def _make_output_figures(self):
"""
Generate the desired figure and save the files according to
self.inputs.output_figure_file
"""
if self.inputs.figure_type == 'matrix':
fig_coh = viz.drawmatrix_channels(self.coherence,
channel_names=self.ROIs,
color_anchor=0)
fig_coh.savefig(fname_presuffix(self.inputs.output_figure_file,
suffix='_coherence'))
fig_dt = viz.drawmatrix_channels(self.delay,
channel_names=self.ROIs,
color_anchor=0)
fig_dt.savefig(fname_presuffix(self.inputs.output_figure_file,
suffix='_delay'))
else:
fig_coh = viz.drawgraph_channels(self.coherence,
channel_names=self.ROIs)
fig_coh.savefig(fname_presuffix(self.inputs.output_figure_file,
suffix='_coherence'))
fig_dt = viz.drawgraph_channels(self.delay,
channel_names=self.ROIs)
fig_dt.savefig(fname_presuffix(self.inputs.output_figure_file,
suffix='_delay'))
def _make_output_figures(self):
"""
Generate the desired figure and save the files according to
self.inputs.output_figure_file
"""
if self.inputs.figure_type == 'matrix':
fig_coh = viz.drawmatrix_channels(self.coherence,
channel_names=self.ROIs,
color_anchor=0)
fig_coh.savefig(fname_presuffix(self.inputs.output_figure_file,
suffix='_coherence'))
fig_dt = viz.drawmatrix_channels(self.delay,
channel_names=self.ROIs,
color_anchor=0)
fig_dt.savefig(fname_presuffix(self.inputs.output_figure_file,
suffix='_delay'))
else:
fig_coh = viz.drawgraph_channels(self.coherence,
channel_names=self.ROIs)
fig_coh.savefig(fname_presuffix(self.inputs.output_figure_file,
suffix='_coherence'))
fig_dt = viz.drawgraph_channels(self.delay,
channel_names=self.ROIs)
fig_dt.savefig(fname_presuffix(self.inputs.output_figure_file,
suffix='_delay'))
def test_drawgraph_channels():
fig04 = drawgraph_channels(C.corrcoef, roi_names)
def test_drawgraph_channels():
fig04 = drawgraph_channels(C.corrcoef, roi_names)
""" Extract the coherence and average across the same frequency bands as before: """ coh = np.mean(coh[:, :, freq_idx], -1) # Averaging on the last dimension """ Finally, in this case, we visualize the adjacency matrix, by creating a network graph of these ROIs (this is done by using the function drawgraph_channels which relies on `networkx <http://networkx.lanl.gov>`_): """ fig04 = drawgraph_channels(coh, roi_names[idx]) """ .. image:: fig/resting_state_fmri_04.png This shows us that there is a stronger connectivity between the left putamen and the left caudate than between the homologous regions in the other hemisphere. In particular, in contrast to the relatively high correlation between the right caudate and the left caudate, there is a rather low coherence between the time-series in these two regions, in this frequency range. Note that the connectivity described by coherency (and other measures of functional connectivity) could arise because of neural connectivity between the two regions, but also due to a common blood supply, or common fluctuations in other physiological measures which affect the BOLD signal measured in both regions. In order to be able to differentiate these two options, we would have
Extract the coherence and average across the same frequency bands as before: """ coh = np.mean(coh[:, :, freq_idx], -1) # Averaging on the last dimension """ Finally, in this case, we visualize the adjacency matrix, by creating a network graph of these ROIs (this is done by using the function drawgraph_channels which relies on `networkx <http://networkx.lanl.gov>`_): """ fig04 = drawgraph_channels(coh, roi_names[idx]) """ .. image:: fig/resting_state_fmri_04.png This shows us that there is a stronger connectivity between the left putamen and the left caudate than between the homologous regions in the other hemisphere. In particular, in contrast to the relatively high correlation between the right caudate and the left caudate, there is a rather low coherence between the time-series in these two regions, in this frequency range. Note that the connectivity described by coherency (and other measures of functional connectivity) could arise because of neural connectivity between the two regions, but also due to a common blood supply, or common fluctuations in other physiological measures which affect the BOLD signal measured in both
#This part is the same as before
TR=1.89
data_rec = csv2rec('data/fmri_timeseries.csv')
roi_names= np.array(data_rec.dtype.names)
n_samples = data_rec.shape[0]
data = np.zeros((len(roi_names),n_samples))
for n_idx, roi in enumerate(roi_names):
data[n_idx] = data_rec[roi]
data = percent_change(data)
T = TimeSeries(data,sampling_interval=TR)
T.metadata['roi'] = roi_names
C = CoherenceAnalyzer(T)
freq_idx = np.where((C.frequencies>0.02) * (C.frequencies<0.15))[0]
idx_lcau = np.where(roi_names=='lcau')[0]
idx_rcau = np.where(roi_names=='rcau')[0]
idx_lput = np.where(roi_names=='lput')[0]
idx_rput = np.where(roi_names=='rput')[0]
idx = np.hstack([idx_lcau,idx_rcau,idx_lput,idx_rput])
idx1 = np.vstack([[idx[i]]*4 for i in range(4)]).ravel()
idx2 = np.hstack(4*[idx])
#Extract the coherence and average across these frequency bands:
coh = C.coherence[idx1,idx2].reshape(4,4,C.frequencies.shape[0])
coh = np.mean(coh[:,:,freq_idx],2) #Averaging on the last dimension
drawgraph_channels(coh,roi_names[idx])
