# Correlation per voxel, in 2D¶

In this exercise, we will take each voxel time course in the brain, and calculate a correlation between the task-on / task-off vector and the voxel time course. We then make a new 3D volume that contains correlation values for each voxel.

You’ve done this before in the exercise Correlation r value for each voxel in the brain, but this time we’ll do it by reshaping the 4D data to 2D, and looping over the long voxels axis instead of over each of the three spatial axes.

We’ve given you the stuff you will have done already for the previous exercise - you can copy-paste into IPython.

# - import common modules
import numpy as np  # the Python array package
import matplotlib.pyplot as plt  # the Python plotting package

# import events2neural from stimuli module
from stimuli import events2neural

import nibabel as nib

# Load the ds114_sub009_t2r1.nii image

# Get the number of volumes in ds114_sub009_t2r1.nii
n_trs = img.shape[-1]


The TR (time between scans) is 2.5 seconds.

TR = 2.5


Call the events2neural function to give you a time course that is 1 for the volumes during the task (thinking of verbs) and 0 for the volumes during rest.

time_course = events2neural('ds114_sub009_t2r1_cond.txt', 2.5, n_trs)

# Using slicing, drop the first 4 volumes, and the first 4 on-off values
data = img.get_data()
data = data[..., 4:]
time_course = time_course[4:]

# Calculate the number of voxels (number of elements in one volume)


Now, reshape the 4D data to a 2D array shape (number of voxels, number of volumes).

# Reshape 4D array to 2D array n_voxels by n_volumes

# Make a 1D array of size (n_voxels,) to hold the correlation values


Now loop over all voxels, calculate the correlation coefficient with time_course at this voxel, and fill in the corresponding entry in your 1D array.

# Loop over voxels filling in correlation at this voxel


Reshape the correlations 1D array back to a 3D array, using the original 3D shape.

# Reshape the correlations array back to 3D


If all went well, you should have generated the same 3D volume of correlations as you did for the original exercise:

# Plot the middle slice of the third axis from the correlations array
plt.imshow(correlations[:, :, 14], cmap='gray')