:orphan: .. _ahelp_3dMEMA: ****** 3dMEMA ****** .. contents:: :local: | .. code-block:: none Usage: ------ 3dMEMA is a program for performing Mixed Effects Meta Analysis at group level that models both within- and across- subjects variability, thereby requiring both regression coefficients, or general linear contrasts among them, and the corresponding t-statistics from each subject as input. To get accurate t-statistics, 3dREMLfit should be used for the linear regression (a GLS regression program using an ARMA(1,1) model for the noise), rather than 3dDeconvolve. It's required to install R (https://www.r-project.org/), plus 'snow' package if parallel computing is desirable. Version 1.0.1, Dec 21, 2016. If you want to cite the analysis approach, use the following at this moment: Chen et al., 2012. FMRI Group Analysis Combining Effect Estimates and Their Variances. NeuroImage. NeuroImage 60: 747-765. The basic usage of 3dMEMA is to derive group effects of a condition, contrast, or linear combination (GLT) of multiple conditions. It can be used to analyze data from one, two, or multiple groups. However, if there are more than two groups or more than one subject-grouping variables (e.g., sex, adolescent/adults, genotypes, etc.) involved in the analysis, dummy coding (zeros and ones) the variables as covariates is required, and extremely caution should be exercised in doing so because different coding strategy may lead to different interpretation. In addition, covariates (quantiative variables) can be incorporated in the model, but centering and potential interactions with other effects in the model should be considered. Basically, 3dMEMA can run one-sample, two-sample, and all types of BETWEEN-SUBJECTS ANOVA and ANCOVA. Within-subject variables mostly cannot be modeled, but there are a few exceptions. For instance, paired-test can be performed through feeding the contrast of the two conditons as input. Multi-way ANOVA can be analyzed under the following two scnearios: 1) all factors have only two levels (e.g., 2 X 2 repeated- measures ANOVA) can be analyzed; or 1) there is only one within-subject (or repeated-measures) factor and it contains two levels only. See more details at https://afni.nimh.nih.gov/sscc/gangc/MEMA.html Notice: When comparing two groups, option "-groups groupA groupB" has to be present, and the output includes the difference of groupB - groupA, which is consistent with most AFNI convention except for 3dttest++ where groupA - groupB is rendered. Example 1 --- One-sample type (one regression coefficient or general linear contrast from each subject in a group): -------------------------------- 3dMEMA -prefix ex1 \ -jobs 4 \ -set happy \ ac ac+tlrc'[14]' ac+tlrc'[15]' \ ejk ejk+tlrc'[14]' ejk+tlrc'[15]' \ ... ss ss+tlrc'[14]' ss+tlrc'[15]' \ -max_zeros 4 \ -model_outliers \ -residual_Z 3dMEMA -prefix ex1 \ -jobs 4 \ -set happy \ ac ac+tlrc'[happy#0_Coef]' ac+tlrc'[happy#0_Tstat]' \ ejk ejk+tlrc'[happy#0_Coef]' ejk+tlrc'[happy#0_Tstat]' \ ... ss ss+tlrc'[happy#0_Coef]' ss+tlrc'[happy#0_Tstat]' \ -missing_data 0 \ -HKtest \ -model_outliers \ -residual_Z Example 2 --- Two-sample type (one regression coefficient or general linear contrast from each subject in two groups with the constrast being the 2nd group subtracing the 1st one), heteroskedasticity (different cross-subjects variability between the two groups), outlier modeling, covariates centering, no payment no interest till Memorial Day next year. Notice that option -groups has to be present in this case, and the output includes the difference of the second group versus the first one. ------------------------------------------------------------------------- 3dMEMA -prefix ex3 \ -jobs 4 \ -groups horses goats \ -set healthy_horses \ ac ac_sad_B+tlrc.BRIK ac_sad_T+tlrc.BRIK \ ejk ejk_sad_B+tlrc.BRIK ejk_sad_T+tlrc.BRIK \ ... ss ss_sad_B+tlrc.BRIK ss_sad_T+tlrc.BRIK \ -set healthy_goats \ jp jp_sad_B+tlrc.BRIK jp_sad_T+tlrc.BRIK \ mb mb_sad_B+tlrc.BRIK mb_sad_T+tlrc.BRIK \ ... trr trr_sad_B+tlrc.BRIK trr_sad_T+tlrc.BRIK \ -n_nonzero 18 \ -HKtest \ -model_outliers \ -unequal_variance \ -residual_Z \ -covariates CovFile.txt \ -covariates_center age = 25 13 weight = 100 150 \ -covariates_model center=different slope=same where file CovFile.txt looks something like this: name age weight ejk 93 117 jcp 3 34 ss 12 200 ac 12 130 jp 65 130 mb 25 630 trr 18 187 delb 9 67 tony 12 4000 Example 3 --- Paired type (difference of two regression coefficients or general linear contrasts from each subject in a group). One scenario of general linear combinations is to test linear or higher order trend at individual level, and then take the trend information to group level. --------------------------------- 3dMEMA -prefix ex2 \ -jobs 4 \ -missing_data happyMiss+tlrc sadMiss+tlrc \ -set happy-sad \ ac ac_hap-sad_B+tlrc ac_hap-sad_T+tlrc \ ejk ejk_hap-sad_B+tlrc ejk_hap-sad_T+tlrc \ ... ss ss_hap-sad_B+tlrc ss_hap-sad_T+tlrc \ Options in alphabetical order: ------------------------------ -cio: Use AFNI's C io functions -conditions COND1 [COND2]: Name of 1 or 2 conditions, tasks, or GLTs. Default is one condition named 'c1' -contrast_name: (no help available) -covariates COVAR_FILE: Specify the name of a text file containing a table for the covariate(s). Each column in the file is treated as a separate covariate, and each row contains the values of these covariates for each subject. Option -unequal_variance may not be used in the presence of covariates with two groups. To avoid confusion, it is best you format COVAR_FILE in this manner with BOTH row and column names: subj age weight Jane 25 300 Joe 22 313 ... .. ... This way, there is no amiguity as to which values are attributed to which subject, nor to the label of the covariate(s). The word 'subj' must be the first word of the first row. You can still get at the values of the columns of such a file with AFNI's 1dcat -ok_text, which will treat the first row, and first column, as all 0s. Alternate, but less recommended ways to specify the covariates: (column names only) age weight 25 300 22 313 .. ... or (no row and column names) 25 300 22 313 .. ... -covariates_center COV_1=CEN_1 [COV_2=CEN_2 ... ]: (for 1 group) -covariates_center COV_1=CEN_1.A CEN_1.B [COV_2=CEN_2.A CEN_2.B ... ]: (for 2 groups) where COV_K is the name assigned to the K-th covariate, either from the header of the covariates file, or from the option -covariates_name. This makes clear which center belongs to which covariate. When two groups are used, you need to specify a center for each of the groups (CEN_K.A, CEN_K.B). Example: If you had covariates age, and weight, you would use: -covariates_center age = 78 55 weight = 165 198 If you want all covariates centered about their own mean, just use -covariates_center mean. Be alert: Default is mean centering! If no centering is desired (e.g.,the covariate values have been pre-centered), set the center value as 0 with -covariates_center. -covariates_model center=different/same slope=different/same: Specify whether to use the same or different intercepts for each of the covariates. Similarly for the slope. -covariates_name COV_1 [... COV_N]: Specify the name of each of the N covariates. This is only needed if the covariates' file has no header. The default is to name the covariates cov1, cov2, ... -dbgArgs: This option will enable R to save the parameters in a file called .3dMEMA.dbg.AFNI.args in the current directory so that debugging can be performed. -equal_variance: Assume same cross-subjects variability between GROUP1 and GROUP2 (homoskedasticity). (Default) -groups GROUP1 [GROUP2]: Name of 1 or 2 groups. This option must be used when comparing two groups. Default is one group named 'G1'. The labels here are used to name the sub-bricks in the output. When there are two groups, the 1st and 2nd labels here are associated with the 1st and 2nd datasets specified respectively through option -set, and their group difference is the second group minus the first one, similar to 3dttest but different from 3dttest++. -help: this help message -HKtest: Perform Hartung-Knapp adjustment for the output t-statistic. This approach is more robust when the number of subjects is small, and is generally preferred. -KHtest is the default with t-statistic output. -jobs NJOBS: On a multi-processor machine, parallel computing will speed up the program significantly. Choose 1 for a single-processor computer. -mask MASK: Process voxels inside this mask only. Default is no masking. -max_zeros MM: Do not compute statistics at any voxel that has more than MM zero beta coefficients or GLTs. Voxels around the edges of the group brain will not have data from some of the subjects. Therefore, some of their beta's or GLTs and t-stats are masked with 0. 3dMEMA can handle missing data at those voxels but obviously too much missing data is not good. Setting -max_zeros to 0.25 means process data only at voxels where no more than 1/4 of the data is missing. The default value is 0 (no missing values allowed). MM can be a positive integer less than the number of subjects, or a fraction between 0 and 1. Alternatively option -missing_data can be used to handle missing data. -missing_data: This option corrects for inflated statistics for the voxels where some subjects do not have any data available due to imperfect spatial alignment or other reasons. The absence of this option means no missing data will be assumed. Two formats of option setting exist as shown below. -missing_data 0: With this format the zero value at a voxel of each subject will be interpreted as missing data. -missing_data File1 [File2]: Information about missing data is specified with file of 1 or 2 groups (the number 1 or 2 and file order should be consistent with those in option -groups). The voxel value of each file indicates the number of sujects with missing data in that group. -model_outliers: Model outlier betas with a Laplace distribution of of subject-specific error. Default is -no_model_outliers -n_nonzero NN: Do not compute statistics at any voxel that has less than NN non-zero beta values. This options is complimentary to -max_zeroes, and matches an option in the interactive 3dMEMA mode. NN is basically (number of unique subjects - MM). Alternatively option -missing_data can be used to handle missing data. -no_HKtest: Do not make the Hartung-Knapp adjustment. -KHtest is the default with t-statistic output. -no_model_outliers: No modeling of outlier betas/GLTs (Default). -no_residual_Z: Do not output residuals and their Z values (Default). -prefix PREFIX: Output prefix (just prefix, no view+suffix needed) -residual_Z: Output residuals and their Z values used in identifying outliers at voxel level. Default is -no_residual_Z -Rio: Use R's io functions -set SETNAME \ SUBJ_1 BETA_DSET T_DSET \ SUBJ_2 BETA_DSET T_DSET \ ... ... ... \ SUBJ_N BETA_DSET T_DSET \ Specify the data for one of two test variables (either group, contrast/GLTs) A & B. SETNAME is the name assigned to the set, which is only for the user's information, and not used by the program. When there are two groups, the 1st and 2nd datasets are associated with the 1st and 2nd labels specified through option -set, and the group difference is the second group minus the first one, similar to 3dttest but different from 3dttest++. SUBJ_K is the label for the subject K whose datasets will be listed next BETA_DSET is the name of the dataset of the beta coefficient or GLT. T_DSET is the name of the dataset containing the Tstat corresponding to BETA_DSET. To specify BETA_DSET, and T_DSET, you can use the standard AFNI notation, which, in addition to sub-brick indices, now allows for the use of sub-brick labels as selectors e.g: -set Placebo Jane pb05.Jane.Regression+tlrc'[face#0_Beta]' \ pb05.Jane.Regression+tlrc'[face#0_Tstat]' \ -show_allowed_options: list of allowed options -unequal_variance: Model cross-subjects variability difference between GROUP1 and GROUP2 (heteroskedasticity). This option may NOT be invoked when covariate is present in the model. Default is -equal_variance (homoskedasticity). This option may not be useded when covariates are involved in the model. -verb VERB: VERB is an integer specifying verbosity level. 0 for quiet (Default). 1 or more: talkative. ####################################################################### Please consider citing the following if this program is useful for you: Chen et al., 2012. FMRI Group Analysis Combining Effect Estimates and Their Variances. NeuroImage. NeuroImage 60: 747-765. https://afni.nimh.nih.gov/sscc/gangc/MEMA.html