Neural space(s) determination and conversion/mapping

[vborghe]

Once the papers have been filtered (#11) - key part of the preprocessing will be

• [x] determining the neural space used (MNI coordinates? atlas? which one?)

Whatever the paper is using,

• [ ] conversion/mapping to the one selected as "standard" here will need to be implemented (#4).

[vborghe]

@pinheirochagas do you think what @complexbrains did here https://github.com/neurocausal/neurocausal_data/pull/5 actually belongs to meta rather than data? regardless of where it ends, clearly helpful for this too, no?

[vborghe]

Next step: adapting the neuroquery code to atlas (rather than coordinates), choose atlas, convert all coordinates (and other atlases) to that one

[vborghe]

@complexbrains's branch now has the MNI coordinates extraction code

templateflow might help with the mapping/conversions, but currently does not cover all the parcellations/atlases we had in mind (mostly MNI-ish spaces)

[vborghe]

neuroparc covers more of the atlases we might need

[vborghe]

@complexbrains @pinheirochagas Following Jerome's suggestion, we might want to stick with MNI coordinates (i.e., convert everything to MNI coordinates) simply putting uniform values to all voxels that fall within a given atlas region

[pinheirochagas]

Here are a set of functions to convert atlas-specific region coordinates to MNI space and plot the regions on a standard MNI brain using nilearn. Some atlases will be fetched directly from nilearn, while others might require manual downloading of the .nii files. @complexbrai @vborghe @margokersey.

Implementation: 1. Fetching the Atlas: We've defined a function fetch_atlas(atlas_name) that fetches the atlas based on its name. Some atlases, like mars_atlas, desikan_killiany, talariach_ba, and talariach_gyrus, aal are placeholders for now and will need the paths to their respective volumetric atlas files (the function already accepts a path to the nii file: e.g.: https://www.oxcns.org/aal3.html). Atlases such as juelich, harvard_oxford and destrieux, can be fetched using nilearn's built-in datasets module.

2. Conversion to MNI Space:

• Fetch the Atlas: Based on the provided atlas name, we fetch the atlas. If the atlas is fetched as a file path, we load it as a Nifti image. Otherwise, we proceed with the provided Nifti image.
• Identify the Region: Using the provided region value (which is a numerical value representing the region in the atlas), we identify the voxels of the region in the atlas.
• Convert to MNI Coordinates: Using the affine transformation of the atlas, we convert the voxels of the region to MNI space.

For the current implementation, regions need to be entered as numerical values. In the future, we should create a dictionary that maps paper_region_name to atlas_label in order to retrieve the atlas_region_value and perform the conversion.

3. Plotting: Once the conversion is done, we have a function plot_anatomical_overlay(binary_mask_img) that plots the MNI coordinates of the region on a standard MNI brain for inspection. It looks great to me!

#%%
import numpy as np
import nibabel as nib
from nilearn import plotting, image
import nilearn
import nilearn.datasets
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
# https://github.com/neurocausal/neurocausal_meta/blob/main/atlas_labels_annotation.csv

#%%
def fetch_atlas(atlas_name):
    if atlas_name == 'mars_atlas':
        return None # find the volumetric atlas probably from file
    elif atlas_name == 'desikan_killiany':
        return None # find the volumetric atlas probably from file
    if atlas_name == 'talariach_ba':
        return None # find the volumetric atlas probably from file
    if atlas_name == 'talariach_gyrus':
        return None # find the volumetric atlas probably from file
    if atlas_name == 'juelich':
        return nilearn.datasets.fetch_atlas_juelich('maxprob-thr25-1mm').maps
    elif atlas_name == 'harvard_oxford':
        return nilearn.datasets.fetch_atlas_harvard_oxford('cort-maxprob-thr25-2mm').maps
    elif atlas_name == 'destrieux':
        return nilearn.datasets.fetch_atlas_destrieux_2009().maps
    elif atlas_name == 'aal':
        atlas_name = '/Users/pinheirochagas/Downloads/AAL3/AAL3v1.nii'
        return atlas_name
    else:
        raise ValueError(f"Atlas {atlas_name} is not recognized or not supported in this function.")

#%%
def convert_to_mni(atlas_name, region_value):
    # Fetch the atlas
    atlas_obj = fetch_atlas(atlas_name)

    # Load the atlas if it's a file path, otherwise proceed with the Nifti image
    if isinstance(atlas_obj, str):
        atlas_img = nib.load(atlas_obj)
    else:
        atlas_img = atlas_obj

    atlas_data = atlas_img.get_fdata()

    # Identify the region
    region_voxels = np.argwhere(atlas_data == region_value)

    # Convert the voxels to MNI space
    mni_coordinates = [nib.affines.apply_affine(atlas_img.affine, voxel) for voxel in region_voxels]

    return mni_coordinates

# %%
def create_mni_mask(mni_coordinates, template_shape, affine):
    """Create a binary mask in MNI space from the given coordinates."""
    mask = np.zeros(template_shape)
    for coord in mni_coordinates:
        voxel = nib.affines.apply_affine(np.linalg.inv(affine), coord).astype(int)
        mask[tuple(voxel)] = 1
    mask_img = nib.Nifti1Image(mask, affine=affine)
    return mask_img

def plot_anatomical_overlay(binary_mask_img):
    """Plot the binary mask on top of a standard MNI brain."""

    # Create a custom colormap that only contains the color red
    cmap = ListedColormap(['#000000', '#FF0000'])

    # Create a matplotlib figure with a specified size
    fig = plt.figure(figsize=(10, 8))

    # Plot
    plotting.plot_stat_map(binary_mask_img, display_mode='ortho', colorbar=False, 
                           threshold=0.5, cmap=cmap, figure=fig)
    plotting.show()

#%%
# Example usage:
region_value = 1  # replace with the value for your region of interest
mni_coords = convert_to_mni('aal', region_value)
template = nilearn.datasets.load_mni152_template()
binary_mask_img = create_mni_mask(mni_coords, template.shape, template.affine)
plot_anatomical_overlay(binary_mask_img)
# %%
atlas_list = ['juelich', 'harvard_oxford', 'destrieux', 'aal']
region_value = 1  # replace with the value for your region of interest
[plot_anatomical_overlay(create_mni_mask(convert_to_mni(atlas, region_value), template.shape, template.affine)) for atlas in atlas_list]

Example: Frontal_Inf_Oper_L from AAL into MNI

[vborghe]

This looks great!! Thanks a lot, Pedro!