{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preprocessing with AFNI\n",
"\n",
"Author: Monika Doerig\n",
"\n",
"Citation:\n",
"\n",
"__Andy's Brain Book:__\n",
"- This AFNI example is based on the [AFNI Tutorial: Commands and Preprocessing](https://andysbrainbook.readthedocs.io/en/latest/AFNI/AFNI_Short_Course/AFNI_04_Preprocessing.html) from Andy’s Brain Book (Jahn, 2022. [doi:10.5281/zenodo.5879293](https://zenodo.org/records/5879294))\n",
"\n",
"__Data from OpenNeuro:__ Flanker Dataset\n",
"\n",
"- Kelly AMC and Uddin LQ and Biswal BB and Castellanos FX and Milham MP (2018). Flanker task (event-related). [OpenNeuro Dataset ds000102](https://openneuro.org/datasets/ds000102/versions/00001/). [Dataset] doi: null\n",
"- Kelly AM, Uddin LQ, Biswal BB, Castellanos FX, Milham MP. Competition between functional brain networks mediates behavioral variability. Neuroimage. 2008 Jan 1;39(1):527-37. doi: [10.1016/j.neuroimage.2007.08.008](https://doi.org/10.1016/j.neuroimage.2007.08.008). Epub 2007 Aug 23. PMID: 17919929.\n",
"- Mennes, M., Kelly, C., Zuo, X.N., Di Martino, A., Biswal, B.B., Castellanos, F.X., Milham, M.P. (2010). Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity. Neuroimage, 50(4):1690-701. doi: [10.1016/j.neuroimage.2010.01.002](https://doi.org/10.1016/j.neuroimage.2010.01.002). Epub 2010 Jan 15. Erratum in: Neuroimage. 2011 Mar 1;55(1):434\n",
"- Mennes, M., Zuo, X.N., Kelly, C., Di Martino, A., Zang, Y.F., Biswal, B., Castellanos, F.X., Milham, M.P. (2011). Linking inter-individual differences in neural activation and behavior to intrinsic brain dynamics. Neuroimage, 54(4):2950-9. doi: [10.1016/j.neuroimage.2010.10.046](https://doi.org/10.1016/j.neuroimage.2010.10.046)"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"## Setup Neurodesk"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [],
"vscode": {
"languageId": "plaintext"
}
},
"outputs": [],
"source": [
"%%capture\n",
"import os\n",
"import sys\n",
"IN_COLAB = 'google.colab' in sys.modules\n",
"\n",
"if IN_COLAB:\n",
" os.environ[\"LD_PRELOAD\"] = \"\";\n",
" os.environ[\"APPTAINER_BINDPATH\"] = \"/content,/tmp,/cvmfs\"\n",
" os.environ[\"MPLCONFIGDIR\"] = \"/content/matplotlib-mpldir\"\n",
" os.environ[\"LMOD_CMD\"] = \"/usr/share/lmod/lmod/libexec/lmod\"\n",
"\n",
" !curl -J -O https://raw.githubusercontent.com/NeuroDesk/neurocommand/main/googlecolab_setup.sh\n",
" !chmod +x googlecolab_setup.sh\n",
" !./googlecolab_setup.sh\n",
"\n",
" os.environ[\"MODULEPATH\"] = ':'.join(map(str, list(map(lambda x: os.path.join(os.path.abspath('/cvmfs/neurodesk.ardc.edu.au/neurodesk-modules/'), x),os.listdir('/cvmfs/neurodesk.ardc.edu.au/neurodesk-modules/')))))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"vendor_id\t: GenuineIntel\n",
"model name\t: Intel(R) Xeon(R) Gold 6126 CPU @ 2.60GHz\n"
]
}
],
"source": [
"# Output CPU information:\n",
"!cat /proc/cpuinfo | grep 'vendor' | uniq\n",
"!cat /proc/cpuinfo | grep 'model name' | uniq"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"### Load packages"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"['afni/21.2.00']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import lmod\n",
"await lmod.load('afni/21.2.00')\n",
"await lmod.list()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Install tcsh"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"scroll-ouput"
]
},
"outputs": [],
"source": [
"%%capture\n",
"!sudo apt update; sudo apt install tcsh"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check the installation:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello from tcsh\n"
]
}
],
"source": [
"%%script tcsh\n",
"set var = \"hello from tcsh\"\n",
"echo $var"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"### Import Python Modules"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"import nibabel as nib\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from ipyniivue import NiiVue\n",
"from IPython.display import display\n",
"from ipywidgets import VBox, Dropdown\n",
"import ipywidgets as widgets"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"## Data preparation"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"editable": true,
"scrolled": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"scroll-output"
],
"vscode": {
"languageId": "plaintext"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning: 0%| | 0.00/2.00 [00:00 \u001b[00m../../.git/annex/objects/Q4/jv/MD5E-s14180--3addf0456b803b7c5ec5147481ecdd62.csv/MD5E-s14180--3addf0456b803b7c5ec5147481ecdd62.csv\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_group.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/6m/q9/MD5E-s98927--d11151f65ae061833e7fd4373adfec3f.pdf/MD5E-s98927--d11151f65ae061833e7fd4373adfec3f.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-01.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/K3/7x/MD5E-s2747349--5d40f2a54fb4194ac4a79f0295ff51c0.pdf/MD5E-s2747349--5d40f2a54fb4194ac4a79f0295ff51c0.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-02.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/Kx/Kv/MD5E-s2803965--56f6b768362bd9b7f0ef501b8cb6dde6.pdf/MD5E-s2803965--56f6b768362bd9b7f0ef501b8cb6dde6.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-03.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/kx/g5/MD5E-s2809843--e90d7a4859ed4be986b55e23f93ca89d.pdf/MD5E-s2809843--e90d7a4859ed4be986b55e23f93ca89d.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-04.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/JK/Zm/MD5E-s2846770--4869146771178dbb01ac79b95b35a8a1.pdf/MD5E-s2846770--4869146771178dbb01ac79b95b35a8a1.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-05.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/zm/FG/MD5E-s2824086--fda634d34556c83005a5eb2ca8c498dd.pdf/MD5E-s2824086--fda634d34556c83005a5eb2ca8c498dd.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-06.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/92/q0/MD5E-s2798058--fdddf0aff1eca8f61ed7c8b04ada9735.pdf/MD5E-s2798058--fdddf0aff1eca8f61ed7c8b04ada9735.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-07.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/39/3K/MD5E-s2795270--29ce2e2352596df940e5f3fae45b5a38.pdf/MD5E-s2795270--29ce2e2352596df940e5f3fae45b5a38.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-08.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/Fx/F4/MD5E-s2727492--b55dad8ffe22fc035110ecf4119d2960.pdf/MD5E-s2727492--b55dad8ffe22fc035110ecf4119d2960.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-09.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/4M/pz/MD5E-s2887144--28ea830af2a4d741147d18ea9c7fda84.pdf/MD5E-s2887144--28ea830af2a4d741147d18ea9c7fda84.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-10.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/0z/Vw/MD5E-s2874045--6542a57a9fc58f97f2a03c2384663c62.pdf/MD5E-s2874045--6542a57a9fc58f97f2a03c2384663c62.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-11.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/wm/76/MD5E-s2781221--1071b83e3c1b4532879521c37c3329da.pdf/MD5E-s2781221--1071b83e3c1b4532879521c37c3329da.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-12.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/GF/19/MD5E-s2817233--bfd24ca3274fa5efd654e2afd927f9ef.pdf/MD5E-s2817233--bfd24ca3274fa5efd654e2afd927f9ef.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-13.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/9Q/X2/MD5E-s2796088--ed299ab7e1662cb03aa01299eed2602b.pdf/MD5E-s2796088--ed299ab7e1662cb03aa01299eed2602b.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-14.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/Wg/55/MD5E-s2558074--cadc9bd81856dcd02677de84e7e6ca90.pdf/MD5E-s2558074--cadc9bd81856dcd02677de84e7e6ca90.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-15.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/43/Q3/MD5E-s2847293--0c678a4b309d055ad9ba4ba25b77351b.pdf/MD5E-s2847293--0c678a4b309d055ad9ba4ba25b77351b.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-16.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/xq/qj/MD5E-s2890454--87c62253c1711f30d53c41b3ac38dc66.pdf/MD5E-s2890454--87c62253c1711f30d53c41b3ac38dc66.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-17.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/zK/M1/MD5E-s2825765--0a91015e22836a3076641b963e1ccfc6.pdf/MD5E-s2825765--0a91015e22836a3076641b963e1ccfc6.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-18.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/w2/Vk/MD5E-s2821624--1d9a3f0b21ce1f9a3b490d44d36f1f11.pdf/MD5E-s2821624--1d9a3f0b21ce1f9a3b490d44d36f1f11.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-19.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/J2/Jq/MD5E-s2453814--533411f3353cb3fa0264485e81f3fcf6.pdf/MD5E-s2453814--533411f3353cb3fa0264485e81f3fcf6.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-20.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/MF/9X/MD5E-s2881144--176c560778c55db87e8468b3246d373c.pdf/MD5E-s2881144--176c560778c55db87e8468b3246d373c.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-21.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/XQ/p1/MD5E-s2330589--b7546dfe5fb43a974cd23111b860c493.pdf/MD5E-s2330589--b7546dfe5fb43a974cd23111b860c493.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-22.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/Fx/k8/MD5E-s2505165--55f0661ad209b742c517cc5b5469436a.pdf/MD5E-s2505165--55f0661ad209b742c517cc5b5469436a.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-23.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/qj/8K/MD5E-s2784018--7e8697a7d4601547a899a27af132166d.pdf/MD5E-s2784018--7e8697a7d4601547a899a27af132166d.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-24.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/G8/Kw/MD5E-s2828817--e86be931adef2a7b0297d557d827d629.pdf/MD5E-s2828817--e86be931adef2a7b0297d557d827d629.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-25.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/XG/kg/MD5E-s2447908--3d392b9d27929dc4146d2b47be16e8dc.pdf/MD5E-s2447908--3d392b9d27929dc4146d2b47be16e8dc.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01manatomical_sub-26.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/8P/42/MD5E-s2850007--6d2f87a305b30d5704aaf4be9b8ff1e6.pdf/MD5E-s2850007--6d2f87a305b30d5704aaf4be9b8ff1e6.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfMRIQC.csv\u001b[0m -> \u001b[00m../../.git/annex/objects/2Z/Ff/MD5E-s21038--cbe73db3db1beb0a1977583cff2a724b.csv/MD5E-s21038--cbe73db3db1beb0a1977583cff2a724b.csv\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_group.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/Kq/xg/MD5E-s90712--7058c3db328fecb86303bc27a9ef0110.pdf/MD5E-s90712--7058c3db328fecb86303bc27a9ef0110.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-01.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/k2/vQ/MD5E-s1157925--e055f942b72b9aabad7a5e3d7b25b201.pdf/MD5E-s1157925--e055f942b72b9aabad7a5e3d7b25b201.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-02.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/X3/X6/MD5E-s1235840--cb32b7f8f1274af250b4f0fc15dacecb.pdf/MD5E-s1235840--cb32b7f8f1274af250b4f0fc15dacecb.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-03.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/Vp/0x/MD5E-s1228507--73ab1cc4cb27712892fcb10a0853ba7c.pdf/MD5E-s1228507--73ab1cc4cb27712892fcb10a0853ba7c.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-04.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/xk/jz/MD5E-s1252659--8ae6d1b02767c1ddb72dd7e6afefe696.pdf/MD5E-s1252659--8ae6d1b02767c1ddb72dd7e6afefe696.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-05.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/Zm/VJ/MD5E-s1258815--dd043691d548a501dd63d1aaf420e43c.pdf/MD5E-s1258815--dd043691d548a501dd63d1aaf420e43c.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-06.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/M5/gq/MD5E-s1247345--9c06bc69792b812ab8deffb01c6656c2.pdf/MD5E-s1247345--9c06bc69792b812ab8deffb01c6656c2.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-07.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/25/35/MD5E-s1229731--99cc64e99df0025ccb0341cd0dcf688b.pdf/MD5E-s1229731--99cc64e99df0025ccb0341cd0dcf688b.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-08.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/MX/vQ/MD5E-s1222308--e13c56f17109d3f142c9c4db60fea674.pdf/MD5E-s1222308--e13c56f17109d3f142c9c4db60fea674.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-09.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/90/0F/MD5E-s1265097--41a69211a0569413917ce3825eac95d6.pdf/MD5E-s1265097--41a69211a0569413917ce3825eac95d6.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-10.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/FZ/gq/MD5E-s1299358--12ccfc4a5f52b077b99481fe25aa8ef1.pdf/MD5E-s1299358--12ccfc4a5f52b077b99481fe25aa8ef1.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-11.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/MJ/mQ/MD5E-s1166014--3465ef6b18514d3cd361c0bffe2b73fc.pdf/MD5E-s1166014--3465ef6b18514d3cd361c0bffe2b73fc.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-12.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/xp/1f/MD5E-s1177325--6fe4937d5aa567fb5b3c3977362fc9af.pdf/MD5E-s1177325--6fe4937d5aa567fb5b3c3977362fc9af.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-13.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/4g/vW/MD5E-s1178873--96f341322d21e2bdeb709edc5b047df0.pdf/MD5E-s1178873--96f341322d21e2bdeb709edc5b047df0.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-14.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/5p/6X/MD5E-s1206987--729f64cf514c9103556c53ccb5430bc4.pdf/MD5E-s1206987--729f64cf514c9103556c53ccb5430bc4.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-15.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/m1/k9/MD5E-s1223617--9239a1c2d968ed18093b69d28fd9e654.pdf/MD5E-s1223617--9239a1c2d968ed18093b69d28fd9e654.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-16.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/jq/wP/MD5E-s1294856--5eb7ec97924a22c7e68fd95373694e7e.pdf/MD5E-s1294856--5eb7ec97924a22c7e68fd95373694e7e.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-17.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/0m/3Q/MD5E-s1238563--129db424a50b7889278024828c08c736.pdf/MD5E-s1238563--129db424a50b7889278024828c08c736.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-18.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/VF/Fm/MD5E-s1197868--3b23e8d53b11d98d49b1adf62ff559df.pdf/MD5E-s1197868--3b23e8d53b11d98d49b1adf62ff559df.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-19.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/Jj/m8/MD5E-s1164028--aea7dfa78e9be2e83a9b313f2ebdc4bd.pdf/MD5E-s1164028--aea7dfa78e9be2e83a9b313f2ebdc4bd.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-20.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/x1/ZQ/MD5E-s1292308--8869b1b640797a2be2aa03be69b89840.pdf/MD5E-s1292308--8869b1b640797a2be2aa03be69b89840.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-21.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/MG/zW/MD5E-s1216271--3d5c5ca0f8f4ba06b3289e197a40defd.pdf/MD5E-s1216271--3d5c5ca0f8f4ba06b3289e197a40defd.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-22.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/5m/pj/MD5E-s1142289--4f9e57d8bfe3d39881c43b959189d69f.pdf/MD5E-s1142289--4f9e57d8bfe3d39881c43b959189d69f.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-23.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/m7/Z2/MD5E-s1233046--7758914aecbf2b5d01cd0825952609be.pdf/MD5E-s1233046--7758914aecbf2b5d01cd0825952609be.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-24.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/mz/5m/MD5E-s1265224--c188bd88fc1c99308389f528ea4df71e.pdf/MD5E-s1265224--c188bd88fc1c99308389f528ea4df71e.pdf\u001b[0m\n",
"│ ├── \u001b[40;31;01mfunctional_sub-25.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/Mk/G6/MD5E-s1260984--1b16abcbbf55ccc7763f1d704d76628f.pdf/MD5E-s1260984--1b16abcbbf55ccc7763f1d704d76628f.pdf\u001b[0m\n",
"│ └── \u001b[40;31;01mfunctional_sub-26.pdf\u001b[0m -> \u001b[00m../../.git/annex/objects/1m/zq/MD5E-s1285726--6838f727d5c4b5593a7b5e0e6b20483a.pdf/MD5E-s1285726--6838f727d5c4b5593a7b5e0e6b20483a.pdf\u001b[0m\n",
"├── \u001b[00mparticipants.tsv\u001b[0m\n",
"├── \u001b[01;34msub-01\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-01_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Pf/6k/MD5E-s10581116--757e697a01eeea5c97a7d6fbc7153373.nii.gz/MD5E-s10581116--757e697a01eeea5c97a7d6fbc7153373.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-01_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/5m/w9/MD5E-s28061534--8e8c44ff53f9b5d46f2caae5916fa4ef.nii.gz/MD5E-s28061534--8e8c44ff53f9b5d46f2caae5916fa4ef.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-01_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-01_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/2F/58/MD5E-s28143286--f0bcf782c3688e2cf7149b4665949484.nii.gz/MD5E-s28143286--f0bcf782c3688e2cf7149b4665949484.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-01_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-02\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-02_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/3m/FF/MD5E-s10737123--cbd4181ee26559e8ec0a441fa2f834a7.nii.gz/MD5E-s10737123--cbd4181ee26559e8ec0a441fa2f834a7.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-02_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/8v/2j/MD5E-s29188378--80050f0deb13562c24f2fc23f8d095bd.nii.gz/MD5E-s29188378--80050f0deb13562c24f2fc23f8d095bd.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-02_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-02_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/fM/Kw/MD5E-s29193540--cc013f2d7d148b448edca8aada349d02.nii.gz/MD5E-s29193540--cc013f2d7d148b448edca8aada349d02.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-02_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-03\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-03_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/7W/9z/MD5E-s10707026--8f1858934cc7c7457e3a4a71cc2131fc.nii.gz/MD5E-s10707026--8f1858934cc7c7457e3a4a71cc2131fc.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-03_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/q6/kF/MD5E-s28755729--b19466702eee6b9385bd6e19e362f94c.nii.gz/MD5E-s28755729--b19466702eee6b9385bd6e19e362f94c.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-03_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-03_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/zV/K1/MD5E-s28782544--8d9700a435d08c90f0c1d534efdc8b69.nii.gz/MD5E-s28782544--8d9700a435d08c90f0c1d534efdc8b69.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-03_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-04\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-04_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/FW/14/MD5E-s10738444--2a9a2ba4ea7d2324c84bf5a2882f196c.nii.gz/MD5E-s10738444--2a9a2ba4ea7d2324c84bf5a2882f196c.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-04_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/9Z/0Q/MD5E-s29062799--27171406951ea275cb5857ea0dc32345.nii.gz/MD5E-s29062799--27171406951ea275cb5857ea0dc32345.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-04_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-04_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/FW/FZ/MD5E-s29071279--f89b61fe3ebab26df1374f2564bd95c2.nii.gz/MD5E-s29071279--f89b61fe3ebab26df1374f2564bd95c2.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-04_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-05\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-05_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/k2/Kj/MD5E-s10753867--c4b5788da5f4c627f0f5862da5f46c35.nii.gz/MD5E-s10753867--c4b5788da5f4c627f0f5862da5f46c35.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-05_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/VZ/z5/MD5E-s29667270--0ce9ac78b6aa9a77fc94c655a6ff5a06.nii.gz/MD5E-s29667270--0ce9ac78b6aa9a77fc94c655a6ff5a06.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-05_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-05_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/z7/MP/MD5E-s29660544--752750dabb21e2cf28e87d1d550a71b9.nii.gz/MD5E-s29660544--752750dabb21e2cf28e87d1d550a71b9.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-05_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-06\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-06_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/5w/G0/MD5E-s10620585--1132eab3830fe59b8a10b6582bb49004.nii.gz/MD5E-s10620585--1132eab3830fe59b8a10b6582bb49004.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-06_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/3x/qj/MD5E-s29386982--e671c0c647ce7d0d4596e35b702ee970.nii.gz/MD5E-s29386982--e671c0c647ce7d0d4596e35b702ee970.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-06_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-06_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/9j/6P/MD5E-s29379265--e513a2746d2b5c603f96044cf48c557c.nii.gz/MD5E-s29379265--e513a2746d2b5c603f96044cf48c557c.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-06_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-07\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-07_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/08/fF/MD5E-s10718092--38481fbc489dfb1ec4b174b57591a074.nii.gz/MD5E-s10718092--38481fbc489dfb1ec4b174b57591a074.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-07_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/z1/7W/MD5E-s28946009--5baf7a314874b280543fc0f91f2731af.nii.gz/MD5E-s28946009--5baf7a314874b280543fc0f91f2731af.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-07_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-07_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Jf/W7/MD5E-s28960603--682e13963bfc49cc6ae05e9ba5c62619.nii.gz/MD5E-s28960603--682e13963bfc49cc6ae05e9ba5c62619.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-07_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-08\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[01;36msub-08_T1w.nii.gz\u001b[0m -> \u001b[01;31m../../.git/annex/objects/mw/MM/MD5E-s10561256--b94dddd8dc1c146aa8cd97f8d9994146.nii.gz/MD5E-s10561256--b94dddd8dc1c146aa8cd97f8d9994146.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[01;36msub-08_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[01;31m../../.git/annex/objects/zX/v9/MD5E-s28641609--47314e6d1a14b8545686110b5b67f8b8.nii.gz/MD5E-s28641609--47314e6d1a14b8545686110b5b67f8b8.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-08_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[01;36msub-08_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[01;31m../../.git/annex/objects/WZ/F0/MD5E-s28636310--4535bf26281e1c5556ad0d3468e7fe4e.nii.gz/MD5E-s28636310--4535bf26281e1c5556ad0d3468e7fe4e.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-08_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-09\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-09_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/QJ/ZZ/MD5E-s10775967--e6a18e64bc0a6b17254a9564cf9b8f82.nii.gz/MD5E-s10775967--e6a18e64bc0a6b17254a9564cf9b8f82.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-09_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/k9/1X/MD5E-s29200533--59e86a903e0ab3d1d320c794ba1f0777.nii.gz/MD5E-s29200533--59e86a903e0ab3d1d320c794ba1f0777.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-09_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-09_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/W3/94/MD5E-s29223017--7f3fb9e260d3bd28e29b0b586ce4c344.nii.gz/MD5E-s29223017--7f3fb9e260d3bd28e29b0b586ce4c344.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-09_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-10\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-10_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/5F/3f/MD5E-s10750712--bde2309077bffe22cb65e42ebdce5bfa.nii.gz/MD5E-s10750712--bde2309077bffe22cb65e42ebdce5bfa.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-10_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/3p/qp/MD5E-s29732696--339715d5cec387f4d44dfe94f304a429.nii.gz/MD5E-s29732696--339715d5cec387f4d44dfe94f304a429.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-10_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-10_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/11/Zx/MD5E-s29724034--16f2bf452524a315182f188becc1866d.nii.gz/MD5E-s29724034--16f2bf452524a315182f188becc1866d.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-10_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-11\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-11_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/kj/xX/MD5E-s10534963--9e5bff7ec0b5df2850e1d05b1af281ba.nii.gz/MD5E-s10534963--9e5bff7ec0b5df2850e1d05b1af281ba.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-11_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/35/fk/MD5E-s28226875--d5012074c2c7a0a394861b010bcf9a8f.nii.gz/MD5E-s28226875--d5012074c2c7a0a394861b010bcf9a8f.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-11_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-11_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/j7/ff/MD5E-s28198976--c0a64e3b549568c44bb40b1588027c9a.nii.gz/MD5E-s28198976--c0a64e3b549568c44bb40b1588027c9a.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-11_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-12\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-12_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/kx/2F/MD5E-s10550168--a7f651adc817b6678148b575654532a4.nii.gz/MD5E-s10550168--a7f651adc817b6678148b575654532a4.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-12_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/M0/fX/MD5E-s28403807--f1c3eb2e519020f4315a696ea845fc01.nii.gz/MD5E-s28403807--f1c3eb2e519020f4315a696ea845fc01.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-12_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-12_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/vW/V0/MD5E-s28424992--8740628349be3c056a0411bf4a852b25.nii.gz/MD5E-s28424992--8740628349be3c056a0411bf4a852b25.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-12_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-13\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-13_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/wM/Xw/MD5E-s10609761--440413c3251d182086105649164222c6.nii.gz/MD5E-s10609761--440413c3251d182086105649164222c6.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-13_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/mf/M4/MD5E-s28180916--aa35f4ad0cf630d6396a8a2dd1f3dda6.nii.gz/MD5E-s28180916--aa35f4ad0cf630d6396a8a2dd1f3dda6.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-13_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-13_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/XP/76/MD5E-s28202786--8caf1ac548c87b2b35f85e8ae2bf72c1.nii.gz/MD5E-s28202786--8caf1ac548c87b2b35f85e8ae2bf72c1.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-13_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-14\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-14_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Zw/0z/MD5E-s9223596--33abfb5da565f3487e3a7aebc15f940c.nii.gz/MD5E-s9223596--33abfb5da565f3487e3a7aebc15f940c.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-14_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Jp/29/MD5E-s29001492--250f1e4daa9be1d95e06af0d56629cc9.nii.gz/MD5E-s29001492--250f1e4daa9be1d95e06af0d56629cc9.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-14_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-14_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/PK/V2/MD5E-s29068193--5621a3b0af8132c509420b4ad9aaf8fb.nii.gz/MD5E-s29068193--5621a3b0af8132c509420b4ad9aaf8fb.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-14_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-15\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-15_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Mz/qq/MD5E-s10752891--ddd2622f115ec0d29a0c7ab2366f6f95.nii.gz/MD5E-s10752891--ddd2622f115ec0d29a0c7ab2366f6f95.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-15_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/08/JJ/MD5E-s28285239--feda22c4526af1910fcee58d4c42f07e.nii.gz/MD5E-s28285239--feda22c4526af1910fcee58d4c42f07e.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-15_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-15_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/9f/0W/MD5E-s28289760--433000a1def662e72d8433dba151c61b.nii.gz/MD5E-s28289760--433000a1def662e72d8433dba151c61b.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-15_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-16\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-16_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/4g/8k/MD5E-s10927450--a196f7075c793328dd6ff3cebf36ea6b.nii.gz/MD5E-s10927450--a196f7075c793328dd6ff3cebf36ea6b.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-16_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/9z/g2/MD5E-s29757991--1a1648b2fa6cc74e31c94f109d8137ba.nii.gz/MD5E-s29757991--1a1648b2fa6cc74e31c94f109d8137ba.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-16_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-16_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/k8/4F/MD5E-s29773832--fe08739ea816254395b985ee704aaa99.nii.gz/MD5E-s29773832--fe08739ea816254395b985ee704aaa99.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-16_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-17\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-17_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/jQ/MQ/MD5E-s10826014--8e2a6b062df4d1c4327802f2b905ef36.nii.gz/MD5E-s10826014--8e2a6b062df4d1c4327802f2b905ef36.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-17_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Wz/2P/MD5E-s28991563--9845f461a017a39d1f6e18baaa0c9c41.nii.gz/MD5E-s28991563--9845f461a017a39d1f6e18baaa0c9c41.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-17_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-17_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/jF/3m/MD5E-s29057821--84ccc041163bcc5b3a9443951e2a5a78.nii.gz/MD5E-s29057821--84ccc041163bcc5b3a9443951e2a5a78.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-17_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-18\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-18_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/3v/pK/MD5E-s10571510--6fc4b5792bc50ea4d14eb5247676fafe.nii.gz/MD5E-s10571510--6fc4b5792bc50ea4d14eb5247676fafe.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-18_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/94/P2/MD5E-s28185776--5b3879ec6fc4bbe1e48efc64984f88cf.nii.gz/MD5E-s28185776--5b3879ec6fc4bbe1e48efc64984f88cf.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-18_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-18_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/qp/6K/MD5E-s28234699--58019d798a133e5d7806569374dd8160.nii.gz/MD5E-s28234699--58019d798a133e5d7806569374dd8160.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-18_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-19\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-19_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Zw/p8/MD5E-s8861893--d338005753d8af3f3d7bd8dc293e2a97.nii.gz/MD5E-s8861893--d338005753d8af3f3d7bd8dc293e2a97.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-19_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/04/k6/MD5E-s28178448--3874e748258cf19aa69a05a7c37ad137.nii.gz/MD5E-s28178448--3874e748258cf19aa69a05a7c37ad137.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-19_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-19_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/mz/P4/MD5E-s28190932--91e6b3e4318ca28f01de8cb967cf8421.nii.gz/MD5E-s28190932--91e6b3e4318ca28f01de8cb967cf8421.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-19_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-20\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-20_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/g1/FF/MD5E-s11025608--5929806a7aa5720fc755687e1450b06c.nii.gz/MD5E-s11025608--5929806a7aa5720fc755687e1450b06c.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-20_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/v5/ZJ/MD5E-s29931631--bf9abb057367ce66961f0b7913e8e707.nii.gz/MD5E-s29931631--bf9abb057367ce66961f0b7913e8e707.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-20_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-20_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/J3/KW/MD5E-s29945590--96cfd5b77cd096f6c6a3530015fea32d.nii.gz/MD5E-s29945590--96cfd5b77cd096f6c6a3530015fea32d.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-20_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-21\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-21_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/K6/6K/MD5E-s8662805--77b262ddd929fa08d78591bfbe558ac6.nii.gz/MD5E-s8662805--77b262ddd929fa08d78591bfbe558ac6.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-21_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Wz/p9/MD5E-s28756041--9ae556d4e3042532d25af5dc4ab31840.nii.gz/MD5E-s28756041--9ae556d4e3042532d25af5dc4ab31840.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-21_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-21_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/xF/M3/MD5E-s28758438--81866411fc6b6333ec382a20ff0be718.nii.gz/MD5E-s28758438--81866411fc6b6333ec382a20ff0be718.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-21_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-22\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-22_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/JG/ZV/MD5E-s9282392--9e7296a6a5b68df46b77836182b6681a.nii.gz/MD5E-s9282392--9e7296a6a5b68df46b77836182b6681a.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-22_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/qW/Gw/MD5E-s28002098--c6bea10177a38667ceea3261a642b3c6.nii.gz/MD5E-s28002098--c6bea10177a38667ceea3261a642b3c6.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-22_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-22_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/VX/Zj/MD5E-s28027568--b34d0df9ad62485aba25296939429885.nii.gz/MD5E-s28027568--b34d0df9ad62485aba25296939429885.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-22_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-23\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-23_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/4Z/4x/MD5E-s10626062--db5a6ba6730b319c6425f2e847ce9b14.nii.gz/MD5E-s10626062--db5a6ba6730b319c6425f2e847ce9b14.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-23_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/VK/8F/MD5E-s28965005--4a9a96d9322563510ca14439e7fd6cea.nii.gz/MD5E-s28965005--4a9a96d9322563510ca14439e7fd6cea.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-23_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-23_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/56/20/MD5E-s29050413--753b0d2c23c4af6592501219c2e2c6bd.nii.gz/MD5E-s29050413--753b0d2c23c4af6592501219c2e2c6bd.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-23_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-24\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-24_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/jQ/fV/MD5E-s10739691--458f0046eff18ee8c43456637766a819.nii.gz/MD5E-s10739691--458f0046eff18ee8c43456637766a819.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-24_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/km/fV/MD5E-s29354610--29ebfa60e52d49f7dac6814cb5fdc2bc.nii.gz/MD5E-s29354610--29ebfa60e52d49f7dac6814cb5fdc2bc.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-24_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-24_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Wj/KK/MD5E-s29423307--fedaa1d7c6e34420735bb3bbe5a2fe38.nii.gz/MD5E-s29423307--fedaa1d7c6e34420735bb3bbe5a2fe38.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-24_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-25\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-25_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Gk/FQ/MD5E-s8998578--f560d832f13e757b485c16d570bf6ebc.nii.gz/MD5E-s8998578--f560d832f13e757b485c16d570bf6ebc.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-25_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/XW/1v/MD5E-s29473003--49b04e7e4b450ec5ef93ff02d4158775.nii.gz/MD5E-s29473003--49b04e7e4b450ec5ef93ff02d4158775.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-25_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-25_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/Qm/M7/MD5E-s29460132--b0e9039e9f33510631f229c8c2193285.nii.gz/MD5E-s29460132--b0e9039e9f33510631f229c8c2193285.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-25_task-flanker_run-2_events.tsv\u001b[0m\n",
"├── \u001b[01;34msub-26\u001b[0m\n",
"│ ├── \u001b[01;34manat\u001b[0m\n",
"│ │ └── \u001b[40;31;01msub-26_T1w.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/kf/9F/MD5E-s10850250--5f103b2660f488e4afa193f9307c1291.nii.gz/MD5E-s10850250--5f103b2660f488e4afa193f9307c1291.nii.gz\u001b[0m\n",
"│ └── \u001b[01;34mfunc\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-26_task-flanker_run-1_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/QV/10/MD5E-s30127491--8e30aa4bbfcc461bac8598bf621283c5.nii.gz/MD5E-s30127491--8e30aa4bbfcc461bac8598bf621283c5.nii.gz\u001b[0m\n",
"│ ├── \u001b[00msub-26_task-flanker_run-1_events.tsv\u001b[0m\n",
"│ ├── \u001b[40;31;01msub-26_task-flanker_run-2_bold.nii.gz\u001b[0m -> \u001b[00m../../.git/annex/objects/3G/Q6/MD5E-s30162480--80fd132e7cb1600ab248249e78f6f1aa.nii.gz/MD5E-s30162480--80fd132e7cb1600ab248249e78f6f1aa.nii.gz\u001b[0m\n",
"│ └── \u001b[00msub-26_task-flanker_run-2_events.tsv\u001b[0m\n",
"└── \u001b[00mtask-flanker_bold.json\u001b[0m\n",
"\n",
"80 directories, 192 files\n"
]
}
],
"source": [
"!tree -L 4 ds000102"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"### Inspecting the anatomical and functional images\n",
"\n",
"To use the AFNI graphical user interface, you would type:\n",
"```javascript\n",
"afni sub-08_T1w.nii.gz\n",
"```\n",
"We will use ipyniivue to look at the data:\n",
"\n",
"Many of the quality checks for the functional images are similar to those for the anatomical images. Look out for excessively bright or dark spots in the grey or white matter, as well as any image distortions, such as abnormal stretching or warping. A common area where slight distortion may occur is in the orbitofrontal region, just above the eyeballs.\n",
"\n",
"Additionally, it’s important to check for excessive motion during the scan."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"anat_image = './ds000102/sub-08/anat/sub-08_T1w.nii.gz'\n",
"func1_image = './ds000102/sub-08/func/sub-08_task-flanker_run-1_bold.nii.gz'\n",
"func2_image = './ds000102/sub-08/func/sub-08_task-flanker_run-2_bold.nii.gz'"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e5a20c63367549709a42c1573e9856ec",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"NiiVue(height=300)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# anatomical image\n",
"nv = NiiVue()\n",
"nv.load_volumes([{\"path\": anat_image}])\n",
"nv"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c3c2f757200d4bb4a0d9ffc48dad8e51",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"NiiVue(height=300)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# functional run 1\n",
"nv = NiiVue()\n",
"nv.load_volumes([{\"path\": func1_image}])\n",
"nv"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"## AFNI Commands and Preprocessing"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"We will be working with the data from subject sub-08. First, we'll introduce basic AFNI commands. Then, we’ll create a processing script using ```afni_proc.py```, which automates the entire preprocessing workflow. AFNI also provides graphical user interfaces (GUIs), such as ```uber_subject.py``` and ```uber_ttest.py``` (for group analysis), which help build processing scripts by combining many commands in the correct order. However, since GUIs cannot be used within a Jupyter Notebook environment, we will focus on writing the scripts directly.\n",
"\n",
"After setting up the automated script, we'll walk through each preprocessing step individually and discuss how to check data quality before and after each step.\n",
"\n",
"- 1: AFNI Commands\n",
" \n",
"- 2: Preprocessing with ```afni_prc.py``` \n",
" - Registration and Normalization \n",
" - Alignment and Motion Correction\n",
" - Smoothing\n",
" - Masking and Scaling\n",
" - Checking Preprocessing"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"### 1. AFNI commands\n",
"AFNI commands typicallly require at least one argument, or input, and they also usually require you to specify what to call the output of the command.\n",
"\n",
"Let’s take skull-stripping, for example - a common preprocessing step that removes the skull from the brain. The AFNI command to do this step is called 3dSkullStrip. Use the ```-h``` flag to print the help page."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"editable": true,
"scrolled": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"scroll-output"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Usage: A program to extract the brain from surrounding.\n",
" tissue from MRI T1-weighted images. \n",
" The simplest command would be:\n",
" 3dSkullStrip <-input DSET>\n",
"\n",
" Also consider the script @SSwarper, which combines the use of\n",
" 3dSkullStrip and nonlinear warping to an MNI template to produce\n",
" a skull-stripped dataset in MNI space, plus the nonlinear warp\n",
" that can used to transform other datasets from the same subject\n",
" (e.g., EPI) to MNI space. (This script only applies to human brain\n",
" images.)\n",
"\n",
" The fully automated process consists of three steps:\n",
" 1- Preprocessing of volume to remove gross spatial image \n",
" non-uniformity artifacts and reposition the brain in\n",
" a reasonable manner for convenience.\n",
" ** Note that in many cases, using 3dUnifize before **\n",
" ** using 3dSkullStrip will give better results. **\n",
" 2- Expand a spherical surface iteratively until it envelopes\n",
" the brain. This is a modified version of the BET algorithm:\n",
" Fast robust automated brain extraction, \n",
" by Stephen M. Smith, HBM 2002 v 17:3 pp 143-155\n",
" Modifications include the use of:\n",
" . outer brain surface\n",
" . expansion driven by data inside and outside the surface\n",
" . avoidance of eyes and ventricles\n",
" . a set of operations to avoid the clipping of certain brain\n",
" areas and reduce leakage into the skull in heavily shaded\n",
" data\n",
" . two additional processing stages to ensure convergence and\n",
" reduction of clipped areas.\n",
" . use of 3d edge detection, see Deriche and Monga references\n",
" in 3dedge3 -help.\n",
" 3- The creation of various masks and surfaces modeling brain\n",
" and portions of the skull\n",
"\n",
" Common examples of usage:\n",
" -------------------------\n",
" o 3dSkullStrip -input VOL -prefix VOL_PREFIX\n",
" Vanilla mode, should work for most datasets.\n",
" o 3dSkullStrip -input VOL -prefix VOL_PREFIX -push_to_edge\n",
" Adds an agressive push to brain edges. Use this option\n",
" when the chunks of gray matter are not included. This option\n",
" might cause the mask to leak into non-brain areas.\n",
" o 3dSkullStrip -input VOL -surface_coil -prefix VOL_PREFIX -monkey\n",
" Vanilla mode, for use with monkey data.\n",
" o 3dSkullStrip -input VOL -prefix VOL_PREFIX -ld 30\n",
" Use a denser mesh, in the cases where you have lots of \n",
" csf between gyri. Also helps when some of the brain is clipped\n",
" close to regions of high curvature.\n",
"\n",
" Tips:\n",
" -----\n",
" I ran the program with the default parameters on 200+ datasets.\n",
" The results were quite good in all but a couple of instances, here\n",
" are some tips on fixing trouble spots:\n",
"\n",
" Clipping in frontal areas, close to the eye balls:\n",
" + Try -push_to_edge option first.\n",
" Can also try -no_avoid_eyes option.\n",
" Clipping in general:\n",
" + Try -push_to_edge option first.\n",
" Can also use lower -shrink_fac, start with 0.5 then 0.4\n",
" Problems down below:\n",
" + Piece of cerebellum missing, reduce -shrink_fac_bot_lim \n",
" from default value.\n",
" + Leakage in lower areas, increase -shrink_fac_bot_lim \n",
" from default value.\n",
" Some lobules are not included:\n",
" + Use a denser mesh. Start with -ld 30. If that still fails,\n",
" try even higher density (like -ld 50) and increase iterations \n",
" (say to -niter 750). \n",
" Expect the program to take much longer in that case.\n",
" + Instead of using denser meshes, you could try blurring the data \n",
" before skull stripping. Something like -blur_fwhm 2 did\n",
" wonders for some of my data with the default options of 3dSkullStrip\n",
" Blurring is a lot faster than increasing mesh density.\n",
" + Use also a smaller -shrink_fac is you have lots of CSF between\n",
" gyri.\n",
" Massive chunks missing:\n",
" + If brain has very large ventricles and lots of CSF between gyri,\n",
" the ventricles will keep attracting the surface inwards. \n",
" This often happens with older brains. In such \n",
" cases, use the -visual option to see what is happening.\n",
" For example, the options below did the trick in various\n",
" instances. \n",
" -blur_fwhm 2 -use_skull \n",
" or for more stubborn cases increase csf avoidance with this cocktail\n",
" -blur_fwhm 2 -use_skull -avoid_vent -avoid_vent -init_radius 75 \n",
" + Too much neck in the volume might throw off the initialization\n",
" step. You can fix this by clipping tissue below the brain with \n",
" @clip_volume -below ZZZ -input INPUT \n",
" where ZZZ is a Z coordinate somewhere below the brain.\n",
"\n",
" Large regions outside brain included:\n",
" + Usually because noise level is high. Try @NoisySkullStrip.\n",
"\n",
" Make sure that brain orientation is correct. This means the image in \n",
" AFNI's axial slice viewer should be close to the brain's axial plane.\n",
" The same goes for the other planes. Otherwise, the program might do a lousy\n",
" job removing the skull.\n",
"\n",
" Eye Candy Mode: \n",
" ---------------\n",
" You can run 3dSkullStrip and have it send successive iterations\n",
" to SUMA and AFNI. This is very helpful in following the\n",
" progression of the algorithm and determining the source\n",
" of trouble, if any.\n",
" Example:\n",
" afni -niml -yesplugouts &\n",
" suma -niml &\n",
" 3dSkullStrip -input Anat+orig -o_ply anat_brain -visual\n",
"\n",
" Help section for the intrepid:\n",
" ------------------------------\n",
" 3dSkullStrip < -input VOL >\n",
" [< -o_TYPE PREFIX >] [< -prefix VOL_PREFIX >] \n",
" [< -spatnorm >] [< -no_spatnorm >] [< -write_spatnorm >]\n",
" [< -niter N_ITER >] [< -ld LD >] \n",
" [< -shrink_fac SF >] [< -var_shrink_fac >] \n",
" [< -no_var_shrink_fac >] [< -shrink_fac_bot_lim SFBL >]\n",
" [< -pushout >] [< -no_pushout >] [< -exp_frac FRAC]\n",
" [< -touchup >] [< -no_touchup >]\n",
" [< -fill_hole R >] [< -NN_smooth NN_SM >]\n",
" [< -smooth_final SM >] [< -avoid_vent >] [< -no_avoid_vent >]\n",
" [< -use_skull >] [< -no_use_skull >] \n",
" [< -avoid_eyes >] [< -no_avoid_eyes >] \n",
" [< -use_edge >] [< -no_use_edge >] \n",
" [< -push_to_edge >] [<-no_push_to_edge>]\n",
" [< -perc_int PERC_INT >] \n",
" [< -max_inter_iter MII >] [-mask_vol | -orig_vol | -norm_vol]\n",
" [< -debug DBG >] [< -node_debug NODE_DBG >]\n",
" [< -demo_pause >]\n",
" [< -monkey >] [< -marmoset >] [<-rat>]\n",
"\n",
" NOTE: Please report bugs and strange failures\n",
" to saadz@mail.nih.gov\n",
"\n",
" Mandatory parameters:\n",
" -input VOL: Input AFNI (or AFNI readable) volume.\n",
" \n",
"\n",
" Optional Parameters:\n",
" -monkey: the brain of a monkey.\n",
" -marmoset: the brain of a marmoset. \n",
" this one was tested on one dataset\n",
" and may not work with non default\n",
" options. Check your results!\n",
" -rat: the brain of a rat.\n",
" By default, no_touchup is used with the rat.\n",
" -surface_coil: Data acquired with a surface coil.\n",
" -o_TYPE PREFIX: prefix of output surface.\n",
" where TYPE specifies the format of the surface\n",
" and PREFIX is, well, the prefix.\n",
" TYPE is one of: fs, 1d (or vec), sf, ply.\n",
" More on that below.\n",
" -skulls: Output surface models of the skull.\n",
" -4Tom: The output surfaces are named based\n",
" on PREFIX following -o_TYPE option below.\n",
" -prefix VOL_PREFIX: prefix of output volume.\n",
" If not specified, the prefix is the same\n",
" as the one used with -o_TYPE.\n",
" The output volume is skull stripped version\n",
" of the input volume. In the earlier version\n",
" of the program, a mask volume was written out.\n",
" You can still get that mask volume instead of the\n",
" skull-stripped volume with the option -mask_vol . \n",
" NOTE: In the default setting, the output volume does not \n",
" have values identical to those in the input. \n",
" In particular, the range might be larger \n",
" and some low-intensity values are set to 0.\n",
" If you insist on having the same range of values as in\n",
" the input, then either use option -orig_vol, or run:\n",
" 3dcalc -nscale -a VOL+VIEW -b VOL_PREFIX+VIEW \\\n",
" -expr 'a*step(b)' -prefix VOL_SAME_RANGE\n",
" With the command above, you can preserve the range\n",
" of values of the input but some low-intensity voxels would\n",
" still be masked. If you want to preserve them, then use\n",
" -mask_vol in the 3dSkullStrip command that would produce \n",
" VOL_MASK_PREFIX+VIEW. Then run 3dcalc masking with voxels\n",
" inside the brain surface envelope:\n",
" 3dcalc -nscale -a VOL+VIEW -b VOL_MASK_PREFIX+VIEW \\\n",
" -expr 'a*step(b-3.01)' -prefix VOL_SAME_RANGE_KEEP_LOW\n",
" -norm_vol: Output a masked and somewhat intensity normalized and \n",
" thresholded version of the input. This is the default,\n",
" and you can use -orig_vol to override it.\n",
" -orig_vol: Output a masked version of the input AND do not modify\n",
" the values inside the brain as -norm_vol would.\n",
" -mask_vol: Output a mask volume instead of a skull-stripped\n",
" volume.\n",
" The mask volume containes:\n",
" 0: Voxel outside surface\n",
" 1: Voxel just outside the surface. This means the voxel\n",
" center is outside the surface but inside the \n",
" bounding box of a triangle in the mesh. \n",
" 2: Voxel intersects the surface (a triangle), but center\n",
" lies outside.\n",
" 3: Voxel contains a surface node.\n",
" 4: Voxel intersects the surface (a triangle), center lies\n",
" inside surface. \n",
" 5: Voxel just inside the surface. This means the voxel\n",
" center is inside the surface and inside the \n",
" bounding box of a triangle in the mesh. \n",
" 6: Voxel inside the surface. \n",
" -spat_norm: (Default) Perform spatial normalization first.\n",
" This is a necessary step unless the volume has\n",
" been 'spatnormed' already.\n",
" -no_spatnorm: Do not perform spatial normalization.\n",
" Use this option only when the volume \n",
" has been run through the 'spatnorm' process\n",
" -spatnorm_dxyz DXYZ: Use DXY for the spatial resolution of the\n",
" spatially normalized volume. The default \n",
" is the lowest of all three dimensions.\n",
" For human brains, use DXYZ of 1.0, for\n",
" primate brain, use the default setting.\n",
" -write_spatnorm: Write the 'spatnormed' volume to disk.\n",
" -niter N_ITER: Number of iterations. Default is 250\n",
" For denser meshes, you need more iterations\n",
" N_ITER of 750 works for LD of 50.\n",
" -ld LD: Parameter to control the density of the surface.\n",
" Default is 20 if -no_use_edge is used,\n",
" 30 with -use_edge. See CreateIcosahedron -help\n",
" for details on this option.\n",
" -shrink_fac SF: Parameter controlling the brain vs non-brain\n",
" intensity threshold (tb). Default is 0.6.\n",
" tb = (Imax - t2) SF + t2 \n",
" where t2 is the 2 percentile value and Imax is the local\n",
" maximum, limited to the median intensity value.\n",
" For more information on tb, t2, etc. read the BET paper\n",
" mentioned above. Note that in 3dSkullStrip, SF can vary across \n",
" iterations and might be automatically clipped in certain areas.\n",
" SF can vary between 0 and 1.\n",
" 0: Intensities < median inensity are considered non-brain\n",
" 1: Intensities < t2 are considered non-brain\n",
" -var_shrink_fac: Vary the shrink factor with the number of\n",
" iterations. This reduces the likelihood of a surface\n",
" getting stuck on large pools of CSF before reaching\n",
" the outer surface of the brain. (Default)\n",
" -no_var_shrink_fac: Do not use var_shrink_fac.\n",
" -shrink_fac_bot_lim SFBL: Do not allow the varying SF to go\n",
" below SFBL . Default 0.65, 0.4 when edge detection is used. \n",
" This option helps reduce potential for leakage below \n",
" the cerebellum.\n",
" In certain cases where you have severe non-uniformity resulting\n",
" in low signal towards the bottom of the brain, you will need to\n",
" reduce this parameter.\n",
" -pushout: Consider values above each node in addition to values\n",
" below the node when deciding on expansion. (Default)\n",
" -no_pushout: Do not use -pushout.\n",
" -exp_frac FRAC: Speed of expansion (see BET paper). Default is 0.1.\n",
" -touchup: Perform touchup operations at end to include\n",
" areas not covered by surface expansion. \n",
" Use -touchup -touchup for aggressive makeup.\n",
" (Default is -touchup)\n",
" -no_touchup: Do not use -touchup\n",
" -fill_hole R: Fill small holes that can result from small surface\n",
" intersections caused by the touchup operation.\n",
" R is the maximum number of pixels on the side of a hole\n",
" that can be filled. Big holes are not filled.\n",
" If you use -touchup, the default R is 10. Otherwise \n",
" the default is 0.\n",
" This is a less than elegant solution to the small\n",
" intersections which are usually eliminated\n",
" automatically. \n",
" -NN_smooth NN_SM: Perform Nearest Neighbor coordinate interpolation\n",
" every few iterations. Default is 72\n",
" -smooth_final SM: Perform final surface smoothing after all iterations.\n",
" Default is 20 smoothing iterations.\n",
" Smoothing is done using Taubin's method, \n",
" see SurfSmooth -help for detail.\n",
" -avoid_vent: avoid ventricles. Default.\n",
" Use this option twice to make the avoidance more\n",
" agressive. That is at times needed with old brains.\n",
" -no_avoid_vent: Do not use -avoid_vent.\n",
" -init_radius RAD: Use RAD for the initial sphere radius.\n",
" For the automatic setting, there is an\n",
" upper limit of 100mm for humans.\n",
" For older brains with lots of CSF, you\n",
" might benefit from forcing the radius \n",
" to something like 75mm\n",
" -avoid_eyes: avoid eyes. Default\n",
" -no_avoid_eyes: Do not use -avoid_eyes.\n",
" -use_edge: Use edge detection to reduce leakage into meninges and eyes.\n",
" Default.\n",
" -no_use_edge: Do no use edges.\n",
" -push_to_edge: Perform aggressive push to edge at the end.\n",
" This option might cause leakage.\n",
" -no_push_to_edge: (Default).\n",
" -use_skull: Use outer skull to limit expansion of surface into\n",
" the skull due to very strong shading artifacts.\n",
" This option is buggy at the moment, use it only \n",
" if you have leakage into skull.\n",
" -no_use_skull: Do not use -use_skull (Default).\n",
" -send_no_skull: Do not send the skull surface to SUMA if you are\n",
" using -talk_suma\n",
" -perc_int PERC_INT: Percentage of segments allowed to intersect\n",
" surface. Ideally this should be 0 (Default). \n",
" However, few surfaces might have small stubborn\n",
" intersections that produce a few holes.\n",
" PERC_INT should be a small number, typically\n",
" between 0 and 0.1. A -1 means do not do\n",
" any testing for intersection.\n",
" -max_inter_iter N_II: Number of iteration to remove intersection\n",
" problems. With each iteration, the program\n",
" automatically increases the amount of smoothing\n",
" to get rid of intersections. Default is 4\n",
" -blur_fwhm FWHM: Blur dset after spatial normalization.\n",
" Recommended when you have lots of CSF in brain\n",
" and when you have protruding gyri (finger like)\n",
" Recommended value is 2..4. \n",
" -interactive: Make the program stop at various stages in the \n",
" segmentation process for a prompt from the user\n",
" to continue or skip that stage of processing.\n",
" This option is best used in conjunction with options\n",
" -talk_suma and -feed_afni\n",
" -demo_pause: Pause at various step in the process to facilitate\n",
" interactive demo while 3dSkullStrip is communicating\n",
" with AFNI and SUMA. See 'Eye Candy' mode below and\n",
" -talk_suma option. \n",
" -fac FAC: Multiply input dataset by FAC if range of values is too\n",
" small.\n",
"\n",
" -visual: Equivalent to using -talk_suma -feed_afni -send_kth 5\n",
"\n",
" -debug DBG: debug levels of 0 (default), 1, 2, 3.\n",
" This is no Rick Reynolds debug, which is oft nicer\n",
" than the results, but it will do.\n",
" -node_debug NODE_DBG: Output lots of parameters for node\n",
" NODE_DBG for each iteration.\n",
" The next 3 options are for specifying surface coordinates\n",
" to keep the program from having to recompute them.\n",
" The options are only useful for saving time during debugging.\n",
" -brain_contour_xyz_file BRAIN_CONTOUR_XYZ.1D\n",
" -brain_hull_xyz_file BRAIN_HULL_XYZ.1D\n",
" -skull_outer_xyz_file SKULL_OUTER_XYZ.1D\n",
" -help: The help you need\n",
"\n",
"\n",
"\n",
"Compile Date:\n",
" Jul 8 2021\n",
"\n",
" Ziad S. Saad SSCC/NIMH/NIH saadz@mail.nih.gov \n"
]
}
],
"source": [
"!3dSkullStrip -h"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One of AFNI’s major advantages is the quality of its documentation and help resources. Each command's usage is thoroughly described, and the purpose behind various options is clearly explained. Example commands are provided to illustrate how to handle different situations — for instance, if too much skull remains after skull-stripping, you might be advised to use a flag like ```-push_to_edge```.\n",
"\n",
"The simplest way to run ```3dSkullStrip``` is by using the ```-input``` option to specify the anatomical dataset for processing. The ```-prefix``` option is also used to output a NIfTI image for visualization with ipyniivue."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The intensity in the output dataset is a modified version\n",
"of the intensity in the input volume.\n",
"To obtain a masked version of the input with identical values inside\n",
"the brain, you can either use 3dSkullStrip's -orig_vol option\n",
"or run the following command:\n",
" 3dcalc -a ./ds000102/sub-08/anat/sub-08_T1w.nii.gz -b ./anat_ss.nii.gz+orig -expr 'a*step(b)' \\\n",
" -prefix ./anat_ss.nii.gz_orig_vol\n",
"to generate a new masked version of the input.\n"
]
}
],
"source": [
"!3dSkullStrip -input $anat_image -prefix anat_ss.nii.gz "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c19a118ee25448dda1eecc2dba1d801c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"NiiVue(height=300)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"volumes = [{\"path\": anat_image}, \n",
" {\"path\": \"./anat_ss.nii.gz\", \"colormap\": \"red\"}]\n",
"\n",
"nv = NiiVue()\n",
"nv.load_volumes(volumes) \n",
"nv"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"### 2. Preprocessing\n",
"To automate preprocessing in AFNI, the powerful ```afni_proc.py``` tool is used. This command generates a fully customizable tcsh script that includes all necessary preprocessing steps, from slice timing correction to scaling.\n",
"\n",
"The basic idea is:\n",
"\n",
"- Specify which processing blocks to apply.\n",
"\n",
"- ```afni_proc.py``` writes a script to carry them out in the correct order.\n",
"\n",
"- The generated script can be reviewed and run, or modified if needed.\n",
" \n",
"\n",
"The processing blocks include both automatic steps (such as setup and initial time concatenation) and default blocks that you can customize, skip, or reorder. For example:\n",
"\n",
"\n",
"- **Automatic blocks**:\n",
"\n",
"-```setup```: Set up subject information, create output directory (subj_id, script, out_dir)\n",
"\n",
"-```tcat```: Remove unwanted initial TRs.\n",
"\n",
"- **Default blocks**:\n",
"\n",
"-```tshift```: Slice timing correction.\n",
"\n",
"-```volreg```: Volume registration (motion correction).\n",
"\n",
"-```blur```: Spatial smoothing.\n",
"\n",
"-```mask```: Create a brain mask from EPI data.\n",
"\n",
"-```scale```: Normalize voxel-wise signal intensities.\n",
"\n",
"-```regress```: (optional) Regression analysis for task-based designs.\n",
"\n",
"\n",
"A full list of options and help can be found in the [AFNI documentation](https://afni.nimh.nih.gov/pub/dist/doc/program_help/afni_proc.py.html).\n",
"\n",
"The following command specifies key preprocessing steps for ```sub-08```, including motion correction, normalization to MNI space, smoothing, and scaling. Additionally, the alignment option ```-giant_move``` is included, which allows for larger shifts and rotations during alignments.\n",
"\n",
"\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"#### Registration and Normalization\n",
"\n",
"Registration aligns the functional and anatomical images. It usually begins by assuming the images are roughly in the same space; if not, their outlines are aligned.\n",
"To fine-tune the alignment, the algorithm uses differences in contrast (mutual information), matching dark areas on one image with bright areas on the other. AFNI's preferred method is Local Pearson Correlation (LPC), which gives greater weight to brighter areas in the functional data.\n",
"Once a good alignment is found, the same transformations can later be applied to warp the functional images to a standard template.\n",
"\n",
"In this example, AFNI’s ```align_epi_anat.py``` command is used to perform the registration. Although this tool can also handle slice-timing correction and functional volume alignment, it will be used here only for registration\n",
"\n",
"The key options used are:\n",
"\n",
"- ```-anat2epi```: Aligns the anatomical image to the functional image (not the other way around), minimizing changes to the functional data.\n",
"\n",
"- ```-suffix``` _al_junk: Adds a suffix to intermediate files, which will be useful for later steps.\n",
"\n",
"- ```-epi```, ```-epi_base```, ```-epi_strip```: Chooses the functional volume with least variability as the reference and strips non-brain tissue using 3dAutomask.\n",
"\n",
"- ```-giant_move```: Helps find an initial rough alignment if the images are very misaligned.\n",
"\n",
"- ```-volreg off```, ```-tshift off```: Indicates that alignment and slice-timing correction are not included in the current command."
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"#### Registration with AFNI`s align_epi_anat.py\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"#### Normalization with AFNI's @auto_tlrc\n",
"\n",
"After registration, the anatomical image is normalized to a standard template (MNI_avg152T1) using AFNI’s ```@auto_tlrc``` command. Since the anatomical image has already been skull-stripped, the ```-no_ss``` option is used. Because the centers of the anatomical and template images were far apart, the `-init_xform AUTO_CENTER` option was included. \n",
"This helps by roughly aligning the centers of the two images before the fine registration step.\n",
"The transformation needed to align the anatomical image to the template is stored as an affine matrix in the header of the anatomical iamge.\n",
"This matrix is then extracted with the ```cat_matvec``` command and saved as ```warp.anat.Xat.1D```, so it can later be applied to the functional images as well."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
"#warp anatomy to standard space\n",
"@auto_tlrc -base MNI_avg152T1+tlrc -input sub-08_T1w_ns+orig -no_ss -init_xform AUTO_CENTER\n",
"\n",
"#store forward transformation matrix in a text file\n",
"cat_matvec sub-08_T1w_ns+tlrc::WARP_DATA -I > warp.anat.Xat.1D\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"#### Alignment and Motion Correction\n",
"Motion correction is done with AFNI’s ```3dvolreg``` command.\n",
"The functional volumes are aligned to a reference image, which is selected as the volume with the fewest outliers (identified using ```3dToutcount``` earlier).\n",
"Motion parameters are saved into a text file (```-1Dfile```) and the corresponding affine transformation matrices into another file (```-1Dmatrix_save```)."
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"
\n",
"
\n",
"# =================== volreg ===================\n",
" ...\n",
"#register and warp\n",
"foreach run ( $runs ) \n",
" \n",
"#register each volume to the base image\n",
" 3dvolreg -verbose -zpad 1 -base vr_base_min_outlier+orig \\n",
" -1Dfile dfile.r$run.1D -prefix rm.epi.volreg.r$run \\n",
" -cubic \\n",
" -1Dmatrix_save mat.r$run.vr.aff12.1D \\\n",
" pb01.$subj.r$run.tshift+orig\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"These motion correction matrices are then concatenated with the matrices from anatomical registration and normalization."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using ```3dAllineate``` and the ```-1Dmatrix_apply``` option, both motion correction and normalization are applied to the functional images in one step."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"#### Smoothing\n",
"Smoothing is done with AFNI’s ```3dmerge``` command, which can be found under the “blur” header.\n",
"\n",
"The ```-1blur_fwhm``` option applies spatial smoothing with a 4mm kernel, and ```-doall``` ensures smoothing is done across all volumes. After smoothing, the images are scaled to a mean intensity of 100, allowing changes to be interpreted as percent signal change. Finally, a brain mask is applied to remove non-brain voxels, preparing the data for statistical analysis."
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"
\n",
"
\n",
"# ==================== blur ====================\n",
"#blur each volume of each run \n",
"foreach run ( $runs )\n",
" 3dmerge -1blur_fwhm 4.0 -doall -prefix pb03.$subj.r$run.blur \\\n",
" pb02.$subj.r$run.volreg+tlrc\n",
"end\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"#### Masking and Scaling\n",
"##### Masking\n",
"\n",
"fMRI datasets include not only brain voxels but also irrelevant areas like the skull, neck, and air. To focus analysis and speed up processing, a mask is applied: voxels inside the mask are kept, and those outside are discarded. AFNI’s ```3dAutomask``` creates these masks by simply specifying the input and output datasets. The additional steps combine individual fMRI masks into a union mask, compute a mask for the anatomical image, and take the intersection of both to define the final analysis region."
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"
\n",
"
\n",
"# ==================== mask ====================\n",
"# create 'full_mask' dataset (union mask) \n",
" \n",
"foreach run ( $runs )\n",
" 3dAutomask -prefix rm.mask_r$run pb03.$subj.r$run.blur+tlrc\n",
"end\n",
"\n",
"#create union of inputs, output type is byte \n",
"3dmask_tool -inputs rm.mask_r*+tlrc.HEAD -union -prefix full_mask.$subj\n",
"\n",
"# ---- create subject anatomy mask ----\n",
"# (resampled from tlrc anat)\n",
"3dresample -master full_mask.$subj+tlrc -input sub-08_T1w_ns+tlrc \\\n",
" -prefix rm.resam.anat\n",
"\n",
"# convert to binary anat mask; fill gaps and holes\n",
"3dmask_tool -dilate_input 5 -5 -fill_holes -input rm.resam.anat+tlrc \\\n",
" -prefix mask_anat.$subj\n",
"\n",
"# compute tighter EPI mask by intersecting with anat mask\n",
"3dmask_tool -input full_mask.$subj+tlrc mask_anat.$subj+tlrc \\\n",
" -inter -prefix mask_epi_anat.$subj\n",
"\n",
"# compute overlaps between anat and EPI masks\n",
"3dABoverlap -no_automask full_mask.$subj+tlrc mask_anat.$subj+tlrc \\\n",
" |& tee out.mask_ae_overlap.txt\n",
"\n",
"# note Dice coefficient of masks, as well\n",
"3ddot -dodice full_mask.$subj+tlrc mask_anat.$subj+tlrc \\\n",
" |& tee out.mask_ae_dice.txt\n",
"\n",
"# ---- create group anatomy mask, mask_group+tlrc ----\n",
"# (resampled from tlrc base anat, MNI_avg152T1+tlrc)\n",
"3dresample -master full_mask.$subj+tlrc -prefix ./rm.resam.group \\\n",
" -input /opt/afni-latest/MNI_avg152T1+tlrc\n",
"\n",
"# convert to binary group mask; fill gaps and holes\n",
"3dmask_tool -dilate_input 5 -5 -fill_holes -input rm.resam.group+tlrc \\\n",
" -prefix mask_group\n",
"\n",
"# note Dice coefficient of anat and template masks\n",
"3ddot -dodice mask_anat.$subj+tlrc mask_group+tlrc \\\n",
" |& tee out.mask_at_dice.txt"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"##### Scaling\n",
"fMRI signal intensities are arbitrary and vary between subjects and runs. To make meaningful comparisons, AFNI scales each voxel's time series to a mean of 100, allowing for consistent signal intensity contrasts between conditions and across subjects."
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"
\n",
"
\n",
"# ==================== scale ====================\n",
"# scale each voxel time series to have a mean of 100\n",
"# (be sure no negatives creep in)\n",
"# (subject to a range of [0,200]) \n",
"foreach run ( $runs )\n",
" 3dTstat -prefix rm.mean_r$run pb03.$subj.r$run.blur+tlrc\n",
" 3dcalc -a pb03.$subj.r$run.blur+tlrc -b rm.mean_r$run+tlrc \\\n",
" -c mask_epi_extents+tlrc \\\n",
" -expr 'c * min(200, a/b*100)*step(a)*step(b)' \\\n",
" -prefix pb04.$subj.r$run.scale\n",
"end "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Effect of Scaling:** Before scaling, time-series values are arbitrary and vary between subjects. After scaling, each subject's data is normalized to the same mean, enabling consistent comparisons across runs and subjects. To visualize both the original and scaled data with Matplotlib, the AFNI format needs to be converted to NIfTI format using the ```3dAFNItonNIFI``` command.\n",
"The following plot compares the signal intensity time series from a voxel at the center of the brain, before and after scaling. The red dashed line indicates the mean signal intensity for each time series."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"++ 3dAFNItoNIFTI: AFNI version=AFNI_21.2.00 (Jul 8 2021) [64-bit]\n",
"\u001b[7m*+ WARNING:\u001b[0m varying brick factors, writing NIfTI as float\n",
"++ 3dAFNItoNIFTI: AFNI version=AFNI_21.2.00 (Jul 8 2021) [64-bit]\n",
"\u001b[7m*+ WARNING:\u001b[0m varying brick factors, writing NIfTI as float\n"
]
}
],
"source": [
"!3dAFNItoNIFTI -prefix ./afni_processing/sub_08.results/pb03.sub_08.r01.blur.nii.gz ./afni_processing/sub_08.results/pb03.sub_08.r01.blur+tlrc\n",
"!3dAFNItoNIFTI -prefix ./afni_processing/sub_08.results/pb04.sub_08.r01.scale.nii.gz ./afni_processing/sub_08.results/pb04.sub_08.r01.scale+tlrc"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"blur_nii = nib.load(\"./afni_processing/sub_08.results/pb03.sub_08.r01.blur.nii.gz\").get_fdata()\n",
"scale_nii = nib.load(\"./afni_processing/sub_08.results/pb04.sub_08.r01.scale.nii.gz\").get_fdata()\n",
"\n",
"# Choose a voxel (e.g., center)\n",
"x, y, z = blur_nii.shape[0]//2, blur_nii.shape[1]//2, blur_nii.shape[2]//2\n",
"time_series1 = blur_nii[x, y, z, :]\n",
"time_series2 = scale_nii[x, y, z, :]\n",
"n_timepoints = time_series1.shape[0]\n",
"\n",
"# Calculate the means of the time series\n",
"mean_time_series1 = time_series1.mean()\n",
"mean_time_series2 = time_series2.mean()\n",
"\n",
"# Create subplots\n",
"fig, axs = plt.subplots(2, 1, figsize=(10, 6), sharex=True)\n",
"\n",
"axs[0].plot(time_series1, color='blue')\n",
"axs[0].set_title(\"Signal intensity before scaling\")\n",
"axs[0].set_ylabel(\"Signal Intensity\")\n",
"axs[0].axhline(mean_time_series1, color='red', linestyle='--', linewidth=0.7, label=f\"Mean: {mean_time_series1:.2f}\")\n",
"axs[0].grid(True)\n",
"axs[0].legend()\n",
"axs[0].set_xlim(0, n_timepoints - 1)\n",
"\n",
"axs[1].plot(time_series2, color='orange')\n",
"axs[1].set_title(\"Signal intensity after scaling\")\n",
"axs[1].set_xlabel(\"Timepoint\")\n",
"axs[1].set_ylabel(\"Signal Intensity\")\n",
"axs[1].axhline(mean_time_series2, color='red', linestyle='--', linewidth=0.7, label=f\"Mean: {mean_time_series2:.2f}\")\n",
"axs[1].grid(True)\n",
"axs[1].legend()\n",
"axs[0].set_xlim(0, n_timepoints - 1)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
"## Results: Checking Preprocessing\n",
"Let's have a look at the results directory. The files containing the **pb** string are the preprocessed functional images at each preprocessing step, and the files with the **T1w** string are the preprocessed anatomical images. Auxiliary functional images are created to assist with specific preprocessing steps, and auxiliary text files contain information about transformation matrices and movement parameter:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"scroll-output"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"@epi_review.sub_08\t\t\t pb01.sub_08.r01.tshift+orig.HEAD\n",
"anat_final.sub_08+tlrc.BRIK\t\t pb01.sub_08.r02.tshift+orig.BRIK\n",
"anat_final.sub_08+tlrc.HEAD\t\t pb01.sub_08.r02.tshift+orig.HEAD\n",
"anat_w_skull_warped+tlrc.BRIK\t\t pb02.sub_08.r01.volreg+tlrc.BRIK\n",
"anat_w_skull_warped+tlrc.HEAD\t\t pb02.sub_08.r01.volreg+tlrc.HEAD\n",
"dfile.r01.1D\t\t\t\t pb02.sub_08.r02.volreg+tlrc.BRIK\n",
"dfile.r02.1D\t\t\t\t pb02.sub_08.r02.volreg+tlrc.HEAD\n",
"dfile_rall.1D\t\t\t\t pb03.sub_08.r01.blur+tlrc.BRIK\n",
"final_epi_vr_base_min_outlier+tlrc.BRIK pb03.sub_08.r01.blur+tlrc.HEAD\n",
"final_epi_vr_base_min_outlier+tlrc.HEAD pb03.sub_08.r01.blur.nii.gz\n",
"full_mask.sub_08+tlrc.BRIK\t\t pb03.sub_08.r02.blur+tlrc.BRIK\n",
"full_mask.sub_08+tlrc.HEAD\t\t pb03.sub_08.r02.blur+tlrc.HEAD\n",
"mask_anat.sub_08+tlrc.BRIK\t\t pb04.sub_08.r01.scale+tlrc.BRIK\n",
"mask_anat.sub_08+tlrc.HEAD\t\t pb04.sub_08.r01.scale+tlrc.HEAD\n",
"mask_epi_anat.sub_08+tlrc.BRIK\t\t pb04.sub_08.r01.scale.nii.gz\n",
"mask_epi_anat.sub_08+tlrc.HEAD\t\t pb04.sub_08.r02.scale+tlrc.BRIK\n",
"mask_epi_extents+tlrc.BRIK\t\t pb04.sub_08.r02.scale+tlrc.HEAD\n",
"mask_epi_extents+tlrc.HEAD\t\t pre.sub-08_T1w_ns+orig.BRIK\n",
"mask_group+tlrc.BRIK\t\t\t pre.sub-08_T1w_ns+orig.HEAD\n",
"mask_group+tlrc.HEAD\t\t\t pre.sub-08_T1w_ns_WarpDrive.log\n",
"mat.basewarp.aff12.1D\t\t\t stimuli\n",
"mat.r01.vr.aff12.1D\t\t\t sub-08_T1w+orig.BRIK\n",
"mat.r01.warp.aff12.1D\t\t\t sub-08_T1w+orig.HEAD\n",
"mat.r02.vr.aff12.1D\t\t\t sub-08_T1w_al_junk+orig.BRIK\n",
"mat.r02.warp.aff12.1D\t\t\t sub-08_T1w_al_junk+orig.HEAD\n",
"motion_sub_08_enorm.1D\t\t\t sub-08_T1w_al_junk_mat.aff12.1D\n",
"out.allcostX.txt\t\t\t sub-08_T1w_ns+orig.BRIK\n",
"out.mask_ae_dice.txt\t\t\t sub-08_T1w_ns+orig.HEAD\n",
"out.mask_ae_overlap.txt\t\t\t sub-08_T1w_ns+tlrc.BRIK\n",
"out.mask_at_dice.txt\t\t\t sub-08_T1w_ns+tlrc.HEAD\n",
"out.min_outlier.txt\t\t\t sub-08_T1w_ns.Xaff12.1D\n",
"out.pre_ss_warn.txt\t\t\t sub-08_T1w_ns.Xat.1D\n",
"outcount.r01.1D\t\t\t\t sub-08_T1w_ns.maskwarp.Xat.1D\n",
"outcount.r02.1D\t\t\t\t sub-08_T1w_ns_shft.1D\n",
"outcount_rall.1D\t\t\t volumized+tlrc.BRIK\n",
"pb00.sub_08.r01.tcat+orig.BRIK\t\t volumized+tlrc.HEAD\n",
"pb00.sub_08.r01.tcat+orig.HEAD\t\t vr_base_min_outlier+orig.BRIK\n",
"pb00.sub_08.r02.tcat+orig.BRIK\t\t vr_base_min_outlier+orig.HEAD\n",
"pb00.sub_08.r02.tcat+orig.HEAD\t\t warp.anat.Xat.1D\n",
"pb01.sub_08.r01.tshift+orig.BRIK\n"
]
}
],
"source": [
"!ls ./afni_processing/sub_08.results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
" \n",
" !\n",
" Warning:\n",
"
\n",
"
\n",
" In AFNI, a +tlrc extension (and the “Talairach View”) indicates that the image has been normalized. However, this does not necessarily mean the image is in Talairach space. The Talairach label is retained for legacy reasons, ensuring compatibility with older versions of the software. To verify which space the image has been warped to, you can use the 3dinfo command and check the “Template Space” field. The three possibilities are: “ORIG” (the image has not been warped), “TLRC” (normalized to Talairach space), and “MNI” (normalized to MNI space).\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizations\n",
"To visualize the preprocessing outputs with ipyniivue, the files are first converted to NIfTI format. Once loaded, check whether the functional and anatomical images are properly aligned to MNI space. Misalignments could indicate issues with the preprocessing steps. Smoothed images will generally appear less detailed than the original images due to the blurring effect; this is expected, but they should not be excessively blurry or too sharp. The scaled functional image will have less anatomical definition, as the signal intensity has been normalized across time, making it more uniform across brain voxels. When reviewing the masks, ensure they appropriately cover the brain regions of interest and match the underlying anatomy. For example, the \"mask_group\" is typically a more liberal mask compared to the \"full_mask\"."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"++ 3dAFNItoNIFTI: AFNI version=AFNI_21.2.00 (Jul 8 2021) [64-bit]\n",
"++ 3dAFNItoNIFTI: AFNI version=AFNI_21.2.00 (Jul 8 2021) [64-bit]\n",
"++ 3dAFNItoNIFTI: AFNI version=AFNI_21.2.00 (Jul 8 2021) [64-bit]\n",
"++ 3dAFNItoNIFTI: AFNI version=AFNI_21.2.00 (Jul 8 2021) [64-bit]\n",
"\u001b[7m*+ WARNING:\u001b[0m varying brick factors, writing NIfTI as float\n",
"++ 3dAFNItoNIFTI: AFNI version=AFNI_21.2.00 (Jul 8 2021) [64-bit]\n",
"++ 3dAFNItoNIFTI: AFNI version=AFNI_21.2.00 (Jul 8 2021) [64-bit]\n"
]
}
],
"source": [
"!3dAFNItoNIFTI -prefix ./afni_processing/mni152.nii.gz /cvmfs/neurodesk.ardc.edu.au/containers/afni_21.2.00_20210714/afni_21.2.00_20210714.simg/opt/afni-latest/MNI_avg152T1+tlrc\n",
"!3dAFNItoNIFTI -prefix ./afni_processing/sub_08.results/anat_w_skull_warped.nii.gz ./afni_processing/sub_08.results/anat_w_skull_warped+tlrc\n",
"!3dAFNItoNIFTI -prefix ./afni_processing/sub_08.results/anat_final.nii.gz ./afni_processing/sub_08.results/anat_final.sub_08+tlrc\n",
"!3dAFNItoNIFTI -prefix ./afni_processing/sub_08.results/pb02.sub_08.r01.volreg+tlrc.nii.gz ./afni_processing/sub_08.results/pb02.sub_08.r01.volreg+tlrc\n",
"!3dAFNItoNIFTI -prefix ./afni_processing/sub_08.results/full_mask.sub_08+tlrc.nii.gz ./afni_processing/sub_08.results/full_mask.sub_08+tlrc\n",
"!3dAFNItoNIFTI -prefix ./afni_processing/sub_08.results/mask_group.nii.gz ./afni_processing/sub_08.results/mask_group+tlrc"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [],
"source": [
"# Paths to files\n",
"anat_skull = './afni_processing/sub_08.results/anat_w_skull_warped.nii.gz'\n",
"anat_final = './afni_processing/sub_08.results/anat_final.nii.gz'\n",
"volreg_func = './afni_processing/sub_08.results/pb02.sub_08.r01.volreg+tlrc.nii.gz'\n",
"mni_nifti = './afni_processing/mni152.nii.gz'\n",
"smoothed = './afni_processing/sub_08.results/pb03.sub_08.r01.blur.nii.gz'\n",
"scaled = './afni_processing/sub_08.results/pb04.sub_08.r01.scale.nii.gz'\n",
"full_mask = './afni_processing/sub_08.results/full_mask.sub_08+tlrc.nii.gz'\n",
"group_mask = './afni_processing/sub_08.results/mask_group.nii.gz'"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "18aa22003af74f5888b073ea34602cc4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Dropdown(description='Overlay:', layout=Layout(width='350px'), options=(('Anatomical aligned to…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nv = NiiVue()\n",
"\n",
"# Paths to all volumes\n",
"volumes = [\n",
" {\"path\": mni_nifti, \"colormap\": \"gray\", \"opacity\": 1.0}, # MNI background\n",
" {\"path\": anat_final, \"colormap\": \"red\", \"opacity\": 1.0}, # default overlay\n",
" {\"path\": volreg_func, \"colormap\": \"red\", \"opacity\": 0.0},\n",
" {\"path\": smoothed, \"opacity\": 0.0},\n",
" {\"path\": scaled, \"opacity\": 0.0},\n",
" {\"path\": full_mask, \"opacity\": 0.0},\n",
" {\"path\": group_mask, \"opacity\": 0.0},\n",
"]\n",
"\n",
"# Load all volumes into viewer\n",
"nv.load_volumes(volumes)\n",
"\n",
"# Dropdown options\n",
"overlay_dropdown = widgets.Dropdown(\n",
" options=[\n",
" (\"Anatomical aligned to MNI\", 1),\n",
" (\"Motion-corrected functional to MNI\", 2),\n",
" (\"Smoothed functional (blur)\", 3),\n",
" (\"Scaled functional (scale)\", 4),\n",
" (\"Union of functional masks (full_mask)\", 5),\n",
" (\"Dilated group mask (group_mask)\", 6)\n",
" ],\n",
" value=1,\n",
" description='Overlay:',\n",
" style={'description_width': 'initial'},\n",
" layout=widgets.Layout(width='350px')\n",
")\n",
"\n",
"# Update function\n",
"def update_overlay(change):\n",
" selected_idx = change.new\n",
"\n",
" # Set all overlays to 0 opacity\n",
" for idx in range(1, len(volumes)):\n",
" nv.volumes[idx].opacity = 0.0\n",
"\n",
" # Activate the selected one\n",
" nv.volumes[selected_idx].opacity = 1.0\n",
"\n",
" # Hide MNI background if functional data is selected\n",
" nv.volumes[0].opacity = 0.0 if selected_idx in [3, 4] else 1.0\n",
"\n",
"# Attach the observer\n",
"overlay_dropdown.observe(update_overlay, names='value')\n",
"\n",
"# Show viewer and dropdown\n",
"display(VBox([overlay_dropdown, nv]))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}