1 st level analysis: design matrix, contrasts, and inference stephane de brito fiona mcnabe
DESCRIPTION
Rebecca Knight Motion correction Smoothing kernel Spatial normalisation Standard template fMRI time-series Statistical Parametric Map General Linear Model Design matrix Parameter Estimates Once the image has been reconstructed, realigned, spatially normalised and smoothed…. The next step is to statistically analyse the data OverviewTRANSCRIPT
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1st level analysis: Design matrix, contrasts, and inference
Stephane De Brito & Fiona McNabe
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Outline What is ‘1st level analysis’?
The General Linear Model and how this relates to the Design Matrix
Design matrix What are we testing for? What do all the black lines mean? What do we need to include?
Contrasts What are they for? t and F contrasts Inferences How do we do that in SPM5?
A B C D
[1 -1 -1 1]
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Rebecca Knight
Motioncorrection
Smoothing
kernel
Spatialnormalisation
Standardtemplate
fMRI time-series Statistical Parametric Map
General Linear Model
Design matrix
Parameter Estimates
Once the image has been reconstructed, realigned, spatially normalised and smoothed….
The next step is to statistically analyse the data
Overview
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1st level analysis – A within subjects analysis where activation is averaged across scans for an individual subject
The Between- subject analysis is referred to as a 2nd level analysis and will be described later on in this course
Design Matrix –The set of regressors that attempts to explain the experimental data using the GLM
A dark-light colour map is used to show the value of each variable at specific time points – 2D, m = regressors, n = time.
The Design Matrix forms part of the General linear model, the majority of statistics at the analysis stage use the GLM
Key concepts
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Y
Generic Model
Aim: To explain as much of the variance in Y by using X, and thus reducing ε
Dependent Variable (What you are measuring)
Independent Variable (What you are manipulating)
Relative Contributionof X to the overalldata (These need tobe estimated)
Error (The difference between the observed data and that which is predicted by the model)
= X x β + ε
Y = X1β1 + X2β2 + ....X n βn.... + ε More than 1 IV ?
General Linear Model
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YMatrix of BOLDat various time points in a single voxel(What you collect)
Design matrix
(This is your model specification in SPM)
Parameters matrix
(These need to be estimated)
Error matrix
(residual error for each voxel)
= X x β + ε
How does this equation translate to the 1st level analysis ?
Each letter is replaced by a set of matrices (2D representations)
Time
(rows)
Voxels (columns)
Time(rows)
Regressors (columns) Param. weights (columns)
Voxels (rows)
Time
(rows)
Voxels
GLM continued
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Rebecca Knight
Y = Matrix of Bold signals
Amplitude/Intensity
Time
(scan every 3 seconds)
fMRI brain scans Voxel time course
1 voxel = ~ 3mm³
Time
‘Y’ in the GLM
Y
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X = Design Matrix
Time(n)
Regressors (m)
‘X’ in the GLM
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Regressors – represent hypothesised contributors to the fMRI time course in your experiment. They are represented by columns in the design matrix (1column = 1 regressor)
Regressors of Interest or Experimental Regressors – represent those variables which you intentionally manipulated. The type of variable used affects how it will be represented in the design matrix (2 types: Covariates and Indicators, next slides)
Regressors of no interest or nuisance regressors – represent those variables which you did not manipulate but you suspect may have an effect. By including nuisance regressors in your design matrix you decrease the amount of error.
E.g. - The 6 movement regressors (rotations x3 & translations x3 ) or physiological factors e.g. heart rate, breathing or others (e.g., scanner known linear drift)
Regressors
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Time(n)
Regressors (m)
Covariates = Regressors that can take any of a continuous range of values (e.g, task difficulty)
A dark-light colour map is used to show the value of each regressor within a specific time point
Black = 0 and illustrates when the regressor is at its smallest value
White = 1 and illustrates when the regressor is at its largest value
Grey represents intermediate values
The representation of each regressor column depends upon the type of variable specified
Regress. of Inter. (Covariates)
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As they indicate conditions they are referred to as indicator variables
Type of dummy code is used to identify the levels of each variable
E.g. Two levels of one variable is on/off, represented as
ON = 1
OFF = 0
When you IV is presented
When you IV is absent (implicit baseline)
Changes in the bold activation associated with the presentation of
a stimulus
Fitted Box-Car
Red box plot of [0 1] doesn’t model the rise and falls
Regress. of inter. (Indicators)
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E.g Movement regressors – not simply just one state or another
The value can take any place along the X,Y,Z continuum for both rotations and translations
Regr. of no inter. (Covariate)
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Scanner Drift Artifact and t-test
E.g., Regress. of no inter.
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Ways to improve your model: modelling haemodynamics
• The brain does not just switch on and off.
• Reshape (convolve) regressors to resemble HRF
HRF basic function
Original
HRF Convolved
week!
Modelling haemodynamic
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The type of design and the type of variables used in your experiment will affect the construction of your design matrix
Another important consideration when designing your matrix is to make sure your regressors are separate
In other words, you should avoid correlations between regressors (collinear regressors) – because correlations in regressors means that variance explained by one regressor could be confused with another regressor
This is illustrated by an example using a 2 x 3 factorial design
Separating regressors
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Motion No Motion
High Medium Low
Design
IV 1 = Movement, 2 levels (Motion and No Motion)
IV 2 = Attentional Load, 3 levels (High, Medium or Low)
High Medium Low
Example
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V A C1 C2 C3
M N h m l If you made each level of the variables a regressor you could get 5 columns and this would enable you to test main effects
BUT what about interactions? How can you test differences between Mh and Nl
This design matrix is flawed – regressors are correlated and therefore a presence of overlapping variance (Grey)
M N h m l
MN h ml
Example Con’t
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h m l h m l M M M N N N If you make each condition a regressor you
create 6 columns and this would enable you to test main effects
AND it enable you to test interactions! You can test differences between Mh and Nl
This design matrix is orthogonal – regressors are NOT correlated and therefore each regressor explains separate variance
M
N
h m l
Mh
Nh
MlMm
Nm Nl
h m l h m l M M M N N N
h m l h m l
M MM N N N
Orthogonal Design Matrix
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YMatrix of BOLD
signals Design matrix Matrix parameters
= X x + εTime
Voxels
Time
Regressors
Regressors
Voxels
Time
Voxels
Error matrix
β
Aim: To explain as much of the variance in Y by using X, and thus reducing ε
β = relative contribution that each regressor has, the larger the β value = the greater the contribution
Next: Examine the effect of regressors and the contrasts
Interim Summary
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BRIEF OVERVIEW:SPECIFY THE 1ST MODEL IN SPM
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Thank you!