why n’ how (i forgot the title) donald g. mclaren, ph.d. department of neurology, mgh/hms grecc,...

Why N’ How (I forgot the title)

Donald G. McLaren, Ph.D.Department of Neurology, MGH/HMS

GRECC, ERNM Veteran’s Hospital

http://www.martinos.org/~mclaren

11/15/2012





Types of Data

Types of Data – Dependent Variable

• Task Data– Single Condition– Multiple Conditions– Multiple Predictors Per Condition

• Functional Connectivity – Correlation• Functional Connectivity -- ICA• Context-Dependent Connectivity• VBM• DTI• Other??

Factors, Levels, Groups, ClassesContinuous Variables/Factors: Age, IQ, Volume, Behavioral measures (emotional scale, memory ability), Images, etc.Discrete Variables/Factors: Gender, Handedness, DiagnosisLevels of Discrete : Handedness: Left and Right Gender: Male and Female Diagnosis: Normal, MCI, ADGroup or Class: Specification of All Discrete Factors:• Left-handed Male MCI• Right-handed Female Normal

Overview• From a line to the GLM and matrices

• Statistical Tests

• Contrasts

• Designs

• Power

• Caveats

General Linear Model(GLM)

Y=aX+b

GLM Theory

HRF AmplitudeIQ, Height, Weight

Independent Variable

Is Activity correlated with Age?

DependentVariable,Measurement

x1 x2

y2

y1

Subject 1

Subject 2

Activity

Age

Of course, you’d need more then twosubjects …

Linear ModelIntercept: b

Slope: m

Activity

Age

x1 x2

y2

y1

System of Linear Equationsy1 = 1*b + x1*my2 = 1*b + x2*m

Y = X*

y1y2

1 x11 x2

bm= *

Matrix Formulation

X = Design Matrix = Regression Coefficients = Parameter estimates = “betas” = Intercepts and Slopes

bm

Intercept = Offset

Hypotheses and ContrastsIs Activity correlated with Age?

Does m = 0?Null Hypothesis: H0: m=0

Intercept: b

Slope: m

Activity

Age

x1 x2

y2

y1

m= [0 1]*bm

= C*?

C=[0 1]: Contrast Matrix

bm

y1y2

1 x11 x2

bm= *

Hypotheses and ContrastsIs Activity different from 0?

Does b = 0?Null Hypothesis: H0: b=0

Intercept: b

Slope: m

Activity

Age

x1 x2

y2

y1

b= [1 0]* bm

= C*?


bm

y1y2

1 x11 x2

bm= *



Intercept: b

Slope: mActivity

Age

x1 x2

y2

y1

b= [1 0]* bm

= C*?


bm

y1y2

1 x11 x2

bm= *



Intercept: b

Activity

Age

x1 x2

y2

y1

b= [1 0]* bm

= C*?


bm

y1y2

1 x11 x1

bm= *



Intercept: b

Activity

Age

x1 x2

y2

y1

b= [1 ]*b

= C*?


b

y1y2

11

b= *

More than Two Data Points

y1 = 1*b + x1*my2 = 1*b + x2*my3 = 1*b + x3*my4 = 1*b + x4*m

y1y2y3y4

1 x11 x21 x31 x4

bm= *

Y = X*+n

Intercept: b

Slope: m

Activity

Age

• Model Error• Noise• Uncertainty

The General Linear Model

npnpnnn

pp

pp

pp

xxxY

xxxY

xxxY

xxxY

,2,21,10

3,32,321,3103

2,22,221,2102

1,12,121,1101

ppXXXY 22110

Y Y observed = predicted + random error

In Matrix Form

Summary of the GLM

Y = X . β + ε

Observed data:

Imaging uses a mass univariate approach – that is each voxel is treated as a separate column vector of data.Y is Dependent Brain Value at various subjects/time points at a single voxel

Design matrix:

Several components which explain the observed data, i.e. the BOLD time series for the voxelTiming info: onset vectors, Om

j, and duration vectors, Dm

j

HRF, hm, describes shape of the expected BOLD response over timeOther regressors, e.g. realignment parameters

At the group level: these are covariates or grouping columns (see later slide)

Parameters:

Define the contribution of each component of the design matrix to the value of YEstimated so as to minimise the error, ε, i.e. least sums of squares

Error:

Difference between the observed data, Y, and that predicted by the model, Xβ.Not assumed to be spherical in fMRI

Brain Imaging

• From the beginning (almost)….

[ 5 6 7 5 ]

25

Spatial Normalization, Atlas Space

Subject 1

Subject 2

Subject 1

Subject 2

MNI305

Native Space MNI305 Space

26

Group Analysis

Does not have to be all positive!

Contrast AmplitudesContrast Amplitudes Variances(Error Bars)

Mass Univariate Analyses

(1) Run the GLM for each voxel.(2) Compute the statistic from the GLM for

each voxel(3) Inferences

28

Statistical Parametric Map (SPM)+3%

0%

-3%

Contrast AmplitudeCON, COPE, CES

Contrast AmplitudeVariance

(Error Bars)VARCOPE, CESVAR

Significance t-Map (p,z,F)(Thresholded

p<.01)sig=-log10(p)

“Massive Univariate Analysis” -- Analyze each voxel separately

SPM/FSL/AFNI/CUSTOM

• It is important to recognize that all programs that utilize the GLM will produce the same result. However, if your design matrices or variance correction methods are different, then you will see differences.

• Some slides show illustrations from FSL, others show illustrations from SPM, MATLAB, or other software. These can be done in all programs.

Types Of Analysis

32

GG

1

1

2

2

2

G

G

iG

G

NDOF

N

t

G

G

“Random Effects (RFx)” Analysis

RFx

33

GG

“Random Effects (RFx)” Analysis

RFx

• Model Subjects as a Random Effect• Variance comes from a single source:

variance across subjects – Mean at the population mean– Variance of the population variance

• Does not take first-level noise into account (assumes 0)

• “Ordinary” Least Squares (OLS)• Usually less activation than individuals

34

“Mixed Effects (MFx)” Analysis

MFx

RFx

• Down-weight each subject based on variance.• Weighted Least Squares vs (“Ordinary” LS)

35

“Mixed Effects (MFx)” Analysis

MFx

• Down-weight each subject based on variance.• Weighted Least Squares vs (“Ordinary” LS)• Protects against unequal variances across

group or groups (“heteroskedasticity”)• May increase or decrease significance with

respect to simple Random Effects• More complicated to compute• “Pseudo-MFx” – simply weight by first-level

variance (easier to compute)

36

“Fixed Effects (FFx)” Analysis

FFx

RFx

i

G

i

G

DOFDOF

N

t

G

G

2

22

2

2i

37

“Fixed Effects (FFx)” Analysis

FFx

i

G

i

G

DOFDOF

N

t

G

G

2

22

2

• As if all subjects treated as a single subject (fixed effect)

• Small error bars (with respect to RFx)• Large DOF• Same mean as RFx• Huge areas of activation• Not generalizable beyond sample.

38

Population vs Sample

Group Population(All members)Hundreds?Thousands?Billions?

Sample18 Subjects

• Do you want to draw inferences beyond your sample?

• Does sample represent entire population?

• Random Draw?

39

fMRI Analysis Overview

Higher Level GLM

First Level GLM Analysis


Subject 3


Subject 4


Subject 1

Subject 2

CX

CX

CX

CX

PreprocessingMC, STC, B0Smoothing

Normalization


Normalization


Normalization


Normalization

Raw Data

Raw Data

Raw Data

Raw Data

CX

Second-Level Modeling

• These are all random effects (because of variance corrections and using beta’s from the first level)

• Mean across subjects divided by variance across subjects.– Low subjects with very low variance between

them can lead to a significant finding, even if no subject was significant at the single subject level

– Implications for analysis (e.g. SLBT??)

Statistical Tests

Implementing the T-test

Variance EstimateSqrt(Var*cT(XTX)-1c)

c = +1 0 0 0 0 0 0 0

T =

contrast ofestimated

parameters

t-test H0: cT = 0

varianceestimate

Implementing the F-test

F = error

varianceestimate

additionalvariance

accounted forby effects of

interest

0 0 1 0 0 0 0 00 0 0 1 0 0 0 00 0 0 0 1 0 0 00 0 0 0 0 1 0 00 0 0 0 0 0 1 00 0 0 0 0 0 0 1

H0: cT = 0c =

Contrasts and the Full Model

ate)(multivariTest -F ˆˆˆF

e)(univariatTest - tˆ)(

ˆ

ˆ

ˆt

Cin Rows J

Estimate VarianceContrast ˆ)(1ˆˆ

Contrast ˆˆ

Variance Residual ˆˆ

ˆ

EstimatesParameter )(ˆ

),0(~ , ,

1JDOF,

21DOF

212

2

1

2

T

nTT

nTT

T

n

TT

n

CXXC

C

CXXCJ

C

DOF

nn

yXXX

NnnsynXy

T/r/F Notes• If F is a single row contrast, then F=T^2• An F-test has no direction• In many programs, T-tests are one-tailed, thus have a

p-value half of the same F-test• There are formulas to convert between T/r and other

statistics (e.g. cohen’s d)• To avoid double-dipping, when you extract an ROI to

plot the correlation and get the correlation value, DO NOT make inferences from the plots, but from the voxel-wise analysis.

Contrasts

• Identify the Null Hypothesis– Ho: A=B

• Make the Null Hypothesis equal 0– Ho: A-B=0

• Identify the columns for A and B, apply their weights– Ho: 1*A+(-1)*B– Contrast [1 -1]

Contrasts

• What if A and B are not individual columns as in the case of A1,A2,B1,B2…– [1 1 -1 -1] would work, but will over estimate the

magnitude of the effect– A is the average A1 A2, or Ho: (A1+A2)/2=0

• [½ ½ 0 0]

– B is the average B1 B2, or Ho: (B1+B2)/2=0• [0 0 ½ ½]

– [½ ½ -½ -½]

51

GG

Higher Level GLM Analysis

=

11111

G

y = X *

Data fromone voxel

Design Matrix(Regressors)

Vector ofRegressionCoefficients(“Betas”)

Obs

erva

tions

(Low

-Lev

el C

ontr

asts

)

Contrast Matrix:C = [1]Contrast = C* = G

One-Sample Group Mean (OSGM)

52

Two Groups GLM Analysis

=

11100

G1

G2

y = X *

Data fromone voxel

Obs

erva

tions

(Low

-Lev

el C

ontr

asts

) 00011

53

Contrasts: Two Groups GLM Analysis

1. Does Group 1 by itself differ from 0?Ho: G1=0; Contrast = C* = G1; C = [1 0]

= 11100

G1

G2

00011


3. Does Group 1 differ from Group 2?Ho: G1= G2; Contrast = C* = G1- G2; C = [1 -1] 4. Does either Group 1 or Group 2 differ from 0? C has two rows: F-test (vs t-test) Concatenation of contrasts #1 and #2

1 00 1

C =

54

One Group, One Covariate (Age)

=

11111

G

Age

y = X *

Data fromone voxel

Obs

erva

tions

(Low

-Lev

el C

ontr

asts

) 2133641747

Intercept: G

Slope: Age

Contrast

Age

55

Contrasts: One Group, One Covariate

1. Does Group offset/intercept differ from 0?

Does Group mean differ from 0 regressing out age?

Ho: G=0; Contrast = C* = G; C = [1 0], (Treat age as nuisance)

= 11111

G

Age

2133641747

2. Does Slope differ from 0?Ho: Age=0; Contrast = C* = Age; C = [0 1]

Intercept: G

Slope: Age

Contrast

Age

56

Contrasts: One Group, One Mean-Centered Covariate

= 11111

G

Age

-15-328-2011

Mean: G

Slope: Age

Contrast

Age


Does Group mean differ from 0 regressing out age?


2. Does Slope differ from 0?Ho: Age=0; Contrast = C* = Age; C = [0 1], ** Same effect as non-mean centered covariate

Group Effects1. Does Activity vary with Disease Status?

2. Does Activity vary with Gender?

1. Is there an Interaction between DS and G?

2x2 Group ANOVA

10

5

13

9

While this design matrix was generated in SPM, you could generate it in any of the MRI Analysis packagees or statistical programs.

Contrasts

• Does Activity vary by Disease Status?– Ho: DS-=DS+– Ho: DS- - DS+ =0– [½ ½ -½ -½]; (group difference based on subgroups) or– [10/15 5/15 -13/22 -9/22] (pure average of subjects)

• Does Activity vary by Gender?– Ho: Male=Female– Ho: Male - Female =0– [½ -½ ½ -½]; or (group difference based on subgroups) or– [10/23 -5/14 13/23 -9/14] (pure average of subjects)

Contrasts

• Average of Subgroups versus Average of Individuals– If you have drawn a random sample and want to talk generally about

all subjects in a group, use the contrast weighted by group size.– If you haven’t drawn a random sample or want to look at the average

effect of the group, then you want to use the contrast that is not weighted by group size.

Contrasts• Is there an interaction?

– Ho: DS-Females-DS-Males= DS+Females-DS+Males– Ho: (DS-Females-DS-Males) – (DS+Females-DS+Males)=0– Ho: DS-Females-DS-Males – DS+Females+DS+Males=0– [1 -1 -1 1]; or

• Are the groups different?– Ho: DS-Females=DS-Males=DS+Females=DS+Males– F-test– DS-Females=DS-Males [1 -1 0 0]– DS-Males=DS+Females [0 1 -1 0]– DS+Females=DS+Males [0 0 1 -1]– [1 -1 0 0; 0 1 -1 0; 0 0 1 -1]

Contrasts

• If there is an interaction, you can not interpret the effects of the individual factors (e.g. disease and gender)

GLM • Important to model all known variables,

even if not experimentally interesting:– e.g. head movement,

block and subject effects – minimise residual error

variance for better stats– effects-of-interest are the

regressors you’re actually interested in

covariates

conditions:effects of interest

64

Contrasts: Two Groups GLM Analysis


= 11100

G1

G2

00011


3. Does Group 1 differ from Group 2?Ho: G1= G2; Contrast = C* = G1- G2; C = [1 -1] 4. Does either Group 1 or Group 2 differ from 0? C has two rows: F-test (vs t-test) Concatenation of contrasts #1 and #2

1 00 1

C =

65

One Group, One Covariate (Age)

=

11111

G

Age

y = X *

Data fromone voxel

Obs

erva

tions

(Low

-Lev

el C

ontr

asts

) 2133641747

Intercept: G

Slope: Age

Contrast

Age

66

Contrasts: One Group, One Covariate


Does Group mean differ from 0 regressing out age (mean-centered)?


= 11111

G

Age

2133641747

2. Does Slope differ from 0?Ho: Age=0; Contrast = C* = Age; C = [0 1]

Intercept: G

Slope: Age

Contrast

Age

One Group, One Covariate

(http://mumford.fmripower.org/mean_centering/)

Two Groups

Do groups differ in Intercept?Do groups differ in Slope?Is average slope different than 0?…

Intercept: b1

Slope: m1

Activity

Age

Intercept: b2

Slope: m2

Two GroupsIntercept: b1

Slope: m1

Activity

Age

Intercept: b2

Slope: m2

y11 = 1*b1 + 0*b2 + x11*m1 + 0*m2y12 = 1*b1 + 0*b2 + x12*m1 + 0*m2y21 = 0*b1 + 1*b2 + 0*m1 + x21*m2y22 = 0*b1 + 1*b2 + 0*m1 + x22*m2

y11y12y21y22

1 0 x11 01 0 x12 00 1 0 x210 1 0 x22

b1b2m1m2

=*

Y = X*

70

Two Groups, One Covariate

• Somewhat more complicated design• Slopes may differ between the groups• What are you interested in?

• Differences between intercepts? Ie, treat covariate as a nuisance?• Differences between slopes? Ie, an interaction between group and covariate?

71

Two Groups, One (Nuisance) Covariate

Is there a difference between the group means?

Synthetic Data

72

Raw Data

Effect of Age Effect After Age “Regressed Out”(e.g. Age=0)

• No difference between groups• Groups are not well matched for age • No group effect after accounting for age• Age is a “nuisance” variable (but important!)• Slope with respect to Age is same across groups•If age was mean-centered, there might be a group effect!!!

•Depends on mean-centering…


73

=

11100

G1

G2

Age

y = X *

Data fromone voxel

Obs

erva

tions

(Low

-Lev

el C

ontr

asts

) 00011

2133641747


One regressor for Age.

Different Offset Same Slope (DOSS)

74

=

11100

G1

G2

Age

00011

2133641747


One regressor for Age indicates that groups have same slope – makes difference between group means/intercepts independent of age.

Different Offset Same Slope (DOSS)

75

Contrasts: Two Groups + Covariate

1. Does Group 1 intercept/mean differ from 0 (after regressing out effect of age)?Ho:G1=0, Contrast = C* = G1, C = [1 0 0]

2. Does Group 2 intercept/mean differ from 0(after regressing out effect of age)?Ho:G2=0, Contrast = C* = G2, C = [0 1 0]

3. Does Group 1 intercept/mean differ from Group 2 intercept/mean (after regressing out effect of age)?Ho: G1=G2, , Contrast = C* = G1- G2, C = [1 -1 0]

=

11100

G1

G2

Age

00011

2133641747

4. Does Slope differ from 0 (after regressing out the effect of group)? Does not have to be a “nuisance”!Ho: Age=0, Contrast = C* = Age, C = [0 0 1]

Two-Groups, One Covariate, Same Slope

1,2

4

3

Model from previous slide


77

• Slope with respect to Age differs between groups• Interaction between Group and Age• Intercept different as well

Group/Covariate InteractionTwo Groups, One Covariate, Different Slopes

78

=

11100

G1

G2

Age1

Age2

y = X *

Data fromone voxel

Obs

erva

tions

(Low

-Lev

el C

ontr

asts

) 00011

213364 0 0

0 0 01747

Group-by-Age Interaction

Different Offset Different Slope (DODS)

Group/Covariate Interaction

79

1. Does Slope differ between groups?Is there an interaction between group and

age? Ho: Age1=Age2, Contrast = C* = Age1-

Age2, C = [0 0 1 -1],


=

11100

G1

G2

Age1

Age2

00011

213364 0 0

0 0 01747

80


=

11100

G1

G2

Age1

Age2

00011

213364 0 0

0 0 01747

Does this contrast make sense?

2. Does Group 1 intercept/mean differ from Group 2 mean (after regressing out effect of age)?Ho: G1- G2, Contrast = C* = G1- G2, C = [1 -1 0 0] Very tricky!This tests for difference at Age=0What about Age = 12?What about Age = 20?

81

Group/Covariate InteractionIf you are interested in the difference between

the means but you are concerned there could be a difference (interaction) in the slopes:

1. Analyze with interaction model (DODS*)2. Test for a difference in slopes3. If there is no difference, re-analyze with

single regressor model (DOSS*)4. If there is a difference, proceed with

caution

* Freesurfer terms



Model from previous slide

1

2

Mean Centering• Across ALL subjects

– Covariate-adjusted group means

• Within each group– Each group would have the same mean as a one-sample t-

test

• Why does it matter?– The interpretation changes– Correlation between group and covariate (e.g. MMSE and

Alzheimer’s diagnosis)

Covariates

• If you have a single group:– Demeaning covariate will not change the slope– Demeaning makes the group term the mean of

the group; whereas not demeaning makes the group term the intercept.

Covariates

• If you have a multiple groups:– Demeaning covariate will not change the slope, no

matter how you demean it– Demeaning within each group controlling for

the covariate, but group means are uneffected– Demeaning across everyone controlling for the

covariate, but group means are effected. If you do this, you should refer to group tests as a comparison of covariate-adjusted means

87

Longitudinal/Repeated-Measures

Did something change between visits?• Drug or Behavioral Intervention?• Training?• Disease Progression?• Aging?• Injury?• Scanner Upgrade?• Multiple tasks in the same session?

88

Longitudinal

Paired DifferencesBetween Subjects

Subject 1, Visit 1

Subject 1, Visit 2

89

Longitudinal Paired Analysis

=

11111

V

y = X *

Paired Diffsfrom one voxel

Design Matrix(Regressors)

Obs

erva

tions

(V1-

V2 D

iffer

ence

s in

Low

-Lev

el C

ontr

asts

)

Ho: V=0Contrast = C* = V Contrast Matrix:C = [1]

One-Sample Group Mean (OSGM): Paired t-Test

GLM – Paired T-Test

GLM – Repeated Measures

Constructing Contrasts


• What is the null hypothesis?

• Make the null hypothesis equal to 0

• Label the columns based on the weighting of the components of the null hypothesis– For repeated measures, form the sub-elements of

the contrast, then apply the weights


• S1G1C1: [1 zeros(1,10) 1 0 1 0 0 1 0 0 0 0 0]• S1G1C2: [1 zeros(1,10) 1 0 0 1 0 0 1 0 0 0 0]• S2G1C1: [0 1 zeros(1,9) 1 0 1 0 0 1 0 0 0 0 0]• G1: [ones(1,6)/6 zeros(1,5) 1 0 1/3 1/3 1/3 1/3 1/3 1/3 0 0 0]• G1vsG2: [ones(1,6)/6 ones(1,5)/5 1 -1 0 0 0 1/3 1/3 1/3 -1/3

-1/3 -1/3] – (NOTE: This is not a valid contrast, even though it can be constructed.)

Contrast Validity• Do you only have between-subject factors?

– All contrasts valid

• Do you only have within-subject factors?– Any contrast comparing levels of a factor/interaction is

valid– Effect of a single level is not valid

• Do you have between- and within-subject factors?– Any contrast comparing levels of a factor/interaction is

valid– Interaction contrasts are valid– Group/between-subject effects are not valid (e.g. G1vG2)– Effect of a single level is not valid


• S1G1C1: [1 zeros(1,10) 1 0 1 0 0 1 0 0 0 0 0]• S1G1C2: [1 zeros(1,10) 1 0 0 1 0 0 1 0 0 0 0]• S2G1C1: [0 1 zeros(1,9) 1 0 1 0 0 1 0 0 0 0 0]• G1C1: [ones(1,6)/6 zeros(1,5) 1 0 1 0 0 1 0 0 0 0 0]• G2C1: [zeros(1,6) ones(1,5)/5 0 1 1 0 0 0 0 0 1 0 0]• *C1:[ones(1,6)/12 ones(1,5)/10 1/2 1/2 1 0 0 1/2 0 0 1/2 0

0]• *C1:[ones(1,11)/11 5/11 6/11 0 0 5/11 0 0 6/11 0 0]• C1vsC2: [zeros(1,11) 0 0 1 -1 0 1/2 -1/2 0 1/2 -1/2 0 ]• C1vsC2: [zeros(1,11) 0 0 1 -1 0 5/11 -5/11 0 6/11 -6/11 0 ]

Power Calculations

• The probability that the test will reject the null hypothesis, when the null hypothesis is false.

• In general, you want to say that you have 80-90% power in your study.

• Estimate your effect size, specify your power, determine the sample size needed.

• CANNOT BE DONE POST-HOC!!!

Power Calculations• Estimate your effect size

– Which brain region?• Minimum N to achieve % power in a set of regions (McLaren

et al. 2010)

– Where to find effect sizes?• Previous studies, pilot studies

• Specify your power (option A)– The higher the better, but more power means a larger N

• Specify your N (option B)– Increasing N will increase the power

Power Calculations - $7600 study

(Mumford et al. 2008)

Programs

• G*Power

• http://fmripower.org/

• http://fmri.wfubmc.edu/cms/talkPowerSampleSizeCalculation voxel-wise

http://fmripower.org/

http://fmri.wfubmc.edu/cms/talkPowerSampleSizeCalculation

http://fmri.wfubmc.edu/cms/talkPowerSampleSizeCalculation

Caveat 1: What is analyzed…

• Missing Data– NaN– Zeros

Also AFNI/FSL

Caveat 2: Designs

• Between-subject Designs

• Within-subject Designs

• Mixed Designs

Pick your design Carefully

All of these designs test the same effect; however only the top 2 give you the correct RFX results and are generalizable to the population. The top right model is a variant of the GLM that creates a second error term (more on this next week).

Pick your design Carefully

Variance Corrections

• The issue of non-sphericity

Repeated Measures in FSL

• Limited to designs that have no violations of sphericity.

Misc. Considerations

Correction for Multiple Comparisons

• Cluster-based– Monte Carlo simulation – Permutation Tests– Surface Gaussian Random Fields (GRF)

• There but not fully tested• False Discovery Rate (FDR) – built into tksurfer

and QDEC. (Genovese, et al, NI 2002)

Clustering1. Choose a voxel/vertex-wise threshold

• Eg, 2 (p<.01), or 3 (p<.001)• Sign (pos, neg, abs)

2. A cluster is a group of connected (neighboring) voxels/vertices above a threshold

3. Cluster has a size (volume in mm3 and area in mm2)

p<.01 (-log10(p)=2)Negative

p<.0001 (-log10(p)=4)Negative

What to report in papers

• Be explicit about the model– What are the factors– What are the covariates– What did you set as the variance and dependence for each

factor

• Be explicit about the contrast you are using• Be explicit about how to interpret the contrast

– Group means, group intercepts, covariate adjusted group means

• Be explicit about the thresholds used– Corrections for multiple comparisons– Small Volume Correction (corrected in SPM8 in late Feb. 2012)

SPM/FSL/AFNI/CUSTOM

• It is important to recognize that all programs that utilize the GLM will produce the same result. However, if your design matrices or variance correction methods are different, then you will see differences.

• Some slides show illustrations from FSL, others show illustrations from SPM, MATLAB, or other software. These can be done in all programs.

Useful Mailing Lists• SPM – http://www.jiscmail.ac.uk/list/spm.html

• FSL -- http://www.jiscmail.ac.uk/list/fsl.html

• Freesurfer -- http://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSupport

• CARET -- http://brainvis.wustl.edu/wiki/index.php/Caret:Mailing_List

• I highly recommend reading the posts on these lists as they will save you time in the future.

why n’ how (i forgot the title) donald g. mclaren, ph.d. department of neurology, mgh/hms grecc,...

Documents

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