Add 0.7

Author: tsbinns

v0.7/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: cb7020c22ee224a00da74b26ee91ab69
+tags: 645f666f9bcd5a90fca523b33c5a78b7

v0.7/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

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+"""
+===============================================================================
+Compute mixed source space connectivity and visualize it using a circular graph
+===============================================================================
+
+This example computes the all-to-all connectivity between 75 regions in a
+mixed source space based on dSPM inverse solutions and a FreeSurfer cortical
+parcellation. The connectivity is visualized using a circular graph which
+is ordered based on the locations of the regions in the axial plane.
+"""
+# Author: Annalisa Pascarella <a.pascarella@iac.cnr.it>
+#
+# License: BSD (3-clause)
+
+import os.path as op
+
+import matplotlib.pyplot as plt
+import mne
+import numpy as np
+from mne import make_forward_solution, setup_source_space, setup_volume_source_space
+from mne.datasets import sample
+from mne.io import read_raw_fif
+from mne.minimum_norm import apply_inverse_epochs, make_inverse_operator
+from mne.viz import circular_layout
+
+from mne_connectivity import spectral_connectivity_epochs
+from mne_connectivity.viz import plot_connectivity_circle
+
+# Set directories
+data_path = sample.data_path()
+subject = "sample"
+data_dir = op.join(data_path, "MEG", subject)
+subjects_dir = op.join(data_path, "subjects")
+bem_dir = op.join(subjects_dir, subject, "bem")
+
+# Set file names
+fname_aseg = op.join(subjects_dir, subject, "mri", "aseg.mgz")
+
+fname_model = op.join(bem_dir, "%s-5120-bem.fif" % subject)
+fname_bem = op.join(bem_dir, "%s-5120-bem-sol.fif" % subject)
+
+fname_raw = op.join(data_dir, "sample_audvis_filt-0-40_raw.fif")
+fname_trans = op.join(data_dir, "sample_audvis_raw-trans.fif")
+fname_cov = op.join(data_dir, "ernoise-cov.fif")
+fname_event = op.join(data_dir, "sample_audvis_filt-0-40_raw-eve.fif")
+
+# List of sub structures we are interested in. We select only the
+# sub structures we want to include in the source space
+labels_vol = [
+    "Left-Amygdala",
+    "Left-Thalamus-Proper",
+    "Left-Cerebellum-Cortex",
+    "Brain-Stem",
+    "Right-Amygdala",
+    "Right-Thalamus-Proper",
+    "Right-Cerebellum-Cortex",
+]
+
+# Setup a surface-based source space, oct5 is not very dense (just used
+# to speed up this example; we recommend oct6 in actual analyses)
+src = setup_source_space(
+    subject, subjects_dir=subjects_dir, spacing="oct5", add_dist=False
+)
+
+# Setup a volume source space
+# set pos=10.0 for speed, not very accurate; we recommend something smaller
+# like 5.0 in actual analyses:
+vol_src = setup_volume_source_space(
+    subject,
+    mri=fname_aseg,
+    pos=10.0,
+    bem=fname_model,
+    add_interpolator=False,  # just for speed, usually use True
+    volume_label=labels_vol,
+    subjects_dir=subjects_dir,
+)
+# Generate the mixed source space
+src += vol_src
+
+# Load data
+raw = read_raw_fif(fname_raw)
+raw.pick_types(meg=True, eeg=False, eog=True, stim=True).load_data()
+events = mne.find_events(raw)
+noise_cov = mne.read_cov(fname_cov)
+
+# compute the fwd matrix
+fwd = make_forward_solution(
+    raw.info, fname_trans, src, fname_bem, mindist=5.0
+)  # ignore sources<=5mm from innerskull
+del src
+
+# Define epochs for left-auditory condition
+event_id, tmin, tmax = 1, -0.2, 0.5
+reject = dict(mag=4e-12, grad=4000e-13, eog=150e-6)
+epochs = mne.Epochs(raw, events, event_id, tmin, tmax, reject=reject, preload=False)
+del raw
+
+# Compute inverse solution and for each epoch
+snr = 1.0  # use smaller SNR for raw data
+inv_method = "dSPM"
+parc = "aparc"  # the parcellation to use, e.g., 'aparc' 'aparc.a2009s'
+
+lambda2 = 1.0 / snr**2
+
+# Compute inverse operator
+inverse_operator = make_inverse_operator(
+    epochs.info, fwd, noise_cov, depth=None, fixed=False
+)
+del fwd
+
+stcs = apply_inverse_epochs(
+    epochs, inverse_operator, lambda2, inv_method, pick_ori=None, return_generator=True
+)
+
+# Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi
+labels_parc = mne.read_labels_from_annot(subject, parc=parc, subjects_dir=subjects_dir)
+
+# Average the source estimates within each label of the cortical parcellation
+# and each sub-structure contained in the source space.
+# When mode = 'mean_flip', this option is used only for the cortical labels.
+src = inverse_operator["src"]
+label_ts = mne.extract_label_time_course(
+    stcs, labels_parc, src, mode="mean_flip", allow_empty=True, return_generator=True
+)
+
+# We compute the connectivity in the alpha band and plot it using a circular
+# graph layout
+fmin = 8.0
+fmax = 13.0
+sfreq = epochs.info["sfreq"]  # the sampling frequency
+con = spectral_connectivity_epochs(
+    label_ts,
+    method="pli",
+    mode="multitaper",
+    sfreq=sfreq,
+    fmin=fmin,
+    fmax=fmax,
+    faverage=True,
+    mt_adaptive=True,
+    n_jobs=1,
+)
+
+# We create a list of Label containing also the sub structures
+labels_aseg = mne.get_volume_labels_from_src(src, subject, subjects_dir)
+labels = labels_parc + labels_aseg
+
+# read colors
+node_colors = [label.color for label in labels]
+
+# We reorder the labels based on their location in the left hemi
+label_names = [label.name for label in labels]
+lh_labels = [name for name in label_names if name.endswith("lh")]
+rh_labels = [name for name in label_names if name.endswith("rh")]
+
+# Get the y-location of the label
+label_ypos_lh = list()
+for name in lh_labels:
+    idx = label_names.index(name)
+    ypos = np.mean(labels[idx].pos[:, 1])
+    label_ypos_lh.append(ypos)
+try:
+    idx = label_names.index("Brain-Stem")
+except ValueError:
+    pass
+else:
+    ypos = np.mean(labels[idx].pos[:, 1])
+    lh_labels.append("Brain-Stem")
+    label_ypos_lh.append(ypos)
+
+
+# Reorder the labels based on their location
+lh_labels = [label for (yp, label) in sorted(zip(label_ypos_lh, lh_labels))]
+
+# For the right hemi
+rh_labels = [
+    label[:-2] + "rh"
+    for label in lh_labels
+    if label != "Brain-Stem" and label[:-2] + "rh" in rh_labels
+]
+
+# Save the plot order
+node_order = lh_labels[::-1] + rh_labels
+
+node_angles = circular_layout(
+    label_names, node_order, start_pos=90, group_boundaries=[0, len(label_names) // 2]
+)
+
+
+# Plot the graph using node colors from the FreeSurfer parcellation. We only
+# show the 300 strongest connections.
+conmat = con.get_data(output="dense")[:, :, 0]
+fig, ax = plt.subplots(figsize=(8, 8), facecolor="black", subplot_kw=dict(polar=True))
+plot_connectivity_circle(
+    conmat,
+    label_names,
+    n_lines=300,
+    node_angles=node_angles,
+    node_colors=node_colors,
+    title="All-to-All Connectivity left-Auditory " "Condition (PLI)",
+    ax=ax,
+)
+fig.tight_layout()
+
+###############################################################################
+# Save the figure (optional)
+# --------------------------
+#
+# By default matplotlib does not save using the facecolor, even though this was
+# set when the figure was generated. If not set via savefig, the labels, title,
+# and legend will be cut off from the output png file.
+#
+# .. code-block:: python
+#
+#     fname_fig = op.join(data_path, 'MEG', 'sample', 'plot_mixed_connect.png')
+#     fig.savefig(fname_fig, facecolor=fig.get_facecolor())

v0.7/_downloads/089119f41c66c5258e6c09e3b0f169ae/mixed_source_space_connectivity.py

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Compute seed-based time-frequency connectivity in sensor space\n\nComputes the connectivity between a seed-gradiometer close to the visual cortex\nand all other gradiometers. The connectivity is computed in the time-frequency\ndomain using Morlet wavelets and the debiased squared weighted phase lag index\n:footcite:`VinckEtAl2011` is used as connectivity metric.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>\n#\n# License: BSD (3-clause)\n\nimport os.path as op\n\nimport mne\nimport numpy as np\nfrom mne import io\nfrom mne.datasets import sample\nfrom mne.time_frequency import AverageTFRArray\n\nfrom mne_connectivity import seed_target_indices, spectral_connectivity_epochs\n\nprint(__doc__)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Set parameters\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "data_path = sample.data_path()\nraw_fname = op.join(data_path, \"MEG\", \"sample\", \"sample_audvis_filt-0-40_raw.fif\")\nevent_fname = op.join(data_path, \"MEG\", \"sample\", \"sample_audvis_filt-0-40_raw-eve.fif\")\n\n# Setup for reading the raw data\nraw = io.read_raw_fif(raw_fname)\nevents = mne.read_events(event_fname)\n\n# Add a bad channel\nraw.info[\"bads\"] += [\"MEG 2443\"]\n\n# Pick MEG gradiometers\npicks = mne.pick_types(\n    raw.info, meg=\"grad\", eeg=False, stim=False, eog=True, exclude=\"bads\"\n)\n\n# Create epochs for left-visual condition\nevent_id, tmin, tmax = 3, -0.2, 0.5\nepochs = mne.Epochs(\n    raw,\n    events,\n    event_id,\n    tmin,\n    tmax,\n    picks=picks,\n    baseline=(None, 0),\n    reject=dict(grad=4000e-13, eog=150e-6),\n    preload=True,\n)\n\n# Use 'MEG 2343' as seed\nseed_ch = \"MEG 2343\"\npicks_ch_names = [raw.ch_names[i] for i in picks]\n\n# Create seed-target indices for connectivity computation\nseed = picks_ch_names.index(seed_ch)\ntargets = np.arange(len(picks))\nindices = seed_target_indices(seed, targets)\n\n# Define wavelet frequencies and number of cycles\ncwt_freqs = np.arange(7, 30, 2)\ncwt_n_cycles = cwt_freqs / 7.0\n\n# Run the connectivity analysis using 2 parallel jobs\nsfreq = raw.info[\"sfreq\"]  # the sampling frequency\ncon = spectral_connectivity_epochs(\n    epochs,\n    indices=indices,\n    method=\"wpli2_debiased\",\n    mode=\"cwt_morlet\",\n    sfreq=sfreq,\n    cwt_freqs=cwt_freqs,\n    cwt_n_cycles=cwt_n_cycles,\n    n_jobs=1,\n)\ntimes = con.times\nfreqs = con.freqs\n\n# Mark the seed channel with a value of 1.0, so we can see it in the plot\ncon.get_data()[np.where(indices[1] == seed)] = 1.0\n\n# Show topography of connectivity from seed\ntitle = \"WPLI2 - Visual - Seed %s\" % seed_ch\n\nlayout = mne.find_layout(epochs.info, \"meg\")  # use full layout\n\n# Note that users of mne < 1.7 should use the `AverageTFR` class\ntfr = AverageTFRArray(epochs.info, con.get_data(), times, freqs, nave=len(epochs))\ntfr.plot_topo(fig_facecolor=\"w\", font_color=\"k\", border=\"k\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n.. footbibliography::\n\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": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}