Is it possible to provide ’Schaefer1000_3mm.nii.gz‘ ？

[Zhangzhixiang-laugh]

Through browsing and searching a lot of literature and Internet materials, I did not find any data on Schaefer1000_3mm.nii.gz (only found Schaefer1000_1mm.nii.gz and Schaefer1000_2mm.nii.gz). At last but not least, thanks for providing Schaefer1000_3mm.nii.gz!!! References: 1.https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations/MNI 2.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6095216/pdf/bhx179.pdf

[snastase]

Sorry for the oversight! I believe I created the Schaefer1000_3mm.nii.gz file offline by using AFNI's 3dresample with the 1 mm resolution Schaefer atlas as -input and one of the functional images from the Narratives dataset as -master, with -rmode NN i.e. nearest-neighbor resampling. In other words, the Schaefer1000_3mm.nii.gz is a version of the original Schaefer atlas resampled to the grid and resolution of the BOLD data.

[Zhangzhixiang-laugh]

Sorry for the oversight! I believe I created the Schaefer1000_3mm.nii.gz file offline by using AFNI's 3dresample with the 1 mm resolution Schaefer atlas as -input and one of the functional images from the Narratives dataset as -master, with -rmode NN i.e. nearest-neighbor resampling. In other words, the Schaefer1000_3mm.nii.gz is a version of the original Schaefer atlas resampled to the grid and resolution of the BOLD data.

Thank you for your reply, I have solved this issue with python tools. The reference code is as follows:

import nibabel as nib
import numpy as np
from scipy.ndimage import zoom

def resample_nii_to_target_resolution(input_nii_path,standard_nii_path, output_nii_path):
    # Load the input and target NIfTI file
    img_src = nib.load(input_nii_path)
    src = img_src.get_fdata()

    img_std = nib.load(standard_nii_path)
    std = img_std.get_fdata()

    # Ensure data is in appropriate format for NIfTI
    src = src.astype(np.int16)  # Use a smaller integer type to save space
    std = std.astype(np.int16)  # Use a smaller integer type to save space

    # Get the original and target resolution from the affine matrix
    original_affine = img_src.affine
    original_resolution = np.abs(original_affine[0, 0])

    standard_affine = img_std.affine
    standard_resolution = np.abs(standard_affine[0, 0])

    # Calculate the resampling factor
    resampling_factor = original_resolution / standard_resolution

    # Resample the data using nearest neighbor interpolation
    resampled_data = zoom(src, zoom=(resampling_factor,  resampling_factor, resampling_factor), order=0)

    # Ensure resampled data is in appropriate format
    resampled_data = resampled_data.astype(np.int16)  # Use a smaller integer type to save space

    # Create a new affine matrix for the target resolution
    new_affine = np.copy(original_affine)
    new_affine[:3, :3] /= resampling_factor

    # Create a new NIfTI image with the resampled data
    resampled_img = nib.Nifti1Image(resampled_data, affine=new_affine)

    # Save the resampled image to the output path with maximum compression
    nib.save(resampled_img, output_nii_path)

# Example usage
input_nii_path = '/mnt/d/github projects/bert-brains-master/data/schaefer1000MNI_1mm.nii.gz' # Replace with the path to your Schaefer NIfTI file
standard_nii_path = '/mnt/d/github projects/bert-brains-master/data/black_data/sub-127.nii.gz'  # Standard file path
output_nii_path = '/mnt/d/github projects/bert-brains-master/data/schaefer1000MNI_3mm.nii.gz'  # Output file path

resample_nii_to_target_resolution(input_nii_path, standard_nii_path,output_nii_path)