Abstract
Functional magnetic resonance imaging (fMRI) is a safe non-invasive technique used for understanding the brain functions against various stimuli and hence to predict the brain disorders. The fMRI signal patterns and brain mapping have been found promising the medical science in the recent days. Adequate contributions have been made in the literature on fMRI signal analysis. This paper identifies notable research works that have contributed on fMRI signal analysis and predicting the brain functions and performs systematic review on them. The review provides the strengths and weaknesses, and the research gaps exist in the works based on 11 useful parameters, such as sparsity, non-linearity, robustness, etc.
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References
Barbé, K.; Van Moer, W.; Nagels, G., “Fractional-Order Time Series Models for Extracting the Haemodynamic Response From Functional Magnetic Resonance Imaging Data”, IEEE Transactions on Biomedical Engineering, Vol. 59, Issue 8, pp 2264-2272, 2012.
K. K. Kwong and D. A. Chesler, “Functional MRI,” in Medical Devices and Systems. Boca Raton: CRC Press, 2006, pp. 22–30.
Katwal, S.B.; Gore, J.C.; Marois, R.; Rogers, B.P., “Unsupervised Spatiotemporal Analysis of FMRI Data Using Graph-Based Visualizations of Self-Organizing Maps”, IEEE Transactions on Biomedical Engineering, Volume 60, Issue 9, pp 2472−2483, 2013.
Yuanqing Li; Namburi, P.; Zhuliang Yu; Cuntai Guan; Jianfeng Feng; Zhenghui Gu, “Voxel Selection in fMRI Data Analysis Based on Sparse Representation”, IEEE Transactions on Biomedical Engineering, Volume 56, Issue 10, pp 2439−2451, 2009.
Michel, V.; Gramfort, A.; Varoquaux, G.; Eger, E.; Thirion, B., “Total Variation Regularization for fMRI-Based Prediction of Behavior”, IEEE Transactions on Medical Imaging, Volume 30, Issue 7, pp 1328−1340, 2011.
C. Genovese, N. Lazar, and T. Nichols, “Thresholding of statistical maps in functional neuroimaging using the false discovery rate,” NeuroImage, Volume 15, pp. 870–878, 2002.
M. Lindquist, J. Loh, L. Atlas, and T. Wager, “Modeling the hemodynamic response function in fMRI: Efficiency, bias and mismodeling,” NeuroImage, Volume 45, no. 1, pp. S187–S196, 2009.
B. Hu, G. Varma, C. Randell, S. Keevil, T. Schaeffter, and P. Glover, “A novel receive-only liquid nitrogen (LN2)-cooled RF coil for high-resolution in vivo imaging on a 3-tesla whole-body scanner,” IEEE Transactions on Instrumentation and Measurement, Volume 61, Issue. 1, pp. 129–139, Jan. 2012.
S. Strother, “Evaluating fMRI preprocessing pipelines—Review of pre-processing steps for BOLD fMRI,” IEEE Engineering in Medicine and Biology Magazine, Volume 25, Issue 2, pp. 27–41, Mar./Apr. 2006.
D. A. Karras and G. B. Mertzios, “New PDE-based methods for image enhancement using SOM and Bayesian inference in various discretization schemes,” Measurement Science and Technology, Volume. 20, Issue 10, 2009.
V. Rallabandi and P. Roy, “Magnetic resonance image enhancement using stochastic resonance in Fourier domain,” Magnetic Resononance Imaging, Volume. 28, Issue 9, pp. 1361–1373, 2010.
X. Yang and B. Baowei Fei, “A wavelet multiscale denoising algorithm for magnetic resonance (MR) images,” Measurement Science and Technology, Volume 22, no. 2, 2011.
J. Sijbers, D. Poot, A. J. den Dekker, and W. Pintjens, “Automatic estimation of the noise variance from the histogram of a magnetic resonance image,”Physics in Medicine and Biology, Volume. 52, pp. 1335–1348, 2007.
C. Goutte, F. A. Nielsen, and L. K. Hansen, “Modeling the haemodynamic response in fMRI using smooth FIR filters,” IEEE Trans. Med. Imag., Volume 19, no. 12, pp. 1188–1201, Dec. 2000.
R. Gibbons, N. Lazar, D. Bhaumik, S. Sclove, H. Chen, K. Thulborn, J. Sweeney, K. Hur, and D. Patterson, “Estimation and classification of fMRI hemodynamic response patterns,” Neuroimage, vol. 22, pp. 804–814, 2004.
A. den Dekker, D. Poot, R. Bos, and J. Sijbers, “Likelihood-based hypothesis tests for brain activation detection from MRI data disturbed by colored noise: A simulation study,” IEEE Trans. Med. Imag., vol. 28, no. 2, pp. 287–296, Feb. 2009.
K. Barb´e, W. Van Moer, and L. Lauwers, “Functional magnetic resonance imaging: An improved short record signal model,” IEEE Trans. Instrum. Meas., vol. 60, no. 5, pp. 1724–1731, May 2011.
X. Descombes, F. Kruggel, and D. Y. von Cramon, “Spatio-temporal fMRI analysis using Markov random fields,” IEEE Trans. Med. Imag., vol. 17, no. 6, pp. 1028–1039, Dec. 1998.
M. Svens´en, F. Kruggel, and D. Y. von Cramen, “Probabilistic modeling of single trial fMRI data,” IEEE Trans. Med. Imag., vol. 19, no. 1, pp. 25–36, Jan. 2000.
S. Faisan, L. Thoraval, J. P. Armspach, J. R. Foucher, M. N. Metz-Lutz, and F. Heitz, “Hidden Markov event sequence models: Toward unspervised functional MRI brain,” Acad. Radiol., vol. 12, no. 1, pp. 25–36, Jan. 2005.
K. J. Friston, A. P. Holmes, K. J. Worsley, J. –P. Poline, C. D. Frith, and R. S. J. Frackowiak, “Statistical parametric maps in functional imaging: A general linear approach,” Human Brain Mapping, vol. 2, no. 4, pp. 189–210, 1994.
E. Zarahn, G. K. Aguirre, and M. D’Esposito, “Empirical analyses of BOLD fMRI statistics,” Neuroimage, vol. 5, no. 3, pp. 179–197, Apr. 1997.
L. K. Hansen, J. Larsen, F. A. Nielsen, S. C. Strother, E. Rostrup, R. Savoy, N. Lange, J. Sidtis, C. Svarer, and O. B. Paulson, “Generalizable patterns in neuroimaging: How many principal components,” Neuroimage, vol.9, no. 5, pp. 534–544, May 1999.
K. H. Chuang, M. H. Chiu, C.C. Lin, and J. H. Chen, “Model-free functional MRI analysis using Kohonen clustering neural network and fuzzy c-means,” IEEE Trans. Med. Imag., vol. 28, no. 12, pp. 1117–1128, Dec. 1999.
Jingyu Liu; Lai Xu; Caprihan, A.; Calhoun, V.D., “Extracting principle components for discriminant analysis of FMRI images”, IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008, pp 449−452, 2008.
S.J. Peltier, T. A. Polk, and D. C. Noll, “Detecting low-frequency functional connectivity in fMRI using a self-organizing map (SOM) algorithm,” Human Brain Mapping, vol. 20, no. 4, pp. 220–226, Aug. 2003.
W. Liao, H. Chen, Q. Yang, and X. Lei, “Analysis of fMRI data using improved self-organizing map and spatio-temporal metric hierarchical clustering,” IEEE Trans. Med. Imag., vol. 27, no. 10, pp. 1472–1483, Oct. 2008.
H. Chen, H. Yuan, D. Yao, L. Chen, and W. Chen, “An integrated neighborhood correlation and hierarchical clustering approach of functional MRI,” IEEE Trans. Biomed. Eng., vol. 53, no. 3, pp. 452–458, Mar. 2006.
Chiew, M.; Graham, S.J.; “BOLD Contrast and Noise Characteristics of Densely Sampled Multi-Echo fMRI Data, IEEE Transactions on Medical Imaging, Vol. 30, No. 9, pp. 1691–1703, 2011.
M. J. McKeown, 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, no. 3, pp. 160–188, 1998.
Honorio, J.; Tomasi, D.; Goldstein, R.Z.; Leung, H.-C.; Samaras, D., “Can a Single Brain Region Predict a Disorder?”, IEEE Transactions on Medical Imaging, Volume 31, Issue: 11, pp 2062−2072, 2012.
Chaari, L.; Vincent, T.; Forbes, F.; Dojat, M.; Ciuciu, P, “Fast Joint Detection-Estimation of Evoked Brain Activity in Event-Related fMRI Using a Variational Approach”, IEEE Transactions on Medical Imaging, Vol. 32, Issue 5, pp 821−837, 2013.
Fan Deng; Dajiang Zhu; Jinglei Lv; Lei Guo; Tianming Liu, “FMRI Signal Analysis Using Empirical Mean Curve Decomposition”, IEEE Transactions on Biomedical Engineering, Volume 60, Issue 1, Part: 1, pp 42−54, 2013.
Babak Afshin-Pour, Seyed-Mohammad Shams, and Stephen Strother, “A Hybrid LDA + gCCA Model for fMRI Data Classification and Visualisation”, IEEE Transactions on Medical Imaging, Volume PP, Issue 99, pp 1–12, 2014.
Chun-An Chou; Kampa, K.; Mehta, S.H.; Tungaraza, R.F.; Chaovalitwongse, W.A.; Grabowski, T.J., “Voxel Selection Framework in Multi-Voxel Pattern Analysis of fMRI Data for Prediction of Neural Response to Visual Stimuli”, IEEE Transactions on Medical Imaging, Volume 33, Issue 4, pp 925−934, 2014.
A. N. Paithane and D. S. Bormane. “Analysis of nonlinear and non-stationary signal to extract the features using Hilbert Huang transform.” Computational Intelligence and Computing Research (ICCIC), 2014 IEEE International Conference on. IEEE, 2014.
A. N. Paithane and D. S. Bormane. “Electrocardiogram signal analysis using empirical mode composition and Hilbert spectrum.” Pervasive Computing (ICPC), 2015 International Conference on. IEEE, 2015.
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Raut, S.V., Yadav, D.M. (2017). A Review on fMRI Signal Analysis and Brain Mapping Methodologies. In: Satapathy, S., Prasad, V., Rani, B., Udgata, S., Raju, K. (eds) Proceedings of the First International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 507. Springer, Singapore. https://doi.org/10.1007/978-981-10-2471-9_30
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DOI: https://doi.org/10.1007/978-981-10-2471-9_30
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