def individual_analysis(bids_path, ID):
raw_intensity = read_raw_bids(bids_path=bids_path, verbose=False)
# Convert signal to haemoglobin and resample
raw_od = optical_density(raw_intensity)
raw_haemo = beer_lambert_law(raw_od)
raw_haemo.resample(0.3)
# Cut out just the short channels for creating a GLM repressor
sht_chans = get_short_channels(raw_haemo)
raw_haemo = get_long_channels(raw_haemo)
# Create a design matrix
design_matrix = make_first_level_design_matrix(raw_haemo, stim_dur=5.0)
# Append short channels mean to design matrix
design_matrix["ShortHbO"] = np.mean(sht_chans.copy().pick(picks="hbo").get_data(), axis=0)
design_matrix["ShortHbR"] = np.mean(sht_chans.copy().pick(picks="hbr").get_data(), axis=0)
# Run GLM
glm_est = run_GLM(raw_haemo, design_matrix)
# Define channels in each region of interest
# List the channel pairs manually
left = [[4, 3], [1, 3], [3, 3], [1, 2], [2, 3], [1, 1]]
right = [[6, 7], [5, 7], [7, 7], [5, 6], [6, 7], [5, 5]]
# Then generate the correct indices for each pair
groups = dict(
Left_Hemisphere=picks_pair_to_idx(raw_haemo, left, on_missing='ignore'),
Right_Hemisphere=picks_pair_to_idx(raw_haemo, right, on_missing='ignore'))
# Extract channel metrics
cha = glm_to_tidy(raw_haemo, glm_est, design_matrix)
cha["ID"] = ID # Add the participant ID to the dataframe
# Compute region of interest results from channel data
roi = pd.DataFrame()
for idx, col in enumerate(design_matrix.columns):
roi = roi.append(glm_region_of_interest(glm_est, groups, idx, col))
roi["ID"] = ID # Add the participant ID to the dataframe
# Contrast left vs right tapping
contrast_matrix = np.eye(design_matrix.shape[1])
basic_conts = dict([(column, contrast_matrix[i])
for i, column in enumerate(design_matrix.columns)])
contrast_LvR = basic_conts['Tapping/Left'] - basic_conts['Tapping/Right']
contrast = compute_contrast(glm_est, contrast_LvR)
con = glm_to_tidy(raw_haemo, contrast, design_matrix)
con["ID"] = ID # Add the participant ID to the dataframe
# Convert to uM for nicer plotting below.
cha["theta"] = [t * 1.e6 for t in cha["theta"]]
roi["theta"] = [t * 1.e6 for t in roi["theta"]]
con["effect"] = [t * 1.e6 for t in con["effect"]]
return raw_haemo, roi, cha, con
def test_GLM_system_test():
fnirs_data_folder = mne.datasets.fnirs_motor.data_path()
fnirs_raw_dir = os.path.join(fnirs_data_folder, 'Participant-1')
raw_intensity = mne.io.read_raw_nirx(fnirs_raw_dir).load_data()
raw_intensity.resample(1.0)
new_des = [des for des in raw_intensity.annotations.description]
new_des = ['Control' if x == "1.0" else x for x in new_des]
new_des = ['Tapping/Left' if x == "2.0" else x for x in new_des]
new_des = ['Tapping/Right' if x == "3.0" else x for x in new_des]
annot = mne.Annotations(raw_intensity.annotations.onset,
raw_intensity.annotations.duration, new_des)
raw_intensity.set_annotations(annot)
raw_intensity.annotations.crop(35, 2967)
raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od)
short_chs = get_short_channels(raw_haemo)
raw_haemo = get_long_channels(raw_haemo)
design_matrix = make_first_level_design_matrix(raw_intensity,
hrf_model='spm',
stim_dur=5.0,
drift_order=3,
drift_model='polynomial')
design_matrix["ShortHbO"] = np.mean(
short_chs.copy().pick(picks="hbo").get_data(), axis=0)
design_matrix["ShortHbR"] = np.mean(
short_chs.copy().pick(picks="hbr").get_data(), axis=0)
glm_est = run_GLM(raw_haemo, design_matrix)
df = glm_to_tidy(raw_haemo, glm_est, design_matrix)
df = _tidy_long_to_wide(df)
a = (df.query('condition in ["Control"]').groupby(['condition', 'Chroma'
]).agg(['mean']))
# Make sure false positive rate is less than 5%
assert a["Significant"].values[0] < 0.05
assert a["Significant"].values[1] < 0.05
a = (df.query('condition in ["Tapping/Left", "Tapping/Right"]').groupby(
['condition', 'Chroma']).agg(['mean']))
# Fairly arbitrary cutoff here, but its more than 5%
assert a["Significant"].values[0] > 0.7
assert a["Significant"].values[1] > 0.7
assert a["Significant"].values[2] > 0.7
assert a["Significant"].values[3] > 0.7
left = [[1, 1], [1, 2], [1, 3], [2, 1], [2, 3], [2, 4], [3, 2], [3, 3],
[4, 3], [4, 4]]
right = [[5, 5], [5, 6], [5, 7], [6, 5], [6, 7], [6, 8], [7, 6], [7, 7],
[8, 7], [8, 8]]
groups = dict(Left_ROI=picks_pair_to_idx(raw_haemo, left),
Right_ROI=picks_pair_to_idx(raw_haemo, right))
df = pd.DataFrame()
for idx, col in enumerate(design_matrix.columns[:3]):
df = df.append(glm_region_of_interest(glm_est, groups, idx, col))
assert df.shape == (12, 8)
def test_roi_picks():
fnirs_data_folder = mne.datasets.fnirs_motor.data_path()
fnirs_raw_dir = os.path.join(fnirs_data_folder, 'Participant-1')
raw = mne.io.read_raw_nirx(fnirs_raw_dir).load_data()
picks = picks_pair_to_idx(raw, [[1, 1], [1, 2], [5, 13], [8, 16]])
assert raw.ch_names[picks[0]] == "S1_D1 760"
assert raw.ch_names[picks[1]] == "S1_D1 850"
assert raw.ch_names[picks[2]] == "S1_D2 760"
assert raw.ch_names[picks[3]] == "S1_D2 850"
assert raw.ch_names[picks[4]] == "S5_D13 760"
assert raw.ch_names[picks[5]] == "S5_D13 850"
assert raw.ch_names[picks[6]] == "S8_D16 760"
assert raw.ch_names[picks[7]] == "S8_D16 850"
# Test what happens when a pair that doesn't exist is requested (15-13)
with pytest.raises(ValueError, match='No matching'):
picks_pair_to_idx(raw, [[1, 1], [1, 2], [15, 13], [8, 16]])
with pytest.warns(RuntimeWarning, match='No matching channels'):
picks = picks_pair_to_idx(raw, [[1, 1], [1, 2], [15, 13], [8, 16]],
on_missing='warning')
assert len(picks) == 6 # Missing should be ignored
picks = picks_pair_to_idx(raw, [[1, 1], [1, 2], [15, 13], [8, 16]],
on_missing='ignore')
assert len(picks) == 6
# Test usage for ROI downstream functions
group_by = dict(Left_ROI=picks_pair_to_idx(raw, [[1, 1], [1, 2], [5, 13]]),
Right_ROI=picks_pair_to_idx(raw, [[3, 3], [3, 11]]))
assert group_by['Left_ROI'] == [0, 1, 2, 3, 34, 35]
assert group_by['Right_ROI'] == [18, 19, 20, 21]
# Ensure we dont match [1, 1] to S1_D11
# Check easy condition
picks = picks_pair_to_idx(raw, [[1, 1]])
assert picks == [0, 1]
# Force in tricky situation
raw.info["ch_names"][2] = 'S1_D11 760'
raw.info["ch_names"][3] = 'S1_D11 850'
picks = picks_pair_to_idx(raw, [[1, 1]])
assert picks == [0, 1]
picks = picks_pair_to_idx(raw, [[21, 91], [91, 2]], on_missing='ignore')
assert picks == []
def test_roi_picks():
fnirs_data_folder = mne.datasets.fnirs_motor.data_path()
fnirs_raw_dir = os.path.join(fnirs_data_folder, 'Participant-1')
raw = mne.io.read_raw_nirx(fnirs_raw_dir).load_data()
picks = picks_pair_to_idx(raw, [[1, 1], [1, 2], [5, 13], [8, 16]])
assert raw.ch_names[picks[0]] == "S1_D1 760"
assert raw.ch_names[picks[1]] == "S1_D1 850"
assert raw.ch_names[picks[2]] == "S1_D2 760"
assert raw.ch_names[picks[3]] == "S1_D2 850"
assert raw.ch_names[picks[4]] == "S5_D13 760"
assert raw.ch_names[picks[5]] == "S5_D13 850"
assert raw.ch_names[picks[6]] == "S8_D16 760"
assert raw.ch_names[picks[7]] == "S8_D16 850"
with pytest.raises(ValueError, match='No matching'):
picks_pair_to_idx(raw, [[1, 1], [1, 2], [15, 13], [8, 16]])
picks_pair_to_idx(raw, [[1, 1], [1, 2], [15, 13], [8, 16]],
on_missing='warning')
picks = picks_pair_to_idx(raw, [[1, 1], [1, 2], [15, 13], [8, 16]],
on_missing='ignore')
assert len(picks) == 6
# Test usage for ROI downstream functions
group_by = dict(Left_ROI=picks_pair_to_idx(raw, [[1, 1], [1, 2],
[5, 13]]),
Right_ROI=picks_pair_to_idx(raw, [[3, 3], [3, 11],
[6, 8]]))
assert group_by['Left_ROI'] == [0, 1, 2, 3, 34, 35]
assert group_by['Right_ROI'] == [18, 19, 20, 21, 40, 41]
'With Short Regression'
]):
axes[column].set_title('{}'.format(condition))
###############################################################################
# Plot hemisphere for each approach
# ---------------------------------
#
# Plot the epoch image for each approach. First we specify the source
# detector pairs for analysis.
left = [[1, 3], [2, 3], [1, 2], [4, 3]]
right = [[5, 7], [6, 7], [5, 6], [8, 7]]
groups = dict(Left_ROI=picks_pair_to_idx(raw_anti.pick(picks='hbo'),
left,
on_missing='warning'),
Right_ROI=picks_pair_to_idx(raw_anti.pick(picks='hbo'),
right,
on_missing='warning'))
evoked_dict = {
'Left/HbO': epochs['Tapping/Left'].average(picks='hbo'),
'Left/HbR': epochs['Tapping/Left'].average(picks='hbr'),
'Right/HbO': epochs['Tapping/Right'].average(picks='hbo'),
'Right/HbR': epochs['Tapping/Right'].average(picks='hbr')
}
for condition in evoked_dict:
evoked_dict[condition].rename_channels(lambda x: x[:-4])
evoked_dict_anti = {
axes[column].set_title('{}'.format(condition))
###############################################################################
# Plot trials for each approach
# -----------------------------
#
# Plot the epoch image for each approach. First we specify the source
# detector pairs for analysis.
left = [[1, 1], [1, 2], [1, 3], [2, 1], [2, 3],
[2, 4], [3, 2], [3, 3], [4, 3], [4, 4]]
right = [[5, 5], [5, 6], [5, 7], [6, 5], [6, 7],
[6, 8], [7, 6], [7, 7], [8, 7], [8, 8]]
groups = dict(Left_ROI=picks_pair_to_idx(raw_anti.pick(picks='hbo'), left),
Right_ROI=picks_pair_to_idx(raw_anti.pick(picks='hbo'), right))
###############################################################################
# First we plot the epochs for the unprocessed data.
epochs['Tapping'].pick(picks='hbo').plot_image(combine='mean',
group_by=groups)
###############################################################################
# New we plot the epochs for the data processed with the Cui anti correlation
# method.
epochs_anti['Tapping'].pick(picks='hbo').plot_image(combine='mean',
# pairs of interest and then determining which channels these correspond to
# within the raw data structure. The channel indices are stored in a
# dictionary for access below.
# The fOLD toolbox can be used to assist in the design of ROIs.
# And consideration should be paid to ensure optimal size ROIs are selected.
#
# In this example, two ROIs are generated. One for the left motor cortex
# and one for the right motor cortex. These are called `Left_Hemisphere` and
# `Right_Hemisphere` and are stored in the `rois` dictionary.
# Specify channel pairs for each ROI
left = [[4, 3], [1, 3], [3, 3], [1, 2], [2, 3], [1, 1]]
right = [[8, 7], [5, 7], [7, 7], [5, 6], [6, 7], [5, 5]]
# Then generate the correct indices for each pair and store in dictionary
rois = dict(Left_Hemisphere=picks_pair_to_idx(raw_haemo, left),
Right_Hemisphere=picks_pair_to_idx(raw_haemo, right))
pprint(rois)
# %%
# Create average waveform per ROI
# -------------------------------
#
# Next, an average waveform is generated per condition per region of interest.
# This allows the researcher to view the responses elicited in different
# regions of the brain for each condition.
# Specify the figure size and limits per chromophore.
fig, axes = plt.subplots(nrows=len(rois),
ncols=len(all_evokeds),
axes[1].set_title("Hemispheres plotted independently")
###############################################################################
# Analyse regions of interest
# ---------------------------
#
# Or alternatively we can summarise the responses across regions of interest
# for each condition. And you can plot it with your favorite software.
left = [[1, 1], [1, 2], [1, 3], [2, 1], [2, 3],
[2, 4], [3, 2], [3, 3], [4, 3], [4, 4]]
right = [[5, 5], [5, 6], [5, 7], [6, 5], [6, 7],
[6, 8], [7, 6], [7, 7], [8, 7], [8, 8]]
groups = dict(Left_ROI=picks_pair_to_idx(raw_haemo, left),
Right_ROI=picks_pair_to_idx(raw_haemo, right))
df = pd.DataFrame()
for idx, col in enumerate(design_matrix.columns[:3]):
df = df.append(glm_region_of_interest(glm_est, groups, idx, col))
###############################################################################
#
# Compute contrasts
# -----------------
#
# We can also define a contrast as described in
# `Nilearn docs <http://nilearn.github.io/auto_examples/04_glm_first_level/plot_localizer_surface_analysis.html>`_
# and plot it.
def individual_analysis(bids_path, ID):
raw_intensity = read_raw_bids(bids_path=bids_path, verbose=False)
# Delete annotation labeled 15, as these just signify the start and end of experiment.
raw_intensity.annotations.delete(raw_intensity.annotations.description == '15.0')
# sanitize event names
raw_intensity.annotations.description[:] = [
d.replace('/', '_') for d in raw_intensity.annotations.description]
# Convert signal to haemoglobin and resample
raw_od = optical_density(raw_intensity)
raw_haemo = beer_lambert_law(raw_od, ppf=0.1)
raw_haemo.resample(0.3)
# Cut out just the short channels for creating a GLM repressor
sht_chans = get_short_channels(raw_haemo)
raw_haemo = get_long_channels(raw_haemo)
# Create a design matrix
design_matrix = make_first_level_design_matrix(raw_haemo, stim_dur=5.0)
# Append short channels mean to design matrix
design_matrix["ShortHbO"] = np.mean(sht_chans.copy().pick(picks="hbo").get_data(), axis=0)
design_matrix["ShortHbR"] = np.mean(sht_chans.copy().pick(picks="hbr").get_data(), axis=0)
# Run GLM
glm_est = run_glm(raw_haemo, design_matrix)
# Define channels in each region of interest
# List the channel pairs manually
left = [[4, 3], [1, 3], [3, 3], [1, 2], [2, 3], [1, 1]]
right = [[8, 7], [5, 7], [7, 7], [5, 6], [6, 7], [5, 5]]
# Then generate the correct indices for each pair
groups = dict(
Left_Hemisphere=picks_pair_to_idx(raw_haemo, left, on_missing='ignore'),
Right_Hemisphere=picks_pair_to_idx(raw_haemo, right, on_missing='ignore'))
# Extract channel metrics
cha = glm_est.to_dataframe()
# Compute region of interest results from channel data
roi = glm_est.to_dataframe_region_of_interest(groups,
design_matrix.columns,
demographic_info=True)
# Define left vs right tapping contrast
contrast_matrix = np.eye(design_matrix.shape[1])
basic_conts = dict([(column, contrast_matrix[i])
for i, column in enumerate(design_matrix.columns)])
contrast_LvR = basic_conts['Tapping_Left'] - basic_conts['Tapping_Right']
# Compute defined contrast
contrast = glm_est.compute_contrast(contrast_LvR)
con = contrast.to_dataframe()
# Add the participant ID to the dataframes
roi["ID"] = cha["ID"] = con["ID"] = ID
# Convert to uM for nicer plotting below.
cha["theta"] = [t * 1.e6 for t in cha["theta"]]
roi["theta"] = [t * 1.e6 for t in roi["theta"]]
con["effect"] = [t * 1.e6 for t in con["effect"]]
return raw_haemo, roi, cha, con
