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Sternberg_Epoched_Beamforming.py
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Sternberg_Epoched_Beamforming.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jan 31 17:51:37 2024
@author: bansals3
"""
# =============================================================================
# Beamforming of sternberg task
# Takes log ratio of the 6 encode vs the 4 encode and plots the
# contrast on an fs average brain
# =============================================================================
import mne
import os, os.path as op
import numpy as np
import glob
import pickle
import copy
from mne.beamformer import make_lcmv, apply_lcmv, apply_lcmv_epochs, apply_lcmv_raw
from mne.datasets import fetch_fsaverage
import pandas as pd
from mne.beamformer import apply_lcmv_cov
import nibabel as nb
import mne_bids
from mne_bids import BIDSPath
import pathlib
n_jobs = 1 #CHANGE
topdir = pathlib.PurePath('X:\\') # topdir = '/data/ML_MEG'
resultsdir = pathlib.PurePath(op.join(topdir, 'results_s_final'))
os.chdir(topdir)
#stcdir = op.join(topdir, resultsdir, f'f_{fmin}_{fmax}')
#lh_stc = mne.read_source_estimate('ON25658_stc.h5-lh.stc')
#rh_stc = mne.read_source_estimate('ON25658_stc.h5-rh.stc')
subj_dir = pathlib.PurePath(op.join(topdir, 'NIH_hvmeg/derivatives/freesurfer/subjects'))
#Setup for fs average warping
subjects_dir = subj_dir
fetch_fsaverage(subjects_dir, verbose=False) # ensure fsaverage src exists
fname_fs_src = subjects_dir.joinpath('fsaverage','bem','fsaverage-ico-5-src.fif')
src_fs = mne.read_source_spaces(fname_fs_src)
fsaverage = 'X:\\NIH_hvmeg/derivatives/freesurfer/subjects/fsaverage'
def visualizer(fmin, fmax):
lims = [0.3, 0.45, 0.6] #color gradient
stc = all_subj_beamformer(fmin, fmax)[0]
src = mne.setup_source_space(fsaverage, spacing='oct6', add_dist='patch')
kwargs = dict(
src=src_fs,
subjects_dir=subj_dir,
subject='fsaverage',
initial_time=0,
verbose=True,
hemi = 'split'
)
stc.plot(clim=dict(kind="value", lims=lims), **kwargs)
def visualizer_grid(fmin, fmax):
lims = [0.3, 0.45, 0.6] #color gradient
stcs = all_subj_beamformer(fmin, fmax)[1]
src = mne.setup_source_space(fsaverage, spacing='oct6', add_dist='patch')
for i in stcs:
kwargs = dict(
src=src_fs,
subjects_dir=subj_dir,
subject='fsaverage',
initial_time=0,
verbose=True,
hemi = 'split'
)
stc.plot(clim=dict(kind="value", lims=lims), **kwargs) #figure out how to get grid of plots
def all_subj_beamformer(fmin, fmax):
all_stcs_ratios = []
directory = pathlib.PurePath(op.join(topdir, 'NIH_hvmeg/derivatives/ica'))
errors = []
for i in os.listdir(directory):
filename = pathlib.PurePath(op.join(directory, i, 'ica_clean.fif'))
print(filename)
try:
stc_ratio_fs=full_process(filename, fmin, fmax)
all_stcs_ratios.append(stc_ratio_fs)
except BaseException as e:
print(f'Error {str(e)} {i}')
errors.append(f'Error {str(e)} {i}')
final_stc_ratio = average_stcs(all_stcs_ratios)
print(errors)
return final_stc_ratio, all_stcs_ratios
'''subj_pd = pd.read_csv('X:\\good_subjects_stern_ica.csv')
good_subjs = subj_pd.iloc[:,1].tolist()
for i in good_subjs:
#filename = pathlib.PurePath(op.join(directory, i, 'ica_clean.fif'))
filename = pathlib.PurePath(i)
print(filename)
try:
stc_ratio_fs=full_process(filename, fmin, fmax)
all_stcs_ratios.append(stc_ratio_fs)
except BaseException as e:
print(f'Error {str(e)} {i}')
errors.append(f'Error {str(e)} {i}')
final_stc_ratio = average_stcs(all_stcs_ratios)
print(errors)
return final_stc_ratio'''
def fsaverage_morph(stc):
#Setup for fs average warping
subjects_dir = subj_dir
fetch_fsaverage(subjects_dir, verbose=False) # ensure fsaverage src exists
fname_fs_src = subjects_dir.joinpath('fsaverage','bem', 'fsaverage-ico-5-src.fif')
src_fs = mne.read_source_spaces(fname_fs_src)
src = get_mri(stc.subject[4:])['src']
morph = mne.compute_source_morph(
src,
subject_from='sub-'+stc.subject[4:],
src_to=src_fs,
subjects_dir=subj_dir,
niter_sdr=[5, 5, 3],
niter_affine=[100, 100, 10],
zooms=3, # just for speed
verbose=True,
)
stc = morph.apply(stc)
return stc
def get_mri(subject):
if subject[0:2] == 'ON':
#subject = 'ON02747'
bids_dir = 'X:\\NIH_hvmeg'
output_dirname = 'sternberg_res_3_6_final' ###RESET NAME!!!!
task_type = 'sternberg'
# =============================================================================
# general variables
# =============================================================================
deriv_dir = op.join(bids_dir, 'derivatives')
output_dir = op.join(deriv_dir, output_dirname)
subjects_dir = op.join(deriv_dir, 'freesurfer', 'subjects')
os.environ['SUBJECTS_DIR']=subjects_dir
bids_path = BIDSPath(root=bids_dir, subject=subject, datatype='meg',
task=task_type, session ='01', run = '01')
anat_bids_path = BIDSPath(root=bids_dir, subject=subject, datatype='anat',
extension='.nii', acquisition = 'MPRAGE', suffix = 'T1w', session = '01')
'''if os.path.exists(anat_bids_path) == False:
anat_bids_path = BIDSPath(root=bids_dir, subject=subject, datatype='anat',
extension='.nii', acquisition = 'FSPGR', suffix = 'T1w', session = '01', recording='SCIC')
else:
pass'''
deriv_path = bids_path.copy().update(root=output_dir, check=False)
deriv_path.directory.mkdir(exist_ok=True, parents=True)
raw_fname = bids_path.copy().update(run = '01', session = '01')
bem_fname = deriv_path.copy().update(suffix='bem', extension='.fif')
fwd_fname = deriv_path.copy().update(suffix='fwd', extension='.fif')
src_fname = deriv_path.copy().update(suffix='src', extension='.fif')
trans_fname = deriv_path.copy().update(suffix='trans',extension='.fif')
raw = mne.io.read_raw_ctf(raw_fname.fpath, system_clock = 'ignore', clean_names =True)
subjects_dir = mne_bids.read.get_subjects_dir()
fs_subject = 'sub-'+bids_path.subject
if not bem_fname.fpath.exists():
mne.bem.make_watershed_bem(fs_subject, subjects_dir=subjects_dir, overwrite=True)
bem = mne.make_bem_model(fs_subject, subjects_dir=f'{subjects_dir}',
conductivity=[0.3])
bem_sol = mne.make_bem_solution(bem)
mne.write_bem_solution(bem_fname, bem_sol, overwrite=True)
else:
bem_sol = mne.read_bem_solution(bem_fname)
if not src_fname.fpath.exists():
src = mne.setup_source_space(fs_subject, spacing='oct6', add_dist='patch',
subjects_dir=subjects_dir)
src.save(src_fname.fpath, overwrite=True)
else:
src = mne.read_source_spaces(src_fname.fpath)
if not trans_fname.fpath.exists():
trans = mne_bids.read.get_head_mri_trans(bids_path, extra_params=dict(system_clock='ignore'),
t1_bids_path=anat_bids_path, fs_subject=fs_subject,
fs_subjects_dir=subjects_dir)
mne.write_trans(trans_fname.fpath, trans, overwrite=True)
else:
trans = mne.read_trans(trans_fname.fpath)
#if fwd_fname.fpath.exists():
# fwd = mne.read_forward_solution(fwd_fname)
#else:
fwd = mne.make_forward_solution(raw.info, trans, src, bem_sol, eeg=False,
n_jobs=n_jobs)
mne.write_forward_solution(fwd_fname.fpath, fwd, overwrite=True)
return {'fwd': fwd, 'trans': trans, 'src': src}
else:
#subject = 'ON02747'
bids_dir = 'X:\\NIH_hvmeg'
output_dirname = 'sternberg_res_3_6_final' ###RESET NAME!!!!
task_type = 'sternberg'
# =============================================================================
# general variables
# =============================================================================
deriv_dir = op.join(bids_dir, 'derivatives')
output_dir = op.join(deriv_dir, output_dirname)
subjects_dir = op.join(deriv_dir, 'freesurfer', 'subjects')
os.environ['SUBJECTS_DIR']=subjects_dir
bids_path = BIDSPath(root=bids_dir, subject=subject, datatype='meg',
task=task_type, session ='1', run = '01')
anat_bids_path = BIDSPath(root=bids_dir, subject=subject, datatype='anat',
extension='.nii', acquisition = 'MPRAGE', suffix = 'T1w', session = '1')
'''if os.path.exists(anat_bids_path) == False:
anat_bids_path = BIDSPath(root=bids_dir, subject=subject, datatype='anat',
extension='.nii', acquisition = 'FSPGR', suffix = 'T1w', session = '1', recording='SCIC')
else:
pass'''
deriv_path = bids_path.copy().update(root=output_dir, check=False)
deriv_path.directory.mkdir(exist_ok=True, parents=True)
raw_fname = bids_path.copy().update(run = '01', session = '1')
bem_fname = deriv_path.copy().update(suffix='bem', extension='.fif')
fwd_fname = deriv_path.copy().update(suffix='fwd', extension='.fif')
src_fname = deriv_path.copy().update(suffix='src', extension='.fif')
trans_fname = deriv_path.copy().update(suffix='trans',extension='.fif')
raw = mne.io.read_raw_ctf(raw_fname.fpath, system_clock = 'ignore', clean_names =True)
subjects_dir = mne_bids.read.get_subjects_dir()
fs_subject = 'sub-'+bids_path.subject
if not bem_fname.fpath.exists():
mne.bem.make_watershed_bem(fs_subject, subjects_dir=subjects_dir, overwrite=True)
bem = mne.make_bem_model(fs_subject, subjects_dir=f'{subjects_dir}',
conductivity=[0.3])
bem_sol = mne.make_bem_solution(bem)
mne.write_bem_solution(bem_fname, bem_sol, overwrite=True)
else:
bem_sol = mne.read_bem_solution(bem_fname)
if not src_fname.fpath.exists():
src = mne.setup_source_space(fs_subject, spacing='oct6', add_dist='patch',
subjects_dir=subjects_dir)
src.save(src_fname.fpath, overwrite=True)
else:
src = mne.read_source_spaces(src_fname.fpath)
if not trans_fname.fpath.exists():
trans = mne_bids.read.get_head_mri_trans(bids_path, extra_params=dict(system_clock='ignore'),
t1_bids_path=anat_bids_path, fs_subject=fs_subject,
fs_subjects_dir=subjects_dir)
mne.write_trans(trans_fname.fpath, trans, overwrite=True)
else:
trans = mne.read_trans(trans_fname.fpath)
#if fwd_fname.fpath.exists():
# fwd = mne.read_forward_solution(fwd_fname)
#else:
fwd = mne.make_forward_solution(raw.info, trans, src, bem_sol, eeg=False,
n_jobs=n_jobs)
mne.write_forward_solution(fwd_fname.fpath, fwd, overwrite=True)
return {'fwd': fwd, 'trans': trans, 'src': src}
def average_stcs(stcs):
stcs_copy=copy.deepcopy(stcs)
#Initialize new source estimate for average
stc_ave = copy.deepcopy(stcs_copy[0])
#Convert to list of numpy arrays
tmp = [i.data for i in stcs_copy]
#Stack list into new dimension and average over this epoch dimension
tmp2 = np.stack(tmp).mean(axis=0)
stc_ave.data=tmp2
return stc_ave
def full_process(filename, fmin, fmax):
subjid = filename.parents[0].name.split('-')[1][:-4]
if subjid[0:2] == 'ON':
fwd_dir = topdir.joinpath('NIH_hvmeg','derivatives',f'sternberg_res_{fmin}_{fmax}_final',f'sub-{subjid}','ses-01','meg')
fwd_fname = fwd_dir.joinpath(f'sub-{subjid}_ses-01_task-sternberg_run-01_fwd.fif')
subject_fs = subjid+'_fs'
ica_fname = topdir.joinpath('NIH_hvmeg','derivatives','ica',f'sub-{subjid}_ses-01_task-sternberg_run-01_meg',f'sub-{subjid}_ses-01_task-sternberg_run-01_meg_0-ica.fif')
noise_fname = topdir.joinpath('NIH_hvmeg',f'sub-{subjid}','ses-01','meg',f'sub-{subjid}_ses-01_task-noise_run-01_meg.ds')
else:
fwd_dir = topdir.joinpath('NIH_hvmeg','derivatives',f'sternberg_res_{fmin}_{fmax}_final',f'sub-{subjid}','ses-1','meg')
fwd_fname = fwd_dir.joinpath(f'sub-{subjid}_ses-1_task-sternberg_run-01_fwd.fif')
subject_fs = subjid+'_fs'
ica_fname = topdir.joinpath('NIH_hvmeg','derivatives','ica',f'sub-{subjid}_ses-1_task-sternberg_run-01_meg',f'sub-{subjid}_ses-1_task-sternberg_run-01_meg_0-ica.fif')
noise_fname = topdir.joinpath('NIH_hvmeg',f'sub-{subjid}','ses-1','meg',f'sub-{subjid}_ses-1_task-noise_run-01_meg.ds')
'''
#subjid = filename.split('/')[2][8:15] #subjid = op.basename(filename).split('_')[0][4:] #
subjid = filename.split('/')[2][4:11] #6 for /data/ML_MEG but 3 for X drive
fwd_dir = op.join(topdir, f'NIH_hvmeg/derivatives/sternberg_res_{fmin}_{fmax}_final/sub-{subjid}/ses-01/meg')
fwd_fname= op.join(fwd_dir, f'sub-{subjid}_ses-01_task-sternberg_run-01_fwd.fif')
subject_fs = subjid+'_fs'
ica_fname = f'X:\\NIH_hvmeg/derivatives/ica/sub-{subjid}_ses-01_task-sternberg_run-01_meg/sub-{subjid}_ses-01_task-sternberg_run-01_meg_0-ica.fif'
noise_fname = f'X:\\NIH_hvmeg/sub-{subjid}/ses-01/meg/sub-{subjid}_ses-01_task-noise_run-01_meg.ds'
'''
raw=mne.io.read_raw_fif(filename) #f'{topdir}/data/movie/{filename}')
raw = raw.load_data() #mne.io.read_raw_ctf(filename, preload=True)
raw.resample(150, n_jobs=n_jobs)
raw.notch_filter([60], n_jobs=n_jobs)
raw.filter(fmin, fmax) #15.0,25)
anat_dict = get_mri(subjid)
fwd = anat_dict['fwd']
evts, evtsid = mne.events_from_annotations(raw)
e = [x for x in evts if x[2] == 1 or x[2] == 2]# x[2] == ?]
e4 = [x for x in evts if x[2] == 1]
e6 = [x for x in evts if x[2] == 2]
epo = mne.Epochs(raw, e, preload=True, tmin=0, tmax=2, baseline=None) # define epo
epo4 = mne.Epochs(raw, e4, preload=True, tmin=0, tmax=2, baseline=None)
epo6 = mne.Epochs(raw, e6, preload=True, tmin=0, tmax=2, baseline=None)
# =============================================================================
# Caculate covariance and beamformer off of cleaned trials
# =============================================================================
full_cov = mne.compute_covariance(epo)
raw_eroom = mne.io.read_raw_ctf(noise_fname, preload=True, clean_names=True, system_clock='ignore') ########################################
epo_noise = mne.Epochs(raw_eroom, e, preload=True)
ica = mne.preprocessing.read_ica(ica_fname) ##FIX THIS
ica.apply(epo_noise)
raw_chs = epo.ch_names
er_chs = epo_noise.ch_names
final_chs=list(set(raw_chs).intersection(set(er_chs)))
epo.pick(final_chs)
epo_noise.pick(final_chs)
#Make list of covariances for each 1s trial
common_cov = mne.compute_covariance(epo, tmin=0, tmax=2,
method='shrunk', cv=5, n_jobs=n_jobs)
#noise_cov = mne.compute_covariance(epo, tmin = -0.4, tmax = 0, method = 'shrunk', cv =5, n_jobs=n_jobs)
noise_cov = mne.compute_covariance(epo_noise)
noise_rank = mne.compute_rank(epo_noise)
epo_rank = mne.compute_rank(epo)
if 'mag' in epo_rank:
if epo_rank['mag'] < noise_rank['mag']:
noise_rank['mag']=epo_rank['mag']
#evoked = epo.average()
filters = make_lcmv(epo.info, fwd, full_cov, noise_cov=noise_cov,
reg=0.05, rank=epo_rank, pick_ori='max-power')
#Adjust filters to align with anatomy
src = fwd['src']
src_anat_ori = np.vstack([i['nn'][i['vertno']] for i in src])
ori_flip = np.sign(np.sum(filters['max_power_ori'] * src_anat_ori, axis=1))
filters['weights'] *= ori_flip[:,np.newaxis]
filters['max_power_ori'] *= ori_flip[:,np.newaxis]
#APPLY FILTER TO MAKE STC LIST OF EPOCHS
stcs4 = apply_lcmv_epochs(epochs=epo4, filters=filters, return_generator=False)
stcs6 = apply_lcmv_epochs(epochs=epo6, filters=filters, return_generator=False)