Name
Title | ||
|---|---|---|
Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models. |
Abstract | ||
|---|---|---|
In this paper, a novel functional magnetic resonance imaging (fMRI) brain mapping method is presented within the statistical modeling framework of hidden semi-Markov event sequence models (HSMESMs). Neural activation detection is formulated at the voxel level in terms of time coupling between the sequence of hemodynamic response onsets (HROs) observed in the fMRI signal, and an HSMESM of the hidden sequence of task-induced neural activations. The sequence of HRO events is derived from a continuous wavelet transform (CWT) of the fMRI signal. The brain activation HSMESM is built from the timing information of the input stimulation protocol. The rich mathematical framework of HSMESMs makes these models an effective and versatile approach for fMRI data analysis. Solving for the HSMESM Evaluation and Learning problems enables the model to automatically detect neural activation embedded in a given set of fMRI signals, without requiring any template basis function or prior shape assumption for the fMRI response. Solving for the HSMESM Decoding problem allows to enrich brain mapping with activation lag mapping, activation mode visualizing, and hemodynamic response function analysis. Activation detection results obtained on synthetic and real epoch-related fMRI data demonstrate the superiority of the HSMESM mapping method with respect to a real application case of the statistical parametric mapping (SPM) approach. In addition, the HSMESM mapping method appears clearly insensitive to timing variations of the hemodynamic response, and exhibits low sensitivity to fluctuations of its shape. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TMI.2004.841225 | IEEE Trans. Med. Imaging |
Keywords | Field | DocType |
biomedical MRI,brain,decoding,haemodynamics,hidden Markov models,medical image processing,medical signal detection,neurophysiology,statistical analysis,unsupervised learning,wavelet transforms,activation lag mapping,activation mode visualizing,active brain functional MRI signal mapping,continuous wavelet transform,decoding,hemodynamic response onsets,hidden semi-Markov event sequence models,neural activation detection,statistical modeling,statistical parametric mapping,time coupling,unsupervised learning,Brain mapping,functional MRI,hidden Markov models,signal processing,wavelet transform | Brain mapping,Voxel,Computer vision,Functional magnetic resonance imaging,Pattern recognition,Computer science,Unsupervised learning,Statistical parametric mapping,Artificial intelligence,Statistical model,Hidden Markov model,Wavelet transform | Journal |
Volume | Issue | ISSN |
24 | 2 | 0278-0062 |
Citations | PageRank | References |
13 | 1.06 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sylvain Faisan | 1 | 32 | 6.99 |
| Laurent Thoraval | 2 | 42 | 7.00 |
| Jean-Paul Armspach | 3 | 221 | 26.60 |
| Marie-Noëlle Metz-Lutz | 4 | 13 | 1.06 |
| Fabrice Heitz | 5 | 401 | 59.55 |