{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb84d8db",
   "metadata": {},
   "source": [
    "# Image-Based Meta-Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ec5c60b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:39:29.416923Z",
     "iopub.status.busy": "2026-09-30T09:39:29.416532Z",
     "iopub.status.idle": "2026-09-30T09:39:31.823844Z",
     "shell.execute_reply": "2026-09-30T09:39:31.823456Z"
    },
    "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 plotting\nfrom repo2data.repo2data import Repo2Data\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\")"
  },
  {
   "cell_type": "markdown",
   "id": "403d2b97",
   "metadata": {},
   "source": [
    "Image-based meta-analysis (IBMA) methods perform a meta-analysis directly on brain images (either whole-brain or partial) rather than on extracted peaks.\n",
    "On paper, IBMA is superior to CBMA in virtually all respects, as the availability of analysis-level parameter and variance estimates at all analyzed voxels allows researchers to use the full complement of standard meta-analysis techniques, instead of having to resort to kernel-based or other methods that require additional spatial assumptions.\n",
    "In principle, given a set of maps that contains no missing values (i.e., where there are _k_ valid pairs of parameter and variance estimates at each voxel), one can simply conduct a voxel-wise version of any standard meta-analysis or meta-regression method commonly used in other biomedical or social science fields.\n",
    "\n",
    "In practice, the utility of IBMA methods has historically been quite limited, as unthresholded statistical maps have been unavailable for the vast majority of neuroimaging studies.\n",
    "However, the introduction and rapid adoption of NeuroVault {cite:p}`Gorgolewski2015-sd`, a database for unthresholded statistical images, has made image-based meta-analysis increasingly viable.\n",
    "Although coverage of the literature remains limited, and IBMAs of maps drawn from the NeuroVault database are likely to omit at least some (and in some cases most) relevant studies due to limited metadata,  we believe the time is ripe for researchers to start including both CBMAs and IBMAs in published meta-analyses, with the aspirational goal of eventually transitioning exclusively to the latter.\n",
    "To this end, NiMARE supports a range of different IBMA methods, including a number of estimators of the gold standard mixed-effects meta-regression model, as well as several alternative estimators suitable for use when some of the traditional inputs are unavailable.\n",
    "\n",
    "```{note}\n",
    "NiMARE's IBMA `Estimator`s are light wrappers around classes from [PyMARE](https://pymare.readthedocs.io), a library for standard (i.e., non-neuroimaging) meta-analyses developed by the same team as NiMARE.\n",
    "```\n",
    "\n",
    "In the optimal situation, meta-analysts have access to both contrast (i.e., parameter estimate) maps and their associated standard error maps for a number of studies.\n",
    "With these data, researchers can fit the traditional random-effects meta-regression model using one of several methods that vary in the way they estimate the between-study variance ($\\tau^{2}$).\n",
    "Currently supported estimators include the **DerSimonian-Laird** method {cite:p}`DerSimonian1986-hu`, the **Hedges** method {cite:p}`Hedges1985-ka`, and **maximum-likelihood** (ML) and **restricted maximum-likelihood** (REML) approaches.\n",
    "NiMARE can also perform fixed-effects meta-regression via weighted least-squares, though there are few IBMA scenarios where a fixed-effects analysis would be indicated.\n",
    "It is worth noting that the non-likelihood-based estimators (i.e., DerSimonian-Laird and Hedges) have a closed-form solution, and are implemented in an extremely efficient way in NiMARE (i.e., computation is performed on all voxels in parallel).\n",
    "However, these estimators also produce more biased estimates under typical conditions (e.g., when sample sizes are very small), implying a tradeoff from the user's perspective.\n",
    "\n",
    "Alternatively, when users only have access to contrast maps and associated sample sizes, they can use the supported **Sample Size-Based Likelihood** estimator, which assumes that within-study variance is constant across studies, and uses maximum-likelihood or restricted maximum-likelihood to estimate between-study variance, as described in {cite:t}`Sangnawakij2019-mq`.\n",
    "When users have access only to contrast maps, they can use the **Permuted OLS** estimator, which uses ordinary least squares and employs a max-type permutation scheme for family-wise error correction {cite:p}`Freedman1983-ld,Anderson2001-uc` that has been validated on neuroimaging data {cite:p}`Winkler2014-wh` and relies on the nilearn library.\n",
    "\n",
    "Finally, when users only have access to z-score maps, they can use either the **Fisher's** {cite:p}`Fisher1925-zh` or the **Stouffer's** {cite:p}`Riley1949-uz` estimators.\n",
    "When sample size information is available, users may incorporate that information into the Stouffer's method, via the method described in {cite:t}`Zaykin2011-fs`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f892e2e",
   "metadata": {},
   "source": [
    "Given the paucity of image-based meta-analytic datasets, we have included the tools to build a Dataset from a NeuroVault collection of 21 pain studies, originally described in {cite:t}`Maumet2016-rr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b2801dfb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:39:31.828837Z",
     "iopub.status.busy": "2026-09-30T09:39:31.828527Z",
     "iopub.status.idle": "2026-09-30T09:39:32.643007Z",
     "shell.execute_reply": "2026-09-30T09:39:32.642624Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nimare.extract.utils:Dataset found in /tmp/paper/data/nimare-paper/data/nidm_21pain\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nimare.utils:Shared path detected: '/tmp/paper/data/nimare-paper/data/nidm_21pain/'\n"
     ]
    }
   ],
   "source": "from nimare import dataset, extract, utils\n\ndset_dir = extract.download_nidm_pain(data_dir=data_path, overwrite=False)\ndset_file = os.path.join(utils.get_resource_path(), \"nidm_pain_dset.json\")\nimg_dset = dataset.Dataset(dset_file)\n\n# Point the Dataset toward the images we've downloaded\nimg_dset.update_path(dset_dir)"
  },
  {
   "cell_type": "markdown",
   "id": "b53746ab",
   "metadata": {},
   "source": [
    "## Transforming images\n",
    "\n",
    "Researchers may share their statistical maps in many forms, some of which are direct transformations of one another.\n",
    "For example, researchers may share test statistic maps with z-statistics or t-statistics, and, as long as we know the degrees of freedom associated with the t-test, we can convert between the two easily. To that end, NiMARE includes a class, {py:class}`~nimare.transforms.ImageTransformer`, which will calculate target image types from available ones, as long as the available images are compatible with said transformation.\n",
    "\n",
    "Here, we use `ImageTransformer` to calculate z-statistic and variance maps for all studies with compatible images.\n",
    "This allows us to apply more image-based meta-analysis algorithms to the `Dataset`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2fc97676",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:39:32.645793Z",
     "iopub.status.busy": "2026-09-30T09:39:32.645492Z",
     "iopub.status.idle": "2026-09-30T09:40:28.604964Z",
     "shell.execute_reply": "2026-09-30T09:40:28.605486Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nimare.utils:Shared path detected: '/tmp/paper/data/nimare-paper/data/nidm_21pain/'\n"
     ]
    }
   ],
   "source": "from nimare import transforms\n\nimg_transformer = transforms.ImageTransformer(target=[\"z\", \"varcope\"], overwrite=False)\nimg_dset = img_transformer.transform(img_dset)"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a783ca3a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:40:28.612290Z",
     "iopub.status.busy": "2026-09-30T09:40:28.611889Z",
     "iopub.status.idle": "2026-09-30T09:40:28.614012Z",
     "shell.execute_reply": "2026-09-30T09:40:28.613747Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": "# Here we delete the recent variables for the sake of reducing memory usage\ndel img_transformer"
  },
  {
   "cell_type": "markdown",
   "id": "7fd98b7b",
   "metadata": {},
   "source": [
    "Now that we have filled in as many gaps in the `Dataset` as possible, we can start running meta-analyses.\n",
    "We will start with a DerSimonian-Laird meta-analysis ({py:class}`~nimare.meta.ibma.DerSimonianLaird`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d337bd36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:40:28.619195Z",
     "iopub.status.busy": "2026-09-30T09:40:28.618575Z",
     "iopub.status.idle": "2026-09-30T09:40:57.338145Z",
     "shell.execute_reply": "2026-09-30T09:40:57.337583Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:nimare.base:Masking out 39113 additional voxels. The updated masker is available in the Estimator.masker attribute.\n"
     ]
    }
   ],
   "source": "from nimare import meta\n\ndsl_meta = meta.ibma.DerSimonianLaird(resample=True)\ndsl_results = dsl_meta.fit(img_dset)\n\n# Retain the z-statistic map for later use\ndsl_img = dsl_results.get_map(\"z\", return_type=\"image\")"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "335b1cf1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:40:57.342051Z",
     "iopub.status.busy": "2026-09-30T09:40:57.341721Z",
     "iopub.status.idle": "2026-09-30T09:40:57.344335Z",
     "shell.execute_reply": "2026-09-30T09:40:57.344124Z"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": "# Here we delete the recent variables for the sake of reducing memory usage\ndel dsl_meta, dsl_results"
  },
  {
   "cell_type": "markdown",
   "id": "7b944a9a",
   "metadata": {},
   "source": [
    "Now we will apply other available IBMA `Estimator`s to the same `Dataset`, and save their results to files for comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "33e06f82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:40:57.402131Z",
     "iopub.status.busy": "2026-09-30T09:40:57.400744Z",
     "iopub.status.idle": "2026-09-30T09:43:10.216324Z",
     "shell.execute_reply": "2026-09-30T09:43:10.215802Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:nimare.base:Masking out 39113 additional voxels. The updated masker is available in the Estimator.masker attribute.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:nimare.base:Masking out 39113 additional voxels. The updated masker is available in the Estimator.masker attribute.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:nimare.base:Masking out 39113 additional voxels. The updated masker is available in the Estimator.masker attribute.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:nimare.base:Masking out 39113 additional voxels. The updated masker is available in the Estimator.masker attribute.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:nimare.base:Masking out 39113 additional voxels. The updated masker is available in the Estimator.masker attribute.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/image/resampling.py:602: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.\n",
      "  fill_value=fill_value)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:nimare.base:Masking out 39113 additional voxels. The updated masker is available in the Estimator.masker attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataset created in /home/jovyan/nilearn_data/fsl\n",
      "\n",
      "Downloading data from http://www.nitrc.org/frs/download.php/9902/HarvardOxford.tgz ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Downloaded 1384448 of 25716861 bytes (5.4%,   17.8s remaining)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Downloaded 5079040 of 25716861 bytes (19.7%,    8.3s remaining)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Downloaded 9560064 of 25716861 bytes (37.2%,    5.3s remaining)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Downloaded 12509184 of 25716861 bytes (48.6%,    4.4s remaining)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Downloaded 15237120 of 25716861 bytes (59.2%,    3.6s remaining)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Downloaded 18522112 of 25716861 bytes (72.0%,    2.4s remaining)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Downloaded 22085632 of 25716861 bytes (85.9%,    1.2s remaining)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " ...done. (9 seconds, 0 min)\n",
      "Extracting data from /home/jovyan/nilearn_data/fsl/f46ad01e95960216e8befa3b5c1ed654/HarvardOxford.tgz..."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".. done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/_utils/niimg.py:62: UserWarning: Non-finite values detected. These values will be replaced with zeros.\n",
      "  \"Non-finite values detected. \"\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:nimare.meta.ibma:A <class 'nilearn.maskers.nifti_labels_masker.NiftiLabelsMasker'> mask has been detected. Masks which average across voxels will likely produce biased results when used with this Estimator.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/pymare/estimators/estimators.py:28: UserWarning: Input contains 48 parallel datasets (in 2nd dim of y and v). The selected estimator will loop over datasets naively, and this may be slow for large numbers of datasets. Consider using the DL, HE, or WLS estimators, which handle parallel datasets more efficiently.\n",
      "  .format(n_iter))\n"
     ]
    }
   ],
   "source": "# Stouffer's\nstouffers_meta = meta.ibma.Stouffers(use_sample_size=False, resample=True)\nstouffers_results = stouffers_meta.fit(img_dset)\nstouffers_img = stouffers_results.get_map(\"z\", return_type=\"image\")\ndel stouffers_meta, stouffers_results\n\n# Stouffer's with weighting based on sample size\nwstouffers_meta = meta.ibma.Stouffers(use_sample_size=True, resample=True)\nwstouffers_results = wstouffers_meta.fit(img_dset)\nwstouffers_img = wstouffers_results.get_map(\"z\", return_type=\"image\")\ndel wstouffers_meta, wstouffers_results\n\n# Fisher's\nfishers_meta = meta.ibma.Fishers(resample=True)\nfishers_results = fishers_meta.fit(img_dset)\nfishers_img = fishers_results.get_map(\"z\", return_type=\"image\")\ndel fishers_meta, fishers_results\n\n# Permuted Ordinary Least Squares\nols_meta = meta.ibma.PermutedOLS(resample=True)\nols_results = ols_meta.fit(img_dset)\nols_img = ols_results.get_map(\"z\", return_type=\"image\")\ndel ols_meta, ols_results\n\n# Weighted Least Squares\nwls_meta = meta.ibma.WeightedLeastSquares(resample=True)\nwls_results = wls_meta.fit(img_dset)\nwls_img = wls_results.get_map(\"z\", return_type=\"image\")\ndel wls_meta, wls_results\n\n# Hedges'\nhedges_meta = meta.ibma.Hedges(resample=True)\nhedges_results = hedges_meta.fit(img_dset)\nhedges_img = hedges_results.get_map(\"z\", return_type=\"image\")\ndel hedges_meta, hedges_results\n\n# Use atlas for likelihood-based estimators\nfrom nilearn import datasets, image, input_data\n\natlas = datasets.fetch_atlas_harvard_oxford(\"cort-maxprob-thr25-2mm\")\n\n# nilearn's NiftiLabelsMasker cannot handle NaNs at the moment,\n# and some of the NIDM-Results packs' beta images have NaNs at the edge of the brain.\n# So, we will create a reduced version of the atlas for this analysis.\nnan_mask = image.math_img(\"~np.any(np.isnan(img), axis=3)\", img=img_dset.images[\"beta\"].tolist())\natlas = image.resample_to_img(atlas[\"maps\"], nan_mask)\nnanmasked_atlas = image.math_img(\"mask * atlas\", mask=nan_mask, atlas=atlas)\nmasker = input_data.NiftiLabelsMasker(nanmasked_atlas)\ndel atlas, nan_mask, nanmasked_atlas\n\n# Variance-Based Likelihood\nvbl_meta = meta.ibma.VarianceBasedLikelihood(method=\"reml\", mask=masker, resample=True)\nvbl_results = vbl_meta.fit(img_dset)\nvbl_img = vbl_results.get_map(\"z\", return_type=\"image\")\ndel vbl_meta, vbl_results\n\n# Sample Size-Based Likelihood\nssbl_meta = meta.ibma.SampleSizeBasedLikelihood(method=\"reml\", mask=masker, resample=True)\nssbl_results = ssbl_meta.fit(img_dset)\nssbl_img = ssbl_results.get_map(\"z\", return_type=\"image\")\ndel ssbl_meta, ssbl_results, masker"
  },
  {
   "cell_type": "markdown",
   "id": "73051b16",
   "metadata": {},
   "source": [
    "## Comparing algorithms\n",
    "\n",
    "Here we load the z-statistic map from each of the IBMA Estimators we've used throughout this chapter and plot them all side by side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5597b320",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-30T09:43:10.234304Z",
     "iopub.status.busy": "2026-09-30T09:43:10.223625Z",
     "iopub.status.idle": "2026-09-30T09:43:30.204263Z",
     "shell.execute_reply": "2026-09-30T09:43:30.203737Z"
    },
    "tags": [
     "hide-output"
    ]
   },
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/nilearn/_utils/niimg.py:62: UserWarning: Non-finite values detected. These values will be replaced with zeros.\n",
      "  \"Non-finite values detected. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 1800x600 with 45 Axes>"
     },
     "metadata": {
      "scrapbook": {
       "mime_prefix": "application/papermill.record/",
       "name": "figure_uncorr_ibma"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": "#| label: figure_uncorr_ibmacell\nmeta_results = {\n    \"DerSimonian-Laird\": dsl_img,\n    \"Stouffer's\": stouffers_img,\n    \"Weighted Stouffer's\": wstouffers_img,\n    \"Fisher's\": fishers_img,\n    \"Ordinary Least Squares\": ols_img,\n    \"Weighted Least Squares\": wls_img,\n    \"Hedges'\": hedges_img,\n    \"Variance-Based Likelihood\": vbl_img,\n    \"Sample Size-Based Likelihood\": ssbl_img,\n}\norder = [\n    [\"Fisher's\", \"Stouffer's\", \"Weighted Stouffer's\"],\n    [\"DerSimonian-Laird\", \"Hedges'\", \"Weighted Least Squares\"],\n    [\"Ordinary Least Squares\", \"Variance-Based Likelihood\", \"Sample Size-Based Likelihood\"],\n]\n\nfig, axes = plt.subplots(figsize=(18, 6), nrows=3, ncols=3)\n\nfor i_row, row_names in enumerate(order):\n    for j_col, name in enumerate(row_names):\n        file_ = meta_results[name]\n        display = plotting.plot_stat_map(\n            file_,\n            annotate=False,\n            axes=axes[i_row, j_col],\n            cmap=\"RdBu_r\",\n            cut_coords=[5, -15, 10],\n            draw_cross=False,\n            figure=fig,\n        )\n        axes[i_row, j_col].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_uncorr_ibma\", fig, display=False)"
  },
  {
   "cell_type": "markdown",
   "id": "429847a4",
   "metadata": {},
   "source": "```{glue:figure} figure_uncorr_ibma\n:name: figure_uncorr_ibma-notebook\n:align: center\n\nAn array of plots of the statistical maps produced by the image-based meta-analysis methods.\nThe likelihood-based meta-analyses are run on atlases instead of voxelwise.\n```"
  }
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