Functional connectivity predicts age group#

This example compares different kinds of functional connectivity between regions of interest : correlation, partial correlation, and tangent space embedding.

The resulting connectivity coefficients can be used to discriminate children from adults. In general, the tangent space embedding outperforms the standard correlations: see Dadi et al.[1] for a careful study.

Note

If you are using Nilearn with a version older than 0.9.0, then you should either upgrade your version or import maskers from the input_data module instead of the maskers module.

That is, you should manually replace in the following example all occurrences of:

from nilearn.maskers import NiftiMasker

with:

from nilearn.input_data import NiftiMasker
try:
    import matplotlib.pyplot as plt
except ImportError:
    raise RuntimeError("This script needs the matplotlib library")

Load brain development fMRI dataset and MSDL atlas#

We study only 60 subjects from the dataset, to save computation time.

from nilearn import datasets

development_dataset = datasets.fetch_development_fmri(n_subjects=60)

We use probabilistic regions of interest (ROIs) from the MSDL atlas.

from nilearn.maskers import NiftiMapsMasker

msdl_data = datasets.fetch_atlas_msdl()
msdl_coords = msdl_data.region_coords

masker = NiftiMapsMasker(
    msdl_data.maps,
    resampling_target="data",
    t_r=2,
    detrend=True,
    low_pass=0.1,
    high_pass=0.01,
    memory="nilearn_cache",
    memory_level=1,
    standardize="zscore_sample",
    standardize_confounds="zscore_sample",
).fit()

masked_data = [
    masker.transform(func, confounds)
    for (func, confounds) in zip(
        development_dataset.func, development_dataset.confounds
    )
]

What kind of connectivity is most powerful for classification?#

we will use connectivity matrices as features to distinguish children from adults. We use cross-validation and measure classification accuracy to compare the different kinds of connectivity matrices.

# prepare the classification pipeline
from sklearn.dummy import DummyClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVC

from nilearn.connectome import ConnectivityMeasure

kinds = ["correlation", "partial correlation", "tangent"]

pipe = Pipeline(
    [
        (
            "connectivity",
            ConnectivityMeasure(
                vectorize=True,
                standardize="zscore_sample",
            ),
        ),
        (
            "classifier",
            GridSearchCV(LinearSVC(dual=True), {"C": [0.1, 1.0, 10.0]}, cv=5),
        ),
    ]
)

param_grid = [
    {"classifier": [DummyClassifier(strategy="most_frequent")]},
    {"connectivity__kind": kinds},
]

We use random splits of the subjects into training/testing sets. StratifiedShuffleSplit allows preserving the proportion of children in the test set.

from sklearn.model_selection import GridSearchCV, StratifiedShuffleSplit
from sklearn.preprocessing import LabelEncoder

groups = [pheno["Child_Adult"] for pheno in development_dataset.phenotypic]
classes = LabelEncoder().fit_transform(groups)

cv = StratifiedShuffleSplit(n_splits=30, random_state=0, test_size=10)
gs = GridSearchCV(
    pipe,
    param_grid,
    scoring="accuracy",
    cv=cv,
    verbose=1,
    refit=False,
    n_jobs=2,
)
gs.fit(masked_data, classes)
mean_scores = gs.cv_results_["mean_test_score"]
scores_std = gs.cv_results_["std_test_score"]
Fitting 30 folds for each of 4 candidates, totalling 120 fits

display the results

plt.figure(figsize=(6, 4))
positions = [0.1, 0.2, 0.3, 0.4]
plt.barh(positions, mean_scores, align="center", height=0.05, xerr=scores_std)
yticks = ["dummy"] + list(gs.cv_results_["param_connectivity__kind"].data[1:])
yticks = [t.replace(" ", "\n") for t in yticks]
plt.yticks(positions, yticks)
plt.xlabel("Classification accuracy")
plt.gca().grid(True)
plt.gca().set_axisbelow(True)
plt.tight_layout()
plot age group prediction cross val

This is a small example to showcase nilearn features. In practice such comparisons need to be performed on much larger cohorts and several datasets. Dadi et al.[1] showed that across many cohorts and clinical questions, the tangent kind should be preferred.

References#

Total running time of the script: (1 minutes 7.058 seconds)

Estimated memory usage: 1188 MB

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