14.2.1. Yoon et al. (2026). Exclusive ipsilateral representation of sequential tactile differences …¶
Introduction¶
Here we present commands used in the following paper:
- Yoon H, Park S, Kim S (2026). Exclusive ipsilateral representation of sequential tactile differences challenges contralateral dominance. (submitted)
Abstract: Tactile perception is traditionally attributed to contralateral somatosensory (S1) processing, yet the functional role of ipsilateral S1 in human tactile discrimination remains unclear. Using fMRI during a sequential vibrotactile discrimination task, we examined how frequency information is represented when participants judged which of two successive stimuli delivered to the same fingertip was higher in frequency. Contralateral S1 showed robust activation, and ipsilateral S1 showed suppression during unilateral stimulation; however, linear mixed-effects analyses revealed reliable frequency-dependent modulation in ipsilateral S1, which was absent when no comparison was required. Multivariate representational analyses further demonstrated that fMRI activity patterns representing the differences between successive stimuli were strengthened under memory demands, particularly within ipsilateral S1 and parietal cortices. Furthermore, the representational separability predicted individual discrimination accuracy. The exclusive representation in the ipsilateral S1 was not hand-specific; that is, the results were consistent for both hands, indicating a bilateral and symmetric encoding scheme. These surprising findings demonstrate that the ipsilateral, not contralateral, S1 contributes to tactile discrimination, challenging classic contralateral models of somatosensory processing. These unprecedented findings highlight interhemispheric coordination as a key mechanism underlying perceptual decisions.
Main programs:
dcm2niix_afni, afni_proc.py, 3dLMEr
Download scripts¶
To download, either:
... click the link(s) in the following table (perhaps Rightclick -> “Save Link As…”):
run
dcm2niix_afnito convert DICOMs to NIFTI volumesrun
afni_proc.pyfor full single subject processingrun
3dLMErfor group-level analysisrun
3dLMErfor group-level analysis... or copy+paste into a terminal:
curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2026_YoonEtal/s1.convert_DICOM.zsh curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2026_YoonEtal/s2-i.create_proc_M.zsh curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2026_YoonEtal/s3.lme_freq_1.sh curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2026_YoonEtal/s4.lme_freq_2.sh
View scripts¶
The scripts shown here contain full examples of the per-subject commands created by the above GitHub repository’s Python implementation.
s1.convert_DICOM.zsh¶
1#!/bin/zsh
2
3# run dcm2niix_afni to convert DICOMs to NIFTI volumes
4#
5# This is an example of running dcm2niix_afni to convert raw DICOM
6# files into NIfTI format and organize structural and functional data.
7#
8# Execution:
9#
10# zsh s1.convert_DICOM.zsh -s TMN01
11#
12##############################################################
13
14## Process command line arguments
15while (( $# )); do
16 key="$1"
17 case $key in
18 -s | --subject)
19 subj="$2"
20 shift # Shift twice if the subject is given
21 ;;
22 esac
23 shift ##takes one argument
24done
25
26# Check if subject is set
27if [ -z "$subj" ]; then
28 echo "Error: Subject is not set."
29 echo "Use -s or --subject to specify the subject."
30 exit 1
31fi
32
33##############################################################
34
35dir_root="/mnt/ext4/TMN/fmri_data"
36dir_raw="$dir_root/raw_data/$subj"
37
38dir_output="$dir_raw/convert_data/$subj"
39if [ ! -d "$dir_output" ]; then
40 mkdir -p -m 755 "$dir_output"
41fi
42
43##############################################################
44
45## T1
46dname_T1=$(find "$dir_raw" -type d -name "T1")
47dcm2niix_afni \
48 -a y -o "$dir_output" -f T1.$subj \
49 "$dname_T1"
50
51## EPI1
52for rr in {1..6}; do
53 dname_E1=$(find "$dir_raw" -type d -name "Nomemory_RUN${rr}")
54 run=$(printf "r%02d" "$rr")
55 dcm2niix_afni \
56 -a y -o "$dir_output" -f func.NM.$run.$subj \
57 "$dname_E1"
58done
59
60##############################################################
61
62## EPI1 reverse
63dname_ER=$(find "$dir_raw" -type d -name "Reverse")
64dcm2niix_afni \
65 -a y -o "$dir_output" -f func.REV.$subj \
66 "$dname_ER"
67
68## EPI2
69for rr in {1..6}; do
70 dname_E2=$(find "$dir_raw" -type d -name "memory_RUN${rr}")
71 run=$(printf "r%02d" "$rr")
72 dcm2niix_afni \
73 -a y -o "$dir_output" -f func.M.$run.$subj \
74 "$dname_E2"
75done
s2-i.create_proc_M.zsh¶
1#!/bin/zsh
2
3# run afni_proc.py to perform full single subject regression
4#
5# This script demonstrates how to construct a single subject
6# afni_proc.py pipeline for the memory condition including EPI
7# distortion correction and nonlinear standard space
8# registration. Running this script automatically creates an
9# afni_proc.py directory and generates the proc.TMN01 execution script
10# inside it.
11#
12# Execution:
13#
14# zsh s2-i.create_proc_M.zsh -s TMN01
15#
16# The above command creates proc.TMN01. This is the generated
17# execution script running the full preprocessing steps for one
18# subject. It is then executed, for example, with:
19#
20# tcsh -xef proc.TMN01 |& tee output.proc.TMN01
21#
22#
23## ==================================================== ##
24
25## $# = the number of arguments
26while (( $# )); do
27 key="$1"
28 case $key in
29 -s | --subject)
30 subj="$2"
31 ;;
32 esac
33 shift ##takes one argument
34done
35
36## ==================================================== ##
37
38dir_root="/mnt/ext4/TMN/fmri_data"
39dir_raw="$dir_root/raw_data/$subj"
40dir_nifti="$dir_raw/convert_data/$subj"
41dir_preproc="$dir_root/preproc_data/M/$subj"
42
43dir_script="/home/harry/TM_pj/Experiment/afni_proc.py"
44if [ ! -d "$dir_script" ]; then
45 mkdir -p -m 755 "$dir_script"
46fi
47
48dir_output="$dir_preproc"
49
50## ==================================================== ##
51
52dsets=($(find "$dir_nifti" -type f -name "func.M.r??.$subj.nii" | sort))
53
54## ==================================================== ##
55
56cd "$dir_script" # Only if needed; otherwise, remove this line
57
58# Notes on options, for resting state FMRI processing
59#
60# + blip up/down (=reverse phase-encoded) datasets are used to reduce
61# distortion due to B0 inhomogeneity
62# + LFF-based bandpassing is _not_ used, to preserve degrees of freedom
63# + while typically the lpc+ZZ cost function is used to align the EPI and
64# anatomical datasets, in this case the lpa cost function performed well
65# + nonlinear alignment and anatomical skullstripping are both
66# performed within this command, but these could be done beforehand
67# with sswarper2, passing the results to afni_proc.py
68
69afni_proc.py \
70 -subj_id "$subj" \
71 -out_dir "$dir_output" \
72 -blocks despike tshift align tlrc volreg blur mask \
73 scale regress \
74 -copy_anat "$dir_nifti/T1.$subj.nii" \
75 -anat_has_skull yes \
76 -anat_uniform_method unifize \
77 -anat_unif_GM yes \
78 -dsets $dsets \
79 -radial_correlate_blocks tcat volreg \
80 -tcat_remove_first_trs 0 \
81 -blip_forward_dset "$dir_nifti/func.M.r01.$subj.nii" \
82 -blip_reverse_dset "$dir_nifti/func.REV.$subj.nii" \
83 -align_opts_aea -cost lpa \
84 -giant_move \
85 -check_flip \
86 -tlrc_NL_warp \
87 -tlrc_base MNI152_2009_template_SSW.nii.gz \
88 -volreg_align_to MIN_OUTLIER \
89 -volreg_align_e2a \
90 -volreg_tlrc_warp \
91 -blur_size 4.0 \
92 -mask_epi_anat yes \
93 -regress_motion_per_run \
94 -regress_censor_motion 0.4 \
95 -regress_censor_outliers 0.05 \
96 -regress_apply_mot_types demean deriv \
97 -html_review_style pythonic
98# -execute # Uncomment if you want to run it immediately
99
100#echo $dsets
101#echo "Directory NIFTI: $dir_nifti"
102#echo "Datasets: $dsets"
s3.lme_freq_1.sh¶
1#!/bin/bash
2
3# run 3dLMEr for group-level linear mixed effects analysis modeling
4# the continuous frequency variable with random intercepts.
5#
6# The model (-model "Freq+(1|Subj)+(1|Subj:Run)") is designed to
7# control for random effects across individual subjects and specific
8# runs, while -gltCode is used to explicitly extract the main linear
9# effect of the frequency.
10
11# ============================================================================
12
13p1=/mnt/ext4/TMN/fmri_data/preproc_data/M/TMN
14
153dLMEr \
16 -prefix output_LME_1_fin.nii \
17 -jobs 16 \
18 -model "Freq+(1|Subj)+(1|Subj:Run)" \
19 -qVars 'Freq' \
20 -gltCode 'Freq_effect' 'Freq :' \
21 -mask ${p1}/full_mask.group.M.nii \
22 -dataTable @newDataTable_fin_SK.txt
s4.lme_freq_2.sh¶
1#!/bin/bash
2
3# run 3dLMEr for group-level linear mixed effects analysis modeling
4# the continuous frequency variable with random intercepts.
5#
6# This script runs a parallel 3dLMEr analysis (to s3*.sh) using an
7# alternative input data table structure for comparative evaluation,
8# applying the same model specifications as in the other 3dLMEr command.
9
10# ============================================================================
11
12p1=/mnt/ext4/TMN/fmri_data/preproc_data/M/TMN
13
143dLMEr \
15 -prefix output_LME_2.nii \
16 -jobs 16 \
17 -model "Freq+(1|Subj)+(1|Subj:Run)" \
18 -qVars 'Freq' \
19 -gltCode 'Freq_effect' 'Freq :' \
20 -mask ${p1}/full_mask.group.M.nii \
21 -dataTable @newDataTable_fin_SK_2.txt
