English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Book Chapter

Functional magnetic resonance imaging adaptation: a technique for studying the properties of neuronal networks

MPS-Authors
/persons/resource/persons84260

Tolias,  AS
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84023

Kourtzi,  Z
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84063

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;

External Resource

https://dl.acm.org/doi/10.5555/949823.949832
(Publisher version)

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Tolias, A., Kourtzi, Z., & Logothetis, N. (2002). Functional magnetic resonance imaging adaptation: a technique for studying the properties of neuronal networks. In F. Sommer, & A. Wichert (Eds.), Exploratory analysis and data modeling in functional neuroimaging (pp. 109-125). Cambridge, MA, USA: MIT Press.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DE34-2
Abstract
Functional magnetic resonance imaging can be used to study the networks of neurons that underline different behaviors. The blood oxygenation level-dependent signal though, measures the activity averaged across heterogeneous population of neurons with different response characteristics. It is therefore often impossible to infer the properties of the underlying imaged neural populations by simply examining the fMRI signal. Here, we describe the use of an adaptation paradigm to study the properties of neuronal populations beyond the spatial resolution of fMRI.