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Meeting Abstract

Multi-resolution classification analysis of ocular dominance columns obtained from human V1 at 7 Tesla: mechanisms underlying decoding signals

MPG-Autoren
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Chaimow,  D
Former Department MRZ, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Logothetis,  NK
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Raddatz,  G
Former Department MRZ, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Shmuel,  A
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen

https://cds.ismrm.org/ismrm-2008/files/1_program.pdf
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Zitation

Chaimow, D., Logothetis, N., Raddatz, G., Shmuel, A., Ugurbil, K., & Yacoub, E. (2008). Multi-resolution classification analysis of ocular dominance columns obtained from human V1 at 7 Tesla: mechanisms underlying decoding signals. In 16th Scientific Meeting and Exhibition of the International Society of Magnetic Resonance in Medicine (ISMRM 2008) (pp. 27).


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-C99B-1
Zusammenfassung
Recent studies have demonstrated that classification algorithms applied to human fMRI data can decode information segregated in cortical columns, although the voxel-size was large relative to the width of columns. The mechanism by which low-resolution imaging decodes information represented at higher resolution is not clear. We show that using GE-fMRI signals, the mechanism underlying the decoding signals involves contributions from both gray matter and macroscopic blood vessels. We hypothesize that draining regions biased towards columns with preference to one eye underlie the specificity of
vessels. Decoding at high-resolution is superior to low-resolution when applied to data from small cortical volumes.