"""
Extracting signals from a brain parcellation
============================================
Here we show how to extract signals from a brain :term:`parcellation`
and compute a correlation matrix.
We also show the importance of defining good confounds signals: the
first correlation matrix is computed after regressing out simple
confounds signals: movement regressors, white matter and CSF signals, ...
The second one is without any confounds: all regions are connected to each
other. Finally we demonstrated the functionality of
:func:`nilearn.interfaces.fmriprep.load_confounds` to flexibly select confound
variables from :term:`fMRIPrep` outputs while following some implementation
guideline of :term:`fMRIPrep` confounds documentation
``_.
One reference that discusses the importance of confounds
is :footcite:t:`Varoquaux2013`.
This is just a code example, see the :ref:`corresponding section in the
documentation ` for more.
.. note::
This example needs SciPy >= 1.0.0 for the reordering of the matrix.
.. include:: ../../../examples/masker_note.rst
"""
# %%
# Retrieve the atlas and the data
# -------------------------------
from nilearn import datasets
dataset = datasets.fetch_atlas_harvard_oxford("cort-maxprob-thr25-2mm")
atlas_filename = dataset.maps
labels = dataset.labels
print(f"Atlas ROIs are located in nifti image (4D) at: {atlas_filename}")
# One subject of brain development fMRI data
data = datasets.fetch_development_fmri(n_subjects=1, reduce_confounds=True)
fmri_filenames = data.func[0]
reduced_confounds = data.confounds[0] # This is a preselected set of confounds
# %%
# Extract signals on a :term:`parcellation` defined by labels
# -----------------------------------------------------------
# Using the NiftiLabelsMasker
from nilearn.maskers import NiftiLabelsMasker
masker = NiftiLabelsMasker(
labels_img=atlas_filename,
standardize="zscore_sample",
standardize_confounds="zscore_sample",
memory="nilearn_cache",
verbose=5,
)
# Here we go from nifti files to the signal time series in a numpy
# array. Note how we give confounds to be regressed out during signal
# extraction
time_series = masker.fit_transform(fmri_filenames, confounds=reduced_confounds)
# %%
# Compute and display a correlation matrix
# ----------------------------------------
from nilearn.connectome import ConnectivityMeasure
correlation_measure = ConnectivityMeasure(
kind="correlation",
standardize="zscore_sample",
)
correlation_matrix = correlation_measure.fit_transform([time_series])[0]
# Plot the correlation matrix
import numpy as np
from nilearn import plotting
# Make a large figure
# Mask the main diagonal for visualization:
np.fill_diagonal(correlation_matrix, 0)
# The labels we have start with the background (0), hence we skip the
# first label
# matrices are ordered for block-like representation
plotting.plot_matrix(
correlation_matrix,
figure=(10, 8),
labels=labels[1:],
vmax=0.8,
vmin=-0.8,
title="Confounds",
reorder=True,
)
# %%
# Extract signals and compute a connectivity matrix without confounds removal
# ---------------------------------------------------------------------------
# After covering the basic of signal extraction and functional connectivity
# matrix presentation, let's look into the impact of confounds to :term:`fMRI`
# signal and functional connectivity. Firstly let's find out what a functional
# connectivity matrix looks like without confound removal.
time_series = masker.fit_transform(fmri_filenames)
# Note how we did not specify confounds above. This is bad!
correlation_matrix = correlation_measure.fit_transform([time_series])[0]
np.fill_diagonal(correlation_matrix, 0)
plotting.plot_matrix(
correlation_matrix,
figure=(10, 8),
labels=labels[1:],
vmax=0.8,
vmin=-0.8,
title="No confounds",
reorder=True,
)
# %%
# Load confounds from file using a flexible strategy with fmriprep interface
# --------------------------------------------------------------------------
# The :func:`nilearn.interfaces.fmriprep.load_confounds` function provides
# flexible parameters to retrieve the relevant columns from the TSV file
# generated by :term:`fMRIPrep`.
# :func:`nilearn.interfaces.fmriprep.load_confounds` ensures two things:
#
# 1. The correct regressors are selected with provided strategy, and
#
# 2. Volumes such as non-steady-state and/or high motion volumes are masked
# out correctly.
#
# Let's try a simple strategy removing motion, white matter signal,
# cerebrospinal fluid signal with high-pass filtering.
from nilearn.interfaces.fmriprep import load_confounds
confounds_simple, sample_mask = load_confounds(
fmri_filenames,
strategy=["high_pass", "motion", "wm_csf"],
motion="basic",
wm_csf="basic",
)
print("The shape of the confounds matrix is:", confounds_simple.shape)
print(confounds_simple.columns)
time_series = masker.fit_transform(
fmri_filenames, confounds=confounds_simple, sample_mask=sample_mask
)
correlation_matrix = correlation_measure.fit_transform([time_series])[0]
np.fill_diagonal(correlation_matrix, 0)
plotting.plot_matrix(
correlation_matrix,
figure=(10, 8),
labels=labels[1:],
vmax=0.8,
vmin=-0.8,
title="Motion, WM, CSF",
reorder=True,
)
# %%
# Motion-based scrubbing
# ----------------------
# With a scrubbing-based strategy,
# :func:`~nilearn.interfaces.fmriprep.load_confounds` returns a `sample_mask`
# that removes the index of volumes exceeding the framewise displacement and
# standardised DVARS threshold, and all the continuous segment with less than
# five volumes. Before applying scrubbing, it's important to access the
# percentage of volumns scrubbed. Scrubbing is not a suitable strategy for
# datasets with too many high motion subjects.
# On top of the simple strategy above, let's add scrubbing to our
# strategy.
confounds_scrub, sample_mask = load_confounds(
fmri_filenames,
strategy=["high_pass", "motion", "wm_csf", "scrub"],
motion="basic",
wm_csf="basic",
scrub=5,
fd_threshold=0.5,
std_dvars_threshold=1.5,
)
print(
f"After scrubbing, {sample_mask.shape[0]} "
f"out of {confounds_scrub.shape[0]} volumes remains"
)
print("The shape of the confounds matrix is:", confounds_simple.shape)
print(confounds_scrub.columns)
time_series = masker.fit_transform(
fmri_filenames, confounds=confounds_scrub, sample_mask=sample_mask
)
correlation_matrix = correlation_measure.fit_transform([time_series])[0]
np.fill_diagonal(correlation_matrix, 0)
plotting.plot_matrix(
correlation_matrix,
figure=(10, 8),
labels=labels[1:],
vmax=0.8,
vmin=-0.8,
title="Motion, WM, CSF, Scrubbing",
reorder=True,
)
# %%
# The impact of global signal removal
# -----------------------------------
# Global signal removes the grand mean from your signal. The benefit is that
# it can remove impacts of physiological artifacts with minimal impact on the
# degrees of freedom. The downside is that one cannot get insight into variance
# explained by certain sources of noise. Now let's add global signal to the
# simple strategy and see its impact.
confounds_minimal_no_gsr, sample_mask = load_confounds(
fmri_filenames,
strategy=["high_pass", "motion", "wm_csf", "global_signal"],
motion="basic",
wm_csf="basic",
global_signal="basic",
)
print("The shape of the confounds matrix is:", confounds_minimal_no_gsr.shape)
print(confounds_minimal_no_gsr.columns)
time_series = masker.fit_transform(
fmri_filenames, confounds=confounds_minimal_no_gsr, sample_mask=sample_mask
)
correlation_matrix = correlation_measure.fit_transform([time_series])[0]
np.fill_diagonal(correlation_matrix, 0)
plotting.plot_matrix(
correlation_matrix,
figure=(10, 8),
labels=labels[1:],
vmax=0.8,
vmin=-0.8,
title="Motion, WM, CSF, GSR",
reorder=True,
)
# %%
# Using predefined strategies
# ---------------------------
# Instead of customising the strategy through
# :func:`nilearn.interfaces.fmriprep.load_confounds`, one can use a predefined
# strategy with :func:`nilearn.interfaces.fmriprep.load_confounds_strategy`.
# Based on the confound variables generated through :term:`fMRIPrep`, and past
# benchmarks studies (:footcite:t:`Ciric2017`, :footcite:t:`Parker2018`):
# `simple`, `scrubbing`, `compcor`, `ica_aroma`.
# The following examples shows how to use the `simple` strategy and overwrite
# the motion default to basic.
from nilearn.interfaces.fmriprep import load_confounds_strategy
# use default parameters
confounds, sample_mask = load_confounds_strategy(
fmri_filenames, denoise_strategy="simple", motion="basic"
)
time_series = masker.fit_transform(
fmri_filenames, confounds=confounds, sample_mask=sample_mask
)
correlation_matrix = correlation_measure.fit_transform([time_series])[0]
np.fill_diagonal(correlation_matrix, 0)
plotting.plot_matrix(
correlation_matrix,
figure=(10, 8),
labels=labels[1:],
vmax=0.8,
vmin=-0.8,
title="simple",
reorder=True,
)
# add optional parameter global signal
confounds, sample_mask = load_confounds_strategy(
fmri_filenames,
denoise_strategy="simple",
motion="basic",
global_signal="basic",
)
time_series = masker.fit_transform(
fmri_filenames, confounds=confounds, sample_mask=sample_mask
)
correlation_matrix = correlation_measure.fit_transform([time_series])[0]
np.fill_diagonal(correlation_matrix, 0)
plotting.plot_matrix(
correlation_matrix,
figure=(10, 8),
labels=labels[1:],
vmax=0.8,
vmin=-0.8,
title="simple with global signal",
reorder=True,
)
plotting.show()
# %%
# References
# ----------
#
# .. footbibliography::
# sphinx_gallery_dummy_images=2