Abstract #4133
Shannon entropy method applied to fMRI data series during evoked and resting state activity
Mauro DiNuzzo 1,2 , Daniele Mascali 1,2 , Marta Moraschi 1,3 , Michela Fratini 1,3 , Tommaso Gili 3 , Girolamo Garreffa 1,3 , Bruno Maraviglia 1,3 , and Federico Giove 1,2
1
MARBILab, Enrico Fermi Center, Rome, Rome,
Italy,
2
Department
of Physics, U Sapienza, Rome, Rome, Italy,
3
Santa
Lucia Foundation IRCCS, Rome, Italy
We applied Shannon entropy method to fMRI time series in
order to examine whether and how information can be
extracted from different experimental paradigms, namely
evoked or resting brain (RS) activity. Shannon entropy
measures information content of the signal without
making a priori assumptions. We found a striking match
between the high-entropy voxels and activated voxels,
while RS data did not reveal any cluster of high-entropy
values. This finding indicates that RS activity cannot
be extracted using a code (i.e., probability
distribution) determined at the voxel-level, paving the
way for different approaches to determine the code
underlying RS activity.
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