0.4_getData_IMGN.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explaining cortical thickness development: IMAGEN CT data\n",
    "\n",
    "Note: As IMAGEN data is under data protection, no IMAGEN data is included in this repo. To run this notebook on your own, you have to apply for [access](https://nda.nih.gov/abcd/request-access.html) to the IMAGEN data, which is only possibly via a specific project proposal. We processed the structural MRI data on our own with Freesurfer 7.1.1. This notebook extracts the required CT data from standard Freesurfer subject folders run with the standard -recon all pipeline. If you want to use it on your own, you will have to change the data paths. \n",
    "\n",
    "If you have successfully gained access to the IMAGEN data and would like to use our Freesurfer data, please feel free to contact me at [leondlotter@gmail.com](mailto:leondlotter@gmail.com)!\n",
    "\n",
    "For data protection, no individual-subject data are shown in this notebook (lines are commented out). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working directory: /Users/llotter/projects/CTdev\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from functools import reduce\n",
    "from glob import glob\n",
    "\n",
    "from os.path import join\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy.stats import iqr\n",
    "\n",
    "# custom functions\n",
    "from scripts.braincharts import get_adapted_predictions #  wrappers around PCNtoolkit/braincharts\n",
    "from scripts.templates import get_destrieux\n",
    "from scripts.utils import reorder_vars, na\n",
    "\n",
    "# working path\n",
    "wd = os.path.dirname(os.path.realpath(\"__file__\"))\n",
    "print(\"Working directory:\", wd)\n",
    "\n",
    "# IMAGEN data paths\n",
    "imgn_data_dir = \"/Users/llotter/data/imagen/freesurfer\"\n",
    "imgn_pheno_dir = \"/Users/llotter/data/imagen/pheno/data/imagen/2.7\"\n",
    "imgn_dir = join(wd, \"data_ABCD-IMGN\")\n",
    "\n",
    "# cortical thickness model path\n",
    "ct_model_dir = join(wd, \"model_rutherford\", \"lifespan_57K_82sites\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Destrieux idps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parcel labels\n",
    "destrieux_idps = get_destrieux()[1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tab keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DAWBA - diagnoses\n",
    "dawba_keys_orig = pd.read_csv(join(imgn_pheno_dir, \"BL\", \"dawba\", \"IMAGEN_dawba_BL.csv\"), \n",
    "                              low_memory=False).columns\n",
    "dawba_keys_orig = [c for c in dawba_keys_orig if c.startswith(\"dc\")] \n",
    "dawba_keys = [\"dawba_\" + k[2:] for k in dawba_keys_orig]\n",
    "\n",
    "# GEN - ethnicity\n",
    "gen_keys = [\"PF_ethnicity\", \"PM_ethnicity\"]\n",
    "\n",
    "# PBQ - risk pregnancy\n",
    "pbq_keys = [\"pbq_05\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get phenotypic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T0: N = 2224, mean age = 14.46\n",
      "T5: N = 1525, mean age = 19.02\n",
      "T8: N = 1315, mean age = 22.58\n"
     ]
    }
   ],
   "source": [
    "imgn = {}\n",
    "for tp, fu in zip([\"T0\", \"T5\", \"T8\"], [\"BL\", \"FU2\", \"FU3\"]):\n",
    "    \n",
    "    imgn_tab_data = {\n",
    "        join(imgn_pheno_dir, fu, \"dawba\", \"IMAGEN_dawba_*.csv\"):  [\"sstartdate\"] + dawba_keys_orig,\n",
    "        join(imgn_pheno_dir, fu, \"psytools\", \"IMAGEN-IMGN_GEN_RC5-BASIC_DIGEST.csv\"):  gen_keys\n",
    "    }\n",
    "    \n",
    "    imgn[tp] = []\n",
    "    for i, k in enumerate(imgn_tab_data):\n",
    "        \n",
    "        # read file\n",
    "        try:\n",
    "            temp = pd.read_csv(glob(k)[0], low_memory=False)\n",
    "        except:\n",
    "            continue\n",
    "        # get ID column and variables of interest\n",
    "        if \"PSC2\" in temp.columns:\n",
    "            temp = temp[[\"PSC2\"] + imgn_tab_data[k]].rename(columns={\"PSC2\": \"id\"})\n",
    "        elif \"User code\" in temp.columns:\n",
    "            temp = temp[[\"User code\"] + imgn_tab_data[k]].rename(columns={\"User code\": \"id\"})\n",
    "        else:\n",
    "            print(\"ID column not found!\")\n",
    "        # clean ID column\n",
    "        try:\n",
    "            temp.id = [int(i.replace(\"-C\", \"\").replace(\"-I\", \"\")) for i in temp.id]\n",
    "        except:\n",
    "            pass\n",
    "        temp.id = [f\"sub-{sub:012d}\" for sub in temp.id]\n",
    "        # add timepoint column\n",
    "        temp = temp.assign(tp=tp)\n",
    "        # save\n",
    "        imgn[tp].append(temp)\n",
    "    \n",
    "    # combine data\n",
    "    imgn[tp] = reduce(lambda  left, right: pd.merge(left, right, on=[\"id\", \"tp\"], how='left'), \n",
    "                      imgn[tp])\n",
    "    \n",
    "    # index\n",
    "    imgn[tp] = imgn[tp].set_index([\"id\", \"tp\"], drop=False)\n",
    "    imgn[tp].index.names = [\"idx_id\", \"idx_tp\"]\n",
    "        \n",
    "    # age  \n",
    "    imgn[tp] = imgn[tp].rename(columns={\"sstartdate\": \"age\"})\n",
    "    imgn[tp].age = imgn[tp].age.astype(\"str\")\n",
    "    imgn[tp].age = [float(a.split(\"/\")[0]) for a in imgn[tp].age]\n",
    "    imgn[tp].age = (imgn[tp].age / 365).round(2)\n",
    "    \n",
    "    print(f\"{tp}: N = {imgn[tp].shape[0]}, mean age = {imgn[tp].age.mean():.02f}\")\n",
    "\n",
    "# combine\n",
    "imgn = pd.concat([imgn[k] for k in imgn.keys()])\n",
    "imgn = imgn.sort_index(axis=\"index\")\n",
    "#imgn"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Rename and create new variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add sex and site\n",
    "temp = pd.read_csv(join(imgn_pheno_dir, \"FU3\", \"participants\", \"IMAGEN_demographics.csv\")) \\\n",
    "    .rename(columns={\"PSC2\": \"id\", \"recruitment centre\": \"site_str\", \"sex\": \"sex_str\"})\n",
    "temp.id = [f\"sub-{sub:012d}\" for sub in temp.id]\n",
    "temp = temp.set_index(\"id\", drop=True)\n",
    "for sub in imgn.id.unique():\n",
    "    try:\n",
    "        imgn.loc[(sub, na()), \"sex_str\"] = temp.loc[sub, \"sex_str\"]\n",
    "        imgn.loc[(sub, na()), \"site_str\"] = temp.loc[sub, \"site_str\"]\n",
    "    except:\n",
    "        pass\n",
    "imgn[\"sex\"] = [0 if s==\"F\" else 1 for s in imgn.sex_str]\n",
    "imgn[\"site\"] = pd.factorize(imgn.site_str)[0] + 1\n",
    "imgn.site = np.where(imgn.site_str.isnull(), pd.NA, imgn.site)\n",
    "\n",
    "# rename remaining variables\n",
    "imgn = imgn.rename(columns=dict(zip(dawba_keys_orig, dawba_keys)))\n",
    "\n",
    "# remove T5 data of one subject that seemed to be scanned with only weeks between T5 and T8\n",
    "imgn = imgn.drop((\"sub-000028982808\", \"T5\"), axis=\"index\")\n",
    "# remove T5 data of one subject that has an age in the T8 range at T5\n",
    "imgn = imgn.drop((\"sub-000032585091\", \"T5\"), axis=\"index\")\n",
    "\n",
    "# remove rows with missing age, sex, or site\n",
    "imgn = imgn.loc[~imgn[[\"age\", \"sex\", \"site\"]].isnull().any(axis=\"columns\"), :]\n",
    "\n",
    "# \"backup\"\n",
    "imgn_orig = imgn.copy()\n",
    "#imgn"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CT data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get FS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUBJECTS_DIR : /Applications/freesurfer/dev/subjects\n",
      "Parsing the .stats files\n",
      "Building the table..\n",
      "Writing the table to /Users/llotter/projects/CTdev/data_ABCD-IMGN/freesurfer_stats_lh.txt\n",
      "SUBJECTS_DIR : /Applications/freesurfer/dev/subjects\n",
      "Parsing the .stats files\n",
      "Building the table..\n",
      "Writing the table to /Users/llotter/projects/CTdev/data_ABCD-IMGN/freesurfer_stats_rh.txt\n",
      "SUBJECTS_DIR : /Applications/freesurfer/dev/subjects\n",
      "Parsing the .stats files\n",
      "Building the table..\n",
      "Writing the table to /Users/llotter/projects/CTdev/data_ABCD-IMGN/freesurfer_stats_volume.txt\n"
     ]
    }
   ],
   "source": [
    "# Use FreeSurfer's aparcstats2table on all subjects/sessions\n",
    "for hemi in [\"lh\", \"rh\"]:\n",
    "    cmd = \\\n",
    "        f\"aparcstats2table \\\n",
    "            --subjects {' '.join(sorted(glob(join(imgn_data_dir,'*'))))} \\\n",
    "            --hemi {hemi} \\\n",
    "            --meas thickness \\\n",
    "            --parc aparc.a2009s \\\n",
    "            --delimiter comma \\\n",
    "            --tablefile {join(imgn_dir, f'freesurfer_stats_{hemi}.txt')}\"\n",
    "    !{cmd}\n",
    "    \n",
    "# asegstats for surface holes\n",
    "cmd = \\\n",
    "    f\"asegstats2table \\\n",
    "        --subjects {' '.join(sorted(glob(join(imgn_data_dir,'*'))))} \\\n",
    "        --delimiter comma \\\n",
    "        --tablefile {join(imgn_dir, f'freesurfer_stats_volume.txt')}\"\n",
    "!{cmd}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FreeSurfer Data from 1529 subjects and 4042 sessions.\n",
      "11 zeros replaces with np.nan\n"
     ]
    }
   ],
   "source": [
    "# sort into dataframe\n",
    "fs_data = pd.concat([\n",
    "    pd.read_csv(join(imgn_dir, f'freesurfer_stats_lh.txt'), index_col=0),\n",
    "    pd.read_csv(join(imgn_dir, f'freesurfer_stats_rh.txt'), index_col=0),\n",
    "    pd.read_csv(join(imgn_dir, f'freesurfer_stats_volume.txt'), index_col=0)\n",
    "], axis=1)\n",
    "fs_data.columns = [c.replace(\"_and_\", \"&\").replace(\"eTIV\", \"EstimatedTotalIntraCranialVol\") for c in fs_data.columns]\n",
    "fs_data.index = pd.MultiIndex.from_tuples(\n",
    "    [tuple(os.path.basename(f).split(\"_\")) for f in fs_data.index],\n",
    "    names=[\"idx_id\", \"idx_tp\"]\n",
    ")\n",
    "fs_data = fs_data.rename(index={\"ses-BL\":\"T0\", \"ses-FU2\":\"T5\", \"ses-FU3\":\"T8\"}, level=\"idx_tp\")\n",
    "fs_data = fs_data[destrieux_idps + [\"EstimatedTotalIntraCranialVol\", \"lhSurfaceHoles\", \"rhSurfaceHoles\", \"SurfaceHoles\"]]\n",
    "fs_data = fs_data.loc[:, ~fs_data.columns.duplicated()]\n",
    "\n",
    "print(f\"FreeSurfer Data from {len(fs_data.index.get_level_values('idx_id').unique())} subjects \"\n",
    "      f\"and {len(fs_data)} sessions.\")\n",
    "\n",
    "# replace zeros with nan\n",
    "print(f\"{(fs_data[destrieux_idps] == 0).sum().sum()} zeros replaces with np.nan\")\n",
    "fs_data[destrieux_idps] = fs_data[destrieux_idps].replace({0: np.nan})\n",
    "\n",
    "#fs_data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combine data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original: 4990 observations, 2158 subjects\n",
      "Complete CT data: 3975 observations, 1528 subjects\n"
     ]
    }
   ],
   "source": [
    "print(f\"Original: {imgn_orig.shape[0]} observations, {len(imgn_orig.id.unique())} subjects\")\n",
    "\n",
    "# original data + freesurfer\n",
    "imgn = imgn_orig.merge(\n",
    "    fs_data, \n",
    "    how=\"left\", left_index=True, right_index=True\n",
    ")\n",
    "\n",
    "# drop subjects with missing data\n",
    "imgn = imgn.loc[~imgn[destrieux_idps + [\"EstimatedTotalIntraCranialVol\", \"SurfaceHoles\"]].isnull().any(axis=\"columns\"), :]\n",
    "print(f\"Complete CT data: {imgn.shape[0]} observations, {len(imgn.id.unique())} subjects\")\n",
    "\n",
    "# # reorder variables\n",
    "imgn = reorder_vars([\"id\", \"tp\", \"site\", \"site_str\", \"age\", \"sex\", \"sex_str\"], imgn, destrieux_idps)\n",
    "#imgn"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre QC: 3975 observations, 1528 subjects\n",
      "Post QC: 3732 observations, 1522 subjects\n"
     ]
    }
   ],
   "source": [
    "qc_thresh = \"iqr\"\n",
    "\n",
    "# get outlier threshold\n",
    "temp = imgn.loc[(imgn.id.unique()), \"SurfaceHoles\"]\n",
    "if qc_thresh==\"quantile\":\n",
    "      thresh = np.quantile(temp, 0.99)\n",
    "elif qc_thresh==\"iqr\":\n",
    "      q3 = np.percentile(temp, 75)\n",
    "      thresh = q3 + 1.5 * iqr(temp)\n",
    "elif qc_thresh==\"sd\":\n",
    "      mean = np.mean(temp)\n",
    "      sd = np.std(temp)\n",
    "      thresh = mean + 3*sd\n",
    "imgn[\"topo_thresh\"] = thresh\n",
    "# get outliers\n",
    "imgn[\"topo_outlier\"] = imgn[\"SurfaceHoles\"] > imgn.topo_thresh\n",
    "\n",
    "# drop outlier subjects\n",
    "imgn_preqc = imgn.copy()\n",
    "print(f\"Pre QC: {imgn.shape[0]} observations, {len(imgn.id.unique())} subjects\")\n",
    "imgn = imgn_preqc.query(\"topo_outlier==False\").copy()\n",
    "print(f\"Post QC: {imgn.shape[0]} observations, {len(imgn.id.unique())} subjects\")\n",
    "\n",
    "# subjects that have longitudinal data after QC\n",
    "imgn[[\"tp_T0T8\", \"tp_T0T5\", \"tp_T5T8\", \"tp_one\"]] = False\n",
    "for sub in imgn.index.get_level_values(\"idx_id\").unique():\n",
    "      temp = imgn.tp[(sub, na())].to_list()\n",
    "      # T0 - T8\n",
    "      if (\"T0\" in temp) & (\"T8\" in temp):\n",
    "            imgn.loc[(sub, [\"T0\", \"T8\"]), \"tp_T0T8\"] = True\n",
    "      # T0 - T5\n",
    "      if (\"T0\" in temp) & (\"T5\" in temp):\n",
    "            imgn.loc[(sub, [\"T0\", \"T5\"]), \"tp_T0T5\"] = True\n",
    "      # T5 - T8\n",
    "      if (\"T5\" in temp) & (\"T8\" in temp):\n",
    "            imgn.loc[(sub, [\"T5\", \"T8\"]), \"tp_T5T8\"] = True\n",
    "      # only one timepoint\n",
    "      if len(temp)==1:\n",
    "            imgn.loc[(sub, temp[0]), \"tp_one\"] = True"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TD subjects and Psychopathology"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### TD subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T0: 922 TD & 404 NTD\n",
      "T5: 910 TD & 402 NTD\n",
      "T8: 743 TD & 351 NTD\n"
     ]
    }
   ],
   "source": [
    "# any subject with psychiatric diagnoses (no info on medical diagnoses)\n",
    "temp = \\\n",
    "      (imgn.dawba_any != 0)\n",
    "      \n",
    "# invert list -> True == TD\n",
    "temp = ~temp\n",
    "\n",
    "# list of all subjects\n",
    "subs = imgn.id.unique().tolist()\n",
    "# iterate subjects\n",
    "for sub in subs:\n",
    "      # a subject is TD if it is TD at all available timepoints\n",
    "      imgn.loc[(sub, na()), \"td\"] = \"TD\" if temp.loc[(sub, na())].all() else \"NTD\"\n",
    "      \n",
    "for tp in [\"T0\", \"T5\", \"T8\"]:\n",
    "      print(f\"{tp}: {len(imgn[(imgn.td=='TD') & (imgn.tp==tp)])} TD & \" \n",
    "            f\"{len(imgn[(imgn.td=='NTD') & (imgn.tp==tp)])} NTD\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adaptation subjects\n",
    "\n",
    "We select 20 adaptation subjects from each site: To retain more subjects for longitudinal analyses, we use only those that have no second time point. Subjects are taken from the \"TD\" cohort defined as having no severe medical or psychiatric condition. Selected subjects are stratified by age (grouped in 0.5-year bins) and split by sex (50/50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selecting n = 20 random adaption subjects from TDs, stratified on age, 50/50 male/female\n",
      "Site: 3, n(F) = 65\n",
      "Dropping group >13.0<=13.5, n = 1\n",
      "Site: 3, n(M) = 68\n",
      "n(adapt) = 20\n",
      "Site: 1, n(F) = 65\n",
      "Site: 1, n(M) = 76\n",
      "n(adapt) = 20\n",
      "Dropping group >15.0<=15.5, n = 1\n",
      "Site: 4, n(F) = 57\n",
      "Site: 4, n(M) = 68\n",
      "n(adapt) = 20\n",
      "Site: 2, n(F) = 66\n",
      "Dropping group >13.5<=14.0, n = 1\n",
      "Site: 2, n(M) = 60\n",
      "n(adapt) = 20\n",
      "Site: 5, n(F) = 60\n",
      "Site: 5, n(M) = 63\n",
      "n(adapt) = 20\n",
      "Site: 6, n(F) = 48\n",
      "Site: 6, n(M) = 53\n",
      "n(adapt) = 20\n",
      "Site: 7, n(F) = 47\n",
      "Dropping group >13.0<=13.5, n = 1\n",
      "Site: 7, n(M) = 49\n",
      "n(adapt) = 20\n",
      "Site: 8, n(F) = 33\n",
      "Dropping group >15.0<=15.5, n = 1\n",
      "Site: 8, n(M) = 39\n",
      "n(adapt) = 20\n",
      "Number of adaptation subjects (tp's combined): 160\n"
     ]
    }
   ],
   "source": [
    "# Find adaptation subjects\n",
    "n_adapt = 20\n",
    "print(f\"Selecting n = {n_adapt} random adaption subjects from TDs, stratified on age, 50/50 male/female\")\n",
    "\n",
    "# form 0.5 year age bins\n",
    "imgn[\"age_group\"] = pd.cut(\n",
    "    imgn.age, \n",
    "    bins=list(np.arange(13,25.1,0.5)),\n",
    "    labels=[f\">{i}<={i+0.5}\" for i in np.arange(13,25,0.5)]\n",
    ")\n",
    "\n",
    "# new variable\n",
    "imgn[\"adapt\"] = False\n",
    "\n",
    "# iterate sites\n",
    "for site in imgn.site.unique():\n",
    "    # iterate sexes\n",
    "    for sex in [\"F\", \"M\"]:\n",
    "        \n",
    "        # df from which subjects are drawn (subjects with T0 but no T2 to keep study cohort large) \n",
    "        temp = imgn.loc[(imgn.site==site) & (imgn.td==\"TD\") & (imgn.tp==\"T0\") & (imgn.sex_str==sex), :].copy()\n",
    "        # drop age groups with less than two subjects \n",
    "        for group in temp.age_group.unique():\n",
    "            if len(temp[temp.age_group==group])==1:\n",
    "                print(f\"Dropping group {group}, n = {len(temp[temp.age_group==group])}\")\n",
    "                temp = temp[temp.age_group!=group]\n",
    "        print(f\"Site: {site}, n({sex}) = {len(temp)}\")\n",
    "        # select adaptation subjects if enough subjects available for site and sex\n",
    "        if len(temp) > int(n_adapt/2):\n",
    "            adapt, _ = train_test_split(\n",
    "                temp.id,\n",
    "                stratify=temp[\"age_group\"],\n",
    "                train_size=int(n_adapt/2),\n",
    "                random_state=42\n",
    "            )\n",
    "        # if not, just use all\n",
    "        else:\n",
    "            adapt = temp.id \n",
    "            \n",
    "        # store if adaptation subject\n",
    "        imgn.loc[(adapt.to_list(), ), \"adapt\"] = True\n",
    "    \n",
    "    print(f\"n(adapt) = {len(imgn[(imgn.site==site) & (imgn.adapt==True) & (imgn.tp=='T0')])}\")\n",
    "           \n",
    "print(\"Number of adaptation subjects (tp's combined):\", len(imgn[(imgn.adapt==True) & (imgn.tp==\"T0\")]))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall number of unique subjects: n = 1522\n",
      "Overall number of unique subjects with longitudinal data: n = 1412\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">All</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Braincharts: Adaptation</th>\n",
       "      <th colspan=\"6\" halign=\"left\">Braincharts: Test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">T0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">T5</th>\n",
       "      <th colspan=\"2\" halign=\"left\">T8</th>\n",
       "      <th colspan=\"2\" halign=\"left\">T0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">T0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">T5</th>\n",
       "      <th colspan=\"2\" halign=\"left\">T8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Age [y]</th>\n",
       "      <th>Male [%]</th>\n",
       "      <th>Age [y]</th>\n",
       "      <th>Male [%]</th>\n",
       "      <th>Age [y]</th>\n",
       "      <th>Male [%]</th>\n",
       "      <th>Age</th>\n",
       "      <th>Male [%]</th>\n",
       "      <th>Age [y]</th>\n",
       "      <th>Male [%]</th>\n",
       "      <th>Age [y]</th>\n",
       "      <th>Male [%]</th>\n",
       "      <th>Age [y]</th>\n",
       "      <th>Male [%]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>1326.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1312.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1094.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1166.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1177.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>985.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sd</th>\n",
       "      <td>0.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.67</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>14.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.42</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.73</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            All                                               \\\n",
       "             T0                T5                T8            \n",
       "        Age [y] Male [%]  Age [y] Male [%]  Age [y] Male [%]   \n",
       "n       1326.00      NaN  1312.00      NaN  1094.00      NaN   \n",
       "mean      14.44      NaN    19.00      NaN    22.56      NaN   \n",
       "%           NaN    47.29      NaN    47.64      NaN    46.62   \n",
       "sd         0.40      NaN     0.77      NaN     0.66      NaN   \n",
       "median    14.42      NaN    18.80      NaN    22.42      NaN   \n",
       "min       13.26      NaN    17.74      NaN    20.73      NaN   \n",
       "max       15.71      NaN    22.55      NaN    25.11      NaN   \n",
       "\n",
       "       Braincharts: Adaptation          Braincharts: Test                    \\\n",
       "                            T0                         T0                T5   \n",
       "                           Age Male [%]           Age [y] Male [%]  Age [y]   \n",
       "n                       160.00      NaN           1166.00      NaN  1177.00   \n",
       "mean                     14.43      NaN             14.45      NaN    19.00   \n",
       "%                          NaN     50.0               NaN    46.91      NaN   \n",
       "sd                        0.39      NaN              0.40      NaN     0.78   \n",
       "median                   14.40      NaN             14.42      NaN    18.79   \n",
       "min                      13.52      NaN             13.26      NaN    17.74   \n",
       "max                      15.30      NaN             15.71      NaN    22.55   \n",
       "\n",
       "                                  \n",
       "                     T8           \n",
       "       Male [%] Age [y] Male [%]  \n",
       "n           NaN  985.00      NaN  \n",
       "mean        NaN   22.55      NaN  \n",
       "%         47.32     NaN    46.09  \n",
       "sd          NaN    0.67      NaN  \n",
       "median      NaN   22.42      NaN  \n",
       "min         NaN   20.73      NaN  \n",
       "max         NaN   25.11      NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Overall number of unique subjects: n =\", len(imgn.id.unique()))\n",
    "print(\"Overall number of unique subjects with longitudinal data: n =\", len(imgn[imgn.tp_one==False].id.unique()))\n",
    "\n",
    "imgn[\"sex_str\"] = pd.Categorical(imgn.sex_str)\n",
    "imgn_sample_stats = dict()\n",
    "for tp in [\"T0\", \"T5\", \"T8\"]:\n",
    "    # All\n",
    "    imgn_sample_stats[(\"All\", tp, \"Age [y]\")] = imgn.loc[(imgn.tp==tp), \"age\"].describe()\n",
    "    imgn_sample_stats[(\"All\", tp, \"Male [%]\")] = pd.Series({\n",
    "        \"count\": len(imgn.loc[(imgn.tp==tp)].sex_str),\n",
    "        \"%\": len(imgn.loc[(imgn.tp==tp) & (imgn.sex_str==\"M\")]),\n",
    "    })\n",
    "    # Test\n",
    "    imgn_sample_stats[(\"Braincharts: Test\", tp, \"Age [y]\")] = imgn.loc[(imgn.adapt==False) & (imgn.tp==tp), \"age\"].describe()\n",
    "    imgn_sample_stats[(\"Braincharts: Test\", tp, \"Male [%]\")] = pd.Series({\n",
    "        \"count\": len(imgn.loc[(imgn.adapt==False) & (imgn.tp==tp)].sex_str),\n",
    "        \"%\": len(imgn.loc[(imgn.adapt==False) & (imgn.tp==tp) & (imgn.sex_str==\"M\")]),\n",
    "    })\n",
    "    # Adapt\n",
    "    if tp==\"T0\":\n",
    "        imgn_sample_stats[(\"Braincharts: Adaptation\", tp, \"Age\")] = imgn.loc[(imgn.adapt==True) & (imgn.tp==tp), \"age\"].describe()\n",
    "        imgn_sample_stats[(\"Braincharts: Adaptation\", tp, \"Male [%]\")] = pd.Series({\n",
    "            \"count\": len(imgn.loc[(imgn.adapt==True) & (imgn.tp==tp)].sex_str),\n",
    "            \"%\": len(imgn.loc[(imgn.adapt==True) & (imgn.tp==tp) & (imgn.sex_str==\"M\")]),\n",
    "        })\n",
    "imgn_sample_stats = pd.DataFrame(imgn_sample_stats).sort_index(axis=1)\n",
    "imgn_sample_stats = imgn_sample_stats.loc[[\"count\", \"mean\", \"%\", \"std\", \"50%\", \"min\", \"max\"],:]\n",
    "imgn_sample_stats = imgn_sample_stats.rename(index={\"count\":\"n\", \"50%\":\"median\", \"std\":\"sd\"})\n",
    "imgn_sample_stats.loc[\"%\",:] = imgn_sample_stats.loc[\"%\",:] / imgn_sample_stats.loc[\"n\",:] * 100\n",
    "imgn_sample_stats.loc[\"n\", (na(), na(), \"Male [%]\")] = np.nan\n",
    "imgn_sample_stats = imgn_sample_stats.round(2)\n",
    "imgn_sample_stats.to_csv(join(imgn_dir, \"imgn_samplestats.csv\"))\n",
    "imgn_sample_stats"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reorder variables\n",
    "imgn = reorder_vars([\"id\", \"tp\", \"tp_T0T5\", \"tp_T0T8\", \"tp_T5T8\", \"tp_one\", \n",
    "                        \"site\", \"site_str\", \"adapt\", \n",
    "                        \"age\", \"age_group\", \n",
    "                        \"sex\", \"sex_str\",\n",
    "                        \"td\"],\n",
    "                        imgn,\n",
    "                        destrieux_idps)\n",
    "# sort rows\n",
    "imgn = imgn.sort_index(axis=\"index\")\n",
    "# save QC dataframe\n",
    "imgn.reset_index(drop=True).to_feather(join(imgn_dir, \"imgn.ftr\"))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview plots"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1296x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Boxplots\n",
    "\n",
    "fig, axes = plt.subplots(1,3, figsize=(15,6), sharey=True)\n",
    "\n",
    "# iterate timepoints\n",
    "for i, tp in enumerate([\"T0\", \"T5\", \"T8\"]):\n",
    "    # data prior to QC at given TP\n",
    "    data = imgn_preqc.query(\"(tp==@tp)\")\n",
    "    # scatters\n",
    "    sns.stripplot(\n",
    "        y=data.SurfaceHoles, \n",
    "        x=data.site_str,\n",
    "        ax=axes[i],\n",
    "        alpha=0.5,\n",
    "        linewidth=0.5,\n",
    "        edgecolor=\"k\",\n",
    "        size=3,\n",
    "        zorder=0\n",
    "    )\n",
    "    # boxplots\n",
    "    sns.boxplot(\n",
    "        y=data.SurfaceHoles, \n",
    "        x=data.site_str,\n",
    "        ax=axes[i],\n",
    "        fliersize=0,\n",
    "    )\n",
    "    # layout\n",
    "    axes[i].set_title(f\"{tp} (n = {len(data)})\", weight=\"semibold\")\n",
    "    axes[i].set_xlabel(\"\")\n",
    "    axes[i].set_ylabel(\"Total number of surface holes\" if tp==\"T0\" else \"\")\n",
    "    axes[i].set_xticklabels([s.get_text().title() for s in axes[i].get_xticklabels()], rotation=30, ha=\"right\")\n",
    "    axes[i].axhline(data.topo_thresh.values[0], c=\"red\")     \n",
    "plt.tight_layout()\n",
    "\n",
    "# save\n",
    "fig.savefig(join(wd, \"plots\", \"abcd-imgn\", \"qc_imgn_boxplots.pdf\"))\n",
    "\n",
    "\n",
    "## Histograms\n",
    "\n",
    "fig, axes = plt.subplots(1,imgn.site.max(), figsize=(18,3))\n",
    "axes = axes.ravel()\n",
    "\n",
    "# iterate sites and tps\n",
    "for i, site in enumerate(imgn.site_str.unique()):\n",
    "    for tp, c in zip([\"T0\", \"T5\", \"T8\"], [\"tab:blue\", \"tab:orange\", \"tab:green\"]):\n",
    "        \n",
    "        # data by TP and site\n",
    "        data = imgn_preqc.query(\"(tp==@tp) & (site_str==@site)\")\n",
    "        \n",
    "        # plot\n",
    "        axes[i].hist(\n",
    "            data.SurfaceHoles,\n",
    "            bins=40,\n",
    "            alpha=0.7,\n",
    "            color=c,\n",
    "            label=tp\n",
    "        )\n",
    "        \n",
    "        # layout\n",
    "        axes[i].legend()\n",
    "        axes[i].set_title(site.title(), weight=\"semibold\")\n",
    "    axes[i].axvline(data.topo_thresh.values[0], c=\"red\")\n",
    "    \n",
    "axes[0].set_ylabel(\"Count\")\n",
    "fig.supxlabel(\"Total number of surface holes\")\n",
    "fig.tight_layout()\n",
    "\n",
    "# save\n",
    "fig.savefig(join(wd, \"plots\", \"abcd-imgn\", \"qc_imgn_histograms.pdf\"))\n",
    "\n",
    "# source data\n",
    "source_data = imgn_preqc.groupby([\"tp\", \"site_str\"])[\"SurfaceHoles\"].describe()\n",
    "source_data[\"threshold\"] = imgn_preqc.topo_thresh.iloc[0]\n",
    "source_data.index.names = [\"timepoint\", \"site\"]\n",
    "source_data.to_csv(join(wd, \"plots\", \"abcd-imgn\", \"qc_imgn_boxplots_source.csv\"))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get predictions and site-corrected CT values by Braincharts model\n",
    "\n",
    "We use code adapted from the notebook associated with Rutherford et al., 2022.  \n",
    "We adapt the normative models to the site effects of the IMGN FreeSurfer 7.1.1 data, make predictions for the new subjects, and use the models to adjust the original CT data from the IMGN subjects for site effects. \n",
    "\n",
    "Sources:  \n",
    "Tutorial notebook: [https://pcntoolkit.readthedocs.io/en/latest/pages/apply_normative_models.html](https://pcntoolkit.readthedocs.io/en/latest/pages/apply_normative_models.html)  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict & Adapt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3bae4efb155b4a6ca595a7d7ec6fe914",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iterating IDPs:   0%|          | 0/149 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDP 0: lh_G&S_frontomargin_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 1: lh_G&S_occipital_inf_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 2: lh_G&S_paracentral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 3: lh_G&S_subcentral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 4: lh_G&S_transv_frontopol_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 5: lh_G&S_cingul-Ant_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 6: lh_G&S_cingul-Mid-Ant_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 7: lh_G&S_cingul-Mid-Post_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 8: lh_G_cingul-Post-dorsal_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 9: lh_G_cingul-Post-ventral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 10: lh_G_cuneus_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 11: lh_G_front_inf-Opercular_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 12: lh_G_front_inf-Orbital_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 13: lh_G_front_inf-Triangul_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 14: lh_G_front_middle_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 15: lh_G_front_sup_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 16: lh_G_Ins_lg&S_cent_ins_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 17: lh_G_insular_short_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 18: lh_G_occipital_middle_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 19: lh_G_occipital_sup_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 20: lh_G_oc-temp_lat-fusifor_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 21: lh_G_oc-temp_med-Lingual_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 22: lh_G_oc-temp_med-Parahip_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 23: lh_G_orbital_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 24: lh_G_pariet_inf-Angular_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 25: lh_G_pariet_inf-Supramar_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 26: lh_G_parietal_sup_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 27: lh_G_postcentral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 28: lh_G_precentral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 29: lh_G_precuneus_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 30: lh_G_rectus_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 31: lh_G_subcallosal_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 32: lh_G_temp_sup-G_T_transv_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 33: lh_G_temp_sup-Lateral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 34: lh_G_temp_sup-Plan_polar_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 35: lh_G_temp_sup-Plan_tempo_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 36: lh_G_temporal_inf_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 37: lh_G_temporal_middle_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 38: lh_Lat_Fis-ant-Horizont_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 39: lh_Lat_Fis-ant-Vertical_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 40: lh_Lat_Fis-post_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 41: lh_Pole_occipital_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 42: lh_Pole_temporal_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 43: lh_S_calcarine_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 44: lh_S_central_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 45: lh_S_cingul-Marginalis_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 46: lh_S_circular_insula_ant_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 47: lh_S_circular_insula_inf_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 48: lh_S_circular_insula_sup_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 49: lh_S_collat_transv_ant_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 50: lh_S_collat_transv_post_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 51: lh_S_front_inf_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 52: lh_S_front_middle_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 53: lh_S_front_sup_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 54: lh_S_interm_prim-Jensen_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 55: lh_S_intrapariet&P_trans_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 56: lh_S_oc_middle&Lunatus_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 57: lh_S_oc_sup&transversal_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 58: lh_S_occipital_ant_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 59: lh_S_oc-temp_lat_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 60: lh_S_oc-temp_med&Lingual_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 61: lh_S_orbital_lateral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 62: lh_S_orbital_med-olfact_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 63: lh_S_orbital-H_Shaped_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 64: lh_S_parieto_occipital_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 65: lh_S_pericallosal_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 66: lh_S_postcentral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 67: lh_S_precentral-inf-part_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 68: lh_S_precentral-sup-part_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 69: lh_S_suborbital_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 70: lh_S_subparietal_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 71: lh_S_temporal_inf_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 72: lh_S_temporal_sup_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 73: lh_S_temporal_transverse_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 74: rh_G&S_frontomargin_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 75: rh_G&S_occipital_inf_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 76: rh_G&S_paracentral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 77: rh_G&S_subcentral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 78: rh_G&S_transv_frontopol_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 79: rh_G&S_cingul-Ant_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 80: rh_G&S_cingul-Mid-Ant_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 81: rh_G&S_cingul-Mid-Post_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 82: rh_G_cingul-Post-dorsal_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 83: rh_G_cingul-Post-ventral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 84: rh_G_cuneus_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 85: rh_G_front_inf-Opercular_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 86: rh_G_front_inf-Orbital_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 87: rh_G_front_inf-Triangul_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 88: rh_G_front_middle_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 89: rh_G_front_sup_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 90: rh_G_Ins_lg&S_cent_ins_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 91: rh_G_insular_short_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 92: rh_G_occipital_middle_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 93: rh_G_occipital_sup_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 94: rh_G_oc-temp_lat-fusifor_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 95: rh_G_oc-temp_med-Lingual_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 96: rh_G_oc-temp_med-Parahip_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 97: rh_G_orbital_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 98: rh_G_pariet_inf-Angular_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 99: rh_G_pariet_inf-Supramar_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 100: rh_G_parietal_sup_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 101: rh_G_postcentral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 102: rh_G_precentral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 103: rh_G_precuneus_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 104: rh_G_rectus_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 105: rh_G_subcallosal_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 106: rh_G_temp_sup-G_T_transv_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 107: rh_G_temp_sup-Lateral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 108: rh_G_temp_sup-Plan_polar_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 109: rh_G_temp_sup-Plan_tempo_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 110: rh_G_temporal_inf_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 111: rh_G_temporal_middle_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 112: rh_Lat_Fis-ant-Horizont_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 113: rh_Lat_Fis-ant-Vertical_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 114: rh_Lat_Fis-post_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 115: rh_Pole_occipital_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 116: rh_Pole_temporal_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 117: rh_S_calcarine_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 118: rh_S_central_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 119: rh_S_cingul-Marginalis_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 120: rh_S_circular_insula_ant_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 121: rh_S_circular_insula_inf_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 122: rh_S_circular_insula_sup_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 123: rh_S_collat_transv_ant_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 124: rh_S_collat_transv_post_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 125: rh_S_front_inf_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 126: rh_S_front_middle_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 127: rh_S_front_sup_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 128: rh_S_interm_prim-Jensen_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 129: rh_S_intrapariet&P_trans_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 130: rh_S_oc_middle&Lunatus_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 131: rh_S_oc_sup&transversal_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 132: rh_S_occipital_ant_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 133: rh_S_oc-temp_lat_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 134: rh_S_oc-temp_med&Lingual_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 135: rh_S_orbital_lateral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 136: rh_S_orbital_med-olfact_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 137: rh_S_orbital-H_Shaped_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 138: rh_S_parieto_occipital_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 139: rh_S_pericallosal_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 140: rh_S_postcentral_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 141: rh_S_precentral-inf-part_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 142: rh_S_precentral-sup-part_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 143: rh_S_suborbital_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 144: rh_S_subparietal_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 145: rh_S_temporal_inf_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 146: rh_S_temporal_sup_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 147: rh_S_temporal_transverse_thickness\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n",
      "IDP 148: EstimatedTotalIntraCranialVol\n",
      "Preparing test data\n",
      "Preparing adaptation data\n",
      "Predict\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Evaluating the model ...\n",
      "Evaluations Writing outputs ...\n",
      "Writing outputs ...\n",
      "Adjust CT data for site effects\n",
      "Loading data ...\n",
      "Prediction by model  1 of 1\n",
      "Writing outputs ...\n",
      "Saving results\n"
     ]
    }
   ],
   "source": [
    "# get datasets (adaptation + all others)\n",
    "data_test = imgn[imgn.adapt==False]\n",
    "data_adapt = imgn[(imgn.adapt==True) & (imgn.tp==\"T0\")]\n",
    "\n",
    "# adapt the Braincharts model to data and get predictions\n",
    "data_pred = get_adapted_predictions(\n",
    "    model_dir=ct_model_dir,\n",
    "    data_test=data_test,\n",
    "    data_adapt=data_adapt,\n",
    "    cols_cov=[\"age\", \"sex\"],\n",
    "    col_site=\"site\",\n",
    "    age_bspline_min=-5, \n",
    "    age_bspline_max=110, \n",
    "    site_ids_tr=list(np.loadtxt(join(wd, \"model_rutherford\", \"site_ids_82sites.txt\"), dtype=str)), \n",
    "    idps=destrieux_idps + [\"EstimatedTotalIntraCranialVol\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Harmonize CT data independent of the braincharts model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[neuroHarmonize]: smoothing more than 10 variables may take several minutes of computation.\n"
     ]
    }
   ],
   "source": [
    "from neuroHarmonize import harmonizationLearn\n",
    "\n",
    "model_harm, data_harm = harmonizationLearn(\n",
    "    data=imgn[destrieux_idps + [\"EstimatedTotalIntraCranialVol\"]].to_numpy(),\n",
    "    covars=imgn[[\"site\", \"age\", \"sex\", \"tp\"]].rename(columns={\"site\":\"SITE\"}).replace({\"T0\":0, \"T5\":1, \"T8\":2}),\n",
    "    smooth_terms=[\"age\"],\n",
    "    smooth_term_bounds=[np.floor(imgn.age.min()), np.ceil(imgn.age.max())],\n",
    "    seed=42\n",
    ")\n",
    "data_harm = pd.DataFrame(\n",
    "    data_harm, \n",
    "    columns=destrieux_idps + [\"EstimatedTotalIntraCranialVol\"], \n",
    "    index=imgn.index\n",
    ")\n",
    "data_harm = data_harm.rename(columns={c: c+\"-harm\" for c in data_harm.columns}).sort_index()\n",
    "#data_harm"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# join with predicted data\n",
    "imgn_pred = imgn.merge(data_pred, left_index=True, right_index=True, how=\"left\", suffixes=(\"\", \"_DUPL\"))\n",
    "\n",
    "# join with harmonized data\n",
    "imgn_pred = imgn_pred.merge(data_harm, left_index=True, right_index=True, suffixes=(\"\", \"_DUPL\"))\n",
    "\n",
    "# remove duplicates\n",
    "imgn_pred = imgn_pred[[c for c in imgn_pred.columns if not c.endswith(\"_DUPL\")]]\n",
    "\n",
    "# reorder variables\n",
    "imgn_pred = reorder_vars([\"id\", \"tp\", \"site\", \"site_str\", \"adapt\", \n",
    "                          \"age\", \"age_group\", \n",
    "                          \"sex\", \"sex_str\",\n",
    "                          \"td\"],\n",
    "                         imgn_pred,\n",
    "                         destrieux_idps)\n",
    "\n",
    "# save predictor dataframe\n",
    "imgn_pred.reset_index().to_feather(join(imgn_dir, \"imgn_pred.ftr\"))\n",
    "#imgn_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.11 ('ntdev')",
   "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": "3.9.11"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "55a5a13bab2c2636ad1654b177bb15c7b38b3aeaddbba167703e2619b356d1fa"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}