14.2.7. Yang et al. (2021). Different activation signatures in the primary sensorimotor …¶
Introduction¶
Here we present commands used in the following paper:
- Yang J, Molfese PJ, Yu Y, Handwerker DA, Chen G, Taylor PA, Ejima Y, Wu J, Bandettini PA (2021). Different activation signatures in the primary sensorimotor and higher-level regions for haptic three-dimensional curved surface exploration. Neuroimage 231:117754.
Abstract: Haptic object perception begins with continuous exploratory contact, and the human brain needs to accumulate sensory information continuously over time. However, it is still unclear how the primary sensorimotor cortex (PSC) interacts with these higher-level regions during haptic exploration over time. This functional magnetic resonance imaging (fMRI) study investigates time-dependent haptic object processing by examining brain activity during haptic 3D curve and roughness estimations. For this experiment, we designed sixteen haptic stimuli (4 kinds of curves x 4 varieties of roughness) for the haptic curve and roughness estimation tasks. Twenty participants were asked to move their right index and middle fingers along the surface twice and to estimate one of the two features-roughness or curvature-depending on the task instruction. We found that the brain activity in several higher-level regions (e.g., the bilateral posterior parietal cortex) linearly increased as the number of curves increased during the haptic exploration phase. Surprisingly, we found that the contralateral PSC was parametrically modulated by the number of curves only during the late exploration phase but not during the early exploration phase. In contrast, we found no similar parametric modulation activity patterns during the haptic roughness estimation task in either the contralateral PSC or in higher-level regions. Thus, our findings suggest that haptic 3D object perception is processed across the cortical hierarchy, whereas the contralateral PSC interacts with other higher-level regions across time in a manner that is dependent upon the features of the object.
Study keywords: task-block, EPI, MPRAGE, human, adult, surface, blip up/down correction,
Main programs:
afni_proc.py, gen_group_command.py, 3dttest++,
3dANOVA2, 3dMVM
Download scripts¶
To download, either:
... click the link(s) in the following table (perhaps Rightclick -> “Save Link As…”):
run
afni_proc.pyfor task analysis on a surface (FreeSurfer’srecon-allwas run prior to this); blip up/down correction is also usedrun
afni_proc.pyfor task analysis on a surface (FreeSurfer’srecon-allwas run prior to this); blip up/down correction is also usedrun
gen_group_command.pyto build3dttest++commandsrun
gen_group_command.pyto build3dANOVA2commands for each hemisphererun
3dMVM... or copy+paste into a terminal:
curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2021_YangEtal/s1.2021_YangEtal_ap.tcsh curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2021_YangEtal/s2.2021_YangEtal_ap.tcsh curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2021_YangEtal/s3.2021_YangEtal_gen_ttest++.tcsh curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2021_YangEtal/s4.2021_YangEtal_gen_ANOVA2.tcsh curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2021_YangEtal/s5.2021_YangEtal_MVM.tcsh
View scripts¶
s1.2021_YangEtal_ap.tcsh¶
Comment: One would probably add -html_review_style pythonic here,
to have the fancier version of the automatically generated QC HTML.
1#!/bin/tcsh
2
3# The afni_proc.py command used to process the curve and roughness
4# estimation runs in the present study. In the stimulus timing files
5# and general linear tests (GLTs), the following abbreviations are
6# used to specify task types: CE = curve estimation tasks; RE =
7# roughness estimation tasks; HMVC = hand motion and visual control
8# task.
9#
10# Used for processing in:
11#
12# Yang J, Molfese PJ, Yu Y, Handwerker DA, Chen G, Taylor PA, Ejima
13# Y, Wu J, Bandettini PA (2021). Different activation signatures
14# in the primary sensorimotor and higher-level regions for haptic
15# three-dimensional curved surface exploration. Neuroimage
16# 231:117754. https://pubmed.ncbi.nlm.nih.gov/33454415/
17#
18# To run for a single subject, type (while providing actual values for
19# SUBJ_ID and TOP_DIR):
20#
21# tcsh s1.2021_YangEtal_ap.tcsh SUBJ_ID TOP_DIR
22#
23# =========================================================================
24
25set subj = $1 # provide subject ID
26set top_dir = $2 # provide top directory location, e.g., for group
27
28afni_proc.py \
29 -subj_id ${subj} \
30 -script proc.${subj} \
31 -scr_overwrite \
32 -blocks tshift align volreg surf blur scale regress \
33 -copy_anat ${top_dir}/${subj}/anat_00/t1w_ns.nii.gz \
34 -anat_has_skull no \
35 -tcat_remove_first_trs 2 \
36 -blip_reverse_dset ${top_dir}/${subj}/blip/blip+orig.HEAD \
37 -dsets ${top_dir}/${subj}/task/run?+orig.HEAD \
38 -volreg_align_to third \
39 -volreg_align_e2a \
40 -align_opts_aea -cmass cmass \
41 -surf_anat ${top_dir}/${subj}/SUMA/${subj}_SurfVol.nii \
42 -surf_spec ${top_dir}/${subj}/SUMA/std.141.${subj}_?h.spec \
43 -blur_size 6.0 \
44 -regress_stim_types IM \
45 -regress_stim_times \
46 ${top_dir}/${subj}/onset/CE1.txt \
47 ${top_dir}/${subj}/onset/CE2.txt \
48 ${top_dir}/${subj}/onset/CE3.txt \
49 ${top_dir}/${subj}/onset/CE4.txt \
50 ${top_dir}/${subj}/onset/HMVC.txt \
51 ${top_dir}/${subj}/onset/RE1.txt \
52 ${top_dir}/${subj}/onset/RE2.txt \
53 ${top_dir}/${subj}/onset/RE3.txt \
54 ${top_dir}/${subj}/onset/RE4.txt \
55 -regress_stim_labels CE1 CE2 CE3 CE4 HMVC RE1 RE2 RE3 RE4 \
56 -regress_basis 'BLOCK(5,1)' \
57 -regress_censor_motion 0.3 \
58 -regress_opts_3dD \
59 -gltsym 'SYM: 0.25*RE1 +0.25*RE2 +0.25*RE3 +0.25*RE4 -HMVC' -glt_label 1 RE-HMVC \
60 -gltsym 'SYM: 0.25*CE1 +0.25*CE2 +0.25*CE3 +0.25*CE4 -HMVC' -glt_label 2 CE-HMVC \
61 -gltsym 'SYM: RE1 -HMVC' -glt_label 3 RE1-HMVC \
62 -gltsym 'SYM: RE2 -HMVC' -glt_label 4 RE2-HMVC \
63 -gltsym 'SYM: RE3 -HMVC' -glt_label 5 RE3-HMVC \
64 -gltsym 'SYM: RE4 -HMVC' -glt_label 6 RE4-HMVC \
65 -gltsym 'SYM: CE1 -HMVC' -glt_label 7 CE1-HMVC \
66 -gltsym 'SYM: CE2 -HMVC' -glt_label 8 CE2-HMVC \
67 -gltsym 'SYM: CE3 -HMVC' -glt_label 9 CE3-HMVC \
68 -gltsym 'SYM: CE4 -HMVC' -glt_label 10 CE4-HMVC \
69 -regress_make_ideal_sum sum_ideal.1D
s2.2021_YangEtal_ap.tcsh¶
Comment: One would probably add -html_review_style pythonic here,
to have the fancier version of the automatically generated QC HTML.
1#!/bin/tcsh
2
3# The afni_proc.py command used to process the somatotopic finger
4# mapping runs in the present study. In the stimulus timing files and
5# general linear tests (GLTs), the following abbreviations are used to
6# specify task types: D1 = index finger; D2 = middle finger; D3 = ring
7# finger; D4 = pinky finger.
8#
9# Used for processing in:
10#
11# Yang J, Molfese PJ, Yu Y, Handwerker DA, Chen G, Taylor PA, Ejima
12# Y, Wu J, Bandettini PA (2021). Different activation signatures
13# in the primary sensorimotor and higher-level regions for haptic
14# three-dimensional curved surface exploration. Neuroimage
15# 231:117754. https://pubmed.ncbi.nlm.nih.gov/33454415/
16#
17# To run for a single subject, type (while providing actual values for
18# SUBJ_ID and TOP_DIR):
19#
20# tcsh s2.2021_YangEtal_ap.tcsh SUBJ_ID TOP_DIR
21#
22# =========================================================================
23
24set subj = $1 # provide subject ID
25set top_dir = $2 # provide top directory location, e.g., for group
26
27afni_proc.py \
28 -subj_id ${subj} \
29 -script proc.${subj} \
30 -scr_overwrite \
31 -blocks tshift align volreg surf blur scale regress \
32 -copy_anat ${top_dir}/${subj}/anat_00/t1w_ns.nii.gz \
33 -anat_has_skull no \
34 -tcat_remove_first_trs 2 \
35 -blip_reverse_dset ${top_dir}/${subj}/blip/blip+orig.HEAD \
36 -dsets ${top_dir}/${subj}/task/lcrun+orig.HEAD \
37 -volreg_align_to third \
38 -volreg_align_e2a \
39 -align_opts_aea -cmass cmass \
40 -surf_anat ${top_dir}/${subj}/SUMA/${subj}_SurfVol.nii \
41 -surf_spec ${top_dir}/${subj}/SUMA/std.141.${subj}_?h.spec \
42 -blur_size 6.0 \
43 -regress_stim_times \
44 ${top_dir}/${subj}/onset/D1.txt \
45 ${top_dir}/${subj}/onset/D2.txt \
46 ${top_dir}/${subj}/onset/D3.txt \
47 ${top_dir}/${subj}/onset/D4.txt \
48 -regress_stim_labels D1 D2 D3 D4 \
49 -regress_basis 'BLOCK(18,1)' \
50 -regress_censor_motion 0.3 \
51 -regress_opts_3dD \
52 -gltsym 'SYM: D1 +D2 +D3 +D4' -glt_label 1 all_digits \
53 -regress_make_ideal_sum sum_ideal.1D
s3.2021_YangEtal_gen_ttest++.tcsh¶
1#!/bin/tcsh
2
3# The command to generate t-tests for group analysis to localize
4# specific S1 sub-regions for the index and middle fingers in the
5# present study. The following abbreviations are used: lh = left
6# hemisphere; rh = right hemisphere; D1 = index finger; D2 = middle
7# finger; D3 = ring finger; D4 = pinky finger.
8#
9# Used for processing in:
10#
11# Yang J, Molfese PJ, Yu Y, Handwerker DA, Chen G, Taylor PA, Ejima
12# Y, Wu J, Bandettini PA (2021). Different activation signatures
13# in the primary sensorimotor and higher-level regions for haptic
14# three-dimensional curved surface exploration. Neuroimage
15# 231:117754. https://pubmed.ncbi.nlm.nih.gov/33454415/
16#
17# To run, type (while providing actual values for TOP_DIR):
18#
19# tcsh s3.2021_YangEtal_ap.tcsh TOP_DIR
20#
21# =========================================================================
22
23set top_dir = $1
24set res_path = ${top_dir}/subject_results/group.LC
25set out_path = ${res_path}/GROUP.LC/ttest
26
27\mkdir -p ${out_path}
28
29# generate t-test scripts and run each to generate dsets.
30foreach hemi ( lh rh )
31 foreach digit ( D1 D2 D3 D4 )
32
33 gen_group_command.py \
34 -command 3dttest++ \
35 -write_script ${out_path}/LC.ttest.${digit}.${hemi}.proc \
36 -prefix ${out_path}/group.LC.${digit}.${hemi} \
37 -dsets ${res_path}/*/*/stats.*.${hemi}.niml.dset \
38 -subs_betas "${digit}#0_Coef" \
39 -set_labels ${digit}
40
41 cd ${out_path}
42 tcsh -exf LC.ttest.${digit}.${hemi}.proc
43 end
44end
s4.2021_YangEtal_gen_ANOVA2.tcsh¶
1#!/bin/tcsh
2
3# The command to generate ANOVAs for group analysis to generate ANOVAs
4# for group analysis to observe the whole-brain activity pattern of CE
5# and RE tasks in the present study. The following abbreviations are
6# used: lh = left hemisphere; rh = right hemisphere; CE = curve
7# estimation task; RE = roughness estimation task; HMVC = hand motion
8# and visual control task.
9#
10# Used for processing in:
11#
12# Yang J, Molfese PJ, Yu Y, Handwerker DA, Chen G, Taylor PA, Ejima
13# Y, Wu J, Bandettini PA (2021). Different activation signatures
14# in the primary sensorimotor and higher-level regions for haptic
15# three-dimensional curved surface exploration. Neuroimage
16# 231:117754. https://pubmed.ncbi.nlm.nih.gov/33454415/
17#
18# To run, type (while providing actual values for TOP_DIR):
19#
20# tcsh s4.2021_YangEtal_ap.tcsh TOP_DIR
21#
22# =========================================================================
23
24set top_dir = $1
25set res_path = ${top_dir}/subject_results/group.HR
26set out_path = ${res_path}/GROUP.HR/group.blur6
27
28\mkdir -p ${out_path}
29
30# --------------------------------------------------------------
31
32foreach hemi ( lh rh )
33 gen_group_command.py \
34 -command 3dANOVA2 \
35 -write_script ${out_path}/HR.ANOVA.RCvsM.${hemi}.proc \
36 -prefix ${out_path}/HR.ANOVA.RCvsM.${hemi} \
37 -dsets ${res_path}/*/*/stats.mean.*.${hemi}.niml.dset \
38 -subs_betas 'RE-HMVC_GLT#0_Coef' 'CE-HMVC_GLT#0_Coef' \
39 -options \
40 -amean 1 RE_HMVC \
41 -amean 2 CE_HMVC \
42 -adiff 1 2 RE_HMVC_CE_HMVC \
43 -adiff 2 1 CE_HMVC_RE_HMVC
44end
s5.2021_YangEtal_MVM.tcsh¶
1#!/bin/tcsh
2
3# The command for MVM analysis with 3dMVM for group analysis to
4# observe the brain regions parametrically modulated by CE and RE
5# tasks in the present study. The following abbreviations are used: lh
6# = left hemisphere; rh = right hemisphere; CE = curve estimation
7# task; RE = roughness estimation task; HMVC = hand motion and visual
8# control task; L1 = degree level one (and similar for L2, etc.).
9#
10# Used for processing in:
11#
12# Yang J, Molfese PJ, Yu Y, Handwerker DA, Chen G, Taylor PA, Ejima
13# Y, Wu J, Bandettini PA (2021). Different activation signatures
14# in the primary sensorimotor and higher-level regions for haptic
15# three-dimensional curved surface exploration. Neuroimage
16# 231:117754. https://pubmed.ncbi.nlm.nih.gov/33454415/
17#
18# To run, type (while providing actual values for TOP_DIR):
19#
20# tcsh s4.2021_YangEtal_ap.tcsh TOP_DIR
21#
22# =========================================================================
23
24
25set top_dir = $1
26set res_path = ${top_dir}/subject_results/group.HR
27set out_path = ${res_path}/GROUP.MVM
28
29\mkdir -p ${out_path}
30
31foreach hemi ( lh rh )
32 3dMVM \
33 -prefix ${out_path}/mvm.${hemi}.niml.dset \
34 -jobs 6 \
35 -wsVars "task*degree" \
36 -SS_type 3 \
37 -num_glt 2 \
38 -gltLabel 1 roughness -gltCode 1 \
39 'task : 1*Roughness degree : -3*L1 -1*L2 1*L3 3*L4' \
40 -gltLabel 2 curve -gltCode 2 \
41 'task : 1*Curve degree : -3*L1 -1*L2 1*L3 3*L4' \
42 -dataTable ${res_path}/data_table_mvm.${hemi}.txt
43end
44
45# --- COMMENT ---
46# The '-dataTable ..' input text file contains 4 columns, with the
47# following column labels:
48# Subj condition degree InputFile
49# where
50# Subj = subject ID (sub01, sub02, sub03, etc.)
51# condition = Roughness|Curve
52# degree = L1|L2|L3|L4
53# InputFile = surface dataset of stats results (stats*.lh.niml.dset
54# or stats*.rh.niml.dset, for the respective data_table*),
55# with the appropriate effect estimate selected by
56# specifying the contrast of interest, e.g.:
57# "[RE1-HMVC_GLT#0_Coef]", "[RE2-HMVC_GLT#0_Coef]",
58# "[CE1-HMVC_GLT#0_Coef]", "[CE2-HMVC_GLT#0_Coef]", etc.
