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Contrasts: It seems easy until you’re all alone setting up your own model on your own data

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Page 1: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

Contrasts:It seems easy until you’re all alone setting up

your own model on your own data

Page 2: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

Setting up regressors in your level 1 model

• Model every task-related thing

• Typically the baseline (fixation) is not modeled– Implicit baseline in your model

• Be careful how much you split up trials– Is it necessary? Do you think the BOLD activation

will change?

Page 3: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

What information is needed for each regressor?

Page 4: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

What information is needed for each regressor?

Page 5: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

Simple model setup

• Task shows pos/neg/neu images to subjects, followed by fixation for 2s and then a neutral face

• What should go into our model?

4s 2s 0.5s 10s

Page 6: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

What your timing file looks likeHow would you split this up for SPM?

Onset time (s) Duration (s) Description

11 4 Negative

17 .5 Face

27.5 4 Neutral

33.5 .5 Face

44 4 Positive

50 .5 Face

60.5 4 Neutral

66.5 .5 Face

.

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.

.

.

.

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.

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Page 7: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

Contrasts

• These combine parameter estimates from your regressors to test hypotheses– Eg. How does activation from negative images

differ from positive images?• We actually need to be more specific since we use 1-

sided hypotheses typically

Page 8: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

SPM example

• You’ll see this in lab today

Page 9: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

Example 1

o Where is the positive IAPS brain activation larger than negative IAPS brain activation?o Positive IAPS > Negative IAPS

oDo math so you end up with “stuff > 0”

o Positive IAPS – Negative IAPS >0o The coefficients of the above go into the contrast

Pos IAPS Neg IAPS Neu IAPS Face after Pos

Face after Neg

Face after Neu

PosIAPS > NegIAPS 1 -1 0 0 0 0

Page 10: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

Example 2

• Where is the positive IAPS brain activation larger than neutral IAPS brain activation?

Pos IAPS Neg IAPS Neu IAPS Face after Pos

Face after Neg

Face after Neu

PosIAPS > NeuIAPS 1 0 -1 0 0 0

Page 11: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

General rule of contrast construction

• Positive parts (if present) sum to 1• Negative parts (if present) sum to -1• Note: There are exceptions to this rule if

you’re working with group level ANOVA models– Just always make sure they are logical– We’ll cover these more later

Page 12: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

Example 1

o Where is both positive and negative IAPS brain activation larger than neutral IAPS brain activation?o Positive IAPS + Negative IAPS > Neutral IAPSo Positive IAPS + Negative IAPS –Netural IAPS>0

Pos IAPS Neg IAPS Neu IAPS Face after Pos

Face after Neg

Face after Neu

1 1 -1 0 0 0WRONG!

Page 13: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

Example 3

o Where is both positive and negative IAPS brain activation larger than neutral IAPS brain activation?o (Positive IAPS + Negative IAPS)/2 > Neutral IAPSo Positive IAPS/2 + Negative IAPS/2 –Netural IAPS>0

Pos IAPS Neg IAPS Neu IAPS Face after Pos

Face after Neg

Face after Neu

0.5 0.5 -1 0 0 0

Page 14: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

Example 4

• Average face activation greater than baseline

Pos IAPS Neg IAPS Neu IAPS Face after Pos

Face after Neg

Face after Neu

0 0 0 1/3 1/3 1/3

Page 15: 4 setting up regressors contrasts - University of Michigan · googledoc –Depending on the complexity, we may or may not get to it, but I’ll try to answer all. Title: 4_setting_up_regressors_contrasts

Questions?

• Let me know if you want more practice doing this?

• Feel free to write out your examples in the googledoc– Depending on the complexity, we may or may not

get to it, but I’ll try to answer all