Hidden Markov multiple event sequence models: A paradigm for the spatio-temporal analysis of fMRI data.
Med Image Anal
; 11(1): 1-20, 2007 Feb.
Article
em En
| MEDLINE
| ID: mdl-17097334
ABSTRACT
This paper presents a novel, completely unsupervised fMRI brain mapping method that addresses the three problems of hemodynamic response function (HRF) variability, hemodynamic event timing, and fMRI response non-linearity. Spatial and temporal information are directly taken into account into the core of the activation detection process. In practice, activation detection at voxel v is formulated in terms of temporal alignment between sequences of hemodynamic response onsets (HROs) detected in the fMRI signal at v and in the spatial neighborhood of v, and the input sequence of stimuli or stimulus onsets. Event-related and epoch paradigms are considered. The multiple event sequence alignment problem is solved within the probabilistic framework of hidden Markov multiple event sequence models (HMMESMs), a new class of hidden Markov models. Results obtained on real and synthetic data significantly outperform those obtained with the popular statistical parametric mapping (SPM2) method without requiring any prior definition of the expected activation patterns, the HMMESM mapping approach being completely unsupervised.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Encéfalo
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Reconhecimento Automatizado de Padrão
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Inteligência Artificial
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Imageamento por Ressonância Magnética
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Interpretação de Imagem Assistida por Computador
Tipo de estudo:
Diagnostic_studies
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Evaluation_studies
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Health_economic_evaluation
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Prognostic_studies
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Risk_factors_studies
Limite:
Adolescent
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Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
Med Image Anal
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2007
Tipo de documento:
Article
País de afiliação:
França