Abstract This tutorial is about the inversion of dynamic input-state-output syst

Abstract This tutorial is about the inversion of dynamic input-state-output syst www.phwiki.com

Abstract This tutorial is about the inversion of dynamic input-state-output syst

Martin, Craig, News Director has reference to this Academic Journal, PHwiki organized this Journal Abstract This tutorial is about the inversion of dynamic input-state-output systems. Identification of the systems parameters proceeds in a Bayesian framework given known, deterministic inputs in addition to observed responses of the [neuronal] system. We develop this approach as long as the analysis of effective connectivity or coupling in the brain, using experimentally designed inputs in addition to fMRI in addition to EEG responses. In this context, the parameters correspond to effective connectivity in addition to , in particular, bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is perturbed or probed using evoked responses (to trial-bound inputs like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (c.f. psychophysiologic interactions). However, unlike previous approaches to connectivity in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown in addition to stochastic. Imaging Clinic Tuesday 26th October: 10AM-4.30PM; Building 26, room 135; Clayton Campus Dynamic Causal Modelling (tutorial) Karl Friston, Wellcome Centre as long as Neuroimaging, UCL Dynamic Causal Modelling State in addition to observation equations Model inversion DCMs as long as fMRI Bilinear models Hemodynamic models Attentional modulation Two-state models DCMs as long as EEG Neural-mass models Perceptual learning in addition to MMN Backward connections DCMs as long as LFP Steady-state responses V1 V4 BA37 STG BA39 Structural perturbations Stimulus-free – u e.g., attention, time Dynamic perturbations Stimuli-bound u e.g., visual words Functional integration in addition to the enabling of specific pathways measurement neuronal network

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Observed data input Forward model (measurement) Model inversion Forward models in addition to their inversion Forward model (neuronal) Model specification in addition to inversion Invert model Inference Define likelihood model Specify priors Neural dynamics Observer function Design experimental inputs Inference on models Inference on parameters Dynamic Causal Modelling State in addition to observation equations Model inversion DCMs as long as fMRI Bilinear models Hemodynamic models Attentional modulation Two-state models DCMs as long as EEG Neural-mass models Perceptual learning in addition to MMN Backward connections Induced responses DCMs as long as LFP Steady-state responses

The bilinear (neuronal) model average connectivity exogenous causes bilinear connectivity Dynamic perturbation Structural perturbation Output: a mixture of intra- in addition to extravascular signal 0 8 16 24 sec Hemodynamic models as long as fMRI basically, a convolution signal flow dHb volume The plumbing Neural population activity BOLD signal change (%) x1 x2 u1 x3 u2 – – A toy example 0 10 20 30 40 50 60 70 80 90 100 0 1 2 3 0 10 20 30 40 50 60 70 80 90 100 -1 0 1 2 3 4 0 10 20 30 40 50 60 70 80 90 100 0 1 2 3 0 10 20 30 40 50 60 70 80 90 100 0 0.1 0.2 0.3 0.4 0 10 20 30 40 50 60 70 80 90 100 0 0.2 0.4 0.6 0 10 20 30 40 50 60 70 80 90 100 0 0.1 0.2 0.3

Stimuli 250 radially moving dots at 4.7 degrees/s Pre-Scanning 5 x 30s trials with 5 speed changes (reducing to 1%) Task: detect change in radial velocity Scanning (no speed changes) 4 100 scan sessions; each comprising 10 scans of 4 conditions F A F N F A F N S F – fixation point A – motion stimuli with attention (detect changes) N – motion stimuli without attention S – no motion Buchel et al 1999 An fMRI study of attention V1 IFG V5 Photic Attention .92 .43 .62 .40 .53 .35 .73 .49 .53 3) Attentional modulation of prefrontal connections sufficient to explain regionally specific attentional effects 2) Segregation of motion in as long as mation to V5 1) Hierarchical architecture Friston et al 1999 SPC Motion FFA PPA MFG time (s) Stephan et al 2008 Nonlinear DCM: modulation of connections in inferotemporal cortex under binocular rivalry

(bottom right). input Single-state DCM Intrinsic (within-region) coupling Extrinsic (between-region) coupling Two-state DCM Modeling excitatory in addition to inhibitory dynamics Andre Marreiros et al Model comparison: where is attention mediated Model comparison Andre Marreiros et al Hierarchical connections in the brain in addition to laminar specificity Dynamic Causal Modelling State in addition to observation equations Model inversion DCMs as long as fMRI Bilinear models Hemodynamic models Attentional modulation Two-state models DCMs as long as EEG Neural-mass models Perceptual learning in addition to MMN Backward connections Induced responses DCMs as long as LFP Steady-state responses

neuronal mass models of distributed sources State equations Output equation Exogenous input Excitatory spiny cells in granular layers Excitatory pyramidal cells in infragranular layers Inhibitory cells in supragranular layers Measured response input ERPs Comparing models (with in addition to without backward connections) A1 A1 STG input STG IFG FB A1 A1 STG input STG IFG F FB vs. F without with Garrido et al 2007 log-evidence The MMN in addition to perceptual learning st in addition to ards deviants Garrido et al 2008

Model comparison: Changes in as long as ward in addition to backward connections Garrido et al 2009 A1 A1 STG STG Forward Backward Lateral input A1 A1 STG STG Forward Backward Lateral input A1 A1 STG Forward Backward Lateral input – STG IFG IFG IFG Forward (F) Backward (B) Forward in addition to Backward (FB) Two subgroups Garrido et al 2008 monotonic phasic Intrinsic connections Extrinsic connections number of presentations The dynamics of plasticity: Repetition suppression Garrido et al 2009

K frequency modes in j-th source Nonlinear (between-frequency) coupling Linear (within-frequency) coupling Extrinsic (between-source) coupling Neuronal model as long as spectral features Data in channel space Inversion of electromagnetic model L input Intrinsic (within-source) coupling DCM as long as induced responses – a different sort of data feature CC Chen et al 2008 Frequency-specific coupling during face-processing CC Chen et al 2008 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 -0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1 -0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1 Right hemisphere Left hemisphere Forward Backward Forward Backward Frequency (Hz) LV RV RF LF input Functional asymmetries in as long as ward in addition to backward connections CC Chen et al 2008

Martin, Craig KPXQ-AM News Director www.phwiki.com

Dynamic Causal Modelling State in addition to observation equations Model inversion DCMs as long as fMRI Bilinear models Hemodynamic models Attentional modulation Two-state models DCMs as long as EEG Neural-mass models Perceptual learning in addition to MMN Backward connections DCMs as long as LFP Steady-state responses DCMs as long as steady-state responses: characterizing coupling parameters Cross-spectral data features 6-OHDA lesion model of Parkinsonism Moran et al 1. Cortex 2. Striatum 3. External globus pallidus (GPe) 4. Subthalamic Nucleus (STN) 6. Thalamus 5. Entopeduncular Nucleus (EPN) Changes in the basal ganglia-cortical circuits Moran et al Control 6-OHDA Lesioned 1 2 3 4 6 4.25 ± 0.17 1.44 ± 0.18 5.24 ± 0.16 6. 91 ± 0.19 0.90 ± 0.21 1.43 ± 0.38 0.29 ± 0.31 0.85 ± 0.36 5 0.72 ± 0.44 1 2 3 4 5 3.43 ± 0.16 3.07 ± 0.17 5.00 ± 0.15 2.33 ± 0.21 1.04 ± 0.20 1.18 ± 0.33 1.03 ± 0.35 6 0.74 ± 0.28 MAP estimates EPN to Thalamus Thalamus to Ctx Ctx to Striatum Ctx to STN Striatum to GPe Striatum to EPN STN to EPN STN to GPe GPe to STN

Thank you And thanks to CC Chen Jean Daunizeau Marta Garrido Lee Harrison Stefan Kiebel Andre Marreiros Rosalyn Moran Will Penny Klaas Stephan And many others

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