13.2.6. Steinhauser et al. (2022). Functional dissection of neural connectivity …¶
Introduction¶
Here we present commands used in the following paper:
- Steinhauser JL, Teed AR, Al-Zoubi O, Hurlemann R, Chen G, Khalsa SS (2022). Functional dissection of neural connectivity in generalized anxiety disorder.
Abstract: Differences in the correlated activity of networked brain regions have been reported in individuals with generalized anxiety disorder (GAD) but an overreliance on the null-hypothesis significance testing (NHST) framework limits the identification and characterization of disorder-relevant relationships. In this preregistered study, we applied a Bayesian statistical framework as well as NHST to the analysis of resting-state fMRI scans from females with GAD and demographically matched healthy comparison females. Eleven a-priori hypotheses about functional correlativity (FC) were evaluated using Bayesian (multilevel model) and frequentist (t-test) inference. Reduced FC between the ventromedial prefrontal cortex (vmPFC) and the posterior-mid insula (PMI) was confirmed by both statistical approaches. FC between the vmPFC-anterior insula, the amygdala-PMI, and the amygdala-dorsolateral prefrontal cortex (dlPFC) region pairs did not survive multiple comparison correction using the frequentist approach. However, the Bayesian model provided evidence for these region pairs having decreased FC in the GAD group. Leveraging Bayesian modeling, we demonstrate decreased FC of the vmPFC, insula, amygdala, and dlPFC in females with GAD. Exploiting the Bayesian framework revealed FC abnormalities between region pairs excluded by the frequentist analysis, as well as other previously undescribed regions, demonstrating the benefits of applying this statistical approach to resting state FC data.
Study keywords: resting state FMRI, EPI, MPRAGE, human, adult, RETROICOR, fast ANATICOR, ROIs
Main programs:
afni_proc.py, MBA (, @SSwarper, recon-all (FS))
Download scripts¶
To download, either:
... click the link(s) in the following table (perhaps Rightclick -> “Save Link As…”):
run
afni_proc.pyfor resting state analysis; note the inclusion of RETRIOCOR (ricor), fast ANATICOR (-regress_anaticor_fast), motion regression with principle components (PCs) from ventricle ROIs, nonlinear warps estimated with@SSwarper, and ROI maps estimated by FreeSurfer’srecon-allrun
MBAfor matrix-based analysis; see the github page (link above) for the data table and ROI list... or copy+paste into a terminal:
curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2022_SteinhauserEtal/preprocessing_command_afniproc.sh curl -O https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/codex/fmri/media/2022_SteinhauserEtal/run_MBA_full.txt
View scripts¶
preprocessing_command_afniproc.sh¶
1#!/bin/bash
2
3# Used for processing in:
4#
5# Steinhauser JL, Teed AR, Al-Zoubi O, Hurlemann R, Chen G, Khalsa
6# SS (2022). Functional dissection of neural connectivity in
7# generalized anxiety disorder.
8# https://www.biorxiv.org/content/10.1101/2022.01.09.475543v1
9#
10# See also the project github page for further scripts and files:
11# https://github.com/Jonas-Ste/GAD_MBA_FC
12#
13# NB: This version contains minor changes to linespacing, from the
14# github repository version (for aesthetic purposes). No content
15# word/command was changed.
16#
17##############################################################################
18
19# these arguments are necessary to be provided when running the command
20subj_id=$1
21session=$2
22epi_file=$3
23output_dir=$4
24
25session_id=$subj_id-$session
26
27fs_dir=$output_dir/../prep_FS
28sswarper_dir=$output_dir/../SSwarper
29
30afni_proc.py \
31 -subj_id $session_id \
32 -blocks despike ricor tshift align tlrc volreg blur mask \
33 scale regress \
34 -radial_correlate_blocks tcat volreg \
35 -copy_anat $sswarper_dir/anatSS.${session_id}.nii \
36 -anat_has_skull no \
37 -anat_follower anat_w_skull anat $sswarper_dir/anatU.${session_id}.nii \
38 -anat_follower_ROI aaseg anat $fs_dir/${session_id}-FS/SUMA/aparc.a2009s+aseg.nii.gz \
39 -anat_follower_ROI aeseg epi $fs_dir/${session_id}-FS/SUMA/aparc.a2009s+aseg.nii.gz \
40 -anat_follower_ROI FSvent epi $fs_dir/${session_id}-FS/SUMA/fs_ap_latvent.nii.gz \
41 -anat_follower_ROI FSWe epi $fs_dir/${session_id}-FS/SUMA/fs_ap_wm.nii.gz \
42 -anat_follower_erode FSvent FSWe \
43 -ricor_regs_nfirst 3 \
44 -ricor_regs $output_dir/../RETROICOR/$session_id-physio.slibase.1D \
45 -dsets $output_dir/../RETROICOR/$epi_file \
46 -regress_polort 3 \
47 -tcat_remove_first_trs 3 \
48 -align_opts_aea -cost lpc+ZZ -giant_move -check_flip \
49 -tlrc_base MNI152_2009_template.nii.gz \
50 -tlrc_NL_warp \
51 -tlrc_NL_warped_dsets \
52 $sswarper_dir/anatQQ.${session_id}.nii \
53 $sswarper_dir/anatQQ.${session_id}.aff12.1D \
54 $sswarper_dir/anatQQ.${session_id}_WARP.nii \
55 -volreg_align_to MIN_OUTLIER \
56 -volreg_align_e2a \
57 -volreg_tlrc_warp \
58 -blur_size 4 \
59 -mask_epi_anat yes \
60 -regress_motion_per_run \
61 -regress_ROI_PC FSvent 3 \
62 -regress_ROI_PC_per_run FSvent \
63 -regress_make_corr_vols aeseg FSvent \
64 -regress_anaticor_fast \
65 -regress_anaticor_label FSWe \
66 -regress_censor_motion 0.2 \
67 -regress_censor_outliers 0.1 \
68 -regress_apply_mot_types demean deriv \
69 -regress_est_blur_epits \
70 -regress_est_blur_errts \
71 -html_review_style pythonic \
72 -script $output_dir/proc.${session_id} \
73 -out_dir $output_dir/${session_id}.results \
74 -execute
75
76# interpolate residuals for censored data
773dTproject \
78 -polort 0 \
79 -input $output_dir/${session_id}.results/pb04.${session_id}.r01.volreg+tlrc. \
80 -censor $output_dir/${session_id}.results/censor_${session_id}_combined_2.1D \
81 -cenmode NTRP \
82 -dsort $output_dir/${session_id}.results/Local_FSWe_rall+tlrc \
83 -ort $output_dir/${session_id}.results/X.nocensor.xmat.1D \
84 -prefix $output_dir/${session_id}.results/errts.${session_id}.fanaticor-interp
85
86exit
run_MBA_full.txt¶
1# Used for processing in:
2#
3# Steinhauser JL, Teed AR, Al-Zoubi O, Hurlemann R, Chen G, Khalsa
4# SS (2022). Functional dissection of neural connectivity in
5# generalized anxiety disorder.
6# https://www.biorxiv.org/content/10.1101/2022.01.09.475543v1
7#
8# See also the project github page for further scripts and files:
9# https://github.com/Jonas-Ste/GAD_MBA_FC
10#
11# NB: This version contains minor changes to linespacing, from the
12# github repository version (for aesthetic purposes). No content
13# word/command was changed.
14#
15##############################################################################
16
17
18MBA \
19 -prefix Iso_MBA_full_output -Y cor_z \
20 -chains 4 \
21 -iterations 1000 \
22 -model '1+group' \
23 -cVars 'group' \
24 -EOI 'group' \
25 -dataTable MBA_input_full.txt \
26 -ROIlist ROIlist.txt
