14.2.9. 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. Elife 11:e73353. doi: 10.7554/eLife.73353.
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…”):
run
@SSwarperfor skullstripping (SS) and nonlinear alignment (warp) estimation to a standard volumetric templaterun
afni_proc.pyfor 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¶
1#!/bin/tcsh
2
3# The @SSwarper command used to perform anatomical dataset
4# skullstripping (SS) and nonlinear alignment (warp) estimation to a
5# reference template.
6#
7# Used for processing in:
8#
9# Atlas LY, Dildine TC, Palacios-Barrios EE, Yu Q, Reynolds RC,
10# Banker LA, Grant SS, Pine DS (2022). Instructions and
11# experiential learning have similar impacts on pain and
12# pain-related brain responses but produce dissociations in
13# value-based reversal learning. 11:e73353. doi: 10.7554/eLife.73353.
14# https://pubmed.ncbi.nlm.nih.gov/36317867/
15#
16# To run for a single subject, type (while providing actual values for
17# SUBJ_ID and DATA_DIR):
18#
19# tcsh s1.2022_AtlasEtal_ssw.tcsh SUBJ_ID DATA_DIR
20#
21# ===========================================================================
22
23# user inputs
24set subj = "$1" # subject ID
25set data_dir = "$2" # path to datasets
26
27set template = MNI152_2009_template_SSW.nii.gz
28
29# set some environment variables
30setenv AFNI_COMPRESSOR GZIP
31setenv OMP_NUM_THREADS 16
32
33cd ${data_dir}
34
35# run skullstripping+warping
36@SSwarper \
37 -input anat_${subj}.nii \
38 -base ${template} \
39 -subid ${subj} \
40 |& tee log.ssw_${subj}.txt
s2.2022_AtlasEtal_ap.tcsh¶
1#!/bin/tcsh
2
3# The afni_proc.py command used to process task-based multi-echo FMRI
4# (ME-FMRI) data in the present study. It incorporates skullstripping
5# (SS) and nonlinear alignment (warp) results from @SSwarper.
6#
7# Used for processing in:
8#
9# Atlas LY, Dildine TC, Palacios-Barrios EE, Yu Q, Reynolds RC,
10# Banker LA, Grant SS, Pine DS (2022). Instructions and
11# experiential learning have similar impacts on pain and
12# pain-related brain responses but produce dissociations in
13# value-based reversal learning. 11:e73353. doi: 10.7554/eLife.73353.
14# https://pubmed.ncbi.nlm.nih.gov/36317867/
15#
16# To run for a single subject, type (while providing actual values for
17# SUBJ_ID and DATA_DIR):
18#
19# tcsh s2.2022_AtlasEtal_ap.tcsh SUBJ_ID DATA_DIR
20#
21# ===========================================================================
22
23# user inputs
24set subj = "$1" # subject ID
25set data_dir = "$2" # path to datasets
26
27set template = MNI152_2009_template_SSW.nii.gz
28
29# set some environment variables
30setenv AFNI_COMPRESSOR GZIP
31setenv OMP_NUM_THREADS 16
32
33# generate+run the ME-FMRI processing script
34afni_proc.py \
35 -subj_id ${subj}_NL \
36 -blocks tshift align tlrc volreg mask combine \
37 scale blur regress \
38 -radial_correlate_blocks tcat volreg \
39 -dsets_me_echo ${data_dir}/epi_run*_e01.nii \
40 -dsets_me_echo ${data_dir}/epi_run*_e02.nii \
41 -dsets_me_echo ${data_dir}/epi_run*_e03.nii \
42 -echo_times 11.0 22.72 34.44 \
43 -reg_echo 2 \
44 -tcat_remove_first_trs 4 \
45 -copy_anat ${data_dir}/anatSS.${subj}.nii \
46 -anat_has_skull no \
47 -anat_uniform_method unifize \
48 -align_opts_aea -cost lpc \
49 -Allineate_opts '-maxscl 1.01' \
50 -source_automask \
51 -align_epi_strip_method 3dSkullStrip \
52 -volreg_align_to MIN_OUTLIER \
53 -volreg_align_e2a \
54 -volreg_tlrc_warp \
55 -volreg_compute_tsnr yes \
56 -tlrc_base ${template} \
57 -tlrc_NL_warp \
58 -tlrc_NL_warped_dsets ${data_dir}/anatQQ.${subj}.nii \
59 ${data_dir}/anatQQ.${subj}.aff12.1D \
60 ${data_dir}/anatQQ.${subj}_WARP.nii \
61 -mask_epi_anat yes \
62 -combine_method OC \
63 -blur_size 4 \
64 -regress_stim_times ${data_dir}/${subj}_onsets_cue.txt \
65 ${data_dir}/${subj}_heatonsets_HH.txt \
66 ${data_dir}/${subj}_heatonsets_LL.txt \
67 ${data_dir}/${subj}_heatonsets_currentHM.txt \
68 ${data_dir}/${subj}_heatonsets_currentLM.txt \
69 ${data_dir}/${subj}_onsets_scale.txt \
70 -regress_stim_labels cue HH LL HM LM scale \
71 -regress_stim_types times times times times times times \
72 -regress_basis_multi 'BLOCK(1,1)' 'BLOCK(8,1)' 'BLOCK(8,1)' \
73 'BLOCK(8,1)' 'BLOCK(8,1)' 'BLOCK(1,1)' \
74 -regress_opts_3dD \
75 -jobs 10 \
76 -gltsym 'SYM: +HH -LL' \
77 -glt_label 1 'HvL' \
78 -gltsym 'SYM: +HM - LM' \
79 -glt_label 2 'CurrentHMvLM' \
80 -gltsym 'SYM: +HH +HM +LM +LL' \
81 -glt_label 3 'HeatFX' \
82 -regress_motion_per_run \
83 -regress_est_blur_epits \
84 -regress_est_blur_errts \
85 -regress_run_clustsim yes \
86 -html_review_style pythonic \
87 -execute
