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The General Linear Model Christophe Phillips Cyclotron Research Centre University of Liège, Belgium SPM Short Course London, May 2011

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Normalisation Statistical Parametric Map Image time-series Parameter estimates General Linear Model RealignmentSmoothing Design matrix Anatomical reference Spatial filter Statistical Inference RFT p <0.05

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Passive word listening versus rest 7 cycles of rest and listening Blocks of 6 scans with 7 sec TR Question: Is there a change in the BOLD response between listening and rest? Stimulus function One session A very simple fMRI experiment

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stimulus function 1.Decompose data into effects and error 2.Form statistic using estimates of effects and error Make inferences about effects of interest Why? How? data statistic Modelling the measured data linear model linear model effects estimate error estimate

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Time BOLD signal Time single voxel time series single voxel time series Voxel-wise time series analysis Model specification Model specification Parameter estimation Parameter estimation Hypothesis Statistic SPM

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BOLD signal Time = 1 2 + + error x1x1 x2x2 e Single voxel regression model

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Mass-univariate analysis: voxel-wise GLM = + y y X X Model is specified by both 1.Design matrix X 2.Assumptions about e Model is specified by both 1.Design matrix X 2.Assumptions about e The design matrix embodies all available knowledge about experimentally controlled factors and potential confounds. N : number of scans p : number of regressors

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one sample t-test two sample t-test paired t-test Analysis of Variance (ANOVA) Factorial designs correlation linear regression multiple regression F-tests fMRI time series models Etc.. GLM: mass-univariate parametric analysis

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Parameter estimation = + Ordinary least squares estimation (OLS) (assuming i.i.d. error): Objective: estimate parameters to minimize y X

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A geometric perspective on the GLM y e Design space defined by X x1x1 x2x2 Smallest errors (shortest error vector) when e is orthogonal to X Ordinary Least Squares (OLS)

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x1x1 x2x2 x2*x2* y Correlated and orthogonal regressors When x 2 is orthogonalized w.r.t. x 1, only the parameter estimate for x 1 changes, not that for x 2 ! Correlated regressors = explained variance is shared between regressors

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= + y X What are the problems of this model?

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1.BOLD responses have a delayed and dispersed form. HRF 2.The BOLD signal includes substantial amounts of low-frequency noise (eg due to scanner drift). 3.Due to breathing, heartbeat & unmodeled neuronal activity, the errors are serially correlated. This violates the i.i.d. assumptions of the noise model in the GLM

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Problem 1: Shape of BOLD response Solution: Convolution model expected BOLD response = input function impulse response function (HRF) expected BOLD response = input function impulse response function (HRF) = Impulses HRF Expected BOLD

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Convolution model of the BOLD response Convolve stimulus function with a canonical hemodynamic response function (HRF): HRF

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blue = data black = mean + low-frequency drift green = predicted response, taking into account low-frequency drift red = predicted response, NOT taking into account low-frequency drift Problem 2: Low-frequency noise Solution: High pass filtering discrete cosine transform (DCT) set

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High pass filtering

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with 1 st order autoregressive process: AR(1) autocovariance function Problem 3: Serial correlations

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Multiple covariance components = 1 + 2 Q1Q1 Q2Q2 Estimation of hyperparameters with ReML (Restricted Maximum Likelihood). V enhanced noise model at voxel i error covariance components Q and hyperparameters

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Parameters can then be estimated using Weighted Least Squares (WLS) Let Then where WLS equivalent to OLS on whitened data and design WLS equivalent to OLS on whitened data and design

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Contrasts & statistical parametric maps Q: activation during listening ? c = 1 0 0 0 0 0 0 0 0 0 0 Null hypothesis:

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Summary Mass univariate approach. Fit GLMs with design matrix, X, to data at different points in space to estimate local effect sizes, GLM is a very general approach Hemodynamic Response Function High pass filtering Temporal autocorrelation

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Thank you for your attention! …and thanks to Guillaume for his slides.

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