We thank the VISTA software group (Brian Wandell and associates, at Stanford).
S. S. was supported by McNair 2280403105,NEI R01-EY109272, and NEI R01-EY024019 and as HHMI Early Carrer Award. A. P. and G. K. was supported by the Max-Planck Society, G. K. was supported by the PLASTICISE project of the 7th Framework Programme of the European Commission, Contract no. HEATH-F2-2009-223524.

1. Data Acquisition
<ol>
	<li>Prepare a stimulus protocol that is effective in eliciting a reliable retinotopic visual response as previously described in Dumoulin and Wandell1 and Lee et al.2. However, other well established paradigms are also applicable depending on the specific experimental question to be addressed.</li>
	<li>Present bar stimuli drifting across the screen sequentially along 8 directions of space, in steps of 45 degrees. Ensure that the motion is in synchrony with scanner frame acquisition (TR ~2 sec) so that the bar moves a step once an fMRI frame starts and stays at the new location until the frame ends.</li>
	<li>To measure a correct baseline signal, add epochs without bar stimulation1.
	<ol>
		<li>Define a field of view (10 to 15&#176; radius) in visual angle over which the stimulus is presented. Present moving or flickering checkerboard patterns (checker size = 0.94 x 0.94 deg2, pattern update rate = 250 msec/pattern) within the bar to elicit strong visual responses.</li>
		<li>Input the following specific parameters: 8 evenly spaced directions of motion, bar width equal to 1.875 deg, and bars move by half the bar width per frame (2 sec). Additional details can be found in Lee et al.2.</li>
		<li>Generate a spot (~0.25&#176;) in the screen center on which the subject&#8217;s eyes fixate during the experiment. Change color of the spot randomly in time.&#160;&#160;</li>
	</ol>
	</li>
	<li>Scan the brain of a subject in an MRI scanner using a typical echo-planar-imaging (EPI) scan that has 192 frames duration (24 frames in each direction of motion). Repeat the scans 4-8 times to increase signal-to-noise ratio.</li>
	<li>Set parameters for the EPI sequence as follows: TR = 2 sec, TE = 40 msec, matrix size = 64 x 64, 28 slices, voxel size = 3 x 3 x 3 mm3, flip angle = 90&#176;, Alternatively, apply sequences with a finer resolution (e.g., 2 x 2 x 2 mm3) or a short TR (e.g., 1-1.5 sec) covering only the visual cortex2.</li>
	<li>Track eye movements with an eyetracker system during functional scans to ensure fixation is maintained to within 1-1.5&#176; of the fixation point. 
	NOTE: Here, a head-coordinate based eyetracker in a goggle system is used, but other suitable eyetracker systems can be used instead.</li>
	<li>Instruct the subjects to fixate the spot on the screen center generated in step 1.3.2. To ensure the subjects are fixating, instruct them to report the color changes of the fixation spot.</li>
	<li>Obtain anatomical scans, at 1 x 1 x 1 mm3 resolution (e.g., T1-MPRAGE; TR = 1,900 msec, TE = 2.26 msec, TI = 900 msec, flip angle = 9&#176;, 176 partitions). 
	NOTE: These anatomical scans will be used for segmentation as well as for aligning the functional images to the anatomy both within and across scans. For better alignment between functional (EPI) images and the anatomy, obtain also an inplane anatomy scan, with resolution identical to the EPI, using T1-weighted fast spoiled gradient echo (SPGR) sequence1.</li>
</ol>
2. Data Pre-processing
NOTE: Prior to estimating pRF properties, several typical fMRI data pre-processing steps are needed, such as head motion correction and alignment of functional volumes to the anatomical scan. In this article, all pre-processing, estimation, analysis and presentation of results obtained are performed using the open source MATLAB-based software toolbox VISTA LAB available on the VISTA software site. http://white.stanford.edu/newlm/index.php/Main_Page.
<ol>
	<li>Load the anatomical scan into MATLAB and prepare a volume anatomy using a function called createVolAnat.</li>
	<li>Segment Gray matter, White matter, and CSF using the function &#8220;ItkGray&#8221;.</li>
	<li>Prepare functional data by converting DICOM (i.e., raw MRI file format for Siemens) files into NIFTI (i.e., standard functional MRI file format) files, and load data into VISTA using a function called mrInit.</li>
	<li>Correct head-motion and align functional images to the anatomy loaded in step 2.1 using rxAlign based on an affine matrix transformation.</li>
	<li>Average functional motion-corrected scans for improving signal-to-noise ratio by clicking mrVISTA Analysis TimeSeries Average tSeries. Exclude from averaging scans during which eye movements deviates from fixation more than 1-1.5&#176;. If signals from different runs have different dc-drifts, average functional scans after removing the dc-drifts.</li>
	<li>Calculate the mapping coordinates between functional scans and Gray matter and identify corresponding Gray-matter voxels in the functional scans by selecting the following menus: mrVISTA Window Open Gray 3-View Window. Assign BOLD signals in the Gray matter voxels by interpolation, choosing one of the options available in mrVISTA.</li>
</ol>
3. Estimation of pRF Topography and Parametric Modeling
<ol>
	<li>Download the code files through the following link: https://sites.google.com/site/leesangkyun/prf/codes.zip, extract the compressed file and place them in a preferred location of the local computer. Add the path of the folder in MATLAB.</li>
	<li>Set the stimulus parameters used in the experiment by selecting the following menus: mrVISTA Analysis Retinotopic Model Set Parameters. Specify the following parameters such as stimulus images, the stimulus size, the canonical hemodynamic function, the frame rate of the fMRI scanner.</li>
	<li>Prior to the pRF estimation, prepare the initial parameter sets (Figure 1B).
	<ol>
		<li>Set the cross-validation sets in &#8220;tprf_set_params.m&#8221; from the code files. Divide timeseries into at least two subsets (one set for testing and the remaining sets for training) that are long enough for the bar to sweep the entire stimulus space. Alternatively, without averaging scans in step 2.4, validate scans by leaving out one scan for testing and using the remaining scans for training.</li>
		<li>Set a coarse parameter set (&#955; in Figure 1; &#955; = [10-2 10-1 1 101 102]) in &#8220;tprf_set_params.m&#8221;. Then, set a fine scale range ([0.1 0.3 0.5 0.7 0.9 1 3 5 7 9]) in &#8220;tprf_set_params.m&#8221;. 
		NOTE: The program uses the coarse set to select the &#955; resulting in the highest explained variance. Then, the program searches the space around the selected &#955; using the fine scale range, further refining the selection of &#955; that yields the highest explained variance.</li>
		<li>Set a threshold (0.2) of the explained variance for visually responsive voxels in &#8220;tprf_set_params.m&#8221;. 
		NOTE: This threshold is used as the reference for selection of visually responsive voxels. Alternatively, make an ROI for a non-visually responsive region (e.g., by drawing a sphere with a radius of 1 cm in a non-visually responsive brain area), where the threshold can be automatically calculated.</li>
		<li>Set a set of thresholds ([0.3, 0.5, 0.7]) for defining the pRF center region in the normalized topography in &#8220;tprf_set_params.m&#8221; (i.e., [0 to 1] or [-1 to 1] with epochs without bar stimulation in step 1.3.1). 
		NOTE: From the set of thresholds the program provided selects the &#8220;best&#8221; threshold, i.e. the threshold that defines a pRF central region for which the pRF center model explains the greatest signal variance. Alternatively, choose a different set of threshold values depending on the characteristics of the topography.</li>
	</ol>
	</li>
	<li>Execute &#8220;tprf_runpRFest.m&#8221; calculate the pRF topography (Figure 1) and fit a 2D anisotropic Gaussian. After specifying all parameters described in this protocol, and running the code, obtain the final estimation results.</li>
</ol>
<img alt="Figure 1" src="/files/ftp_upload/51811/51811fig1highres.jpg" width="700" /> 
Figure 1: PRF estimation process. (A) Schematic illustration of the process followed for pRF topography estimation. h(t): hemodynamic response function, A(t): stimulus, m: pRF, Reg: L2-norm regularization. (B) Specific steps for pRF topography estimation and pRF center modeling. The set of parameters required for the estimation is listed in each step. A one-dimensional section of topography and its model are illustrated. Under &#8220;Model Fitting&#8221;, black and red curves represent the topography and its pRF center model with a center threshold of 0.5, respectively. The blue dashed line indicates a threshold for the pRF central region.

<ol>
	<li>Dumoulin, S. O., Wandell, B. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Population+receptive+field+estimates+in+human+visual+cortex.">Population receptive field estimates in human visual cortex.</a> Neuroimage. 39, 647-660 (2008).</li><li>Lee, S., Papanikolaou, A., Logothetis, N. K., Smirnakis, S. M., Keliris, G. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+method+for+estimating+population+receptive+field+topography+in+visual+cortex.">A new method for estimating population receptive field topography in visual cortex.</a> Neuroimage. 81, 144-157 (2013).</li><li>Hastie, T., Tibshirani, R., Friedman, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The elements of statistical learning : data mining, inference, and prediction. , (2009).</li><li>Sereno, M. I., 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=Borders+of+multiple+visual+areas+in+humans+revealed+by+functional+magnetic+resonance+imaging.">Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging.</a> Science. 268, 889-893 (1995).</li><li>Engel, S. A., Glover, G. H., Wandell, B. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Retinotopic+organization+in+human+visual+cortex+and+the+spatial+precision+of+functional+MRI.">Retinotopic organization in human visual cortex and the spatial precision of functional MRI.</a> Cereb Cortex. 7, 181-192 (1997).</li><li>Engel, S. A., 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=fMRI+of+human+visual+cortex.">fMRI of human visual cortex.</a> Nature. 369, 525 (1994).</li><li>DeYoe, E. A., 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=Mapping+striate+and+extrastriate+visual+areas+in+human+cerebral+cortex.">Mapping striate and extrastriate visual areas in human cerebral cortex.</a> Proc Natl Acad Sci U S A. 93, 2382-2386 (1996).</li><li>Greene, C. A., Dumoulin, S. O., Harvey, B. M., Ress, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measurement+of+population+receptive+fields+in+human+early+visual+cortex+using+back-projection+tomography.">Measurement of population receptive fields in human early visual cortex using back-projection tomography.</a> J Vis. , (2014).</li><li>Zuiderbaan, W., Harvey, B. M., Dumoulin, S. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+center-surround+configurations+in+population+receptive+fields+using+fMRI.">Modeling center-surround configurations in population receptive fields using fMRI.</a> J Vis. , (2012).</li><li>Harvey, B. M., Dumoulin, S. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+relationship+between+cortical+magnification+factor+and+population+receptive+field+size+in+human+visual+cortex:+constancies+in+cortical+architecture.">The relationship between cortical magnification factor and population receptive field size in human visual cortex: constancies in cortical architecture.</a> J Neurosci. 31, 13604-13612 (2011).</li><li>Haak, K. V., Cornelissen, F. W., Morland, A. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Population+receptive+field+dynamics+in+human+visual+cortex.">Population receptive field dynamics in human visual cortex.</a> PLoS One. 7, e37686 (2012).</li></ol>

The authors declare that they have no competing financial interests.

This article demonstrates how to estimate the topography of visual population receptive fields in human visual cortex and how to use it to select an appropriate parametric model for the receptive field. For a successful retinotopy, an appropriate stimulation protocol and an efficient analysis method should be selected, and the subject&#8217;s experimental parameters (motion and fixation) should be optimized. Bar stimuli moving sequentially across the visual field are an efficient stimulus paradigm for pRF estimation as i...

Functional magnetic resonance imaging (fMRI) measures non-invasively the functional organization of visual cortex at a macroscopic scale (typically on the order of millimeters). Early fMRI retinotopy studies used a coherence measure between stimulus location and elicited BOLD responses4-7. These studies typically did not estimate population receptive field size. Later, Dumoulin and Wandell1 proposed a method to overcome such a limitation by explicitly modeling the pRF location and size, using a linear function of this model to predict the BOLD response. However, one limitation of this pioneering method is that the parametric pRF model has to be chosen a-priori, and may lead to erroneous pRF estimates if it turns out not to be appropriate. 
To overcome limitations of the parametric pRF-model method, new methods have been developed recently. These methods directly predict the BOLD response to the stimulus by reconstructing the pRF topography. A method8 proposed by Greene and colleagues reconstructs the pRF topography by back-projecting the BOLD responses to the individual 1D stimulus spaces and building the pRF topography in the 2D stimulus space like a typical computer tomography technique. On the other hand, the method2 proposed by us directly estimates the 2D pRF topography by using linear regression and applying a regularization technique. In this method, the pRF topography is represented as a set of weights which is multiplied by the stimulus to estimate the neuronal population response of a given voxel. Then, the final Blood Oxygen Level-Dependent (BOLD) response evoked by the stimulus is estimated by convolving the neuronal population response and the canonical hemodynamic response function. In order to solve the under-constrained linear system, additionally, ridge regression regularization is used to enforce sparseness (see Figure 1 below). The regularization technique suppresses noise and artifacts and thus allows our method to estimate the pRF topography more robustly.
The topographical methods do not force the pRF shape to have a certain parametric shape, and therefore can uncover the actual pRF structure. An appropriate parametric model can then be chosen based on the pRF topography. For example, the pRF topography can be used to separate the pRF center and surround, and then the subsequent pRF center modeling can be more accurate by minimizing the influence of surround suppression as well as the influence of other potential artifacts arising in areas distant to the pRF center. We have recently performed a quantitative comparison between our method and several other methods that directly (i.e. before estimating the topography) fit isotropic Gaussian1, anisotropic Gaussian, and difference of isotropic Gaussians to the pRF9. It was found that the topography-based method outperformed these methods with respect to pRF center modeling by achieving higher explained variance of the BOLD signal time series.
Accurate estimation of pRF properties in various areas reveals how they cover the visual field and is important for investigating the functional organization of the visual cortex particularly as it relates to visual perception. Properties such as how pRF size changes with eccentricity1,10 and pRF center surround organization9 are well studied in the human literature. The proposed method for estimating the pRF topography results in more accurate pRF parameter modeling and is more likely to reveal unknown regularities, not easily modeled a-priori in the direct parametric models. This approach will be especially suitable for studying pRF organization in patients with visual pathway lesions, for whom pRF structure is not necessarily predictable a-priori. Below is described how to estimate the pRF topography and how to use the topography to model the pRF center.

<table border="0" cellpadding="0" cellspacing="0" width="1009">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:225px;">Name of Equipment/Software</td>
			<td style="width:219px;">Company/Provider</td>
			<td style="width:219px;"></td>
			<td style="width:347px;">Web address</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">MRI scanner</td>
			<td>Siemens/Philips/GE</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">MATLAB</td>
			<td>The Mathworks, Inc.</td>
			<td></td>
			<td>http://www.mathworks.com&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">VISTA software</td>
			<td>VISTA software group</td>
			<td></td>
			<td>http://white.stanford.edu/newlm/index.php/Software</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">PsychoToolbox</td>
			<td>PsychoToolbox&#160;</td>
			<td></td>
			<td>http://psychtoolbox.org&#160;</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Eye Tracker(VisuaStimDigital)</td>
			<td>Resonance Technology Inc</td>
			<td></td>
			<td>http://mrivideo.com/</td>
		</tr>
	</tbody>
</table>

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.

Accurate pRF modeling requires capturing pRF shapes correctly. Without knowing the pRF topography, the selection of circularly symmetric models used in prior studies1,9-11 is a reasonable choice. This is because, if the local retinotopic organization is homogeneous in all directions of visual field, a local population response could be represented as a circularly symmetric cumulative aggregate of neuronal responses. However, our observations demonstrate that this is not necessarily the case (Figure 2</...

Watch this Scientific Journal Video about Topographical Estimation of Visual Population Receptive Fields by fMRI at JoVE.com

Topographical Estimation of Visual Population Receptive Fields by fMRI

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 ...

topographical-estimation-of-visual-population-receptive-fields-by-fmri

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9.2K Views.  Baylor College of Medicine. 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.

Video: Topographical Estimation of Visual Population Receptive Fields by fMRI

Functional Magnetic Resonance Imaging (fMRI) with Auditory Stimulation in Songbirds

The neurobiology of birdsong, as a model for human speech, is a pronounced area of research in behavioral neuroscience. Whereas electrophysiology and molecular approaches allow the investigation of either different stimuli on few neurons, or one stimulus in large parts of the brain, blood oxygenation level dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) allows combining both advantages, i.e. compare the neural activation induced by different stimuli in the entire brain at once. fMRI in songbirds is challenging because of the small size of their brains and because their bones and especially their skull comprise numerous air cavities, inducing important susceptibility artifacts. Gradient-echo (GE) BOLD fMRI has been successfully applied to songbirds 1-5 (for a review, see 6). These studies focused on the primary and secondary auditory brain areas, which are regions free of susceptibility artifacts. However, because processes of interest may occur beyond these regions, whole brain BOLD fMRI is required using an MRI sequence less susceptible to these artifacts. This can be achieved by using spin-echo (SE) BOLD fMRI 7,8 . In this article, we describe how to use this technique in zebra finches (Taeniopygia guttata), which are small songbirds with a bodyweight of 15-25 g extensively studied in behavioral neurosciences of birdsong. The main topic of fMRI studies on songbirds is song perception and song learning. The auditory nature of the stimuli combined with the weak BOLD sensitivity of SE (compared to GE) based fMRI sequences makes the implementation of this technique very challenging.

Functional Magnetic Resonance Imaging fMRI with Auditory Stimulation in Songbirds

This article shows an optimized procedure for imaging of the neural substrates of auditory stimulation in the songbird brain using functional Magnetic Resonance Imaging (fMRI). It describes the preparation of the sound stimuli, the positioning of the subject and the acquisition and subsequent analysis of the fMRI data.

The neurobiology of birdsong, as a model for human speech, is a pronounced area of research in behavioral neuroscience. Whereas electrophysiology and ...

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 ...

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.

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 ...

Methods to Test Visual Attention Online

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative data samples than can typically be collected on college campuses. However, before such methods can be widely adopted, a number of technological challenges must be overcome &#8211; in particular in experiments where tight control over stimulus properties is necessary. Here we present methods for collecting performance data on two tests of visual attention. Both tests require control over the visual angle of the stimuli (which in turn requires knowledge of the viewing distance, monitor size, screen resolution, etc.) and the timing of the stimuli (as the tests involve either briefly flashed stimuli or stimuli that move at specific rates). Data collected on these tests from over 1,700 online participants were consistent with data collected in laboratory-based versions of the exact same tests. These results suggest that with proper care, timing/stimulus size dependent tasks can be deployed in web-based settings.

To replicate laboratory settings, online data collection methods for visual tasks require tight control over stimulus presentation. We outline methods for the use of a web application to collect performance data on two tests of visual attention.

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative ...

Visualizing Visual Adaptation

Many techniques have been developed to visualize how an image would appear to an individual with a different visual sensitivity: e.g., because of optical or age differences, or a color deficiency or disease. This protocol describes a technique for incorporating sensory adaptation into the simulations. The protocol is illustrated with the example of color vision, but is generally applicable to any form of visual adaptation. The protocol uses a simple model of human color vision based on standard and plausible assumptions about the retinal and cortical mechanisms encoding color and how these adjust their sensitivity to both the average color and range of color in the prevailing stimulus. The gains of the mechanisms are adapted so that their mean response under one context is equated for a different context. The simulations help reveal the theoretical limits of adaptation and generate &#34;adapted images&#34; that are optimally matched to a specific environment or observer. They also provide a common metric for exploring the effects of adaptation within different observers or different environments. Characterizing visual perception and performance with these images provides a novel tool for studying the functions and consequences of long-term adaptation in vision or other sensory systems.

This article describes a novel method for simulating and studying adaptation in the visual system.

Many techniques have been developed to visualize how an image would appear to an individual with a different visual sensitivity: e.g., because of optical ...

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

This protocol describes how to conduct temporal-order experiments to measure visual processing speed and the attentional resource distribution. The proposed method is based on a new and synergistic combination of three components: the temporal-order judgments (TOJ) paradigm, Bundesen&#39;s Theory of Visual Attention (TVA), and a hierarchical Bayesian estimation framework. The method provides readily interpretable parameters, which are supported by the theoretical and neurophysiological underpinnings of TVA. Using TOJs, TVA-based estimates can be obtained for a broad range of stimuli, whereas traditional paradigms used with TVA are mainly limited to letters and digits. Finally, the meaningful parameters of the proposed model allow for the establishment of a hierarchical Bayesian model. Such a statistical model allows assessing results in one coherent analysis both on the subject and the group level.
To demonstrate the feasibility and versatility of this new approach, three experiments are reported with attention manipulations in synthetic pop-out displays, natural images, and a cued letter-report paradigm.

Temporal-order judgments can be used to estimate processing speed parameters and attentional weights and thereby to infer the mechanisms of attentional processing. This methodology can be applied to a wide range of visual stimuli and works with many attention manipulations.

This protocol describes how to conduct temporal-order experiments to measure visual processing speed and the attentional resource distribution. The ...

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

The neural correlates of consonance and dissonance perception have been widely studied, but not the neural correlates of consonance and dissonance production. The most straightforward manner of musical production is singing, but, from an imaging perspective, it still presents more challenges than listening because it involves motor activity. The accurate singing of musical intervals requires integration between auditory feedback processing and vocal motor control in order to correctly produce each note. This protocol presents a method that permits the monitoring of neural activations associated with the vocal production of consonant and dissonant intervals. Four musical intervals, two consonant and two dissonant, are used as stimuli, both for an auditory discrimination test and a task that involves first listening to and then reproducing given intervals. Participants, all female vocal students at the conservatory level, were studied using functional Magnetic Resonance Imaging (fMRI) during the performance of the singing task, with the listening task serving as a control condition. In this manner, the activity of both the motor and auditory systems was observed, and a measure of vocal accuracy during the singing task was also obtained. Thus, the protocol can also be used to track activations associated with singing different types of intervals or with singing the required notes more accurately. The results indicate that singing dissonant intervals requires greater participation of the neural mechanisms responsible for the integration of external feedback from the auditory and sensorimotor systems than does singing consonant intervals.

The neural correlates of listening to consonant and dissonant intervals have been widely studied, but the neural mechanisms associated with production of consonant and dissonant intervals are less well known. In this article, behavioral tests and fMRI are combined with interval identification and singing tasks to describe these mechanisms.

The neural correlates of consonance and dissonance perception have been widely studied, but not the neural correlates of consonance and dissonance ...

Automated Analysis of a Nematode Population-based Chemosensory Preference Assay

The nematode, Caenorhabditis elegans' compact nervous system of only 302 neurons underlies a diverse repertoire of behaviors. To facilitate the dissection of the neural circuits underlying these behaviors, the development of robust and reproducible behavioral assays is necessary. Previous C. elegans behavioral studies have used variations of a "drop test", a "chemotaxis assay", and a "retention assay" to investigate the response of C. elegans to soluble compounds. The method described in this article seeks to combine the complementary strengths of the three aforementioned assays. Briefly, a small circle in the middle of each assay plate is divided into four quadrants with the control and experimental solutions alternately placed. After the addition of the worms, the assay plates are loaded into a behavior chamber where microscope cameras record the worms' encounters with the treated regions. Automated video analysis is then performed and a preference index (PI) value for each video is generated. The video acquisition and automated analysis features of this method minimizes the experimenter's involvement and any associated errors. Furthermore, minute amounts of the experimental compound are used per assay and the behavior chamber's multi-camera setup increases experimental throughput. This method is particularly useful for conducting behavioral screens of genetic mutants and novel chemical compounds. However, this method is not appropriate for studying stimulus gradient navigation due to the close proximity of the control and experimental solution regions. It should also not be used when only a small population of worms is available. While suitable for assaying responses only to soluble compounds in its current form, this method can be easily modified to accommodate multimodal sensory interaction and optogenetic studies. This method can also be adapted to assay the chemosensory responses of other nematode species.

We present a behavior recording setup and protocol that enables automated analysis of the nematode, Caenorhabditis elegans' preference for soluble compounds in a population-based assay. This article describes the construction of a behavior chamber, the behavioral assay protocol, and video analysis software usage.

The nematode, Caenorhabditis elegans' compact nervous system of only 302 neurons underlies a diverse repertoire of behaviors. To facilitate the dissection ...

Visual Classical Conditioning in Wood Ants

Several species of insects have become model systems for studying learning and memory formation. Although many studies focus on freely moving animals, studies implementing classical conditioning paradigms with harnessed insects have been important for investigating the exact cues that individuals learn and the neural mechanisms underlying learning and memory formation. Here we present a protocol for evoking visual associative learning in wood ants through classical conditioning. In this paradigm, ants are harnessed and presented with a visual cue (a blue cardboard), the conditional stimulus (CS), paired with an appetitive sugar reward, the unconditional stimulus (US). Ants perform a Maxilla-Labium Extension Reflex (MaLER), the unconditional response (UR), which can be used as a readout for learning. Training consists of 10 trials, separated by a 5-minute intertrial interval (ITI). Ants are also tested for memory retention 10 minutes or 1 hour after training. This protocol has the potential to allow researchers to analyze, in a precise and controlled manner, the details of visual memory formation and the neural basis of learning and memory formation in wood ants.

We present a protocol for the classical conditioning of harnessed ants that permits researchers to study visual learning and memory formation at a level of analysis not possible with freely moving individuals.

Several species of insects have become model systems for studying learning and memory formation. Although many studies focus on freely moving animals, ...

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

This experimental protocol was designed to investigate whether visual attention is obligatorily deployed at the endpoint of saccades. To this end, we recorded the eye position of human participants engaged in a saccade task via eye tracking and assessed visual orientation discrimination performance at various locations during saccade preparation. Importantly, instead of using a single saccade target paradigm for which the saccade endpoint typically coincides roughly with the target, this protocol comprised the presentation of two nearby saccade targets, leading to a distinct spatial dissociation between target locations and saccade endpoint on a substantial number of trials. The paradigm allowed us to compare presaccadic visual discrimination performance at the endpoint of accurate saccades (landing at one of the saccade targets) and of averaging saccades (landing at an intermediate location in between the two targets). We observed a selective enhancement of visual sensitivity at the endpoint of accurate saccades but not at the endpoint of averaging saccades. Rather, before the execution of averaging saccades, visual sensitivity was equally enhanced at both targets, suggesting that saccade averaging follows from unresolved attentional selection among the saccade targets. These results argue against a mandatory coupling between visual attention and saccade programming based on a direct measure of presaccadic visual sensitivity rather than saccadic reaction times, which have been used in other protocols to draw similar conclusions. While our protocol provides a useful framework to investigate the relationship between visual attention and saccadic eye movements at the behavioral level, it can also be combined with electrophysiological measures to extend insights at the neuronal level.

This experimental protocol combined eye tracking and the assessment of presaccadic visual sensitivity in a dual task paradigm, consisting of a free choice saccade task and a visual discrimination task, to investigate the deployment of visual spatial attention before both, accurate and averaging saccades.

This experimental protocol was designed to investigate whether visual attention is obligatorily deployed at the endpoint of saccades. To this end, we ...