def dailyfc_visual(files):
for onefile in files:
lfpdata, chnAreas, fs = lfp_extract([onefile])
if lfpdata.shape[2] < 80:
continue
print(onefile)
ciCOHs = calc_ciCOHs_rest(lfpdata)
# permutation test: use the lfp data whose ciCOHs are the largest to get distribution
[i, j] = np.unravel_index(np.argmax(ciCOHs), shape=ciCOHs.shape)
lfp1, lfp2 = lfpdata[i, :, :], lfpdata[j, :, :]
_, mu, std = pval_permciCOH_rest(lfp1,
lfp2,
ciCOHs[i, j],
shuffleN=1000)
pvals = norm.sf(abs(ciCOHs), loc=mu, scale=std) * 2
# multiple comparison correction, get weights
reject, pval_corr = fdr_correction(pvals, alpha=0.05, method='indep')
[rows, cols] = np.where(reject == True)
weight = np.zeros(ciCOHs.shape)
if len(rows) > 0:
weight[rows, cols] = ciCOHs[rows, cols]
# visual and save
filename = os.path.basename(onefile)
datestr = re.search('[0-9]{8}', filename).group()
cond = re.search('_[a-z]*_[0-9]{8}', filename).group()[1:-9]
freqstr = 'freq' + re.search('_filtered[0-9]*_[0-9]*',
filename).group()[len('_filtered'):]
save_prefix = 'all'
saveFCGraph = os.path.join(
savefolder,
freqstr + '_' + cond + '_' + save_prefix + '_' + datestr + '.png')
texts = dict()
texts[cond + ',' + datestr] = [-80, 50, 15]
weight_visual_save(weight,
chnInf=assign_coord2chnArea(
area_coord_file=area_coord_file,
chnAreas=chnAreas),
savefile=saveFCGraph,
texts=texts,
threds_edge=None)
del texts, datestr, cond, weight
def subArea_dailyfc_visual(files):
for onefile in files:
lfpdata, chnAreas, fs = lfp_extract([onefile])
if lfpdata.shape[2] < 80:
continue
print(onefile)
ciCOHs = calc_ciCOHs_rest(lfpdata)
# permutation test: use the lfp data whose ciCOHs are the largest to get distribution
[i, j] = np.unravel_index(np.argmax(ciCOHs), shape = ciCOHs.shape)
lfp1, lfp2 = lfpdata[i, :, :], lfpdata[j, :, :]
_, mu, std = pval_permciCOH_rest(lfp1, lfp2, ciCOHs[i, j], shuffleN = 1000)
cond = re.search('_[a-z]*_[0-9]{8}', files[0]).group()[1:-9]
datestr = re.search('[0-9]{8}', os.path.basename(onefile)).group()
### left thalamus and SMA/M1 ###
save_prefix = 'leftThaCor_'
areas_used = ['lVA', 'lVLo/VPLo', 'lSMA', 'rSMA','M1']
# subareas selection
ciCOH_new, chnAreas_new = ciCOH_select(ciCOHs, chnAreas, areas_used)
# multiple comparison correction, get weight matrix
pvals = norm.sf(abs(ciCOH_new), loc = mu, scale = std) * 2
reject, pval_corr = fdr_correction(pvals, alpha = 0.05, method='indep')
[rows, cols]= np.where(reject == True)
weight = np.zeros(ciCOH_new.shape)
if len(rows) > 0:
weight[rows, cols] = ciCOH_new[rows, cols]
# visual and save
saveFCGraph = os.path.join(savefolder, cond + '_' + save_prefix + '_' + datestr + '.png')
texts = dict()
texts[datestr] = [80, 50, 15]
weight_visual_save(weight, chnInf = assign_coord2chnArea(area_coord_file, chnAreas_new),
savefile = saveFCGraph, texts = None, threds_edge = None)
del ciCOH_new, chnAreas_new, save_prefix, areas_used
del saveFCGraph, weight
def imcohs_daily_calc(onefile):
lfpdata, chnAreas, fs = lfp_extract([onefile])
if lfpdata.shape[2] < 30:
return None
[imcohs, _, _, _,
_] = spectral_connectivity(data=np.transpose(lfpdata, axes=(2, 0, 1)),
method='imcoh',
sfreq=fs,
fmin=26,
fmax=28,
faverage=True)
imcohs = np.squeeze(imcohs)
imcohs = imcohs + np.transpose(imcohs, axes=(1, 0))
# permutation test: use the lfp data whose ciCOHs are the largest to get distribution
[i, j] = np.unravel_index(np.argmax(imcohs), shape=imcohs.shape)
lfp1, lfp2 = lfpdata[i, :, :], lfpdata[j, :, :]
mu, std = pval_perm_imcohMNE_rest(lfp1,
lfp2,
fs=fs,
fmin=freqs[0],
fmax=freqs[1],
shuffleN=300)
pvals = norm.sf(abs(imcohs), loc=mu, scale=std) * 2
del lfp1, lfp2
fc = dict()
fc['imcohs'] = imcohs
fc['pvals'] = pvals
fc['chnAreas'] = chnAreas
# save
filename = os.path.basename(onefile)
datestr = re.search('[0-9]{8}', filename).group()
cond = re.search('_[a-z]*_[0-9]{8}', filename).group()[1:-9]
freqstr = 'freq' + str(freqs[0]) + '_' + str(freqs[1])
fcfile_pickle = os.path.join(
savefolder, freqstr + '_' + cond + '_' + datestr + '.pickle')
with open(fcfile_pickle, 'wb') as f:
pickle.dump(fc, f)
return fcfile_pickle
def segfc_visual(onefile):
# lfpdata: nchns * ntemp * nsegs
lfpdata, chnAreas, fs = lfp_extract([onefile])
nchns, _, nsegs = lfpdata.shape
seg_ciCOHs = np.zeros(shape=(nchns, nchns, nsegs))
for segi in range(nsegs):
seg_ciCOHs[:, :, segi] = calc_ciCOHs_rest(
np.expand_dims(lfpdata[:, :, segi], axis=2))
# permutation test: use the lfp data whose ciCOHs are the largest to get distribution
[i, j] = np.unravel_index(np.argmax(ciCOHs), shape=ciCOHs.shape)
lfp1, lfp2 = lfpdata[i, :, :], lfpdata[j, :, :]
_, mu, std = pval_permciCOH_rest(lfp1, lfp2, ciCOHs[i, j], shuffleN=1000)
pvals = norm.sf(abs(ciCOHs), loc=mu, scale=std) * 2
# multiple comparison correction, get weights
reject, pval_corr = fdr_correction(pvals, alpha=0.05, method='indep')
[rows, cols] = np.where(reject == True)
weight = np.zeros(ciCOHs.shape)
if len(rows) > 0:
weight[rows, cols] = ciCOHs[rows, cols]
# visual and save
filename = os.path.basename(onefile)
datestr = re.search('[0-9]{8}', filename).group()
cond = re.search('_[a-z]*_[0-9]{8}', filename).group()[1:-9]
save_prefix = 'all'
saveFCGraph = os.path.join(
savefolder, cond + '_' + save_prefix + '_' + datestr + '.png')
weight_visual_save(weight,
chnInf=assign_coord2chnArea(
area_coord_file=area_coord_file, chnAreas=chnAreas),
savefile=saveFCGraph,
texts=None,
threds_edge=None)