Abstract
An efficient method for detecting activation on single and multiple epoch functional MRI (fMRI) data based on power spectral density of time-series and hidden Markov model is presented. Conventional methods of analysis of fMRI data are generally based on time-domain correlation analysis concentrating mainly on the multiple epoch data and generally do not provide good results for single epoch data. The main focus of this study is the analysis of single epoch data, constrained by certain experiments such as pain response, sleep, administration of pharmacological agents, which can only have a single or very few stimulus cycles. Further, our method obviates the need to exclusively model the hemodynamic response function and correctly identifies the voxels with delayed activation. We demonstrate the efficacy of our method in detecting brain activation by using both synthetic and real fMRI data.
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Farckowiak RSJ, Friston KJ, Frith CD, Dolan RJ, Mazziota JC (1997) Human brain function. Academic, New York
Friston KJ, Jezzard P, Turner R (1994) Analysis of functional MRI time-series. Hum Brain Mapp 1:153–171
Friston KJ, Holmes AP, Worsley KJ, et al. (1995) Statistical parametric maps in functional imaging: a general linear approach. Hum Brain Mapp 2:189–210
Weaver JB (1995) Efficient calculation of the principal components of imaging data. J Cereb Blood Flow Metab 15(5):892–894
Beckmann CF, Smith SM (2004) Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imag 23(2):137–152
Lu W, Rajapakse JC (2005) Approach and applications of constrained ICA. IEEE Trans Neural Netw 16(1):203–212
Lu W, Rajapakse JC (2006) ICA with Reference. Neurocomputing 69:2244–2257
Ruttimann UE, Unser M, Rawlings RR, Rio D, Ramsey NF, Mattay VS, Hommer DW, Frank JA, Weinberger DR (1998) Statistical analysis of functional MRI data in the wavelet domain. IEEE Trans Med Imaging 17(2):142–154
Rajapakse JC, Piyaratna J (2001) Bayesian approach to segmentation of statistical parametric maps. IEEE Trans Biomed Eng 48(10):1186–1194
Wang Y, Rajapakse JC (2006) Contextual modelling of functional MR images with conditional random fields. IEEE Trans Med Imaging 25(6):804–812
Mitra PP, Pesaran B (1999) Analysis of dynamic brain imaging data. Biophys J 76:691–708
Bou-Ghazale SE, Hansen JHL (1996) Synthesis of stressed speech from isolated neutral speech using HMM-based models. In: ICSLP-96: International conference on spoken language processing, vol. 3. Philadelphia, pp 1860–1863
Huang X, Acer A, Hon H, Ju Y, Liu J, Meredith S, Plumpe M (1997) Recent improvements on Microsoft’s trainable text-to-speech system-whistler. Proc. ICASSP, pp 959–962
Kenny P, Lennig M, Mermelstein P (1990) A linear predictive HMM for vector-valued observations with applications to speech recognition. IEEE Trans Acoust Speech Signal Proc ASSP-38(2):220–225
Rabiner LR (1989) A tutorial on hidden markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286
Vaseghi SV (2000) Advanced digital signal processing and noise reduction. Wiley, Chichester
Rajapakse JC, Kruggel F, Maisog JM, Cramon DY (1998) Modeling hemodynamic response for analysis of functional MRI time-series. Hum Brain Mapp 6:283–300
Zhou J, Rajapakse JC (2005) Segmentation of subcortical brain structures using fuzzy templates. Neuroimage 28(4):927–936
Rajapakse JC, Giedd JN, DeCarli C, Snell JW, McLaughlin A, Vauss YC, Krain AL, Hamburger S, Rapoport JL (1996) A technique for single-channel MR brain tissue segmentation: Application to a pediatric sample. Magn Reson Imaging 14(9):1053–1065
Rajapakse JC, DeCarli C, Mclaughlin A, Giedd JN, Krain AL, Hamburger SD, Rapoport JL (1996) Cerebral magnetic resonance image segmentation using data fusion. J Comput Assist Tomogr 20(2):206–218
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Kumar, A., Rajapakse, J.C. Power spectral based detection of brain activation from fMR images. Neural Comput & Applic 16, 551–557 (2007). https://doi.org/10.1007/s00521-007-0102-1
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DOI: https://doi.org/10.1007/s00521-007-0102-1