adding quick bold masking with automask

Author: birajstha

CPAC/func_preproc/func_motion.py

@@ -87,11 +87,19 @@ def calc_motion_stats(wf, cfg, strat_pool, pipe_num, opt=None):
         gen_motion_stats,
         "inputspec.motion_correct",
     )
-    wf.connect(
-        *strat_pool.get_data("space-bold_desc-brain_mask"),
-        gen_motion_stats,
-        "inputspec.mask",
-    )
+
+    try :
+        wf.connect(*strat_pool.get_data("space-bold_desc-brain_mask"),
+            gen_motion_stats,
+            "inputspec.mask",
+        )
+    except :
+        mask_bold = pe.Node(interface=afni.MaskTool(), name=f"mask_bold_{pipe_num}")
+        mask_bold.inputs.outputtype = "NIFTI_GZ"
+        node, out = strat_pool.get_data("desc-preproc_bold")
+        wf.connect(node, out, mask_bold, "in_file")
+        wf.connect(mask_bold, "out_file", gen_motion_stats, "inputspec.mask") 
+
     wf.connect(
         *strat_pool.get_data("desc-movementParameters_motion"),
         gen_motion_stats,

CPAC/func_preproc/func_preproc.py

@@ -1611,7 +1611,7 @@ def bold_mask_anatomical_resampled(wf, cfg, strat_pool, pipe_num, opt=None):
 
     Adapted from `DCAN Lab's BOLD mask method from the ABCD pipeline <https://github.com/DCAN-Labs/DCAN-HCP/blob/1d90814/fMRIVolume/scripts/OneStepResampling.sh#L121-L132>`_.
     """
-    anat_brain_to_func_res = anat_brain_to_bold_res(wf_name="anat_brain_to_bold_res", cfg, pipe_num)
+    anat_brain_to_func_res = anat_brain_to_bold_res(wf_name="anat_brain_to_bold_res", cfg=cfg, pipe_num=pipe_num)
 
     node, out = strat_pool.get_data("space-template_desc-preproc_T1w")
     wf.connect(

CPAC/pipeline/cpac_pipeline.py

@@ -99,6 +99,7 @@
     bold_mask_fsl,
     bold_mask_fsl_afni,
     bold_masking,
+    template_space_bold_masking,
     func_despike,
     func_despike_template,
     func_mean,
@@ -1401,13 +1402,6 @@ def build_workflow(subject_id, sub_dict, cfg, pipeline_name=None):
         if cfg.voxel_mirrored_homotopic_connectivity["run"]:
             pipeline_blocks += [create_func_to_T1template_symmetric_xfm]
 
-    # Template space functional masking
-    if cfg.functional_preproc["template_space_func_masking"]["run"]:
-        if not rpool.check_rpool("space-template_desc-bold_mask"):
-            pipeline_blocks += bold_mask_anatomical_resampled,
-        if cfg.functional_preproc["template_space_func_masking"]["apply_func_mask_in_template_space"]:
-            pipeline_blocks += apply_func_mask_to_template,
-
     # Nuisance Correction
     generate_only = (
         True not in cfg["nuisance_corrections", "2-nuisance_regression", "run"]
@@ -1518,6 +1512,13 @@ def build_workflow(subject_id, sub_dict, cfg, pipeline_name=None):
             warp_bold_mask_to_EPItemplate,
             warp_deriv_mask_to_EPItemplate,
         ]
+        
+    # Template space functional masking
+    if cfg.functional_preproc["template_space_func_masking"]["run"]:
+        if not rpool.check_rpool("space-template_desc-bold_mask"):
+            pipeline_blocks += bold_mask_anatomical_resampled,
+        if cfg.functional_preproc["template_space_func_masking"]["apply_func_mask_in_template_space"]:
+            pipeline_blocks += template_space_bold_masking,
 
     # Template-space nuisance regression
     nuisance_template = (

CPAC/resources/configs/pipeline_config_abcd-options.yml

@@ -210,11 +210,16 @@ registration_workflows:
         # input: ['Mean_Functional', 'Selected_Functional_Volume', 'fmriprep_reference']
         input: [Selected_Functional_Volume]
 
+        # Mask the sbref created by coregistration input prep nodeblocks above before registration
+        mask_sbref: Off
+
       # Choose coregistration interpolation
       interpolation: spline
 
       # Choose coregistration degree of freedom
       dof: 12
+      
+
 
     func_registration_to_template:
 
@@ -311,7 +316,7 @@ functional_preproc:
 
 nuisance_corrections:
   2-nuisance_regression:
-
+    run: Off
     # Select which nuisance signal corrections to apply
     Regressors:
       - Name: default
@@ -324,6 +329,9 @@ nuisance_corrections:
           include_delayed_squared: On
           include_squared: On
 
+    # switch to Off if nuisance regression is off and you don't want to write out the regressors
+    create_regressors: Off
+    
     # Process and refine masks used to produce regressors and time series for
     # regression.
     regressor_masks:
@@ -380,10 +388,10 @@ regional_homogeneity:
 # -----------------------
 post_processing:
   spatial_smoothing:
-    run: On
+    run: Off
 
   z-scoring:
-    run: On
+    run: Off
 
 seed_based_correlation_analysis: