Name: fmri validation of fnirs measurements during a naturalistic task
Uploaded: 2015-06-15T13:00:00
Duration: 10 min 36 s
Description: Watch this Scientific Journal Video about fMRI Validation of fNIRS Measurements During a Naturalistic Task at JoVE.com

This research was supported in part by the following funding sources: JSPS Grant-in-Aid for Scientific Research (C) 25350642 (AT), a research grant from Hayao Nakayama Foundation for Science &#38; Technology and Culture (SS &#38; YO), and a Health Games Research grant from The Robert Wood Johnson Foundation (Grant #66729) (SB &#38; JAN).

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)
<ol>
	<li>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.
	<ol>
		<li>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 &#8220;1&#8221;, &#8220;0&#8221;, or &#8220;M&#8221;. Define left, up, down, or right button presses for each measure. Use &#8220;1&#8221; for an arrow, use &#8220;0&#8221; for blank, and use &#8220;M&#8221; for a mine in the rest epochs.</li>
	</ol>
	</li>
	<li>Using the game-song &#8220;Butterfly&#8221; (originally performed by Smile.dk and available on the original Dance Dance Revolution 3rd 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).</li>
</ol>
<img alt="Figure 1" src="/files/ftp_upload/52116/52116fig1.jpg" /> 
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 &#8220;bomb&#8221; 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. <a href="https://www.jove.com/files/ftp_upload/52116/52116fig1highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="3">
	<li>Repeat the alternating interval five times per run to assure specificity in contrast between rest and active periods.</li>
	<li>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).</li>
	<li>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.</li>
</ol>
<img alt="Figure 2" src="/files/ftp_upload/52116/52116fig2.jpg" /> 
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. <a href="https://www.jove.com/files/ftp_upload/52116/52116fig2highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. fMRI Testing and Analysis
<ol>
	<li>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.</li>
	<li>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&#176;. 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.</li>
	<li>Instruct subjects to play game using the paradigm above but using only left and right arrows to reduce motion artifact.</li>
	<li>Perform fMRI BOLD signal analyses using SPM85, implemented in MATLAB 7.0.
	<ol>
		<li>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 &#8216;rigid body&#8217; spatial transformation.</li>
		<li>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).</li>
		<li>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.</li>
		<li>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 &#60;0.01 and cluster size threshold of 100 voxels.</li>
		<li>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 tool13,14</li>
	</ol>
	</li>
</ol>
3. fNIRS Setup and Data Acquisition
<ol>
	<li>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).
	<ol>
		<li>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.</li>
	</ol>
	</li>
	<li>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).</li>
	<li>Sample raw analog optode source-detector pair light data into the computer at 7.9 Hz using the computer graphic user interface.</li>
</ol>
<img alt="Figure 3" src="/files/ftp_upload/52116/52116fig3.jpg" /> 
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. <a href="https://www.jove.com/files/ftp_upload/52116/52116fig3highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="4">
	<li>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).</li>
</ol>
<img alt="Figure 4" src="/files/ftp_upload/52116/52116fig4.jpg" /> 
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. <a href="https://www.jove.com/files/ftp_upload/52116/52116fig4highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="5">
	<li>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.</li>
</ol>
<img alt="Figure 5" src="/files/ftp_upload/52116/52116fig5.jpg" /> 
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. <a href="https://www.jove.com/files/ftp_upload/52116/52116fig5highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="6">
	<li>Process the captured 3D coordinates using the registration option in NIRS-SPM16,17 within MATLAB 7.0 (Figure 6). From the main menu of SPM, pick stand-alone spatial registration. On the next screen, select &#8220;With 3D Digitizer&#8221; and choose the previously saved others and origin text files using the corresponding dialog. From the software dialog, choose the &#8220;Registration (use the NFRI function)&#8221; to determine spatial representation</li>
</ol>
<img alt="Figure 6" src="/files/ftp_upload/52116/52116fig6.jpg" /> 
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. <a href="https://www.jove.com/files/ftp_upload/52116/52116fig6highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="7">
	<li>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).</li>
</ol>
<img alt="Figure 7" src="/files/ftp_upload/52116/52116fig7.jpg" /> 
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. <a href="https://www.jove.com/files/ftp_upload/52116/52116fig7highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
4. fNIRS Data Collection
<ol>
	<li>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.</li>
	<li>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.</li>
	<li>Use a standard 4 button floor mat button response system for gameplay during fNIRS testing (Figure 7A).</li>
	<li>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.
	<ol>
		<li>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.</li>
	</ol>
	</li>
</ol>
5. fNIRS Data Analysis
<ol>
	<li>Use a modified Beer&#8211;Lambert approach18 to calculate relative signals reflecting the oxygenated hemoglobin (oxy-Hb), deoxygenated hemoglobin (deoxy-Hb), and total hemoglobin (total-Hb) concentration changes as &#916;oxyHb, &#916;deoxyHb, and &#916;totalHb respectively in an arbitrary unit (&#956;M cm) using the following equations: 
	&#160;&#160;&#160;&#160;&#916;oxyHb = -1.4887 &#215; &#916;abs780 + 0.5970 &#215; &#916;abs805 + 1.4847 &#215; &#916;abs830 
	&#160;&#160;&#160;&#160;&#916;deoxyHb = 1.8545 &#215; &#916;abs780 + (-0.2394) &#215; &#916;abs805 + (-1.0947) &#215; &#916;abs830 
	&#160;&#160;&#160;&#160;&#916;totalHb = &#916;oxyHb + &#916;deoxyHb; 
	where &#916;abs indicates changes in light absorption at the corresponding wavelength.</li>
	<li>Low-pass filter raw data of hemodynamic signals from individual through a 25th order Savitzky&#8211;Golay filter and average19.</li>
	<li>Apply baseline correction to the averaged data with the onset set to zero.</li>
	<li>Normalize hemodynamic signal amplitude by dividing the averaged values by the standard deviation of the signal recorded 10 sec prior to the task.</li>
	<li>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.</li>
</ol>
6. Comparison of fMRI and fNIRS Signals
<ol>
	<li>Use the results function in SPM8 to determine super-threshold voxels at T &#62;2.6 or a corresponding P value &#60;0.01. Determine the region of interest (ROI) using overlapping super-threshold voxels to define a cluster inside of an anatomical region.
	<ol>
		<li>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.</li>
	</ol>
	</li>
	<li>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.</li>
	<li>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).</li>
	<li>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.</li>
	<li>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.</li>
	<li>Compare fNIRS and fMRI signals by correlation of the two groups.</li>
</ol>

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R., Zeff, B. W., Berger, A. J., Culver, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain+specificity+of+diffuse+optical+imaging:+improvements+from+superficial+signal+regression+and+tomography.">Brain specificity of diffuse optical imaging: improvements from superficial signal regression and tomography.</a> Frontiers in Neuroenergetics. 2, 14 (2010).</li><li>Kirilina, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+physiological+origin+of+task-evoked+systemic+artefacts+in+functional+near+infrared+spectroscopy.">The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy.</a> Neuroimage. 61, 70-81 (2012).</li><li>Strangman, G., Franceschini, M. A., Boas, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Factors+affecting+the+accuracy+of+near-infrared+spectroscopy+concentration+calculations+for+focal+changes+in+oxygenation+parameters.">Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters.</a> NeuroImage. 18, 865-879 (2003).</li></ol>

Publication fees for this article are sponsored by Shimadzu.

fNIRS is a functional brain imaging technique that has the promise of allowing the study of the neural correlates of natural activities. Development of those techniques is an active research direction. We outline here a methodology that has been effective for recording functional brain activity in a single region of interest associated with participation in a dance video game.
The number of publications investigating motor learning paradigms with fNIRS have increased rapidly in recent years with the introduction of multi-channel fNIRS units from Hitachi and Shimadzu (as well as others) capable of recording functional activities in multiple regions of the brain concurrently20,21. We have shown previously that it is possible to determine anatomical specificity of fNIRS signals as well as to use the signals to determine how variations in timing and amplitude of the signals contribute to behavioral performance of motor tasks11. A recent special issue of the journal Neuroimage focused on current applications of NIRS technology and included a number of studies specific to motor learning22. Even with this snowballing interest in NIRS technology, few studies have focused on neural mechanisms of naturalistic behaviors validated by fMRI. While this special issue and numerous other publications23-26 clearly outline the future role of fNIRS in studying motor behaviors, the protocol described here was developed to validate a uniform methodology for comparing fMRI and fNIRS data from naturalistic behaviors.
The results indicate a high correlation between groups for fNIRS and fMRI signals in an integration area of the temporal lobe and the correlation between the signals is in-line with that shown before in concurrent fMRI/fNIRS studies3. Figure 9 indicates that fNIRS activity in the temporal lobe is not only cortical in nature with differential oxy- and deoxyHb chromofore absorbance, but also that activity in the temporal lobe is quite different from that seen in the frontal lobe does not correlate with fMRI activity in the temporal lobe. We stress a few critical aspects of the study that allow data to be compared between the two techniques. First, the calibration of the optode locations was outlined as a key strategy in confirming cortical anatomy and interpretation of the results. The modifications we made to software and hardware allowed us to synchronize our paradigm with a block design protocol optimized to yield strong cortical responses in both procedures. We also outline key strategies to assure high signal to noise ratio (hair removal from optode surface) and reduction of artifacts including motion and facial stimulation (subjects were specifically instructed not to touch their head or face).
These results and the results of previous studies investigating concurrent fNIRS/fMRI imaging procedures confirm a high correlation between the signals as theoretically predicted1-3. One limitation to the interpretation of the data presented here is that we were unable to utilize newer techniques in optimization of NIRS recording that show it is possible to increase the resolution of the individual channel data using high density recordings. This high density technique has also been used to separate superficial components from the cortical NIRS signals27 in addition to other techniques that remove artifacts from blood pressure, heart rate and other systemic variables6,7,28. It has also been shown that probe placement and chromaphore choice can be used to control for false positives29 and adaptive filtering of NIRS signals can be used effectively in the case of high signal to noise ratio. The task that we have employed here and previously using commercial NIRS systems with paradigms presented in block design10,11 has produced data with large signals and has not required further analysis techniques or hardware to show similarities between signals. However, it is possible that the data presented here could be improved further through the use of these and other techniques in NIRS signal processing.
Current methodologies in functional NIRS will not replace the need for fMRI scanning; rather, as we suggest here, the two imaging procedures (in addition to EEG and others) can be used to complement each other. In the case of a group of individuals that are contraindicated for fMRI scanning, fNIRS may prove the only viable technique to ascertain the benefits of a training program such as fall risk prevention programs for individuals with Parkinson&#8217;s disease. Further, fNIRS has a number of future directions that can also be used to add information to the anatomical detail provided through MR scanning. High density optode placement and increased optodes will yield higher temporal resolution that can be used for connectivity and principle component analysis as well as increased accuracy of BOLD signal modeling.

The goal of the methods described here was to develop a working protocol to compare fMRI (functional magnetic resonance imaging) and fNIRS (functional near-infrared spectroscopy) signals in similar multi-modal tasks. Specifically we aimed to develop a functional imaging procedure for use with individuals contraindicated for traditional fMRI scans due to tremor, dyskinesia, or multiple implanted devices. While many effective training and rehabilitation programs exist for individuals at risk for falling, there is no confirmation of the neural mechanisms underlying efficacy of these programs. Often, individuals participating in these training programs are contra-indicated for the reasons cited. Our rationale for this study was to determine the efficacy of functional near-infrared spectroscopy (fNIRS) to determine patterns of brain activity associated with a naturalistic motor task involving whole body movements. Long-term goals include development of a tool to study not only motor learning paradigms but also serve to confirm the appropriateness of fNIRS for a wider variety of tasks that are not possible to perform in a naturalistic fashion using traditional methods.
fNIRS activity has previously been shown to be highly correlated (r = 0.77-0.94) for homologous regions with fMRI BOLD (blood oxygen level dependent) signal recordings in limited studies which measured concurrent fNIRS and fMRI functional activity using classic psychological and simple finger tapping paradigms1-3. The results of these studies confirm fNIRS is valid and reliable to determine cortical activity associated with a reduced environmental task compatible with fMRI. However, fNIRS has numerous advantages over fMRI as a neuroimaging method. fNIRS, importantly, is much less sensitive to motion artifact than fMRI and allows subjects to behave as they would in a naturalistic environment as opposed to fMRI studies which restrict motor paradigms4. The temporal accuracy associated with fNIRS allows for determination of changes in neural response functions with increased granularity due to the increased sampling frequency. Finally, the cost of fNIRS is lower than fMRI scanning and allows for studies to be conducted at less cost. However, there are disadvantages of fNIRS compared to fMRI including limited depth of penetration, limited spatial resolution, and more recently it has been shown that certain physiologic phenomena such as blood pressure, heart rate, and scalp blood flow effects can introduce false positives to the cortical hemodynamic signal5-9. While a number of methodologies and hardware developments specific to NIRS, including signal processing, hardware to provide high-density optode gradients are currently being developed, it is still important to develop mixed methodologies that allow fMRI and fNIRS procedures to complement each other.
This study serves to test a method of fNIRS for determining neural mechanisms engaged during a naturalistic dance video game task. The aim of the study was to compare activity in an integration center of the brain (the superior and middle temporal gyrus) between a group of individuals imaged using fMRI to another group of subjects using a naturalistic version of the task imaged using fNIRS. Our group has previously investigated this area using similar interactive game paradigms and has shown activity in the temporal lobe responds to both oxy- and de-oxyHb chromophores in accordance with cortical activations10. We have also previously attempted to control for systemic artifacts previously by showing responses in this area are modulated in amplitude with respect to the cortical load of the task and not systemic responses related to the activity itself11. The superior and middle temporal gyrus has known activity associated with integration of multi-modal sensory stimuli and we have previously shown this area to be active in Dance Dance Revolution (DDR) game play in pilot fMRI studies in addition to our fNIRS publications10-12. Our hypothesis for the current study was that functional activity in this area as recorded using fNIRS would be significantly correlated with functional activity recorded using fMRI in a similar but limited dance game protocol.
The protocol described here includes how to modify a dance video game to be used as a neuroimaging paradigm in both fNIRS and fMRI protocols. However the overall procedure is not specific to the paradigm of video game play and could be appropriate for any number of tasks which are not possible to perform in the constraints of an fMRI protocol, including language and locomotor tasks. This protocol further outlines the procedure to use the anatomical specificity of fMRI to develop specific regions of interest (ROI) which can be further studied during real-real world tasks using fNIRS.

<table border="0" cellpadding="0" cellspacing="0" width="800">
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			<td height="17" style="height:17px;width:208px;">Equipment</td>
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			<td height="20" style="height:20px;">Shimadzu OMM-3000</td>
			<td></td>
			<td></td>
			<td>http://www.shimadzu.com/an/lifescience/imaging/nirs/nirs_top.html; The OMM-3000 used in this study has been replaced by the LABNIRS</td>
		</tr>
		<tr height="17">
			<td height="17" style="height:17px;">Polhemus Patriot 3D Digitizer</td>
			<td></td>
			<td></td>
			<td>http://polhemus.com/motion-tracking/all-trackers/patriot/</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">GE Twin-Speed 1.5T MRI scanner</td>
			<td></td>
			<td></td>
			<td>http://www3.gehealthcare.com/en/Products/Categories/Magnetic_Resonance_Imaging; The Twin-Speed 1.5T scanner has been retired.&#160; A number of new scanners are available to replicate this procedure</td>
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			<td height="17" style="height:17px;">Software</td>
			<td></td>
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		</tr>
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			<td height="17" style="height:17px;">Stepmania</td>
			<td></td>
			<td></td>
			<td>http://www.stepmania.com/</td>
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		<tr height="17">
			<td height="17" style="height:17px;">Matlab</td>
			<td></td>
			<td></td>
			<td>http://www.mathworks.com/</td>
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			<td height="17" style="height:17px;">NIRS-SPM</td>
			<td></td>
			<td></td>
			<td>http://bisp.kaist.ac.kr/NIRS-SPM.html</td>
		</tr>
		<tr height="17">
			<td height="17" style="height:17px;">WFU Pick Atlas</td>
			<td></td>
			<td></td>
			<td>http://fmri.wfubmc.edu/software/PickAtlas</td>
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	</tbody>
</table>

fmri validation of fnirs measurements during a naturalistic task

We present a method to compare brain activity recorded with near-infrared spectroscopy (fNIRS) in a dance video game task to that recorded in a reduced version of the task using fMRI (functional magnetic resonance imaging). Recently, it has been shown that fNIRS can accurately record functional brain activities equivalent to those concurrently recorded with functional magnetic resonance imaging for classic psychophysical tasks and simple finger tapping paradigms. However, an often quoted benefit of fNIRS is that the technique allows for studying neural mechanisms of complex, naturalistic behaviors that are not possible using the constrained environment of fMRI. Our goal was to extend the findings of previous studies that have shown high correlation between concurrently recorded fNIRS and fMRI signals to compare neural recordings obtained in fMRI procedures to those separately obtained in naturalistic fNIRS experiments. Specifically, we developed a modified version of the dance video game Dance Dance Revolution (DDR) to be compatible with both fMRI and fNIRS imaging procedures. In this methodology we explain the modifications to the software and hardware for compatibility with each technique as well as the scanning and calibration procedures used to obtain representative results. The results of the study show a task-related increase in oxyhemoglobin in both modalities and demonstrate that it is possible to replicate the findings of fMRI using fNIRS in a naturalistic task. This technique represents a methodology to compare fMRI imaging paradigms which utilize a reduced-world environment to fNIRS in closer approximation to naturalistic, full-body activities and behaviors. Further development of this technique may apply to neurodegenerative diseases, such as Parkinson&#8217;s disease, late states of dementia, or those with magnetic susceptibility which are contraindicated for fMRI scanning.

The results of the experiment indicate activity obtained from integration centers in the superior and middle temporal gyri using functional magnetic resonance imaging (fMRI) have a high correlation to functional near-infrared spectroscopy (fNIRS) signals form the same area in naturalistic version of the task. Figure 8 shows the normalized and averaged raw data from 16 subjects from the fMRI scanning procedure (blue trace) and the 26 subjects from the fNIRS protocol (red trace). The data were pinned at time 0 and the active phase of game interaction took place in the first 30 sec of the graph (labelled task). The vertical dashed line indicates the transition of the green to red background in the task and the start of the rest phase (indicated as rest) Both data sets show increases in responses in the active phase and decline in the rest phase with increased variability seen in the fMRI signal. Comparisons were made between the traces by first regressing the fMRI signal to minimize the root mean square difference between the two traces. The resultant correlation coefficient between the two signals was 0.78 and p value for the correlation was 0.03.
Figure 9 represents a brain rendering with superimposed channel locations from a single subject. Averaged traces from channels 1 and 22 are shown with solid lines representing oxyhemoglobin signals and dashed lines representing deoxyhemoglobin signals. Red and blue traces represent two variations on gameplay, music and non-music, respectively. Signals from channel 1 serve as a control example to compare with the region of interest. Signals in these two regions differ with respect to task-response.
<img alt="Figure 8" src="/files/ftp_upload/52116/52116fig8.jpg" /> 
Figure 8: Correlation between fNIRS and fMRI signals from Middle Temporal Gyrus. The fNIRS (red) and fMRI (blue) signals are shown for each group &#177; SEM represented by shaded edges. The regression coefficient between the two is 0.78; p = 0.03. The insert shows a rendering of the ROI determined from the activity in fMRI with peak activity at MNI coordinate (-66, -24, 0) with a cluster size = 571, peak t = 5.73. <a href="https://www.jove.com/files/ftp_upload/52116/52116fig8highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" src="/files/ftp_upload/52116/52116fig9.jpg" /> 
Figure 9: Differences in Signals in the Temporal and Frontal Lobes. Time-course of &#916;oxyHb (solid line) and &#916;deoxyHb (dotted line) responses of a single subject from two single trials of the dance game showing two conditions &#8220;with music (red)&#8221; and &#8220;no music (blue)&#8221; conditions. This figure has been modified from Ono et al.10.

Watch this Scientific Journal Video about fMRI Validation of fNIRS Measurements During a Naturalistic Task at JoVE.com

fMRI Validation of fNIRS Measurements During a Naturalistic Task

We present a method to compare functional brain activity recorded during a naturalistic task using fNIRS with activity recorded during fMRI.

We present a method to compare brain activity recorded with near-infrared spectroscopy (fNIRS) in a dance video game task to that recorded in a reduced ...

fmri-validation-of-fnirs-measurements-during-a-naturalistic-task

Research

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Behavior

Method for Simultaneous fMRI/EEG Data Collection during a Focused Attention Suggestion for Differential Thermal Sensation

In the present work, we demonstrate a method for concurrent collection of EEG/fMRI data. In our setup, EEG data are collected using a high-density 256-channel sensor net. The EEG amplifier itself is contained in a field isolation containment system (FICS), and MRI clock signals are synchronized with EEG data collection for subsequent MR artifact characterization and removal. We demonstrate this method first for resting state data collection. Thereafter, we demonstrate a protocol for EEG/fMRI data recording, while subjects listen to a tape asking them to visualize that their left hand is immersed in a cold-water bath and referred to, here, as the cold glove paradigm. Thermal differentials between each hand are measured throughout EEG/fMRI data collection using an MR compatible temperature sensor that we developed for this purpose. We collect cold glove EEG/fMRI data along with simultaneous differential hand temperature measurements both before and after hypnotic induction. Between pre and post sessions, single modality EEG data are collected during the hypnotic induction and depth assessment process. Our representative results demonstrate that significant changes in the EEG power spectrum can be measured during hypnotic induction, and that hand temperature changes during the cold glove paradigm can be detected rapidly using our MR compatible differential thermometry device.

We present a protocol for concurrent collection of EEG/fMRI data, and synchronized MR clock signal recording. We demonstrate this method using a unique paradigm whereby subjects receive &#x2018;cold glove&#x2019; instructions during scanning, and EEG/fMRI data are recorded along with hand temperature measurements both before and after hypnotic induction.

In the present work, we demonstrate a method for concurrent collection of EEG/fMRI data. In our setup, EEG data are collected using a high-density ...

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Simultaneous EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the high spatial accuracy of fMRI. The data from these two modalities can be combined in a number of ways, but all rely on the acquisition of high quality EEG and fMRI data. EEG data acquired during simultaneous fMRI are affected by several artifacts, including the gradient artefact (due to the changing magnetic field gradients required for fMRI), the pulse artefact (linked to the cardiac cycle) and movement artifacts (resulting from movements in the strong magnetic field of the scanner, and muscle activity). Post-processing methods for successfully correcting the gradient and pulse artifacts require a number of criteria to be satisfied during data acquisition. Minimizing head motion during EEG-fMRI is also imperative for limiting the generation of artifacts.
Interactions between the radio frequency (RF) pulses required for MRI and the EEG hardware may occur and can cause heating. This is only a significant risk if safety guidelines are not satisfied. Hardware design and set-up, as well as careful selection of which MR sequences are run with the EEG hardware present must therefore be considered.
The above issues highlight the importance of the choice of the experimental protocol employed when performing a simultaneous EEG-fMRI experiment. Based on previous research we describe an optimal experimental set-up. This provides high quality EEG data during simultaneous fMRI when using commercial EEG and fMRI systems, with safety risks to the subject minimized. We demonstrate this set-up in an EEG-fMRI experiment using a simple visual stimulus. However, much more complex stimuli can be used. Here we show the EEG-fMRI set-up using a Brain Products GmbH (Gilching, Germany) MRplus, 32 channel EEG system in conjunction with a Philips Achieva (Best, Netherlands) 3T MR scanner, although many of the techniques are transferable to other systems.

Simultaneous electroencephalography (EEG) and functional Magnetic Resonance imaging (fMRI) is a powerful neuroimaging tool. However, the inside of an MRI scanner forms a difficult environment for EEG data recording and safety must be considered whenever operating EEG equipment inside a scanner. Here, we present an optimised EEG-fMRI data acquisition protocol.

Simultaneous  EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the  high spatial accuracy of fMRI. The data  from these two ...

The 5-Choice Serial Reaction Time Task: A Task of Attention and Impulse Control for Rodents

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. Test day challenges, modifications to the standard task, can be used to systematically tax the neural systems controlling either attention or impulse control. Importantly, these challenges have consistent effects on behavior across laboratories in intact animals and can reveal either enhancements or deficits in cognitive function that are not apparent when rats are only tested on the standard task. The variety of behavioral measures that are collected can be used to determine if other factors (i.e., sedation, motivation deficits, locomotor impairments) are contributing to changes in performance. The versatility of the 5CSRTT is further enhanced because it is amenable to combination with pharmacological, molecular, and genetic techniques.

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. Test day challenges, which are modifications of the standard task, increase flexibility of the task and can be combined with other manipulations to more fully characterize behavior.

This protocol describes the 5-choice serial reaction time task, which is an operant based task used to study attention and impulse control in rodents. ...

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies

As cognitive neuroscience methods develop, established experimental tasks are used with emerging brain imaging modalities. Here transferring a paradigm (the visual oddball task) with a long history of behavioral and electroencephalography (EEG) experiments to a functional magnetic resonance imaging (fMRI) experiment is considered. The aims of this paper are to briefly describe fMRI and when its use is appropriate in cognitive neuroscience; illustrate how task design can influence the results of an fMRI experiment, particularly when that task is borrowed from another imaging modality; explain the practical aspects of performing an fMRI experiment. It is demonstrated that manipulating the task demands in the visual oddball task results in different patterns of blood oxygen level dependent (BOLD) activation. The nature of the fMRI BOLD measure means that many brain regions are found to be active in a particular task. Determining the functions of these areas of activation is very much dependent on task design and analysis. The complex nature of many fMRI tasks means that the details of the task and its requirements need careful consideration when interpreting data. The data show that this is particularly important in those tasks relying on a motor response as well as cognitive elements and that covert and overt responses should be considered where possible. Furthermore, the data show that transferring an EEG paradigm to an fMRI experiment needs careful consideration and it cannot be assumed that the same paradigm will work equally well across imaging modalities. It is therefore recommended that the design of an fMRI study is pilot tested behaviorally to establish the effects of interest and then pilot tested in the fMRI environment to ensure appropriate design, implementation and analysis for the effects of interest.

Transferring a paradigm with a history of use in EEG experiments to an fMRI experiment is considered. It is demonstrated that manipulating the task demands in the visual oddball task resulted in different patterns of BOLD activation and illustrated how task design is crucial in fMRI experiments.

As cognitive neuroscience methods develop, established experimental tasks are used with emerging brain imaging modalities. Here transferring a paradigm ...

Topographical Estimation of Visual Population Receptive Fields by fMRI

Visual cortex is retinotopically organized so that neighboring populations of cells map to neighboring parts of the visual field. Functional magnetic resonance imaging allows us to estimate voxel-based population receptive fields (pRF), i.e., the part of the visual field that activates the cells within each voxel. Prior, direct, pRF estimation methods1 suffer from certain limitations: 1) the pRF model is chosen a-priori and may not fully capture the actual pRF shape, and 2) pRF centers are prone to mislocalization near the border of the stimulus space. Here a new topographical pRF estimation method2 is proposed that largely circumvents these limitations. A linear model is used to predict the Blood Oxygen Level-Dependent (BOLD) signal by convolving the linear response of the pRF to the visual stimulus with the canonical hemodynamic response function. PRF topography is represented as a weight vector whose components represent the strength of the aggregate response of voxel neurons to stimuli presented at different visual field locations. The resulting linear equations can be solved for the pRF weight vector using ridge regression3, yielding the pRF topography. A pRF model that is matched to the estimated topography can then be chosen post-hoc, thereby improving the estimates of pRF parameters such as pRF-center location, pRF orientation, size, etc. Having the pRF topography available also allows the visual verification of pRF parameter estimates allowing the extraction of various pRF properties without having to make a-priori assumptions about the pRF structure. This approach promises to be particularly useful for investigating the pRF organization of patients with disorders of the visual system.

It is important to obtain unbiased estimates of visual population receptive fields (pRFs) by functional magnetic resonance imaging. We use mild regularization constraints to estimate pRF topography without a-priori assumptions about pRF shape, allowing us to choose specific pRF models post-hoc. This is particularly advantageous in subjects with visual-pathway lesions.

Visual cortex is retinotopically organized so that neighboring populations of cells map to neighboring parts of the visual field. Functional magnetic ...

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Fear of certain threat and anxiety about uncertain threat are distinct emotions with unique behavioral, cognitive-attentional, and neuroanatomical components. Both anxiety and fear can be studied in the laboratory by measuring the potentiation of the startle reflex. The startle reflex is a defensive reflex that is potentiated when an organism is threatened and the need for defense is high. The startle reflex is assessed via electromyography (EMG) in the orbicularis oculi muscle elicited by brief, intense, bursts of acoustic white noise (i.e., &#8220;startle probes&#8221;). Startle potentiation is calculated as the increase in startle response magnitude during presentation of sets of visual threat cues that signal delivery of mild electric shock relative to sets of matched cues that signal the absence of shock (no-threat cues). In the Threat Probability Task, fear is measured via startle potentiation to high probability (100% cue-contingent shock; certain) threat cues whereas anxiety is measured via startle potentiation to low probability (20% cue-contingent shock; uncertain) threat cues. Measurement of startle potentiation during the Threat Probability Task provides an objective and easily implemented alternative to assessment of negative affect via self-report or other methods (e.g., neuroimaging) that may be inappropriate or impractical for some researchers. Startle potentiation has been studied rigorously in both animals (e.g., rodents, non-human primates) and humans which facilitates animal-to-human translational research. Startle potentiation during certain and uncertain threat provides an objective measure of negative affective and distinct emotional states (fear, anxiety) to use in research on psychopathology, substance use/abuse and broadly in affective science. As such, it has been used extensively by clinical scientists interested in psychopathology etiology and by affective scientists interested in individual differences in emotion.

Potentiation of the startle reflex is measured via electromyography of the orbicularis oculi muscle during low (uncertain) and high (certain) probability electric shock threat in the Threat Probability Task. This provides an objective measure of distinct negative emotional states (fear/anxiety) for research on psychopathology, substance use/abuse, and broad affective science.

Fear of certain threat and anxiety about uncertain threat are distinct emotions with unique behavioral, cognitive-attentional, and neuroanatomical ...

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants&#8217; leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.

A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning ...

Acquisition of a High-precision Skilled Forelimb Reaching Task in Rats

Movements are the main measurable output of central nervous system function. Developing behavioral paradigms that allow detailed analysis of motor learning and execution is of critical importance in order to understand the principles and processes that underlie motor function. Here we present a paradigm to study movement acquisition within a daily session of training (within-session) representing the fast learning component and primary acquisition as well as skilled motor learning over several training sessions (between-session) representing the slow learning component and consolidation of the learned task. This behavioral paradigm increases the degree of difficulty and complexity of the motor skill task due to two features: First, the animal realigns its body prior to each pellet retrieval forcing renewed orientation and preventing movement execution from the same angle. Second, pellets are grasped from a vertical post that matches the diameter of the pellet and is placed in front of the cage. This requires a precise grasp for successful pellet retrieval and thus prevents simple pulling of the pellet towards the animal. In combination with novel genetics, imaging and electrophysiological technologies, this behavioral method will aid to understand the morphological, anatomical and molecular underpinnings of motor learning and memory.

A paradigm is presented to analyze the acquisition of a high-precision skilled forelimb reaching task in rats.

Movements are the main measurable output of central nervous system function. Developing behavioral paradigms that allow detailed analysis of motor ...

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Spatial intelligence is often linked to success in engineering education and engineering professions. The use of electroencephalography enables comparative calculation of individuals&#39; neural efficiency as they perform successive tasks requiring spatial ability to derive solutions. Neural efficiency here is defined as having less beta activation, and therefore expending fewer neural resources, to perform a task in comparison to other groups or other tasks. For inter-task comparisons of tasks with similar durations, these measurements may enable a comparison of task type difficulty. For intra-participant and inter-participant comparisons, these measurements provide potential insight into the participant&#39;s level of spatial ability and different engineering problem solving tasks. Performance on the selected tasks can be analyzed and correlated with beta activities. This work presents a detailed research protocol studying the neural efficiency of students engaged in the solving of typical spatial ability and Statics problems. Students completed problems specific to the Mental Cutting Test (MCT), Purdue Spatial Visualization test of Rotations (PSVT:R), and Statics. While engaged in solving these problems, participants&#39; brain waves were measured with EEG allowing data to be collected regarding alpha and beta brain wave activation and use. The work looks to correlate functional performance on pure spatial tasks with spatially intensive engineering tasks to identify the pathways to successful performance in engineering and the resulting improvements in engineering education that may follow.

This manuscript describes an approach to measure neural activity of humans while solving spatially focused engineering problems. The electroencephalogram methodology helps interpret beta brain wave measurements in terms of neural efficiency, with the aim of ultimately enabling comparisons of task performance both between problem types and between participants.

Spatial intelligence is often linked to success in engineering education and engineering professions. The use of electroencephalography enables ...

Feeding Experimentation Device (FED): Construction and Validation of an Open-source Device for Measuring Food Intake in Rodents

Food intake measurements are essential for many research studies. Here, we provide a detailed description of a novel solution for measuring food intake in mice: the Feeding Experimentation Device (FED). FED is an open-source system that was designed to facilitate flexibility in food intake studies. Due to its compact and battery powered design, FED can be placed within standard home cages or other experimental equipment. Food intake measurements can also be synchronized with other equipment in real-time via FED's transistor-transistor logic (TTL) digital output, or in post-acquisition processing as FED timestamps every event with a real-time clock. When in use, a food pellet sits within FED's food well where it is monitored via an infrared beam. When the pellet is removed by the mouse, FED logs the timestamp onto its internal secure digital (SD) card and dispenses another pellet. FED can run for up to 5 days before it is necessary to charge the battery and refill the pellet hopper, minimizing human interference in data collection. Assembly of FED requires minimal engineering background, and off-the-shelf materials and electronics were prioritized in its construction. We also provide scripts for analysis of food intake and meal patterns. Finally, FED is open-source and all design and construction files are online, to facilitate modifications and improvements by other researchers.

Feeding Experimentation Device FED: Construction and Validation of an Open-source Device for Measuring Food Intake in Rodents

Feeding Experimentation Device (FED) is an open-source device for measuring food intake in mice. FED can also synchronize food intake measurements with other techniques via a real-time digital output. Here, we provide a step-by-step tutorial for the construction, validation, and usage of FED.

Food intake measurements are essential for many research studies. Here, we provide a detailed description of a novel solution for measuring food intake in ...