def signal_confound_design(self, run):
"""Build a matrix of signal confound variables."""
analysis_dir = PROJECT["analysis_dir"]
if self.exp == "dots":
tr = 2
elif self.exp == "sticks":
tr = .72
fstem = op.join(analysis_dir, self.exp, self.subj, "preproc",
"run_{}".format(run))
motion = (pd.read_csv(op.join(fstem, "realignment_params.csv")).filter(
regex="rot|trans").apply(stats.zscore))
nuisance = (pd.read_csv(op.join(
fstem,
"nuisance_variables.csv")).filter(regex="wm_").apply(stats.zscore))
artifacts = (pd.read_csv(op.join(fstem, "artifacts.csv")).any(axis=1))
confounds = pd.concat([motion, nuisance], axis=1)
dmat = glm.DesignMatrix(confounds=confounds,
artifacts=artifacts,
hpf_kernel=self.hpf_kernel,
tr=tr)
return dmat.design_matrix
def estimate_voxel_params(subj, data, model, runs, conditions):
"""Fit a univariate model in each voxel of a ROI data array."""
# Load the task design
design_file = design_temp.format(subj, model)
design = pd.read_csv(design_file)
# Precompute the highpass filter kernel and HRF
ntp = data.shape[1]
hpf_kernel = glm.fsl_highpass_matrix(ntp, 128)
hrf = glm.GammaDifferenceHRF()
# Build a design matrix for each run separately and then combine
Xs = []
for run in runs:
run_design = design.query("run == @run")
X = glm.DesignMatrix(run_design,
hrf,
ntp,
hpf_kernel=hpf_kernel,
condition_names=conditions)
Xs.append(X.design_matrix)
X = pd.concat(Xs).reset_index(drop=True)
# Rotate the data around to stack runs together
np.testing.assert_equal(len(data), len(runs))
data = data.reshape(-1, data.shape[-1])
# Fit the model
model = sm.OLS(data, X).fit()
# Return the params
return model.params
def confound_design(exp, subj, run, hpf_kernel):
"""Build a matrix of confound variables."""
analysis_dir = PROJECT["analysis_dir"]
if exp == "dots":
tr = 1
elif exp == "sticks":
tr = .72
# Load in the confound information
fstem = op.join(analysis_dir, exp, subj, "preproc", "run_{}".format(run))
motion = (pd.read_csv(op.join(fstem, "realignment_params.csv")).filter(
regex="rot|trans").apply(stats.zscore))
nuisance = (pd.read_csv(op.join(
fstem,
"nuisance_variables.csv")).filter(regex="wm_").apply(stats.zscore))
artifacts = (pd.read_csv(op.join(fstem, "artifacts.csv")).any(axis=1))
# Upsample dots data
if exp == "dots":
motion = pd.DataFrame(moss.upsample(motion, 2))
nuisance = pd.DataFrame(moss.upsample(nuisance, 2))
new_tps = np.arange(0, 229.5, .5)
artifacts = artifacts.reindex(new_tps).ffill().reset_index(drop=True)
# Build this portion of the design matrix
confounds = pd.concat([motion, nuisance], axis=1)
dmat = glm.DesignMatrix(confounds=confounds,
artifacts=artifacts,
hpf_kernel=hpf_kernel,
tr=tr)
return dmat.design_matrix
def estimate_subject_roi_fir(subj, mask, model, conditions=None):
# Load the cached ROI dataset
data = load_cached_roi_data(subj, mask)
# Average the data over voxels
data = data.mean(axis=-1)
# Upsample the data to 1s resolution
data = moss.upsample(data.T, 2).T
# Convert the data to percent signal change over runs
data = percent_change(data, 1)
# Count the number of timepoints
ntp = data.shape[1]
# Concatenate the data into one long vector
data = np.concatenate(data)
# Load the design, make events impulses, get a list of conditions
design = pd.read_csv(design_temp.format(subj, model))
design["duration"] = 0
if conditions is None:
conditions = design["condition"].unique()
# Precache the hpf kernel
hpf_kernel = glm.fsl_highpass_matrix(ntp, 128)
# Make a design matrix for each run and then concatenate
Xs = []
for run, run_df in design.groupby("run"):
X = glm.DesignMatrix(run_df,
glm.FIR(tr=1, nbasis=24, offset=-2),
condition_names=conditions,
hpf_cutoff=None,
hpf_kernel=hpf_kernel,
ntp=ntp,
tr=1,
oversampling=1)
Xs.append(X.design_matrix)
X = pd.concat(Xs)
# Fit the model
model = sm.OLS(data, X).fit()
# Add metadata about the beta for each timepoint and condition
params = model.params.reset_index(name="coef")
params["timepoint"] = params["index"].str[-2:].astype(int)
params["timepoint"] -= 1
params["condition"] = params["index"].str[:-3]
params["subj"] = subj
params["roi"] = mask
# Return the model parameters
return params
def schedule_efficiency(schedule, nbasis, leadout_trs):
"""Compute the FIR design matrix efficiency for a given schedule."""
par = pd.DataFrame(dict(onset=schedule.trial_time_tr,
condition=schedule.context))
fir = glm.FIR(tr=1, nbasis=nbasis, offset=-1)
ntp = par.onset.max() + leadout_trs
X = glm.DesignMatrix(par, fir, ntp,
hpf_cutoff=None,
tr=1, oversampling=1).design_matrix.values
eff = 1 / np.trace(inv(X.T.dot(X)))
return schedule, eff
def regress_task(exp, subj, data):
"""Fit a model of the task and return the residuals."""
if exp == "dots":
ntp = 459
design, _ = dots_design(subj)
design.loc[:, "duration"] = 0
hpf_kernel = glm.fsl_highpass_matrix(ntp, 128, 1)
elif exp == "sticks":
ntp = 515
design, _ = sticks_design(subj)
design.loc[:, "duration"] = 0
design.loc[:, "onset"] = (design["onset"] / .72).round()
hpf_kernel = glm.fsl_highpass_matrix(ntp, 178, 1)
conditions = design["condition"].unique()
# Upsample the dots data to match the design resolution
if exp == "dots":
run_data = np.split(data, 12)
data = np.concatenate([moss.upsample(d, 2) for d in run_data])
# Make a design matrix for each run and then concatenate
Xs = []
for run, run_df in design.groupby("run"):
Xrun = glm.DesignMatrix(run_df,
glm.FIR(tr=1, nbasis=24, offset=-2),
condition_names=conditions,
hpf_kernel=hpf_kernel,
ntp=ntp,
tr=1,
oversampling=1).design_matrix
# Regress confounds out of the design matrix
confounds = confound_design(exp, subj, run, hpf_kernel)
assert len(confounds) == len(Xrun)
confounds.index = Xrun.index
Xrun = OLS(Xrun, confounds).fit().resid
Xs.append(Xrun)
X = pd.concat(Xs)
resid = OLS(data, X).fit().resid.values
return resid
def _run_interface(self, runtime):
# Get all the information for the design
design_kwargs = self.build_design_information()
# Initialize the design matrix object
X = glm.DesignMatrix(**design_kwargs)
# Report on the design
self.design_report(self.inputs.exp_info, X, design_kwargs)
# Write out the design object as a pkl to pass to the report function
X.to_pickle("design.pkl")
# Finally, write out the design files in FSL format
X.to_fsl_files("design", self.inputs.exp_info["contrasts"])
return runtime
def residualize_roi_data(subj, mask, model, conditions=None):
"""Residualize cached ROI data against task model."""
orig_data = load_cached_roi_data(subj, mask)
# De-mean the data by run and voxel
orig_data = signal.detrend(orig_data, axis=1, type="constant")
# Precompute the highpass filter kernel and HRF
ntp = orig_data.shape[1]
hpf_kernel = glm.fsl_highpass_matrix(ntp, 128)
hrf = glm.GammaDifferenceHRF(temporal_deriv=True)
# Load the task design
design_file = op.join(data_dir, subj, "design", model + ".csv")
design = pd.read_csv(design_file)
if conditions is None:
conditions = sorted(design["condition"].unique())
# Set up the output data structure
out_data = np.empty_like(orig_data)
# Loop over the runs and get the residual data for each
for run_i, run_data in enumerate(orig_data):
# Generate the design matrix
run_design = design.query("run == (@run_i + 1)")
X = glm.DesignMatrix(run_design,
hrf,
ntp,
condition_names=conditions,
hpf_kernel=hpf_kernel)
# Fit the model
ols = sm.OLS(run_data, X.design_matrix).fit()
# Save the residuals
out_data[run_i] = ols.resid
# Z-score the residuals by run and voxel
out_data = stats.zscore(out_data, axis=1)
assert not np.any(np.isnan(out_data))
return out_data
def deconvolve(self):
"""Fit a model for each run to get condition amplitude estimates."""
beta_list = []
# Set experiment-specific variables
if self.exp == "dots":
tr = 2
ntp = 230
condition_names = ["context", "trial_type", "cue"]
context_map = dict(motion=0, color=1)
elif self.exp == "sticks":
tr = .72
ntp = 515
condition_names = ["context", "cue", "ori_diff", "hue_diff"]
context_map = dict(ori=0, hue=1)
# Initialize the data and design
design, info = self.design_info()
all_conditions = design.condition.sort(inplace=False).unique()
run_data = np.split(self.data, 12, axis=0)
hrf_model = glm.GammaDifferenceHRF(temporal_deriv=True, tr=tr)
# Keep track of voxels with nonzero variance
assert np.unique([d.shape[0] for d in run_data]).size == 1
n_voxels = run_data[0].shape[1]
good_voxels = np.ones(n_voxels, np.bool)
for run, run_design in design.groupby("run"):
# Build the design matrix
dmat = glm.DesignMatrix(run_design,
hrf_model,
ntp,
hpf_kernel=self.hpf_kernel,
condition_names=all_conditions,
tr=tr)
# Set up the regression variables
X = dmat.design_matrix
Y = pd.DataFrame(run_data[run - 1], index=X.index)
# Regress signal confounds out of the design matrix
# (They have already been removed from the data)
signal_confounds = self.signal_confound_design(run)
X = OLS(X, signal_confounds).fit().resid
# Fit the experiment model and extract the condition betas
betas = OLS(Y, X).fit().params.ix[info.ix[run].index]
beta_list.append(betas)
# Identify bad voxels
good_voxels &= (Y.var(axis=0) > 0).values
# Reformat the condition information by each variable
conditions = pd.DataFrame(
(info.index.get_level_values("condition").str.split("-").tolist()),
columns=condition_names)
# Build the relevant objects for classification
samples = conditions.index
runs = pd.Series(info.index.get_level_values("run"), index=samples)
betas = pd.concat(beta_list, ignore_index=True)
rt = pd.Series(info["rt"].values, index=samples)
y = conditions.context.map(context_map)
# Remove null or single observation samples
use = pd.Series(info["count"].values > 1, index=samples)
runs, betas, rt, y = runs[use], betas[use], rt[use], y[use]
# Assign instance attributes
self.design = design
self.betas = betas
self.runs = runs
self.rt = rt
self.y = y
self.good_voxels = good_voxels
def _run_interface(self, runtime):
subject = self.inputs.subject
session = self.inputs.session
run = self.inputs.run
exp_info = Bunch(self.inputs.exp_info)
model_info = Bunch(self.inputs.model_info)
data_dir = self.inputs.data_dir
# Load the timeseries
ts_img = nib.load(self.inputs.ts_file)
affine, header = ts_img.affine, ts_img.header
# Load the anatomical segmentation and fine analysis mask
run_mask = nib.load(self.inputs.mask_file).get_data() > 0
seg_img = nib.load(self.inputs.seg_file)
seg = seg_img.get_data()
mask = (seg > 0) & (seg < 5) & run_mask
n_vox = mask.sum()
mask_img = nib.Nifti1Image(mask.astype(np.int8), affine, header)
# Load the noise segmentation
# TODO implement noisy voxel removal
noise_img = nib.load(self.inputs.noise_file)
# Spatially filter the data
fwhm = model_info.smooth_fwhm
# TODO use smooth_segmentation instead?
signals.smooth_volume(ts_img, fwhm, mask_img, noise_img, inplace=True)
if model_info.surface_smoothing:
vert_data = nib.load(self.inputs.surf_file).get_data()
for i, mesh_file in enumerate(self.inputs.mesh_files):
sm = surface.SurfaceMeasure.from_file(mesh_file)
vert_img = nib.Nifti1Image(vert_data[..., i], affine)
signals.smooth_surface(ts_img,
vert_img,
sm,
fwhm,
noise_img,
inplace=True)
# Compute the mean image for later
# TODO limit to gray matter voxels?
data = ts_img.get_data()
mean = data.mean(axis=-1)
mean_img = nib.Nifti1Image(mean, affine, header)
# Temporally filter the data
n_tp = ts_img.shape[-1]
hpf_matrix = glm.highpass_filter_matrix(n_tp, model_info.hpf_cutoff,
exp_info.tr)
data[mask] = np.dot(hpf_matrix, data[mask].T).T
# TODO remove the mean from the data
# data[gray_mask] += mean[gray_mask, np.newaxis]
data[~mask] = 0 # TODO this is done within smoothing actually
# Define confound regressons from various sources
# TODO
mc_data = pd.read_csv(self.inputs.mc_file)
# Detect artifact frames
# TODO
# Convert to percent signal change?
# TODO
# Build the design matrix
# TODO move out of moss and simplify
design_file = op.join(data_dir, subject, "design",
model_info.name + ".csv")
design = pd.read_csv(design_file)
run_rows = (design.session == session) & (design.run == run)
design = design.loc[run_rows]
# TODO better error when this fails (maybe check earlier too)
assert len(design) > 0
dmat = mossglm.DesignMatrix(design, ntp=n_tp, tr=exp_info.tr)
X = dmat.design_matrix.values
# Save out the design matrix
design_file = self.define_output("design_file", "design.csv")
dmat.design_matrix.to_csv(design_file, index=False)
# Prewhiten the data
ts_img = nib.Nifti1Image(data, affine)
WY, WX = glm.prewhiten_image_data(ts_img, mask_img, X)
# Fit the final model
B, SS, XtXinv, E = glm.iterative_ols_fit(WY, WX)
# TODO should we re-compute the tSNR on the residuals?
# Convert outputs to image format
beta_img = matrix_to_image(B.T, mask_img)
error_img = matrix_to_image(SS, mask_img)
XtXinv_flat = XtXinv.reshape(n_vox, -1)
ols_img = matrix_to_image(XtXinv_flat.T, mask_img)
resid_img = matrix_to_image(E, mask_img, ts_img)
# Write out the results
self.write_image("mask_file", "mask.nii.gz", mask_img)
self.write_image("beta_file", "beta.nii.gz", beta_img)
self.write_image("error_file", "error.nii.gz", error_img)
self.write_image("ols_file", "ols.nii.gz", ols_img)
if model_info.save_residuals:
self.write_image("resid_file", "resid.nii.gz", resid_img)
# Make some QC plots
# We want a version of the resid data with an intact mean so that
# the carpet plot can compute percent signal change.
# (Maybe carpetplot should accept a mean image and handle that
# internally)?
# TODO standarize the representation of mean in this method
resid_data = np.zeros(ts_img.shape, np.float32)
resid_data += np.expand_dims(mean * mask, axis=-1)
resid_data[mask] += E.T
resid_img = nib.Nifti1Image(resid_data, affine, header)
p = CarpetPlot(resid_img, seg_img, mc_data)
self.write_visualization("resid_plot", "resid.png", p)
# Plot the deisgn matrix
# TODO update when improving design matrix code
design_plot = self.define_output("design_plot", "design.png")
dmat.plot(fname=design_plot, close=True)
# Plot the sigma squares image for QC
error_m = Mosaic(mean_img, error_img, mask_img)
error_m.plot_overlay("cube:.8:.2", 0, fmt=".0f")
self.write_visualization("error_plot", "error.png", error_m)
return runtime
def design_report(self, exp_info, X, design_kwargs):
"""Generate static images summarizing the design."""
# Plot the design itself
design_png = op.abspath("design.png")
X.plot(fname=design_png, close=True)
with sns.axes_style("whitegrid"):
# Plot the eigenvalue spectrum
svd_png = op.abspath("design_singular_values.png")
X.plot_singular_values(fname=svd_png, close=True)
# Plot the correlations between design elements and confounds
corr_png = op.abspath("design_correlation.png")
if design_kwargs["design"] is None:
with open(corr_png, "wb"):
pass
else:
X.plot_confound_correlation(fname=corr_png, close=True)
# Build a list of images sumarrizing the model
report = [design_png, corr_png, svd_png]
# Now plot the information loss from the high-pass filter
design_kwargs["hpf_cutoff"] = None
X_unfiltered = glm.DesignMatrix(**design_kwargs)
tr = design_kwargs["tr"]
ntp = design_kwargs["ntp"]
# Plot for each contrast
for i, (name, cols, weights) in enumerate(exp_info["contrasts"], 1):
# Compute the contrast predictors
C = X.contrast_vector(cols, weights)
y_filt = X.design_matrix.dot(C)
y_unfilt = X_unfiltered.design_matrix.dot(C)
# Compute the spectral density for filtered and unfiltered
fs, pxx_filt = signal.welch(y_filt, 1. / tr, nperseg=ntp)
fs, pxx_unfilt = signal.welch(y_unfilt, 1. / tr, nperseg=ntp)
# Draw the spectral density
with sns.axes_style("whitegrid"):
f, ax = plt.subplots(figsize=(9, 3))
ax.fill_between(fs, pxx_unfilt, color="#C41E3A")
if exp_info["hpf_cutoff"] is not None:
ax.axvline(1.0 / exp_info["hpf_cutoff"],
c=".3",
ls=":",
lw=1.5)
ax.fill_between(fs, pxx_filt, color=".5")
# Label the plot
ax.set(xlabel="Frequency",
ylabel="Spectral Density",
xlim=(0, .15))
plt.tight_layout()
# Save the plot
fname = op.abspath("cope%d_filter.png" % i)
f.savefig(fname, dpi=100)
plt.close(f)
report.append(fname)
# Store the report files for later
self.report_files = report
