Dissociation of the Neural Networks Recruited during a Haptic Object-Recognition Task : Complementary Results with a Tensorial Independent Component Analysis
Identifieur interne : 000A41 ( PascalFrancis/Corpus ); précédent : 000A40; suivant : 000A42Dissociation of the Neural Networks Recruited during a Haptic Object-Recognition Task : Complementary Results with a Tensorial Independent Component Analysis
Auteurs : C. Habas ; E. A. CabanisSource :
- American journal of neuroradiology [ 0195-6108 ] ; 2008.
Descripteurs français
- Pascal (Inist)
English descriptors
Abstract
BACKGROUND AND PURPOSE: The cerebral and cerebellar networks involved in bimanual object recognition were assessed by blood oxygen level-dependent functional MR imaging by using multi-variate model-free analysis, because conventional univariate model-based analysis, such as the general linear model (GLM), does not allow investigation of resting, background, and transiently task-related brain activities. MATERIALS AND METHODS: Data from 14 healthy right-handed volunteers, scanned while successively performing bilateral finger movements and a bimanual tactile-tactile matching discrimination task were analyzed by using tensor-independent component analysis (TICA), which computes statistically independent spatiotemporal processes (P >.7) thought to reflect specific and distinct anatomofunctional neural networks. These results were compared with the network obtained in a previous study by using the same paradigm based on GLM to evaluate the advantages of TICA. RESULTS: TICA characterized and distinguished the following: 1) resting-state networks such as the default-mode networks, 2) networks transiently synchronized with the beginning and end of the task, such as temporo-pericentral and temporo-pericentral-occipital networks, and 3) task-related networks such as cerebello-fronto-parietal, cerebello-prefrontocingulo-insular, and cerebello-parietal networks. CONCLUSION: Bimanual tactile-tactile matching discrimination specifically recruits a complex neural network, which can be dissociated into 3 distinct but cooperative neural subnetworks related to sensorimotor function, salience detection, executive control, and, possibly, sensory expectation. This tripartite network involved in bimanual object recognition could not be demonstrated by GLM. Moreover, TICA allowed monitoring of the temporal succession of the networks recruited during the resting phase, audition of the "go" and "stop" signals, and the tactile discrimination task.
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Format Inist (serveur)
| NO : | FRANCIS 08-0500105 INIST |
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| ET : | Dissociation of the Neural Networks Recruited during a Haptic Object-Recognition Task : Complementary Results with a Tensorial Independent Component Analysis |
| AU : | HABAS (C.); CABANIS (E. A.) |
| AF : | Service de Neurolmagerie, Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts/Paris/France (1 aut., 2 aut.) |
| DT : | Publication en série; Niveau analytique |
| SO : | American journal of neuroradiology; ISSN 0195-6108; Coden AAJNDL; Etats-Unis; Da. 2008; Vol. 29; No. 9; Pp. 1715-1721; Bibl. 50 ref. |
| LA : | Anglais |
| EA : | BACKGROUND AND PURPOSE: The cerebral and cerebellar networks involved in bimanual object recognition were assessed by blood oxygen level-dependent functional MR imaging by using multi-variate model-free analysis, because conventional univariate model-based analysis, such as the general linear model (GLM), does not allow investigation of resting, background, and transiently task-related brain activities. MATERIALS AND METHODS: Data from 14 healthy right-handed volunteers, scanned while successively performing bilateral finger movements and a bimanual tactile-tactile matching discrimination task were analyzed by using tensor-independent component analysis (TICA), which computes statistically independent spatiotemporal processes (P >.7) thought to reflect specific and distinct anatomofunctional neural networks. These results were compared with the network obtained in a previous study by using the same paradigm based on GLM to evaluate the advantages of TICA. RESULTS: TICA characterized and distinguished the following: 1) resting-state networks such as the default-mode networks, 2) networks transiently synchronized with the beginning and end of the task, such as temporo-pericentral and temporo-pericentral-occipital networks, and 3) task-related networks such as cerebello-fronto-parietal, cerebello-prefrontocingulo-insular, and cerebello-parietal networks. CONCLUSION: Bimanual tactile-tactile matching discrimination specifically recruits a complex neural network, which can be dissociated into 3 distinct but cooperative neural subnetworks related to sensorimotor function, salience detection, executive control, and, possibly, sensory expectation. This tripartite network involved in bimanual object recognition could not be demonstrated by GLM. Moreover, TICA allowed monitoring of the temporal succession of the networks recruited during the resting phase, audition of the "go" and "stop" signals, and the tactile discrimination task. |
| CC : | 770D03F01 |
| FD : | Pathologie du système nerveux; Réseau neuronal; Reconnaissance; Radiodiagnostic; Perception haptique |
| ED : | Nervous system diseases; Neural network; Recognition; Radiodiagnosis; Haptic perception |
| SD : | Sistema nervioso patología; Red neuronal; Reconocimiento; Radiodiagnóstico |
| LO : | INIST-19668.354000185750690220 |
| ID : | 08-0500105 |
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Francis:08-0500105Le document en format XML
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Cabanis</name><affiliation><inist:fA14 i1="01"><s1>Service de Neurolmagerie, Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts</s1><s2>Paris</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">08-0500105</idno><date when="2008">2008</date><idno type="stanalyst">FRANCIS 08-0500105 INIST</idno><idno type="RBID">Francis:08-0500105</idno><idno type="wicri:Area/PascalFrancis/Corpus">000A41</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Dissociation of the Neural Networks Recruited during a Haptic Object-Recognition Task : Complementary Results with a Tensorial Independent Component Analysis</title><author><name sortKey="Habas, C" sort="Habas, C" uniqKey="Habas C" first="C." last="Habas">C. Habas</name><affiliation><inist:fA14 i1="01"><s1>Service de Neurolmagerie, Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts</s1><s2>Paris</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Cabanis, E A" sort="Cabanis, E A" uniqKey="Cabanis E" first="E. A." last="Cabanis">E. A. Cabanis</name><affiliation><inist:fA14 i1="01"><s1>Service de Neurolmagerie, Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts</s1><s2>Paris</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14></affiliation></author></analytic><series><title level="j" type="main">American journal of neuroradiology</title><title level="j" type="abbreviated">Am. j. neuroradiol.</title><idno type="ISSN">0195-6108</idno><imprint><date when="2008">2008</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">American journal of neuroradiology</title><title level="j" type="abbreviated">Am. j. neuroradiol.</title><idno type="ISSN">0195-6108</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Haptic perception</term><term>Nervous system diseases</term><term>Neural network</term><term>Radiodiagnosis</term><term>Recognition</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Pathologie du système nerveux</term><term>Réseau neuronal</term><term>Reconnaissance</term><term>Radiodiagnostic</term><term>Perception haptique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">BACKGROUND AND PURPOSE: The cerebral and cerebellar networks involved in bimanual object recognition were assessed by blood oxygen level-dependent functional MR imaging by using multi-variate model-free analysis, because conventional univariate model-based analysis, such as the general linear model (GLM), does not allow investigation of resting, background, and transiently task-related brain activities. MATERIALS AND METHODS: Data from 14 healthy right-handed volunteers, scanned while successively performing bilateral finger movements and a bimanual tactile-tactile matching discrimination task were analyzed by using tensor-independent component analysis (TICA), which computes statistically independent spatiotemporal processes (P >.7) thought to reflect specific and distinct anatomofunctional neural networks. These results were compared with the network obtained in a previous study by using the same paradigm based on GLM to evaluate the advantages of TICA. RESULTS: TICA characterized and distinguished the following: 1) resting-state networks such as the default-mode networks, 2) networks transiently synchronized with the beginning and end of the task, such as temporo-pericentral and temporo-pericentral-occipital networks, and 3) task-related networks such as cerebello-fronto-parietal, cerebello-prefrontocingulo-insular, and cerebello-parietal networks. CONCLUSION: Bimanual tactile-tactile matching discrimination specifically recruits a complex neural network, which can be dissociated into 3 distinct but cooperative neural subnetworks related to sensorimotor function, salience detection, executive control, and, possibly, sensory expectation. This tripartite network involved in bimanual object recognition could not be demonstrated by GLM. Moreover, TICA allowed monitoring of the temporal succession of the networks recruited during the resting phase, audition of the "go" and "stop" signals, and the tactile discrimination task.</div></front></TEI><inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>0195-6108</s0></fA01><fA02 i1="01"><s0>AAJNDL</s0></fA02><fA03 i2="1"><s0>Am. j. neuroradiol.</s0></fA03><fA05><s2>29</s2></fA05><fA06><s2>9</s2></fA06><fA08 i1="01" i2="1" l="ENG"><s1>Dissociation of the Neural Networks Recruited during a Haptic Object-Recognition Task : Complementary Results with a Tensorial Independent Component Analysis</s1></fA08><fA11 i1="01" i2="1"><s1>HABAS (C.)</s1></fA11><fA11 i1="02" i2="1"><s1>CABANIS (E. 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All rights reserved.</s1></fA44><fA45><s0>50 ref.</s0></fA45><fA47 i1="01" i2="1"><s0>08-0500105</s0></fA47><fA60><s1>P</s1></fA60><fA61><s0>A</s0></fA61><fA64 i1="01" i2="1"><s0>American journal of neuroradiology</s0></fA64><fA66 i1="01"><s0>USA</s0></fA66><fC01 i1="01" l="ENG"><s0>BACKGROUND AND PURPOSE: The cerebral and cerebellar networks involved in bimanual object recognition were assessed by blood oxygen level-dependent functional MR imaging by using multi-variate model-free analysis, because conventional univariate model-based analysis, such as the general linear model (GLM), does not allow investigation of resting, background, and transiently task-related brain activities. MATERIALS AND METHODS: Data from 14 healthy right-handed volunteers, scanned while successively performing bilateral finger movements and a bimanual tactile-tactile matching discrimination task were analyzed by using tensor-independent component analysis (TICA), which computes statistically independent spatiotemporal processes (P >.7) thought to reflect specific and distinct anatomofunctional neural networks. These results were compared with the network obtained in a previous study by using the same paradigm based on GLM to evaluate the advantages of TICA. RESULTS: TICA characterized and distinguished the following: 1) resting-state networks such as the default-mode networks, 2) networks transiently synchronized with the beginning and end of the task, such as temporo-pericentral and temporo-pericentral-occipital networks, and 3) task-related networks such as cerebello-fronto-parietal, cerebello-prefrontocingulo-insular, and cerebello-parietal networks. CONCLUSION: Bimanual tactile-tactile matching discrimination specifically recruits a complex neural network, which can be dissociated into 3 distinct but cooperative neural subnetworks related to sensorimotor function, salience detection, executive control, and, possibly, sensory expectation. This tripartite network involved in bimanual object recognition could not be demonstrated by GLM. 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A.)</AU><AF>Service de Neurolmagerie, Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts/Paris/France (1 aut., 2 aut.)</AF><DT>Publication en série; Niveau analytique</DT><SO>American journal of neuroradiology; ISSN 0195-6108; Coden AAJNDL; Etats-Unis; Da. 2008; Vol. 29; No. 9; Pp. 1715-1721; Bibl. 50 ref.</SO><LA>Anglais</LA><EA>BACKGROUND AND PURPOSE: The cerebral and cerebellar networks involved in bimanual object recognition were assessed by blood oxygen level-dependent functional MR imaging by using multi-variate model-free analysis, because conventional univariate model-based analysis, such as the general linear model (GLM), does not allow investigation of resting, background, and transiently task-related brain activities. MATERIALS AND METHODS: Data from 14 healthy right-handed volunteers, scanned while successively performing bilateral finger movements and a bimanual tactile-tactile matching discrimination task were analyzed by using tensor-independent component analysis (TICA), which computes statistically independent spatiotemporal processes (P >.7) thought to reflect specific and distinct anatomofunctional neural networks. These results were compared with the network obtained in a previous study by using the same paradigm based on GLM to evaluate the advantages of TICA. RESULTS: TICA characterized and distinguished the following: 1) resting-state networks such as the default-mode networks, 2) networks transiently synchronized with the beginning and end of the task, such as temporo-pericentral and temporo-pericentral-occipital networks, and 3) task-related networks such as cerebello-fronto-parietal, cerebello-prefrontocingulo-insular, and cerebello-parietal networks. CONCLUSION: Bimanual tactile-tactile matching discrimination specifically recruits a complex neural network, which can be dissociated into 3 distinct but cooperative neural subnetworks related to sensorimotor function, salience detection, executive control, and, possibly, sensory expectation. This tripartite network involved in bimanual object recognition could not be demonstrated by GLM. Moreover, TICA allowed monitoring of the temporal succession of the networks recruited during the resting phase, audition of the "go" and "stop" signals, and the tactile discrimination task.</EA><CC>770D03F01</CC><FD>Pathologie du système nerveux; Réseau neuronal; Reconnaissance; Radiodiagnostic; Perception haptique</FD><ED>Nervous system diseases; Neural network; Recognition; Radiodiagnosis; Haptic perception</ED><SD>Sistema nervioso patología; Red neuronal; Reconocimiento; Radiodiagnóstico</SD><LO>INIST-19668.354000185750690220</LO><ID>08-0500105</ID></server></inist></record>Pour manipuler ce document sous Unix (Dilib)
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