def test_run_plot_GLM_topo():
raw_intensity = _load_dataset()
raw_intensity.crop(450, 600) # Keep the test fast
design_matrix = make_first_level_design_matrix(raw_intensity,
drift_order=1,
drift_model='polynomial')
raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od, ppf=0.1)
glm_estimates = run_glm(raw_haemo, design_matrix)
fig = plot_glm_topo(raw_haemo, glm_estimates.data, design_matrix)
# 5 conditions (A,B,C,Drift,Constant) * two chroma + 2xcolorbar
assert len(fig.axes) == 12
# Two conditions * two chroma + 2 x colorbar
fig = plot_glm_topo(raw_haemo,
glm_estimates.data,
design_matrix,
requested_conditions=['A', 'B'])
assert len(fig.axes) == 6
# Two conditions * one chroma + 1 x colorbar
with pytest.warns(RuntimeWarning, match='Reducing GLM results'):
fig = plot_glm_topo(raw_haemo.copy().pick(picks="hbo"),
glm_estimates.data,
design_matrix,
requested_conditions=['A', 'B'])
assert len(fig.axes) == 3
# One conditions * two chroma + 2 x colorbar
fig = plot_glm_topo(raw_haemo,
glm_estimates.data,
design_matrix,
requested_conditions=['A'])
assert len(fig.axes) == 4
# One conditions * one chroma + 1 x colorbar
with pytest.warns(RuntimeWarning, match='Reducing GLM results'):
fig = plot_glm_topo(raw_haemo.copy().pick(picks="hbo"),
glm_estimates.data,
design_matrix,
requested_conditions=['A'])
assert len(fig.axes) == 2
# One conditions * one chroma + 0 x colorbar
with pytest.warns(RuntimeWarning, match='Reducing GLM results'):
fig = plot_glm_topo(raw_haemo.copy().pick(picks="hbo"),
glm_estimates.data,
design_matrix,
colorbar=False,
requested_conditions=['A'])
assert len(fig.axes) == 1
# Ensure warning thrown if glm estimates is missing channels from raw
glm_estimates_subset = {
a: glm_estimates.data[a]
for a in raw_haemo.ch_names[0:3]
}
with pytest.raises(RuntimeError, match="does not match regression"):
plot_glm_topo(raw_haemo, glm_estimates_subset, design_matrix)
def test_run_plot_GLM_topo():
raw_intensity = _load_dataset()
raw_intensity.crop(450, 600) # Keep the test fast
design_matrix = make_first_level_design_matrix(raw_intensity,
drift_order=1,
drift_model='polynomial')
raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od)
glm_estimates = run_GLM(raw_haemo, design_matrix)
fig = plot_glm_topo(raw_haemo, glm_estimates, design_matrix)
# 5 conditions (A,B,C,Drift,Constant) * two chroma + 2xcolorbar
assert len(fig.axes) == 12
fig = plot_glm_topo(raw_haemo, glm_estimates, design_matrix,
requested_conditions=['A', 'B'])
# Two conditions * two chroma + 2xcolorbar
assert len(fig.axes) == 6
plt.hlines([0.0], 0, 2)
###############################################################################
# Fit GLM to all data and view topographic distribution
# -----------------------------------------------------
#
# Lastly we can run the GLM analysis on all sensors and plot the result on a
# topomap.
# We see the same result as in the MNE tutorial,
# that activation is largest
# contralateral to the tapping side. Also note that HbR tends to be the
# negative sof HbO as expected.
glm_est = run_GLM(raw_haemo, design_matrix)
plot_glm_topo(raw_haemo,
glm_est,
design_matrix,
requested_conditions=['Tapping/Left', 'Tapping/Right'])
###############################################################################
#
# Compute contrasts
# -----------------
#
# We can also define a contrast as described in
# `Nilearn docs <https://5874-1235740-gh.circle-artifacts.com/0/doc/_build/html/auto_examples/04_glm_first_level_models/plot_localizer_surface_analysis.html>`_
# and plot it.
# Here we contrast the response to tapping on the left hand with the response
# from tapping on the right hand.
contrast_matrix = np.eye(design_matrix.shape[1])
basic_conts = dict([(column, contrast_matrix[i])
###############################################################################
# Fit GLM to all data and view topographic distribution
# -----------------------------------------------------
#
# Lastly we can run the GLM analysis on all sensors and plot the result on a
# topomap.
# We see the same result as in the MNE tutorial,
# that activation is largest
# contralateral to the tapping side. Also note that HbR tends to be the
# negative of HbO as expected.
glm_est = run_GLM(raw_haemo, design_matrix)
plot_glm_topo(raw_haemo, glm_est, design_matrix,
requested_conditions=['Tapping/Left',
'Tapping/Right'])
###############################################################################
#
# Note that the topographic visualisation is a high level representation
# of the underlying data. This visual representation fits a smoothed surface
# to the data and makes many assumptions including that the data is
# spatially smooth and that the sensors sufficiently cover the scalp surface.
# These assumptions can be violated with fNIRS due to the improved spatial
# sensitivity (relative to EEG) and typically low number of sensors that are
# unevenly distributed over the scalp.
# As such, researchers should understand the underlying data and ensure that
# the figure accurately reflects the effect of interest.
#