def test_statsmodel_to_df(func):
func = getattr(smf, func)
np.random.seed(0)
amplitude = 1.432
df_cha = pd.DataFrame()
for n in range(5):
raw = simulate_nirs_raw(sfreq=3.,
amplitude=amplitude,
sig_dur=300.,
stim_dur=5.,
isi_min=15.,
isi_max=45.)
raw._data += np.random.normal(0, np.sqrt(1e-12), raw._data.shape)
design_matrix = make_first_level_design_matrix(raw, stim_dur=5.0)
glm_est = run_glm(raw, design_matrix)
with pytest.warns(RuntimeWarning, match='Non standard source detect'):
cha = glm_est.to_dataframe()
cha["ID"] = '%02d' % n
df_cha = pd.concat([df_cha, cha], ignore_index=True)
df_cha["theta"] = df_cha["theta"] * 1.0e6
roi_model = func("theta ~ -1 + Condition", df_cha,
groups=df_cha["ID"]).fit()
df = statsmodels_to_results(roi_model)
assert type(df) == pd.DataFrame
assert_allclose(df["Coef."]["Condition[A]"], amplitude, rtol=0.1)
assert df["Significant"]["Condition[A]"]
assert df.shape == (8, 8)
roi_model = smf.rlm("theta ~ -1 + Condition", df_cha,
groups=df_cha["ID"]).fit()
df = statsmodels_to_results(roi_model)
assert type(df) == pd.DataFrame
assert_allclose(df["Coef."]["Condition[A]"], amplitude, rtol=0.1)
assert df["Significant"]["Condition[A]"]
assert df.shape == (8, 8)
# Create path to file based on experiment info
bids_path = dataset.update(subject=sub)
# Analyse data and return both ROI and channel results
raw_haemo, channel = individual_analysis(bids_path, sub)
# Append individual results to all participants
df_cha = pd.concat([df_cha, channel], ignore_index=True)
ch_summary = df_cha.query("Condition in ['Tapping_Right']")
assert len(ch_summary)
ch_summary = ch_summary.query("Chroma in ['hbo']")
ch_model = smf.mixedlm("theta ~ -1 + ch_name",
ch_summary,
groups=ch_summary["ID"]).fit(method='nm')
model_df = statsmodels_to_results(ch_model,
order=raw_haemo.copy().pick("hbo").ch_names)
# %%
# Plot surface projection of GLM results
# --------------------------------------
#
# Finally, we can project the GLM results from each channel to the nearest cortical surface
# and overlay the sensor positions and two different regions of interest.
# In this example we also highlight the premotor cortex and auditory association cortex
# in green and blue respectively.
# Plot the projection and sensor locations
brain = plot_glm_surface_projection(raw_haemo.copy().pick("hbo"),
model_df,
colorbar=True)
brain.add_sensors(raw_haemo.info,
#
# Now we can summarise the output of the second level model.
# This figure shows that the control condition has small responses that
# are not significantly different to zero for both HbO
# and HbR in both hemispheres.
# Whereas clear significant responses are show for the two tapping conditions.
# We also observe the the tapping response is
# larger in the contralateral hemisphere.
# Filled symbols represent HbO, unfilled symbols represent HbR.
# Regenerate the results from the original group model above
grp_results = df_roi.query("Condition in ['Control','Tapping_Left', 'Tapping_Right']")
roi_model = smf.mixedlm("theta ~ -1 + ROI:Condition:Chroma",
grp_results, groups=grp_results["ID"]).fit(method='nm')
df = statsmodels_to_results(roi_model)
sns.catplot(x="Condition", y="Coef.", hue="ROI", data=df.query("Chroma == 'hbo'"), ci=None, palette="muted", height=4, s=10)
# %%
# Group topographic visualisation
# -------------------------------
#
# We can also view the topographic representation of the data
# (rather than the ROI summary above).
# Here we just plot the oxyhaemoglobin for the two tapping conditions.
# First we compute the mixed effects model for each channel (rather
# than region of interest as above).
# Then we pass these results to the topomap function.
# The model is summarised below, and is not displayed here.
# You can display the model output using: lme.summary()
# %%
# Summarise group-level findings
# ---------------------------------------------------------------------
#
# Next the values from the model above are extracted into a dataframe for
# more convenient analysis below.
# A subset of the results is displayed, illustrating the estimated coefficients
# for oxyhaemoglobin (HbO) for the right hand tapping condition.
# Create a dataframe from LME model for plotting below
df_sum = statsmodels_to_results(lme)
df_sum["delay"] = [int(n) for n in df_sum["delay"]]
df_sum = df_sum.sort_values('delay')
# Print the result for the oxyhaemoglobin data in the tapping condition
df_sum.query("TidyCond in ['Tapping']").query("Chroma in ['hbo']")
# %%
# Note in the output above that there are 10 FIR delays.
# A coefficient estimate has been calculated for each delay.
# These coefficients must be multiplied by the FIR function to obtain the
# morphology of the fNIRS response.
# %%
# Plot the response from a single condition
# ---------------------------------------------------------------------
###############################################################################
# Visualise group results
# -----------------------
#
# Now we can summarise the output of the second level model.
# This figure shows that the control condition has small responses that
# are not significantly different to zero for both HbO
# and HbR in both hemispheres.
# Whereas clear significant responses are show for the two tapping conditions.
# We also observe the the tapping response is
# larger in the contralateral hemisphere.
# Filled symbols represent HbO, unfilled symbols represent HbR.
df = statsmodels_to_results(roi_model)
ggplot(df.query("Chroma == 'hbo'"),
aes(x='Condition', y='Coef.', color='Significant', shape='ROI')) \
+ geom_hline(y_intercept=0, linetype="dashed", size=1) \
+ geom_point(size=5) \
+ scale_shape_manual(values=[16, 17]) \
+ ggsize(800, 300) \
+ geom_point(data=df.query("Chroma == 'hbr'")
.query("ROI == 'Left_Hemisphere'"), size=5, shape=1) \
+ geom_point(data=df.query("Chroma == 'hbr'")
.query("ROI == 'Right_Hemisphere'"), size=5, shape=2)
###############################################################################
# Group topographic visualisation
