Mixed-Effects Meta Analysis (MEMA)
The conventional approach for FMRI group analysis is to take regression coefficients (typically referred to as beta values) and run t-test, AN(C)OVA, or other types of analysis, and ignore the within-subject variability (sampling error of each condition/task/event type at individual subject level). The underlying rationale for such practice is built on the following two assumptions:
Within-subject (or intra-subject) variability is relatively small
compared to between-subjects (group) variability;
(2) Within-subject variability is roughly equal across all subjects.
evidences show that up to 40% or even more of variability is usually
accounted for with intra-subject variation among all the variability
(sum of within- and cross-subject variability) in an activated region of
interest. Also there are a lot of variations in within-subject
variability across subjects. Violation of either assumption could render
a suboptimal or even
invalid group analysis, and most of the time it reduces the statistical
power at group level. Therefore the conventional group analysis method,
typically referred to as random/mixed-effects modeling, is mostly a
variability measures how precise/reliable the percent signal change
(beta) estimate is from individual subject time series regression model.
Since such quantity, contained in the corresponding t-statistic,
is conveniently available, there is no reason, except theoretical
complexity and computation cost, to waste this piece of information in
3dMEMA is developed in R with a mixed-effects meta analysis (MEMA) approach. It effectively takes advantage of estimate precision from each subject, and assigns each subject's contribution in the final result based on weighting instead of equal treatment. More specifically, a more precise beta estimate (meaning higher t-statistic) from a subject will have more say in the group effect; conversely, a less reliable beta estimate (i.e., lower t-statistic) from a subject will be discounted in the MEMA model. Such strategy can be surprisingly tolerant of and robust against some types of outliers compared to the conventional group analysis method. More theoretical considerations of MEMA can be found in the following literature:
Chen et al., 2012. FMRI Group Analysis Combining Effect Estimates and Their Variances. NeuroImage 60, 747-765.
3dMEMA handles the following model types:
(1) random-effects analysis: one-sample, paired-sample
(2) mixed-effects analysis: two-sample (two groups of subjects)
(3) mixed-effects analysis: multiple between-subjects factors
(4) either of the above three types plus between-subjects covariate(s)
This basically covers whatever t-test you
could do with 3dttest/3dANOVAx/3dMVM/3dLME/3dRegAna before. Noticeably it
may give an impression that it can't directly deal with sophisticated ANOVA
designs, but that is not necessarily the case. F-tests for main
effects and interactions in ANOVA, for example, provide a concise
summary for the factors and their relationship, but eventually most of
the time everything boils down to single (not composite) effect testing.
In other words, almost all those t-tests in
3dttest/3dANOVAx/3dRegAna/3dMVM/3dLME can be run with 3dMEMA.
Putting in a different perspective, you can run 3dMEMA if your analysis can be conceptualized into one of the following types:
A. one condition
within one group;
B. two conditions within one group;
C. one condition within two or more groups with homoskedasticity;
D. one condition within two or more groups with heteroskedasticity.
Most tests from a
sophisticated analysis design should find their niche in the above list.
For example, suppose we have factor A, coding for two groups of
subjects, and factor B, representing two levels (e.g., house and face)
of an experiment condition. Usually we can get all the tests we want
with one batch script: 3dANOVA3 -type 5 .... However, whatever we can
get with options such as -fa, -fb, -fab, -amean, -bmean, -adiff, -bdiff,
-acontr, -bcontr, -aBdiff, -Abdiff, -aBcontr, and -Abcontr, they are
essentially all t-tests (including those F-tests for main
effects and interaction with ONE numerator degree of freedom), which
means we can deal with them with 3dMEMA without any problems, but just
one test a time! Moreover, these F-tests are disadvantageous and
not as informative compared to their counterpart with t-tests
because F hides the directionality (sign) of the effect of
interest. For instance, the F-test for the interaction between factors A
and B (both with 2 levels) in this example is essentially equivalent to
the t-test (A1B1-A1B2)-(A2B1-A2B2)
but we can say more with the t-test than F: a positive t-value
shows A1B1-A1B2 > A2B1-A2B2
and A1B1-A2B1 > A1B2-A2B2.
The only tests that 3dMEMA can't currently handle are those F-tests with multiple numerator degrees of freedom, but hopefully this F-test limitation will change in the future. See more discussion here or here regarding 3dMEMA/3dttest/3dtest++ vs. traditional ANOVA framework.
As beta precision estimate
is important for MEMA, it is suggested (not mandated, of course) that
all input files, beta and more importantly t-statistic, come
from 3dREMLfit output instead of 3dDeconvolve;
(2) warping to standard space be performed before spatial smoothing and individual subject regression analysis to avoid the troubling step of warping on t-statistic;
(3) no masking be applied at individual subject analysis level so that no data is lost at group level along the edge of (and sometimes inside) the brain.
If you don't have R installed yet on your computer,
choose a mirror site
geographically close to you, and download the appropriate binary for
your platform (or the source code and then compile yourself). Set your
path appropriately. For example, my R executable is under
/Applications/R.app/Contents/MacOS on my Mac OS X, so I add
/Applications/R.app/Contents/MacOS as one of the search paths in my C
shell startup configuration file .cshrc
If the installation is
successful, start the R interface with the following command on the
You can also work with the GUI version of R on Mac OS and Windows. Special note for Mac OS X users when using the GUI R: Don't start the GUI R through clicking the icon because it would fail to initialize all the path setup on the X11. Instead run the GUI version by typing/copying the following on the terminal (or, even better, setting an alias
The usage of 3dMEMA can be found at the terminal"
How to use 3dMEMA to handle multiple groups
Suppose at group level we have three categorical variables (factors): one within-subject factor condition with two levels, positive (pos) and negative (neg); two between-subjects factors, sex (male and female) and genotypes (FF, TT, and FT). This would be a mixed 2 (sex) x 3 (genotype) x 2 (condition) ANOVA, but we would like to use 3dMEMA to tackle the analysis.
First we need the contrast of positive and negative conditions and its t-statistic from each subject (with 3dREMLfit). Then we treat the two between-subjects factors and their interactions as covariate via dummy coding in a text file with the five columns. Note that dummy coding works like this: for each factor choose one level as base (also called reference) and code it with 0, and a factor with k levels is represented with k-1 columns each of which codes for one level (except for the base level).
F-M TT-FF FT-FF int1 int2If you call the above text file as cov.txt, then add the following line in the 3dMEMA script:
0 0 0 0 0
0 1 0 0 0
0 0 1 0 0
1 0 0 0 0
1 1 0 1 0
1 0 1 0 1
-covariates_center F-M = 0 TT-FF = 0 FT-FF = 0 int1 = 0 int2 = 0Interpretation of the output:
the first two sub-bricks corresponds to the base of all between-subjects factors: the contrast of positive and negative conditions of male with FF genotype.
the next two sub-bricks corresponds to the first column of the covariate file: the sex difference in the contrast of positive and negative conditions (or the interaction between sex and condition)
the next two sub-bricks corresponds to the second column of the covariate file: the genotype difference between TT and FF in the contrast of positive and negative conditions
the next two sub-bricks corresponds to the third column of the covariate file: the genotype difference between FT and FF in the contrast of positive and negative conditions
the next two sub-bricks corresponds to the fourth column of the covariate file: three-way interaction
the next two sub-bricks corresponds to the fifth column of the covariate file: another three-way interaction
Running 3dMEMA inside R
Alternatively 3dMEMA works in a procedural or streamlined fashion with a string of information about modeling parameters, input files (beta and t-statistic) and options. Hopefully anything else should be self-evident from there as shown below with user input underlined in bold face (Note: input files with sub-brick selector are allowed, but no quotes are needed around the square brackets. See example below):
 " ================== Welcome to 3dMEMA.R ================== "
 "AFNI Meta-Analysis Modeling Package!"
 "Version 0.1.3, Jan. 8, 2010"
 "Author: Gang Chen (firstname.lastname@example.org)"
 "Website: http://afni.nimh.nih.gov/sscc/gangc/3dMEMA.html"
 "SSCC/NIMH, National Institutes of Health, Bethesda MD 20892"
 "Please consider citing the following if this program is useful for you:"
Gang Chen, Manual or manuscript coming soon.
 "Use CNTL-C on Unix or ESC on GUI version of R to stop at any moment."
Output file name (just prefix, no view+suffix needed, e.g., myOutput): myOutput
 "On a multi-processor machine, parallel computing will speed up the program significantly."
 "Choose 1 for a single-processor computer."
Number of parallel jobs for the running (e.g., 2)? 4
Number of groups (1 or 2)? 1
 "The following types of group analysis are currently available:"
 "The following types of group analysis are currently available:"
 "1: one condition with one group;"
 "2: one condition across 2 groups with homoskedasticity (same variability);"
 "3: two conditions with one group;"
 "4: one condition across 2 groups with heteroskedasticity (different variability)."
Which analysis type (1, 2, 3): 3
 "Since the contrast between the 2 conditions will be the 1st minus the 2nd, choose"
 "an appropriate order between the 2 conditions to get the desirable contrast."
Label for the contrast? myContrast
Number of subjects: 18
Number of subjects in group Female (e.g., 12)? 24
No. 1 subject label in group: S1
No. 18 subject label in group: S18
Label for condition 1? conditon1
No. 1 subject file for beta or linear combination of betas with condition1: subj1_con1_B+tlrc.BRIK
No. 1 subject file for the corresponding t-statistic with condition1: subj1_con1_T+tlrc.BRIK
No. 2 subject file for beta or linear combination of betas with condition1: subj2_con1_B+tlrc.BRIK
No. 2 subject file for the corresponding t-statistic with condition1: subj2_con1_T+tlrc.BRIK
Label for condition 2? conditon2
No. 1 subject file for beta or linear combination of betas with condition2: subj1_con2_B+tlrc.BRIK
No. 1 subject file for the corresponding t-statistic with condition2: subj1_con2_T+tlrc.BRIK
No. 2 subject file for beta or linear combination of betas with condition2: subj2_con2_B+tlrc.BRIK
No. 2 subject file for the corresponding t-statistic with condition2: subj2_con2_T+tlrc.BRIK
Number of subjects with non-zero t-statistic? (0-18) 12
 "t-statistic is a little more conservative but also more appropriate for significance testing than Z"
 "especially when sample size, number of subjects, is relatively small."
Z- or t-statistic for the output? (0: Z; 1: t) 0
 "Masking is optional, but will alleviate unnecessary penalty on q values of FDR correction."
Any mask (0: no; 1: yes)? myMask+tlrc.BRIK
 "Covariates are continuous variables (e.g., age, behavioral data) that can be partialled out in the model."
Any covariates (0: no; 1: yes)? 0
 "If outliers exist at voxel/subject level, a special model can be adopted to account for outliers"
 "in the data, leading to increased statistical power at slightly higher computation cost."
Model outliers (0: no; 1: yes)? 1
 "The Z-score of residuals indicates the significance level a subject is an outlier at a voxel."
 "Turn off this option if memory allocation problem occurs later on."
Want residuals Z-score for each subject (0: no; 1: yes)? 1
 "Totally 43 slices in the data."
 "Package snow successfully loaded!"
Z slice # 1 done: 04/30/09 14:06:17.290
Z slice # 2 done: 04/30/09 14:06:17.982
Z slice # 43 done: 04/30/09 14:17:27.149
 "Analysis finished: 04/30/09 14:17:27.151"
++ 3drefit: AFNI version=AFNI_2008_07_18_1710 (Apr 8 2009) [32-bit]
++ Authored by: RW Cox
++ Processing AFNI dataset myOutput+orig
+ Changed dataset view type and filenames.
+ created 2 FDR curves in dataset header
++ 3drefit processed 1 datasets
user system elapsed
39.847 13.348 743.834
input files should only contain ONE (either beta or t-statistic)
sub-brick. You don't have to type those input file names. Instead I
suggest that you list all those files by executing 'ls -1 *.BRIK'
(number ONE, not letter L ) on the terminal, and copy and taste them
onto the 3dMEMA interface. Directories can be included as part of the
file name, that is, those input files don't have to be in the same
directory where you run 3dMEMA. It's always a good habit, for records
and for running it again (or a different analysis) in batch mode later
on, to save all the input items in a pure text file with content like
the following (don't include those interpretive words after the pound
myOutput # output file name (no view and appendix needed)
4 # number of parallel jobs
1 # number of groups of subjects
3 # paired-sample type
myContrast # label for condition 1 vs. condition 2
18 # total number of subjects
conditon1 # condition 1 label
subj1_con1_B+tlrc.BRIK # beta value for subject 1
subj1_con1_T+tlrc.BRIK # t-statistic for subject 1
conditon2 # condition 2 label
12 # minimum number of subjects allowed to have ZERO t-statistic at a voxel
0 # want Z or t-statistic
1 # yes a mask will be provided; otherwise 0
myMask+tlrc.BRIK # mask
0 # no covariates
1 # handle outliers with a special model
1 # Z-score for residuals
are two output files, one includes all the major effects plus
the associated statistics, while the other output, if requested,
contains two values at a voxel for each subject: lambda measures the
percentage of within-subject variability relative the total variability,
and Z-score shows the significance level that voxel is an outlier
relative to the group effect.
The runtime can be significantly reduced through parallel computing: If multiple cores are available on your computer, simply specify the number of parallel jobs in the program. Once you know the exact answers for those sequential questions, you may want to run 3dMEMA.R in a batch mode for a slightly different analysis by creating a file like one above (or multiple ones concatenated), calling it Cmds.R, for example (again don't include those interpretive words). Type one of the following two commands at the terminal prompt (not inside R):
R CMD BATCH Cmds.R myDiary &
Rscript Cmds.R |& tee myDiary &
or in the same fashion but remotely:
nohup R CMD BATCH Cmds.R myDiary &
nohup Rscript Cmds.R > myDiary &
"myDiary" contains the progression of the running including error
message. In case you encounter some problem with 3dMEMA, please send me
the whole file myDiary.
To quit R, type
hit letter "d" while holding down CTRL key on UNIX-based systems).
I'd like to thank Jarrod Hadfield for directing my attention to meta analysis, Wolfgang Viechtbauer for theoretical consultation and programming support, Xianggui Qu for help in formula derivation, and James Bjork for help in testing the program and for providing feedback.
Last modified 2012-10-23 13:43