{ "cells": [ { "cell_type": "markdown", "id": "72d66668-4a09-487e-b637-57dfd7bbd3ec", "metadata": {}, "source": [ "# Mean-centred analysis of surface source-space data\n", "\n", "This notebook will illustrate how to identify condition-wise differences in (surface) source-space data using mean-centred PLS." ] }, { "cell_type": "markdown", "id": "22fcd745-25ff-43cf-b204-c283bf5be316", "metadata": {}, "source": [ "## Getting source-space data" ] }, { "cell_type": "code", "execution_count": 6, "id": "57c8f4a8-4561-47e3-92ad-9b6a3cffd3c1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading inverse operator decomposition from C:\\Users\\isaac\\mne_data\\MNE-sample-data\\MEG\\sample\\sample_audvis-meg-oct-6-meg-inv.fif...\n", " Reading inverse operator info...\n", " [done]\n", " Reading inverse operator decomposition...\n", " [done]\n", " 305 x 305 full covariance (kind = 1) found.\n", " Read a total of 4 projection items:\n", " PCA-v1 (1 x 102) active\n", " PCA-v2 (1 x 102) active\n", " PCA-v3 (1 x 102) active\n", " Average EEG reference (1 x 60) active\n", " Noise covariance matrix read.\n", " 22494 x 22494 diagonal covariance (kind = 2) found.\n", " Source covariance matrix read.\n", " 22494 x 22494 diagonal covariance (kind = 6) found.\n", " Orientation priors read.\n", " 22494 x 22494 diagonal covariance (kind = 5) found.\n", " Depth priors read.\n", " Did not find the desired covariance matrix (kind = 3)\n", " Reading a source space...\n", " Computing patch statistics...\n", " Patch information added...\n", " Distance information added...\n", " [done]\n", " Reading a source space...\n", " Computing patch statistics...\n", " Patch information added...\n", " Distance information added...\n", " [done]\n", " 2 source spaces read\n", " Read a total of 4 projection items:\n", " PCA-v1 (1 x 102) active\n", " PCA-v2 (1 x 102) active\n", " PCA-v3 (1 x 102) active\n", " Average EEG reference (1 x 60) active\n", " Source spaces transformed to the inverse solution coordinate frame\n", "Opening raw data file C:\\Users\\isaac\\mne_data\\MNE-sample-data\\MEG\\sample\\sample_audvis_filt-0-40_raw.fif...\n", " Read a total of 4 projection items:\n", " PCA-v1 (1 x 102) idle\n", " PCA-v2 (1 x 102) idle\n", " PCA-v3 (1 x 102) idle\n", " Average EEG reference (1 x 60) idle\n", " Range : 6450 ... 48149 = 42.956 ... 320.665 secs\n", "Ready.\n" ] } ], "source": [ "import mne\n", "from mne.datasets import sample\n", "from mne.minimum_norm import apply_inverse_epochs, read_inverse_operator\n", "import mne_plsc\n", "\n", "data_path = sample.data_path()\n", "meg_path = data_path / \"MEG\" / \"sample\"\n", "fname_inv = meg_path / \"sample_audvis-meg-oct-6-meg-inv.fif\"\n", "fname_raw = meg_path / \"sample_audvis_filt-0-40_raw.fif\"\n", "fname_event = meg_path / \"sample_audvis_filt-0-40_raw-eve.fif\"\n", "\n", "# Load data\n", "inverse_operator = read_inverse_operator(fname_inv)\n", "raw = mne.io.read_raw_fif(fname_raw)\n", "events = mne.read_events(fname_event)\n", "\n", "# Add a bad channel\n", "raw.info[\"bads\"] += [\"EEG 053\"] # bads + 1 more\n", "\n", "# Create epochs\n", "picks = mne.pick_types(\n", " raw.info, meg=True, eeg=False, stim=False, eog=True, exclude=\"bads\"\n", ")\n", "epochs = mne.Epochs(\n", " raw,\n", " events,\n", " event_id={'left': 1, 'right': 2},\n", " tmin=-0.2, tmax=0.5,\n", " picks=picks,\n", " baseline=(None, 0),\n", " reject=dict(mag=4e-12, grad=4000e-13, eog=150e-6),\n", " preload=True,\n", " verbose=False\n", ")\n", "\n", "# Crop to small time window for speed\n", "epochs.crop(0.05, 0.1)\n", "\n", "# Apply inverse operator\n", "snr = 3.0\n", "lambda2 = 1.0 / snr**2\n", "method = \"dSPM\" \n", "stcs = apply_inverse_epochs(\n", " epochs,\n", " inverse_operator,\n", " lambda2,\n", " method,\n", " pick_ori=\"normal\",\n", " verbose=False\n", ")" ] }, { "cell_type": "markdown", "id": "a079f3d7-fe23-460d-9235-6ef1741f94c2", "metadata": {}, "source": [ "## Fitting and assessing model\n", "\n", "First, we will extract the epoch labels and use them to fit a mean-centred model. Then we will assess the significance of the model using permutation testing. We will do a small number of permutations for speed here; a real analysis requires many more." ] }, { "cell_type": "code", "execution_count": 9, "id": "7309c354-f39f-432b-9c22-56bc92c857f6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Getting permutations: 100%|████████████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 32979.27it/s]\n", "Permuting: 100%|██████████████████████████████████████████████████████████████████████████████████| 100/100 [00:05<00:00, 17.91it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " LV index singular value variance explained p value\n", "0 0 57.759548 1.0 0.009901\n", "1 1 0.000000 0.0 NaN\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "labels = mne_plsc.utils.get_epoch_labels(epochs)\n", "res = mne_plsc.fit_mc(data=stcs,\n", " between=labels)\n", "res.permute(100)\n", "print(res.summary())" ] }, { "cell_type": "markdown", "id": "663bab4c-c230-4d3d-8c5d-408d5c785d86", "metadata": {}, "source": [ "As we can see, the model has identified a pattern differentiating between the left and right ear auditory conditions." ] }, { "cell_type": "markdown", "id": "40a04fba-0266-44fc-bc0e-7c498adae690", "metadata": {}, "source": [ "## Cluster analysis\n", "\n", "To characterize the spatial and temporal distribution of the pattern that differentiates the conditions, we can do cluster analysis. For this, we first need to add information about the source space: both the `SourceSpaces` object corresponding to the inverse operator and the freesurfer subjects directory. After this, we can add an adjacency matrix and identify clusters. Here we will perform clustering on the raw saliences and skip bootstrap resampling for speed." ] }, { "cell_type": "code", "execution_count": 10, "id": "e5baabad-c128-47e6-a632-d4cf98538f4e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-- number of adjacent vertices : 8196\n", "Clustering saliences\n", "Defaulting to unsigned clustering\n", "Computing clusters for lv_idx 0...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\isaac\\Projects\\mne-plsc\\src\\mne_plsc\\__init__.py:318: RuntimeWarning: 8.5% of original source space vertices have been omitted, tri-based adjacency will have holes.\n", "Consider using distance-based adjacency or morphing data to all source space vertices.\n", " spatial_adj = mne.spatial_src_adjacency(self.template.src)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "110 clusters\n" ] } ], "source": [ "res.add_source_info(src=inverse_operator['src'],\n", " subjects_dir=data_path / 'subjects')\n", "res.add_adjacency()\n", "res.cluster(threshold=0.01)" ] }, { "cell_type": "markdown", "id": "f3be057c-3eb7-4eba-bef0-0731d9858d58", "metadata": {}, "source": [ "As we can see, there are two major clusters:" ] }, { "cell_type": "code", "execution_count": 11, "id": "337e60b9-e422-42b4-9ccd-e243eed69d83", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res.plot_cluster_sizes(lv_idx=0)" ] }, { "cell_type": "markdown", "id": "4bee94a6-9c47-42ef-aab7-a21cc17bf2a2", "metadata": {}, "source": [ "## Visualizing clusters\n", "\n", "We can visualize these clusters using the `plot_cluster_spatial()` method, which produces and interactive 3D rendering. This is not possible in a static HTML notebook, so it has to be run on your own computer.\n", "\n", "However, we can still see the temporal distribution of the clusters:" ] }, { "cell_type": "code", "execution_count": 13, "id": "d22b4667-1193-482f-8be1-d3a67dd6c867", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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", 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