Correlation per voxel, the faster wayΒΆ

You are going to use your new super-fast correlation routine to recalculate the voxel correlation volumne

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

If you don’t have them already you will need these files in your working directory:

First, import sys and append the directory containing your module to sys.path:

# Import sys, and append directory to sys.path

Check you can import pearson and stimuli. Call over someone to help if you can’t.

import pearson
import stimuli

Now load up the data, and get the on-off time course (neural prediction):

# - 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

# Load the ds114_sub009_t2r1.nii image
import nibabel as nib
img = nib.load('ds114_sub009_t2r1.nii')

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

# Time between 3D volumes in seconds
TR = 2.5

# Get on-off timecourse
time_course = events2neural('ds114_sub009_t2r1_cond.txt', 2.5, n_trs)

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

Now the real work.

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

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

Transpose the array to make a (number of volumes, number of voxels) array.

# Transpose 2D array to give n_volumes, n_voxels array

Use the pearson_2d function to return the correlation coefficients with time_course at each voxel:

# Calculate 1D vector length n_voxels of correlation coefficients

You might have noticed this is much faster than doing the correlation by looping over each 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')