English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Neuronal and hemodynamic events from fMRI time-series

MPS-Authors
/persons/resource/persons19800

Kruggel,  Frithjof J.
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons20125

Zysset,  Stefan
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons20070

von Cramon,  D. Yves
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Rajapakse, J., Kruggel, F. J., Zysset, S., & von Cramon, D. Y. (1998). Neuronal and hemodynamic events from fMRI time-series. Journal of Advanced Computational Intelligence, 2(6), 185-194. doi:10.20965/jaciii.1998.p0185.


Cite as: http://hdl.handle.net/21.11116/0000-0003-3A2D-B
Abstract
Time-series provided by high-resolution functional MR imaging (fMRI) bear rich information of underlying physiological processes and associated hemodynamic events of human brain activation during sensory and cognitive stimulation. A computational model to represent neuronal and hemodynamic events in fMRI time-series is presented where the transient neuronal activities are modeled with exponential functions and coupling between neuronal response and hemodynamic response is approximated by a linear convolution. The hemodynamic parameters, namely lag and dispersion, and neuronal parameters, namely rise time and fall time, quantitate some of the neuronal and hemodynamic events following a sensory, motor, or cognitive task. Methods to estimate neuronal responses with hemodynamic demodulation and parameters assuming exponential transient changes are presented. Experiments with simulated time-series demonstrate the robustness of the parameter estimation scheme and with fMRI data obtained in a memory retrieval task is used to illustrate how the model parameters can improve detection of relevant activation in fMRI. This paper highlights the potentials of fMRI to study neuronal populations and the use of the proposed model in identifying neurophysiological events of brain function.