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The general linear model and Statistical Parametric Mapping

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Presentation on theme: "The general linear model and Statistical Parametric Mapping"— Presentation transcript:

1 The general linear model and Statistical Parametric Mapping
Stefan Kiebel Wellcome Dept. of Imaging Neuroscience Institute of Neurology, UCL, London

2 Passive word listening
fMRI example One session Time series of BOLD responses in one voxel Passive word listening versus rest 7 cycles of rest and listening Each epoch 6 scans with 7 sec TR Stimulus function Question: Is there a change in the BOLD response between listening and rest?

3 Make inferences about effects of interest
Why modelling? Why? Make inferences about effects of interest Decompose data into effects and error Form statistic using estimates of effects and error How? Stimulus function effects estimate statistic linear model data error estimate

4 Modelling with SPM General linear model Preprocessing SPMs
functional data Design matrix Contrasts Smoothed normalised data Parameter estimates General linear model Preprocessing SPMs Random field theory templates Variance components

5 Voxel by voxel Time Time model specification parameter estimation
hypothesis statistic Time Intensity single voxel time series SPM

6 Classical statistics General linear model etc... etc...
one sample t-test correlation ANOVA multiple regression General linear model Fourier transform wavelet transform etc... etc...

7 identically distributed)
Regression model error = + + Time (error is normal and independently and identically distributed) Intensity Question: Is there a change in the BOLD response between listening and rest? Hypothesis test: b1 = 0? (using t-statistic)

8 Regression model = + + error Time Intensity
(error is normal and independently and identically distributed) Intensity Stimulus function is not expected BOLD response Data is serially correlated Something wrong with this model?

9 Regression model = + +

10 Design matrix = +

11 Overview General linear model:
Partition data into effects (of interest and no interest) and error Model error using variance components Ordinary Least-Squares (Restricted) Maximum-Likelihood t-contrast: tests for single dimension in parameter space F-contrast: tests for multiple dimensions inference at first or second level (fixed or random effects) over conditions or groups: main effect, difference, interaction: average over time window parametric modulation with extrinsic variable power comparison in time-frequency domain Inference Classical statistics Hypothesis test using contrast

12 General Linear Model = +
Model is specified by Design matrix X Assumptions about e N: number of scans p: number of regressors

13 Ordinary least squares (OLS) estimator
Parameter estimation Assume iid error = + residuals Estimate parameters Ordinary least squares (OLS) estimator such that minimal

14 Estimation, example Assume iid error residuals and Least squares
estimate Another estimate

15 Convolve stimulus function with model of BOLD response
Improved model Convolve stimulus function with model of BOLD response Haemodynamic response function fitted data

16 Confounds and noise expected BOLD response modelled BOLD response
high pass filter Scanner drift movement-related regressors Cardiac-respiratory cycle head movements non-modelled neuronal events serial correlation HRF shape different

17 High-pass filter High-pass ‚filter‘ implemented by
modelling low frequencies discrete cosine transform set

18 data and three different models
High-pass filter data and three different models

19 autoregressive process of order 1 (AR(1))
Serial correlation with autoregressive process of order 1 (AR(1)) autocovariance function COMPUTATIONAL EFFICIENCY To test a hypothesis a lot of images (on the order of 104) are accessed. Acceptable for interpolated channel data, but probably not for source reconstructed 3D-data. For fMRI, this procedure is ok, because there are not really many hypotheses (HRF up or HRF down + differences/interactions) For EEG, there are many more hypotheses to test, because there are many potential different hypotheses Some hypotheses cannot be tested with conventional model: any contrasts with lots of different regressors at 2nd level!!

20 Error covariance matrix
i.i.d. AR(1) sampled error covariance matrix COMPUTATIONAL EFFICIENCY To test a hypothesis a lot of images (on the order of 104) are accessed. Acceptable for interpolated channel data, but probably not for source reconstructed 3D-data. For fMRI, this procedure is ok, because there are not really many hypotheses (HRF up or HRF down + differences/interactions) For EEG, there are many more hypotheses to test, because there are many potential different hypotheses Some hypotheses cannot be tested with conventional model: any contrasts with lots of different regressors at 2nd level!! Serial correlations

21 Mass-univariate approach
= +

22 Restricted Maximum Likelihood
observed ReML estimated correlation matrix COMPUTATIONAL EFFICIENCY To test a hypothesis a lot of images (on the order of 104) are accessed. Acceptable for interpolated channel data, but probably not for source reconstructed 3D-data. For fMRI, this procedure is ok, because there are not really many hypotheses (HRF up or HRF down + differences/interactions) For EEG, there are many more hypotheses to test, because there are many potential different hypotheses Some hypotheses cannot be tested with conventional model: any contrasts with lots of different regressors at 2nd level!!

23 Inference - t-statistic
boxcar parameter > 0 ? Null hypothesis: COMPUTATIONAL EFFICIENCY To test a hypothesis a lot of images (on the order of 104) are accessed. Acceptable for interpolated channel data, but probably not for source reconstructed 3D-data. For fMRI, this procedure is ok, because there are not really many hypotheses (HRF up or HRF down + differences/interactions) For EEG, there are many more hypotheses to test, because there are many potential different hypotheses Some hypotheses cannot be tested with conventional model: any contrasts with lots of different regressors at 2nd level!!

24 t-statistic - Computations
least squares estimates cT = COMPUTATIONAL EFFICIENCY To test a hypothesis a lot of images (on the order of 104) are accessed. Acceptable for interpolated channel data, but probably not for source reconstructed 3D-data. For fMRI, this procedure is ok, because there are not really many hypotheses (HRF up or HRF down + differences/interactions) For EEG, there are many more hypotheses to test, because there are many potential different hypotheses Some hypotheses cannot be tested with conventional model: any contrasts with lots of different regressors at 2nd level!! compute df using Satterthwaite approximation ReML

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