Wanna try ncRFs for your data???
Tutorial on real dataset is now available here.
The magnetoencephalography (MEG) response to continuous auditory stimuli, such as speech, is commonly described using a linear filter, the auditory temporal response function (TRF). Though components of the sensor level TRFs have been well characterized, the cortical distributions of the underlying neural responses are not well-understood. In our recent work, we provide a unified framework for determining the TRFs of neural sources directly from the MEG data, by integrating the TRF and distributed forward source models into one, and casting the joint estimation task as a Bayesian optimization problem. Though the resulting problem emerges as non-convex, we propose efficient solutions that leverage recent advances in evidence maximization. For more details please refer to [1], [2].
This repository contains the implementation of our direct TRF estimation algorithm in python (version 3.6 and above).
Eelbrain (Download/ Installation Instructions)
After successfully installing Eelbrain, one can follow either of the methods to install different versions the repository.
The following comment will install the latest released version:
pip install ncrf
Note: yet to be released on PyPI yet.
After cloning the GitHub repository, install using pip:
git clone https://github.com/proloyd/neuro-currentRF.git
cd neuro-currentRF
pip install -e .
and there you go!
If the last command throws any error, Cython is most likely not installed. Then try installing Cython by following
command:
conda install cython
then enter the last command again, and that should do the trick.
run
model = fit_ncrf(meg, stim, lead_field, noise, mu='auto', tstop=1.0, nlevels=2, n_splits=3, normalize='l1')to perform a 3-fold cross-validation and then construct the model for 1s long TRF with the regularization weight among the given range that gives least generalization error. The signature of the meg and stim could be as follows:
[ megA (case, sensor, time), megB (case, sensor, time), ...]
[[featA1 (case, time), featA2 (case, time), ...], [featB1 (case, time), featB2 (case, time), ...], ...]
For more options, please look at the docstring. The learned TRF, h can be retrieved using:
h = model.hWe applied the algorithm on a subset of MEG data collected from 17 adults (aged 18-27 years) under an auditory task described in the papers. In short, during the task, the participants listened to 1 min long segments from an audio-book recording of The Legend of Sleepy Hollow by Washington Irving, narrated by a male speaker. We consider localizing the TRFs using a total of 6 min data from each participant. MNE-python 0.14 was used in pre-processing the raw data to automatically detect and discard flat channels, remove extraneous artifacts, and to band-pass filter the data in the range 1 - 80 Hz. The six 1 min long data epochs were then down-sampled to 200 Hz. As the stimulus variable, we used the speech envelope reflecting the momentary acoustic power, by averaging the auditory spectrogram representation (generated using a model of the
auditory periphery) across the frequency bands, sampled at 200 Hz. A volume source space for individual subjects was defined on a 3D regular grid with a resolution of 7 mm in each direction. The lead-field matrix was then computed by placing free orientation virtual dipoles on the resulting 3322 grid points. The consistent components of our estimated 1 s-long 3D TRFs across all 17 subjects looks like following:
Isn't that cool? Do expect to see something like that with any other source localization method? If you realize you could use this method on your data, please feel free to use the codes. You can reach me at proloy@umd.edu if you have any issues with the codes. And don't forget to go over the papers/ posters before applying the algorithm.
Since this is a dev version, and I will be adding more functionality over time, so feel free to ask me to add any other functionality, or report if anything is broken. If you want to contact the author, you can do so under the following email-address: proloy@umd.edu
This repository is free software, covered by the Apache License 2.0. However since they have been mainly developed for academic use, the author would appreciate being given academic credit for it. Whenever you use this software to produce a publication or talk, please cite the following references.
[1] P. Das, C. Brodbeck, J. Z. Simon, B. Babadi, Direct Cortical Localization of the MEG Auditory Temporal Response Function: a Non-Convex Optimization Approach; Proceedings of the 47th Annual Neuroscience Meeting (SfN 2018), Nov. 2-7, San Diego, CA.
[2] P. Das, C. Brodbeck, J. Z. Simon, B. Babadi, Cortical Localization of the Auditory Temporal Response Function from MEG via Non-Convex Optimization; 2018 Asilomar Conference on Signals, Systems, and Computers, Oct. 28–31, Pacific Grove, CA (invited)
