{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "00ab5950",
   "metadata": {},
   "source": [
    "# Meta-Analytic Coactivation Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c48de7b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T11:37:27.547479Z",
     "iopub.status.busy": "2026-09-30T11:37:27.547117Z",
     "iopub.status.idle": "2026-09-30T11:37:34.363537Z",
     "shell.execute_reply": "2026-09-30T11:37:34.362683Z"
    },
    "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 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\nneurosynth_dset = dataset.Dataset.load(os.path.join(data_path, \"neurosynth_dataset.pkl.gz\"))"
  },
  {
   "cell_type": "markdown",
   "id": "3433b101",
   "metadata": {},
   "source": [
    "Meta-analytic coactivation modeling (MACM) {cite:p}`Laird2009-gc,Robinson2010-iv,Eickhoff2010-vx`, also known as meta-analytic connectivity modeling, uses meta-analytic data to measure co-occurrence of activations between brain regions providing evidence of functional connectivity of brain regions across tasks.\n",
    "In coordinate-based MACM, whole-brain studies within the database are selected based on whether or not they report at least one peak in a region of interest specified for the analysis.\n",
    "These studies are then subjected to a meta-analysis, often comparing the selected studies to those remaining in the database.\n",
    "In this way, the significance of each voxel in the analysis corresponds to whether there is greater convergence of foci at the voxel among studies which also report foci in the region of interest than those which do not.\n",
    "\n",
    "<!-- TODO: Determine appropriate citation style here. -->\n",
    "\n",
    "MACM results have historically been accorded a similar interpretation to task-related functional connectivity (e.g., {cite:p}`Hok2015-lt,Kellermann2013-en`), although this approach is quite removed from functional connectivity analyses of task fMRI data (e.g., beta-series correlations, psychophysiological interactions, or even seed-to-voxel functional connectivity analyses on task data).\n",
    "Nevertheless, MACM analyses do show high correspondence with resting-state functional connectivity {cite:p}`Reid2017-ez`.\n",
    "MACM has been used to characterize the task-based functional coactivation of the cerebellum {cite:p}`Riedel2015-tx`, lateral prefrontal cortex {cite:p}`Reid2016-ba`, fusiform gyrus {cite:p}`Caspers2014-ja`, and several other brain regions.\n",
    "\n",
    "Within NiMARE, MACMs can be performed by selecting studies in a Dataset based on the presence of activation within a target mask or coordinate-centered sphere.\n",
    "While some algorithms, such as SCALE, may have been designed with MACMs in mind, in practice MACMs may be performed with any valid Estimator.\n",
    "\n",
    "In this section, we will perform two MACMs- one with a target mask and one with a coordinate-centered sphere.\n",
    "For the former, we use {py:meth}`~nimare.dataset.Dataset.get_studies_by_mask`.\n",
    "For the latter, we use {py:meth}`~nimare.dataset.Dataset.get_studies_by_coordinate`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eebd764e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T11:37:34.424722Z",
     "iopub.status.busy": "2026-09-30T11:37:34.424101Z",
     "iopub.status.idle": "2026-09-30T11:37:43.952135Z",
     "shell.execute_reply": "2026-09-30T11:37:43.951367Z"
    }
   },
   "outputs": [],
   "source": "# Create Dataset only containing studies with peaks within the amygdala mask\namygdala_mask = os.path.join(data_path, \"amygdala_roi.nii.gz\")\namygdala_ids = neurosynth_dset.get_studies_by_mask(amygdala_mask)\ndset_amygdala = neurosynth_dset.slice(amygdala_ids)\n\n# Create Dataset only containing studies with peaks within the sphere ROI\nsphere_ids = neurosynth_dset.get_studies_by_coordinate([[24, -2, -20]], r=6)\ndset_sphere = neurosynth_dset.slice(sphere_ids)"
  },
  {
   "cell_type": "markdown",
   "id": "26ac1fa2",
   "metadata": {},
   "source": [
    "```{important}\n",
    "The amygdala dataset includes more than 1300 studies.\n",
    "Running a meta-analysis on such a large dataset may require more than 4 GB of RAM, which is NeuroLibre's limit.\n",
    "Therefore, we will further reduce the Dataset to its first 500 studies, in order to run the meta-analysis successfully on NeuroLibre's server.\n",
    "For publication-quality analyses, we would recommend using the entire Dataset.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ab83f96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T11:37:43.958378Z",
     "iopub.status.busy": "2026-09-30T11:37:43.957902Z",
     "iopub.status.idle": "2026-09-30T11:37:44.052108Z",
     "shell.execute_reply": "2026-09-30T11:37:44.052385Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset(1369 experiments, space='mni152_2mm')\n",
      "Dataset(500 experiments, space='mni152_2mm')\n"
     ]
    }
   ],
   "source": "print(dset_amygdala)\ndset_amygdala = dset_amygdala.slice(dset_amygdala.ids[:500])\nprint(dset_amygdala)"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ef6ed650",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T11:37:44.555186Z",
     "iopub.status.busy": "2026-09-30T11:37:44.554646Z",
     "iopub.status.idle": "2026-09-30T11:37:52.545658Z",
     "shell.execute_reply": "2026-09-30T11:37:52.546005Z"
    },
    "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 600x400 with 8 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "figure_macm_rois"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": "#| label: figure_macm_roiscell\nimport numpy as np\nfrom nilearn import input_data, plotting\n\n# In order to plot a sphere with a precise radius around a coordinate with\n# nilearn, we need to use a NiftiSpheresMasker\nmask_img = neurosynth_dset.masker.mask_img\nsphere_masker = input_data.NiftiSpheresMasker([[24, -2, -20]], radius=6, mask_img=mask_img)\nsphere_masker.fit(mask_img)\nsphere_img = sphere_masker.inverse_transform(np.array([[1]]))\n\nfig, axes = plt.subplots(figsize=(6, 4), nrows=2)\ndisplay = plotting.plot_roi(\n    amygdala_mask,\n    annotate=False,\n    draw_cross=False,\n    axes=axes[0],\n    figure=fig,\n)\naxes[0].set_title(\"Amygdala ROI\")\ndisplay = plotting.plot_roi(\n    sphere_img,\n    annotate=False,\n    draw_cross=False,\n    axes=axes[1],\n    figure=fig,\n)\naxes[1].set_title(\"Spherical ROI\")\nglue(\"figure_macm_rois\", fig, display=False)"
  },
  {
   "cell_type": "markdown",
   "id": "f4c98ca6",
   "metadata": {},
   "source": "```{glue:figure} figure_macm_rois\n:name: figure_macm_rois-notebook\n:align: center\n\nRegion of interest masks for (1) a target mask-based MACM and (2) a coordinate-based MACM.\n```"
  },
  {
   "cell_type": "markdown",
   "id": "bd8debc5",
   "metadata": {},
   "source": [
    "Once the `Dataset` has been reduced to studies with coordinates within the mask or sphere requested, any of the supported CBMA Estimators can be run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ac27a49c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T11:37:52.620046Z",
     "iopub.status.busy": "2026-09-30T11:37:52.617291Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:nimare.utils:Metadata field 'sample_sizes' not found. Set a constant value for this field as an argument, if possible.\n"
     ]
    }
   ],
   "source": "from nimare import meta\n\nmeta_amyg = meta.cbma.ale.ALE(kernel__sample_size=20)\nresults_amyg = meta_amyg.fit(dset_amygdala)\n\nmeta_sphere = meta.cbma.ale.ALE(kernel__sample_size=20)\nresults_sphere = meta_sphere.fit(dset_sphere)"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58975822",
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": "#| label: figure_macmcell\nmeta_results = {\n    \"Amygdala ALE MACM\": results_amyg.get_map(\"z\", return_type=\"image\"),\n    \"Sphere ALE MACM\": results_sphere.get_map(\"z\", return_type=\"image\"),\n}\n\nfig, axes = plt.subplots(figsize=(6, 4), nrows=2)\nfor i_meta, (name, file_) in enumerate(meta_results.items()):\n    display = plotting.plot_stat_map(\n        file_,\n        annotate=False,\n        axes=axes[i_meta],\n        cmap=\"Reds\",\n        cut_coords=[24, -2, -20],\n        draw_cross=False,\n        figure=fig,\n    )\n    axes[i_meta].set_title(name)\n\n    colorbar = display._cbar\n    colorbar_ticks = colorbar.get_ticks()\n    if colorbar_ticks[0] < 0:\n        new_ticks = [colorbar_ticks[0], 0, colorbar_ticks[-1]]\n    else:\n        new_ticks = [colorbar_ticks[0], colorbar_ticks[-1]]\n    colorbar.set_ticks(new_ticks, update_ticks=True)\n\nglue(\"figure_macm\", fig, display=False)"
  },
  {
   "cell_type": "markdown",
   "id": "4133bb20",
   "metadata": {},
   "source": "```{glue:figure} figure_macm\n:name: figure_macm-notebook\n:align: center\n\nUnthresholded z-statistic maps for (1) the target mask-based MACM and (2) the coordinate-based MACM.\n```"
  }
 ],
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