def test_load_motpars_break(motion_parameters):
"""Break aroma.utils.load_motpars."""
with pytest.raises(Exception):
utils.load_motpars("dog.dog", source="auto")
with pytest.raises(ValueError):
utils.load_motpars(motion_parameters["FSL"], source="dog")
def test_load_motpars_auto(motion_parameters):
"""Test aroma.utils.load_motpars with automatic source determination."""
fsl = utils.load_motpars(motion_parameters["FSL"], source="auto")
afni = utils.load_motpars(motion_parameters["AfNI"], source="auto")
spm = utils.load_motpars(motion_parameters["SPM"], source="auto")
fmriprep = utils.load_motpars(motion_parameters["fMRIPrep"], source="auto")
assert np.allclose(fsl, afni)
assert np.allclose(fsl, spm)
assert np.allclose(fsl, fmriprep)
def test_motpars_afni2fsl(motion_parameters):
"""Test aroma.utils.motpars_afni2fsl."""
fsl = utils.load_motpars(motion_parameters["FSL"], source="fsl")
afni = utils.motpars_afni2fsl(motion_parameters["AfNI"])
assert np.allclose(fsl, afni)
with pytest.raises(ValueError):
utils.motpars_afni2fsl(5)
bad_data = np.random.random((200, 7))
with pytest.raises(ValueError):
utils.motpars_afni2fsl(bad_data)
def test_motpars_fmriprep2fsl(motion_parameters):
"""Test aroma.utils.motpars_fmriprep2fsl."""
fsl = utils.load_motpars(motion_parameters["FSL"], source="fsl")
fmriprep = utils.motpars_fmriprep2fsl(motion_parameters["fMRIPrep"])
assert np.allclose(fsl, fmriprep)
with pytest.raises(ValueError):
utils.motpars_fmriprep2fsl(5)
bad_data = np.random.random((200, 7))
with pytest.raises(ValueError):
utils.motpars_fmriprep2fsl(bad_data)
def feature_time_series(mel_mix, mc, metric_metadata=None):
"""Extract maximum motion parameter correlation scores from components.
This function determines the maximum robust correlation of each component
time series with a model of 72 realignment parameters.
Parameters
----------
mel_mix : numpy.ndarray of shape (T, C)
Mixing matrix in shape T (time) by C (component).
mc : str or array_like
Full path of the text file containing the realignment parameters.
Motion parameters are (time x 6), with the first three columns being
rotation parameters (in radians) and the final three being translation
parameters (in mm).
metric_metadata : None or dict, optional
A dictionary containing metadata about the AROMA metrics.
If provided, metadata for the ``max_RP_corr`` metric will be added.
Otherwise, no operations will be performed on this parameter.
Returns
-------
max_RP_corr : array_like
Array of the maximum RP correlation feature scores for the components
of the melodic_mix file.
metric_metadata : None or dict
If the ``metric_metadata`` input was None, then None will be returned.
Otherwise, this will be a dictionary containing existing information,
as well as new metadata for the ``max_RP_corr`` metric.
"""
if isinstance(metric_metadata, dict):
metric_metadata["max_RP_corr"] = {
"LongName":
"Maximum motion parameter correlation",
"Description":
("The maximum correlation coefficient between each component and "
"a set of 36 regressors derived from the motion parameters. "
"The derived regressors are the raw six motion parameters (6), "
"their derivatives (6), "
"the parameters and their derivatives time-shifted one TR forward (12), and "
"the parameters and their derivatives time-shifted one TR backward (12). "
"The correlations are performed on a series of 1000 permutations, "
"in which 90 percent of the volumes are selected from both the "
"component time series and the motion parameters. "
"The correlation is performed between each permuted component time series and "
"each permuted regressor in the motion parameter model, "
"as well as the squared versions of both. "
"The maximum correlation coefficient from each permutation is retained and these "
"correlation coefficients are averaged across permutations for the final metric."
),
"Units":
"arbitrary",
}
if isinstance(mc, str):
rp6 = utils.load_motpars(mc, source="auto")
else:
rp6 = mc
if (rp6.ndim != 2) or (rp6.shape[1] != 6):
raise ValueError(
f"Motion parameters must of shape (n_trs, 6), not {rp6.shape}")
if rp6.shape[0] != mel_mix.shape[0]:
raise ValueError(
f"Number of rows in mixing matrix ({mel_mix.shape[0]}) does not match "
f"number of rows in motion parameters ({rp6.shape[0]}).")
# Determine the derivatives of the RPs (add zeros at time-point zero)
_, nparams = rp6.shape
rp6_der = np.vstack((np.zeros(nparams), np.diff(rp6, axis=0)))
# Create an RP-model including the RPs and its derivatives
rp12 = np.hstack((rp6, rp6_der))
# add the fw and bw shifted versions
rp12_1fw = np.vstack((np.zeros(2 * nparams), rp12[:-1]))
rp12_1bw = np.vstack((rp12[1:], np.zeros(2 * nparams)))
rp_model = np.hstack((rp12, rp12_1fw, rp12_1bw))
# Determine the maximum correlation between RPs and IC time-series
nsplits = 1000
nmixrows, nmixcols = mel_mix.shape
nrows_to_choose = int(round(0.9 * nmixrows))
# Max correlations for multiple splits of the dataset (for a robust
# estimate)
max_correls = np.empty((nsplits, nmixcols))
for i in range(nsplits):
# Select a random subset of 90% of the dataset rows
# (*without* replacement)
chosen_rows = np.random.choice(a=range(nmixrows),
size=nrows_to_choose,
replace=False)
# Combined correlations between RP and IC time-series, squared and
# non squared
correl_nonsquared = utils.cross_correlation(mel_mix[chosen_rows],
rp_model[chosen_rows])
correl_squared = utils.cross_correlation(mel_mix[chosen_rows]**2,
rp_model[chosen_rows]**2)
correl_both = np.hstack((correl_squared, correl_nonsquared))
# Maximum absolute temporal correlation for every IC
max_correls[i] = np.abs(correl_both).max(axis=1)
# Feature score is the mean of the maximum correlation over all the random
# splits
# Avoid propagating occasional nans that arise in artificial test cases
max_RP_corr = np.nanmean(max_correls, axis=0)
return max_RP_corr, metric_metadata
def aroma_workflow(
in_file,
mc,
mixing,
component_maps,
out_dir,
den_type="nonaggr",
TR=None,
overwrite=False,
generate_plots=True,
debug=False,
quiet=False,
mc_source="auto",
):
"""Run the AROMA workflow.
Parameters
----------
in_file : str
Path to MNI-space functional run to denoise.
mc : str
Path to motion parameters.
mixing : str
Path to mixing matrix.
component_maps : str
Path to thresholded z-statistic component maps.
out_dir : str
Output directory.
den_type : {"nonaggr", "aggr", "both", "no"}, optional
Denoising approach to use.
TR : float or None, optional
Repetition time of data in in_file and mixing.
If None, this will be extracted from the header of in_file.
overwrite : bool
generate_plots : bool
debug : bool
quiet : bool
mc_source : {"auto"}, optional
What format is the mc file in?
"""
if not op.isfile(in_file):
raise FileNotFoundError(f"Input file does not exist: {in_file}")
if not op.isfile(mc):
raise FileNotFoundError(f"Motion parameters file does not exist: {mc}")
if not op.isfile(mixing):
raise FileNotFoundError(f"Mixing matrix file does not exist: {mixing}")
if not op.isfile(component_maps):
raise FileNotFoundError(
f"Component maps file does not exist: {component_maps}")
# Create output directory if needed
if op.isdir(out_dir) and not overwrite:
LGR.info(
f"Output directory {out_dir},"
"""already exists.
AROMA will not continue.
Rerun with the -overwrite option to explicitly overwrite
existing output.""", )
return
elif op.isdir(out_dir) and overwrite:
LGR.warning("Output directory {} exists and will be overwritten."
"\n".format(out_dir))
shutil.rmtree(out_dir)
os.makedirs(out_dir)
else:
os.makedirs(out_dir)
# Create logfile name
basename = 'aroma_'
extension = 'tsv'
isotime = datetime.datetime.now().strftime('%Y-%m-%dT%H%M%S')
logname = os.path.join(out_dir, (basename + isotime + '.' + extension))
# Set logging format
log_formatter = logging.Formatter(
'%(asctime)s\t%(name)-12s\t%(levelname)-8s\t%(message)s',
datefmt='%Y-%m-%dT%H:%M:%S')
# Set up logging file and open it for writing
log_handler = logging.FileHandler(logname)
log_handler.setFormatter(log_formatter)
sh = logging.StreamHandler()
# add logger mode options
if quiet:
logging.basicConfig(level=logging.WARNING,
handlers=[log_handler, sh],
format='%(levelname)-10s %(message)s')
elif debug:
logging.basicConfig(level=logging.DEBUG,
handlers=[log_handler, sh],
format='%(levelname)-10s %(message)s')
else:
logging.basicConfig(level=logging.INFO,
handlers=[log_handler, sh],
format='%(levelname)-10s %(message)s')
version_number = _version.get_versions()['version']
LGR.info(f'Currently running ICA-AROMA version {version_number}')
# Check if the type of denoising is correctly specified, when specified
if den_type not in ("nonaggr", "aggr", "both", "no"):
LGR.warning(
"Type of denoising was not correctly specified. Non-aggressive "
"denoising will be run.")
den_type = "nonaggr"
# Prepare
# Get TR of the fMRI data, if not specified
if not TR:
in_img = nib.load(in_file)
TR = in_img.header.get_zooms()[3]
# Check TR
if TR == 1:
LGR.warning("Please check whether the determined TR (of " + str(TR) +
"s) is correct!\n")
elif TR == 0:
raise Exception(
"TR is zero. ICA-AROMA requires a valid TR and will therefore "
"exit. Please check the header, or define the TR as an additional "
"argument.\n"
"-------------- ICA-AROMA IS CANCELED ------------\n")
# Load more inputs
motion_params = utils.load_motpars(mc, source=mc_source) # T x 6
mixing = np.loadtxt(mixing) # T x C
component_maps = nib.load(component_maps) # X x Y x Z x C
if mixing.shape[1] != component_maps.shape[3]:
raise ValueError(
f"Number of columns in mixing matrix ({mixing.shape[1]}) does not match "
f"fourth dimension of component maps file ({component_maps.shape[3]})."
)
if mixing.shape[0] != motion_params.shape[0]:
raise ValueError(
f"Number of rows in mixing matrix ({mixing.shape[0]}) does not match "
f"number of rows in motion parameters ({motion_params.shape[0]}).")
LGR.info(" - extracting the CSF & Edge fraction features")
metric_metadata = {}
features_df = pd.DataFrame()
(features_df["edge_fract"], features_df["csf_fract"],
metric_metadata) = features.feature_spatial(component_maps,
metric_metadata)
LGR.info(" - extracting the Maximum RP correlation feature")
features_df["max_RP_corr"], metric_metadata = features.feature_time_series(
mixing,
motion_params,
metric_metadata,
)
LGR.info(" - extracting the High-frequency content feature")
# Should probably check that the frequencies match up with MELODIC's outputs
mel_FT_mix, FT_freqs = utils.get_spectrum(mixing, TR)
features_df["HFC"], metric_metadata = features.feature_frequency(
mel_FT_mix,
TR,
metric_metadata,
)
LGR.info(" - classification")
features_df, metric_metadata = utils.classification(
features_df, metric_metadata)
motion_ICs = utils.write_metrics(features_df, out_dir, metric_metadata)
if generate_plots:
from . import plotting
plotting.classification_plot(
op.join(out_dir, "desc-AROMA_metrics.tsv"), out_dir)
if den_type != "no":
LGR.info("Step 3) Data denoising")
utils.denoising(in_file, out_dir, mixing, den_type, motion_ICs)
LGR.info("Finished")
