Dynamic analysis of Probabilistic Boolean Network for fMRI study in Parkinson's disease.
Identifieur interne : 002392 ( Main/Merge ); précédent : 002391; suivant : 002393Dynamic analysis of Probabilistic Boolean Network for fMRI study in Parkinson's disease.
Auteurs : Zheng Ma [Canada] ; Z Jane WangSource :
- Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference [ 1557-170X ] ; 2008.
English descriptors
- KwdEn :
- Artificial Intelligence, Brain (physiopathology), Diagnosis, Computer-Assisted (methods), Evoked Potentials, Motor, Humans, Logistic Models, Magnetic Resonance Imaging (methods), Models, Neurological, Models, Statistical, Neural Networks (Computer), Parkinson Disease (diagnosis), Parkinson Disease (physiopathology).
- MESH :
- diagnosis : Parkinson Disease.
- methods : Diagnosis, Computer-Assisted, Magnetic Resonance Imaging.
- physiopathology : Brain, Parkinson Disease.
- Artificial Intelligence, Evoked Potentials, Motor, Humans, Logistic Models, Models, Neurological, Models, Statistical, Neural Networks (Computer).
Abstract
Probabilistic Boolean Networks (PBNs) have recently been applied to infer functional connectivity between brain regions of interested (ROIs), and to identify the existence of connectivity abnormality in Parkinson's Disease (PD). In addition to PBNs' promising application in inferring significant brain connections, PBN modeling for brain ROIs also enables researchers to study dynamic activities of the system under stochastic condition, gaining essential information regarding asymptotic behaviors of ROIs for potential therapeutic intervention in PD. In this paper, we will present a PBN model for fMRI analysis and study its asymptotic behavior. The PBN results indicate significant differences in asymptotic behaviors between PD patients and normal subjects. Hypothesizing the observed feature states for normal subject as the desired functional states, we further explore possible methods to manipulate the dynamical network behavior of PD patients in the favor of the desired states from the view of random perturbation as well as intervention.
DOI: 10.1109/IEMBS.2008.4649115
PubMed: 19162618
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pubmed:19162618Le document en format XML
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Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference</title><idno type="ISSN">1557-170X</idno><imprint><date when="2008" type="published">2008</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Artificial Intelligence</term><term>Brain (physiopathology)</term><term>Diagnosis, Computer-Assisted (methods)</term><term>Evoked Potentials, Motor</term><term>Humans</term><term>Logistic Models</term><term>Magnetic Resonance Imaging (methods)</term><term>Models, Neurological</term><term>Models, Statistical</term><term>Neural Networks (Computer)</term><term>Parkinson Disease (diagnosis)</term><term>Parkinson Disease (physiopathology)</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Diagnosis, Computer-Assisted</term><term>Magnetic Resonance Imaging</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Brain</term><term>Parkinson Disease</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Artificial Intelligence</term><term>Evoked Potentials, Motor</term><term>Humans</term><term>Logistic Models</term><term>Models, Neurological</term><term>Models, Statistical</term><term>Neural Networks (Computer)</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Probabilistic Boolean Networks (PBNs) have recently been applied to infer functional connectivity between brain regions of interested (ROIs), and to identify the existence of connectivity abnormality in Parkinson's Disease (PD). In addition to PBNs' promising application in inferring significant brain connections, PBN modeling for brain ROIs also enables researchers to study dynamic activities of the system under stochastic condition, gaining essential information regarding asymptotic behaviors of ROIs for potential therapeutic intervention in PD. In this paper, we will present a PBN model for fMRI analysis and study its asymptotic behavior. The PBN results indicate significant differences in asymptotic behaviors between PD patients and normal subjects. Hypothesizing the observed feature states for normal subject as the desired functional states, we further explore possible methods to manipulate the dynamical network behavior of PD patients in the favor of the desired states from the view of random perturbation as well as intervention.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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