Presentation is loading. Please wait.

Presentation is loading. Please wait.

Bayesian Model Comparison Will Penny Wellcome Department of Imaging Neuroscience, University College London, UK UCL, June 7, 2003 Large-Scale Neural Network.

Similar presentations


Presentation on theme: "Bayesian Model Comparison Will Penny Wellcome Department of Imaging Neuroscience, University College London, UK UCL, June 7, 2003 Large-Scale Neural Network."— Presentation transcript:

1 Bayesian Model Comparison Will Penny Wellcome Department of Imaging Neuroscience, University College London, UK UCL, June 7, 2003 Large-Scale Neural Network models fitted to fMRI data

2 Attention to Visual Motion Stimuli 250 radially moving dots at 4.7 degrees/s Pre-Scanning 5 x 30s trials with 5 speed changes (reducing to 1%) 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 e.g. F A F N F A F N S................. F – fixation S – stationary dots N – moving dots A – attended moving dots 1.Photic Stimulation, S,N,A 2.Motion, N,A 3.Attention, A Experimental Factors Buchel et al. 1997

3 Motion Sensitive Areas y = Xw + e Mass Univariate Analyses Where is effect, w, eg. of motion, significantly non-zero. Analysis of 360 images each containing 100,000 voxels, ie. 100,000 time series. New image every 3 seconds.

4 Network Analysis IFG SPC V5 V1 Photic Motion Attention Inputs and Outputs

5 DCM: A network model for fMRI Set u 2 Stimuli u 1 Input State Output Friston et al. 2003

6 Bilinear Dynamics: Positive transients - Z2Z2 Stimuli u 1 Set u 2 Z1Z1 + + - - - + u 1 Z 1 u 2 Z 2

7 Hemodynamics Impulse response BOLD is sluggish

8 V1IFG V5SPC Motion Photic Attention..82 (100%).42 (100%).37 (90%).69 (100%).47 (100%).65 (100%).52 (98%).56 (99%) Motion modulates bottom-up V1-V5 connection Attention modulates top-down IFG-SPC and SPC-V5 connections DCM for Attention Data Friston et al. 2003

9 Bayesian Model Comparison We need to compute the Bayesian Evidence: Posterior Probability of Model:

10 m=1 V1PFC V5 PPC Motion Photic Attention Motion Photic Attention Motion Photic Attention Motion Photic Attention m=2 V1PFC V5 PPC Motion Photic Attention V1PFCV5PPC Motion Photic Attention V1PFCV5PPC Motion Photic Attention m=3 m=4

11 m=2 V1PFC V5 PPC Motion Photic Attention V1PFC V5 PPC Motion Photic Attention V1PFC V5 PPC Motion Photic Attention m=3 m=4


Download ppt "Bayesian Model Comparison Will Penny Wellcome Department of Imaging Neuroscience, University College London, UK UCL, June 7, 2003 Large-Scale Neural Network."

Similar presentations


Ads by Google