🐛 Bugfixes for many pipelines project

CHANGELOG.md

@@ -24,6 +24,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Required positional parameter "wf" in input and output of `ingress_pipeconfig_paths` function, where a node to reorient templates is added to the `wf`.
 - Required positional parameter "orientation" to `resolve_resolution`.
 - Optional positional argument "cfg" to `create_lesion_preproc`.
+- Allow enabling `overwrite_transform` only when the registration method is `ANTS`.
+- `resource_inventory` utility to inventory NodeBlock function inputs and outputs.
 
 ### Changed
 

CPAC/_entrypoints/run.py

@@ -454,6 +454,14 @@ def run_main():
         action="store_true",
     )
 
+    parser.add_argument(
+        "--freesurfer_dir",
+        "--freesurfer-dir",
+        help="Specify path to pre-computed FreeSurfer outputs "
+        "to pull into C-PAC run",
+        default=False,
+    )
+
     # get the command line arguments
     args = parser.parse_args(
         sys.argv[1 : (sys.argv.index("--") if "--" in sys.argv else len(sys.argv))]
@@ -743,6 +751,9 @@ def run_main():
             c["pipeline_setup", "system_config", "num_participants_at_once"],
         )
 
+        if args.freesurfer_dir:
+            c["pipeline_setup"]["freesurfer_dir"] = args.freesurfer_dir
+
         if not args.data_config_file:
             WFLOGGER.info("Input directory: %s", bids_dir)
 
@@ -783,7 +794,6 @@ def run_main():
             sub_list = load_cpac_data_config(
                 args.data_config_file, args.participant_label, args.aws_input_creds
             )
-        list(sub_list)
         sub_list = sub_list_filter_by_labels(
             sub_list, {"T1w": args.T1w_label, "bold": args.bold_label}
         )

CPAC/anat_preproc/anat_preproc.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-# Copyright (C) 2012-2023  C-PAC Developers
+# Copyright (C) 2012-2025  C-PAC Developers
 
 # This file is part of C-PAC.
 
@@ -2572,7 +2572,7 @@ def brain_mask_acpc_niworkflows_ants_T2(wf, cfg, strat_pool, pipe_num, opt=None)
     config=["anatomical_preproc", "brain_extraction"],
     option_key="using",
     option_val="UNet",
-    inputs=["desc-preproc_T2w", "T1w-brain-template", "T1w-template", "unet_model"],
+    inputs=["desc-preproc_T2w", "T1w-brain-template", "T1w-template", "unet-model"],
     outputs=["space-T2w_desc-brain_mask"],
 )
 def brain_mask_unet_T2(wf, cfg, strat_pool, pipe_num, opt=None):
@@ -2586,7 +2586,7 @@ def brain_mask_unet_T2(wf, cfg, strat_pool, pipe_num, opt=None):
     config=["anatomical_preproc", "brain_extraction"],
     option_key="using",
     option_val="UNet",
-    inputs=["desc-preproc_T2w", "T1w-brain-template", "T1w-template", "unet_model"],
+    inputs=["desc-preproc_T2w", "T1w-brain-template", "T1w-template", "unet-model"],
     outputs=["space-T2w_desc-acpcbrain_mask"],
 )
 def brain_mask_acpc_unet_T2(wf, cfg, strat_pool, pipe_num, opt=None):

CPAC/func_preproc/func_preproc.py

@@ -1503,6 +1503,91 @@ def bold_mask_anatomical_based(wf, cfg, strat_pool, pipe_num, opt=None):
     return (wf, outputs)
 
 
+def anat_brain_to_bold_res(wf_name, cfg, pipe_num):
+    wf = pe.Workflow(name=f"{wf_name}_{pipe_num}")
+
+    inputNode = pe.Node(
+        util.IdentityInterface(
+            fields=["T1w-template-funcreg", "space-template_desc-preproc_T1w"]
+        ),
+        name="inputspec",
+    )
+    outputNode = pe.Node(
+        util.IdentityInterface(fields=["space-template_res-bold_desc-brain_T1w"]),
+        name="outputspec",
+    )
+
+    # applywarp --rel --interp=spline -i ${T1wImage} -r ${ResampRefIm} --premat=$FSLDIR/etc/flirtsch/ident.mat -o ${WD}/${T1wImageFile}.${FinalfMRIResolution}
+    anat_brain_to_func_res = pe.Node(
+        interface=fsl.ApplyWarp(), name=f"resample_anat_brain_in_standard_{pipe_num}"
+    )
+
+    anat_brain_to_func_res.inputs.interp = "spline"
+    anat_brain_to_func_res.inputs.premat = cfg.registration_workflows[
+        "anatomical_registration"
+    ]["registration"]["FSL-FNIRT"]["identity_matrix"]
+
+    wf.connect(
+        inputNode, "space-template_desc-preproc_T1w", anat_brain_to_func_res, "in_file"
+    )
+    wf.connect(inputNode, "T1w-template-funcreg", anat_brain_to_func_res, "ref_file")
+
+    wf.connect(
+        anat_brain_to_func_res,
+        "out_file",
+        outputNode,
+        "space-template_res-bold_desc-brain_T1w",
+    )
+    return wf
+
+
+def anat_brain_mask_to_bold_res(wf_name, cfg, pipe_num):
+    # Create brain masks in this space from the FreeSurfer output (changing resolution)
+    # applywarp --rel --interp=nn -i ${FreeSurferBrainMask}.nii.gz -r ${WD}/${T1wImageFile}.${FinalfMRIResolution} --premat=$FSLDIR/etc/flirtsch/ident.mat -o ${WD}/${FreeSurferBrainMaskFile}.${FinalfMRIResolution}.nii.gz
+    wf = pe.Workflow(name=f"{wf_name}_{pipe_num}")
+    inputNode = pe.Node(
+        util.IdentityInterface(
+            fields=["space-template_desc-brain_mask", "space-template_desc-preproc_T1w"]
+        ),
+        name="inputspec",
+    )
+    outputNode = pe.Node(
+        util.IdentityInterface(fields=["space-template_desc-bold_mask"]),
+        name="outputspec",
+    )
+
+    anat_brain_mask_to_func_res = pe.Node(
+        interface=fsl.ApplyWarp(),
+        name=f"resample_anat_brain_mask_in_standard_{pipe_num}",
+    )
+
+    anat_brain_mask_to_func_res.inputs.interp = "nn"
+    anat_brain_mask_to_func_res.inputs.premat = cfg.registration_workflows[
+        "anatomical_registration"
+    ]["registration"]["FSL-FNIRT"]["identity_matrix"]
+
+    wf.connect(
+        inputNode,
+        "space-template_desc-brain_mask",
+        anat_brain_mask_to_func_res,
+        "in_file",
+    )
+    wf.connect(
+        inputNode,
+        "space-template_desc-preproc_T1w",
+        anat_brain_mask_to_func_res,
+        "ref_file",
+    )
+    wf.connect(
+        anat_brain_mask_to_func_res,
+        "out_file",
+        outputNode,
+        "space-template_desc-bold_mask",
+    )
+
+    return wf
+
+
 @nodeblock(
     name="bold_mask_anatomical_resampled",
     switch=[
@@ -1528,39 +1613,35 @@ 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>`_.
     """
-    # applywarp --rel --interp=spline -i ${T1wImage} -r ${ResampRefIm} --premat=$FSLDIR/etc/flirtsch/ident.mat -o ${WD}/${T1wImageFile}.${FinalfMRIResolution}
-    anat_brain_to_func_res = pe.Node(
-        interface=fsl.ApplyWarp(), name=f"resample_anat_brain_in_standard_{pipe_num}"
-    )
-
-    anat_brain_to_func_res.inputs.interp = "spline"
-    anat_brain_to_func_res.inputs.premat = cfg.registration_workflows[
-        "anatomical_registration"
-    ]["registration"]["FSL-FNIRT"]["identity_matrix"]
+    anat_brain_to_func_res = anat_brain_to_bold_res(wf, cfg, pipe_num)
 
     node, out = strat_pool.get_data("space-template_desc-preproc_T1w")
-    wf.connect(node, out, anat_brain_to_func_res, "in_file")
+    wf.connect(
+        node, out, anat_brain_to_func_res, "inputspec.space-template_desc-preproc_T1w"
+    )
 
     node, out = strat_pool.get_data("T1w-template-funcreg")
-    wf.connect(node, out, anat_brain_to_func_res, "ref_file")
+    wf.connect(node, out, anat_brain_to_func_res, "inputspec.T1w-template-funcreg")
 
     # Create brain masks in this space from the FreeSurfer output (changing resolution)
     # applywarp --rel --interp=nn -i ${FreeSurferBrainMask}.nii.gz -r ${WD}/${T1wImageFile}.${FinalfMRIResolution} --premat=$FSLDIR/etc/flirtsch/ident.mat -o ${WD}/${FreeSurferBrainMaskFile}.${FinalfMRIResolution}.nii.gz
-    anat_brain_mask_to_func_res = pe.Node(
-        interface=fsl.ApplyWarp(),
-        name=f"resample_anat_brain_mask_in_standard_{pipe_num}",
+    anat_brain_mask_to_func_res = anat_brain_mask_to_bold_res(
+        wf_name="anat_brain_mask_to_bold_res", cfg=cfg, pipe_num=pipe_num
     )
 
-    anat_brain_mask_to_func_res.inputs.interp = "nn"
-    anat_brain_mask_to_func_res.inputs.premat = cfg.registration_workflows[
-        "anatomical_registration"
-    ]["registration"]["FSL-FNIRT"]["identity_matrix"]
-
     node, out = strat_pool.get_data("space-template_desc-brain_mask")
-    wf.connect(node, out, anat_brain_mask_to_func_res, "in_file")
+    wf.connect(
+        node,
+        out,
+        anat_brain_mask_to_func_res,
+        "inputspec.pace-template_desc-brain_mask",
+    )
 
     wf.connect(
-        anat_brain_to_func_res, "out_file", anat_brain_mask_to_func_res, "ref_file"
+        anat_brain_to_func_res,
+        "outputspec.space-template_res-bold_desc-brain_T1w",
+        anat_brain_mask_to_func_res,
+        "inputspec.space-template_desc-preproc_T1w",
     )
 
     # Resample func mask in template space back to native space
@@ -1574,15 +1655,24 @@ def bold_mask_anatomical_resampled(wf, cfg, strat_pool, pipe_num, opt=None):
     func_mask_template_to_native.inputs.outputtype = "NIFTI_GZ"
 
     wf.connect(
-        anat_brain_mask_to_func_res, "out_file", func_mask_template_to_native, "in_file"
+        anat_brain_mask_to_func_res,
+        "outputspec.space-template_desc-bold_mask",
+        func_mask_template_to_native,
+        "in_file",
     )
 
     node, out = strat_pool.get_data("desc-preproc_bold")
     wf.connect(node, out, func_mask_template_to_native, "master")
 
     outputs = {
-        "space-template_res-bold_desc-brain_T1w": (anat_brain_to_func_res, "out_file"),
-        "space-template_desc-bold_mask": (anat_brain_mask_to_func_res, "out_file"),
+        "space-template_res-bold_desc-brain_T1w": (
+            anat_brain_to_func_res,
+            "outputspec.space-template_res-bold_desc-brain_T1w",
+        ),
+        "space-template_desc-bold_mask": (
+            anat_brain_mask_to_func_res,
+            "outputspec.space-template_desc-bold_mask",
+        ),
         "space-bold_desc-brain_mask": (func_mask_template_to_native, "out_file"),
     }
 

CPAC/nuisance/nuisance.py

@@ -75,8 +75,8 @@ def choose_nuisance_blocks(cfg, rpool, generate_only=False):
     ]
     apply_transform_using = to_template_cfg["apply_transform"]["using"]
     input_interface = {
-        "default": ("desc-preproc_bold", ["desc-preproc_bold", "bold"]),
-        "abcd": ("desc-preproc_bold", "bold"),
+        "default": ("desc-preproc_bold", ["desc-preproc_bold", "desc-reorient_bold"]),
+        "abcd": ("desc-preproc_bold", "desc-reorient_bold"),
         "single_step_resampling_from_stc": ("desc-preproc_bold", "desc-stc_bold"),
     }.get(apply_transform_using)
     if input_interface is not None:

CPAC/pipeline/engine.py

@@ -1,4 +1,4 @@
-# Copyright (C) 2021-2024  C-PAC Developers
+# Copyright (C) 2021-2025  C-PAC Developers
 
 # This file is part of C-PAC.
 
@@ -17,13 +17,15 @@
 import ast
 import copy
 import hashlib
+from importlib.resources import files
 from itertools import chain
 import json
 import os
 import re
 from typing import Optional
 import warnings
 
+import pandas as pd
 from nipype import config, logging
 from nipype.interfaces import afni
 from nipype.interfaces.utility import Rename
@@ -1007,6 +1009,19 @@ def post_process(self, wf, label, connection, json_info, pipe_idx, pipe_x, outs)
             for label_con_tpl in post_labels:
                 label = label_con_tpl[0]
                 connection = (label_con_tpl[1], label_con_tpl[2])
+                if "desc-" not in label:
+                    if "space-template" in label:
+                        new_label = label.replace(
+                            "space-template", "space-template_desc-zstd"
+                        )
+                    else:
+                        new_label = f"desc-zstd_{label}"
+                else:
+                    for tag in label.split("_"):
+                        if "desc-" in tag:
+                            newtag = f"{tag}-zstd"
+                            new_label = label.replace(tag, newtag)
+                            break
                 if label in Outputs.to_zstd:
                     zstd = z_score_standardize(f"{label}_zstd_{pipe_x}", input_type)
 
@@ -1015,20 +1030,6 @@ def post_process(self, wf, label, connection, json_info, pipe_idx, pipe_x, outs)
                     node, out = self.get_data(mask, pipe_idx=mask_idx)
                     wf.connect(node, out, zstd, "inputspec.mask")
 
-                    if "desc-" not in label:
-                        if "space-template" in label:
-                            new_label = label.replace(
-                                "space-template", "space-template_desc-zstd"
-                            )
-                        else:
-                            new_label = f"desc-zstd_{label}"
-                    else:
-                        for tag in label.split("_"):
-                            if "desc-" in tag:
-                                newtag = f"{tag}-zstd"
-                                new_label = label.replace(tag, newtag)
-                                break
-
                     post_labels.append((new_label, zstd, "outputspec.out_file"))
 
                     self.set_data(
@@ -1188,7 +1189,7 @@ def gather_pipes(self, wf, cfg, all=False, add_incl=None, add_excl=None):
                 key
                 for json_info in all_jsons
                 for key in json_info.get("CpacVariant", {}).keys()
-                if key not in (*MOVEMENT_FILTER_KEYS, "regressors")
+                if key not in (*MOVEMENT_FILTER_KEYS, "timeseries")
             }
             if "bold" in unlabelled:
                 all_bolds = list(
@@ -2411,15 +2412,17 @@ def strip_template(data_label, dir_path, filename):
     return data_label, json
 
 
+def template_dataframe() -> pd.DataFrame:
+    """Return the template dataframe."""
+    template_csv = files("CPAC").joinpath("resources/cpac_templates.csv")
+    return pd.read_csv(str(template_csv), keep_default_na=False)
+
+
 def ingress_pipeconfig_paths(wf, cfg, rpool, unique_id, creds_path=None):
     # ingress config file paths
     # TODO: may want to change the resource keys for each to include one level up in the YAML as well
 
-    import pandas as pd
-    import pkg_resources as p
-
-    template_csv = p.resource_filename("CPAC", "resources/cpac_templates.csv")
-    template_df = pd.read_csv(template_csv, keep_default_na=False)
+    template_df = template_dataframe()
     desired_orientation = cfg.pipeline_setup["desired_orientation"]
 
     for row in template_df.itertuples():