13.2.3. Atlas et al. (2022). Instructions and experiential learning have similar …

Introduction

Here we present commands used in the following paper:

  • Atlas LY, Dildine TC, Palacios-Barrios EE, Yu Q, Reynolds RC, Banker LA, Grant SS, Pine DS (2022). Instructions and experiential learning have similar impacts on pain and pain-related brain responses but produce dissociations in value-based reversal learning. (submitted)

Abstract:

Recent data suggest that interactions between systems involved in higher order knowledge and associative learning drive responses during appetitive and aversive learning. However, it is unknown how these systems impact subjective responses, such as pain. We tested how instructions and reversal learning influence pain and pain-evoked brain activation. Healthy volunteers (n = 40) were either instructed about contingencies between cues and aversive outcomes or learned through experience in a paradigm where contingencies reversed three times. We measured predictive cue effects on pain and heat-evoked brain responses using functional magnetic resonance imaging. Predictive cues dynamically modulated pain perception as contingencies changed, regardless of whether participants received contingency instructions. Heat-evoked responses in the insula, anterior cingulate, and putamen updated as contingencies changed, whereas the periaqueductal gray and thalamus responded to initial contingencies throughout the task. Quantitative modeling revealed that expected value was shaped purely by instructions in the Instructed Group, whereas expected value updated dynamically in the Uninstructed Group as a function of error-based learning. These differences were accompanied by dissociations in the neural correlates of value-based learning in the rostral anterior cingulate, medial prefrontal cortex, and orbitofrontal cortex. These results show how predictions impact subjective pain. Moreover, imaging data delineate three types of networks involved in pain generation and value-based learning: those that respond to initial contingencies, those that update dynamically during feedback-driven learning as contingencies change, and those that are sensitive to instruction. Together, these findings provide multiple points of entry for therapies designs to impact pain.

Study keywords: task-block, EPI, multi-echo FMRI (ME-FMRI), optimally combined (OC) echoes, MPRAGE, human, adult,

Main programs: @SSwarper, afni_proc.py

Download scripts

To download, either:

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

    s1.2022_AtlasEtal_ssw.tcsh

    run @SSwarper for skullstripping (SS) and nonlinear alignment (warp) estimation to a standard volumetric template

    s2.2022_AtlasEtal_ap.tcsh

    run afni_proc.py for task analysis on ME-FMRI data

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

    curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2022_AtlasEtal/s1.2022_AtlasEtal_ssw.tcsh
    curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2022_AtlasEtal/s2.2022_AtlasEtal_ap.tcsh
    

View scripts

s1.2022_AtlasEtal_ssw.tcsh

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#!/bin/tcsh 

# The @SSwarper command used to perform anatomical dataset
# skullstripping (SS) and nonlinear alignment (warp) estimation to a
# reference template.
#
# Used for processing in: 
#
#    Atlas LY, Dildine TC, Palacios-Barrios EE, Yu Q, Reynolds RC,
#    Banker LA, Grant SS, Pine DS (2022). Instructions and
#    experiential learning have similar impacts on pain and
#    pain-related brain responses but produce dissociations in
#    value-based reversal learning. (submitted)
#    https://www.biorxiv.org/content/10.1101/2021.08.25.457682v1
#
# To run for a single subject, type (while providing actual values for
# SUBJ_ID and DATA_DIR):
#
#    tcsh s1.2022_AtlasEtal_ssw.tcsh  SUBJ_ID  DATA_DIR
# 
# ===========================================================================

# user inputs 
set subj     = "$1"           # subject ID
set data_dir = "$2"           # path to datasets

set template = MNI152_2009_template_SSW.nii.gz

# set some environment variables
setenv AFNI_COMPRESSOR GZIP
setenv OMP_NUM_THREADS 16

cd ${data_dir}

# run skullstripping+warping
@SSwarper                                                                \
    -input  anat_${subj}.nii                                             \
    -base   ${template}                                                  \
    -subid  ${subj}                                                      \
    |& tee  log.ssw_${subj}.txt

s2.2022_AtlasEtal_ap.tcsh

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#!/bin/tcsh 

# The afni_proc.py command used to process task-based multi-echo FMRI
# (ME-FMRI) data in the present study.  It incorporates skullstripping
# (SS) and nonlinear alignment (warp) results from @SSwarper.
#
# Used for processing in: 
#
#    Atlas LY, Dildine TC, Palacios-Barrios EE, Yu Q, Reynolds RC,
#    Banker LA, Grant SS, Pine DS (2022). Instructions and
#    experiential learning have similar impacts on pain and
#    pain-related brain responses but produce dissociations in
#    value-based reversal learning. (submitted)
#    https://www.biorxiv.org/content/10.1101/2021.08.25.457682v1
#
# To run for a single subject, type (while providing actual values for
# SUBJ_ID and DATA_DIR):
#
#    tcsh s2.2022_AtlasEtal_ap.tcsh  SUBJ_ID  DATA_DIR
# 
# ===========================================================================

# user inputs 
set subj     = "$1"           # subject ID
set data_dir = "$2"           # path to datasets

set template = MNI152_2009_template_SSW.nii.gz

# set some environment variables
setenv AFNI_COMPRESSOR GZIP
setenv OMP_NUM_THREADS 16

# generate+run the ME-FMRI processing script
afni_proc.py                                                                 \
    -subj_id                  ${subj}_NL                                     \
    -blocks                   tshift align tlrc volreg mask combine          \
                              scale blur regress                             \
    -radial_correlate_blocks  tcat volreg                                    \
    -dsets_me_echo            ${data_dir}/epi_run*_e01.nii                   \
    -dsets_me_echo            ${data_dir}/epi_run*_e02.nii                   \
    -dsets_me_echo            ${data_dir}/epi_run*_e03.nii                   \
    -echo_times               11.0 22.72 34.44                               \
    -reg_echo                 2                                              \
    -tcat_remove_first_trs    4                                              \
    -copy_anat                ${data_dir}/anatSS.${subj}.nii                 \
    -anat_has_skull           no                                             \
    -anat_uniform_method      unifize                                        \
    -align_opts_aea           -cost lpc                                      \
                              -Allineate_opts '-maxscl 1.01'                 \
                              -source_automask                               \
                              -align_epi_strip_method 3dSkullStrip           \
    -volreg_align_to          MIN_OUTLIER                                    \
    -volreg_align_e2a                                                        \
    -volreg_tlrc_warp                                                        \
    -volreg_compute_tsnr      yes                                            \
    -tlrc_base                ${template}                                    \
    -tlrc_NL_warp                                                            \
    -tlrc_NL_warped_dsets     ${data_dir}/anatQQ.${subj}.nii                 \
                              ${data_dir}/anatQQ.${subj}.aff12.1D            \
                              ${data_dir}/anatQQ.${subj}_WARP.nii            \
    -mask_epi_anat            yes                                            \
    -combine_method           OC                                             \
    -blur_size                4                                              \
    -regress_stim_times       ${data_dir}/${subj}_onsets_cue.txt             \
                              ${data_dir}/${subj}_heatonsets_HH.txt          \
                              ${data_dir}/${subj}_heatonsets_LL.txt          \
                              ${data_dir}/${subj}_heatonsets_currentHM.txt   \
                              ${data_dir}/${subj}_heatonsets_currentLM.txt   \
                              ${data_dir}/${subj}_onsets_scale.txt           \
    -regress_stim_labels      cue HH LL HM LM scale                          \
    -regress_stim_types       times times times times times times            \
    -regress_basis_multi      'BLOCK(1,1)' 'BLOCK(8,1)' 'BLOCK(8,1)'         \
                              'BLOCK(8,1)' 'BLOCK(8,1)' 'BLOCK(1,1)'         \
    -regress_opts_3dD                                                        \
        -jobs                 10                                             \
        -gltsym               'SYM: +HH -LL'                                 \
        -glt_label            1 'HvL'                                        \
        -gltsym               'SYM: +HM - LM'                                \
        -glt_label            2 'CurrentHMvLM'                               \
        -gltsym               'SYM: +HH +HM +LM +LL'                         \
        -glt_label            3 'HeatFX'                                     \
    -regress_motion_per_run                                                  \
    -regress_est_blur_epits                                                  \
    -regress_est_blur_errts                                                  \
    -regress_run_clustsim     yes                                            \
    -html_review_style        pythonic                                       \
    -execute