials. The vertical lines at t = 0 and t = 12 indicate the start and the end of the right-hand ME task.
FIGURE 7
FIGURE 7
2-D topographical maps, representing the changes of oxy-Hb (top row) and deoxy-Hb (bottom row) over the scalp, divided into four time-ranges (t1 = −5 to 0 s, t2 = 0–7 s, t3 = 7–14 s, t4 = 14–20 s). The colors orange/red indicate an increase of the concentration, green/blue a decrease.
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