1 / 24

SPM short course Functional integration and connectivity

SPM short course Functional integration and connectivity. Christian Büchel Karl Friston The Wellcome Department of Cognitive Neurology, UCL London UK http//:www.fil.ion.ucl.ac.uk/spm. p <0.05. Data analysis. Design matrix. fMRI time-series. Kernel.

aideen
Télécharger la présentation

SPM short course Functional integration and connectivity

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. SPM short course Functional integration and connectivity Christian Büchel Karl Friston The Wellcome Department of Cognitive Neurology, UCL London UK http//:www.fil.ion.ucl.ac.uk/spm

  2. p <0.05 Data analysis Design matrix fMRI time-series Kernel Inference with Gaussian field theory Realignment Smoothing General linear model Normalisation Adjusted regional data spatial modes and effective connectivity Template Parameter estimates

  3. Functional brain architectures • Functional segregation • Univariate analyses of regionally specific effects • Functional integration • Multivariate analyses of regional interactions • Functional connectivity • “the temporal correlation between neurophysiological events” • an operational definition • Effective connectivity • “the influence one neuronal system exerts over another” • a model-dependent definition

  4. Issues in functional integration • Functional Connectivity • Eigenimage analysis and PCA • Effective Connectivity • Psychophysiological Interactions • State space Models (Variable parameter regression) • Structural Equation Modelling • Volterra series

  5. B A C Effective vs. functional connectivity Model: A = V1 fMRI time-series B = 0.5 * A + e1 C = 0.3 * A + e2 Correlations: A B C 1 0.49 1 0.30 0.12 1 0.49 Correct model -0.02 2=0.5, ns. 0.31

  6. Eigenimages - the basic concept A time-series of 1D images 128 scans of 40 “voxels” Expression of 1st 3 “eigenimages” Eigenvalues and spatial “modes” The time-series ‘reconstituted’

  7. Eigenimages and SVD V1 V2 V3 voxels APPROX. OF Y U1 U2 APPROX. OF Y U3 APPROX. OF Y s1 + s2 + s3 = + ... Y (DATA) time Y = USVT = s1U1V1T + s2U2V2T + ...

  8. An example from PET Eigenimage analysis of a PET word generation study Word generation G Word repetition R R G R G R G.........

  9. Dynamic changes in effective connectivityAttentional modulation of V5 responses to visual motion • Psychophysiological interactions • Attentional modulation of V2 to V5 connections • State space models and variable parameter regression • Attentional modulation of V5 to PPC connections • Models of effective connectivity • The mediating role of posterior parietal cortex • in attentional modulation • Structural Equation modelling • Volterra formulation

  10. The fMRI study 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 e.g. 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

  11. Psychophysiological interactions: Attentional modulation of V2 -> V5 influences SPM{Z} V5 activity Attention time attention V2 V5 activity V5 no attention V2 activity

  12. AttentionFixation No attention 0.8 regression coefficient 0.5 • bt Time (scans) x = V5y = PP Regression with time-varying coefficients Fixed regression model (one coefficient for entire time-series) y = x*b + e Time varying regression model (coefficient changes over time) yt = xt.bt + et bt = bt-1+ht Coefficient b of the explanatory variable (V5) is modelled as a time-varying random walk. Estimation by Kalman filter.

  13. The source of modulatory afferents Anterior cingulate Dorsolateral prefrontal cortex “Modulatory” sources identified as regions correlated with bt R R p<0.05 corrected

  14. u v a V U c b W w Structural equation modelling (SEM) Minimise the difference between the observed (S) and implied () covariances by adjusting the path coefficients (a, b, c) The implied covariance structure: x = x.B + zx = z.(I - B)-1 x : matrix of time-series of regions U, V and W B: matrix of unidirectional path coefficients (a,b,c) Variance-covariance structure: xT . x =  = (I-B)-T. C.(I-B)-1 where C = zT z xT.x is the implied variance covariance structure  C contains the residual variances (u,v,w) and covariances The free parameters are estimated by minimising a [maximum likelihood] function of S and 

  15. 0.43 0.75 0.47 0.76 No attention Attention Attention - No attention

  16. PP V1 0.14 V5 V1xPP V5 The use of moderator or interaction variables 2 =11, p<0.01 = Modulatory influence of parietal cortex on V1 to V5

  17. PP 0.2 V5 PFC 2=13.6, p<0.01 0.1 2=5.9, p<0.01 V1 LGN Hierarchical architectures

  18. PP ITa ITp LP V1 DE V1 ITp E R E R Changes in effective connectivity over time: Learning • Paired associates learning • Pairing • Object (Snodgrass) with • Location • fMRI, 48 axial slices, TR 4.1s, 8 scans/cond • 8 cycles (E)ncoding (C)ontrol (R)etrieval • 3 sessions (each with new objects & locations) C C C

  19. PP PP LP LP DE DE V1 V1 ITp ITp ITa ITa SEM: Encoding Early vs. Late 0.59 0.61 0.37 0.41 0.46 2 =6.3p<0.05diff. = 0.16 0.26 0.57 0.15 0.13 -0.03 Late Early 0.38 0.45 0.27 0.35 Single subjects: +0.27*, +0.21, +0.37*, +0.24*, +0.19, +0.31** p < 0.05

  20. Changes in effective connectivity predict learning 1 k = .60 k = .35 learning rate k k=.95 k = .63 k = .71 0.4 k =.44 r = 0.64 % correct learning block 1 2 3 4 5 6 7 Length of EARLY (in learning blocks) that maximised the EARLY vs. LATE difference in connectivity (PP -> ITP)

  21. [u(t)] input[s] u(t) response y(t) Regional activities estimate kernels (h) Volterra series - a general nonlinear input-output model y(t) = 1[u(t)] + 2[u(t)] + .... + n[u(t)] + .... n[u(t)] = ....  hn(t1,..., tn)u(t - t1) .... u(t - tn)d t1 .... d tn

  22. Volterra series approximation • Trying to explain activity in region A by • past and present activity in other regions (1st order) • direct effects (eg. effect of B on A) • past and present activity in other regions (pairwise = 2nd order) • non-linear (eg. effect of B2 on A) • modulatory (eg. effect of AB on A) • A = a1B + a2C + a3AA + a4BB + a5CC + a6AB + a7AC + a8BC • All terms can be seen as regressors and their impact can be tested with the general linear model • direct effect of B on A : B and BB as covariates of interests, others confounds • modulatory effect of B on A : AB and BC as covariates of interest, others confounds

  23. PPC FEF IFS PPC V3a V5 areas showing attentional effects PPC V1/V2 V5 Pul regional interactions examined

  24. Changes in V5 response to V2 inputs with PPC activity i.e. a modulatory (activity-dependent) component of V5 responses PPC activity = 1 SPM{F} PPC activity = 0

More Related