Author: Mark Alexander Henney
Principle Investigator: Robert Oostenveld
Collaborators: Henrik Hansen, Marcus Carstensen, Kristoffer Madsen, Eelke Spaak
This repository contains the files required to run the experiment, currate the data, and run the analyses.
MEG-AHAT is run at the Donders Center for Cognitive Neuroimaing (DCCN: PPM ethical committee code: 3031001.04) in collaboration with the Technical University of Denmark and OptoCeutics ApS (OC). It investigates the effects of 40 Hz visible and invisible spectral flicker (see Carstensen et. al. 2020) on the brain during a visual attention (VA) task and a non-visual working memory (WM) task with simultaneous megnetoencephalography (MEG).
See also the DCCN project proposal slides.
For the purpose of transparency, interpretability, and reproducibility, this project aims to conduct data analysis on BIDS-compliant data from the get-go. Thus, the source data is converted to BIDS prior to data analysis. However, the conversion takes place in two steps to incrementally enrich the data structure.
Data analysis is conducted solely on the completely enriched data set (raw2 below).
The project pilot-data exists in several versions that can all be accessed in the MEG-AHAT
project directory on the DCCN cluster under /project/3031004.01/pilot-data. These versions
include:
- Source data (
/source/) contains the data files as they were obtained from their respective recording devices and includes MEG scans, simultaneous eye-tracking and presentation log-files, polhemous positional data, and MRI scans. - First iteration raw data (
/raw1/) contains a BIDS-compliant version with minimal changes. One implication of this is that events are not yet alligned, as the recorded behavioural data is not set on a time axis at the point of recording. - Second iteration raw data (
/raw2/) contains a BIDS-compliant version of the data in which the behevioural data recorded along with MEG has been moved to the /meg directory, and MEG, eyetrack, and behavioural events are enriched and aligned in time.
Converting data between versions is non-trivial and requires both commonly used public
tools and custom scripts. This projects uses the FieldTrip tool data2bids
and custom matlab scripts.
Source data is converted to raw1 using the
/data_curration/convert_source_to_raw1.m script.
Raw1 data is converted to raw2 using the
/data_curration/convert_raw1_to_raw2.m script.
Currently, eyetracking data is not covered by BIDS, but is added with the BIDS extension proposal BEP020.
With this change, eyetracker data is allocated to the eyetrack/ scans directory and has the _eyetrack suffix.
pilot-data
|-- raw1
| |-- dataset_description.json
| |-- participants.tsv
| `-- sub-099
| |-- ses-001
| | |-- anat
| | |-- beh
| | |-- eyetrack
| | |-- meg
| | `-- sub-099_ses-001_scans.tsv
| |-- ses-002
| | |-- beh
| | |-- eyetrack
| | |-- meg
| | `-- sub-099_ses-002_scans.tsv
| `-- sub-099_sessions.tsv
`-- source
`-- sub-099
|-- ses-001
| |-- 099_1.ds
| |-- behaviour
| |-- pilot001_3031000.01_20231212_01.ds
| |-- sub-099.pos
| |-- sub-099_ses-001-eyetracking.asc
| |-- sub-099_ses-001-eyetracking.edf
| `-- sub-20231212T163000
`-- ses-002
|-- 099_2.ds
|-- behaviour
|-- pilot002_3031000.01_20231214_01.ds
|-- sub-099_ses-002-eyetracking.asc
`-- sub-099_ses-002-eyetracking.edf
Manual trial rejection takes place at the point of 40 Hz Artefact Removal .
Trials are rejected based on excessive variance using ft_rejectvisual.
The handling of trial rejection is done by /analysis/grab_trial_rej_history.m,
and books are kept in data/derivatives/trial_rejections.json.
Data analysis is done in Matlab2023b using a fork of FieldTrip with modifications to accomodate handling of eyetracking data: https://github.com/henneysq/fieldtrip/tree/bep020.
The overall data analysis pipeline is defined by the flowchart, in which .m and .py files are found in the /analysis/ directory:
graph TD;
A[raw2/meg]-->a(artefact_rejection.m)-->B[derivatives/meg];
B-->b(sensor_level_analysis.m)-->D[Sensor level results]
C[raw2/anat]-->c( make_headmodel.m)-->E[derivatives/anat]
d(beamformer.m)
B-->d-->F[Source level results]
E-->d
G[raw2/beh]-->e(inspect_beh.py)-->H[Behavioural results]
style a fill:#414b65,stroke:#01579b,stroke-width:2px,color:#FFFFFF,stroke-dasharray: 5 5
style b fill:#414b65,stroke:#01579b,stroke-width:2px,color:#FFFFFF,stroke-dasharray: 5 5
style c fill:#414b65,stroke:#01579b,stroke-width:2px,color:#FFFFFF,stroke-dasharray: 5 5
style d fill:#414b65,stroke:#01579b,stroke-width:2px,color:#FFFFFF,stroke-dasharray: 5 5
style e fill:#414b65,stroke:#01579b,stroke-width:2px,color:#FFFFFF,stroke-dasharray: 5 5
style A fill:#e2bf70,stroke:#333,stroke-width:4px
style B fill:#e2bf70,stroke:#333,stroke-width:4px
style C fill:#e2bf70,stroke:#333,stroke-width:4px
style D fill:#e2bf70,stroke:#333,stroke-width:4px
style E fill:#e2bf70,stroke:#333,stroke-width:4px
style F fill:#e2bf70,stroke:#333,stroke-width:4px
style G fill:#e2bf70,stroke:#333,stroke-width:4px
style H fill:#e2bf70,stroke:#333,stroke-width:4px
The physical experimental setup has current running through cables and LEDs in the magnetically shielded room which introduces a 40 Hz artifact, confounded with the 40 Hz brain signal.
graph LR
subgraph Magnetically shielded room
led1
led2
meg(MEG scanner)
subject(Subject brain)
end
driver1[40 Hz LED Driver]-- ~700 mA AC -->led1[Visual stimulator 1]
driver2[40 Hz LED Driver]-- ~700 mA AC -->led2[Visual stimulator 2]
led1-. 40 Hz visual stimulation .-> subject
led2-. 40 Hz visual stimulation .-> subject
led1-. 40 Hz magnetic field oscillation .-> meg
led2-. 40 Hz magnetic field oscillation .-> meg
subject-. 40 Hz magnetic field oscillation .-> meg
The 40 Hz cable noise is removed using artifact_rejection.m.
The experiments require visual presentations and participant feedback is implemented in Python with psychopy, and light stimulation with custom and proprietary equipment and drivers are provided by OC. The required software can be installed by:
pip install -r requirements.txt
Note that the proprietary libLEDController package is not publicly available.
Git is not installed on the DCCN MEG presentation PC, which is why the path to a (portable) Git executable can be set with test_util.set_git_executable_path. Some additional GitPython wrappers are also contained in test_util.
Two scripts are provided in the root directory to prepare, run, and save outputs for the experiments:
run_experiment_visual_attention.py and run_experiment_working_memory.py.
Update the SUB, SES, and RUN constants appropriately before executing the script.
Control of the two experiments, trial condition randomisation, and progress monitoring is implemented with the two classes
ExperimentManagerVA
and ExperimentManagerWM for the VA- and WM-experiments, respectively. Internally, they are built on a common parent class ExperimentManagerBase which handles most of the shared data- and experiment flow management.
Experimental settings such as durations, blocks, repetitions etc. are configured manually in experiment_va_settings.py and experiment_wm_settings.py for the VA and WM experiments, repectively. These are loaded by the manager classes.
Serial interface with the BITSI trigger system is controlled by the ExperimentTrigger class. It inherits from the Serial object as a thin wrapper with standard values specified in the DCCN BITSI documentation.
At instantiation of the ExperimentTrigger object, the BITSI is automatically programmed to trigger mode, and the trigger length set to 30 ms.
It implements a function send_trigger, which takes a single unsigned 8-bit integer, encodes it as an ASCII character, and writes this to the BITSI. The experiment managers use this to send distinct trigger codes for each event in a trial.
## Example of using the ExperimentTrigger
# Import the experiment trigger
from experiment_management.experiment_trigger import ExperimentTrigger as ET
et = ET() # The BITSI is reprogrammed by the `prepare_trigger` method
et.prepare_trigger()
et.send_trigger(65) # 65 is encoded as ASCII to 'A' and written to serialSeveral handy features are implemented to handle the experiments.
A custom root directory can be defined in which experiment data is stored. This can be done either at instantiation of the experiment objects, when making experiment data with make_and_save_experiment_data, when loading existing experiment data from a file with load_experiment_data - all via the root argument.
In the quick-start scripts, the root is specified at instantiation:
from pathlib import Path
from experiment_management.experiment_manager_va import VisualAttentionExperimentManager
SUB = 42
SES = 42
RUN = 42
ROOT = Path(__file__).parent / "data"
experiment_manager = VisualAttentionExperimentManager(
sub=SUB, ses=SES, run=RUN, root=ROOT
)Note that the root argument is required the first time calling make_and_save_experiment_data and load_experiment_data if it was not given at instantiation. In these cases, the root is subsequently stored in experiment_manager.root property.
In case of a crash during the experiment, it can be restarted at a customly selected trial. The experiment data
is saved to the >ROOT</data/ directory, and completed trials are indicates in the "completed" column.
To start an experiment after the already completed trials, first set the trial progress, then run the experiment:
trial_progress = 10 # NOTE: trial numbers are 0-indexed
experiment_manager.set_trial_progress(trial_progress)
experiment_manager.run_experiment()In this case, the experiment will run from trial 10 (the 11th).
When creating experiment data, the conditions are randomised uniquely based on the provided sub, ses, and run arguments at instantiation. In make_and_save_experiment_data, a custom seed can be provided via the seed argument, though this is not necessarly desireble.
A test suite is provided in tests/ based on the unittest-module available in the standard Python library. To run this locally, use:
python -m unittest discover tests/