Cluster permutation statistics for PAC

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1
  • MNE version: 1.8.0
  • operating system:Windows 10

Hi All,

I am trying to run permutation cluster stats on my phase-amplitude coupling (PAC) data and having some issues with implementation.

My data structure:

pac_all - numpy array of shape (24, 60, 20, 20) (participants x electrodes x phase_frequency x amplitude_frequency)
I don’t have epochs dimension, since the PAC values vere computed on concatenated epochs for each participant.

There is no contrast between conditions so far, I want to know the significant PAC values for each electrode separately.

Here’s my code:

# Compute adjacency matrix
sensor_adjacency, ch_names = mne.channels.find_ch_adjacency(epochs.info, "eeg")

adjacency = mne.stats.combine_adjacency(
   sensor_adjacency, pac_all.shape[2], pac_all.shape[3]
)

sensor_adjacency.shape = (60, 60)
adjacency.shape = (24000, 24000)

# We want a one-tailed test, since PAC values are positive numbers
tail = 1
t_thresh = scipy.stats.t.ppf(1 - 0.01, df=degrees_of_freedom) # np.float64(2.4998667394943976)

# Set the number of permutations to run
n_permutations = 1000

# Run the analysis
T_obs, clusters, cluster_p_values, H0 = permutation_cluster_1samp_test(
    pac_all,
    n_permutations=n_permutations,
    threshold=12,
    tail=tail,
    adjacency=adjacency,
    out_type="mask",
    max_step=1,
    verbose=True,
)

T_obs.mean() = np.float64(8.314733192984257)
len(clusters) = 1
cluster_p_values = array([0.001])

Why do I only get one cluster?
Even if I increase the threshold to 8 and get 9 clusters, all the cluster_p_values are equal to 0.001, which does not seem right.

How can I visualise the output for each electrode to see what is going on?
(to see significant PAC values in colour and all the rest - in black and white)

Am I passing the right structure to the test at all?
I am using the tutorial for TF stats analysis as a reference, where the structure of the data is (n_obs x electrodes x phase_frequency x amplitude_frequency)

2

Hi,

I don’t know what is wrong, but since you are interested in analyzing each channel separately couldn’t you try doing the analysis per channel anyway, as in this example, and see if the results are same or not?

Cheers,

3

Hey,

Thanks a lot for your reply!

I’ve actually tried to z-score my PAC values and it helped to get more meaningful output.
I would assume that since the modulation indices were small, almost any difference reached significance level and normalization halped to get rid of this issue.