Note
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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
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/himanshu/Desktop/nilearn_work/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()
/home/himanshu/Desktop/nilearn_work/nilearn/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.
warnings.warn(
References#
Total running time of the script: (0 minutes 2.324 seconds)
Estimated memory usage: 9 MB
