Abstract
Most methods used in functional MRI (fMRI) brain mapping require restrictive prior knowledge about the shape of the active blood-oxygenation-level-dependent (BOLD) response, thus leading to suboptimal or invalid inference. To solve this problem, we propose to assess local neural activity in terms of time alignment between the sequence of BOLD dynamics changes of interest and an Hidden Semi-Markov Event Sequence Model (HSMESM) of brain activation. The topology of the HSMESM is built from the deterministic transitions of the input stimulation paradigm and its parameters are automatically and iteratively learned from all intracranial fMRI signals. The brain mapping results achieved by HSMESMs in language processing demonstrate the relevance of such models in BOLD fMRI, especially to cope with strong variabilities of the active BOLD signal across time, brain, experiments and subjects.
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Thoraval, L., Armspach, J.-P., Nammer, I.: Analysis of brain functional MRI time series based on continuous wavelet transform and stimulation-response coupling distance. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 881–888. Springer, Heidelberg (2001)
Friston, K.J., Jezzard, P., Turner, R.: The analysis of functional MRI time-series. Human Brain Mapping 1, 153–171 (1994)
Lange, N., Zeger, S.L.: Non-linear fourier time series analysis for human brain mapping by functional magnetic resonance imaging. Applied Statistics, Journal of the Royal Statistical Society, Series C 46, 1–29 (1997)
Rajapakse, J.C., Kruggel, F., Maisog, J.M., von Cramon, D.Y.: Modeling Hemodynamic Response for Analysis of Functional MRI Time-Series. Human Brain Mapping 6, 283–300 (1998)
Frackowiak, R.S.J., Friston, K.J., Frith, C.D., Dolan, R.J., Mazziota, J.C. (eds.): Human Brain Function. Academic Press, USA (1997)
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77, 257–286 (1989)
Ferguson, J.D.: Variable duration models for speech. In: Proc. Symposium on the Application of Hidden Markov Models to Text and Speech, pp. 143–179 (1980)
Russell, M., Moore, R.: Explicit modelling of state occupancy in Hidden Markov Models for automatic speech recognition. In: Proc. ICASSP, pp. 5–8 (1985)
Levinson, S.E.: Continuously variable duration hidden Markov models for automatic speech recognition. Computer Speech and Language 1, 29–45 (1986)
Metz-Lutz, M.-N., Namer, I.J., Gounot, D., Kleitz, C., Armspach, J.-P., Kehrli, P.: Language functional neuro-imaging changes following focal left thalamic infarction. Neuroreport 11, 2907–2911 (2000)
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Faisan, S., Thoraval, L., Armspach, JP., Heitz, F. (2003). Unsupervised Learning and Mapping of Brain fMRI Signals Based on Hidden Semi-Markov Event Sequence Models. In: Ellis, R.E., Peters, T.M. (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003. MICCAI 2003. Lecture Notes in Computer Science, vol 2879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39903-2_10
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DOI: https://doi.org/10.1007/978-3-540-39903-2_10
Publisher Name: Springer, Berlin, Heidelberg
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