ParkinsonCanadaV1, PascalFrancis, Curation, bibRecord, 000580

Revealing the dynamic causal interdependence between neural and muscular signals in Parkinsonian tremor

Identifieur interne : 000580 ( PascalFrancis/Curation ); précédent : 000579; suivant : 000581

Revealing 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.

RBID : Pascal:07-0277567

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.

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Le document en format XML

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<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>
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<fA11 i1="07" i2="1"><s1>LIU (X.)</s1>
</fA11>
<fA12 i1="01" i2="1"><s1>NANDI (Asoke K.)</s1>
<s9>ed.</s9>
</fA12>
<fA12 i1="02" i2="1"><s1>RANGAYYAN (Rangaraj M.)</s1>
<s9>ed.</s9>
</fA12>
<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>
</fA14>
<fA14 i1="02"><s1>Department of Biomedical Engineering, University of Florida</s1>
<s3>USA</s3>
<sZ>2 aut.</sZ>
<sZ>3 aut.</sZ>
</fA14>
<fA14 i1="03"><s1>Centre for Scientific Computing, University of Warwick</s1>
<s3>GBR</s3>
<sZ>4 aut.</sZ>
</fA14>
<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>
</fA14>
<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>
</fA14>
<fA15 i1="01"><s1>Department of Electrical Engineering and Electronics, The University of Liverpool, Brownlow Hill</s1>
<s2>Liverpool, L69 3GJ</s2>
<s3>GBR</s3>
<sZ>1 aut.</sZ>
</fA15>
<fA15 i1="02"><s1>Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW</s1>
<s2>Calgary, Alberta, T2N 1N4</s2>
<s3>CAN</s3>
<sZ>2 aut.</sZ>
</fA15>
<fA20><s1>180-195</s1>
</fA20>
<fA21><s1>2007</s1>
</fA21>
<fA23 i1="01"><s0>ENG</s0>
</fA23>
<fA43 i1="01"><s1>INIST</s1>
<s2>555</s2>
<s5>354000147096050020</s5>
</fA43>
<fA44><s0>0000</s0>
<s1>© 2007 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45><s0>26 ref.</s0>
</fA45>
<fA47 i1="01" i2="1"><s0>07-0277567</s0>
</fA47>
<fA60><s1>P</s1>
</fA60>
<fA61><s0>A</s0>
</fA61>
<fA64 i1="01" i2="1"><s0>Journal of the Franklin Institute</s0>
</fA64>
<fA66 i1="01"><s0>GBR</s0>
</fA66>
<fC01 i1="01" l="ENG"><s0>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.</s0>
</fC01>
<fC02 i1="01" i2="X"><s0>002B24D</s0>
</fC02>
<fC02 i1="02" i2="X"><s0>001D04A05</s0>
</fC02>
<fC03 i1="01" i2="X" l="FRE"><s0>Traitement signal</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="ENG"><s0>Signal processing</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="SPA"><s0>Procesamiento señal</s0>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="X" l="FRE"><s0>Application médicale</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG"><s0>Medical application</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA"><s0>Aplicación medical</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE"><s0>Electromyographie</s0>
<s5>06</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG"><s0>Electromyography</s0>
<s5>06</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA"><s0>Electromiografía</s0>
<s5>06</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Parkinson maladie</s0>
<s5>20</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Parkinson disease</s0>
<s5>20</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Parkinson enfermedad</s0>
<s5>20</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Tremblement</s0>
<s5>21</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Tremor</s0>
<s5>21</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Temblor</s0>
<s5>21</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Système nerveux pathologie</s0>
<s5>22</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Nervous system diseases</s0>
<s5>22</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Sistema nervioso patología</s0>
<s5>22</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Corrélation</s0>
<s5>23</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Correlation</s0>
<s5>23</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Correlación</s0>
<s5>23</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Cohérence</s0>
<s5>24</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Coherence</s0>
<s5>24</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Coherencia</s0>
<s5>24</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Résistance mécanique</s0>
<s5>25</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Strength</s0>
<s5>25</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Resistencia mecánica</s0>
<s5>25</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Dépendance du temps</s0>
<s5>26</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Time dependence</s0>
<s5>26</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Dependencia del tiempo</s0>
<s5>26</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Phénomène transitoire</s0>
<s5>27</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Transients</s0>
<s5>27</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Fenómeno transitorio</s0>
<s5>27</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Réponse transitoire</s0>
<s5>28</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Transient response</s0>
<s5>28</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Respuesta transitoria</s0>
<s5>28</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Intermittence</s0>
<s5>29</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Intermittency</s0>
<s5>29</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Intermitencia</s0>
<s5>29</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Causalité</s0>
<s5>30</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Causality</s0>
<s5>30</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Causalidad</s0>
<s5>30</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Méthode adaptative</s0>
<s5>31</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Adaptive method</s0>
<s5>31</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Método adaptativo</s0>
<s5>31</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Modèle autorégressif</s0>
<s5>32</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Autoregressive model</s0>
<s5>32</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Modelo autorregresivo</s0>
<s5>32</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Analyse régression</s0>
<s5>33</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Regression analysis</s0>
<s5>33</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Análisis regresión</s0>
<s5>33</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Modélisation</s0>
<s5>34</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Modeling</s0>
<s5>34</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Modelización</s0>
<s5>34</s5>
</fC03>
<fC03 i1="19" i2="X" l="FRE"><s0>Fonction cohérence</s0>
<s5>35</s5>
</fC03>
<fC03 i1="19" i2="X" l="ENG"><s0>Coherence function</s0>
<s5>35</s5>
</fC03>
<fC03 i1="19" i2="X" l="SPA"><s0>Función coherencia</s0>
<s5>35</s5>
</fC03>
<fC03 i1="20" i2="X" l="FRE"><s0>Méthode domaine temps fréquence</s0>
<s5>36</s5>
</fC03>
<fC03 i1="20" i2="X" l="ENG"><s0>Time frequency domain method</s0>
<s5>36</s5>
</fC03>
<fC03 i1="20" i2="X" l="SPA"><s0>Método dominio tiempo frecuencia</s0>
<s5>36</s5>
</fC03>
<fN21><s1>183</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
</inist>
</record>

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	La maladie de Parkinson au Canada (serveur d'exploration)
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La maladie de Parkinson au Canada (serveur d'exploration)

Revealing the dynamic causal interdependence between neural and muscular signals in Parkinsonian tremor

Revealing the dynamic causal interdependence between neural and muscular signals in Parkinsonian tremor

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