Revealing the dynamic causal interdependence between neural and muscular signals in Parkinsonian tremor
Identifieur interne : 002648 ( Main/Exploration ); précédent : 002647; suivant : 002649Revealing the dynamic causal interdependence between neural and muscular signals in Parkinsonian tremor
Auteurs : S. Wang [Royaume-Uni] ; Y. Chen [États-Unis] ; M. Ding [États-Unis] ; J. Feng [Royaume-Uni] ; J. F. Stein [Royaume-Uni] ; T. Z. Aziz [Royaume-Uni] ; X. Liu [Royaume-Uni]Source :
- Journal of the Franklin Institute [ 0016-0032 ] ; 2007.
Descripteurs français
- Pascal (Inist)
- Traitement signal, Application médicale, Electromyographie, Parkinson maladie, Tremblement, Système nerveux pathologie, Corrélation, Cohérence, Résistance mécanique, Dépendance du temps, Phénomène transitoire, Réponse transitoire, Intermittence, Causalité, Méthode adaptative, Modèle autorégressif, Analyse régression, Modélisation, Fonction cohérence, Méthode domaine temps fréquence.
English descriptors
- KwdEn :
- Adaptive method, Autoregressive model, Causality, Coherence, Coherence function, Correlation, Electromyography, Intermittency, Medical application, Modeling, Nervous system diseases, Parkinson disease, Regression analysis, Signal processing, Strength, Time dependence, Time frequency domain method, Transient response, Transients, Tremor.
Abstract
Functional correlation between oscillatory neural and muscular signals during tremor can be revealed by coherence estimation. The coherence value in a defined frequency range reveals the interaction strength between the two signals. However, coherence estimation does not provide directional information, preventing the further dissection of the relationship between the two interacting signals. We have therefore investigated causal correlations between the subthalamic nucleus (STN) and muscle in Parkinsonian tremor using adaptive Granger autoregressive (AR) modeling. During resting tremor we analyzed the inter-dependence of local field potentials (LFPs) recorded from the STN and surface electromyograms (EMGs) recorded from the contralateral forearm muscles using an adaptive Granger causality based on AR modeling with a running window to reveal the time-dependent causal influences between the LFP and EMG signals in comparison with coherence estimation. Our results showed that during persistent tremor, there was a directional causality predominantly from EMGs to LFPs corresponding to the significant coherence between LFPs and EMGs at the tremor frequency; and over episodes of transient resting tremor, the inter-dependence between EMGs and LFPs was bi-directional and alternatively varied with time. Further time-frequency analysis showed a significant suppression in the beta band (10-30 Hz) power of the STN LFPs preceded the onset of resting tremor which was presented as the increases in the power at the tremor frequency (3.0-4.5 Hz) in both STN LFPs and surface EMGs. We conclude that the functional correlation between the STN and muscle is dynamic, bi-directional, and dependent on the tremor status. The Granger causality and time-frequency analysis are effective to characterize the dynamic correlation of the transient or intermittent events between simultaneously recorded neural and muscular signals at the same and across different frequencies.
Affiliations:
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Chen</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Department of Biomedical Engineering, University of Florida</s1><s3>USA</s3><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>États-Unis</country><wicri:noRegion>Department of Biomedical Engineering, University of Florida</wicri:noRegion></affiliation></author><author><name sortKey="Ding, M" sort="Ding, M" uniqKey="Ding M" first="M." last="Ding">M. Ding</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Department of Biomedical Engineering, University of Florida</s1><s3>USA</s3><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>États-Unis</country><wicri:noRegion>Department of Biomedical Engineering, University of Florida</wicri:noRegion></affiliation></author><author><name sortKey="Feng, J" sort="Feng, J" uniqKey="Feng J" first="J." last="Feng">J. 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Wang</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>University Laboratory of Physiology, University of Oxford. Parks Road</s1><s2>Oxford 0X1 3PT</s2><s3>GBR</s3><sZ>1 aut.</sZ><sZ>5 aut.</sZ></inist:fA14><country>Royaume-Uni</country><wicri:noRegion>Oxford 0X1 3PT</wicri:noRegion></affiliation></author><author><name sortKey="Chen, Y" sort="Chen, Y" uniqKey="Chen Y" first="Y." last="Chen">Y. Chen</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Department of Biomedical Engineering, University of Florida</s1><s3>USA</s3><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>États-Unis</country><wicri:noRegion>Department of Biomedical Engineering, University of Florida</wicri:noRegion></affiliation></author><author><name sortKey="Ding, M" sort="Ding, M" uniqKey="Ding M" first="M." last="Ding">M. Ding</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Department of Biomedical Engineering, University of Florida</s1><s3>USA</s3><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>États-Unis</country><wicri:noRegion>Department of Biomedical Engineering, University of Florida</wicri:noRegion></affiliation></author><author><name sortKey="Feng, J" sort="Feng, J" uniqKey="Feng J" first="J." last="Feng">J. Feng</name><affiliation wicri:level="1"><inist:fA14 i1="03"><s1>Centre for Scientific Computing, University of Warwick</s1><s3>GBR</s3><sZ>4 aut.</sZ></inist:fA14><country>Royaume-Uni</country><wicri:noRegion>Centre for Scientific Computing, University of Warwick</wicri:noRegion></affiliation></author><author><name sortKey="Stein, J F" sort="Stein, J F" uniqKey="Stein J" first="J. F." last="Stein">J. F. Stein</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>University Laboratory of Physiology, University of Oxford. Parks Road</s1><s2>Oxford 0X1 3PT</s2><s3>GBR</s3><sZ>1 aut.</sZ><sZ>5 aut.</sZ></inist:fA14><country>Royaume-Uni</country><wicri:noRegion>Oxford 0X1 3PT</wicri:noRegion></affiliation></author><author><name sortKey="Aziz, T Z" sort="Aziz, T Z" uniqKey="Aziz T" first="T. Z." last="Aziz">T. Z. Aziz</name><affiliation wicri:level="1"><inist:fA14 i1="04"><s1>Oxford Functional Neurosurgery, Department of Neurosurgery, Radcliffe Infirmary, Woodstock Road</s1><s2>Oxford OX2 6HE</s2><s3>GBR</s3><sZ>6 aut.</sZ></inist:fA14><country>Royaume-Uni</country><wicri:noRegion>Oxford OX2 6HE</wicri:noRegion></affiliation></author><author><name sortKey="Liu, X" sort="Liu, X" uniqKey="Liu X" first="X." last="Liu">X. Liu</name><affiliation wicri:level="1"><inist:fA14 i1="05"><s1>The Movement Disorders and Neurostimulation Group, Department of Neuroscience, Charing Cross Hospital & Division of Newosciences and Mental Health, Imperial College London, Fulham Palace Road</s1><s2>London W6 8RF</s2><s3>GBR</s3><sZ>7 aut.</sZ></inist:fA14><country>Royaume-Uni</country><wicri:noRegion>London W6 8RF</wicri:noRegion></affiliation></author></analytic><series><title level="j" type="main">Journal of the Franklin Institute</title><title level="j" type="abbreviated">J. Franklin Inst.</title><idno type="ISSN">0016-0032</idno><imprint><date when="2007">2007</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Journal of the Franklin Institute</title><title level="j" type="abbreviated">J. Franklin Inst.</title><idno type="ISSN">0016-0032</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adaptive method</term><term>Autoregressive model</term><term>Causality</term><term>Coherence</term><term>Coherence function</term><term>Correlation</term><term>Electromyography</term><term>Intermittency</term><term>Medical application</term><term>Modeling</term><term>Nervous system diseases</term><term>Parkinson disease</term><term>Regression analysis</term><term>Signal processing</term><term>Strength</term><term>Time dependence</term><term>Time frequency domain method</term><term>Transient response</term><term>Transients</term><term>Tremor</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Traitement signal</term><term>Application médicale</term><term>Electromyographie</term><term>Parkinson maladie</term><term>Tremblement</term><term>Système nerveux pathologie</term><term>Corrélation</term><term>Cohérence</term><term>Résistance mécanique</term><term>Dépendance du temps</term><term>Phénomène transitoire</term><term>Réponse transitoire</term><term>Intermittence</term><term>Causalité</term><term>Méthode adaptative</term><term>Modèle autorégressif</term><term>Analyse régression</term><term>Modélisation</term><term>Fonction cohérence</term><term>Méthode domaine temps fréquence</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Functional correlation between oscillatory neural and muscular signals during tremor can be revealed by coherence estimation. The coherence value in a defined frequency range reveals the interaction strength between the two signals. However, coherence estimation does not provide directional information, preventing the further dissection of the relationship between the two interacting signals. We have therefore investigated causal correlations between the subthalamic nucleus (STN) and muscle in Parkinsonian tremor using adaptive Granger autoregressive (AR) modeling. During resting tremor we analyzed the inter-dependence of local field potentials (LFPs) recorded from the STN and surface electromyograms (EMGs) recorded from the contralateral forearm muscles using an adaptive Granger causality based on AR modeling with a running window to reveal the time-dependent causal influences between the LFP and EMG signals in comparison with coherence estimation. Our results showed that during persistent tremor, there was a directional causality predominantly from EMGs to LFPs corresponding to the significant coherence between LFPs and EMGs at the tremor frequency; and over episodes of transient resting tremor, the inter-dependence between EMGs and LFPs was bi-directional and alternatively varied with time. Further time-frequency analysis showed a significant suppression in the beta band (10-30 Hz) power of the STN LFPs preceded the onset of resting tremor which was presented as the increases in the power at the tremor frequency (3.0-4.5 Hz) in both STN LFPs and surface EMGs. We conclude that the functional correlation between the STN and muscle is dynamic, bi-directional, and dependent on the tremor status. The Granger causality and time-frequency analysis are effective to characterize the dynamic correlation of the transient or intermittent events between simultaneously recorded neural and muscular signals at the same and across different frequencies.</div></front></TEI><affiliations><list><country><li>Royaume-Uni</li><li>États-Unis</li></country></list><tree><country name="Royaume-Uni"><noRegion><name sortKey="Wang, S" sort="Wang, S" uniqKey="Wang S" first="S." last="Wang">S. Wang</name></noRegion><name sortKey="Aziz, T Z" sort="Aziz, T Z" uniqKey="Aziz T" first="T. Z." last="Aziz">T. Z. Aziz</name><name sortKey="Feng, J" sort="Feng, J" uniqKey="Feng J" first="J." last="Feng">J. Feng</name><name sortKey="Liu, X" sort="Liu, X" uniqKey="Liu X" first="X." last="Liu">X. Liu</name><name sortKey="Stein, J F" sort="Stein, J F" uniqKey="Stein J" first="J. F." last="Stein">J. F. Stein</name></country><country name="États-Unis"><noRegion><name sortKey="Chen, Y" sort="Chen, Y" uniqKey="Chen Y" first="Y." last="Chen">Y. Chen</name></noRegion><name sortKey="Ding, M" sort="Ding, M" uniqKey="Ding M" first="M." last="Ding">M. Ding</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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