docs/builtin.rst

@@ -365,6 +365,16 @@ Available
        | resting-state functional connectivity.
        | NeuroImage, Volume 273 (2023).
        | https://doi.org/10.1016/j.neuroimage.2023.120010
+   * - Brainnetome
+     - ``threshold``
+     - ``Brainnetome_thr0``, ``Brainnetome_thr25``, ``Brainnetome_thr50``
+     - ``MNI152NLin6Asym``
+     - 0.0.4
+     - | Fan, L., Li, H., Zhuo, J., et al.
+       | The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional
+       | Architecture
+       | Cerebral Cortex, Volume 26(8), Pages 3508–3526 (2016).
+       | https://doi.org/10.1093/cercor/bhw157
 
 
 Planned

docs/changes/newsfragments/275.feature

@@ -0,0 +1 @@
+Add ``Brainnetome 246`` parcellation to ``junifer.data`` by `Synchon Mandal`_

junifer/data/parcellations.py

@@ -127,6 +127,13 @@ for n_rois in range(100, 1001, 100):
         "kong_networks": 17,
         "space": "MNI152NLin6Asym",
     }
+# Add Brainnetome parcellation info
+for threshold in [0, 25, 50]:
+    _available_parcellations[f"Brainnetome_thr{threshold}"] = {
+        "family": "Brainnetome",
+        "threshold": threshold,
+        "space": "MNI152NLin6Asym",
+    }
 
 
 def register_parcellation(
@@ -523,6 +530,9 @@ def _retrieve_parcellation(
             Number of Yeo networks to use (default None).
         ``kong_networks`` : {17}, optional
             Number of Kong networks to use (default None).
+    * Brainnetome :
+        ``threshold`` : {0, 25, 50}
+            Threshold for the probabilistic maps of subregion.
 
     Returns
     -------
@@ -586,6 +596,12 @@ def _retrieve_parcellation(
             resolution=resolution,
             **kwargs,
         )
+    elif family == "Brainnetome":
+        parcellation_fname, parcellation_labels = _retrieve_brainnetome(
+            parcellations_dir=parcellations_dir,
+            resolution=resolution,
+            **kwargs,
+        )
     else:
         raise_error(
             f"The provided parcellation name {family} cannot be retrieved."
@@ -1555,6 +1571,122 @@ def _retrieve_yan(
     return parcellation_fname, labels
 
 
+def _retrieve_brainnetome(
+    parcellations_dir: Path,
+    resolution: Optional[float] = None,
+    threshold: Optional[int] = None,
+) -> Tuple[Path, List[str]]:
+    """Retrieve Brainnetome parcellation.
+
+    Parameters
+    ----------
+    parcellations_dir : pathlib.Path
+        The path to the parcellation data directory.
+    resolution : {1.0, 1.25, 2.0}, optional
+        The desired resolution of the parcellation to load. If it is not
+        available, the closest resolution will be loaded. Preferably, use a
+        resolution higher than the desired one. By default, will load the
+        highest one (default None). Available resolutions for this
+        parcellation are 1mm, 1.25mm and 2mm.
+    threshold : {0, 25, 50}, optional
+        The threshold for the probabilistic maps of subregion (default None).
+
+    Returns
+    -------
+    pathlib.Path
+        File path to the parcellation image.
+    list of str
+        Parcellation labels.
+
+    Raises
+    ------
+    RuntimeError
+        If there is a problem fetching files.
+    ValueError
+        If invalid value is provided for ``threshold``.
+
+    """
+    logger.info("Parcellation parameters:")
+    logger.info(f"\tresolution: {resolution}")
+    logger.info(f"\tthreshold: {threshold}")
+
+    # Check resolution
+    _valid_resolutions = [1.0, 1.25, 2.0]
+    resolution = closest_resolution(resolution, _valid_resolutions)
+
+    # Check threshold value
+    _valid_threshold = [0, 25, 50]
+    if threshold not in _valid_threshold:
+        raise_error(
+            f"The parameter `threshold` ({threshold}) needs to be one of the "
+            f"following: {_valid_threshold}"
+        )
+    # Correct resolution for further stuff
+    if resolution in [1.0, 2.0]:
+        resolution = int(resolution)
+
+    parcellation_fname = (
+        parcellations_dir
+        / "BNA246"
+        / f"BNA-maxprob-thr{threshold}-{resolution}mm.nii.gz"
+    )
+
+    # Check for existence of parcellation
+    if not parcellation_fname.exists():
+        # Set URL
+        url = f"http://neurovault.org/media/images/1625/BNA-maxprob-thr{threshold}-{resolution}mm.nii.gz"
+
+        logger.info(f"Downloading Brainnetome from {url}")
+        # Make HTTP request
+        try:
+            resp = httpx.get(url, follow_redirects=True)
+            resp.raise_for_status()
+        except httpx.HTTPError as exc:
+            raise_error(
+                f"Error response {exc.response.status_code} while "
+                f"requesting {exc.request.url!r}",
+                klass=RuntimeError,
+            )
+        else:
+            # Create local directory if not present
+            parcellation_fname.parent.mkdir(parents=True, exist_ok=True)
+            # Create file if not present
+            parcellation_fname.touch(exist_ok=True)
+            # Open file and write bytes
+            parcellation_fname.write_bytes(resp.content)
+
+    # Load labels
+    labels = (
+        sorted([f"SFG_L(R)_7_{i}" for i in range(1, 8)] * 2)
+        + sorted([f"MFG_L(R)_7_{i}" for i in range(1, 8)] * 2)
+        + sorted([f"IFG_L(R)_6_{i}" for i in range(1, 7)] * 2)
+        + sorted([f"OrG_L(R)_6_{i}" for i in range(1, 7)] * 2)
+        + sorted([f"PrG_L(R)_6_{i}" for i in range(1, 7)] * 2)
+        + sorted([f"PCL_L(R)_2_{i}" for i in range(1, 3)] * 2)
+        + sorted([f"STG_L(R)_6_{i}" for i in range(1, 7)] * 2)
+        + sorted([f"MTG_L(R)_4_{i}" for i in range(1, 5)] * 2)
+        + sorted([f"ITG_L(R)_7_{i}" for i in range(1, 8)] * 2)
+        + sorted([f"FuG_L(R)_3_{i}" for i in range(1, 4)] * 2)
+        + sorted([f"PhG_L(R)_6_{i}" for i in range(1, 7)] * 2)
+        + sorted([f"pSTS_L(R)_2_{i}" for i in range(1, 3)] * 2)
+        + sorted([f"SPL_L(R)_5_{i}" for i in range(1, 6)] * 2)
+        + sorted([f"IPL_L(R)_6_{i}" for i in range(1, 7)] * 2)
+        + sorted([f"PCun_L(R)_4_{i}" for i in range(1, 5)] * 2)
+        + sorted([f"PoG_L(R)_4_{i}" for i in range(1, 5)] * 2)
+        + sorted([f"INS_L(R)_6_{i}" for i in range(1, 7)] * 2)
+        + sorted([f"CG_L(R)_7_{i}" for i in range(1, 8)] * 2)
+        + sorted([f"MVOcC _L(R)_5_{i}" for i in range(1, 6)] * 2)
+        + sorted([f"LOcC_L(R)_4_{i}" for i in range(1, 5)] * 2)
+        + sorted([f"LOcC_L(R)_2_{i}" for i in range(1, 3)] * 2)
+        + sorted([f"Amyg_L(R)_2_{i}" for i in range(1, 3)] * 2)
+        + sorted([f"Hipp_L(R)_2_{i}" for i in range(1, 3)] * 2)
+        + sorted([f"BG_L(R)_6_{i}" for i in range(1, 7)] * 2)
+        + sorted([f"Tha_L(R)_8_{i}" for i in range(1, 9)] * 2)
+    )
+
+    return parcellation_fname, labels
+
+
 def merge_parcellations(
     parcellations_list: List["Nifti1Image"],
     parcellations_names: List[str],

junifer/data/tests/test_parcellations.py

@@ -16,6 +16,7 @@ from numpy.testing import assert_array_almost_equal, assert_array_equal
 
 from junifer.data.parcellations import (
     _retrieve_aicha,
+    _retrieve_brainnetome,
     _retrieve_parcellation,
     _retrieve_schaefer,
     _retrieve_shen,
@@ -955,6 +956,77 @@ def test_retrieve_yan_incorrect_kong_networks(tmp_path: Path) -> None:
         )
 
 
+@pytest.mark.parametrize(
+    "resolution, threshold",
+    [
+        (1.0, 0),
+        (1.0, 25),
+        (1.0, 50),
+        (1.25, 0),
+        (1.25, 25),
+        (1.25, 50),
+        (2, 0),
+        (2, 25),
+        (2, 50),
+    ],
+)
+def test_brainnetome(
+    tmp_path: Path,
+    resolution: float,
+    threshold: int,
+) -> None:
+    """Test Brainnetome parcellation.
+
+    Parameters
+    ----------
+    tmp_path : pathlib.Path
+        The path to the test directory.
+    resolution : float
+        The parametrized resolution values.
+    threshold : int
+        The parametrized threshold values.
+
+    """
+    parcellations = list_parcellations()
+    parcellation_name = f"Brainnetome_thr{threshold}"
+    assert parcellation_name in parcellations
+
+    # Fix resolution
+    if resolution in [1.0, 2.0]:
+        resolution = int(resolution)
+
+    parcellation_file = f"BNA-maxprob-thr{threshold}-{resolution}mm.nii.gz"
+    # Load parcellation
+    img, label, img_path, space = load_parcellation(
+        name=parcellation_name,
+        parcellations_dir=tmp_path,
+        resolution=resolution,
+    )
+    assert img is not None
+    assert img_path.name == parcellation_file
+    assert space == "MNI152NLin6Asym"
+    assert len(label) == 246
+    assert_array_equal(
+        img.header["pixdim"][1:4], 3 * [resolution]  # type: ignore
+    )
+
+
+def test_retrieve_brainnetome_incorrect_threshold(tmp_path: Path) -> None:
+    """Test retrieve Brainnetome with incorrect threshold.
+
+    Parameters
+    ----------
+    tmp_path : pathlib.Path
+        The path to the test directory.
+
+    """
+    with pytest.raises(ValueError, match="The parameter `threshold`"):
+        _retrieve_brainnetome(
+            parcellations_dir=tmp_path,
+            threshold=100,
+        )
+
+
 def test_merge_parcellations() -> None:
     """Test merging parcellations."""
     # load some parcellations for testing