Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences
<p>Channel quality. (<b>A</b>) A 10-s segment and (<b>B</b>) the frequency power spectrum in log and (<b>C</b>) normal scale are shown for a 830 nm raw time-course (<b>D</b>, red). The typical cardiac waveform in the time domain (<b>A</b>) and the typical peak near the 1 Hz cardiac frequency (<b>B</b>,<b>C</b>) can be observed. Additionally, both wavelengths are plotted (<b>D</b>). (<b>E</b>–<b>H</b>) show the same figures as (<b>A</b>–<b>D</b>) for a channel with low signal to noise ratio.</p> "> Figure 2
<p>Motion correction. An example of motion correction applied to a signal to successfully remove large spike artifacts. (<b>A</b>) Uncorrected; (<b>B</b>) motion corrected with the Homer Wavelet motion correction (MC#1) method.</p> "> Figure 3
<p>Oscillations underlying fNIRS signals. An fNIRS signal is bandpass filtered to visualize specific frequencies bands that are known to arise from different physiological parameters (VLF—Very Low Frequency < 0.01 Hz; LF—Low Frequency 0.01–0.2 Hz; Respiration 0.2–0.6 Hz; and Cardiac 0.6–2.5 Hz).</p> "> Figure 4
<p>NIRS set-up and processing steps. (<b>A</b>) Head-cap configuration, based on the 10–20 system. Sources (red) and detectors (green) are shown on the right hemisphere of the configuration. The symmetrical configuration is presented on the left hemisphere, with channel number rather than source or detector (small green and red circles). Channels are in the same color when the source is identical for these channels. The green arrow on the left hemisphere marks the left primary motor region (M1 coordinate). (<b>B</b>) Diagram of processing steps evaluated here. The GLM results later shown in this paper are based on the bolded pre-processing choice. The reasons for these choices are further described in each paragraph in the method and discussion section. Blue represents processing on raw data, yellow on optical density data, and green on concentration data.</p> "> Figure 5
<p>Channel exclusion criteria. Three channel exclusion methods are compared: visual inspection (VI), coefficient of variation (CV), and PHOEBE. (<b>A</b>) For each subject, the number of channels excluded are shown in addition to their overlap with visual inspection (‘*’, lighter blue for CV and lighter red for PHOEBE). (<b>B</b>) The comparison shows that PHOEBE is the most conservative measurement, excluding more channels than VI or CV.</p> "> Figure 6
<p>Motion correction. Comparison between two Wavelet MC algorithms, MC#1 and MC#2 (after bandpass filter) for all participants shown as boxplots (<b>left</b>). In addition, an example time course for Subject 1 channel 12 is plotted after bandpass filter and PCA (<b>right</b>). Shaded sections depict finger tapping.</p> "> Figure 7
<p>Filters. (<b>A</b>) Comparison between the standard HOMER2 Butterworth filter (BW#1) and a Butterworth filter of the same order designed in MATLAB (BW#2), as well as a zero-delay trapezoidal FFT filter (FFT). (<b>B</b>) Visualization of power spectra of the random data, (<b>C</b>) filtered with all three filters (BW#1 = blue, BW#2 = red, FFT = yellow). Enlargements of all filters in (<b>C</b>) can be seen in (<b>D</b>) for BW#1, (<b>E</b>) for BW#2 and (<b>F</b>) for FFT.</p> "> Figure 8
<p>Low-frequency (LF) de-noising methods. Visualization of three commonly used LF de-noising methods, namely short distance adaptive filtering (SSD*, 20 mm source-detector distance), principal component analysis (PCA) and delay-corrected global averaging (GloAvg). Example of changes in oxyhemoglobin from subject #1 in channel 12. Shaded sections depict finger tapping.</p> "> Figure 9
<p>Similarity of LF de-noising methods. (<b>A</b>) Correlation between time courses after LF de-noising, with example ROI channel 12. Upper triangle (red frame) depicts mean oxyhemoglobin correlation between the time courses of the different methods and lower triangle (blue frame) shows deoxyhemoglobin. (<b>B</b>) Correlation of LF de-noising methods with no de-noising with example ROI channel 12 for oxy- (red) and deoxyhemoglobin (blue).</p> "> Figure 10
<p>Evaluation of LF methods in regard to Cohen’s d and contrast to noise ratio. (<b>A</b>) Mean and standard deviation for maximum Cohen’s d value in the ROI (channels 4, 5, 10 and 12) is presented for no de-noising as well as all three LF de-noising methods. No significant difference was found. (<b>B</b>) Mean and standard deviation for maximum contrast to noise ratio (CNR) in the ROI. Red represents oxy- and blue deoxyhemoglobin measures. Each circle represents the maximum value for one subject.</p> "> Figure 11
<p>GLM analysis results. (<b>A</b>) Results were evaluated with oxy- or deoxyhemoblobin only, (<b>B</b>) as well as combined, i.e., when a channel showed a significant increase in oxy- accompanied by a significant decrease in deoxyhemoglobin. The figure shows how many times (in percentage) a channel was regarded as significant over all participants. Outliers are shown as red ‘+’ outside the boxplots 95th percentile and the channels 4, 5, 10 and 12 are shown as yellow, purple, orange and blue colored circles respectively. The larger black circle represents the percentage of times the ROI was activated over all participants, regardless of which or how many channels of the ROI was significantly activated. (<b>C</b>) The maximum <span class="html-italic">t</span>-values for each subject in the ROI when using the combined hemoglobin condition in (<b>B</b>).</p> "> Figure 12
<p>Group level visualization of each channel. Percentage of participants with significant activation in the depicted channel is projected on the simplified channel structure from <a href="#algorithms-11-00067-f003" class="html-fig">Figure 3</a>a for all hemoglobin conditions. Lower percentages of participants having a particular channel activated are blue versus higher percentages are red.</p> "> Figure 13
<p>GLM analysis with and without derivatives. (<b>A</b>) GLM analysis was originally run with derivatives for the oxy- and deoxyhemoglobin combined condition (see <a href="#algorithms-11-00067-f011" class="html-fig">Figure 11</a>b replicated here as comparison) and (<b>B</b>) is shown here run without including the first order derivatives to adjust for shape and delay differences in the hemodynamic response function.</p> ">
Abstract
:1. Introduction
1.1. Channel Exclusion Criteria
1.2. Motion Correction
1.3. Filtering and De-Noising for Removal of Systemic Physiology
1.4. Statistical Evaluation of Task-Evoked Hemodynamics
2. Methods
2.1. Participants
2.2. Task
2.3. NIRS
2.4. Pre-Processing
2.4.1. Channel Exclusion Criteria
2.4.2. Motion Correction
2.4.3. Filter
2.4.4. LF De-Noising Methods
2.5. Post-Processing
3. Results
3.1. Channel Exclusion Criteria
3.2. Motion Correction
3.3. Filter
3.4. LF De-Noising Methods
3.5. GLM Analysis
4. Discussion
4.1. Channel Exclusion
4.2. Motion Correction and Filter
4.3. LF De-Noising Methods
4.4. Hemoglobin
4.5. GLM
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
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Oxyhemoglobin | Deoxyhemoglobin | Oxy- & Deoxyhemoglobin | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Channel | None | SSD* | PCA | GloAvg | None | SSD* | PCA | GloAvg | None | SSD* | PCA | GloAvg |
Ch4 | 75 | 69 | 81 | 69 | 63 | 56 | 63 | 75 | 56 | 50 | 63 | 63 |
Ch5 | 63 | 69 | 75 | 75 | 75 | 75 | 75 | 81 | 56 | 63 | 63 | 69 |
Ch10 | 43 | 57 | 43 | 36 | 50 | 50 | 64 | 71 | 21 | 50 | 29 | 29 |
Ch12 | 69 | 69 | 75 | 69 | 69 | 69 | 69 | 63 | 44 | 56 | 56 | 56 |
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Hocke, L.M.; Oni, I.K.; Duszynski, C.C.; Corrigan, A.V.; Frederick, B.D.; Dunn, J.F. Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences. Algorithms 2018, 11, 67. https://doi.org/10.3390/a11050067
Hocke LM, Oni IK, Duszynski CC, Corrigan AV, Frederick BD, Dunn JF. Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences. Algorithms. 2018; 11(5):67. https://doi.org/10.3390/a11050067
Chicago/Turabian StyleHocke, Lia M., Ibukunoluwa K. Oni, Chris C. Duszynski, Alex V. Corrigan, Blaise DeB. Frederick, and Jeff F. Dunn. 2018. "Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences" Algorithms 11, no. 5: 67. https://doi.org/10.3390/a11050067