Get normalization figure working.

src/fmripost_aroma/data/io_spec.json

@@ -81,6 +81,8 @@
             },
             "anat_dseg": {
                 "datatype": "anat",
+                "task": null,
+                "run": null,
                 "space": null,
                 "res": null,
                 "den": null,

src/fmripost_aroma/data/reports-spec-func.yml

@@ -10,17 +10,15 @@ sections:
   reportlets:
   - bids: {datatype: figures, desc: summary, suffix: bold}
   - bids: {datatype: figures, desc: validation, suffix: bold}
-  - bids: {datatype: figures, desc: aroma, suffix: bold}
-  - bids: {datatype: figures, desc: metrics, suffix: bold}
-  - bids: {datatype: figures, desc: coreg, suffix: bold}
+  - bids: {datatype: figures, desc: normalization, suffix: bold}
     caption: This panel shows the alignment of the reference EPI (BOLD) image to the
-      anatomical (T1-weighted) image.
-      The reference EPI has been contrast enhanced and susceptibility-distortion
-      corrected (if applicable) for improved anatomical fidelity.
-      The anatomical image has been resampled into EPI space, as well as the
-      anatomical white matter mask, which appears as a red contour.
+      MNI152NLin6Asym template.
+      The anatomical white matter mask has been warped to MNI152NLin6Asym space
+      and appears as a red contour.
     static: false
-    subtitle: Alignment of functional and anatomical MRI data (coregistration)
+    subtitle: Alignment of functional and template MRI data (normalization)
+  - bids: {datatype: figures, desc: aroma, suffix: bold}
+  - bids: {datatype: figures, desc: metrics, suffix: bold}
   - bids: {datatype: figures, desc: preprocCarpetplot, suffix: bold}
     title: Preprocessed BOLD
   - bids: {datatype: figures, desc: nonaggrCarpetplot, suffix: bold}

src/fmripost_aroma/data/reports-spec.yml

@@ -10,17 +10,15 @@ sections:
   reportlets:
   - bids: {datatype: figures, desc: summary, suffix: bold}
   - bids: {datatype: figures, desc: validation, suffix: bold}
-  - bids: {datatype: figures, desc: aroma, suffix: bold}
-  - bids: {datatype: figures, desc: metrics, suffix: bold}
-  - bids: {datatype: figures, desc: coreg, suffix: bold}
+  - bids: {datatype: figures, desc: normalization, suffix: bold}
     caption: This panel shows the alignment of the reference EPI (BOLD) image to the
-      anatomical (T1-weighted) image.
-      The reference EPI has been contrast enhanced and susceptibility-distortion
-      corrected (if applicable) for improved anatomical fidelity.
-      The anatomical image has been resampled into EPI space, as well as the
-      anatomical white matter mask, which appears as a red contour.
+      MNI152NLin6Asym template.
+      The anatomical white matter mask has been warped to MNI152NLin6Asym space
+      and appears as a red contour.
     static: false
-    subtitle: Alignment of functional and anatomical MRI data (coregistration)
+    subtitle: Alignment of functional and template MRI data (normalization)
+  - bids: {datatype: figures, desc: aroma, suffix: bold}
+  - bids: {datatype: figures, desc: metrics, suffix: bold}
   - bids: {datatype: figures, desc: preprocCarpetplot, suffix: bold}
     title: Preprocessed BOLD
   - bids: {datatype: figures, desc: nonaggrCarpetplot, suffix: bold}

src/fmripost_aroma/interfaces/nilearn.py

@@ -21,7 +21,7 @@
     )
     mask_file = File(
         exists=True,
-        mandatory=True,
+        mandatory=False,
         desc='A binary brain mask.',
     )
     out_file = File(
@@ -46,11 +46,19 @@
     output_spec = _MeanImageOutputSpec
 
     def _run_interface(self, runtime):
+        import nibabel as nb
         from nilearn.masking import apply_mask, unmask
+        from nipype.interfaces.base import isdefined
+
+        if isdefined(self.inputs.mask_file):
+            data = apply_mask(self.inputs.bold_file, self.inputs.mask_file)
+            mean_data = data.mean(axis=0)
+            mean_img = unmask(mean_data, self.inputs.mask_file)
+        else:
+            in_img = nb.load(self.inputs.bold_file)
+            mean_data = in_img.get_fdata().mean(axis=3)
+            mean_img = nb.Nifti1Image(mean_data, in_img.affine, in_img.header)
 
-        data = apply_mask(self.inputs.bold_file, self.inputs.mask_file)
-        mean_data = data.mean(axis=0)
-        mean_img = unmask(mean_data, self.inputs.mask_file)
         self._results['out_file'] = os.path.join(runtime.cwd, self.inputs.out_file)
         mean_img.to_filename(self._results['out_file'])
 

src/fmripost_aroma/workflows/outputs.py

@@ -55,37 +55,38 @@
     source_file
         Input BOLD images
     """
     from nireports.interfaces.reporting.base import (
         SimpleBeforeAfterRPT as SimpleBeforeAfter,
     )
+    from templateflow.api import get as get_template
 
     from fmripost_aroma.interfaces.nilearn import MeanImage
 
     workflow = pe.Workflow(name=name)
 
     inputfields = [
         'source_file',
         'bold_mni6',
         'anat_dseg',
         'anat2std_xfm',
     ]
     inputnode = pe.Node(niu.IdentityInterface(fields=inputfields), name='inputnode')
 
     # Average the BOLD image over time
     calculate_mean_bold = pe.Node(
         MeanImage(),
         name='calculate_mean_bold',
         mem_gb=1,
     )
     workflow.connect([(inputnode, calculate_mean_bold, [('bold_mni6', 'bold_file')])])
 
     # Warp the tissue segmentation to MNI
     dseg_to_mni6 = pe.Node(
         ApplyTransforms(interpolation='MultiLabel'),
         name='dseg_to_mni6',
         mem_gb=1,
     )
     workflow.connect([
         (inputnode, dseg_to_mni6, [
             ('anat_dseg', 'input_image'),
             ('anat2std_xfm', 'transforms'),
@@ -93,43 +94,51 @@
         ]),
     ])  # fmt:skip
 
     mni6_wm = pe.Node(
         niu.Function(function=dseg_label),
         name='mni6_wm',
         mem_gb=DEFAULT_MEMORY_MIN_GB,
     )
     mni6_wm.inputs.label = 2  # BIDS default is WM=2
-    workflow.connect([(dseg_to_mni6, mni6_wm, [('output_image', 'in_file')])])
+    workflow.connect([(dseg_to_mni6, mni6_wm, [('output_image', 'in_seg')])])
 
     # EPI-MNI registration
     epi_mni_report = pe.Node(
         SimpleBeforeAfter(
-            before_label='T1w',
-            after_label='EPI',
+            after=str(
+                get_template(
+                    'MNI152NLin6Asym',
+                    resolution=2,
+                    desc='brain',
+                    suffix='T1w',
+                    extension=['.nii', '.nii.gz'],
+                )
+            ),
+            before_label='EPI',
+            after_label='MNI152NLin6Asym',
             dismiss_affine=True,
         ),
         name='epi_mni_report',
         mem_gb=0.1,
     )
     workflow.connect([
-        (inputnode, epi_mni_report, [('coreg_boldref', 'after')]),
         (calculate_mean_bold, epi_mni_report, [('out_file', 'before')]),
-        (mni6_wm, epi_mni_report, [('output_image', 'wm_seg')]),
+        (mni6_wm, epi_mni_report, [('out', 'wm_seg')]),
     ])  # fmt:skip
 
     ds_epi_mni_report = pe.Node(
         DerivativesDataSink(
             base_directory=output_dir,
-            desc='coreg',
+            desc='normalization',
             suffix='bold',
             datatype='figures',
             dismiss_entities=dismiss_echo(),
         ),
         name='ds_epi_mni_report',
     )
     workflow.connect([
         (inputnode, ds_epi_mni_report, [('source_file', 'source_file')]),
         (epi_mni_report, ds_epi_mni_report, [('out_report', 'in_file')]),
     ])  # fmt:skip
 
     return workflow