Self-organizing maps and entropy applied to data analysis of functional magnetic resonance images

Kohonen self-organizing maps (SOM) and Shannon entropy were applied together for the analysis of data from functional magnetic resonance imaging (fMRI). To increase the efficiency of SOM in the search for patterns of activation in fMRI data, first, we applied the Shannon entropy in order to eliminate signals possibly related to noise sources. The procedure with these techniques was applied to simulated data and on real hearing experiment, the results showed that the application of entropy and SOM is a good tool to the identification of areas of activity.

[1]  G. Radda,et al.  Oxygenation dependence of the transverse relaxation time of water protons in whole blood at high field. , 1982, Biochimica et biophysica acta.

[2]  Karl J. Friston,et al.  Bayesian Estimation of Dynamical Systems: An Application to fMRI , 2002, NeuroImage.

[3]  Scott J Peltier,et al.  Detecting low‐frequency functional connectivity in fMRI using a self‐organizing map (SOM) algorithm , 2003, Human brain mapping.

[4]  C. Windischberger,et al.  Quantification in functional magnetic resonance imaging: fuzzy clustering vs. correlation analysis. , 1998, Magnetic resonance imaging.

[5]  Karl J. Friston,et al.  Movement‐Related effects in fMRI time‐series , 1996, Magnetic resonance in medicine.

[6]  Claus Svarer,et al.  Cluster analysis of activity‐time series in motor learning , 2002, Human brain mapping.

[7]  L. Shah,et al.  Functional magnetic resonance imaging. , 2010, Seminars in roentgenology.

[8]  Stanley L. Sclove,et al.  Estimation and classification of fMRI hemodynamic response patterns , 2004, NeuroImage.

[9]  Qin Yang,et al.  Analysis of fMRI Data Using Improved Self-Organizing Mapping and Spatio-Temporal Metric Hierarchical Clustering , 2008, IEEE Transactions on Medical Imaging.

[10]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[11]  Lin Chen,et al.  An integrated neighborhood correlation and hierarchical clustering approach of functional MRI , 2006, IEEE Transactions on Biomedical Engineering.

[12]  Oswaldo Baffa,et al.  Shannon entropy applied to the analysis of event-related fMRI time series , 2003, NeuroImage.

[13]  Vince D. Calhoun,et al.  Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering , 2011, NeuroImage.

[14]  Essa Yacoub,et al.  Node merging in Kohonen's self-organizing mapping of fMRI data , 2002, Artif. Intell. Medicine.

[15]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[16]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[17]  R. Turner,et al.  Event-Related fMRI: Characterizing Differential Responses , 1998, NeuroImage.

[18]  M J Brammer,et al.  The analysis of functional magnetic resonance images , 1997, Statistical methods in medical research.

[19]  Yul-Wan Sung,et al.  Functional magnetic resonance imaging , 2004, Scholarpedia.

[20]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[21]  J Hennig,et al.  Neural network‐based analysis of MR time series , 1999, Magnetic resonance in medicine.

[22]  C. D. Coryell,et al.  The Magnetic Properties and Structure of Hemoglobin, Oxyhemoglobin and Carbonmonoxyhemoglobin , 1936, Proceedings of the National Academy of Sciences.

[23]  B. Biswal,et al.  Blind source separation of multiple signal sources of fMRI data sets using independent component analysis. , 1999, Journal of computer assisted tomography.

[24]  Rajesh Nandy,et al.  Cluster analysis of fMRI data using dendrogram sharpening , 2003, Human brain mapping.

[25]  Nestor Caticha,et al.  Multigrid priors for a Bayesian approach to fMRI , 2004, NeuroImage.

[26]  G. Glover.  Deconvolution of Impulse Response in Event-Related BOLD fMRI1 , 1999, NeuroImage.

[27]  Huafeng Liu,et al.  Nonlinear Analysis of the BOLD Signal , 2009, EURASIP J. Adv. Signal Process..

[28]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[29]  D. Tank,et al.  Brain magnetic resonance imaging with contrast dependent on blood oxygenation. , 1990, Proceedings of the National Academy of Sciences of the United States of America.