Visual Cognitive Neuroscience

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<html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><title>Zotero Report</title><link rel="stylesheet" type="text/css" href="Zotero%20Report_retinotopy_stimulus_methods_files/detail.css"><link rel="stylesheet" type="text/css" media="screen,projection" href="Zotero%20Report_retinotopy_stimulus_methods_files/detail_screen.css"><link rel="stylesheet" type="text/css" media="print" href="Zotero%20Report_retinotopy_stimulus_methods_files/detail_print.css"></head><body><ul class="report combineChildItems"><li id="item-21488" class="item journalArticle"><h2><a href="zotero://select/items/0_BU2K5VP2">Borders, map clusters, and supra-areal organization in visual cortex</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Randy L. Buckner</td></tr><tr class="creator author"><th class="author">Author</th><td>B. T. Thomas Yeo</td></tr><tr class="url"><th class="url">URL</th><td><a href="http://www.sciencedirect.com/science/article/pii/S1053811913012561">http://www.sciencedirect.com/science/article/pii/S1053811913012561</a></td></tr><tr class="volume"><th class="volume">Volume</th><td>93, Part 2</td></tr><tr class="pages"><th class="pages">Pages</th><td>292-297</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>NeuroImage</td></tr><tr class="date"><th class="date">Date</th><td>June 2014</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/10.1016/j.neuroimage.2013.12.036">10.1016/j.neuroimage.2013.12.036</a></td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>V1
 is a canonical cortical area with clearly delineated architectonic 
boundaries and a continuous topographic representation of the visual 
hemifield. It thus serves as a touchstone for understanding what new 
mapping methods can tell us about cortical organization. By parcellating
 human cortex using local gradients in functional connectivity, Wig et 
al. (2014--in this issue) detected the V1 border with V2. By contrast, 
previously-published clustering methods that focus on global similarity 
in connectivity reveal a supra-areal organization that emphasizes 
eccentricity bands spanning V1 and its neighboring extrastriate areas; 
i.e. in the latter analysis, the V1 border is not evident. Thus the 
focus on local connectivity gradients emphasizes qualitatively different
 features of cortical organization than are captured by global 
similarity measures. What is intriguing to consider is that each kind of
 information might be telling us something unique about cortical 
organization. Global similarity measures may be detecting map clusters 
and other supra-areal arrangements that reflect a fundamental level of 
organization.</td></tr></tbody></table></li><li id="item-12506" class="item journalArticle"><h2><a href="zotero://select/items/0_J9GUGTEU">Retinotopic organization in children measured with fMRI</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Ian P. Conner</td></tr><tr class="creator author"><th class="author">Author</th><td>Saloni Sharma</td></tr><tr class="creator author"><th class="author">Author</th><td>Susan K. Lemieux</td></tr><tr class="creator author"><th class="author">Author</th><td>Janine D. Mendola</td></tr><tr class="url"><th class="url">URL</th><td><a href="http://www.journalofvision.org/content/4/6/10">http://www.journalofvision.org/content/4/6/10</a></td></tr><tr class="volume"><th class="volume">Volume</th><td>4</td></tr><tr class="issue"><th class="issue">Issue</th><td>6</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>Journal of Vision</td></tr><tr class="date"><th class="date">Date</th><td>06/18/2004</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/10.1167/4.6.10">10.1167/4.6.10</a></td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>Many
 measures of visual function reach adult levels by about age 5, but some
 visual abilities continue to develop throughout adolescence. Little is 
known about the underlying functional anatomy of visual cortex in human 
infants or children. We used fMRI to measure the retinotopic 
organization of visual cortex in 15 children aged 7–12 years. Overall, 
we obtained adult-like patterns for most children tested. We found that 
significant head motion accounted for poor quality maps in a few tested 
children who were excluded from further analysis. When the maps from 10 
children were compared with those obtained from 10 adults, the magnitude
 of retinotopic signals in visual areas V1, V2, V3, V3A, VP, and V4v was
 essentially the same between children and adults. Furthermore, one 
measure of intra-area organization, the cortical magnification function,
 did not significantly differ between adults and children for V1 or V2. 
However, quantitative analysis of visual area size revealed some 
significant differences beyond V1. Adults had larger extrastriate areas 
(V2, V3, VP, and V4v), when measured absolutely or as a proportion of 
the entire cortical sheet. We found that the extent and laterality of 
retinotopic signals beyond these classically defined areas, in parietal 
and lateral occipital cortex, showed some differences between adults and
 children. These data serve as a useful reference for studies of higher 
cognitive function in pediatric populations and for studies of children 
with vision disorders, such as amblyopia.</td></tr></tbody></table></li><li id="item-15395" class="item journalArticle"><h2><a href="zotero://select/items/0_Q546TI2X">The development and use of phase-encoded functional MRI designs</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Stephen A Engel</td></tr><tr class="volume"><th class="volume">Volume</th><td>62</td></tr><tr class="issue"><th class="issue">Issue</th><td>2</td></tr><tr class="pages"><th class="pages">Pages</th><td>1195-1200</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>NeuroImage</td></tr><tr class="date"><th class="date">Date</th><td>Aug 15, 2012</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/10.1016/j.neuroimage.2011.09.059">10.1016/j.neuroimage.2011.09.059</a></td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>Phase-encoded
 designs advanced the early development of functional MRI, enabling the 
"killer app" of retinotopic mapping, which helped demonstrate fMRI's 
value to a skeptical scientific public. The design, also called "the 
traveling wave", remains in wide use today, due to its ability to easily
 measure neural activity in a parameterized set of experimental 
conditions. In phase-encoded designs, stimuli defined by a numerical 
parameter, for example visual eccentricity, are presented continuously 
in the order specified by the parameter. The stimulus parameter that 
produces maximum response can be recovered from the timing of neural 
activity, i.e. its phase. From the outset, phase-encoded designs were 
used for two related, but complementary purposes: 1) to measure 
aggregate response properties of neurons in a voxel, for example the 
average visual field location of receptive fields, and 2) to segregate 
the set of voxels that corresponds to an organized cortical region, for 
example a retinotopically mapped visual area. This short review will 
cover the history and current uses of phase-encoded fMRI, while noting 
the ongoing tension in the field between the brain mapping and 
computational neuroimaging approaches.</td></tr></tbody></table></li><li id="item-10280" class="item journalArticle"><h2><a href="zotero://select/items/0_KXR8Q2PE">Retinotopic
 Maps, Spatial Tuning, and Locations of Human Visual Areas in Surface 
Coordinates Characterized with Multifocal and Blocked fMRI Designs</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Linda Henriksson</td></tr><tr class="creator author"><th class="author">Author</th><td>Juha Karvonen</td></tr><tr class="creator author"><th class="author">Author</th><td>Niina Salminen-Vaparanta</td></tr><tr class="creator author"><th class="author">Author</th><td>Henry Railo</td></tr><tr class="creator author"><th class="author">Author</th><td>Simo Vanni</td></tr><tr class="url"><th class="url">URL</th><td><a href="http://dx.doi.org/10.1371/journal.pone.0036859">http://dx.doi.org/10.1371/journal.pone.0036859</a></td></tr><tr class="volume"><th class="volume">Volume</th><td>7</td></tr><tr class="issue"><th class="issue">Issue</th><td>5</td></tr><tr class="pages"><th class="pages">Pages</th><td>e36859</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>PLoS ONE</td></tr><tr class="date"><th class="date">Date</th><td>May 9, 2012</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/10.1371/journal.pone.0036859">10.1371/journal.pone.0036859</a></td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>The
 localization of visual areas in the human cortex is typically based on 
mapping the retinotopic organization with functional magnetic resonance 
imaging (fMRI). The most common approach is to encode the response phase
 for a slowly moving visual stimulus and to present the result on an 
individual's reconstructed cortical surface. The main aims of this study
 were to develop complementary general linear model (GLM)-based 
retinotopic mapping methods and to characterize the inter-individual 
variability of the visual area positions on the cortical surface. We 
studied 15 subjects with two methods: a 24-region multifocal 
checkerboard stimulus and a blocked presentation of object stimuli at 
different visual field locations. The retinotopic maps were based on 
weighted averaging of the GLM parameter estimates for the stimulus 
regions. In addition to localizing visual areas, both methods could be 
used to localize multiple retinotopic regions-of-interest. The two 
methods yielded consistent retinotopic maps in the visual areas V1, V2, 
V3, hV4, and V3AB. In the higher-level areas IPS0, VO1, LO1, LO2, TO1, 
and TO2, retinotopy could only be mapped with the blocked stimulus 
presentation. The gradual widening of spatial tuning and an increase in 
the responses to stimuli in the ipsilateral visual field along the 
hierarchy of visual areas likely reflected the increase in the average 
receptive field size. Finally, after registration to Freesurfer's 
surface-based atlas of the human cerebral cortex, we calculated the mean
 and variability of the visual area positions in the spherical 
surface-based coordinate system and generated probability maps of the 
visual areas on the average cortical surface. The inter-individual 
variability in the area locations decreased when the midpoints were 
calculated along the spherical cortical surface compared with volumetric
 coordinates. These results can facilitate both analysis of individual 
functional anatomy and comparisons of visual cortex topology across 
studies.</td></tr></tbody></table></li><li id="item-8514" class="item journalArticle"><h2><a href="zotero://select/items/0_VVKW3SGM">Quantifying spatial uncertainty of visual area boundaries in neuroimaging data</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Dean Kirson</td></tr><tr class="creator author"><th class="author">Author</th><td>Alexander C Huk</td></tr><tr class="creator author"><th class="author">Author</th><td>Lawrence K Cormack</td></tr><tr class="url"><th class="url">URL</th><td><a href="http://www.ncbi.nlm.nih.gov/pubmed/19146352">http://www.ncbi.nlm.nih.gov/pubmed/19146352</a></td></tr><tr class="volume"><th class="volume">Volume</th><td>8</td></tr><tr class="issue"><th class="issue">Issue</th><td>10</td></tr><tr class="pages"><th class="pages">Pages</th><td>10.1-15</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>Journal of Vision</td></tr><tr class="date"><th class="date">Date</th><td>2008</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/10.1167/8.10.10">10.1167/8.10.10</a></td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>Functional
 magnetic resonance imaging (fMRI) of the human brain has provided much 
information about visual cortex. These insights hinge on researchers' 
ability to identify cortical areas based on stimulus selectivity and 
retinotopic mapping. However, border identification around regions of 
interest or between retinotopic maps is often performed without 
characterizing the degree of certainty associated with the location of 
these key features; ideally, assertions about the location of boundaries
 would have an associated spatial confidence interval. We describe an 
approach that allows researchers to transform estimates of error in the 
intensive dimension (i.e., activation of voxels) to the spatial 
dimension (i.e., the location of features evident in patterns across 
voxels). We implement the approach by bootstrapping, with applications 
to: (1) the location of human MT+ and (2) the location of the V1/V2 
boundary. The transformation of intensive to spatial error furnishes 
graphical, intuitive characterizations of spatial uncertainty akin to 
error bars on the borders of visual areas, instead of the conventional 
practice of computing and thresholding p-values for voxels. This 
approach provides a general, unbiased arena for evaluating: (1) 
competing conceptions of visual area organization; (2) analysis 
technique efficacy; and (3) data quality.</td></tr></tbody></table></li><li id="item-22861" class="item journalArticle"><h2><a href="zotero://select/items/0_FMQ9IIII">Comparison of randomized multifocal mapping and temporal phase mapping of visual cortex for clinical use</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Yan Ma</td></tr><tr class="creator author"><th class="author">Author</th><td>B. Douglas Ward</td></tr><tr class="creator author"><th class="author">Author</th><td>Kristina M. Ropella</td></tr><tr class="creator author"><th class="author">Author</th><td>Edgar A. DeYoe</td></tr><tr class="url"><th class="url">URL</th><td><a href="http://www.sciencedirect.com/science/article/pii/S2213158213001046">http://www.sciencedirect.com/science/article/pii/S2213158213001046</a></td></tr><tr class="volume"><th class="volume">Volume</th><td>3</td></tr><tr class="pages"><th class="pages">Pages</th><td>143-154</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>NeuroImage: Clinical</td></tr><tr class="date"><th class="date">Date</th><td>2013</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/10.1016/j.nicl.2013.08.004">10.1016/j.nicl.2013.08.004</a></td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>fMRI
 is becoming an important clinical tool for planning and guidance of 
surgery to treat brain tumors, arteriovenous malformations, and 
epileptic foci. For visual cortex mapping, the most popular paradigm by 
far is temporal phase mapping, although random multifocal stimulation 
paradigms have drawn increased attention due to their ability to 
identify complex response fields and their random properties. In this 
study we directly compared temporal phase and multifocal vision mapping 
paradigms with respect to clinically relevant factors including: time 
efficiency, mapping completeness, and the effects of noise. Randomized, 
multifocal mapping accurately decomposed the response of single voxels 
to multiple stimulus locations and made correct retinotopic assignments 
as noise levels increased despite decreasing sensitivity. Also, 
multifocal mapping became less efficient as the number of stimulus 
segments (locations) increased from 13 to 25 to 49 and when duty cycle 
was increased from 25% to 50%. Phase mapping, on the other hand, 
activated more extrastriate visual areas, was more time efficient in 
achieving statistically significant responses, and had better 
sensitivity as noise increased, though with an increase in systematic 
retinotopic mis-assignments. Overall, temporal phase mapping is likely 
to be a better choice for routine clinical applications though random 
multifocal mapping may offer some unique advantages for selected 
applications.</td></tr></tbody></table></li><li id="item-5668" class="item journalArticle"><h2><a href="zotero://select/items/0_86EVEQAN">How much luxury is there in 'luxury perfusion'? An analysis of the BOLD response in the visual areas V1 and V2</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Valentine L Marcar</td></tr><tr class="creator author"><th class="author">Author</th><td>Thomas Loenneker</td></tr><tr class="creator author"><th class="author">Author</th><td>Andrea Straessle</td></tr><tr class="creator author"><th class="author">Author</th><td>Franck Girard</td></tr><tr class="creator author"><th class="author">Author</th><td>Ernst Martin</td></tr><tr class="url"><th class="url">URL</th><td><a href="http://www.ncbi.nlm.nih.gov/pubmed/15288132">http://www.ncbi.nlm.nih.gov/pubmed/15288132</a></td></tr><tr class="volume"><th class="volume">Volume</th><td>22</td></tr><tr class="issue"><th class="issue">Issue</th><td>7</td></tr><tr class="pages"><th class="pages">Pages</th><td>921-928</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>Magnetic Resonance Imaging</td></tr><tr class="date"><th class="date">Date</th><td>Sep 2004</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/10.1016/j.mri.2004.02.013">10.1016/j.mri.2004.02.013</a></td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>We
 re-analyzed the functional magnetic resonance imaging data from a study
 involving awake, adult, human volunteers in order to examine the 
influence of vascular density on the blood oxygenation level-dependent 
(BOLD) response. We employed a flashed and reversing stimulus paradigm 
where the latter stimulated twice the number of receptive fields and 
with it doubled the neuronal metabolic load (CMRO2) compared to the 
former stimulus. The blood flow increase to these stimuli was identical,
 so that differences in the BOLD response are due to differences in the 
oxygen extraction fraction. By comparing the BOLD response in human 
striate cortex (V1) and its neighbor, extra-striate area V2 to the two 
stimuli, we were able to determine the influence of the higher vascular 
density of striate cortex on the BOLD response. In striate cortex, the 
extent of activation, as measured by the number of activated voxels, was
 larger for the flashed than for the reversing stimulus. In 
extra-striate area V2, no such difference in the extent of activation 
was noted. Gauging the local concentration of HbR using deltaR2*, we 
found it to be significantly lower for the flashed than for the 
reversing checkerboard. We estimated the HbR concentration in 
extra-striate area V2 to be double that of striate cortex independent of
 the stimulus presented. A frequency distribution of the deltaR2* values
 for the flashed and reversing checkerboard revealed a shift consistent 
with an increase in the HbR concentration between areas V1 and V2. The 
metabolically most demanding stimulus, the reversing checkerboard was 
associated with the highest HbR concentration and with the largest 
number of voxels with a negative BOLD response.</td></tr></tbody></table></li><li id="item-9273" class="item journalArticle"><h2><a href="zotero://select/items/0_QRE6EICP">Quantitative multifocal fMRI shows active suppression in human V1</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Miika Pihlaja</td></tr><tr class="creator author"><th class="author">Author</th><td>Linda Henriksson</td></tr><tr class="creator author"><th class="author">Author</th><td>Andrew C James</td></tr><tr class="creator author"><th class="author">Author</th><td>Simo Vanni</td></tr><tr class="url"><th class="url">URL</th><td><a href="http://www.ncbi.nlm.nih.gov/pubmed/18381768">http://www.ncbi.nlm.nih.gov/pubmed/18381768</a></td></tr><tr class="volume"><th class="volume">Volume</th><td>29</td></tr><tr class="issue"><th class="issue">Issue</th><td>9</td></tr><tr class="pages"><th class="pages">Pages</th><td>1001-1014</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>Human Brain Mapping</td></tr><tr class="date"><th class="date">Date</th><td>Sep 2008</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/10.1002/hbm.20442">10.1002/hbm.20442</a></td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>Multifocal
 functional magnetic resonance imaging has recently been introduced as 
an alternative method for retinotopic mapping, and it enables effective 
functional localization of multiple regions-of-interest in the visual 
cortex. In this study we characterized interactions in V1 with spatially
 and temporally identical stimuli presented alone, or as a part of a 
nine-region multifocal stimulus. We compared stimuli at different 
contrasts, collinear and orthogonal orientations and spatial frequencies
 one octave apart. Results show clear attenuation of BOLD signal from 
the central region in the multifocal condition. The observed modulation 
in BOLD signal could be produced either by neural suppression resulting 
from stimulation of adjacent regions of visual field, or alternatively 
by hemodynamic saturation or stealing effects in V1. However, we find 
that attenuation of the central response persists through a range of 
contrasts, and that its strength varies with relative orientation and 
spatial frequency of the central and surrounding stimulus regions, 
indicating active suppression mechanisms of neural origin. Our results 
also demonstrate that the extent of the signal spreading is commensurate
 with the extent of the horizontal connections in primate V1.</td></tr></tbody></table></li><li id="item-6110" class="item journalArticle"><h2><a href="zotero://select/items/0_QDTBBS2T">Localizing Retinotopic fMRI Activation in Human Primary Visual Cortex via Dynamic Programming</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Anqi Qiu</td></tr><tr class="creator author"><th class="author">Author</th><td>Benjamin Rosenau</td></tr><tr class="creator author"><th class="author">Author</th><td>Adam Greenberg</td></tr><tr class="creator author"><th class="author">Author</th><td>Patrick Barta</td></tr><tr class="creator author"><th class="author">Author</th><td>Steven Yantis</td></tr><tr class="creator author"><th class="author">Author</th><td>Michael Miller</td></tr><tr class="url"><th class="url">URL</th><td><a href="http://www.ncbi.nlm.nih.gov/pubmed/17282437">http://www.ncbi.nlm.nih.gov/pubmed/17282437</a></td></tr><tr class="volume"><th class="volume">Volume</th><td>2</td></tr><tr class="pages"><th class="pages">Pages</th><td>1313-1316</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>Conference
 Proceedings: ... Annual International Conference of the IEEE 
Engineering in Medicine and Biology Society. IEEE Engineering in 
Medicine and Biology Society. Conference</td></tr><tr class="date"><th class="date">Date</th><td>2005</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/10.1109/IEMBS.2005.1616668">10.1109/IEMBS.2005.1616668</a></td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>This
 paper presents an approach for automatically delineating the borders of
 human primary visual cortex and finding ridges of maximal response due 
to static phase-encoding stimuli on fMRI t-statistical maps via dynamic 
programming. The sensitivity of such an approach to the choice of 
initial starting and ending points and the identification of the ridge 
path over a wide response region are addressed. Moreover, retinotopic 
maps for left and right visual cortex are shown in a population of two 
normal subjects.</td></tr></tbody></table></li><li id="item-8821" class="item journalArticle"><h2><a href="zotero://select/items/0_PACSVFU6">Estimating linear cortical magnification in human primary visual cortex via dynamic programming</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Anqi Qiu</td></tr><tr class="creator author"><th class="author">Author</th><td>Benjamin J Rosenau</td></tr><tr class="creator author"><th class="author">Author</th><td>Adam S Greenberg</td></tr><tr class="creator author"><th class="author">Author</th><td>Monica K Hurdal</td></tr><tr class="creator author"><th class="author">Author</th><td>Patrick Barta</td></tr><tr class="creator author"><th class="author">Author</th><td>Steven Yantis</td></tr><tr class="creator author"><th class="author">Author</th><td>Michael I Miller</td></tr><tr class="volume"><th class="volume">Volume</th><td>31</td></tr><tr class="issue"><th class="issue">Issue</th><td>1</td></tr><tr class="pages"><th class="pages">Pages</th><td>125-38</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>NeuroImage</td></tr><tr class="date"><th class="date">Date</th><td>May 15, 2006</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/S1053-8119%2805%2902519-X">S1053-8119(05)02519-X</a></td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>Human
 primary visual cortex is organized retinotopically, with adjacent 
locations in cortex representing adjacent locations on the retina. The 
spatial sampling in cortex is highly nonuniform: the amount of cortex 
devoted to a unit area of retina decreases with increasing retinal 
eccentricity. This sampling property can be quantified by the linear 
cortical magnification factor, which is expressed in terms of 
millimeters of cortex per degree of visual angle. In this paper, we 
present a new method using dynamic programming and fMRI retinotopic 
eccentricity mapping to estimate the linear cortical magnification 
factor in human primary visual cortex (V1). We localized cortical 
activity while subjects viewed each of seven stationary contrast- 
reversing radial checkerboard rings of equal thickness that tiled the 
visual field from 1.62 to 12.96 degrees of eccentricity. Imaging data 
from all epochs of each ring were contrasted with data from fixation 
epochs on a subject-by-subject basis. The resulting t statistic maps 
were then superimposed on a local coordinate system constructed from the
 gray/white matter boundary surface of each individual subject's 
occipital lobe, separately for each ring. Smoothed maps of functional 
activity on the cortical surface were constructed using orthonormal 
bases of the Laplace-Beltrami operator that incorporate the geometry of 
the cortical surface. This allowed us to stably track the ridge of 
maximum activation due to each ring via dynamic programming optimization
 over all possible paths on the cortical surface. We estimated the 
linear cortical magnification factor by calculating geodesic distances 
between activation ridges on the cortical surface in a population of 
five normal subjects. The reliability of these estimates was assessed by
 comparing results based on data from one quadrant to those based on 
data from the full hemifield along with a split-half reliability 
analysis.</td></tr></tbody></table></li><li id="item-9194" class="item journalArticle"><h2><a href="zotero://select/items/0_TZ64WP3I">Two-dimensional mapping of the central and parafoveal visual field to human visual cortex</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Mark M Schira</td></tr><tr class="creator author"><th class="author">Author</th><td>Alex R Wade</td></tr><tr class="creator author"><th class="author">Author</th><td>Christopher W Tyler</td></tr><tr class="volume"><th class="volume">Volume</th><td>97</td></tr><tr class="issue"><th class="issue">Issue</th><td>6</td></tr><tr class="pages"><th class="pages">Pages</th><td>4284-95</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>Journal of Neurophysiology</td></tr><tr class="date"><th class="date">Date</th><td>Jun 2007</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/00972.2006">00972.2006</a></td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>Primate
 visual cortex contains a set of maps of visual space. These maps are 
fundamental to early visual processing, yet their form is not fully 
understood in humans. This is especially true for the central and most 
important part of the visual field--the fovea. We used functional 
magnetic resonance imaging (fMRI) to measure the mapping geometry of 
human V1 and V2 down to 0.5 degrees of eccentricity. By applying 
automated atlas fitting procedures to parametrize and average 
retinotopic measurements of eight brains, we provide a reference 
standard for the two-dimensional geometry of human early visual cortex 
of unprecedented precision and analyze this high-quality mean dataset 
with respect to the 2-dimensional cortical magnification morphometry. 
The analysis indicates that 1) area V1 has meridional isotropy in areal 
projection: equal areas of visual space are mapped to equal areas of 
cortex at any given eccentricity. 2) V1 has a systematic pattern of 
local anisotropies: cortical magnification varies between isopolar and 
isoeccentricity lines, and 3) the shape of V1 deviates systematically 
from the complex-log model, the fit of which is particularly poor close 
to the fovea. We therefore propose that human V1 be fitted by models 
based on an equal-area principle of its two-dimensional magnification. 
4) V2 is elongated by a factor of 2 in eccentricity direction relative 
to V1 and has significantly more local anisotropy. We propose that V2 
has systematic intrinsic curvature, but V1 is intrinsically flat.</td></tr></tbody></table></li><li id="item-15373" class="item journalArticle"><h2><a href="zotero://select/items/0_WCVGWBSA">Analysis of retinotopic maps in extrastriate cortex</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>M I Sereno</td></tr><tr class="creator author"><th class="author">Author</th><td>C T McDonald</td></tr><tr class="creator author"><th class="author">Author</th><td>J M Allman</td></tr><tr class="volume"><th class="volume">Volume</th><td>4</td></tr><tr class="issue"><th class="issue">Issue</th><td>6</td></tr><tr class="pages"><th class="pages">Pages</th><td>601-620</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>Cerebral cortex (New York, N.Y.: 1991)</td></tr><tr class="date"><th class="date">Date</th><td>1994 Nov-Dec</td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>Two
 new techniques for analyzing retinotopic maps--arrow diagrams and 
visual field sign maps--are demonstrated with a large 
electrophysiological mapping data set from owl monkey extrastriate 
visual cortex. An arrow diagram (vectors indicating receptive field 
centers placed at cortical coordinates) provides a more compact and 
understandable representation of retinotopy than does a standard 
receptive field chart (accompanied by a penetration map) or a double 
contour map (e.g., isoeccentricity and isopolar angle as a function of 
cortical x, y-coordinates). None of these three representational 
techniques, however, make separate areas easily visible, especially in 
data sets containing numerous areas with partial, distorted 
representations of the visual hemifield. Therefore, we computed visual 
field sign maps (non-mirror-image vs mirror-image visual field 
representation) from the angle between the direction of the cortical 
gradient in receptive field eccentricity and the cortical gradient in 
receptive field angle for each small region of the cortex. Visual field 
sign is a local measure invariant to cortical map orientation and 
distortion but also to choice of receptive field coordinate system. To 
estimate the gradients, we first interpolated the eccentricity and polar
 angle data onto regular grids using a distance-weighted smoothing 
algorithm. The visual field sign technique provides a more objective 
method for using retinotopy to outline multiple visual areas. In order 
to relate these arrow and visual field sign maps accurately to 
architectonic features visualized in the stained, flattened cortex, we 
also developed a deformable template algorithm for warping the 
photograph-derived penetration map using the final observed location of a
 set of marking lesions.</td></tr></tbody></table></li><li id="item-21465" class="item conferencePaper"><h2><a href="zotero://select/items/0_P2WHC4JB">Characterizing human retinotopic mapping with conformal geometry: a preliminary study</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Duyan Ta</td></tr><tr class="creator author"><th class="author">Author</th><td>Jie Shi</td></tr><tr class="creator author"><th class="author">Author</th><td>Brian Barton</td></tr><tr class="creator author"><th class="author">Author</th><td>Alyssa Brewer</td></tr><tr class="creator author"><th class="author">Author</th><td>Zhong-Lin Lu</td></tr><tr class="creator author"><th class="author">Author</th><td>Yalin Wang</td></tr><tr class="url"><th class="url">URL</th><td><a href="http://dx.doi.org/10.1117/12.2043570">http://dx.doi.org/10.1117/12.2043570</a></td></tr><tr class="volume"><th class="volume">Volume</th><td>9034</td></tr><tr class="pages"><th class="pages">Pages</th><td>90342A-90342A-10</td></tr><tr class="date"><th class="date">Date</th><td>2014</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/10.1117/12.2043570">10.1117/12.2043570</a></td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>Functional
 magnetic resonance imaging (fMRI) has been widely used to measure the 
retinotopic organization of early visual cortex in the human brain. 
Previous studies have identified multiple visual field maps (VFMs) based
 on statistical analysis of fMRI signals, but the resulting geometry has
 not been fully characterized with mathematical models. Here we test 
whether VFMs V1 and V2 obey the least restrictive of all geometric 
mappings; that is, whether they are anglepreserving and therefore 
maintain conformal mapping. We measured retinotopic organization in 
individual subjects using standard traveling-wave fMRI methods. Visual 
stimuli consisted of black and white, drifting checkerboards comprising 
rotating wedges and expanding rings to measure the cortical 
representations of polar angle and eccentricity, respectively. These 
representations were then projected onto a 3D cortical mesh of each 
hemisphere. By generating a mapped unit disk that is conformal of the 
VFMs using spherical stereographic projection and computing the 
parameterized coordinates of the eccentricity and polar angle gradients,
 we computed Beltrami coefficients to check whether the mapping from the
 visual field to the V1 and V2 cortical representations is conformal. We
 find that V1 and V2 exhibit local conformality. Our analysis of the 
Beltrami coefficient shows that selected regions of V1 and V2 that 
contain reasonably smooth eccentricity and polar angle gradients do show
 significant local conformality, warranting further investigation of 
this approach for analysis of early and higher visual cortex. These 
results suggest that such a mathematical model can be used to 
characterize the early VFMs in human visual cortex.</td></tr></tbody></table></li><li id="item-9242" class="item journalArticle"><h2><a href="zotero://select/items/0_4546TWCH">fMRI retinotopic mapping at 3 T: Benefits gained from correcting the spatial distortions due to static field inhomogeneity</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Flor Vasseur</td></tr><tr class="creator author"><th class="author">Author</th><td>Chantal Delon-Martin</td></tr><tr class="creator author"><th class="author">Author</th><td>Cécile Bordier</td></tr><tr class="creator author"><th class="author">Author</th><td>Jan Warnking</td></tr><tr class="creator author"><th class="author">Author</th><td>Laurent Lamalle</td></tr><tr class="creator author"><th class="author">Author</th><td>Christoph Segebarth</td></tr><tr class="creator author"><th class="author">Author</th><td>Michel Dojat</td></tr><tr class="url"><th class="url">URL</th><td><a href="http://www.journalofvision.org/content/10/12/30.abstract">http://www.journalofvision.org/content/10/12/30.abstract</a></td></tr><tr class="volume"><th class="volume">Volume</th><td>10</td></tr><tr class="issue"><th class="issue">Issue</th><td>12</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>Journal of Vision</td></tr><tr class="date"><th class="date">Date</th><td>October 25 , 2010</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/10.1167/10.12.30">10.1167/10.12.30</a></td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>fMRI
 retinotopic mapping usually relies upon Fourier analysis of functional 
responses to periodic visual stimuli that encode eccentricity or polar 
angle in the visual field. Generally, phase estimations are assigned to a
 surface model of the cerebral cortex and borders between retinotopic 
areas are eventually determined following ad hoc phase analysis on the 
surface model. Assigning functional responses to a surface model of the 
cortex is particularly sensitive to geometric distortions of the 3D 
functional data due to static field inhomogeneity. Here, we assess and 
document the benefits gained from correcting the fMRI data for these 
effects, under standard experimental conditions (echo-planar imaging, 
3.0-T field strength) and with well-chosen acquisition parameters 
(regarding slice orientation and phase-encoding direction). While it 
appears that, in the absence of correction, errors in the estimates of 
the borders between low-order visual areas do not then significantly 
exceed the variance of statistical origin, about half of the functional 
responses in a retinotopic experiment are misassigned to neighboring 
functional areas. Therefore, correction of the effects due to geometric 
distortions is important in any retinotopic mapping experiment and by 
extension in any fMRI experiment on the visual system.</td></tr></tbody></table></li><li id="item-21466" class="item journalArticle"><h2><a href="zotero://select/items/0_9BUA3RGA">Visual Field Maps of the Human Visual Cortex for Central and Peripheral Vision</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Bin Wang</td></tr><tr class="creator author"><th class="author">Author</th><td>Hiroki Yamamoto</td></tr><tr class="creator author"><th class="author">Author</th><td>Jinglong Wu</td></tr><tr class="creator author"><th class="author">Author</th><td>Yoshimichi Ejima</td></tr><tr class="volume"><th class="volume">Volume</th><td>1</td></tr><tr class="issue"><th class="issue">Issue</th><td>2</td></tr><tr class="pages"><th class="pages">Pages</th><td>102-110</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>Neuroscience and Biomedical Engineering</td></tr><tr class="date"><th class="date">Date</th><td>2013-09-01T00:00:00///</td></tr><tr class="abstractNote"><th class="abstractNote">Abstract</th><td>In
 humans, visual information in the peripheral visual field is processed 
differently from such information in the central visual field. For 
example, peripheral vision prefers coarser information, while central 
vision prefers finer details. Recent advances in neuroimaging allowed us
 to non-invasively explore the neural substrates underlying the 
distinctiveness of peripheral and central vision. In the human visual 
cortex, there is a mosaic of orderly representations of the visual 
field, and this organization is called a visual field map. In this 
review, we summarize the various strands of research on visual field 
maps, which are crucial to understand human peripheral vision. We first 
describe the techniques that are used to measure visual field maps by 
means of functional magnetic resonance imaging (fMRI). We then review 
several studies that have tried to locate human visual areas using these
 techniques. We focused particularly on the findings of retinotopic 
organization for “far” peripheral visual field and tried to find the 
cortical regions that are crucial for peripheral vision.</td></tr></tbody></table></li><li id="item-6509" class="item journalArticle"><h2><a href="zotero://select/items/0_C5XP7SQV">Development of a wide‐view visual presentation system for visual retinotopic mapping during functional MRI</a></h2><table><tbody><tr class="creator author"><th class="author">Author</th><td>Tianyi Yan</td></tr><tr class="creator author"><th class="author">Author</th><td>Fengzhe Jin</td></tr><tr class="creator author"><th class="author">Author</th><td>Jiping He</td></tr><tr class="creator author"><th class="author">Author</th><td>Jinglong Wu</td></tr><tr class="url"><th class="url">URL</th><td><a href="http://onlinelibrary.wiley.com/doi/10.1002/jmri.22404/abstract">http://onlinelibrary.wiley.com/doi/10.1002/jmri.22404/abstract</a></td></tr><tr class="volume"><th class="volume">Volume</th><td>33</td></tr><tr class="issue"><th class="issue">Issue</th><td>2</td></tr><tr class="pages"><th class="pages">Pages</th><td>441-447</td></tr><tr class="publicationTitle"><th class="publicationTitle">Publication</th><td>Journal of Magnetic Resonance Imaging</td></tr><tr class="date"><th class="date">Date</th><td>2011/02/01</td></tr><tr class="DOI"><th class="DOI">DOI</th><td><a href="http://doi.org/10.1002/jmri.22404">10.1002/jmri.22404</a></td></tr></tbody></table></li><li id="item-15407" class="item webpage"><h2><a href="zotero://select/items/0_URHEKK43">VISTA LAB</a></h2><table><tbody><tr class="url"><th class="url">URL</th><td><a href="https://www.stanford.edu/group/vista/cgi-bin/home/">https://www.stanford.edu/group/vista/cgi-bin/home/</a></td></tr></tbody></table></li><li id="item-15409" class="item webpage"><h2><a href="zotero://select/items/0_UMZ4UZEJ">Retinotopy Tutorial - Vista Lab Manual</a></h2><table><tbody><tr class="url"><th class="url">URL</th><td><a href="http://white.stanford.edu/newlm/index.php/Retinotopy_Tutorial">http://white.stanford.edu/newlm/index.php/Retinotopy_Tutorial</a></td></tr></tbody></table></li><li id="item-15411" class="item webpage"><h2><a href="zotero://select/items/0_RSEFHRDM">Atlas - Vista Lab Manual</a></h2><table><tbody><tr class="url"><th class="url">URL</th><td><a href="http://white.stanford.edu/newlm/index.php/Atlas">http://white.stanford.edu/newlm/index.php/Atlas</a></td></tr></tbody></table></li></ul></body></html>