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Merged
Gab-D-G merged 2 commits into from
May 25, 2026
Merged

Select input bold node #497

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bd7bd52
small fix: the number of bins in the frequency spectrum is now based …
Gab-D-G May 25, 2026
8afa7b4
format_bold_node and input_bold_node were redundant nodes, they are b…
Gab-D-G May 25, 2026
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2 changes: 1 addition & 1 deletion rabies/analysis_pkg/diagnosis_pkg/diagnosis_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -224,7 +224,7 @@ def plot_freqs(ax,timeseries, TR, frame_mask):

from scipy.signal import welch
fs = 1/TR
N = timeseries.shape[0]
N = timeseries_.shape[0]
# N//4 : proxy to scale the resolution of the filter to the number of data points
# minimum of 130 to be able to detect frequency spikes in the spectrum
nperseg = max(N // 4, 130)
Expand Down
2 changes: 1 addition & 1 deletion rabies/confound_correction_pkg/main_wf.py
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Expand Up @@ -456,7 +456,7 @@ def read_preproc_workflow(preproc_output, nativespace_analysis, preprocess_opts)

node_dict = get_workflow_dict(preproc_workflow_file)

match_targets = {'input_bold':['main_wf.input_bold', 'selected_file'],
match_targets = {'input_bold':['main_wf.select_input_bold', 'selected_file'],
'commonspace_mask':['main_wf.bold_main_wf.mask_commonspace_trans_wf.brain_mask_resample', 'resampled_file'],
'motion_params_csv':['main_wf.bold_main_wf.estimate_motion_node', 'motion_params_csv'],
'FD_csv':['main_wf.bold_main_wf.estimate_motion_node', 'FD_csv'],
Expand Down
22 changes: 10 additions & 12 deletions rabies/preprocess_pkg/bold_main_wf.py
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Expand Up @@ -51,8 +51,6 @@ def init_bold_main_wf(opts, output_folder, number_functional_scans, inho_cor_onl

**Outputs**

input_bold
The provided input BOLD file
bold_ref
Initial EPI median volume subsequently used as 3D reference EPI volume
motcorr_params
Expand Down Expand Up @@ -113,14 +111,14 @@ def init_bold_main_wf(opts, output_folder, number_functional_scans, inho_cor_onl
workflow = pe.Workflow(name=name)

inputnode = pe.Node(niu.IdentityInterface(
fields=['bold', 'inho_cor_anat', 'inho_cor_mask', 'coreg_anat', 'coreg_mask',
fields=['bold_file', 'inho_cor_anat', 'inho_cor_mask', 'coreg_anat', 'coreg_mask',
'anat_to_commonspace_transform_list','anat_to_commonspace_inverse_list',
'commonspace_to_anat_transform_list','commonspace_to_anat_inverse_list',
'commonspace_ref']),
name="inputnode")

outputnode = pe.Node(niu.IdentityInterface(
fields=['input_bold', 'bold_ref', 'motcorr_params', 'init_denoise', 'denoise_mask', 'corrected_EPI',
fields=['bold_ref', 'motcorr_params', 'init_denoise', 'denoise_mask', 'corrected_EPI',
'output_warped_bold', 'bold_to_anat_affine', 'bold_to_anat_warp', 'bold_to_anat_inverse_warp',
'native_bold', 'native_bold_ref', 'native_brain_mask', 'native_WM_mask', 'native_CSF_mask', 'native_vascular_mask',
'motion_params_csv', 'FD_voxelwise', 'pos_voxelwise', 'FD_csv', 'commonspace_bold', 'commonspace_mask',
Expand Down Expand Up @@ -181,12 +179,12 @@ def init_bold_main_wf(opts, output_folder, number_functional_scans, inho_cor_onl
function=apply_despike),
name='despike')
workflow.connect([
(inputnode, despike, [('bold', 'in_file')]),
(inputnode, despike, [('bold_file', 'in_file')]),
(despike, boldbuffer, [('out_file', 'bold_file')]),
])
else:
workflow.connect([
(inputnode, boldbuffer, [('bold', 'bold_file')]),
(inputnode, boldbuffer, [('bold_file', 'bold_file')]),
])

if opts.detect_dummy:
Expand Down Expand Up @@ -237,7 +235,7 @@ def remove_dummy(bold_file):
(inputnode, inho_cor_wf, [
('inho_cor_anat', 'inputnode.anat_ref'),
('inho_cor_mask', 'inputnode.anat_mask'),
('bold', 'inputnode.name_source'),
('bold_file', 'inputnode.name_source'),
]),
(template_inputnode, inho_cor_wf, [
("template_anat", "template_inputnode.template_anat"),
Expand Down Expand Up @@ -409,7 +407,7 @@ def transforms_buffernode(bold_to_commonspace_transform_list, bold_to_commonspac
('corrected_EPI', 'corrected_EPI'),
]),
(inputnode, estimate_motion_node, [
('bold', 'boldspace_bold'),
('bold_file', 'boldspace_bold'),
]),
(bold_hmc_wf, estimate_motion_node, [
('outputnode.motcorr_params', 'motcorr_params'),
Expand Down Expand Up @@ -438,7 +436,7 @@ def transforms_buffernode(bold_to_commonspace_transform_list, bold_to_commonspac

workflow.connect([
(inputnode, boldspace_brain_mask, [
('bold', 'name_source'),
('bold_file', 'name_source'),
]),
(transitionnode, boldspace_brain_mask, [
('bold_ref', 'ref_file'),
Expand Down Expand Up @@ -510,10 +508,10 @@ def transforms_buffernode(bold_to_commonspace_transform_list, bold_to_commonspac
(bold_hmc_wf, bold_native_trans_wf, [
('outputnode.motcorr_params', 'inputnode.motcorr_params')]),
(inputnode, bold_native_trans_wf, [
('bold', 'inputnode.name_source'),
('bold_file', 'inputnode.name_source'),
]),
(inputnode, mask_native_trans_wf, [
('bold', 'inputnode.name_source'),
('bold_file', 'inputnode.name_source'),
]),
(bold_native_trans_wf, mask_native_trans_wf, [
('outputnode.bold_ref','inputnode.ref_file'),
Expand Down Expand Up @@ -544,7 +542,7 @@ def transforms_buffernode(bold_to_commonspace_transform_list, bold_to_commonspac
(bold_hmc_wf, bold_commonspace_trans_wf, [
('outputnode.motcorr_params', 'inputnode.motcorr_params')]),
(inputnode, bold_commonspace_trans_wf, [
('bold', 'inputnode.name_source'),
('bold_file', 'inputnode.name_source'),
('commonspace_ref', 'inputnode.ref_file'),
]),
(bold_commonspace_trans_wf, outputnode, [
Expand Down
59 changes: 18 additions & 41 deletions rabies/preprocess_pkg/main_wf.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
import os
import pathlib
from nipype.pipeline import engine as pe
from nipype.interfaces import utility as niu
from nipype.interfaces.io import DataSink
Expand Down Expand Up @@ -30,9 +29,6 @@ def init_main_wf(data_dir_path, output_folder, opts, name='main_wf'):

**Outputs**


input_bold
Input EPIs to the preprocessing
commonspace_resampled_template
the anatomical commonspace template after initial resampling
anat_preproc
Expand Down Expand Up @@ -100,7 +96,7 @@ def init_main_wf(data_dir_path, output_folder, opts, name='main_wf'):

# set output node
outputnode = pe.Node(niu.IdentityInterface(
fields=['input_bold', 'commonspace_resampled_template', 'anat_preproc', 'initial_bold_ref', 'inho_cor_bold', 'bold_to_anat_affine',
fields=['commonspace_resampled_template', 'anat_preproc', 'initial_bold_ref', 'inho_cor_bold', 'bold_to_anat_affine',
'bold_to_anat_warp', 'bold_to_anat_inverse_warp', 'inho_cor_bold_warped2anat', 'native_bold', 'native_bold_ref', 'motion_params_csv',
'FD_voxelwise', 'pos_voxelwise', 'FD_csv', 'native_brain_mask', 'native_WM_mask', 'native_CSF_mask', 'native_vascular_mask',
'commonspace_bold', 'commonspace_mask', 'commonspace_WM_mask', 'commonspace_CSF_mask', 'commonspace_vascular_mask',
Expand Down Expand Up @@ -178,38 +174,25 @@ def init_main_wf(data_dir_path, output_folder, opts, name='main_wf'):
'''
MATCHING INPUTS TO MAIN ITERABLES
'''
format_bold_node = pe.Node(Function(input_names=['scan_info', 'run', 'joined_scan_info', 'joined_run', 'joined_file_list'],
output_names=['selected_file'],
function=match_iterables),
name='format_bold')

input_bold_node = pe.Node(Function(input_names=['scan_info', 'run', 'joined_scan_info', 'joined_run', 'joined_file_list'],
select_input_bold_node = pe.Node(Function(input_names=['scan_info', 'run', 'joined_scan_info', 'joined_run', 'joined_file_list'],
output_names=['selected_file'],
function=match_iterables),
name='input_bold')

name='select_input_bold')
workflow.connect([
(prep_input_wf, format_bold_node, [
("outputnode.prep_bold_list", "joined_file_list"),
("outputnode.joined_scan_info", "joined_scan_info"),
]),
(main_split, format_bold_node, [
("scan_info", "scan_info"),
]),
(prep_input_wf, input_bold_node, [
(prep_input_wf, select_input_bold_node, [
("outputnode.input_bold_list", "joined_file_list"),
("outputnode.joined_scan_info", "joined_scan_info"),
]),
(main_split, input_bold_node, [
(main_split, select_input_bold_node, [
("scan_info", "scan_info"),
]),
])

if opts.bold_only:
format_bold_node.inputs.run = None
input_bold_node.inputs.run = None
format_bold_node.inputs.joined_run = None
input_bold_node.inputs.joined_run = None
select_input_bold_node.inputs.run = None
select_input_bold_node.inputs.joined_run = None
else:
run_split = pe.Node(niu.IdentityInterface(fields=['run', 'split_name']),
name="run_split")
Expand All @@ -220,16 +203,10 @@ def init_main_wf(data_dir_path, output_folder, opts, name='main_wf'):
(main_split, run_split, [
("split_name", "split_name"),
]),
(prep_input_wf, format_bold_node, [
("outputnode.joined_run", "joined_run"),
]),
(run_split, format_bold_node, [
("run", "run"),
]),
(prep_input_wf, input_bold_node, [
(prep_input_wf, select_input_bold_node, [
("outputnode.joined_run", "joined_run"),
]),
(run_split, input_bold_node, [
(run_split, select_input_bold_node, [
("run", "run"),
]),
])
Expand Down Expand Up @@ -341,8 +318,8 @@ def init_main_wf(data_dir_path, output_folder, opts, name='main_wf'):

# MAIN WORKFLOW STRUCTURE #######################################################
workflow.connect([
(format_bold_node, bold_main_wf, [
("selected_file", "inputnode.bold"),
(select_input_bold_node, bold_main_wf, [
("selected_file", "inputnode.bold_file"),
]),
(resample_template_node, template_diagnosis, [
("registration_template", "anat_template"),
Expand Down Expand Up @@ -393,13 +370,13 @@ def init_main_wf(data_dir_path, output_folder, opts, name='main_wf'):
("outputnode.corrected_EPI", "final_denoise"),
("outputnode.denoise_mask", "warped_mask"),
]),
(input_bold_node, bold_inho_cor_diagnosis,
(select_input_bold_node, bold_inho_cor_diagnosis,
[("selected_file", "name_source")]),
(bold_main_wf, temporal_diagnosis, [
("outputnode.motion_params_csv", "motion_params_csv"),
("outputnode.FD_csv", "FD_csv"),
]),
(input_bold_node, temporal_diagnosis,
(select_input_bold_node, temporal_diagnosis,
[("selected_file", "name_source")]),
(temporal_diagnosis, outputnode, [
("tSNR_filename", "tSNR_filename"),
Expand Down Expand Up @@ -491,8 +468,8 @@ def init_main_wf(data_dir_path, output_folder, opts, name='main_wf'):
("registration_template", "template_inputnode.template_anat"),
("registration_mask", "template_inputnode.template_mask"),
]),
(format_bold_node, inho_cor_bold_main_wf, [
("selected_file", "inputnode.bold"),
(select_input_bold_node, inho_cor_bold_main_wf, [
("selected_file", "inputnode.bold_file"),
]),
(EPI_target_buffer, inho_cor_bold_main_wf, [
("EPI_template", "inputnode.inho_cor_anat"),
Expand Down Expand Up @@ -522,7 +499,7 @@ def init_main_wf(data_dir_path, output_folder, opts, name='main_wf'):
preprocess_visual_QC.PlotOverlap(), name='PlotOverlap_EPI2Anat')
PlotOverlap_EPI2Anat_node.inputs.out_dir = output_folder+'/preprocess_QC_report/EPI2Anat'
workflow.connect([
(input_bold_node, PlotOverlap_EPI2Anat_node,
(select_input_bold_node, PlotOverlap_EPI2Anat_node,
[("selected_file", "name_source")]),
(anat_inho_cor_wf, PlotOverlap_EPI2Anat_node,
[("outputnode.corrected", "fixed")]),
Expand All @@ -533,7 +510,7 @@ def init_main_wf(data_dir_path, output_folder, opts, name='main_wf'):

# fill the datasinks
workflow.connect([
(input_bold_node, bold_datasink, [
(select_input_bold_node, bold_datasink, [
("selected_file", "input_bold"),
]),
(outputnode, motion_datasink, [
Expand Down
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