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Hemodynamic Traveling Waves in Human Visual Cortex

Overview of attention for article published in PLoS Computational Biology, March 2012
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Title
Hemodynamic Traveling Waves in Human Visual Cortex
Published in
PLoS Computational Biology, March 2012
DOI 10.1371/journal.pcbi.1002435
Pubmed ID
Authors

Kevin M. Aquino, Mark M. Schira, P. A. Robinson, Peter M. Drysdale, Michael Breakspear

Abstract

Functional MRI (fMRI) experiments rely on precise characterization of the blood oxygen level dependent (BOLD) signal. As the spatial resolution of fMRI reaches the sub-millimeter range, the need for quantitative modelling of spatiotemporal properties of this hemodynamic signal has become pressing. Here, we find that a detailed physiologically-based model of spatiotemporal BOLD responses predicts traveling waves with velocities and spatial ranges in empirically observable ranges. Two measurable parameters, related to physiology, characterize these waves: wave velocity and damping rate. To test these predictions, high-resolution fMRI data are acquired from subjects viewing discrete visual stimuli. Predictions and experiment show strong agreement, in particular confirming BOLD waves propagating for at least 5-10 mm across the cortical surface at speeds of 2-12 mm s-1. These observations enable fundamentally new approaches to fMRI analysis, crucial for fMRI data acquired at high spatial resolution.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 2%
Netherlands 2 1%
United Kingdom 2 1%
Italy 1 <1%
Cuba 1 <1%
Chile 1 <1%
Australia 1 <1%
Canada 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 139 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 28%
Researcher 39 25%
Student > Master 16 10%
Professor > Associate Professor 8 5%
Professor 6 4%
Other 22 14%
Unknown 19 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 20%
Neuroscience 25 16%
Psychology 14 9%
Physics and Astronomy 13 8%
Engineering 13 8%
Other 31 20%
Unknown 27 18%