# Correlation r value for each voxel in the brainΒΆ

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.

Import the `events2neural`

function from the `stimuli`

module:

```
# import events2neural from stimuli module
```

Load the `ds114_sub009_t2r1.nii`

image, and calculate the number of volumes:

```
# Load the ds114_sub009_t2r1.nii image
# Get number of volumes
```

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. Plot this time course:

```
# 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 corresponding on-off values
```

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

```
# Make array to hold the correlation values
correlations = np.zeros(data.shape[:-1])
```

- 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 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 correlation image
```