Cerebral modeling and dynamic Bayesian networks.

Détails

ID Serval
serval:BIB_C9F5B7BCA33F
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Cerebral modeling and dynamic Bayesian networks.
Périodique
Artificial Intelligence In Medicine
Auteur(s)
Labatut V., Pastor J., Ruff S., Démonet J.F., Celsis P.
ISSN
0933-3657 (Print)
ISSN-L
0933-3657
Statut éditorial
Publié
Date de publication
02/2004
Peer-reviewed
Oui
Volume
30
Numéro
2
Pages
119-139
Langue
anglais
Notes
Publication types: Journal ArticlePublication Status: ppublish
Résumé
The understanding and the prediction of the clinical outcomes of focal or degenerative cerebral lesions, as well as the assessment of rehabilitation procedures, necessitate knowing the cerebral substratum of cognitive or sensorimotor functions. This is achieved by activation studies, where subjects are asked to perform a specific task while data of their brain functioning are obtained through functional neuroimaging techniques. Such studies, as well as animal experiments, have shown that sensorimotor or cognitive functions are the offspring of the activity of large-scale networks of anatomically connected cerebral regions. However, no one-to-one correspondence between activated networks adn functions can be found. Our research aims at understanding how the activation of large-scale networks derives from cerebral information processing mechanisms, which can only explain apparently conflicting activation data. Our work falls at the crossroads of neuroimaging interpretation techniques and computational neuroscience. Since knowledge in cognitive neuroscience is permanently evolving, our research aims more precisely at defining a new modeling formalism and at building a flexible simulator, allowing a quick implementation of the models, for a better interpretation of cerebral functional images. It also aims at providing plausible models, at eht level of large-scale networks, of cerebral information processing mechanisms in humans. In this paper, we propose a formalism, based on dynamic Bayesian networks (DBNs), that respects the following constraints: an oriented, networks architecture, whose nodes (the cerebral structures) can all be different, the implementation of causality--the activation of the structure is caused by upstream nodes' activation--the explicit representation of different time scales (from 1 ms for the cerebral activity to many seconds for a PET scan image acquisition), the representation of cerebral information at the integrated level of neuronal populations, the imprecision of functional neuroimaging data, the nonlinearity and the uncertainty in cerebral mechanisms, and brain's plasticity (learning, reorganization, modulation). One of the main problems, nonlinearity, has been tackled thanks to new extensions of the Kalman filter. The capabilities of the formalism's current version are illustrated by the modeling of a phoneme categorization process, explaining the different cerebral activations in normal and dyslexic subjects.
Mots-clé
Bayes Theorem, Brain Diseases/pathology, Brain Diseases/rehabilitation, Brain Injuries/pathology, Brain Injuries/rehabilitation, Cerebral Cortex/physiology, Cognition Disorders/etiology, Cognition Disorders/rehabilitation, Dyslexia, Humans, Prognosis, Tomography, Emission-Computed
Pubmed
Web of science
Création de la notice
24/03/2013 19:39
Dernière modification de la notice
03/03/2018 21:23
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