Presentation on theme: "Will Penny DCM for Time-Frequency DCM Course, Paris, 2012 1. DCM for Induced Responses 2. DCM for Phase Coupling."— Presentation transcript:
Will Penny DCM for Time-Frequency DCM Course, Paris, 2012 1. DCM for Induced Responses 2. DCM for Phase Coupling
Dynamic Causal Models NeurophysiologicalPhenomenological DCM for ERP DCM for SSR DCM for Induced Responses DCM for Phase Coupling spiny stellate cells inhibitory interneurons Pyramidal Cells Time Frequency Phase Source locations not optimizedElectromagnetic forward model included
Region 1 Region 2 ? ? Changes in power caused by external input and/or coupling with other regions Model comparisons: Which regions are connected? E.g. Forward/backward connections (Cross-)frequency coupling: Does slow activity in one region affect fast activity in another ? 1. DCM for Induced Responses Time Frequency Time
Connection to Neural Mass Models First and Second order Volterra kernels From Neural Mass model. Strong (saturating) input leads to cross-frequency coupling
Single region u2u2 u1u1 z1z1 z2z2 z1z1 u1u1 a 11 c cf. Neural state equations in DCM for fMRI
Multiple regions u2u2 u1u1 z1z1 z2z2 z1z1 z2z2 u1u1 a 11 a 22 c a 21 cf. DCM for fMRI
Modulatory inputs u2u2 u1u1 z1z1 z2z2 u2u2 z1z1 z2z2 u1u1 a 11 a 22 c a 21 b 21 cf. DCM for fMRI
u1u1 u2u2 z1z1 z2z2 a 11 a 22 c a 12 a 21 b 21 Reciprocal connections u2u2 u1u1 z1z1 z2z2 cf. DCM for fMRI
dg(t)/dt=A∙g(t)+C∙u(t) DCM for induced responses Where g(t) is a K x 1 vector of spectral responses A is a K x K matrix of frequency coupling parameters Also allow A to be changed by experimental condition Time Frequency
G=USV’ Use of Frequency Modes Where G is a K x T spectrogram U is K x K’ matrix with K frequency modes V is K x T and contains spectral mode responses over time Hence A is only K’ x K’, not K x K Time Frequency
Connection to Neurobiology From Neural Mass model. Strong (saturating) input leads to cross-frequency coupling
Connection to Neurobiology Strong (saturating) input leads to cross-frequency coupling
Differential equation model for spectral energy Nonlinear (between-frequency) coupling Linear (within-frequency) coupling Extrinsic (between-source) coupling Intrinsic (within-source) coupling How frequency K in region j affects frequency 1 in region i
FLBLFLBL FNBLFNBL FLBNFLBN *F N B N -59890 -16308 -16306 -11895 -70000 -60000 -50000 -40000 -30000 -20000 -10000 0 -8000 -7000 -6000 -5000 -4000 -3000 -2000 -1000 0 1000 backward linearbackward nonlinear forward linear forward nonlinear Model Inference Winning model: F N B N Both forward and backward connections are nonlinear
Parameter Inference: gamma affects alpha Right backward - inhibitory - suppressive effect of gamma-alpha coupling in backward connections Left forward - excitatory - activating effect of gamma-alpha coupling in the forward connections From 32 Hz (gamma) to 10 Hz (alpha) t = 4.72 ; p = 0.002 4 12 20 28 36 44 44 36 28 20 12 4 SPM t df 72; FWHM 7.8 x 6.5 Hz Frequency (Hz) From 30Hz To 10Hz
For studying synchronization among brain regions Relate change of phase in one region to phase in others Region 1 Region 3 Region 2 ? ? 2. DCM for Phase Coupling Phase Interaction Function
Synchronization achieved by phase coupling between regions Model comparisons: Which regions are connected? E.g. ‘master-slave’/mutual connections Parameter inference: (frequency-dependent) coupling values Region 1 Region 2 ? ?
Synchronization Gamma sync synaptic plasticity, forming ensembles Theta sync system-wide distributed control (phase coding) Pathological (epilepsy, Parkinsons) Phase Locking Indices, Phase Lag etc are useful characterising systems in their steady state Sync – Steve Strogatz
Questions Duzel et al. find different patterns of theta-coupling in the delay period dependent on task. Pick 3 regions based on [previous source reconstruction] 1. Right MTL [27,-18,-27] mm 2. Right VIS [10,-100,0] mm 3. Right IFG [39,28,-12] mm Find out if structure of network dynamics is Master-Slave (MS) or (Partial/Total) Mutual Entrainment (ME) Which connections are modulated by memory task ?
Analysis Source reconstruct activity in areas of interest (with fewer sources than sensors and known location, then pinv will do; Baillet 01) Bandpass data into frequency range of interest Hilbert transform data to obtain instantaneous phase. Data that we model are unwrapped phase time series in multiple regions. Use multiple trials per experimental condition Model inversion
Your consent to our cookies if you continue to use this website.