Denoising using clean_img function + SPM first level analysis. "Please check your data: There are no significant voxels." error in model estimation mode

Hi everyone,
Now I am trying to denoise my data and conduct first level analysis on this denoised data. I am using nipype workflow and nodes, SPM first level analysis and nilearn clean_img function. In addition, I am using physiological regressors calculated by PhysIO toolbox. So here is my code

def denoise_img(func_path, mask_path, phys_regressors, bids_dir):
    import nibabel as nib
    import pandas as pd
    from nilearn import image as niimg
    import os
    denoise_path = os.path.join(bids_dir, "derivatives", "fMRI_Denoise", "denoised_img.nii")
    phys_regressors = phys_regressors.to_numpy()
    clean_img = niimg.clean_img(func_path,confounds= phys_regressors, axis=0,detrend=True,standardize=True,
                         low_pass=0.1,high_pass=0.009,t_r= 2, mask_img=mask_path), denoise_path)
    return denoise_path

def physio_toolbox(script_mat, subject, session, task, table, derivatives_dir):
    from nipype.interfaces.matlab import MatlabCommand
    import pandas as pd 
    filtered_data = table[(table['subject'] == subject) & (
        table['session'] == session) & (table['task'] == task)]
    tsv_paths = None
    tsv_path = ''
    if not filtered_data.empty:
        tsv_paths = filtered_data.iloc[0]['tsv_physio']
        # Check if the list is not empty before accessing the first item
        if tsv_paths:
            tsv_path = tsv_paths[0]
            with open(script_mat, 'r') as file:
                script_mat =
            script_mat = script_mat.replace('physio.log_files.cardiac = {};', f"physio.log_files.cardiac = {{'{tsv_path}'}};").replace("physio_out",
            if task == "selfref":
                script_mat = script_mat.replace("Nscans = 575", "Nscans = 545")
            elif task == "faces":
                script_mat = script_mat.replace("Nscans = 575", "Nscans = 134").replace("TR = 0.735", "TR = 2").replace("Nslices = 64", "Nslices = 34").replace("onset_slice = 32", "onset_slice =17")
            elif task == "reversal":
                script_mat.replace("Nscans = 575", "Nscans = 1820")
            matlab = MatlabCommand()
            matlab.inputs.script = script_mat
            phys_regressors = f'{derivatives_dir}/sub-{subject}/ses-{session}/physio_out_{task}/multiple_regressors.txt'
            with open(phys_regressors, 'r') as file:
                phys_regressors = pd.read_csv(phys_regressors, delim_whitespace=True, header=None)
    return phys_regressors

physio_correction = Node(Function(input_names=["script_mat", "subject", "session", "task", "table", "derivatives_dir"],
                                     function= physio_toolbox),
physio_correction.inputs.script_mat = script_mat_file
physio_correction.inputs.table = table
physio_correction.inputs.derivatives_dir = derivatives_dir
physio_correction.inputs.subject = "ZI004"
physio_correction.inputs.session = "03"
physio_correction.inputs.task = "faces"

denoising_node = Node(Function(input_names = ["func_path", "mask_path", "phys_regressors", "bids_dir"],
                               output_names = ["denoise_path"],
                               function = denoise_img),
                      name = "denoising")
denoising_node.inputs.func_path = func_path
denoising_node.inputs.mask_path = mask_path
denoising_node.inputs.bids_dir = bids_dir
# Get Subject Info - get subject specific condition information
getsubjectinfo = Node(Function(input_names=['subject', 'session', "task", "rawdata_dir"],
getsubjectinfo.inputs.subject = "ZI004"
getsubjectinfo.inputs.session = "03"
getsubjectinfo.inputs.task = "faces"
getsubjectinfo.inputs.rawdata_dir = rawdata_dir

from nipype.algorithms.misc import Gunzip
gunzip = Node(Gunzip(), name='gunzip')
gunzip.inputs.in_file = "/data/Stepan_Tikhomirov/EPIsoDE/derivatives/fmriprep-preprocess/fmriprep/sub-ZI004/ses-03/func/sub-ZI004_ses-03_task-faces_run-1_space-MNI152NLin6Asym_res-2_desc-preproc_bold.nii.gz"
from nipype.interfaces.spm import Smooth
smooth = Node(Smooth(), name = "smooth")
smooth.inputs.fwhm = [4, 4, 4]

from nipype.algorithms.modelgen import SpecifySPMModel, SpecifyModel
from nipype.interfaces.spm import Level1Design, EstimateModel, EstimateContrast
modelspec = Node(SpecifySPMModel(concatenate_runs=False,
                                 time_repetition= 2,

level1design = Node(Level1Design(bases={'hrf': {'derivs': [1, 0]}},
                                 interscan_interval= 2,

# EstimateModel - estimate the parameters of the model
level1estimate = Node(EstimateModel(estimation_method={'Classical': 1}),

# EstimateContrast - estimates contrasts
level1conest = Node(EstimateContrast(contrasts= contrast), name="level1conest")

datasink = Node(DataSink(base_directory=derivatives_dir,
                         container= "ZI004"),
workflow1 = Workflow(name = "workflow1")

workflow1.connect(physio_correction, "phys_regressors", denoising_node, "phys_regressors") 
workflow1.connect(getsubjectinfo, 'subject_info', modelspec, "subject_info")
workflow1.connect(denoising_node, "denoise_path", modelspec, "functional_runs")
workflow1.connect(modelspec, "session_info", level1design, "session_info")
workflow1.connect(level1design, "spm_mat_file", level1estimate, "spm_mat_file")
workflow1.connect([(level1estimate, level1conest, [('spm_mat_file',
workflow1.connect([(level1conest, datasink, [('spm_mat_file', '1stLevel.spm_mat'),
                                              ('spmT_images', '1stLevel.spmT'),
                                              ('con_images', '1stLevel.con_images'),

I tried to do it with and without smoothing. Also i tried to denoise my data using motion regressors calculated by fmriprep. But when model esimate node is running I get this error :
“Error using spm_est_non_sphericity Please check your data: There are no significant voxels.”
This error occurred all the time: i tried to set detrend and standard to False, changed different parameters in first level analysis, nothing changed. though the denoised timeseries looks okay.
When i set model_serial_correlations to “none” in level1design node, i got another error :
Failed ‘Model estimation’
Error using spm_spm
Please check your data: There are no inmask voxels.
I used the mask created by fmriprep. and it also looks fine.
The last thing I’d like to underline is that when i run first level analysis without denoising, or by adding regressors to subject_info, then everything works. But I would like to try subtracting regressors before GLM.
So, what can be the reason of this error?
Thank you

Random thing to check.
The denoised images you are passing to the first level “make sense”?

Please check your data: There are no inmask voxels

This message does not refer to the mask you used during denoising but to the implicit mask that spm implicitly comoutws to determine which voxels to include in the GLM.

SPM usually includes voxels that have enough signals in them. Hence why I wondering if your denoising has not been a bit too “agressive”.

Random question.

Any reason why you want to clean your images this way rather than just include the regressors in the GLM?

FYI I was raised at a “don’t touch the data, change the model” school of thought.

Denoised image looks like this. Maybe I am mistaken, as I am new to this topic, but it seems to be fine.

Well, we are trying different approaches now. We wanted to try removing regressors before to check if it worked better for our task. But of course if it doesn’t work we’ll just add it to GLM.

hard to tell but at least from the screenshot I am not seeing anything obvious

And how can I estimate if the denoising was too ‘aggressive’ then? Sorry for these questions.