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Dynamic Causal Modelling (DCM) for induced responses

Dynamic Causal Modelling (DCM) for induced responses. CC Chen Wellcome Trust Centre for Neuroimaging Institute of Neurology, UCL, London Dr. James Kilnear , Dr. Stefan Kiebel, Prof. Karl Friston and Dr. Nick Ward. Contents. Evoked and Induced activities

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Dynamic Causal Modelling (DCM) for induced responses

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  1. Dynamic Causal Modelling (DCM)for induced responses CC Chen Wellcome Trust Centre for Neuroimaging Institute of Neurology, UCL, London Dr. James Kilnear , Dr. Stefan Kiebel, Prof. Karl Friston and Dr. Nick Ward

  2. Contents • Evoked and Induced activities • Methods for frequency-based analyses • DCM for induced responses • One condition, Single subject result -- intrinsic nonlinearity and force modulation in motor system • Two conditions, group result -- functional asymmetries between forward and backward connections induced by face processing • Summary

  3. Evoked and Induced activities Trends Cogn Sci. 1999 Apr;3(4):151-162

  4. Task-related power changes occur in several frequency bands (10-12, 14-18, and 36-40 Hz) with different time courses and duration Power fluctuation is associated with the functional organization of brain and is important aspects of network function Task-related changes in frequency-specific power Pfurtscheller G., F.H. Lopes da Silva, Clin Neurophysiol. 1999 ;110(11):1842-57

  5. Frequency-based analyses • measuring connectivity • linear (within frequency coupling) or/and non-linear (cross frequency coupling) Trends Cogn Sci. 2007 Jul;11(7):267-9. Epub 2007 Jun 4.

  6. Brain is a dynamic system and follows some basic principles which link the inputs to outputs -- neuronal state equations Dynamic causal modelling Nat Rev Neurosci. 2001 Apr;2(4):229-39

  7. Linear (within-frequency) coupling Intrinsic (within-source) coupling Extrinsic (between-source) coupling Nonlinear (between-frequency) coupling Neuronal model for spectral features • Spectral dynamics • of sources

  8. input Feature selection Inversion of electromagnetic model L • In theory, we can consider the states as spectral densities at a discrete number of frequencies. • In practice, we use only several significant singular components (modes) obtained by SVD of the spectral responses over time and sources so that we reduce the problem to modelling only the coupling among modes that cover all frequencies in different proportions Data in channel space K frequency modes in j-th source more than one state per area

  9. Intrinsic (within-source) coupling Extrinsic (between-source) coupling Neuronal model for spectral features Linear (within-frequency) coupling Nonlinear (between-frequency) coupling

  10. Dynamic Causal Modelling • Specify the DCM: • areas • Exogenous inputs (stimuli) • Extrinsic and intrinsic coupling • Specify a generative model based on specific hypotheses • The coupling parameters embody linear and non-linear connections. • Inversion of the models using a Bayesian procedure • With Bayesian model selection (BMC), one can use model evidence to compare several plausible models and identify the architecture that best explains the data.

  11. Experimental examples-- A nonlinear neural code of motor programme during handgrip tasks(one condition)-- Forward and backward connections in the brain: A DCM study of functional asymmetries in face processing(faces v.s. scrambled faces )

  12. Intrinsic connections: between frequency coupling • The power changes of LFPs from subthalamic areaat two rhythms below 50 Hz are separately modulated by antiparkinsoniam medication • There are several functional sub-loops between the subthalamic area and cerebral cortical motor regions Priori A, et al. Exp Neurol 2004; 189: 369-79

  13. SMA LSMI RSMI Hypothesis ? • Whether there exists between-frequency (nonlinear) coupling in the neural circuitry between left and right primary sensorimotor areas (LSMI and RSMI, respectively) and supplementary motor area (SMA) ? ?

  14. Motor task • Single isometric hand grip (Ward et al. 2003) • Inter-movement interval : 7 s + time jitter • The target forces: 15% and 45% of the right hand maximum voluntary contraction (MVC) • 100 trials of each event type

  15. Four models • Intrinsic Linear & Extrinsic Linear (ILEL) • Intrinsic Linear & Extrinsic Nonlinear(ILEN) • Intrinsic Nonlinear & Extrinsic Linear(LNEL) • Intrinsic Nonlinear & Extrinsic Nonlinear (LNEN)

  16. Linear Linear + nonlinear SMA SMA input input LSMI LSMI RSMI RSMI input input input input Model 1: INEL Model 2: INEN SMA SMA input input LSMI LSMI RSMI RSMI input input input input Model 4: ILEL Model 3: ILEN

  17. Log-evidence INEL INEN ILEN ILEL Probability

  18. 3 1 2

  19. input Linear Linear+nonlinear SMA LSMI RSMI input input

  20. LSMI LSMI SMA LSMI At single subject level, we found significant nonlinear coupling within MI and SMA. This intrinsic nonlinearity is important because it may serve as functional integrations of different inputs.  our results suggest that nonlinear coupling within and between areas is essential of forming the neural code of motor programme.

  21. Forward and backward connections in the brain: A DCM study of functional asymmetries in face processing (faces v.s. scrambled faces )

  22. Hierarchical connections and functional asysmetries • The brain has a hierarchical organisation that is largely defined by asysmetries in extrinsic cortico-cortical connections (anatomical and physiological evidence) • From animal and fMRI studies, it has been shown that there is direct evidence for modulatory effect of backward connections is nonlinear

  23. Hypothesis • If there are any functional asymmetries in forward and backward connections during face processing • The models with nonlinearities in the backward modulation would be better than equivalent models with nonlinear forward modulation.

  24. NFBC model NFC model NFB model NBC model NF model NB model LFA LFA RFA RFA LFA RFA LFA LFA RFA RFA LFA RFA LFA RFA LVA LVA RVA RVA LVA RVA LVA LVA RVA RVA LVA RVA LVA RVA input input input input input input input LFB model LF model LB model Modulation of connectivity Linear + nonlinear Linear LFA RFA LFA RFA LVA RVA LVA RVA input input

  25. BMC at the group level Log-evidence NFBC NFC NBC* NFB NF NB LFB LF LB * Bayes Factor (NBC,NFBC)>150

  26. -1.5 % (alpha-gamma ) +1.6 % (gamma-alpha ) LVA RVA NBC model • SPM of left backward coupling gain 5 15 25 35 45 5 15 25 35 45 LFA RFA 5 15 25 35 45 5 15 25 35 45 +5.6 % (alpha-beta ) • SPM of right backward coupling gain 5 15 25 35 45 5 15 25 35 45 input 5 15 25 35 45 5 15 25 35 45

  27. conclusions • we were able to characterise frequency-specific causal influences mediating the observed spectral responses. • we provide evidence for functional asymmetries in forward and backward connections .

  28. Summary In DCM for induced responses : • modelling the second-order features of the data (i.e., the spectrum) (broadband approach) • disambiguating between linear and non-linear coupling • making inferences about causal coupling • Making inference about task manipulation • DCM is not for a surrogate for widely used linear models (e.g., coherence, correlations) but represents a complementary approach to disclose cross-frequency interactions

  29. Thank you for your attention SPM5 / SPM8 Software available from http://www.fil.ion.ucl.ac.uk/spm/

  30. Linear Linear + nonlinear LPM RPM SMA LMI RMI The best model is not necessary to be the more complex one m3 LPM RPM SMA LMI RMI m1 m3 m2 m1 m2 LPM RPM SMA LMI RMI m1 m3 m2

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