1_spatialHeterogeneity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preterm brain development is heterogeneous\n",
    "For each modality (i.e., CT, SA, and measures of cerebral tissue volume) and each dataset, we will now show that average dysmaturation outcomes, derived from classic case-control studies, are not representative for individual brain outcomes. Results demonstrate that brain development after preterm birth is highly heterogeneous. \n",
    "For this, we will:\n",
    "1. Calculate the average dysmaturation outcome by comparing preterm subjects to full-term controls using a linear model (brain_measure ~ diagnosis + age + sex)\n",
    "2. Calculate the individual brain abnormality pattern (IBAP) for each subject based on the BrainChart framework\n",
    "3. Calculate the percentage of subjects with extranormal deviations\n",
    "4. Compare spatial similarity of IBAPs among preterm individuals using cross-correlation of binarized IBAPs\n",
    "\n",
    "\n",
    "In step 2, we will refit the pretrained braincharts model to the selected dataset. Since this requires some computational power, several cores can be used in parallel to speed up the process. This requires the following data:\n",
    "- some functions from the original BrainChart project written by R. Bethlehem and J. Seidlitz are needed which are not yet openly available.\n",
    "- the pretrained model which is also not openly shared yet. Adjust `braincharts_sw_dir` accordingly.\n",
    "Both will be made available upon publication of the manuscript.\n",
    "\n",
    "Please contact the authors for further information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# manually set path to R if there are problems with the automatic detection\n",
    "# import os\n",
    "# os.environ['R_HOME'] = '/Library/Frameworks/R.framework/R'  # adapt to your R installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# Use rpy2 to run R in python notebook for braincharts\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import join\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import gc  # for clearing unnecessary variables\n",
    "\n",
    "sns.set(style='white')\n",
    "\n",
    "# import custom functions\n",
    "import sys\n",
    "sys.path.append('code')\n",
    "from analysis_helpers import get_roi_names, group_comparison\n",
    "from plotting import get_significant_cortical_vals_for_plotting, plot_brain_map, plot_group_difference_global, plot_individual_brain_maps\n",
    "from spatial_heterogeneity import *\n",
    "\n",
    "# directories\n",
    "data_dir = 'data'\n",
    "outputs_dir = join('outputs', 'spatial_heterogeneity')\n",
    "os.makedirs(outputs_dir, exist_ok=True)\n",
    "\n",
    "#braincharts_sw_dir = 'code/BrainCharts'  # where the pretrained models and code from BrainCharts are stored; '/Users/melissa/Dokumente/PhD/sw/BrainCharts'\n",
    "braincharts_sw_dir = '/Users/melissa/Dokumente/PhD/sw/BrainCharts'\n",
    "\n",
    "# some settings\n",
    "brain_measure = 'CT'  # CT or SA\n",
    "dataset = 'BLS-26'\n",
    "scale = 1  # scale for brain plots; run with scale = 10 for higher resolution\n",
    "\n",
    "out_dir_main = join(outputs_dir, dataset)\n",
    "os.makedirs(out_dir_main, exist_ok=True)\n",
    "\n",
    "\n",
    "refit_models = True  # whether to repeat adaptation of the pretrained models to the new data, computationally intense"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get list of ROIs\n",
    "rois = get_roi_names(brain_measure, global_vars=True)\n",
    "if brain_measure == 'CT':\n",
    "    rois.append('meanCT2')\n",
    "\n",
    "# load preprocessed data\n",
    "if dataset.startswith('BLS') or dataset.startswith('ABCD'):\n",
    "    dataset_name = dataset[:-3]\n",
    "else:\n",
    "    dataset_name = dataset\n",
    "data = pd.read_csv(join(data_dir, dataset_name, 'derivatives', f'{dataset}_{brain_measure}_preprocessed.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Average dysmaturation outcome after preterm birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_dir = join(out_dir_main, 'average_dysmaturation')\n",
    "os.makedirs(out_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t_statistic</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_fdr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROI</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CT_bankssts</th>\n",
       "      <td>-2.969</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_caudalanteriorcingulate</th>\n",
       "      <td>1.010</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_caudalmiddlefrontal</th>\n",
       "      <td>-1.041</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_cuneus</th>\n",
       "      <td>-0.565</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_entorhinal</th>\n",
       "      <td>-1.347</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_fusiform</th>\n",
       "      <td>-2.666</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_inferiorparietal</th>\n",
       "      <td>-4.632</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>&lt;0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_inferiortemporal</th>\n",
       "      <td>-1.746</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_isthmuscingulate</th>\n",
       "      <td>3.071</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_lateraloccipital</th>\n",
       "      <td>-0.054</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_lateralorbitofrontal</th>\n",
       "      <td>-0.458</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_lingual</th>\n",
       "      <td>-1.220</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_medialorbitofrontal</th>\n",
       "      <td>3.398</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_middletemporal</th>\n",
       "      <td>-6.518</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>&lt;0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_parahippocampal</th>\n",
       "      <td>-0.478</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_paracentral</th>\n",
       "      <td>1.630</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_parsopercularis</th>\n",
       "      <td>-1.900</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_parsorbitalis</th>\n",
       "      <td>-6.108</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>&lt;0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_parstriangularis</th>\n",
       "      <td>-2.909</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_pericalcarine</th>\n",
       "      <td>0.877</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_postcentral</th>\n",
       "      <td>-2.251</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_posteriorcingulate</th>\n",
       "      <td>2.393</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_precentral</th>\n",
       "      <td>-2.752</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_precuneus</th>\n",
       "      <td>-1.003</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_rostralanteriorcingulate</th>\n",
       "      <td>0.823</td>\n",
       "      <td>0.411</td>\n",
       "      <td>0.504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_rostralmiddlefrontal</th>\n",
       "      <td>-3.565</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_superiorfrontal</th>\n",
       "      <td>0.799</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_superiorparietal</th>\n",
       "      <td>-2.858</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_superiortemporal</th>\n",
       "      <td>-1.029</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_supramarginal</th>\n",
       "      <td>-3.749</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_frontalpole</th>\n",
       "      <td>-0.305</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_temporalpole</th>\n",
       "      <td>-1.443</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_transversetemporal</th>\n",
       "      <td>0.031</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT_insula</th>\n",
       "      <td>-2.969</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMV</th>\n",
       "      <td>-4.686</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>&lt;0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMV</th>\n",
       "      <td>-5.298</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>&lt;0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sGMV</th>\n",
       "      <td>-9.216</td>\n",
       "      <td>&lt;0.001</td>\n",
       "      <td>&lt;0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meanCT2</th>\n",
       "      <td>-2.423</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             t_statistic p_value   p_fdr\n",
       "ROI                                                     \n",
       "CT_bankssts                       -2.969   0.003   0.011\n",
       "CT_caudalanteriorcingulate         1.010   0.314   0.415\n",
       "CT_caudalmiddlefrontal            -1.041   0.299   0.415\n",
       "CT_cuneus                         -0.565   0.572   0.659\n",
       "CT_entorhinal                     -1.347   0.179   0.284\n",
       "CT_fusiform                       -2.666   0.008    0.02\n",
       "CT_inferiorparietal               -4.632  <0.001  <0.001\n",
       "CT_inferiortemporal               -1.746   0.082   0.149\n",
       "CT_isthmuscingulate                3.071   0.002   0.009\n",
       "CT_lateraloccipital               -0.054   0.957   0.975\n",
       "CT_lateralorbitofrontal           -0.458   0.648   0.703\n",
       "CT_lingual                        -1.220   0.224    0.34\n",
       "CT_medialorbitofrontal             3.398   0.001   0.003\n",
       "CT_middletemporal                 -6.518  <0.001  <0.001\n",
       "CT_parahippocampal                -0.478   0.633   0.703\n",
       "CT_paracentral                     1.630   0.105   0.181\n",
       "CT_parsopercularis                -1.900   0.059   0.112\n",
       "CT_parsorbitalis                  -6.108  <0.001  <0.001\n",
       "CT_parstriangularis               -2.909   0.004   0.012\n",
       "CT_pericalcarine                   0.877   0.381   0.483\n",
       "CT_postcentral                    -2.251   0.026   0.051\n",
       "CT_posteriorcingulate              2.393   0.018   0.037\n",
       "CT_precentral                     -2.752   0.006   0.016\n",
       "CT_precuneus                      -1.003   0.317   0.415\n",
       "CT_rostralanteriorcingulate        0.823   0.411   0.504\n",
       "CT_rostralmiddlefrontal           -3.565  <0.001   0.002\n",
       "CT_superiorfrontal                 0.799   0.425   0.505\n",
       "CT_superiorparietal               -2.858   0.005   0.013\n",
       "CT_superiortemporal               -1.029   0.305   0.415\n",
       "CT_supramarginal                  -3.749  <0.001   0.001\n",
       "CT_frontalpole                    -0.305    0.76   0.803\n",
       "CT_temporalpole                   -1.443   0.151   0.249\n",
       "CT_transversetemporal              0.031   0.975   0.975\n",
       "CT_insula                         -2.969   0.003   0.011\n",
       "WMV                               -4.686  <0.001  <0.001\n",
       "GMV                               -5.298  <0.001  <0.001\n",
       "sGMV                              -9.216  <0.001  <0.001\n",
       "meanCT2                           -2.423   0.016   0.036"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tvals_mr = group_comparison(rois, data, covariates=['age_days', 'sex'])\n",
    "tvals_mr.to_csv(join(out_dir, f'{dataset}_{brain_measure}_average_dysmaturation.csv'), index=False)\n",
    "\n",
    "# show table and round every value to 3 decimal places, replace 0.000 with <0.001\n",
    "display(tvals_mr.set_index('ROI').round(3).replace(0.000, '<0.001'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/average_dysmaturation/BLS-26_CT_avg_dysmaturation_term-preterm.svg\n"
     ]
    }
   ],
   "source": [
    "# plot the t-statistic only for significant ROIs after FDR-correction\n",
    "rois_cortical = get_roi_names(brain_measure, global_vars=False)\n",
    "sig_t = get_significant_cortical_vals_for_plotting(data=tvals_mr, rois_cortical=rois_cortical, statistic='t_statistic', bilateral=True)\n",
    "\n",
    "outname = join(out_dir, f'{dataset}_{brain_measure}_avg_dysmaturation_term-preterm.svg')\n",
    "plot_brain_map(sig_t, outname, scale=scale, limits=(-5, 5))  # larger scale improves image quality\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "CN vs. preterm: t-test independent samples with Benjamini-Hochberg correction, P_val:4.573e-05 t=4.168e+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/melissa/Dokumente/PhD/01B_preterm_normative/scripts/normative_modeling_gamlss/final_repo/code/plotting.py:89: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(['Term', 'Preterm'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/melissa/Dokumente/PhD/01B_preterm_normative/scripts/normative_modeling_gamlss/final_repo/code/plotting.py:89: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(['Term', 'Preterm'])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted group difference for GMV and saved it to outputs/spatial_heterogeneity/BLS-26/average_dysmaturation/BLS-26_GMV_avg_dysmaturation_term-preterm.svg\n",
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "CN vs. preterm: t-test independent samples with Benjamini-Hochberg correction, P_val:4.224e-05 t=4.187e+00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted group difference for WMV and saved it to outputs/spatial_heterogeneity/BLS-26/average_dysmaturation/BLS-26_WMV_avg_dysmaturation_term-preterm.svg\n",
      "p-value annotation legend:\n",
      "      ns: 5.00e-02 < p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "CN vs. preterm: t-test independent samples with Benjamini-Hochberg correction, P_val:6.341e-14 t=8.069e+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/melissa/Dokumente/PhD/01B_preterm_normative/scripts/normative_modeling_gamlss/final_repo/code/plotting.py:89: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(['Term', 'Preterm'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted group difference for sGMV and saved it to outputs/spatial_heterogeneity/BLS-26/average_dysmaturation/BLS-26_sGMV_avg_dysmaturation_term-preterm.svg\n"
     ]
    }
   ],
   "source": [
    "# plot measures of cerebral tissue volume\n",
    "for ctv_measure in ['GMV', 'WMV', 'sGMV']:\n",
    "    outname = join(out_dir, f'{dataset}_{ctv_measure}_avg_dysmaturation_term-preterm.svg')\n",
    "    # in dHCP,x one outlier with sGMV = 0 was removed\n",
    "    data = data[data['sGMV'] > 0]\n",
    "    plot_group_difference_global(data=data, x='dx', y=ctv_measure, yaxis_label = f'{ctv_measure} [mm$^3$]', outname=outname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Individual brain aberration patterns (IBAP) for each subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_dir = join(out_dir_main, f'braincharts_model_adapted_{brain_measure}')\n",
    "os.makedirs(out_dir, exist_ok=True)\n",
    "\n",
    "out_dir_analysis = join(out_dir_main, f'IBAP_{brain_measure}')\n",
    "os.makedirs(out_dir_analysis, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adjust settings for ABCD: longitudinal analysis but split into two timepoints after model adaptation again\n",
    "if dataset_name == 'ABCD':\n",
    "    # combine two timepoints for longitudinal estimation of random effect of study\n",
    "    data = pd.read_csv(join(data_dir, dataset_name, 'derivatives', f'{dataset_name}_{brain_measure}_preprocessed.csv'))\n",
    "    print('Loaded both timepoints for ABCD. Data shape:', data.shape)\n",
    "    \n",
    "    if dataset == 'ABCD-10':\n",
    "        abcd_session = 1\n",
    "    elif dataset == 'ABCD-12':\n",
    "        abcd_session = 2\n",
    "        out_dir = join(outputs_dir, 'ABCD-10', f'braincharts_model_adapted_{brain_measure}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Refit the pretrained model and calculate deviation scores\n",
    "The calculation will be done in R. Since the calculation will take some time, it is recommended to adjust the number of cores in `cpu_num` for higher processing speed. However, stick to the limits of your system!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpu_num = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──\n",
      "✔ dplyr     1.1.4     ✔ readr     2.1.5\n",
      "✔ forcats   1.0.0     ✔ stringr   1.5.1\n",
      "✔ lubridate 1.9.3     ✔ tibble    3.2.1\n",
      "✔ purrr     1.0.2     ✔ tidyr     1.3.1\n",
      "── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "✖ dplyr::collapse() masks nlme::collapse()\n",
      "✖ dplyr::filter()   masks stats::filter()\n",
      "✖ dplyr::lag()      masks stats::lag()\n",
      "ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors\n",
      "[1] \"MODELLING:  CT_bankssts ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_caudalanteriorcingulate ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_caudalmiddlefrontal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_cuneus ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_entorhinal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_fusiform ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_inferiorparietal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_inferiortemporal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_isthmuscingulate ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_lateraloccipital ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_lateralorbitofrontal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_lingual ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_medialorbitofrontal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_middletemporal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_parahippocampal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_paracentral ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_parsopercularis ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_parsorbitalis ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_parstriangularis ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_pericalcarine ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_postcentral ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_posteriorcingulate ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_precentral ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_precuneus ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_rostralanteriorcingulate ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_rostralmiddlefrontal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_superiorfrontal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_superiorparietal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_superiortemporal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_supramarginal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_frontalpole ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_temporalpole ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_transversetemporal ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  CT_insula ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  WMV ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  GMV ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  sGMV ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n",
      "[1] \"MODELLING:  meanCT2 ....................\"\n",
      "[1] \"/Users/melissa/Dokumente/PhD/sw/BrainCharts/BrainCharts_refit_28022024\"\n",
      "NewData inspected and any novel studies identified. Finding Maximum Likelihood estimates for random-effects. \n",
      "Computing novel random-effects for bootstrap replicates using 2 cores. This may take some time.\n",
      "Novel bootstrap estimaton complete.\n",
      "Novel study random-effects calculated.\n",
      "Computing bootstrap intervals using 2 cores. This may take some time.\n",
      "Bootstrap intervals complete\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Lade nötiges Paket: splines\n",
       "Lade nötiges Paket: gamlss.data\n",
       "\n",
       "Attache Paket: ‘gamlss.data’\n",
       "\n",
       "Das folgende Objekt ist maskiert ‘package:datasets’:\n",
       "\n",
       "    sleep\n",
       "\n",
       "Lade nötiges Paket: gamlss.dist\n",
       "Lade nötiges Paket: nlme\n",
       " **********   GAMLSS Version 5.4-22  ********** \n",
       "For more on GAMLSS look at https://www.gamlss.com/\n",
       "Type gamlssNews() to see new features/changes/bug fixes.\n",
       "\n",
       "Zusätzlich: Es gab 20 Warnungen (Anzeige mit warnings())\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i data -i rois -i out_dir -i braincharts_sw_dir -i cpu_num -i refit_models\n",
    "\n",
    "# load required functions\n",
    "source(file.path(braincharts_sw_dir, '920.calc-novel-wo-subset-function.r'))\n",
    "source('code/braincharts_helpers.R')\n",
    "\n",
    "if (refit_models == TRUE) {\n",
    "    # only if refitting is necessary\n",
    "    \n",
    "    for (roi in rois) { \n",
    "        # adapt the pretrained model for each ROI for the current dataset and calculate deviation scores for each subject in dataset\n",
    "        if (roi == 'SA_entorhinal') {\n",
    "            # skip SA entorhinal as BrainChart estimation failed due to low data consistency\n",
    "            next\n",
    "        }\n",
    "        result <- fit_braincharts_model(roi, data, braincharts_sw_dir, out_dir, plotting=FALSE, ncores=cpu_num)\n",
    "    } \n",
    "\n",
    "} else {\n",
    "    # Check if the directory exists\n",
    "    if (!file.exists(out_dir)) {\n",
    "        warning('Models not yet adapted, please set refit_models to TRUE.')\n",
    "    } else {\n",
    "        message('Models already adapted, skipping refitting.')\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot BrainChart trajectories\n",
    "As an example region, we will plot the growth trajectory of the insula based on adapted braincharts models. Code from the BrainChart project can be obtained from here: https://github.com/brainchart/Lifespan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "roi = f'{brain_measure}_insula'\n",
    "best_analysis_dir = out_dir\n",
    "\n",
    "# save current dir for later use\n",
    "cwd = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"PLOTTING CT_insula .........\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Zusätzlich: Warnmeldungen:\n",
       "1: In eval(ei, envir) :\n",
       "  Have set a fixed random seed, all runs will be identical unless this is changed (may be good or bad, depending on what you want)\n",
       "2: In model.matrix.default(Model.Formula, Model.Frame, contrasts.arg = FITParam$contrasts,  :\n",
       "  Variable 'fs_version' fehlt, ihre Kontraste werden ignoriert\n",
       "3: Use of `term_data_best[[r]]` is discouraged.\n",
       "ℹ Use `.data[[r]]` instead. \n",
       "4: Use of `preterm_data_best[[r]]` is discouraged.\n",
       "ℹ Use `.data[[r]]` instead. \n",
       "5: Use of `term_data_best[[r]]` is discouraged.\n",
       "ℹ Use `.data[[r]]` instead. \n",
       "6: Use of `preterm_data_best[[r]]` is discouraged.\n",
       "ℹ Use `.data[[r]]` instead. \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i braincharts_sw_dir -i out_dir -i cwd -i roi -i best_analysis_dir -i refit_models\n",
    "\n",
    "if (refit_models == TRUE) {\n",
    "    library(ggplot2)\n",
    "    setwd(braincharts_sw_dir)\n",
    "    source('100.common-variables.r')\n",
    "    source('101.common-functions.r')\n",
    "    source('300.variables.r')\n",
    "    source('301.functions.r')\n",
    "    setwd(cwd)\n",
    "    source('code/braincharts_helpers.R')\n",
    "\n",
    "    plot_pop_curves(roi, braincharts_sw_dir, out_dir, best_analysis_dir)\n",
    "} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the corresponding DK region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/braincharts_model_adapted_CT/CT_insula_DK_region.svg\n"
     ]
    }
   ],
   "source": [
    "# mark the relevant rois as 1, the rest is 0\n",
    "marked_roi = pd.DataFrame(rois_cortical)\n",
    "marked_roi['filter'] = (marked_roi[0] == roi).astype(int)\n",
    "roi1 = marked_roi['filter'].to_numpy().squeeze()\n",
    "\n",
    "# duplicate for visualization\n",
    "roi1 = np.tile(roi1,(1,2)).squeeze()\n",
    "\n",
    "# plot\n",
    "outname = os.path.join(out_dir, f'{roi}_DK_region.svg')\n",
    "plot_brain_map(roi1, outname, cmap='Greys', limits=(0,1), scale=scale, fill=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarize results\n",
    "Deviation scores per subject and number of infra- and supranormal regions per subject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (203, 58)\n"
     ]
    }
   ],
   "source": [
    "# clean up not neeced variables to make program run faster\n",
    "gc.collect()\n",
    "\n",
    "# summarize deviation scores per subject\n",
    "cent_df = get_centile_scores_per_subject(out_dir, rois, base_file=f'result_deviation_{brain_measure}_bankssts.csv')\n",
    "cent_df = cent_df.merge(data.drop(columns=rois), on=['participant', 'session'])\n",
    "\n",
    "# calculate the amount of cortical infra- and supranormal deviation per subject\n",
    "cent_scores = cent_df.copy()\n",
    "cent_scores.set_index(['participant', 'session'], inplace=True)\n",
    "cent_scores = cent_scores.filter(regex=f'centile_{brain_measure}')  # filter only for brain_measure\n",
    "\n",
    "cent_scores = get_amount_infra_supra(cent_scores)\n",
    "\n",
    "# merge with cent_df\n",
    "cent_df = pd.merge(cent_df, cent_scores[['participant','session','amount_infranormal', 'amount_supranormal']], on=['participant', 'session'])\n",
    "\n",
    "# display results for random participants as example\n",
    "cent_df[['dx', f'centile_{brain_measure}_middletemporal', 'amount_infranormal', 'amount_supranormal']].iloc[50:55,:].head()\n",
    "\n",
    "# save\n",
    "print('Data shape:', cent_df.shape)\n",
    "cent_df.to_csv(join(out_dir_analysis, f'{dataset}_{brain_measure}_centiles_per_subject.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot some example subject's deviation scores for each ROI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT/BEST-MUC-103_CT_individual_centiles.svg\n",
      "Plotted individual rCTD map for subject BEST-MUC-103 and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT\n",
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT/BEST-MUC-103_CT_individual_centiles_extranormal.svg\n",
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT/BEST-MUC-108_CT_individual_centiles.svg\n",
      "Plotted individual rCTD map for subject BEST-MUC-108 and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT\n",
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT/BEST-MUC-108_CT_individual_centiles_extranormal.svg\n",
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT/BEST-MUC-148_CT_individual_centiles.svg\n",
      "Plotted individual rCTD map for subject BEST-MUC-148 and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT\n",
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT/BEST-MUC-148_CT_individual_centiles_extranormal.svg\n",
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT/BEST-BN-069_CT_individual_centiles.svg\n",
      "Plotted individual rCTD map for subject BEST-BN-069 and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT\n",
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT/BEST-BN-069_CT_individual_centiles_extranormal.svg\n",
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT/BEST-MUC-041_CT_individual_centiles.svg\n",
      "Plotted individual rCTD map for subject BEST-MUC-041 and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT\n",
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT/BEST-MUC-041_CT_individual_centiles_extranormal.svg\n",
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT/BEST-MUC-069_CT_individual_centiles.svg\n",
      "Plotted individual rCTD map for subject BEST-MUC-069 and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT\n",
      "Plotted brain map and saved it to outputs/spatial_heterogeneity/BLS-26/IBAP_CT/BEST-MUC-069_CT_individual_centiles_extranormal.svg\n"
     ]
    }
   ],
   "source": [
    "# plot example subjects for BLS-26\n",
    "if dataset == 'BLS-26':\n",
    "    cent_df_pt = cent_df[cent_df['dx']=='preterm']\n",
    "    cent_df_ft = cent_df[cent_df['dx']=='CN']\n",
    "    cent_df_pt_ctx = cent_df_pt.filter(regex=f'centile_{brain_measure}|participant')\n",
    "\n",
    "    subjects = ['BEST-MUC-103', 'BEST-MUC-108', 'BEST-MUC-148',\n",
    "                'BEST-BN-069', 'BEST-MUC-041', 'BEST-MUC-069']\n",
    "\n",
    "    for subject in subjects:\n",
    "        plot_individual_brain_maps(cent_df_pt_ctx, subject, outdir=out_dir_analysis, scale=scale, cmap='RdBu_r', \n",
    "                                    limits=(0, 1), infra_supra=True, brain_measure=brain_measure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subjects with at least one extranormal deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Percentage of subjects with extranormal deviations\n",
    "Calculate the percentage of subjects with infra- or supranormal deviations for each modality and dataset. For ABCD, BrainChart random study effect were calculated based on longitudinal data. The following results, however, are presented separately for each timepoint. Therefore, the dataset is split again here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# separate groups\n",
    "if dataset_name == 'ABCD':\n",
    "    # split the dataset into the 2 timepoints again here to report results separately\n",
    "    cent_df = cent_df[cent_df['session'] == abcd_session].copy()\n",
    "    ft_scores_df = cent_df[cent_df['dx']=='CN']\n",
    "    pt_scores_df = cent_df[cent_df['dx']=='preterm']\n",
    "else: \n",
    "    ft_scores_df = cent_df[cent_df['dx']=='CN']\n",
    "    pt_scores_df = cent_df[cent_df['dx']=='preterm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full-term subjects with at least one extranormality: 84%\n",
      "Preterm subjects with at least one extranormality: 97%\n"
     ]
    }
   ],
   "source": [
    "# subjects with at least one extranormal deviation\n",
    "percent_ft_extranormal = percent_extranormal(ft_scores_df)\n",
    "print(f'Full-term subjects with at least one extranormality: {percent_ft_extranormal:.0f}%')\n",
    "\n",
    "percent_pt_extranormal = percent_extranormal(pt_scores_df)\n",
    "print(f'Preterm subjects with at least one extranormality: {percent_pt_extranormal:.0f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing extranormal deviations for dataset: BLS-26\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# adjust yticklabels\n",
    "if brain_measure == 'CT':\n",
    "    brain_measure_name = 'CTh'\n",
    "elif brain_measure == 'SA':\n",
    "    brain_measure_name = 'SA'\n",
    "\n",
    "# calculate the percentage of subjects with extranormal deviation per ROI\n",
    "print('Showing extranormal deviations for dataset:', dataset)\n",
    "rois_cortical_cent = ['centile_' + roi for roi in rois_cortical]\n",
    "infra_supra_all = calc_infra_supra_percentage(rois_cortical_cent, ft_scores_df, pt_scores_df)\n",
    "\n",
    "\n",
    "\n",
    "# plot Fig 1d\n",
    "fsize=8\n",
    "plt.close('all')\n",
    "\n",
    "ax = infra_supra_all.plot(x='rois', y=['infranormal_ft', 'supranormal_ft', 'infranormal_pt', 'supranormal_pt'], \n",
    "                            kind=\"barh\", rot=0, figsize=(3,12),fontsize=fsize,\n",
    "                            color=['dimgray','lightgray', 'mediumblue', 'red'],\n",
    "                            legend=True, width=0.75)\n",
    "ax.legend(['Infranormal FT', 'Supranormal FT', 'Infranormal PT', 'Supranormal PT'], fontsize=fsize+1)\n",
    "\n",
    "ax.invert_yaxis()  # labels read top-to-bottom\n",
    "ax.set_xlabel('Subjects with extranormal deviation [%]', fontsize=fsize)\n",
    "ax.set_ylabel('')\n",
    "rois_cortical_names = [brain_measure_name + ' ' + roi.split('_')[1] for roi in rois_cortical]\n",
    "ax.set_yticklabels(rois_cortical_names)\n",
    "\n",
    "# set the x-axis limit\n",
    "xlim = np.round(infra_supra_all[['infranormal_ft','supranormal_ft','infranormal_pt','supranormal_pt']].max().max()*1.1)\n",
    "plt.xlim(0, xlim)\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "plt.savefig(join(out_dir_analysis, f'{dataset}_{brain_measure}_individual_percentage_outside_norm.svg'), \n",
    "            bbox_inches=\"tight\", dpi=300, format='svg')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x141.176 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# global measures\n",
    "rois_global = ['centile_GMV', 'centile_sGMV', 'centile_WMV']\n",
    "infra_supra_global = calc_infra_supra_percentage(rois_global, ft_scores_df, pt_scores_df)\n",
    "fsize=8\n",
    "plt.close('all')\n",
    "\n",
    "ax = infra_supra_global.plot(x='rois', y=['infranormal_ft','supranormal_ft','infranormal_pt','supranormal_pt'], \n",
    "                            kind=\"barh\", rot=0,figsize=(3,12/34*4),fontsize=fsize,\n",
    "                            color=['dimgray','lightgray', 'mediumblue', 'red'],\n",
    "                            legend=False)\n",
    "\n",
    "ax.invert_yaxis()  # labels read top-to-bottom\n",
    "ax.set_xlabel('Subjects with extranormal deviation [%]',fontsize=fsize)\n",
    "ax.set_ylabel('')\n",
    "rois_cortical_names = [roi.split('_')[1] for roi in rois_global]\n",
    "ax.set_yticklabels(rois_cortical_names)\n",
    "\n",
    "# set the x-axis limit\n",
    "xlim = np.round(infra_supra_global[['infranormal_ft','supranormal_ft','infranormal_pt','supranormal_pt']].max().max()*1.1)\n",
    "plt.xlim(0, xlim)\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "plt.savefig(join(out_dir_analysis, f'{dataset}_individual_percentage_outside_norm_global.svg'), \n",
    "            bbox_inches=\"tight\", dpi=300, format='svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Compare spatial similarity of IBAPs among preterm individuals \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_dir = join(out_dir_main, f'IBAP_similarity_{brain_measure}')\n",
    "os.makedirs(out_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 250x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# binarize extranormal deviations of preterm subjects (Fig. 1e)\n",
    "plt.close('all')\n",
    "cent_df_pt = cent_df[cent_df['dx']=='preterm']\n",
    "dev_scores_stripped_pt = cent_df_pt.filter(regex=f'centile_{brain_measure}')\n",
    "dev_scores_stripped_pt_bin = binarize_extranormal(dev_scores_stripped_pt)\n",
    "\n",
    "# plot binarized values per subject\n",
    "outname = join(out_dir, f'bin_{brain_measure}_deviations_pt.svg')\n",
    "plot_binarized_extranormal(dev_scores_stripped_pt_bin, title='', outname=outname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/melissa/Dokumente/PhD/01B_preterm_normative/scripts/normative_modeling_gamlss/final_repo/code/spatial_heterogeneity.py:175: UserWarning: \n",
      "\n",
      "The `vertical` parameter is deprecated; assigning data to `y`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(row_means, ax=ax_kde, vertical=True, fill=False, color='k')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subject x subject correlation matrix (Fig. 1f+g)\n",
    "corr_matrix_pt_subjects_01 = dev_scores_stripped_pt_bin.T.corr(method='spearman')\n",
    "\n",
    "if dataset == 'BLS-26' and brain_measure == 'CT':\n",
    "    # plot extended version for Fig.2f+g\n",
    "    outname = join(out_dir, f'corrmatrix_bin_{brain_measure}_deviations.svg')\n",
    "    plot_correlation_matrix_kde(corr_matrix_pt_subjects_01, outname)\n",
    "    \n",
    "else:\n",
    "    # plot simplified version for all others\n",
    "    outname = join(out_dir, f'corrmatrix_bin_{brain_measure}_deviations.svg')\n",
    "    title = 'Mean Spearman rho per preterm subject'\n",
    "    plot_mean_rho(corr_matrix_pt_subjects_01, title, outname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}