fMRI Validation of fNIRS Measurements During a Naturalistic Task

Prior to participation, all subjects provide informed consent in accordance with institutional guidelines. In this case, the protocol was approved by the institutional human protection program from Meiji University (Kanagawa, Japan), Columbia University Medical Center (transferred to Yale School of Medicine for data analysis), and Long Island University, Brooklyn Campus for this study.

1. Software and Hardware Modification and Development for Functional Neuroimaging (fMRI and fNIRS)

1. Modify the game Dance Dance Revolution (DDR) by editing the details of the configuration files (.sm) using the open source clone of DDR, Stepmania, to change timing, graphics and music for the region of interest studies using fMRI prior to fNIRS imaging.
   1. In the .sm file specify the variables: background, music, offset (start music at scan time), samplestart, samplelength, bpms, and bgchanges. Specify the arrow patterns for each measure in the .sm file by defining arrows per measure as a value of “1”, “0”, or “M”. Define left, up, down, or right button presses for each measure. Use “1” for an arrow, use “0” for blank, and use “M” for a mine in the rest epochs.
2. Using the game-song “Butterfly” (originally performed by Smile.dk and available on the original Dance Dance Revolution 3^rd Mix game CD for Sony PlayStation), allow subjects to play using an alternating block design built into the game mechanics as modified in the .sm configuration file. Alternate 30 sec game times with 30 sec rest periods with background graphics indicating to the player when to play (green) and when to relax (red; Figure 1).

Figure 1: Paradigm Design. (A) DDR Graphic User Interface. Arrows at the bottom of the screen moved towards the top of the screen. These arrows indicated to subjects which button to press. When the arrows reached the top action area (gray arrows at the top of the screen), subjects responded by pressing the correct button. Play time was indicated with a green background. Rest time was indicated by a red background. During the rest time, arrows were replaced with “bomb” animations. These had no function with respect to gameplay or score, but were used to serve as a place holder during rest epochs. (B) The block design used for scanning consisted of a total of 5 min of play and rest epochs. The pre-scan was 10 sec in length, followed by alternating 30 sec play and rest blocks. Please click here to view a larger version of this figure.

3. Repeat the alternating interval five times per run to assure specificity in contrast between rest and active periods.
4. In the case of fMRI data collection, restrict movements to left and right arrow button presses in fMRI recordings using foot buttons. The overall number of button presses should remain equal for both tasks (Figure 2).
5. Prior to the scanning procedure, explain the rudiments of the game to subjects and allow subjects a few practice runs prior to imaging. Instruct subjects to press the corresponding arrow button with their foot as close to the perfect time outlined at the top of the path of the moving arrow, but to minimize head movement as much as possible.

Figure 2: Experimental setup for fMRI. (A) Subjects lay in the MRI scanner while observing the projected interactive environment using a mirror mounted to the head coil above the subject. Figure 2B. A modified foot platform consisting of two buttons allowed subjects to respond with left or right toe taps in real time during gameplay. Please click here to view a larger version of this figure.

2. fMRI Testing and Analysis

1. Obtain a structural image for each subject prior to gameplay with a 3D spoiled gradient echo sequence (SPGR) (124 slices, 256 x 256, field of view = 220 mm), with a total scan time of 10 min 38 sec.
2. Obtain functional magnetic resonance images during scanning using the following settings for echo planar (EPI) T2*-weighted gradient echo sequence: echo time = 51 msec, repetition time = 3 sec, flip angle= 83°. Acquire 27 consecutive axial slice images of the brain with the following dimensions: 192 x 192 mm field of view with a 128 x 128 grid for a total resolution of 1.56 x 1.56 mm and a z-axis resolution of 4.5 mm.
3. Instruct subjects to play game using the paradigm above but using only left and right arrows to reduce motion artifact.
4. Perform fMRI BOLD signal analyses using SPM8⁵, implemented in MATLAB 7.0.
   1. Discard the first 10 sec of the EPI series to minimize the T2* relaxation artifact, functional data are motion corrected via a least squares 6-parameter ‘rigid body’ spatial transformation.
   2. Normalize the realigned EPI scans to MNI (Montreal Neurological Institute) template which has a 2 mm³ resolution followed by spatial smoothing with Gaussian kernel of 8 mm full width at half maximum (FWHM).
   3. Perform subject-level statistical analyses using the general linear model (GLM) to create statistical parametric maps for comparing the active condition (DDR) compared against the rest condition.
   4. Perform group analysis with individual results using standard statistical parametric mapping (SPM) second-level random effects approach. Obtain region of interest based on group analysis results with a threshold of p <0.01 and cluster size threshold of 100 voxels.
   5. Define the region of interest as the conjunction between the functional cluster and the anatomical mask of superior and middle temporal gyrus, obtained from WFU PickAtlas tool^13,14

3. fNIRS Setup and Data Acquisition

1. Use a 22-channel fNIRS topography system to record data from optodes arranged into a 3 x 5 array. The inter-optode distance for each source-detector pair is 3 cm (Figure 3A, B).
   1. Orient the elastic cap containing the array of optical sensors so that it is lined up from the left prefrontal cortex to the left temporal lobe (Figure 3A, B). Assure the optode in the lowest row at the most anterior position is centered on Fpz of the international 10-10 system¹⁵. Align the inferior row of optodes in parallel with the line between anatomical landmarks Fpz and T7.
2. Tighten the optical probe array to the head of the subject and assure it is securely attached using the straps and the chin-strap. Attention should be paid to the displacement of optodes from the head surface so that source-detector pairs are tight to the head but not uncomfortable to the subject (Figure 3C).
3. Sample raw analog optode source-detector pair light data into the computer at 7.9 Hz using the computer graphic user interface.

Figure 3: Optode setup for NIRS recordings. (A) The cap consists of an elastic sheet fitted with bendable plastic coupled to and holding 3 cm spaced optode holders. Straps are fitted to the cap to allow it to be tightly fitted to the head. The cap is larger and allows for more optodes than the 3 x 5 array (shown in yellow) used in this study, but is necessary to secure it securely to the heads of subjects. (B) The optode cap and positioned over the left prefrontal to the temporal lobes. Example of the optode cap on the head of the subject providing coverage of 3 x 5 array over the left prefrontal area to the left temporal lobe. (C) Optode placement in cap showing cap secured to head with tightening straps and chinstrap. Please click here to view a larger version of this figure.

4. Test calibration and signal strength and signal to noise ratio using the system control software provided by the manufacturer prior to recording. In the case of high noise being detected, remove optodes and any interfering hair from the channel using an LED illuminated plastic rod (Figure 4).

Figure 4: Optimization of optode signals. Hair was moved from each channel using an illuminated plastic tool to displace hair from the channel center to assure optimal signal quality. Please click here to view a larger version of this figure.

5. Use a 3D digitizing pen to determine spatial values of the source and detector optode locations in each channel of the elastic cap. Use the digitizer to identify the spatial coordinates of nasion, inion, auricles and Cz of each subject immediately before data collection and game play (Figure 5). Save text files with source and detector locations to others.txt and anatomical coordinates to origin.txt files.

Figure 5: Calibration of optode position. A magnetic digitizing tool was used to determine placement of 10-20 landmarks on the head and position of the optode channels. Please click here to view a larger version of this figure.

6. Process the captured 3D coordinates using the registration option in NIRS-SPM^16,17 within MATLAB 7.0 (Figure 6). From the main menu of SPM, pick stand-alone spatial registration. On the next screen, select “With 3D Digitizer” and choose the previously saved others and origin text files using the corresponding dialog. From the software dialog, choose the “Registration (use the NFRI function)” to determine spatial representation

Figure 6: Sample output of NIRS calibration data. Digitizer data was used to determine the probability of each channel in specific regions of the brain. Channel 22 in this subject showed a probability of 0.4129 in the Middle Temporal Gyrus, and 0.47419 in the Superior Temporal Gyrus. The channel is defined by the area between emitter and detector pairs. The circle around channel 22 in the figure represents an approximation of the area contributing to the signal recorded from the optode pairs in this subject. Please click here to view a larger version of this figure.

7. When all optode channel positions are digitized and each channel provides sufficient signal strength as indicated in the GUI of the software interface provided by the manufacturer, ask subjects to stand and prepare for DDR testing (Figure 7A, B).

Figure 7: fNIRS data collection during dance game play. (A) Subjects stand to play the game using the block paradigm on a standard dance game mat while being tethered to the NIRS machine. (B) Alternative view of data collection showing raw data on background screen collected in real time from subject. Please click here to view a larger version of this figure.

4. fNIRS Data Collection

1. Prior to setting up the fNIRS recording optodes, provide subjects with a brief introduction to gameplay and allow practice for familiarity with the game as in fMRI testing.
2. For fNIRS testing, use an identical paradigm to that of fMRI testing with the addition of the up/down arrows compared to only left and right arrows used for fMRI. Ensure that the total number of arrow presses are identical between fMRI and fNIRS tasks and that only the pattern differs.
3. Use a standard 4 button floor mat button response system for gameplay during fNIRS testing (Figure 7A).
4. Once comfortable in the basics of gameplay, instruct subjects to play 30 sec game times with 30 sec rest periods as in step 1.2. Repeat this 5 min game twice with each subjects.
   1. Instruct subjects specifically to not touch their face or nose and particularly their hair or head near the optodes. Instruct subjects to minimize rotation, yawl or pitch movements of their head during game play.

5. fNIRS Data Analysis

1. Use a modified Beer–Lambert approach¹⁸ to calculate relative signals reflecting the oxygenated hemoglobin (oxy-Hb), deoxygenated hemoglobin (deoxy-Hb), and total hemoglobin (total-Hb) concentration changes as ΔoxyHb, ΔdeoxyHb, and ΔtotalHb respectively in an arbitrary unit (μM cm) using the following equations:
       ΔoxyHb = -1.4887 × Δabs₇₈₀ + 0.5970 × Δabs₈₀₅ + 1.4847 × Δabs₈₃₀
       ΔdeoxyHb = 1.8545 × Δabs₇₈₀ + (-0.2394) × Δabs₈₀₅ + (-1.0947) × Δabs₈₃₀
       ΔtotalHb = ΔoxyHb + ΔdeoxyHb;
   where Δabs indicates changes in light absorption at the corresponding wavelength.
2. Low-pass filter raw data of hemodynamic signals from individual through a 25^th order Savitzky–Golay filter and average¹⁹.
3. Apply baseline correction to the averaged data with the onset set to zero.
4. Normalize hemodynamic signal amplitude by dividing the averaged values by the standard deviation of the signal recorded 10 sec prior to the task.
5. Choose channels to be analyzed based on 3D digitizer information. Here, use a channel to utilize for analysis that has a registration probability of 80% or more in the Middle and Superior Temporal Gyrus) according to the output of the registration process.

6. Comparison of fMRI and fNIRS Signals

1. Use the results function in SPM8 to determine super-threshold voxels at T >2.6 or a corresponding P value <0.01. Determine the region of interest (ROI) using overlapping super-threshold voxels to define a cluster inside of an anatomical region.
   1. In this case, define the superior and middle temporal gyrus using the AAL atlas included in the WFU Pick Atlas. In this case, the resulting cluster has 572 2 x 2 x 2 mm voxels located in the middle temporal gyrus with a peak voxel at coordinate (-66, -24, 0) and peak T = 5.73 fNIRS.
2. Determine the channel of interest from fNIRS data using 3D digitized coordinates which are converted to MNI coordinates using NIRS-SPM in step 3.5.1 above. In this case, channel 22 in most subjects had the highest probability of activity in the ROI defined in step 6.1.
3. Determine the averaged, event-triggered response in the ROI for fMRI and corresponding channel in fNIRS for the duration of the 60 sec block (active and rest, combined).
4. For each subject, average the blood oxygen level dependent (BOLD) raw signals for the voxels within the cluster to generate fMRI event triggered average data.
5. Compare fMRI and fNIRS by scaling fMRI data to optimally match the fNIRS data using a linear regression using fNIRS = b* fMRI, where the regression method obtains the b value so that the root mean square value of fNIRS -b* fMRI is minimized.
6. Compare fNIRS and fMRI signals by correlation of the two groups.