neuroscience-imaging

Neuroscience Imaging

Overview

Neuroimaging analysis encompasses structural MRI (morphometry, volumetrics), task fMRI (GLM-based activation), and resting-state fMRI (functional connectivity, ICA) workflows. This skill uses nilearn and nibabel for analysis and visualization, compatible with fMRIPrep-preprocessed data and BIDS-formatted datasets.

When to Use This Skill

• Loading and visualizing NIfTI (.nii/.nii.gz) brain images
• Extracting time series from ROIs using brain atlases (Schaefer, AAL, Harvard-Oxford)
• Computing functional connectivity matrices and networks
• Running GLM-based task fMRI analysis
• Independent Component Analysis (ICA) for resting-state networks
• Brain parcellation and morphometric analysis
• Whole-brain searchlight and mass-univariate analysis
• Visualizing brain maps, glass brains, and surface plots

Quick Start

Loading NIfTI Data

import nibabel as nib
import numpy as np

# Load NIfTI image
img = nib.load("sub-01_task-rest_bold.nii.gz")
data = img.get_fdata()
affine = img.affine
header = img.header

print(f"Image shape: {data.shape}")  # (x, y, z, time)
print(f"Voxel size: {header.get_zooms()}")
print(f"TR: {header.get_zooms()[3]:.2f} s")
print(f"# timepoints: {data.shape[3]}")

Brain Visualization

from nilearn import plotting, image
import matplotlib.pyplot as plt

# Glass brain plot (activation map)
stat_img = "sub-01_contrast-faces_stat.nii.gz"
plotting.plot_glass_brain(
    stat_img,
    threshold=3.5,
    colorbar=True,
    plot_abs=False,
    display_mode="lyrz",
    title="Face vs. Object Contrast",
    output_file="glass_brain.png",
)

# Anatomical underlay
plotting.plot_stat_map(
    stat_img,
    bg_img="sub-01_T1w_MNI.nii.gz",
    threshold=3.5,
    display_mode="z",
    cut_coords=8,
    colorbar=True,
    title="Face activation (z-score)",
    output_file="stat_map_slices.png",
)
print("Brain plots saved.")

Functional Connectivity (ROI-to-ROI)

from nilearn import datasets, input_data
from nilearn.connectome import ConnectivityMeasure
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load atlas
atlas = datasets.fetch_atlas_schaefer_2018(n_rois=200, yeo_networks=7)
atlas_img = atlas.maps
labels = atlas.labels

# Extract time series from ROIs
masker = input_data.NiftiLabelsMasker(
    labels_img=atlas_img,
    standardize=True,
    detrend=True,
    high_pass=0.01,
    low_pass=0.1,
    t_r=2.0,  # TR in seconds
    resampling_target="labels",
)

# Load preprocessed fMRI (fMRIPrep output)
fmri_img = "sub-01_task-rest_space-MNI152NLin2009cAsym_res-2_desc-preproc_bold.nii.gz"
time_series = masker.fit_transform(fmri_img)
print(f"Time series shape: {time_series.shape}")  # (timepoints, n_ROIs)

# Compute correlation matrix
correlation_measure = ConnectivityMeasure(kind="correlation")
corr_matrix = correlation_measure.fit_transform([time_series])[0]
np.fill_diagonal(corr_matrix, 0)  # Zero diagonal

# Plot connectivity matrix
fig, ax = plt.subplots(figsize=(12, 10))
sns.heatmap(
    corr_matrix,
    cmap="RdBu_r",
    center=0,
    vmin=-0.8,
    vmax=0.8,
    xticklabels=False,
    yticklabels=False,
    ax=ax,
)
ax.set_title("Functional Connectivity Matrix (Schaefer 200 ROIs)", fontsize=13)
plt.tight_layout()
plt.savefig("connectivity_matrix.png", dpi=150)