Predicted time series and residuals

Here we fit a First Level GLM with the minimize_memory-argument set to False. By doing so, the FirstLevelModel-object stores the residuals, which we can then inspect. Also, the predicted time series can be extracted, which is useful to assess the quality of the model fit.

dataset

This example uses the SPM auditory dataset.

import pandas as pd

from nilearn import image, masking
from nilearn.datasets import fetch_spm_auditory
from nilearn.plotting import plot_stat_map, show

# load fMRI data
subject_data = fetch_spm_auditory()
fmri_img = subject_data.func[0]

# Make an average
mean_img = image.mean_img(fmri_img)
mask = masking.compute_epi_mask(mean_img)

# Clean and smooth data
fmri_img = image.clean_img(fmri_img, standardize=None)
fmri_img = image.smooth_img(fmri_img, 5.0)

# load events
events = pd.read_csv(subject_data.events, sep="\t")

[fetch_spm_auditory] Dataset directory found: /home/runner/work/nilearn/nilearn/nilearn_data/spm_auditory

Fit model

Note that minimize_memory is set to False so that FirstLevelModel stores and plot the residuals. signal_scaling is set to False, so we keep the same scaling as the original data in fmri_img.

from nilearn.glm.first_level import FirstLevelModel

fmri_glm = FirstLevelModel(
    t_r=subject_data.t_r,
    drift_model="cosine",
    signal_scaling=False,
    mask_img=mask,
    minimize_memory=False,
    verbose=1,
)

fmri_glm = fmri_glm.fit(fmri_img, events)

/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_predictions_residuals.py:57: RuntimeWarning:

[MultiNiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.

[FirstLevelModel.fit] Computing run 1 out of 1 runs (go take a coffee, a big one).
[FirstLevelModel.fit] Performing mask computation.
[FirstLevelModel.fit] Masking took 0 seconds.
[FirstLevelModel.fit] Performing GLM computation.
[FirstLevelModel.fit] GLM took 1 seconds.
[FirstLevelModel.fit] Computation of 1 runs done in 00 HR 00 MIN 01 SEC.

Calculate and plot contrast

z_map = fmri_glm.compute_contrast("listening")

threshold = 3.1
plot_stat_map(
    z_map,
    bg_img=mean_img,
    threshold=threshold,
    title=f"listening > rest (t-test; |Z|>{threshold})",
)

show()

Extract the most significant clusters

We can extract the clusters surviving our threshold and get the x, y, and z coordinates of their peaks. We then extract the time series from a sphere around each coordinate.

from nilearn.maskers import NiftiSpheresMasker
from nilearn.reporting import get_clusters_table

table, _ = get_clusters_table(
    z_map,
    stat_threshold=threshold,
    cluster_threshold=20,
    return_label_maps=True,
)
print(table)

   Cluster ID     X     Y     Z  Peak Stat Cluster Size (mm3)
0           1 -60.0  -6.0  42.0  11.543910              20196
1          1a -51.0 -12.0  39.0  10.497958
2          1b -39.0 -12.0  39.0  10.118978
3          1c -63.0  12.0  33.0   9.754835
4           2  60.0   0.0  36.0  11.088600              32157
..        ...   ...   ...   ...        ...                ...
58         21  27.0 -72.0  27.0   4.422543                729
59        21a  24.0 -66.0  18.0   4.346647
60        21b  24.0 -78.0  21.0   3.428392
61         22 -30.0 -24.0  90.0   3.877083                648
62        22a -33.0 -18.0  72.0   3.791863

[63 rows x 6 columns]

Find the coordinates of the peaks of the 6 most significant clusters. Clusters are sorted by peak statistic and sub-peaks (with a letter in their “Cluster ID”, for example “1a”) are excluded.

is_main_peak = table["Cluster ID"].astype(str).str.isdigit()
coords = table.loc[is_main_peak, ["X", "Y", "Z"]].head(6).to_numpy()
print(coords)

masker = NiftiSpheresMasker(coords, verbose=1).fit(mean_img)

# let's have a look at the report to make sure the spheres are well placed.
report = masker.generate_report()
report

[[-60.  -6.  42.]
 [ 60.   0.  36.]
 [ 36.   0.  15.]
 [ 57.  21.  75.]
 [ 12.  45.  78.]
 [ 39.  33.  54.]]
[NiftiSpheresMasker.fit] Loading data from <nibabel.nifti1.Nifti1Image object at 0x72031dc02090>
[NiftiSpheresMasker.fit] Finished fit
/home/runner/work/nilearn/nilearn/examples/04_glm_first_level/plot_predictions_residuals.py:105: UserWarning:

`generate_report()` received 'displayed_spheres=10' to be displayed. But masker only has 6 sphere(s). 'displayed_spheres' was set to 6.

NiftiSpheresMasker Class for masking of Niimg-like objects using seeds. NiftiSpheresMasker is useful when data from given seeds should be extracted. Use case: summarize brain signals from seeds that were obtained from prior knowledge.

WARNING

• `generate_report()` received 'displayed_spheres=10' to be displayed. But masker only has 6 sphere(s). 'displayed_spheres' was set to 6.

Previous Sphere

Next Sphere

This report shows the regions defined by the spheres of the masker.

The masker has 6 spheres in total (displayed together on the first image).

They can be individually browsed using Previous and Next buttons.

Regions summary

seed number	coordinates	position	radius	size (in mm^3)	size (in voxels)	relative size (in %)
0	[-60.0, -6.0, 42.0]	[51, 29, 36]	1.0	4.19	not implemented	not implemented
1	[60.0, 0.0, 36.0]	[11, 31, 34]	1.0	4.19	not implemented	not implemented
2	[36.0, 0.0, 15.0]	[19, 31, 27]	1.0	4.19	not implemented	not implemented
3	[57.0, 21.0, 75.0]	[12, 38, 47]	1.0	4.19	not implemented	not implemented
4	[12.0, 45.0, 78.0]	[27, 46, 48]	1.0	4.19	not implemented	not implemented
5	[39.0, 33.0, 54.0]	[18, 42, 40]	1.0	4.19	not implemented	not implemented

	Value
Parameter
allow_overlap	False
detrend	False
high_variance_confounds	False
memory_level	1
reports	True
seeds	[[-60.0, -6.0, 42.0], [60.0, 0.0, 36.0], [36.0, 0.0, 15.0], [57.0, 21.0, 75.0], [12.0, 45.0, 78.0], [39.0, 33.0, 54.0]]
standardize_confounds	True
verbose	1

Plot predicted and time series of the most significant clusters

We can plot the predicted and observed time series together for a more direct comparison.

time_series_dfs, figs = fmri_glm.plot_predicted_signal_and_residuals(
    coords=coords[0:2]
)

• 
• 

Plot R-squared

Because we stored the residuals, we can plot the R-squared: the proportion of explained variance of the GLM as a whole. Note that the R-squared is markedly lower deep down the brain, where there is more physiological noise and we are further away from the receive coils. However, R-Squared should be interpreted with a grain of salt. The R-squared value will necessarily increase with the addition of more factors (such as rest, active, drift, motion) into the GLM. Additionally, we are looking at the overall fit of the model, so we are unable to say whether a voxel/region has a large R-squared value because the voxel/region is responsive to the experiment (such as active or rest) or because the voxel/region fits the noise factors (such as drift or motion) that could be present in the GLM. To isolate the influence of the experiment, we can use an F-test as shown in the next section.

plot_stat_map(
    fmri_glm.r_square_[0],
    bg_img=mean_img,
    threshold=0.1,
    display_mode="z",
    cut_coords=7,
    cmap="inferno",
    title="R-squared",
    vmin=0,
    symmetric_cbar=False,
)

<nilearn.plotting.displays._slicers.ZSlicer object at 0x720314defe10>

Calculate and Plot F-test

The F-test tells you how well the GLM fits effects of interest such as the active and rest conditions together. This is different from R-squared, which tells you how well the overall GLM fits the data, including active, rest and all the other columns in the design matrix such as drift and motion.

# f-test for 'listening'
z_map_ftest = fmri_glm.compute_contrast(
    "listening", stat_type="F", output_type="z_score"
)

plot_stat_map(
    z_map_ftest,
    bg_img=mean_img,
    threshold=threshold,
    display_mode="z",
    cut_coords=7,
    cmap="inferno",
    title=f"listening > rest (F-test; Z>{threshold})",
    symmetric_cbar=False,
    vmin=0,
)

show()

Estimated memory usage: 962 MB