{
"cells": [
{
"cell_type": "markdown",
"id": "abd678aa-fb2a-47b7-bd31-2123bbea17e4",
"metadata": {},
"source": [
"# Replication: Agosta *et al*, 2013\n",
"\n",
"## Introduction\n",
"\n",
"This notebook attempts to replicate the following paper with the [PPMI](http://ppmi-info.org) dataset:\n",
"\n",
"<div class=\"alert alert-block alert-success\">\n",
"Agosta, Federica, et al. <a href=\"https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.22101?casa_token=vStnHL7zZ9MAAAAA:Ua2XOtP-ZFkkN-ebLPWi-84jCctqFpC8I89zyE3Ia9Lvk8bJWl7wj7-15xciyX50gzG05TlCvv6DFk0\">\n",
"The topography of brain damage at different stages of Parkinson's disease.</a>, Human brain mapping 34.11 (2013): 2798-2807.\n",
"</div>"
]
},
{
"cell_type": "markdown",
"id": "ab86edb8-d5ab-402a-b133-2a77530600a4",
"metadata": {},
"source": [
"<img src='images/table.png' width=800/>"
]
},
{
"cell_type": "markdown",
"id": "9189c63b-94ad-469b-b9d0-328eb52cc8b8",
"metadata": {},
"source": [
"## Initial setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "52b4e9c2-4f75-43bc-b956-dbe9f1ba608d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Installing notebook dependencies (see log in install.log)... \n",
"This notebook was run on 2022-07-21 15:38:37 UTC +0000\n"
]
}
],
"source": [
"import livingpark_utils\n",
"\n",
"utils = livingpark_utils.LivingParkUtils(\"agosta-etal\")\n",
"utils.notebook_init()\n",
"random_seed = 1"
]
},
{
"cell_type": "markdown",
"id": "ff5646ca-49a8-4072-b28c-cf8a82debb42",
"metadata": {
"tags": []
},
"source": [
"## PPMI cohort preparation"
]
},
{
"cell_type": "markdown",
"id": "003cc8c9-96aa-4772-af38-fb769c4fdff3",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Study data download"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "be13ea39-957e-440f-b27b-32113c81f6a0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Download skipped: No missing files!\n"
]
}
],
"source": [
"required_files = [\n",
" \"iu_genetic_consensus_20220310.csv\",\n",
" \"Cognitive_Categorization.csv\",\n",
" \"Primary_Clinical_Diagnosis.csv\",\n",
" \"Demographics.csv\", # SEX\n",
" \"Socio-Economics.csv\", # EDUCYRS\n",
" \"PD_Diagnosis_History.csv\", # Disease duration\n",
" \"Montreal_Cognitive_Assessment__MoCA_.csv\",\n",
" \"REM_Sleep_Behavior_Disorder_Questionnaire.csv\", # STROKE\n",
"]\n",
"\n",
"utils.download_ppmi_metadata(required_files)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a7719ba4-28c1-4b6e-a8a7-08e163c2dd94",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"File /home/glatard/code/livingpark/agosta-etal/inputs/study_files/MDS_UPDRS_Part_III_clean.csv is now available\n",
"File /home/glatard/code/livingpark/agosta-etal/inputs/study_files/MRI_info.csv is now available\n"
]
}
],
"source": [
"# H&Y scores\n",
"\n",
"# TODO: move this to livingpark_utils\n",
"import os.path as op\n",
"\n",
"file_path = op.join(utils.study_files_dir, \"MDS_UPDRS_Part_III_clean.csv\")\n",
"if not op.exists(file_path):\n",
" !(cd {utils.study_files_dir} && python -m wget \"https://raw.githubusercontent.com/LivingPark-MRI/ppmi-treatment-and-on-off-status/main/PPMI medication and ON-OFF status.ipynb\") # use requests to improve portability\n",
" npath = op.join(utils.study_files_dir, \"PPMI medication and ON-OFF status.ipynb\")\n",
" %run \"{npath}\"\n",
"print(f\"File {file_path} is now available\")\n",
"\n",
"# TODO: move this to livingpark_utils\n",
"import os.path as op\n",
"\n",
"file_path = op.join(utils.study_files_dir, \"MRI_info.csv\")\n",
"if not op.exists(file_path):\n",
" !(cd {utils.study_files_dir} && python -m wget \"https://raw.githubusercontent.com/LivingPark-MRI/ppmi-MRI-metadata/main/MRI metadata.ipynb\") # use requests to improve portability\n",
" npath = op.join(utils.study_files_dir, \"MRI metadata.ipynb\")\n",
" %run \"{npath}\"\n",
"print(f\"File {file_path} is now available\")"
]
},
{
"cell_type": "markdown",
"id": "01671975-07d0-48a0-8774-4349abc3f090",
"metadata": {
"tags": []
},
"source": [
"### Inclusion criteria\n",
"\n",
"We obtain the following group sizes:\n",
"\n",
"<!-- and a cutoff score of 6 to identify RBD subjects among PD subjects, consistently with the results presented in [[2]](https://www.sciencedirect.com# /science/article/pii/S138994571100164X). -->"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7984652c-62b1-465f-bfdb-dccbb9191dcb",
"metadata": {},
"outputs": [],
"source": [
"# H&Y during OFF time\n",
"# Exclusion criteria: Patients were excluded if they had:\n",
"# (a) parkin, leucine-rich repeat kinase 2 (LRRK2), and glu-\n",
"# cocerebrosidase (GBA) gene mutations; OK\n",
"# (b) cerebrovascular\n",
"# disorders, traumatic brain injury history, or intracranial\n",
"# mass; only stroke found in\n",
"#\n",
"# (c) other major neurological and medical diseases;\n",
"# (d) dementia: OK\n",
"\n",
"# DARTEL: 8-mm full width\n",
"# at half maximum (FWHM) Gaussian filter"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b7589aaf-1a2f-411a-afbe-6aa8840fdddd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Download skipped: No missing files!\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"# UPDRS3\n",
"updrs3 = pd.read_csv(op.join(utils.study_files_dir, \"MDS_UPDRS_Part_III_clean.csv\"))[\n",
" [\"PATNO\", \"EVENT_ID\", \"PDSTATE\", \"NHY\", \"NP3TOT\"]\n",
"]\n",
"# Genetics\n",
"genetics = pd.read_csv(\n",
" op.join(utils.study_files_dir, \"iu_genetic_consensus_20220310.csv\")\n",
")[[\"PATNO\", \"GBA_PATHVAR\", \"LRRK2_PATHVAR\", \"NOTES\"]]\n",
"# Cognitive Categorization\n",
"cog_cat = pd.read_csv(op.join(utils.study_files_dir, \"Cognitive_Categorization.csv\"))[\n",
" [\"PATNO\", \"EVENT_ID\", \"COGSTATE\"]\n",
"]\n",
"# Diagnosis\n",
"diag = pd.read_csv(op.join(utils.study_files_dir, \"Primary_Clinical_Diagnosis.csv\"))[\n",
" [\"PATNO\", \"EVENT_ID\", \"PRIMDIAG\", \"OTHNEURO\"]\n",
"]\n",
"# MRI\n",
"mri = pd.read_csv(op.join(utils.study_files_dir, \"MRI_info.csv\"))[\n",
" [\"Subject ID\", \"Visit code\", \"Description\", \"Age\"]\n",
"]\n",
"mri.rename(columns={\"Subject ID\": \"PATNO\", \"Visit code\": \"EVENT_ID\"}, inplace=True)\n",
"# Demographics\n",
"demographics = pd.read_csv(op.join(utils.study_files_dir, \"Demographics.csv\"))[\n",
" [\"PATNO\", \"SEX\"]\n",
"]\n",
"# Soci-economics\n",
"socio_eco = pd.read_csv(op.join(utils.study_files_dir, \"Socio-Economics.csv\"))[\n",
" [\"PATNO\", \"EDUCYRS\"]\n",
"]\n",
"# Disease duration\n",
"disease_dur = utils.disease_duration()\n",
"# MoCA\n",
"moca = pd.read_csv(\n",
" op.join(utils.study_files_dir, \"Montreal_Cognitive_Assessment__MoCA_.csv\")\n",
")[[\"PATNO\", \"EVENT_ID\", \"MCATOT\"]]\n",
"moca[\"MMSETOT\"] = moca[\"MCATOT\"].apply(utils.moca2mmse)\n",
"# Stroke\n",
"rem = pd.read_csv(\n",
" op.join(utils.study_files_dir, \"REM_Sleep_Behavior_Disorder_Questionnaire.csv\")\n",
")[[\"PATNO\", \"EVENT_ID\", \"STROKE\"]]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7bdfd288-bf39-44c5-a194-a3baa873be64",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of controls: 46\n",
"Number of PD subjects: 125\n",
"Number of PD subject visits by H&Y score:\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PATNO</th>\n",
" </tr>\n",
" <tr>\n",
" <th>NHY</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PATNO\n",
"NHY \n",
"1 157\n",
"2 537\n",
"3 34\n",
"4 6"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Remove genetics data that should be excluded according to PPMI doc\n",
"genetics_clean = genetics[\n",
" ~(\n",
" (genetics[\"NOTES\"].notna())\n",
" & (\n",
" genetics[\"NOTES\"].str.contains(\"exclude GBA\")\n",
" | genetics[\"NOTES\"].str.contains(\"exclude LRRK2\")\n",
" )\n",
" )\n",
"]\n",
"\n",
"# Inclusion / exclusion criteria\n",
"subjects = (\n",
" updrs3[\n",
" (updrs3[\"PDSTATE\"] == \"OFF\") & (updrs3[\"NHY\"].notna()) & (updrs3[\"NHY\"] != \"UR\")\n",
" ] # H&Y score is available and was obtained during OFF time\n",
" .merge(\n",
" genetics_clean[ # No pathogenic GBA or LRRK2 variant present (Note: on-going email thread with Madeleine)\n",
" (genetics_clean[\"GBA_PATHVAR\"] == 0)\n",
" & (genetics_clean[\"LRRK2_PATHVAR\"] == 0)\n",
" ],\n",
" how=\"inner\",\n",
" on=\"PATNO\",\n",
" )\n",
" .merge(\n",
" cog_cat[cog_cat[\"COGSTATE\"] != 3], how=\"inner\", on=[\"PATNO\", \"EVENT_ID\"]\n",
" ) # No dementia\n",
" .merge(\n",
" diag[\n",
" (diag[\"PRIMDIAG\"].isin([1, 17])) & (diag[\"OTHNEURO\"].isna())\n",
" ], # Primary diagnosis is Idiopathic PD or healthy control\n",
" how=\"inner\",\n",
" on=[\"PATNO\", \"EVENT_ID\"],\n",
" )\n",
" .merge(mri, how=\"inner\", on=[\"PATNO\", \"EVENT_ID\"])\n",
" .merge(demographics, how=\"left\", on=\"PATNO\")\n",
" .merge(socio_eco, how=\"left\", on=\"PATNO\")\n",
" .merge(disease_dur, how=\"left\", on=[\"PATNO\", \"EVENT_ID\"])\n",
" .merge(moca, how=\"left\", on=[\"PATNO\", \"EVENT_ID\"])\n",
" .merge(rem, how=\"inner\", on=[\"PATNO\", \"EVENT_ID\"])\n",
")\n",
"\n",
"pds = subjects[\n",
" (subjects[\"PRIMDIAG\"] == 1) & (subjects[\"STROKE\"] == 0)\n",
"] # Include only idiopathic PD with no history of stroke\n",
"controls = subjects[\n",
" (subjects[\"PRIMDIAG\"] == 17) & (subjects[\"NHY\"] == \"0\") & (subjects[\"STROKE\"] == 0)\n",
"] # Exclude controls with H&Y > 0 and history of stroke\n",
"subjects = pd.concat([pds, controls])\n",
"\n",
"\n",
"print(f\"Number of controls: {len(pd.unique(controls['PATNO']))}\")\n",
"print(f\"Number of PD subjects: {len(pd.unique(pds['PATNO']))}\")\n",
"\n",
"print(f\"Number of PD subject visits by H&Y score:\")\n",
"pds.groupby([\"NHY\"], dropna=False).count()[[\"PATNO\"]]"
]
},
{
"cell_type": "markdown",
"id": "e7bb0228-4b3a-443f-89da-c94bca364f2b",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Cohort matching\n",
"\n",
"Matching sex balance as much as possible."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6f8b6fee-c179-4330-a264-999eafd91300",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selecting {'total': 12, 'women': 5} visits of unique patients with H&Y=4\n",
"Warning: found only 2 visits of unique patients with H&Y=4 while 12 were required. Including them all regardless of sex balance.\n",
"Selecting {'total': 14, 'women': 7} visits of unique patients with H&Y=3\n",
"Warning: found only 9 visits of unique patients with H&Y=3 while 14 were required. Including them all regardless of sex balance.\n",
"Selecting {'total': 46, 'women': 15} visits of unique patients with H&Y=2\n",
"Selecting {'total': 17, 'women': 7} visits of unique patients with H&Y=1\n",
"Selecting {'total': 42, 'women': 17} visits of unique patients with H&Y=0\n",
"Warning: found only 14 women with H&Y=0 while 17 were required: including them all\n"
]
}
],
"source": [
"sample_size = {\n",
" 0: {\"total\": 42, \"women\": 17},\n",
" 1: {\"total\": 17, \"women\": 7},\n",
" 2: {\"total\": 46, \"women\": 15},\n",
" 3: {\"total\": 14, \"women\": 7},\n",
" 4: {\"total\": 12, \"women\": 5},\n",
"}\n",
"\n",
"samples = {}\n",
"\n",
"subjects_ = subjects.copy() # copy subjects DF to leave it intact\n",
"for x in sorted(\n",
" sample_size, reverse=True\n",
"): # sampling patients in descending H&Y score order as there are less patients with high H&Y scores\n",
" print(f\"Selecting {sample_size[x]} visits of unique patients with H&Y={x}\")\n",
"\n",
" # Sample visits with H&Y=x and make sure they belong to different patients\n",
" hy = (\n",
" subjects_[subjects_[\"NHY\"] == str(x)]\n",
" .groupby([\"PATNO\"])\n",
" .sample(1, random_state=random_seed)\n",
" )\n",
"\n",
" # Sample subjects in hy\n",
" if len(hy) < sample_size[x][\"total\"]:\n",
" print(\n",
" f'Warning: found only {len(hy)} visits of unique patients with H&Y={x} while {sample_size[x][\"total\"]} were required. '\n",
" + \"Including them all regardless of sex balance.\"\n",
" )\n",
" samples[x] = hy\n",
" else:\n",
" # We have enough subjects. Trying to match sex balance\n",
" hy_women = hy[hy[\"SEX\"] == 0]\n",
" required_women = sample_size[x][\"women\"]\n",
"\n",
" hy_men = hy[hy[\"SEX\"] == 1]\n",
" required_men = sample_size[x][\"total\"] - required_women\n",
"\n",
" if len(hy_women) < required_women: # We don't have enough women\n",
" print(\n",
" f\"Warning: found only {len(hy_women)} women with H&Y={x} while {required_women} were required: including them all\"\n",
" )\n",
" # We have enough men since we have enough subjects\n",
" required_men += required_women - len(hy_women)\n",
" required_women = len(hy_women)\n",
"\n",
" if len(hy_men) < required_men:\n",
" # We don't have enough men\n",
" print(\n",
" f\"Warning: found only {len(hy_men)} men with H&Y={x} while {required_men} were required: including them all\"\n",
" )\n",
" # We have enough women since we have enough subjects\n",
" required_women += required_men - len(hy_men)\n",
" required_men = len(hy_men)\n",
"\n",
" samples[x] = pd.concat(\n",
" [\n",
" hy_women.sample(required_women, random_state=random_seed),\n",
" hy_men.sample(required_men, random_state=random_seed),\n",
" ]\n",
" )\n",
"\n",
" # Remove sampled patients from subjects_\n",
" subjects_ = subjects_[~(subjects_[\"PATNO\"].isin(samples[x][\"PATNO\"]))]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c86d3e08-d089-4dcd-a404-f41fc3416aab",
"metadata": {},
"outputs": [],
"source": [
"cohort = pd.concat([samples[x] for x in samples])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c1c616ca-5e4f-4ed9-82f7-d1d3f86dead7",
"metadata": {},
"outputs": [],
"source": [
"def num_stats(column_name, skip_healthy=False):\n",
" def round_(x):\n",
" if str(x) == \"nan\":\n",
" return float(\"nan\")\n",
" else:\n",
" return round(x)\n",
"\n",
" a = [\n",
" f'{round_(cohort[cohort[\"NHY\"]==str(x)][column_name].mean())} '\n",
" + f'+/- {round_(cohort[cohort[\"NHY\"]==str(x)][column_name].std())} '\n",
" + f'({round_(cohort[cohort[\"NHY\"]==str(x)][column_name].min())}-'\n",
" + f'{round_(cohort[cohort[\"NHY\"]==str(x)][column_name].max())})'\n",
" for x in range(5)\n",
" ]\n",
" if skip_healthy:\n",
" a[0] = \"—\"\n",
" return a\n",
"\n",
"\n",
"cohort_stats = pd.DataFrame(\n",
" columns=[\"Healthy controls\"] + [f\"HY {x}\" for x in range(1, 5)]\n",
")\n",
"cohort_stats.loc[\"Number\"] = [len(cohort[cohort[\"NHY\"] == str(x)]) for x in range(5)]\n",
"cohort_stats.loc[\"Women / men \"] = [\n",
" f'{len(cohort[(cohort[\"NHY\"]==str(x)) & (cohort[\"SEX\"]==0)])} / {len(cohort[(cohort[\"NHY\"]==str(x)) & (cohort[\"SEX\"]==1)])}'\n",
" for x in range(5)\n",
"]\n",
"cohort_stats.loc[\"Age (years)\"] = num_stats(\"Age\")\n",
"cohort_stats.loc[\"Education (years)\"] = num_stats(\"EDUCYRS\")\n",
"cohort_stats.loc[\"Disease duration (years)\"] = num_stats(\"PDXDUR\", skip_healthy=True)\n",
"cohort_stats.loc[\"UPDRS III\"] = num_stats(\"NP3TOT\", skip_healthy=True)\n",
"cohort_stats.loc[\"MMSE\"] = num_stats(\"MMSETOT\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "23529c9e-e8a6-4dbf-9a3e-7e50226a9c93",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Healthy controls</th>\n",
" <th>HY 1</th>\n",
" <th>HY 2</th>\n",
" <th>HY 3</th>\n",
" <th>HY 4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Number</th>\n",
" <td>42</td>\n",
" <td>17</td>\n",
" <td>46</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Women / men</th>\n",
" <td>14 / 28</td>\n",
" <td>7 / 10</td>\n",
" <td>15 / 31</td>\n",
" <td>4 / 5</td>\n",
" <td>1 / 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age (years)</th>\n",
" <td>61 +/- 12 (33-83)</td>\n",
" <td>62 +/- 10 (40-79)</td>\n",
" <td>63 +/- 10 (42-78)</td>\n",
" <td>69 +/- 8 (55-80)</td>\n",
" <td>69 +/- 6 (64-73)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Education (years)</th>\n",
" <td>16 +/- 3 (12-24)</td>\n",
" <td>14 +/- 3 (8-20)</td>\n",
" <td>15 +/- 3 (9-22)</td>\n",
" <td>14 +/- 3 (8-18)</td>\n",
" <td>20 +/- nan (20-20)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Disease duration (years)</th>\n",
" <td>—</td>\n",
" <td>4 +/- 3 (0-10)</td>\n",
" <td>4 +/- 3 (0-10)</td>\n",
" <td>4 +/- 2 (0-7)</td>\n",
" <td>2 +/- 2 (1-4)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>UPDRS III</th>\n",
" <td>—</td>\n",
" <td>14 +/- 5 (6-22)</td>\n",
" <td>26 +/- 10 (9-51)</td>\n",
" <td>40 +/- 9 (29-56)</td>\n",
" <td>nan +/- nan (nan-nan)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MMSE</th>\n",
" <td>30 +/- 1 (28-30)</td>\n",
" <td>29 +/- 1 (26-30)</td>\n",
" <td>29 +/- 2 (22-30)</td>\n",
" <td>28 +/- 2 (26-30)</td>\n",
" <td>29 +/- 0 (29-29)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Healthy controls HY 1 \\\n",
"Number 42 17 \n",
"Women / men 14 / 28 7 / 10 \n",
"Age (years) 61 +/- 12 (33-83) 62 +/- 10 (40-79) \n",
"Education (years) 16 +/- 3 (12-24) 14 +/- 3 (8-20) \n",
"Disease duration (years) — 4 +/- 3 (0-10) \n",
"UPDRS III — 14 +/- 5 (6-22) \n",
"MMSE 30 +/- 1 (28-30) 29 +/- 1 (26-30) \n",
"\n",
" HY 2 HY 3 \\\n",
"Number 46 9 \n",
"Women / men 15 / 31 4 / 5 \n",
"Age (years) 63 +/- 10 (42-78) 69 +/- 8 (55-80) \n",
"Education (years) 15 +/- 3 (9-22) 14 +/- 3 (8-18) \n",
"Disease duration (years) 4 +/- 3 (0-10) 4 +/- 2 (0-7) \n",
"UPDRS III 26 +/- 10 (9-51) 40 +/- 9 (29-56) \n",
"MMSE 29 +/- 2 (22-30) 28 +/- 2 (26-30) \n",
"\n",
" HY 4 \n",
"Number 2 \n",
"Women / men 1 / 1 \n",
"Age (years) 69 +/- 6 (64-73) \n",
"Education (years) 20 +/- nan (20-20) \n",
"Disease duration (years) 2 +/- 2 (1-4) \n",
"UPDRS III nan +/- nan (nan-nan) \n",
"MMSE 29 +/- 0 (29-29) "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cohort_stats"
]
},
{
"cell_type": "markdown",
"id": "713fd44e-d137-42da-97d5-9c30369d730b",
"metadata": {
"tags": []
},
"source": [
"## Image analysis\n",
"\n",
"Structural MRI analysis in the original paper is a straightforward VBM analysis implemented with SPM using the DARTEL toolbox. To replicate it, we mostly followed the excellent tutorial on VBM in SPM available at https://www.fil.ion.ucl.ac.uk/~john/misc/VBMclass15.pdf"
]
},
{
"cell_type": "markdown",
"id": "bb80d0ea-0767-40d3-a4c6-0af0024f65d0",
"metadata": {},
"source": [
"### Imaging data download\n",
"\n",
"Let's first check if the Nifti image files associated with the subjects and visits selected in our cohort are available in cache:"
]
},
{
"cell_type": "markdown",
"id": "3b44e1a4-64c1-4444-875b-eef795c2b41d",
"metadata": {},
"source": [
"We will now download the missing image files and move them to the data cache:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e6e48c07-487d-48fd-b87b-20751618cae6",
"metadata": {},
"outputs": [],
"source": [
"# utils.download_missing_nifti_files(cohort, link_in_outputs=True)"
]
}
],
"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": "3.10.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}