def getCorrelation(run, encoding, subject_id):
# obtains the correlation between empirical functional connectivity and simulated functional connectivity
key = str(run) + "_" + str(encoding) + "_" + str(subject_id) + "_rsHRF"
input_path_sim = "weights" + key + "fMRI.txt"
em_fc_matrix = get_FC(run, encoding, subject_id)
sampling_interval = 0.72
uidx = np.triu_indices(379, 1)
em_fc_z = np.arctanh(em_fc_matrix)
em_fc = em_fc_z[uidx]
tsr = np.loadtxt(input_path_sim)
T = TimeSeries(tsr, sampling_interval=sampling_interval)
C = CorrelationAnalyzer(T)
sim_fc = np.arctanh(C.corrcoef)[uidx]
sim_fc = np.nan_to_num(sim_fc)
pearson_corr, _ = stat.pearsonr(sim_fc, em_fc)
os.remove(input_path_sim)
return pearson_corr
def getCorrelation(group_id, subject_id):
# obtains the correlation between empirical functional connectivity and simulated functional connectivity
input_path_sim = "fMRI.txt"
input_path_em = get_path(group_id, subject_id, "T1") + "FC.mat"
em_mat = scipy.io.loadmat(input_path_em)
em_fc_matrix = em_mat["FC_cc_DK68"]
sampling_interval = em_mat["TR"][0][0]
uidx = np.triu_indices(68, 1)
em_fc_z = np.arctanh(em_fc_matrix)
em_fc = em_fc_z[uidx]
tsr = np.loadtxt(input_path_sim)
T = TimeSeries(tsr, sampling_interval=sampling_interval)
C = CorrelationAnalyzer(T)
sim_fc = np.arctanh(C.corrcoef)[uidx]
sim_fc = np.nan_to_num(sim_fc)
pearson_corr, _ = stat.pearsonr(sim_fc, em_fc)
return pearson_corr
def connectivity(x):
data = np.vstack(x)
ts = TimeSeries(data, sampling_interval=1.)
return CorrelationAnalyzer(ts).corrcoef
mode="mean")
ht_pln_bs = mne.baseline.rescale(ht_pln,
times,
baseline=(-3.8, -3.3),
mode="mean")
ht_int_bs = mne.baseline.rescale(ht_pln,
times,
baseline=(-3.8, -3.3),
mode="mean")
for ts in ht_cls_bs:
nits = TimeSeries(ts[:, tois[toi][0]:tois[toi][1]],
sampling_rate=1000) # epochs_normal.info["sfreq"])
corr_cls += [CorrelationAnalyzer(nits)]
for ts in ht_pln_bs:
nits = TimeSeries(ts[:, tois[toi][0]:tois[toi][1]],
sampling_rate=1000) # epochs_normal.info["sfreq"])
corr_pln += [CorrelationAnalyzer(nits)]
for ts in ht_int_bs:
nits = TimeSeries(ts[:, tois[toi][0]:tois[toi][1]],
sampling_rate=1000) # epochs_normal.info["sfreq"])
corr_int += [CorrelationAnalyzer(nits)]
results_cls = np.asarray([c.corrcoef for c in corr_cls])
results_pln = np.asarray([c.corrcoef for c in corr_pln])
nx.__class__ # will raise an error if nx could not be imported in viz
no_networkx = False
no_networkx_msg = ''
except ImportError, e:
no_networkx = True
no_networkx_msg = e.args[0]
roi_names = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
data = np.random.rand(10, 1024)
T = TimeSeries(data, sampling_interval=np.pi)
T.metadata['roi'] = roi_names
#Initialize the correlation analyzer
C = CorrelationAnalyzer(T)
def test_drawmatrix_channels():
fig01 = drawmatrix_channels(C.corrcoef,
roi_names,
size=[10., 10.],
color_anchor=0)
def test_plot_xcorr():
xc = C.xcorr_norm
fig02 = plot_xcorr(xc, ((0, 1), (2, 3)), line_labels=['a', 'b'])
verbose=True))
# reshape ROI matrix
allROIS = reshapeTS(t_fix)
numRuns = allROIS.shape[1]
corr_all[subject] = np.zeros((numRuns, len(rois), len(rois))) * np.nan
coh_all[subject] = np.zeros((numRuns, len(rois), len(rois))) * np.nan
# Get roi correlations and coherence
for runNum in range(allROIS.shape[1]):
#need to load timeseries by run
fixTS = ts.TimeSeries(allROIS[:, runNum, :], sampling_interval=TR)
fixTS.metadata['roi'] = roi_names
# Get plot and correlations
C = CorrelationAnalyzer(fixTS)
fig01 = drawmatrix_channels(C.corrcoef,
roi_names,
size=[10., 10.],
color_anchor=0,
title='Correlation Results Run %i' %
runNum)
plt.show()
# Save correlation
corr_all[subject][runNum] = C.corrcoef
# Get coherence
Coh = CoherenceAnalyzer(fixTS)
Coh.method['NFFT'] = NFFT
Coh.method['n_overlap'] = n_overlap
def speed_connectivity(timecourses):
ts = TimeSeries(timecourses, sampling_interval=1.)
C = CorrelationAnalyzer(ts)
return z_fisher(C.corrcoef)
np.abs(ht_vol[:, :, :, j])**2,
times,
baseline=(-1.85, -1.5),
mode="percent")
ht_invol_bs = mne.baseline.rescale(
np.abs(ht_invol[:, :, :, j])**2,
times,
baseline=(-1.85, -1.5),
mode="percent")
for ts in ht_vol_bs:
nits = TimeSeries(ts[:, 768:1024],
sampling_rate=512)
corr_vol += [CorrelationAnalyzer(nits)]
for ts in ht_invol_bs:
nits = TimeSeries(ts[:, 768:1024],
sampling_rate=512)
corr_invol += [CorrelationAnalyzer(nits)]
results_vol[band] = np.asarray([c.corrcoef for c in corr_vol])
results_invol[band] = np.asarray([c.corrcoef for c in corr_invol])
np.save(graph_data + "%s_vol_pow.npy" % subject,
results_vol)
np.save(graph_data + "%s_inv_pow.npy" % subject,
results_invol)
