\(\newcommand{L}[1]{\| #1 \|}\newcommand{VL}[1]{\L{ \vec{#1} }}\newcommand{R}[1]{\operatorname{Re}\,(#1)}\newcommand{I}[1]{\operatorname{Im}\, (#1)}\)

Voxel correlation exercise

>>> #: import common modules
>>> import numpy as np  # the Python array package
>>> import matplotlib.pyplot as plt  # the Python plotting package
>>> import nibabel as nib

Import the events2neural function from the stimuli.py module:

>>> #- import events2neural from stimuli module

If you don’t have it already, download the ds114_sub009_t2r1.nii image. Load it with nibabel.

>>> #- Load the ds114_sub009_t2r1.nii image

>>> #- Get the number of volumes in ds114_sub009_t2r1.nii

The TR (time between scans) is 2.5 seconds.

>>> #: TR
>>> 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.

>>> #- Call the events2neural function to generate the on-off values for
>>> #- each volume.  Plot these values.

Using slicing, drop the first 4 volumes, and the corresponding on-off values:

>>> #- Drop the first 4 volumes, and the first 4 on-off values.

Make a single brain-volume-sized array of all zero to hold the correlations:

>>> #- Make a brain-volume-size array of 0 to hold the correlations
  • Loop over all voxel indices on the first, then second, then third dimension;
  • extract the voxel time courses at each voxel coordinate in the image;
  • get the correlation between the voxel time course and neural prediction;
  • fill in the value in the correlations array.
>>> #- Loop over all voxel indices.
>>> #- Extract the voxel time courses at each voxel.
>>> #- Get correlation value for voxel time course with on-off vector.
>>> #- Fill value in the correlations array.

Plot the middle slice (plane) of the third axis from the correlations array. Can you see any sign of activity (high correlation) in the frontal lobe?

>>> #- Plot the middle slice of the third axis from the correlations array