docs/builtin.rst

@@ -308,6 +308,26 @@ Available
        | patterns of brain connectivity.
        | Nature Neuroscience, Volume 18(11), Pages 1664-1671 (2015).
        | https://doi:10.1038/nn.4135
+   * - Yan
+     - ``n_rois``, ``yeo_networks``, ``kong_networks``
+     - | ``Yan100xYeo7``, ``Yan200xYeo7``, ``Yan300xYeo7``,
+       | ``Yan400xYeo7``, ``Yan500xYeo7``, ``Yan600xYeo7``,
+       | ``Yan700xYeo7``, ``Yan800xYeo7``, ``Yan900xYeo7``,
+       | ``Yan1000xYeo7``,
+       | ``Yan100xYeo17``, ``Yan200xYeo17``, ``Yan300xYeo17``,
+       | ``Yan400xYeo17``, ``Yan500xYeo17``, ``Yan600xYeo17``,
+       | ``Yan700xYeo17``, ``Yan800xYeo17``, ``Yan900xYeo17``,
+       | ``Yan1000xYeo17``,
+       | ``Yan100xKong17``, ``Yan200xKong17``, ``Yan300xKong17``,
+       | ``Yan400xKong17``, ``Yan500xKong17``, ``Yan600xKong17``,
+       | ``Yan700xKong17``, ``Yan800xKong17``, ``Yan900xKong17``,
+       | ``Yan1000xKong17``
+     - 0.0.3
+     - | Yan, X., Kong, R., Xue, A., et al.
+       | Homotopic local-global parcellation of the human cerebral cortex from
+       | resting-state functional connectivity.
+       | NeuroImage, Volume 273 (2023).
+       | https://doi.org/10.1016/j.neuroimage.2023.120010
 
 
 Planned

docs/changes/newsfragments/225.feature

@@ -0,0 +1 @@
+Add ``Yan 2023`` parcellation to ``junifer.data`` by `Synchon Mandal`_

junifer/data/parcellations.py

@@ -103,6 +103,21 @@ for year in (2013, 2015, 2019):
             "year": 2019,
             "n_rois": 368,
         }
+# Add Yan parcellation info
+for n_rois in range(100, 1001, 100):
+    # Add Yeo networks
+    for yeo_network in [7, 17]:
+        _available_parcellations[f"Yan{n_rois}xYeo{yeo_network}"] = {
+            "family": "Yan",
+            "n_rois": n_rois,
+            "yeo_networks": yeo_network,
+        }
+    # Add Kong networks
+    _available_parcellations[f"Yan{n_rois}xKong17"] = {
+        "family": "Yan",
+        "n_rois": n_rois,
+        "kong_networks": 17,
+    }
 
 
 def register_parcellation(
@@ -273,7 +288,7 @@ def _retrieve_parcellation(
 
     Parameters
     ----------
-    family : {"Schaefer", "SUIT", "Tian", "AICHA", "Shen"}
+    family : {"Schaefer", "SUIT", "Tian", "AICHA", "Shen", "Yan"}
         The name of the parcellation family.
     parcellations_dir : str or pathlib.Path, optional
         Path where the retrieved parcellations file are stored. The default
@@ -316,6 +331,13 @@ def _retrieve_parcellation(
             Number of ROIs to use. Can be ``50, 100, or 150`` for
             ``year = 2013`` but is fixed at ``268`` for ``year = 2015`` and at
             ``368`` for ``year = 2019``.
+    * Yan :
+        ``n_rois`` : {100, 200, 300, 400, 500, 600, 700, 800, 900, 1000}
+            Granularity of the parcellation to be used.
+        ``yeo_networks`` : {7, 17}, optional
+            Number of Yeo networks to use (default None).
+        ``kong_networks`` : {17}, optional
+            Number of Kong networks to use (default None).
 
     Returns
     -------
@@ -373,6 +395,12 @@ def _retrieve_parcellation(
             resolution=resolution,
             **kwargs,
         )
+    elif family == "Yan":
+        parcellation_fname, parcellation_labels = _retrieve_yan(
+            parcellations_dir=parcellations_dir,
+            resolution=resolution,
+            **kwargs,
+        )
     else:
         raise_error(
             f"The provided parcellation name {family} cannot be retrieved."
@@ -1089,6 +1117,188 @@ def _retrieve_shen(  # noqa: C901
     return parcellation_fname, labels
 
 
+def _retrieve_yan(
+    parcellations_dir: Path,
+    resolution: Optional[float] = None,
+    n_rois: Optional[int] = None,
+    yeo_networks: Optional[int] = None,
+    kong_networks: Optional[int] = None,
+) -> Tuple[Path, List[str]]:
+    """Retrieve Yan parcellation.
+
+    Parameters
+    ----------
+    parcellations_dir : pathlib.Path
+        The path to the parcellation data directory.
+    resolution : float, 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 and 2mm.
+    n_rois : {100, 200, 300, 400, 500, 600, 700, 800, 900, 1000}, optional
+        Granularity of the parcellation to be used (default None).
+    yeo_networks : {7, 17}, optional
+        Number of Yeo networks to use (default None).
+    kong_networks : {17}, optional
+        Number of Kong networks to use (default None).
+
+    Returns
+    -------
+    pathlib.Path
+        File path to the parcellation image.
+    list of str
+        Parcellation labels.
+
+    Raises
+    ------
+    ValueError
+        If invalid value is provided for ``n_rois``, ``yeo_networks`` or
+        ``kong_networks`` or if there is a problem fetching the parcellation.
+
+    """
+    logger.info("Parcellation parameters:")
+    logger.info(f"\tresolution: {resolution}")
+    logger.info(f"\tn_rois: {n_rois}")
+    logger.info(f"\tyeo_networks: {yeo_networks}")
+    logger.info(f"\tkong_networks: {kong_networks}")
+
+    # Allow single network type
+    if (not yeo_networks and not kong_networks) or (
+        yeo_networks and kong_networks
+    ):
+        raise_error(
+            "Either one of `yeo_networks` or `kong_networks` need to be "
+            "specified."
+        )
+
+    # Check resolution
+    _valid_resolutions = [1, 2]
+    resolution = closest_resolution(resolution, _valid_resolutions)
+
+    # Check n_rois value
+    _valid_n_rois = list(range(100, 1001, 100))
+    if n_rois not in _valid_n_rois:
+        raise_error(
+            f"The parameter `n_rois` ({n_rois}) needs to be one of the "
+            f"following: {_valid_n_rois}"
+        )
+
+    if yeo_networks:
+        # Check yeo_networks value
+        _valid_yeo_networks = [7, 17]
+        if yeo_networks not in _valid_yeo_networks:
+            raise_error(
+                f"The parameter `yeo_networks` ({yeo_networks}) needs to be "
+                f"one of the following: {_valid_yeo_networks}"
+            )
+        # Define image and label file according to network
+        parcellation_fname = (
+            parcellations_dir
+            / "Yan_2023"
+            / (
+                f"{n_rois}Parcels_Yeo2011_{yeo_networks}Networks_FSLMNI152_"
+                f"{resolution}mm.nii.gz"
+            )
+        )
+        parcellation_lname = (
+            parcellations_dir
+            / "Yan_2023"
+            / f"{n_rois}Parcels_Yeo2011_{yeo_networks}Networks_LUT.txt"
+        )
+    elif kong_networks:
+        # Check kong_networks value
+        _valid_kong_networks = [17]
+        if kong_networks not in _valid_kong_networks:
+            raise_error(
+                f"The parameter `kong_networks` ({kong_networks}) needs to be "
+                f"one of the following: {_valid_kong_networks}"
+            )
+        # Define image and label file according to network
+        parcellation_fname = (
+            parcellations_dir
+            / "Yan_2023"
+            / (
+                f"{n_rois}Parcels_Kong2022_{kong_networks}Networks_FSLMNI152_"
+                f"{resolution}mm.nii.gz"
+            )
+        )
+        parcellation_lname = (
+            parcellations_dir
+            / "Yan_2023"
+            / f"{n_rois}Parcels_Kong2022_{kong_networks}Networks_LUT.txt"
+        )
+
+    # Check for existence of parcellation:
+    if not parcellation_fname.exists() and not parcellation_lname.exists():
+        logger.info(
+            "At least one of the parcellation files are missing, fetching."
+        )
+
+        # Set URL based on network
+        if yeo_networks:
+            img_url = (
+                "https://raw.githubusercontent.com/ThomasYeoLab/CBIG/"
+                "master/stable_projects/brain_parcellation/Yan2023_homotopic/"
+                f"parcellations/MNI/yeo{yeo_networks}/{n_rois}Parcels_Yeo2011"
+                f"_{yeo_networks}Networks_FSLMNI152_{resolution}mm.nii.gz"
+            )
+            label_url = (
+                "https://raw.githubusercontent.com/ThomasYeoLab/CBIG/"
+                "master/stable_projects/brain_parcellation/Yan2023_homotopic/"
+                f"parcellations/MNI/yeo{yeo_networks}/freeview_lut/{n_rois}"
+                f"Parcels_Yeo2011_{yeo_networks}Networks_LUT.txt"
+            )
+        elif kong_networks:
+            img_url = (
+                "https://raw.githubusercontent.com/ThomasYeoLab/CBIG/"
+                "master/stable_projects/brain_parcellation/Yan2023_homotopic/"
+                f"parcellations/MNI/kong17/{n_rois}Parcels_Kong2022"
+                f"_17Networks_FSLMNI152_{resolution}mm.nii.gz"
+            )
+            label_url = (
+                "https://raw.githubusercontent.com/ThomasYeoLab/CBIG/"
+                "master/stable_projects/brain_parcellation/Yan2023_homotopic/"
+                f"parcellations/MNI/kong17/freeview_lut/{n_rois}Parcels_"
+                "Kong2022_17Networks_LUT.txt"
+            )
+
+        # Initiate a session and make HTTP requests
+        session = requests.Session()
+        # Download parcellation file
+        logger.info(f"Downloading Yan 2023 parcellation from {img_url}")
+        try:
+            img_resp = session.get(img_url)
+            img_resp.raise_for_status()
+        except (ConnectionError, ReadTimeout, HTTPError) as err:
+            raise_error(
+                f"Failed to download Yan 2023 parcellation due to: {err}"
+            )
+        else:
+            parcellation_img_path = Path(parcellation_fname)
+            parcellation_img_path.parent.mkdir(parents=True, exist_ok=True)
+            parcellation_img_path.touch(exist_ok=True)
+            with open(parcellation_img_path, "wb") as f:
+                f.write(img_resp.content)
+        # Download label file
+        logger.info(f"Downloading Yan 2023 labels from {label_url}")
+        try:
+            label_resp = session.get(label_url)
+            label_resp.raise_for_status()
+        except (ConnectionError, ReadTimeout, HTTPError) as err:
+            raise_error(f"Failed to download Yan 2023 labels due to: {err}")
+        else:
+            parcellation_labels_path = Path(parcellation_lname)
+            parcellation_labels_path.touch(exist_ok=True)
+            with open(parcellation_labels_path, "wb") as f:
+                f.write(label_resp.content)
+
+    # Load label file
+    labels = pd.read_csv(parcellation_lname, sep=" ", header=None)[1].to_list()
+
+    return parcellation_fname, labels
+
+
 def merge_parcellations(
     parcellations_list: List["Nifti1Image"],
     parcellations_names: List[str],

junifer/data/tests/test_parcellations.py

@@ -21,6 +21,7 @@ from junifer.data.parcellations import (
     _retrieve_shen,
     _retrieve_suit,
     _retrieve_tian,
+    _retrieve_yan,
     list_parcellations,
     load_parcellation,
     merge_parcellations,
@@ -728,6 +729,204 @@ def test_retrieve_shen_incorrect_param_combo(
         )
 
 
+@pytest.mark.parametrize(
+    "resolution, n_rois, yeo_networks, kong_networks",
+    [
+        (1.0, 100, 7, None),
+        (1.0, 200, 7, None),
+        (1.0, 300, 7, None),
+        (1.0, 400, 7, None),
+        (1.0, 500, 7, None),
+        (1.0, 600, 7, None),
+        (1.0, 700, 7, None),
+        (1.0, 800, 7, None),
+        (1.0, 900, 7, None),
+        (1.0, 1000, 7, None),
+        (2.0, 100, 7, None),
+        (2.0, 200, 7, None),
+        (2.0, 300, 7, None),
+        (2.0, 400, 7, None),
+        (2.0, 500, 7, None),
+        (2.0, 600, 7, None),
+        (2.0, 700, 7, None),
+        (2.0, 800, 7, None),
+        (2.0, 900, 7, None),
+        (2.0, 1000, 7, None),
+        (1.0, 100, 17, None),
+        (1.0, 200, 17, None),
+        (1.0, 300, 17, None),
+        (1.0, 400, 17, None),
+        (1.0, 500, 17, None),
+        (1.0, 600, 17, None),
+        (1.0, 700, 17, None),
+        (1.0, 800, 17, None),
+        (1.0, 900, 17, None),
+        (1.0, 1000, 17, None),
+        (2.0, 100, 17, None),
+        (2.0, 200, 17, None),
+        (2.0, 300, 17, None),
+        (2.0, 400, 17, None),
+        (2.0, 500, 17, None),
+        (2.0, 600, 17, None),
+        (2.0, 700, 17, None),
+        (2.0, 800, 17, None),
+        (2.0, 900, 17, None),
+        (2.0, 1000, 17, None),
+        (1.0, 100, None, 17),
+        (1.0, 200, None, 17),
+        (1.0, 300, None, 17),
+        (1.0, 400, None, 17),
+        (1.0, 500, None, 17),
+        (1.0, 600, None, 17),
+        (1.0, 700, None, 17),
+        (1.0, 800, None, 17),
+        (1.0, 900, None, 17),
+        (1.0, 1000, None, 17),
+        (2.0, 100, None, 17),
+        (2.0, 200, None, 17),
+        (2.0, 300, None, 17),
+        (2.0, 400, None, 17),
+        (2.0, 500, None, 17),
+        (2.0, 600, None, 17),
+        (2.0, 700, None, 17),
+        (2.0, 800, None, 17),
+        (2.0, 900, None, 17),
+        (2.0, 1000, None, 17),
+    ],
+)
+def test_yan(
+    tmp_path: Path,
+    resolution: float,
+    n_rois: int,
+    yeo_networks: int,
+    kong_networks: int,
+) -> None:
+    """Test Yan parcellation.
+
+    Parameters
+    ----------
+    tmp_path : pathlib.Path
+        The path to the test directory.
+    resolution : float
+        The parametrized resolution values.
+    n_rois : int
+        The parametrized ROI count values.
+    yeo_networks : int
+        The parametrized Yeo networks values.
+    kong_networks : int
+        The parametrized Kong networks values.
+
+    """
+    parcellations = list_parcellations()
+    if yeo_networks:
+        parcellation_name = f"Yan{n_rois}xYeo{yeo_networks}"
+        assert parcellation_name in parcellations
+        parcellation_file = (
+            f"{n_rois}Parcels_Yeo2011_{yeo_networks}Networks_FSLMNI152_"
+            f"{int(resolution)}mm.nii.gz"
+        )
+    elif kong_networks:
+        parcellation_name = f"Yan{n_rois}xKong{kong_networks}"
+        assert parcellation_name in parcellations
+        parcellation_file = (
+            f"{n_rois}Parcels_Kong2022_{kong_networks}Networks_FSLMNI152_"
+            f"{int(resolution)}mm.nii.gz"
+        )
+    # Load parcellation
+    img, label, img_path = load_parcellation(
+        name=parcellation_name,  # type: ignore
+        parcellations_dir=tmp_path,
+        resolution=resolution,
+    )
+    assert img is not None
+    assert img_path.name == parcellation_file  # type: ignore
+    assert len(label) == n_rois
+    assert_array_equal(
+        img.header["pixdim"][1:4], 3 * [resolution]  # type: ignore
+    )
+
+
+def test_retrieve_yan_incorrect_networks(tmp_path: Path) -> None:
+    """Test retrieve Yan with incorrect networks.
+
+    Parameters
+    ----------
+    tmp_path : pathlib.Path
+        The path to the test directory.
+
+    """
+    with pytest.raises(
+        ValueError, match="Either one of `yeo_networks` or `kong_networks`"
+    ):
+        _retrieve_yan(
+            parcellations_dir=tmp_path,
+            n_rois=31418,
+            yeo_networks=100,
+            kong_networks=100,
+        )
+
+    with pytest.raises(
+        ValueError, match="Either one of `yeo_networks` or `kong_networks`"
+    ):
+        _retrieve_yan(
+            parcellations_dir=tmp_path,
+            n_rois=31418,
+            yeo_networks=None,
+            kong_networks=None,
+        )
+
+
+def test_retrieve_yan_incorrect_n_rois(tmp_path: Path) -> None:
+    """Test retrieve Yan with incorrect ROIs.
+
+    Parameters
+    ----------
+    tmp_path : pathlib.Path
+        The path to the test directory.
+
+    """
+    with pytest.raises(ValueError, match="The parameter `n_rois`"):
+        _retrieve_yan(
+            parcellations_dir=tmp_path,
+            n_rois=31418,
+            yeo_networks=7,
+        )
+
+
+def test_retrieve_yan_incorrect_yeo_networks(tmp_path: Path) -> None:
+    """Test retrieve Yan with incorrect Yeo networks.
+
+    Parameters
+    ----------
+    tmp_path : pathlib.Path
+        The path to the test directory.
+
+    """
+    with pytest.raises(ValueError, match="The parameter `yeo_networks`"):
+        _retrieve_yan(
+            parcellations_dir=tmp_path,
+            n_rois=100,
+            yeo_networks=27,
+        )
+
+
+def test_retrieve_yan_incorrect_kong_networks(tmp_path: Path) -> None:
+    """Test retrieve Yan with incorrect Kong networks.
+
+    Parameters
+    ----------
+    tmp_path : pathlib.Path
+        The path to the test directory.
+
+    """
+    with pytest.raises(ValueError, match="The parameter `kong_networks`"):
+        _retrieve_yan(
+            parcellations_dir=tmp_path,
+            n_rois=100,
+            kong_networks=27,
+        )
+
+
 def test_merge_parcellations() -> None:
     """Test merging parcellations."""
     # load some parcellations for testing