{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "82a1621b",
   "metadata": {},
   "source": [
    "# Meta-Analytic Subtraction Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0052dfd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T11:33:18.125581Z",
     "iopub.status.busy": "2026-09-30T11:33:18.125194Z",
     "iopub.status.idle": "2026-09-30T11:33:21.039705Z",
     "shell.execute_reply": "2026-09-30T11:33:21.039373Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": "# First, import the necessary modules and functions\nimport os\n\nimport matplotlib.pyplot as plt\nfrom myst_nb import glue\nfrom nilearn import image, plotting\nfrom repo2data.repo2data import Repo2Data\n\nfrom nimare import dataset\n\n# Install the data if running locally, or points to cached data if running on neurolibre\nDATA_REQ_FILE = os.path.abspath(\"../binder/data_requirement.json\")\nrepo2data = Repo2Data(DATA_REQ_FILE)\ndata_path = repo2data.install()\ndata_path = os.path.join(data_path[0], \"data\")\n\n# Now, load the Datasets we will use in this chapter\nsleuth_dset1 = dataset.Dataset.load(os.path.join(data_path, \"sleuth_dset1.pkl.gz\"))\nsleuth_dset2 = dataset.Dataset.load(os.path.join(data_path, \"sleuth_dset2.pkl.gz\"))"
  },
  {
   "cell_type": "markdown",
   "id": "baf2ff65",
   "metadata": {},
   "source": [
    "Subtraction analysis refers to the voxel-wise comparison of two meta-analytic samples.\n",
    "In image-based meta-analysis, comparisons between groups of maps can generally be accomplished within the standard meta-regression framework (i.e., by adding a covariate that codes for group membership).\n",
    "However, coordinate-based subtraction analysis requires special extensions for CBMA algorithms.\n",
    "\n",
    "Subtraction analysis to compare the results of two ALE meta-analyses was originally implemented by {cite:t}`Laird2005-qh` and later extended by {cite:t}`Eickhoff2012-hk`.\n",
    "In this approach, two groups of experiments (A and B) are compared using a group assignment randomization procedure in which voxel-wise null distributions are generated by randomly reassigning experiments between the two groups and calculating ALE-difference scores for each permutation.\n",
    "Real ALE-difference scores (i.e., the ALE values for one group minus the ALE values for the other) are compared against these null distributions to determine voxel-wise significance.\n",
    "In the original implementation of the algorithm, this procedure is performed separately for a group A > B contrast and a group B > A contrast, where each contrast is limited to voxels that were significant in the first group's original meta-analysis.\n",
    "\n",
    "```{important}\n",
    "In NiMARE, we use an adapted version of the subtraction analysis method in {py:class}`~nimare.meta.cbma.ale.ALESubtraction`.\n",
    "The NiMARE implementation analyzes all voxels, rather than only those that show a significant effect of A alone or B alone as in the original implementation.\n",
    "```\n",
    "\n",
    "```{important}\n",
    "Running a subtraction analysis with the standard number of iterations (10000) may require more than 4 GB of RAM, which is NeuroLibre's limit.\n",
    "We will instead use only 1000 iterations, so that the analysis will run successfully on NeuroLibre's server.\n",
    "For publication-quality subtraction analyses, we recommend using the standard 10000 iterations.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7b91f3fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T11:33:21.057552Z",
     "iopub.status.busy": "2026-09-30T11:33:21.054511Z",
     "iopub.status.idle": "2026-09-30T11:37:19.161374Z",
     "shell.execute_reply": "2026-09-30T11:37:19.158809Z"
    }
   },
   "outputs": [],
   "source": "from nimare import meta\n\nkern = meta.kernel.ALEKernel()\nsub_meta = meta.cbma.ale.ALESubtraction(kernel_transformer=kern, n_iters=1000)\nsub_results = sub_meta.fit(sleuth_dset1, sleuth_dset2)"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2214647d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T11:37:19.319616Z",
     "iopub.status.busy": "2026-09-30T11:37:19.242314Z",
     "iopub.status.idle": "2026-09-30T11:37:24.137521Z",
     "shell.execute_reply": "2026-09-30T11:37:24.137988Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/plotting/img_plotting.py:348: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0\n",
      "  anat_img = load_mni152_template()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 600x200 with 5 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "figure_subtraction"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": "#| label: figure_subtractioncell\nfig, ax = plt.subplots(figsize=(6, 2))\ndisplay = plotting.plot_stat_map(\n    sub_results.get_map(\"z_desc-group1MinusGroup2\", return_type=\"image\"),\n    annotate=False,\n    axes=ax,\n    cmap=\"RdBu_r\",\n    cut_coords=[0, 0, 0],\n    draw_cross=False,\n    figure=fig,\n)\nax.set_title(\"ALE Subtraction\")\n\ncolorbar = display._cbar\ncolorbar_ticks = colorbar.get_ticks()\nif colorbar_ticks[0] < 0:\n    new_ticks = [colorbar_ticks[0], 0, colorbar_ticks[-1]]\nelse:\n    new_ticks = [colorbar_ticks[0], colorbar_ticks[-1]]\ncolorbar.set_ticks(new_ticks, update_ticks=True)\nglue(\"figure_subtraction\", fig, display=False)"
  },
  {
   "cell_type": "markdown",
   "id": "7b7a337e",
   "metadata": {},
   "source": "```{glue:figure} figure_subtraction\n:name: figure_subtraction-notebook\n:align: center\n\nUnthresholded z-statistic map for the subtraction analysis of the two example Sleuth-based `Datasets`.\n```\n\nAlternatively, MKDA Chi-squared analysis is inherently a subtraction analysis method, in that it compares foci from two groups of studies.\nGenerally, one of these groups is a sample of interest, while the other is a meta-analytic database (minus the studies in the sample).\nWith this setup, meta-analysts can infer whether there is greater convergence of foci in a voxel as compared to the baseline across the field (as estimated with the meta-analytic database), much like SCALE.\nHowever, if the database is replaced with a second sample of interest, the analysis ends up comparing convergence between the two groups."
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
