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><tbody><tr><td>MRI scanner</td><td>Siemens/Philips/GE</td><td></td><td></td></tr><tr><td>MATLAB</td><td>The Mathworks, Inc.</td><td></td><td>http://www.mathworks.com&amp;nbsp;</td></tr><tr><td>VISTA software</td><td>VISTA software group</td><td></td><td>http://white.stanford.edu/newlm/index.php/Software</td></tr><tr><td>PsychoToolbox</td><td>PsychoToolbox&amp;nbsp;</td><td></td><td>http://psychtoolbox.org&amp;nbsp;</td></tr><tr><td>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 ...

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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 ...

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 ...

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon measuring activity on the surface of cerebral cortex rather than in subcortical regions such as midbrain and brainstem. Subcortical fMRI must overcome two challenges: spatial resolution and physiological noise. Here we describe an optimized set of techniques developed to perform high-resolution fMRI in human SC, a structure on the dorsal surface of the midbrain; the methods can also be used to image other brainstem and subcortical structures.
High-resolution (1.2 mm voxels) fMRI of the SC requires a non-conventional approach. The desired spatial sampling is obtained using a multi-shot (interleaved) spiral acquisition1. Since, T2* of SC tissue is longer than in cortex, a correspondingly longer echo time (TE ~ 40 msec) is used to maximize functional contrast. To cover the full extent of the SC, 8-10 slices are obtained. For each session a structural anatomy with the same slice prescription as the fMRI is also obtained, which is used to align the functional data to a high-resolution reference volume.
In a separate session, for each subject, we create a high-resolution (0.7 mm sampling) reference volume using a T1-weighted sequence that gives good tissue contrast. In the reference volume, the midbrain region is segmented using the ITK-SNAP software application2. This segmentation is used to create a 3D surface representation of the midbrain that is both smooth and accurate3. The surface vertices and normals are used to create a map of depth from the midbrain surface within the tissue4.
Functional data is transformed into the coordinate system of the segmented reference volume. Depth associations of the voxels enable the averaging of fMRI time series data within specified depth ranges to improve signal quality. Data is rendered on the 3D surface for visualization.
In our lab we use this technique for measuring topographic maps of visual stimulation and covert and overt visual attention within the SC1. As an example, we demonstrate the topographic representation of polar angle to visual stimulation in SC.

This article describes techniques to perform high-resolution functional magnetic resonance imaging with 1.2 mm sampling in human midbrain and subcortical structures using a 3T scanner. Use of these techniques to resolve topographic maps of visual stimulation in the human superior colliculus (SC) is given as an example.

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon ...

Neuroscience

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when studying the brain, as the functional role that a brain area fulfills may be largely defined by its connections to other brain areas. In this report, we describe a novel, non-invasive approach for relating brain structure and function using magnetic resonance imaging (MRI). This approach, a combination of structural imaging of long-range fiber connections and functional imaging data, is illustrated in two distinct cognitive domains, visual attention and face perception. Structural imaging is performed with diffusion-weighted imaging (DWI) and fiber tractography, which track the diffusion of water molecules along white-matter fiber tracts in the brain (Figure 1). By visualizing these fiber tracts, we are able to investigate the long-range connective architecture of the brain. The results compare favorably with one of the most widely-used techniques in DWI, diffusion tensor imaging (DTI). DTI is unable to resolve complex configurations of fiber tracts, limiting its utility for constructing detailed, anatomically-informed models of brain function. In contrast, our analyses reproduce known neuroanatomy with precision and accuracy. This advantage is partly due to data acquisition procedures: while many DTI protocols measure diffusion in a small number of directions (e.g., 6 or 12), we employ a diffusion spectrum imaging (DSI)1, 2 protocol which assesses diffusion in 257 directions and at a range of magnetic gradient strengths. Moreover, DSI data allow us to use more sophisticated methods for reconstructing acquired data. In two experiments (visual attention and face perception), tractography reveals that co-active areas of the human brain are anatomically connected, supporting extant hypotheses that they form functional networks. DWI allows us to create a "circuit diagram" and reproduce it on an individual-subject basis, for the purpose of monitoring task-relevant brain activity in networks of interest.

We describe a novel approach for simultaneous analysis of brain function and structure using magnetic resonance imaging (MRI). We assess brain structure with high-resolution diffusion-weighted imaging and white-matter fiber tractography. Unlike standard structural MRI, these techniques allow us to directly relate anatomical connectivity to functional properties of brain networks.

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when ...

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Pre-stimulus oscillatory brain activity influences upcoming perception. The characteristics of this pre-stimulus activity can predict whether a near-threshold stimulus will be perceived or not perceived, but can they also predict which one of two competing stimuli with different perceptual contents is perceived? Ambiguous visual stimuli, which can be seen in one of two possible ways at a time, are ideally suited to investigate this question. Magnetoencephalography (MEG) is a neurophysiological measurement technique that records magnetic signals emitted as a result of brain activity. The millisecond temporal resolution of MEG allows for a characterization of oscillatory brain states from as little as 1 second of recorded data. Presenting an empty screen around 1 second prior to the ambiguous stimulus onset therefore provides a time window in which one can investigate whether pre-stimulus oscillatory activity biases the content of upcoming perception, as indicated by participants&#39;&#160;reports. The spatial resolution of MEG is not excellent, but sufficient to localise sources of brain activity at the centimetre scale. Source reconstruction of MEG activity then allows for testing hypotheses about the oscillatory activity of specific regions of interest, as well as the time- and frequency-resolved connectivity between regions of interest. The described protocol enables a better understanding of the influence of spontaneous, ongoing brain activity on visual perception.

This article describes how to set up an experiment that allows detecting pre-stimulus source-level influences on object perception using magnetoencephalography (MEG). It covers stimulus material, experimental design, MEG recording, and data analysis.

Pre-stimulus oscillatory brain activity influences upcoming perception. The characteristics of this pre-stimulus activity can predict whether a ...

Mapping Regional Homogeneity and Functional Connectivity of the Visual Cortex in Resting-State fMRI

A combined regional homogeneity (ReHo) and functional connectivity (FC) method, a type of noninvasive functional magnetic resonance imaging (fMRI) method, has been used to evaluate synchronous neuronal activity changes in retinitis pigmentosa (RP). The purpose of this study is to describe our method for analysis of intra- and interregional synchronizations of changes in neuronal activity in RP patients. The advantages of the combined ReHo and FC method are that it is both noninvasive and sufficiently sensitive to investigate changes in cerebral synchronous neuronal activity changes in vivo. Here, 16 RP patients and 14 healthy controls closely matched in age, sex, and education underwent resting-state fMRI scans. Two sample t-tests were conducted to compare ReHo and FC across groups. Our results showed that visual network disconnection and reorganization of the retino-thalamocortical pathway and dorsal visual stream occurred in the RP patients. Here, we describe the details of this method, its use, and the impact of its key parameters in a step-by-step manner.

We present a protocol for analyzing functional magnetic resonance imaging data to investigate spontaneous neural activity alterations in retinitis pigmentosa patient using a combined regional homogeneity and functional connectivity method.

A combined regional homogeneity (ReHo) and functional connectivity (FC) method, a type of noninvasive functional magnetic resonance imaging (fMRI) method, ...

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Functional near-infrared spectroscopy (fNIRS) is a portable neuroimaging methodology, more robust to motion and more cost-effective than functional magnetic resonance imaging (fMRI), which makes it highly suitable for conducting naturalistic studies of brain function and for use with developmental and clinical populations. Both fNIRS and fMRI methodologies detect changes in cerebral blood oxygenation during functional brain activation, and prior studies have shown high spatial and temporal correspondence between the two signals. There is, however, no quantitative comparison of the two signals collected simultaneously from the same subjects with whole-head fNIRS coverage. This comparison is necessary to comprehensively validate area-level activations and functional connectivity against the fMRI gold standard, which in turn has the potential to facilitate comparisons of the two signals across the lifespan. We address this gap by describing a protocol for simultaneous data collection of fMRI&#160;and fNIRS signals that: i) provides whole-head fNIRS coverage; ii) includes short-distance measurements for regression of the non-cortical, systemic physiological signal; and iii) implements two different methods for optode-to-scalp co-registration of fNIRS measurements. fMRI and fNIRS data from three subjects are presented, and recommendations for adapting the protocol to test developmental and clinical populations are discussed. The current setup with adults allows scanning sessions for an average of approximately 40 min, which includes both functional and structural scans. The protocol outlines the steps required to adapt the fNIRS equipment for use in the magnetic resonance (MR) environment, provides recommendations for both data recording and optode-to-scalp co-registration, and discusses potential modifications of the protocol to fit the specifics of the available MR-safe fNIRS system. Representative subject-specific responses from a flashing-checkerboard task illustrate the feasibility of the protocol to measure whole-head fNIRS signals in the MR environment. This protocol will be particularly relevant for researchers interested in validating fNIRS signals against fMRI across the lifespan.

We present a method for simultaneously collecting fMRI and fNIRS signals from the same subjects with whole-head fNIRS coverage. The protocol has been tested with three young adults and can be adapted for data collection for developmental studies and clinical populations.

Functional near-infrared spectroscopy (fNIRS) is a portable neuroimaging methodology, more robust to motion and more cost-effective than functional ...

Advancing Visual Cortex Mapping for Enhanced Evaluation of Vision Disorders

Functional Magnetic Resonance Imaging (fMRI) of the Visual Cortex with Wide-View Retinotopic Stimulation

High-resolution retinotopic blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) with a wide-view presentation can be used to functionally map the peripheral and central visual cortex. This method for measuring functional changes of the visual brain allows for functional mapping of the occipital lobe, stimulating &gt;100&#176; (&plusmn;50&#176;) or more of the visual field, compared to standard fMRI visual presentation setups which usually cover &lt;30&#176; of the visual field. A simple wide-view stimulation system for BOLD fMRI can be set up using common MR-compatible projectors by placing a large mirror or screen close to the subject's face and using only the posterior half of a standard head coil to provide a wide-viewing angle without obstructing their vision. The wide-view retinotopic fMRI map can then be imaged using various retinotopic stimulation paradigms, and the data can be analyzed to determine the functional activity of visual cortical regions corresponding to central and peripheral vision. This method provides a practical, easy-to-implement visual presentation system that can be used to evaluate changes in the peripheral and central visual cortex due to eye diseases such as glaucoma and the vision loss that may accompany them.

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping 

We have developed techniques for mapping the visual cortex function utilizing more of the visual field than is commonly used. This approach has the potential to enhance the evaluation of vision disorders and eye diseases.

High-resolution retinotopic blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) with a wide-view presentation can be ...

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

An EEG-fMRI multimodal imaging method, known as the spatiotemporal fMRI-constrained EEG source imaging method, is described here. The presented method employs conditionally-active fMRI sub-maps, or priors, to guide EEG source localization in a manner that improves spatial specificity and limits erroneous results.

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain ...

Biology

Visual Attention: fMRI Investigation of Object-based Attentional Control

Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel&#8212; University of Southern California
The human visual system is incredibly sophisticated and capable of processing large amounts of information very quickly. However, the brain's capacity to process information is not an unlimited resource. Attention, the ability to selectively process information that is relevant to current goals and to ignore information that is not, is therefore an essential part of visual perception. Some aspects of attention are automatic, while others are subject to voluntary, conscious control. In this experiment we explore the mechanisms of voluntary, or "top-down" attentional control on visual processing. 
This experiment leverages the orderly organization of visual cortex to examine how top-down attention can selectively modulate the processing of visual stimuli. Certain regions of the visual cortex appear to be specialized for processing specific visual items. Specifically, work by Kanwisher et al.1 has identified an area in the fusiform gyrus of the inferior temporal lobe that is significantly more active when subjects view faces compared to when they observe other common objects. This area has come to be known as the Fusiform Face Area (FFA). Another brain region, known as the Parahippocampal Place Area (PPA), responds strongly to houses and places, but not to faces.2 Given that we know how these regions respond to specific types of stimuli, their activity can be further explored to identify a key component of vision-visual attention. 
This video shows how to use fMRI to localize the FFA and PPA in the brain, and then examines how object-based attentional control modulates activity in these areas. The use of a functional localizer to restrict subsequent hypothesis testing is a powerful technique in functional imaging. Participants will undergo functional MRI while being presented with an overlaid image of a face and a house. Even though both a face and a house are presented in each stimulus, we predict that patterns of activity in their FFA and PPA will change based on which item is being attended to.3

fMRI Investigation of Object-based Visual Attentional Control

Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel&#8212; University of Southern California
The human visual system is incredibly sophisticated ...