{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n\n# Compute MNE-dSPM inverse solution on evoked data in volume source space\n\nCompute dSPM inverse solution on MNE evoked dataset in a volume source\nspace and stores the solution in a nifti file for visualisation.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n#\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from nilearn.image import index_img\nfrom nilearn.plotting import plot_stat_map\n\nfrom mne import read_evokeds\nfrom mne.datasets import sample\nfrom mne.minimum_norm import apply_inverse, read_inverse_operator\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nmeg_path = data_path / \"MEG\" / \"sample\"\nfname_inv = meg_path / \"sample_audvis-meg-vol-7-meg-inv.fif\"\nfname_evoked = meg_path / \"sample_audvis-ave.fif\"\n\nsnr = 3.0\nlambda2 = 1.0 / snr**2\nmethod = \"dSPM\"  # use dSPM method (could also be MNE or sLORETA)\n\n# Load data\nevoked = read_evokeds(fname_evoked, condition=0, baseline=(None, 0))\ninverse_operator = read_inverse_operator(fname_inv)\nsrc = inverse_operator[\"src\"]\n\n# Compute inverse solution\nstc = apply_inverse(evoked, inverse_operator, lambda2, method)\nstc.crop(0.0, 0.2)\n\n# Export result as a 4D nifti object\nimg = stc.as_volume(src, mri_resolution=False)  # set True for full MRI resolution\n\n# Save it as a nifti file\n# nib.save(img, f\"mne_{method}_inverse.nii.gz\")\n\nt1_fname = data_path / \"subjects\" / \"sample\" / \"mri\" / \"T1.mgz\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot with nilearn:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_stat_map(\n    index_img(img, 61),\n    str(t1_fname),\n    threshold=8.0,\n    title=f\"{method} (t={stc.times[61]:.1f} s.)\",\n)"
      ]
    }
  ],
  "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": "3.12.2"
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  },
  "nbformat": 4,
  "nbformat_minor": 0
}