[ENH]: Add support for native space

docs/changes/newsfragments/252.change

@@ -0,0 +1 @@
+Add ``native_t1w`` parameter to :class:`.DataladAOMICID1000`, :class:`.DataladAOMICPIOP1`, :class:`.DataladAOMICPIOP2`, enabling fetching of T1w data in subject-native space by `Synchon Mandal`_

docs/changes/newsfragments/252.feature

@@ -0,0 +1 @@
+Add support for subject-native space by `Synchon Mandal`_ and `Fede Raimondo`_

junifer/datagrabber/aomic/id1000.py

@@ -28,13 +28,16 @@ class DataladAOMICID1000(PatternDataladDataGrabber):
            "probseg_WM", "DWI"} or a list of the options, optional
         AOMIC data types. If None, all available data types are selected.
         (default None).
+    native_t1w : bool, optional
+        Whether to use T1w in native space (default False).
 
     """
 
     def __init__(
         self,
         datadir: Union[str, Path, None] = None,
         types: Union[str, List[str], None] = None,
+        native_t1w: bool = False,
     ) -> None:
         # The patterns
         patterns = {
@@ -84,6 +87,27 @@ def __init__(
                 "sub-{subject}_desc-preproc_dwi.nii.gz"
             ),
         }
+        # Use native T1w assets
+        self.native_t1w = False
+        if native_t1w:
+            self.native_t1w = True
+            patterns.update(
+                {
+                    "T1w": (
+                        "derivatives/fmriprep/sub-{subject}/anat/"
+                        "sub-{subject}_desc-preproc_T1w.nii.gz"
+                    ),
+                    "T1w_mask": (
+                        "derivatives/fmriprep/sub-{subject}/anat/"
+                        "sub-{subject}_desc-brain_mask.nii.gz"
+                    ),
+                    "Warp": (
+                        "derivatives/fmriprep/sub-{subject}/anat/"
+                        "sub-{subject}_from-MNI152NLin2009cAsym_to-T1w_"
+                        "mode-image_xfm.h5"
+                    ),
+                }
+            )
         # Set default types
         if types is None:
             types = list(patterns.keys())
@@ -126,5 +150,8 @@ def get_item(self, subject: str) -> Dict:
         if out.get("T1w"):
             out["T1w"]["mask_item"] = "T1w_mask"
             # Add space information
-            out["T1w"].update({"space": "native"})
+            if self.native_t1w:
+                out["T1w"].update({"space": "native"})
+            else:
+                out["T1w"].update({"space": "MNI152NLin2009cAsym"})
         return out

junifer/datagrabber/aomic/piop1.py

@@ -34,6 +34,8 @@ class DataladAOMICPIOP1(PatternDataladDataGrabber):
             "gstroop", "workingmemory"} or list of the options, optional
         AOMIC PIOP1 task sessions. If None, all available task sessions are
         selected (default None).
+    native_t1w : bool, optional
+        Whether to use T1w in native space (default False).
 
     """
 
@@ -42,6 +44,7 @@ def __init__(
         datadir: Union[str, Path, None] = None,
         types: Union[str, List[str], None] = None,
         tasks: Union[str, List[str], None] = None,
+        native_t1w: bool = False,
     ) -> None:
         # Declare all tasks
         all_tasks = [
@@ -114,6 +117,27 @@ def __init__(
                 "sub-{subject}_desc-preproc_dwi.nii.gz"
             ),
         }
+        # Use native T1w assets
+        self.native_t1w = False
+        if native_t1w:
+            self.native_t1w = True
+            patterns.update(
+                {
+                    "T1w": (
+                        "derivatives/fmriprep/sub-{subject}/anat/"
+                        "sub-{subject}_desc-preproc_T1w.nii.gz"
+                    ),
+                    "T1w_mask": (
+                        "derivatives/fmriprep/sub-{subject}/anat/"
+                        "sub-{subject}_desc-brain_mask.nii.gz"
+                    ),
+                    "Warp": (
+                        "derivatives/fmriprep/sub-{subject}/anat/"
+                        "sub-{subject}_from-MNI152NLin2009cAsym_to-T1w_"
+                        "mode-image_xfm.h5"
+                    ),
+                }
+            )
         # Set default types
         if types is None:
             types = list(patterns.keys())
@@ -171,7 +195,10 @@ def get_item(self, subject: str, task: str) -> Dict:
         if out.get("T1w"):
             out["T1w"]["mask_item"] = "T1w_mask"
             # Add space information
-            out["T1w"].update({"space": "native"})
+            if self.native_t1w:
+                out["T1w"].update({"space": "native"})
+            else:
+                out["T1w"].update({"space": "MNI152NLin2009cAsym"})
         return out
 
     def get_elements(self) -> List:

junifer/datagrabber/aomic/piop2.py

@@ -34,6 +34,8 @@ class DataladAOMICPIOP2(PatternDataladDataGrabber):
             or list of the options, optional
         AOMIC PIOP2 task sessions. If None, all available task sessions are
         selected (default None).
+    native_t1w : bool, optional
+        Whether to use T1w in native space (default False).
 
     """
 
@@ -42,6 +44,7 @@ def __init__(
         datadir: Union[str, Path, None] = None,
         types: Union[str, List[str], None] = None,
         tasks: Union[str, List[str], None] = None,
+        native_t1w: bool = False,
     ) -> None:
         # Declare all tasks
         all_tasks = [
@@ -111,6 +114,27 @@ def __init__(
                 "sub-{subject}_desc-preproc_dwi.nii.gz"
             ),
         }
+        # Use native T1w assets
+        self.native_t1w = False
+        if native_t1w:
+            self.native_t1w = True
+            patterns.update(
+                {
+                    "T1w": (
+                        "derivatives/fmriprep/sub-{subject}/anat/"
+                        "sub-{subject}_desc-preproc_T1w.nii.gz"
+                    ),
+                    "T1w_mask": (
+                        "derivatives/fmriprep/sub-{subject}/anat/"
+                        "sub-{subject}_desc-brain_mask.nii.gz"
+                    ),
+                    "Warp": (
+                        "derivatives/fmriprep/sub-{subject}/anat/"
+                        "sub-{subject}_from-MNI152NLin2009cAsym_to-T1w_"
+                        "mode-image_xfm.h5"
+                    ),
+                }
+            )
         # Set default types
         if types is None:
             types = list(patterns.keys())
@@ -171,5 +195,8 @@ def get_item(self, subject: str, task: str) -> Dict:
         if out.get("T1w"):
             out["T1w"]["mask_item"] = "T1w_mask"
             # Add space information
-            out["T1w"].update({"space": "native"})
+            if self.native_t1w:
+                out["T1w"].update({"space": "native"})
+            else:
+                out["T1w"].update({"space": "MNI152NLin2009cAsym"})
         return out

junifer/datagrabber/hcp1200/hcp1200.py

@@ -98,15 +98,17 @@ def __init__(
                 )
         suffix = "_hp2000_clean" if ica_fix else ""
         # The types of data
-        types = ["BOLD"]
+        types = ["BOLD", "T1w", "Warp"]
         # The patterns
         patterns = {
             "BOLD": (
                 "{subject}/MNINonLinear/Results/"
                 "{task}_{phase_encoding}/"
                 "{task}_{phase_encoding}"
                 f"{suffix}.nii.gz"
-            )
+            ),
+            "T1w": "{subject}/T1w/T1w_acpc_dc_restore.nii.gz",
+            "Warp": "{subject}/MNINonLinear/xfms/standard2acpc_dc.nii.gz",
         }
         # The replacements
         replacements = ["subject", "task", "phase_encoding"]
@@ -147,6 +149,12 @@ def get_item(self, subject: str, task: str, phase_encoding: str) -> Dict:
         out = super().get_item(
             subject=subject, task=new_task, phase_encoding=phase_encoding
         )
+        # Add space for BOLD data type
+        if "BOLD" in out:
+            out["BOLD"].update({"space": "MNI152NLin6Asym"})
+        # Add space for T1w data type
+        if "T1w" in out:
+            out["T1w"].update({"space": "native"})
         return out
 
     def get_elements(self) -> List:

junifer/datareader/default.py

@@ -104,6 +104,10 @@ def _fit_transform(
             params = {}
         # For each type of data, try to read it
         for type_ in input.keys():
+            # Skip Warp data type
+            if type_ == "Warp":
+                continue
+
             # Check for malformed datagrabber specification
             if "path" not in input[type_]:
                 warn_with_log(