Source-level RSA using a searchlight on surface data¶

This example demonstrates how to perform representational similarity analysis (RSA) on source localized MEG data, using a searchlight approach.

In the searchlight approach, representational similarity is computed between the model and searchlight “patches”. A patch is defined by a seed vertex on the cortex and all vertices within a given radius. By default, patches are created using each vertex as a seed point, so you can think of it as a “searchlight” that scans along the cortex.

The radius of a searchlight can be defined in space, in time, or both. In this example, our searchlight will have a spatial radius of 2 cm. and a temporal radius of 20 ms.

The dataset will be the MNE-sample dataset: a collection of 288 epochs in which the participant was presented with an auditory beep or visual stimulus to either the left or right ear or visual field.

# sphinx_gallery_thumbnail_number=2

# Import required packages
from matplotlib import pyplot as plt
import mne
import mne_rsa

mne.set_log_level(False)  # Be less verbose
mne.viz.set_3d_backend("pyvista")

'pyvistaqt'

We’ll be using the data from the MNE-sample set. To speed up computations in this example, we’re going to use one of the sparse source spaces from the testing set.

sample_root = mne.datasets.sample.data_path(verbose=True)
testing_root = mne.datasets.testing.data_path(verbose=True)
sample_path = sample_root / "MEG" / "sample"
testing_path = testing_root / "MEG" / "sample"
subjects_dir = sample_root / "subjects"

Creating epochs from the continuous (raw) data. We downsample to 100 Hz to speed up the RSA computations later on.

raw = mne.io.read_raw_fif(sample_path / "sample_audvis_filt-0-40_raw.fif")
events = mne.read_events(sample_path / "sample_audvis_filt-0-40_raw-eve.fif")
event_id = {"audio/left": 1, "audio/right": 2, "visual/left": 3, "visual/right": 4}
epochs = mne.Epochs(raw, events, event_id, preload=True)
epochs.resample(100)

It’s important that the model RDM and the epochs are in the same order, so that each row in the model RDM will correspond to an epoch. The model RDM will be easier to interpret visually if the data is ordered such that all epochs belonging to the same experimental condition are right next to each-other, so patterns jump out. This can be achieved by first splitting the epochs by experimental condition and then concatenating them together again.

epoch_splits = [
    epochs[cl] for cl in ["audio/left", "audio/right", "visual/left", "visual/right"]
]
epochs = mne.concatenate_epochs(epoch_splits)

Now that the epochs are in the proper order, we can create a RDM based on the experimental conditions. This type of RDM is referred to as a “sensitivity RDM”. Let’s create a sensitivity RDM that will pick up the left auditory response when RSA-ed against the MEG data. Since we want to capture areas where left beeps generate a large signal, we specify that left beeps should be similar to other left beeps. Since we do not want areas where visual stimuli generate a large signal, we specify that beeps must be different from visual stimuli. Furthermore, since in areas where visual stimuli generate only a small signal, random noise will dominate, we also specify that visual stimuli are different from other visual stimuli. Finally left and right auditory beeps will be somewhat similar.

def sensitivity_metric(event_id_1, event_id_2):
    """Determine similarity between two epochs, given their event ids."""
    if event_id_1 == 1 and event_id_2 == 1:
        return 0  # Completely similar
    if event_id_1 == 2 and event_id_2 == 2:
        return 0.5  # Somewhat similar
    elif event_id_1 == 1 and event_id_2 == 2:
        return 0.5  # Somewhat similar
    elif event_id_1 == 2 and event_id_1 == 1:
        return 0.5  # Somewhat similar
    else:
        return 1  # Not similar at all


model_rdm = mne_rsa.compute_rdm(epochs.events[:, 2], metric=sensitivity_metric)
mne_rsa.plot_rdms(model_rdm, title="Model RDM")

<Figure size 200x200 with 2 Axes>

This example is going to be on source-level, so let’s load the inverse operator and apply it to obtain a cortical surface source estimate for each epoch. To speed up the computation, we going to load an inverse operator from the testing dataset that was created using a sparse source space with not too many vertices.

inv = mne.minimum_norm.read_inverse_operator(
    testing_path / "sample_audvis_trunc-meg-eeg-oct-4-meg-inv.fif"
)
epochs_stc = mne.minimum_norm.apply_inverse_epochs(epochs, inv, lambda2=0.1111)

Performing the RSA. This will take some time. Consider increasing n_jobs to parallelize the computation across multiple CPUs.

rsa_vals = mne_rsa.rsa_stcs(
    epochs_stc,  # The source localized epochs
    model_rdm,  # The model RDM we constructed above
    src=inv["src"],  # The inverse operator has our source space
    stc_rdm_metric="correlation",  # Metric to compute the MEG RDMs
    rsa_metric="kendall-tau-a",  # Metric to compare model and EEG RDMs
    spatial_radius=0.02,  # Spatial radius of the searchlight patch
    temporal_radius=0.02,  # Temporal radius of the searchlight path
    tmin=0,
    tmax=0.3,  # To save time, only analyze this time interval
    n_jobs=1,  # Only use one CPU core. Increase this for more speed.
    verbose=False,
)  # Set to True to display a progress bar

# Find the searchlight patch with highest RSA score
peak_vertex, peak_time = rsa_vals.get_peak(vert_as_index=True)

# Plot the result at the timepoint where the maximum RSA value occurs.
rsa_vals.plot("sample", subjects_dir=subjects_dir, initial_time=peak_time)

<mne.viz._brain._brain.Brain object at 0x0000024D29BBE6E0>

Plot the RSA timecourse at the peak vertex

plt.figure()
plt.plot(rsa_vals.times, rsa_vals.data[peak_vertex])
plt.xlabel("Time (s)")
plt.ylabel("Kendall-Tau (alpha)")
plt.title(f"RSA values at vert {peak_vertex}")

Text(0.5, 1.0, 'RSA values at vert 127')

Total running time of the script: (3 minutes 18.580 seconds)

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