Effective Connectivity

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Presentation transcript:

Effective Connectivity Lee Harrison Wellcome Department of Imaging Neuroscience, University College London, UK SPM Short Course, May 2004

Outline Motivation & concepts Models of effective connectivity An example

Outline Motivation & concepts Models of effective connectivity An example

Functional Specialization Q. In what areas does the ‘motion’ factor change activity ? Univariate Analysis

Functional Integration To estimate and make inferences about the influence that one neural system exerts over another (2) how this is affected by the experimental context Z2 Z4 Z3 Z5

Concepts Brain as a physical system System identification Evoked response to input System identification Parameterised models In terms of connectivity Classification of models Black box & hidden states

Concepts (continued) Linear vs nonlinear systems Balance mathematical tractability and biological plausibility Generalization of General Linear Model Bilinear models Inputs Perturbing & contextual Stochastic & deterministic use of design matrix Experimental design 22 factorial design

Concepts (continued) Linear vs nonlinear systems Balance mathematical tractability and biological plausibility Generalization of General Linear Model Bilinear models Inputs Perturbing & contextual Stochastic & deterministic use of design matrix Experimental design 22 factorial design

Model of Neuronal Activity Z2 Z1 Z4 Z3 Z5 Stimuli u1 Set u2 Nonlinear, systems-level model

Bilinear Dynamics Z4 Z2 Psycho-physiological interaction Z5 Z1 Z3 Set Stimuli u1 Set u2 Psycho-physiological interaction

Bilinear Dynamics a53 Psycho-physiological interaction Set Stimuli u2

Bilinear Dynamics: Positive transients Stimuli u1 Set u2 u 1 Z 2 - + Z1 - + + Z2 - -

Concepts (continued) Linear vs nonlinear systems Balance mathematical tractability and biological plausibility Generalization of General Linear Model Bilinear models Inputs Perturbing & contextual Stochastic & deterministic use of design matrix Experimental design 22 factorial design

Outline Motivation & concepts Models of effective connectivity An example

Practical steps 1) Standard Analysis of fMRI Data Design matrix 1) Standard Analysis of fMRI Data 2) Statistical Parametric Maps 3) Anatomical model 4) Connectivity model 5) Estimation & inference of model parameters SPMs Z2 Z4 Z3 Z5

Outline Motivation & concepts Models of effective connectivity Linear regression Convolution State-Space An example

Outline Motivation & concepts Models of effective connectivity Linear regression Convolution State-Space An example

Structural Equation Modelling y1 y2 y3

Inference in SEMs V1 V5 PPC V1 V5 PPC V1 V5 PPC V1 V5 PPC V1 V5 PPC vs V1 V5 PPC V1 V5 PPC V1 V5 PPC PFC V1 V5 PPC PFC vs PPIV5xPFC PPIV5xPFC Attentional set Attentional set

Outline Motivation & concepts Models of effective connectivity Linear regression Convolution State-Space An example

Bilinear Convolution Model

Outline Motivation & concepts Models of effective connectivity Linear regression Convolution State-Space An example

Outline Motivation & concepts Models of effective connectivity Linear regression Convolution State-Space Dynamic Causal Modelling An example

The DCM and its bilinear approximation neuronal changes intrinsic connectivity induced connectivity induced response Input u(t) The bilinear model activity z2(t) activity z3(t) activity z1(t) y y y Hemodynamic model

The hemodynamic model y

Overview Models of Constraints on Bayesian estimation Hemodynamics in a single region Neuronal interactions Constraints on Connections Hemodynamic parameters Bayesian estimation

Practical steps 1) Standard Analysis of fMRI Data Design matrix 1) Standard Analysis of fMRI Data 2) Statistical Parametric Maps 3) Anatomical model 4) Connectivity model 5) Estimation & inference of model parameters SPMs Z2 Z4 Z3 Z5

Outline Motivation & concepts Models of effective connectivity Linear regression Convolution State-Space An example DCM for visual motion processing

A fMRI study of attentional modulation 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) 6 normal subjects, 4 100 scan sessions; each session comprising 10 scans of 4 different condition F A F N F A F N S ................. F - fixation point only A - motion stimuli with attention (detect changes) N - motion stimuli without attention S - no motion PPC V5+ Buchel et al 1999

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

Summary Studies of functional integration look at experimentally induced changes in connectivity Neurodynamics and hemodynamics DCM Inferences about large-scale neuronal networks