Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(348.98KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Detection of Neural Activities in FMRI Using Jensen-Shannon Divergence
Jayanta Basak
Pages - 113 - 122     |    Revised - 15-09-2012     |    Published - 24-10-2012
Volume - 6   Issue - 5    |    Publication Date - October 2012  Table of Contents
MORE INFORMATION
KEYWORDS
Speckle Noise, Frost Filter, Fuzzy Level Set Method
ABSTRACT
In this paper, we present a statistical technique based on Jensen-Shanon divergence for detecting the regions of activity in fMRI images. The method is model free and we exploit the metric property of the square root of Jensen-Shannon divergence to accumulate the variations between successive time frames of fMRI images. Theoretically and experimentally we show the effectiveness of our algorithm.
CITED BY (2)  
1 Barcaru, A., & Vivó-Truyols, G. (2016). Use of Bayesian Statistics for Pairwise Comparison of Megavariate Data Sets: Extracting Meaningful Differences between GCxGC-MS Chromatograms Using Jensen–Shannon Divergence. Analytical chemistry, 88(4), 2096-2104.
2 Vallabhadas, D. K. (2013). Comparative study of distance metrics for t-closeness (Doctoral dissertation).
1 Google Scholar
2 CiteSeerX
3 Scribd
4 SlideShare
5 PdfSR
1 B. Biswal, F. Z. Yetkin, V. M. Haughton, and J. S. Hyde, “Functional connectivity in the motor cortex of resting human brain using echo-planar MRI,” Magn. Reson. Med., vol. 34, pp. 537–541,1995.
2 W. Richter, P. M. Andersen, A. P. Georgopoulos, and S. G. Kim, “Sequential activity in human motor areas during a delayed cued finger movement task studied by time-resolved fMRI,” Neuro Report, vol. 24, pp. 1–15, 1997.
3 J. B. Brewer, J. E. Desmond, G. H. Glover, and J. D. E. Gabrieli, “Making memories: Brain activity predicts how well visual experience will be remembered,” Science, vol. 281, pp. 1185–1187, 1998.
4 A. D. Wagner, D. L. Schacter, M. Rotte, W. Koutstaal, A. Marial, A. M. Dale, B. R. Rosen, and R. L. Bucker, “Building memories: Remembering and forgetting of verbal experiences and predicted by brain activity,” Science, vol. 281, pp. 1188–1190, 1998.
5 S. Y. Bookheimer, M. H. Strojwas, M. S. Cohen, A. M. Saunders, M. A. Pericak-Vance, J. C.Mazziotta, and G. W. Small, “Patterns of brain activation in people at risk for alzheimer’s disease,” New England Journal of Medicine, vol. 343, pp. 450–456, 2000.
6 S. Gold, B. Christian, S. Arndt, G. Zeien, T. Cizadlo, D. L. Johnson, M. Flaum, and N. C.Andreasen, “Functional MRI statistical software packages : A comparative analysis,” Human Brain Mapping, vol. 6, pp. 73–84, 1998.
7 S. Ogawa, T. M. Lee, A. R. Kay, and D. W. Tank, “Brain magnetic resonance imaging with contrast dependent on blood oxygenation,” Proc. National Academy of Science, USA, vol. 87, pp.9868–9872, 1990.
8 K. K. Kwong, “Functional resonance imaging with echoplanar imaging,” Magn. Reson. Q., vol.11, pp. 1–20, 1995.
9 E. Bullmore, S. C. Brammer, M. Williams, S. Rabe-Hesketh, N. Janot, A. David, J. Mellers, R.Howard, and P. Sham, “Statistical methods of estimation and inference for functional MR image analysis,” Magn. Resonance Med., vol. 35, pp. 261–277, 1996.
10 S. Clare, Functional Magnetic Resonance Imaging: Methods and Applications. PhD thesis,University of Nottingham, 1997.
11 Y. Benjamini and Y. Hochberg, “Controlling the false discovery rate: A practical and powerful approach to multiple testing,” Journal of the Royal Statistical Society, Series B, vol. 57, pp. 289–300, 1995.
12 E. Salli, H. H. Aronen, S. Savolainen, A. Korvenoja, and A. Visa, “Contextual clustering for analysis of functional fMRI data,” IEEE Transactions on Medical Imaging, vol. 20, pp. 403–414,2001.
13 P. A. Bandettini, A. Jesmanowicz, E. C. Wong, and J. S. Hyde, “Processing strategies for time-course data sets in functional MRI of the human brain,” Magn. Reson. Med., vol. 30, pp.161–173, 1993.
14 K. Kuppusamy, W. Lin, and E. M. Haacke, “Statistical assesment of crosscorrelation and variance methods and the importance of electrocardiogram gating in functional magnetic resonance imaging,” Magn. Resonance Imaging, vol. 15, pp. 169–181, 1997.
15 U. E. Ruttimann, M. Unser, R. R. Rawlings, D. Rio, N. F. Ramsey, V. S. Mattay, D. W. Hommer, J. A. Frank, and D. R. Weinberger, “Statistical analysis of functional MRI data in the wavelet domain,” IEEE Transactions on Medical Imaging, vol. 17, pp. 142–154, 1998.
16 M. J. Brammer, “Multidimensional wavelet analysis of functional magnetic resonance images,” Human Brain Mapping, vol. 6, pp. 378–382, 1998.
17 W. Backfrieder, R. Baumgartner, M. Smal, E. Moser, and H. Bergmann, “Quantification of intensity variations in functional MR images using rotated principal components,” Phys. Med.Biol., vol. 41, pp. 1425–1438, 1996.
18 M. M. J., S. Makeig, G. G. Brown, T. P. Jung, S. S. Kindermann, A. J. Bell, and T. J. Sejnowski, “Analysis of fMRI data by blind separation into independent spatial components,”Human Brain Mapping, vol. 6, pp. 160–188, 1998.
19 J. V. Stone, J. Porrill, C. Buchel, and K. Friston, “Spatial, temporal, and spatiotemporal independent component analysis of fMRI data,” in 18th Leeds Statistical Research Workshop on Spatio-temporal modeling and its applications, University of Leeds, 1999.
20 B. A. Ardekani, J. Kershaw, K. Kashikura, and I. Kanno, “Activation detection in functional MRI using subspacemodeling and maximum likelihood estimation,” IEEE Trans. Medical Imaging,vol. 18, pp. 101–114, 1996.
21 C. Goutte, P. Toft, E. Rostrup, F. A. Nielsen, and L. K. Hansen, “On clustering fMRI time series,” NeuroImage, vol. 9, pp. 298–310, 1999.
22 K. J. Friston, K. J. Worsley, R. S. J. Frackowiak, J. C. Mazziotta, and A. C. Evans,“Assessing the significance of focal activations using their spatial extent,” Human Brain Mapping,vol. 1, pp. 214–220, 1994.
23 C. R. Rao, “Diversity : Its measurement, decomposition, appointment and analysis,”Sankhya: The Indian Journal of Statistics, vol. 11(A), pp. 1–22, 1982.
24 A. K. C. Wong and M. You, “Entropy and distance of random graphs with application to structural pattern recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 7, pp.599–609, 1985.
25 J. Lin, “Divergence measures based on the Shannon entropy,” IEEE Trans. Information Theory, vol. 37, pp. 145–151, 1991.
26 C. Atae-Allah, J. F. G´omez-Lopera, J. Mart´inez-Aroza, Rom´an-Rold´an, and P. LuqueEscamilla,“Image segmentation by Jensen-Shannon divergence : Application to measurement of interfacial tension,” in Proc. Int. Conference on Pattern Recognition (ICPR00), Barcelona, Spain,vol. 3, 2000.
27 D. M. Endres and J. E. Schindelin, “A new metric for probability distributions,” IEEE Trans.Information Theory, vol. 49, pp. 1858–60, 2003.
28 F. ¨Osterreicher and I. Vajda, “A new class of metric divergences on probability spaces and its statistical applications,” Ann. Inst. Statist. Math., vol. 55, pp. 639–653, 2003.
29 G. H. Glover, “Deconvolution of impulse response in event-related bold fmri,” NeuroImage,vol. 9, pp. 416–429, 1999.
30 fMRIDC, “fmri data center,” http://www.fmridc.org/f/fmridc.
31 J. Hirsch, D. Rodriguez Moreno, and K. H. S. Kim, “Interconnected large-scale systems for three fundamental cognitive tasks revealed by fMRI,” Journal of Cognitive Neuroscience, vol. 13,pp. 389–405, 2001.
Dr. Jayanta Basak
NetApp - India
basakjayanta@yahoo.com