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source_connectivity_permutation.py
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source_connectivity_permutation.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Sep 9 08:41:17 2015.
@author: mje
"""
import numpy as np
import numpy.random as npr
import os
import socket
import mne
# import pandas as pd
from mne.connectivity import spectral_connectivity
from mne.minimum_norm import (apply_inverse_epochs, read_inverse_operator)
# Permutation test.
def permutation_resampling(case, control, num_samples, statistic):
"""
Permutation test.
Return p-value that statistic for case is different
from statistc for control.
"""
observed_diff = abs(statistic(case) - statistic(control))
num_case = len(case)
combined = np.concatenate([case, control])
diffs = []
for i in range(num_samples):
xs = npr.permutation(combined)
diff = np.mean(xs[:num_case]) - np.mean(xs[num_case:])
diffs.append(diff)
pval = (np.sum(diffs > observed_diff) +
np.sum(diffs < -observed_diff))/float(num_samples)
return pval, observed_diff, diffs
def permutation_test(a, b, num_samples, statistic):
"""
Permutation test.
Return p-value that statistic for a is different
from statistc for b.
"""
observed_diff = abs(statistic(b) - statistic(a))
num_a = len(a)
combined = np.concatenate([a, b])
diffs = []
for i in range(num_samples):
xs = npr.permutation(combined)
diff = np.mean(xs[:num_a]) - np.mean(xs[num_a:])
diffs.append(diff)
pval = np.sum(np.abs(diffs) >= np.abs(observed_diff)) / float(num_samples)
return pval, observed_diff, diffs
# Setup paths and prepare raw data
hostname = socket.gethostname()
if hostname == "Wintermute":
data_path = "/home/mje/mnt/caa/scratch/"
n_jobs = 1
else:
data_path = "/projects/MINDLAB2015_MEG-CorticalAlphaAttention/scratch/"
n_jobs = 1
subjects_dir = data_path + "fs_subjects_dir/"
# change dir to save files the rigth place
os.chdir(data_path)
fname_inv = data_path + '0001-meg-oct-6-inv.fif'
fname_epochs = data_path + '0001_p_03_filter_ds_ica-mc_tsss-epo.fif'
fname_evoked = data_path + "0001_p_03_filter_ds_ica-mc_raw_tsss-ave.fif"
# Parameters
snr = 1.0 # Standard assumption for average data but using it for single trial
lambda2 = 1.0 / snr ** 2
method = "dSPM" # use dSPM method (could also be MNE or sLORETA)
# Load data
inverse_operator = read_inverse_operator(fname_inv)
epochs = mne.read_epochs(fname_epochs)
# Get labels for FreeSurfer 'aparc' cortical parcellation with 34 labels/hemi
#labels = mne.read_labels_from_annot('0001', parc='PALS_B12_Lobes',
labels = mne.read_labels_from_annot('0001', parc='PALS_B12_Brodmann',
regexp="Brodmann",
subjects_dir=subjects_dir)
labels_occ = labels[6:12]
# labels = mne.read_labels_from_annot('subject_1', parc='aparc.DKTatlas40',
# subjects_dir=subjects_dir)
for cond in epochs.event_id.keys():
stcs = apply_inverse_epochs(epochs[cond], inverse_operator, lambda2,
method, pick_ori="normal")
exec("stcs_%s = stcs" % cond)
labels_name = [label.name for label in labels_occ]
for label in labels_occ:
labels_name += [label.name]
# Extract time series
ts_ctl_left = mne.extract_label_time_course(stcs_ctl_left,
labels_occ,
src=inverse_operator["src"],
mode = "mean_flip")
ts_ent_left = mne.extract_label_time_course(stcs_ent_left,
labels_occ,
src=inverse_operator["src"],
mode = "mean_flip")
stcs_all_left = stcs_ctl_left + stcs_ent_left
ts_all_left = np.asarray(mne.extract_label_time_course(stcs_all_left,
labels_occ,
src=inverse_operator["src"],
mode = "mean_flip"))
number_of_permutations = 2000
index = np.arange(0, len(ts_all_left))
permutations_results = np.empty(number_of_permutations)
fmin, fmax = 7, 12
tmin, tmax = 0, 1
con_method = "plv"
diff_permuatation = np.empty([6, 6, number_of_permutations])
# diff
con_ctl, freqs_ctl, times_ctl, n_epochs_ctl, n_tapers_ctl =\
spectral_connectivity(
ts_ctl_left,
method=con_method,
mode='multitaper',
sfreq=250,
fmin=fmin, fmax=fmax,
faverage=True,
tmin=tmin, tmax=tmax,
mt_adaptive=False,
n_jobs=1,
verbose=None)
con_ent, freqs_ent, times_ent, n_epochs_ent, n_tapers_ent =\
spectral_connectivity(
ts_ent_left,
method=con_method,
mode='multitaper',
sfreq=250,
fmin=fmin, fmax=fmax,
faverage=True,
tmin=tmin, tmax=tmax,
mt_adaptive=False,
n_jobs=1,
verbose=None)
diff = con_ctl[:, :, 0] - con_ent[:, :, 0]
for i in range(number_of_permutations):
index = np.random.permutation(index)
tmp_ctl = ts_all_left[index[:64], :, :]
tmp_case = ts_all_left[index[64:], :, :]
con_ctl, freqs_ctl, times_ctl, n_epochs_ctl, n_tapers_ctl =\
spectral_connectivity(
tmp_ctl,
method=con_method,
mode='multitaper',
sfreq=250,
fmin=fmin, fmax=fmax,
faverage=True,
tmin=tmin, tmax=tmax,
mt_adaptive=False,
n_jobs=1)
con_case, freqs_case, times_case, n_epochs_case, n_tapers_case =\
spectral_connectivity(
tmp_case,
method=con_method,
mode='multitaper',
sfreq=250,
fmin=fmin, fmax=fmax,
faverage=True,
tmin=tmin, tmax=tmax,
mt_adaptive=False,
n_jobs=1)
diff_permuatation[:, :, i] = con_ctl[:, :, 0] - con_case[:, :, 0]
pval = np.empty_like(diff)
for h in range(diff.shape[0]):
for j in range(diff.shape[1]):
if diff[h, j] != 0:
pval[h, j] = np.sum(np.abs(diff_permuatation[h, h, :] >=
np.abs(diff[h, j, :])))/float(number_of_permutations)
# np.sum(np.abs(diff[h, j]) >= np.abs(
# diff_permuatation[h, j, :]))\
# / float(number_of_permutations)