View source for MEICA

==About==
MEICA is the '''''M'''ulti-'''E'''cho '''I'''ndependent '''C'''omponent '''A'''nalysis'' package created by Prantik Kundu during his PhD at Cambridge. It can be applied to fMRI datasets that have been acquired with at least 3 different echo times (TE). The working principle behind MEICA is that genuine BOLD signal depends on TE but artifacts don't. The ICA part of MEICA is used to define a set of components which are individually classified as BOLD or non-BOLD based on their TE dependence (quantified using two variables ''kappa'' and ''rho''). See [http://dx.doi.org/10.1016/j.neuroimage.2011.12.028 Kundu et al 2012].

Please refer to the [https://bitbucket.org/manfredg/me-ica Bitbucket repository] for the source code and all the gory implementation details.

==Usage==
<code>
Usage: meica.py [options]                                                                                                                 

Options:
  -h, --help            show this help message and exit
  -e TES                ex: -e 14.5,38.5,62.5  Echo times (in ms)
  -d DSINPUTS           ex: -d RESTe1.nii.gz,RESTe2.nii.gz,RESTe3.nii.gz
  -a ANAT               ex: -a mprage.nii.gz  Anatomical dataset (optional)
  -b BASETIME           ex: -b 10s OR -b 10v  Time to steady-state
                        equilibration in seconds(s) or volumes(v). Default 0.
  --MNI                 Warp to MNI space using high-resolution template

  Additional processing options:
    --qwarp             Nonlinear anatomical normalization to MNI (or --space
                        template) using 3dQWarp, after affine
    --fres=FRES         Specify functional voxel dim. in mm (iso.) for
                        resampling during preprocessing. Default none. ex:
                        --fres=2.5
    --space=SPACE       Path to specific standard space template for affine
                        anatomical normalization
    --no_skullstrip     Anatomical is already intensity-normalized and skull-
                        stripped (if -a provided)
    --no_despike        Do not de-spike functional data. Default is to de-
                        spike, recommended.
    --no_axialize       Do not re-write dataset in axial-first order. Default
                        is to axialize, recommended.
    --mask_mode=MASK_MODE
                        Mask functional with help from anatomical or standard
                        space images: use 'anat' or 'template'
    --coreg_mode=COREG_MODE
                        Coregistration with Local Pearson and T2* weights
                        (default), or use align_epi_anat.py (edge method): use
                        'lp-t2s' or 'aea'
    --smooth=FWHM       Data FWHM smoothing (3dBlurInMask). Default off. ex:
                        --smooth 3mm
    --align_base=ALIGN_BASE
                        Explicitly specify base dataset for volume
                        registration
    --TR=TR             The TR. Default read from input dataset header
    --tpattern=TPATTERN
                        Slice timing (i.e. alt+z, see 3dTshift -help). Default
                        from header. (N.B. This is important!)
    --align_args=ALIGN_ARGS
                        Additional arguments to anatomical-functional co-
                        registration routine
    --seed=SEED         Seed used for ICA. Default 42.
    --ted_args=TED_ARGS
                        Additional arguments to TE-dependence analysis routine

  Run options:
    --prefix=PREFIX     Prefix for final ME-ICA output datasets.
    --cpus=CPUS         Maximum number of CPUs (OpenMP threads) to use.
                        Default 2.
    --label=LABEL       Label to tag ME-ICA analysis folder.
    --test_proc         Align and preprocess 1 dataset then exit, for testing
    --script_only       Generate script only, then exit
    --pp_only           Preprocess only, then exit.
    --keep_int          Keep preprocessing intermediates. Default delete.
    --skip_check        Skip dependency checks during initialization.
    --OVERWRITE         If meica.xyz directory exists, overwrite.
</code>

==Example==
A typical meica invokation might look like this:

<code>
python -u ~/me-ica/meica.py -d "rest_e[0,1,2].nii.gz" -e 15,34,54 -a mprage.nii.gz --qwarp --space /usr/local/afni/MNI152_T1_2009c+tlrc --fres=2.33 --cpus=12 2>&1 | tee my_meica.log
</code>

===Arguments explained===
;    -d "rest_e[0,1,2].nii.gz"
:input files rest_e0.nii.gz rest_e1.nii.gz rest_e2.nii.gz
;    -e 15,34,54 
:echo times TE in milli-seconds for the three input files respectively
;    -a mprage.nii.gz
:anatomical image of the same subject for co-registration
;    --qwarp --space /usr/local/afni/MNI152_T1_2009c+tlrc 
:will use the lores MNI152 template that comes with afni as reference space into which the output is warped
;    --fres=2.33 
:resample to 2.33mm iso voxel size
;    --cpu=12 
:use parallel processing for warping and the internal PCA and ICA calculations
;    | tee my_meica.log
: save output to my_meica.log while also writing it out to the terminal

==See also==
The actual component identification is performed by the [[Tedana|tedana.py]] subroutine which is called from within [[MEICA|meica.py]] at the end of the afni based preprocessing.