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CRIS Workshop: Computational Neuroscience and Bayesian Modelling

CRIS Workshop: Computational Neuroscience and Bayesian Modelling Monday 25th October; 2-5PM; Building 26, room 135; Clayton Campus Effective and functional connectivity Karl Friston, Wellcome Centre for Neuroimaging, UCL. Abstract

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CRIS Workshop: Computational Neuroscience and Bayesian Modelling

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  1. CRIS Workshop: Computational Neuroscience and Bayesian Modelling Monday 25th October; 2-5PM; Building 26, room 135; Clayton Campus Effective and functional connectivity Karl Friston, Wellcome Centre for Neuroimaging, UCL Abstract This talk will highlight the fundamental difference between effective and functional connectivity by demonstrating the nature of biophysical models used to infer effective connectivity. I will use DCM studies of reciprocal connections in the brain to illustrate what can be achieved using anatomically and physiologically informed models of distributed neuronal interactions. The examples chosen will focus on functional asymmetries in forward and backward connections and try to cover (i) different data features (e.g., fMRI and ERPs) and (ii) different scales (macroscopic and microscopic).

  2. Functional and Effective connectivity • Dynamic Causal Modelling • DCM and fMRI • DCM and EEG • DCM and DTI

  3. Effective connectivity Causal influence among systems Functional connectivity Statistical dependence between systems DCM DAG Tests for conditional independence: Structural causal modeling Bayesian model comparison: Dynamic causal modeling DCM Bayesian networks PCA and ICA Path analysis (SEM) Ganger causality (MAR)

  4. Functional and Effective connectivity • Dynamic Causal Modelling • DCM and fMRI • DCM and EEG • DCM and DTI

  5. Forward models and their inversion Observed data Forward model (measurement) Model inversion Forward model (neuronal) input

  6. Model specification and inversion Design experimental inputs Neural dynamics Define likelihood model Observer function Specify priors Invert model Inference on parameters Inference on models Inference

  7. Input The bilinear (neuronal) model Dynamic perturbation Structural perturbation bilinear and nonlinear connectivity exogenous causes average connectivity

  8. Functional and Effective connectivity • Dynamic Causal Modelling • DCM and fMRI • DCM and EEG • DCM and DTI

  9. Hemodynamic models for fMRI basically, a convolution signal The plumbing flow volume dHb 0 8 16 24 sec Output: a mixture of intra- and extravascular signal

  10. Neural population activity 0.4 0.3 0.2 0.1 0 0 10 20 30 40 50 60 70 80 90 100 u2 0.6 0.4 A toy example x3 0.2 0 0 10 20 30 40 50 60 70 80 90 100 0.3 0.2 0.1 BOLD signal change (%) 0 0 10 20 30 40 50 60 70 80 90 100 x1 x2 u1 3 2 1 – – 0 0 10 20 30 40 50 60 70 80 90 100 4 3 2 1 0 -1 0 10 20 30 40 50 60 70 80 90 100 3 2 1 0 0 10 20 30 40 50 60 70 80 90 100

  11. PPC V5+ An fMRI study of attention 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

  12. 3) Attentional modulation of prefrontal connections sufficient to explain regionally specific attentional effects 1) Hierarchical architecture Attention .43 .53 SPC Photic .40 .49 .62 .92 V1 IFG .35 .53 2) Segregation of motion information to V5 Motion V5 .73 Friston et al 1999

  13. Functional and Effective connectivity • Dynamic Causal Modelling • DCM and fMRI • DCM and EEG • DCM and DTI

  14. neuronal mass models of distributed sources input Inhibitory cells in supragranular layers Exogenous input Excitatory spiny cells in granular layers State equations Excitatory pyramidal cells in infragranular layers Output equation Measured response

  15. 0 0 400 200 IFG A1 A1 0 STG STG 0 200 400 Comparing models (with and without backward connections) ERPs log-evidence FB vs. F IFG IFG FB F STG STG STG STG without with A1 A1 A1 A1 input input Garrido et al 2007

  16. Functional and Effective connectivity • Dynamic Causal Modelling • DCM and fMRI • DCM and EEG • DCM and DTI

  17. FG FG LG LG LD|LVF FG (x3) FG (x4) Probabilistic constraints (priors) on effective connectivity LD LD LG (x2) LG (x1) LD|RVF RVF stim. LVF stim. BVF stim. DTI data and tractography Probabilistic structural connectivity

  18. Optimizing structural constraints Model-space search (scoring)

  19. 1 1 1 1 1 1 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 Model-space search - results

  20. 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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