Generate an events.tsv file for the NeuroSpin localizer task

Create a BIDS-compatible events.tsv file from onset/trial-type information.

The protocol described is the so-called “ARCHI Standard” functional localizer task.

For details on the task, please see Pinel et al.[1].

from nilearn.plotting import plot_event

Define the onset times in seconds. These are typically extracted from the stimulation software used, but we will use hardcoded values in this example.

# fmt: off
onsets = [
    0.0,   2.4,   8.7,   11.4,  15.0,  18.0,  20.7,  23.7,  26.7,  29.7, # noqa
    33.0,  35.4,  39.0,  41.7,  44.7,  48.0,  56.4,  59.7,  62.4,  69.0, # noqa
    71.4,  75.0,  83.4,  87.0,  89.7,  96.0,  108.0, 116.7, 119.4, 122.7, # noqa
    125.4, 131.4, 135.0, 137.7, 140.4, 143.4, 146.7, 149.4, 153.0, 156.0,
    159.0, 162.0, 164.4, 167.7, 170.4, 173.7, 176.7, 188.4, 191.7, 195.0,
    198.0, 201.0, 203.7, 207.0, 210.0, 212.7, 215.7, 218.7, 221.4, 224.7,
    227.7, 230.7, 234.0, 236.7, 246.0, 248.4, 251.7, 254.7, 257.4, 260.4,
    264.0, 266.7, 269.7, 275.4, 278.4, 284.4, 288.0, 291.0, 293.4, 296.7,
]
# fmt: on

Associated trial types: these are numbered between 0 and 9, hence corresponding to 10 different conditions.

# fmt: off
trial_type_idx = [
    7, 7, 0, 2, 9, 4, 9, 3, 5, 9, 1, 6, 8, 8, 6, 6, 8, 0, 3, 4, 5, 8, 6,
    2, 9, 1, 6, 5, 9, 1, 7, 8, 6, 6, 1, 2, 9, 0, 7, 1, 8, 2, 7, 8, 3, 6,
    0, 0, 6, 8, 7, 7, 1, 1, 1, 5, 5, 0, 7, 0, 4, 2, 7, 9, 8, 0, 6, 3, 3,
    7, 1, 0, 0, 4, 1, 9, 8, 4, 9, 9
]
# fmt: on

We may want to map these indices to explicit condition names. For that, we define a list of 10 strings.

condition_ids = [
    "horizontal checkerboard",
    "vertical checkerboard",
    "right button press, auditory instructions",
    "left button press, auditory instructions",
    "right button press, visual instructions",
    "left button press, visual instructions",
    "mental computation, auditory instructions",
    "mental computation, visual instructions",
    "visual sentence",
    "auditory sentence",
]

trial_types = [condition_ids[i] for i in trial_type_idx]

We must also define a duration (required by BIDS conventions). In this case, all trials lasted one second, except for button response that are modeled as events with instantaneous duration (0 second).

null_duration_trials = [2, 3, 4, 5]
durations = []
for i in trial_type_idx:
    if i in null_duration_trials:
        durations.append(0)
    else:
        durations.append(1)

Form a pandas DataFrame from this information.

import pandas as pd

events = pd.DataFrame(
    {
        "trial_type": trial_types,
        "onset": onsets,
        "duration": durations,
    }
)

Take a look at the new DataFrame

trial_type onset duration
0 mental computation, visual instructions 0.0 1
1 mental computation, visual instructions 2.4 1
2 horizontal checkerboard 8.7 1
3 right button press, auditory instructions 11.4 0
4 auditory sentence 15.0 1
... ... ... ...
75 auditory sentence 284.4 1
76 visual sentence 288.0 1
77 right button press, visual instructions 291.0 0
78 auditory sentence 293.4 1
79 auditory sentence 296.7 1

80 rows × 3 columns



Export them to a tsv file.

from pathlib import Path

output_dir = Path.cwd() / "results" / "plot_write_events_file"
output_dir.mkdir(exist_ok=True, parents=True)
tsvfile = output_dir / "localizer_events.tsv"
events.to_csv(tsvfile, sep="\t", index=False)
print(f"The event information has been saved to {tsvfile}")
The event information has been saved to /home/runner/work/nilearn/nilearn/examples/04_glm_first_level/results/plot_write_events_file/localizer_events.tsv

Optionally, the events can be visualized using the plot_event function.

import matplotlib.pyplot as plt

plot_event(events, figsize=(15, 5))
plt.show()
plot write events file
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/nilearn/glm/first_level/experimental_paradigm.py:142: UserWarning:

The following conditions contain events with null duration:
right button press, auditory instructions, right button press, visual instructions, left button press, auditory instructions, left button press, visual instructions.

Parametric modulation

We may want to modulate the way we model our events in our fMRI analysis. This type of parametric modulation can be done by adding a “modulation” column to the dataframe containing our events.

Here we will assume that when a trial is the same condition as the previous one, it will elicit a less intense response.

modulations = []
conditions_to_modulate = [
    "horizontal checkerboard",
    "vertical checkerboard",
    "mental computation, auditory instructions",
    "mental computation, visual instructions",
    "visual sentence",
    "auditory sentence",
]
for i, trial in enumerate(trial_types):
    if (
        i > 0
        and trial in conditions_to_modulate
        and trial == trial_types[i - 1]
    ):
        modulations.append(0.5)
    else:
        modulations.append(1)

modulated_events = pd.DataFrame(
    {
        "trial_type": trial_types,
        "onset": onsets,
        "duration": durations,
        "modulation": modulations,
    }
)

# Now lets plot the modulated and unmodulated events side by side.
plot_event([events, modulated_events], figsize=(15, 5))
plt.show()
plot write events file
/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/nilearn/glm/first_level/experimental_paradigm.py:142: UserWarning:

The following conditions contain events with null duration:
right button press, auditory instructions, right button press, visual instructions, left button press, auditory instructions, left button press, visual instructions.

/opt/hostedtoolcache/Python/3.12.4/x64/lib/python3.12/site-packages/nilearn/glm/first_level/experimental_paradigm.py:142: UserWarning:

The following conditions contain events with null duration:
right button press, auditory instructions, right button press, visual instructions, left button press, auditory instructions, left button press, visual instructions.

A 'modulation' column was found in the given events data and is used.

References

Total running time of the script: (0 minutes 3.223 seconds)

Estimated memory usage: 10 MB

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