13.2.9. Chen et al. (2016). Untangling the Relatedness among Correlations, Part II: …

Introduction

Here we present commands used in the following paper:

Abstract: It has been argued that naturalistic conditions in FMRI studies provide a useful paradigm for investigating perception and cognition through a synchronization measure, inter-subject correlation (ISC). However, one analytical stumbling block has been the fact that the ISC values associated with each single subject are not independent, and our previous paper (Chen et al., 2016) used simulations and analyses of real data to show that the methodologies adopted in the literature do not have the proper control for false positives. In the same paper, we proposed nonparametric subject-wise bootstrapping and permutation testing techniques for one and two groups, respectively, which account for the correlation structure, and these greatly outperformed the prior methods in controlling the false positive rate (FPR); that is, subject-wise bootstrapping (SWB) worked relatively well for both cases with one and two groups, and subject-wise permutation (SWP) testing was virtually ideal for group comparisons. Here we seek to explicate and adopt a parametric approach through linear mixed-effects (LME) modeling for studying the ISC values, building on the previous correlation framework, with the benefit that the LME platform offers wider adaptability, more powerful interpretations, and quality control checking capability than nonparametric methods. We describe both theoretical and practical issues involved in the modeling and the manner in which LME with crossed random effects (CRE) modeling is applied. A data-doubling step further allows us to conveniently track the subject index, and achieve easy implementations. We pit the LME approach against the best nonparametric methods, and find that the LME framework achieves proper control for false positives. The new LME methodologies are shown to be both efficient and robust, and they will be publicly available in AFNI (http://afni.nimh.nih.gov).

Study keywords: naturalistic, EPI, MPRAGE, human, control, adult, Talairach space, nonlinear align, FreeSurfer, fANATICOR

Main programs: recon-all (FS), @SUMA_Make_Spec_FS, afni_proc.py

Download scripts

To download, either:

  • ... click the link(s) in the following table (perhaps Rightclick -> “Save Link As…”):

    s.2016_ChenEtal_01_init.tcsh

    FreeSurfer segmentation with recon-all; @SUMA_Make_Spec_FS; tissue selection

    s.2016_ChenEtal_02_ap.tcsh

    afni_proc.py command

  • ... or copy+paste into a terminal:

    curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2016_ChenEtal/s.2016_ChenEtal_01_init.tcsh
    curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2016_ChenEtal/s.2016_ChenEtal_02_ap.tcsh
    

View scripts

s.2016_ChenEtal_01_init.tcsh

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#!/bin/tcsh

# --------------------------------------------------------------------
# Script: s.2016_ChenEtal_01_init.tcsh
#
# From:
# Chen GC, Taylor PA, Shin Y-W, Reynolds RC, Cox RW (2016). Untangling
# the Relatedness among Correlations, Part II: Inter-Subject
# Correlation Group Analysis through Linear Mixed-Effects
# Modeling. Neuroimage (in press).
#
# Originally run using: AFNI_16.1.16
#
# ** Note: this code is a "fixed" version, with the selection of WM
# ** regions updated *not* to include voxels with value '16' in line 34.
#
# --------------------------------------------------------------------


# Commands run prior to afni_proc.py, each in appropriate 
# directories for the data sets for each subject

# Run FreeSurfer on the anatomical, and then use
# SUMA to convert the FS output to NIFTI for AFNI to use.
recon-all -all -subject $subj -i $anat
@SUMA_Make_Spec_FS -sid $subj -NIFTI

# Select the ventricle maps from the FS output.
3dcalc -a aparc+aseg.nii -datum byte -prefix FSmask_vent.nii \
     -expr 'amongst(a,4,43)'

# Select the WM maps from the FS output. 
3dcalc -a aparc+aseg.nii -datum byte -prefix FSmask_WM.nii \
     -expr 'amongst(a,2,7,41,46,251,252,253,254,255)'

s.2016_ChenEtal_02_ap.tcsh

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#!/bin/tcsh

# --------------------------------------------------------------------
# Script: s.2016_ChenEtal_02_ap.tcsh
#
# From:
# Chen GC, Taylor PA, Shin Y-W, Reynolds RC, Cox RW (2016). Untangling
# the Relatedness among Correlations, Part II: Inter-Subject
# Correlation Group Analysis through Linear Mixed-Effects
# Modeling. Neuroimage (in press).
#
# Originally run using: AFNI_16.1.16
# --------------------------------------------------------------------


# FMRI processing script, ISC movie data.
# Assumes previously run FS and SUMA commands, respectively: 
# $ recon-all -all -subject $subj -i $anat
# $ @SUMA_Make_Spec_FS -sid $subj -NIFTI

# Set top level directory structure
set subj    = $1
set topdir  = TOP_LEVEL_FILE_LOCATION
set task    = movie
set fsroot  = $topdir/freesurfer/subjects
set outroot = $topdir/subject_results/$task.6

# Input directory: unprocessed FMRI data
set indir   = $topdir/orig.data
# Input directory: FreeSurfer + @SUMA_MakeSpec_FS results
set fsindir = $fsroot/$subj/SUMA

# Output directory: name for output
set outdir  = $outroot/$subj

# Input data: list of partitioned EPIs (movie clips)
set epi_dpattern = "movie*.HEAD"

# Input data: FreeSurfer results (anatomy, ventricle and WM maps)
set fsanat = ${subj}_SurfVol.nii
set fsvent = FSmask_vent.nii
set fswm   = FSmask_WM.nii

afni_proc.py -subj_id $subj.$task                                       \
    -blocks despike tshift align tlrc volreg blur mask regress          \
    -copy_anat $fsindir/$fsanat                                         \
    -anat_follower_ROI aaseg  anat $fsindir/aparc.a2009s+aseg_rank.nii  \
    -anat_follower_ROI aeseg  epi  $fsindir/aparc.a2009s+aseg_rank.nii  \
    -anat_follower_ROI FSvent epi  $fsindir/$fsvent                     \
    -anat_follower_ROI FSWMe  epi  $fsindir/$fswm                       \
    -anat_follower_erode FSvent FSWMe                                   \
    -dsets $epi_dpattern                                                \
    -tcat_remove_first_trs 0                                            \
    -tlrc_base TT_N27+tlrc                                              \
    -tlrc_NL_warp                                                       \
    -volreg_align_to MIN_OUTLIER                                        \
    -volreg_align_e2a                                                   \
    -volreg_tlrc_warp                                                   \
    -regress_ROI_PC FSvent 3                                            \
    -regress_make_corr_vols aeseg FSvent                                \
    -regress_anaticor_fast                                              \
    -regress_anaticor_label FSWMe                                       \
    -regress_censor_motion 0.2                                          \
    -regress_censor_outliers 0.1                                        \
    -regress_apply_mot_types demean deriv                               \
    -regress_est_blur_epits                                             \
    -regress_est_blur_errts                                             \
    -regress_run_clustsim no