Source Reconstructed MEG Data for Adaptive Circuit Dynamics Across Human Cortex During Evidence Accumulation in Changing Environments
This dataset contains source reconstructed MEG data for:
Murphy PR, Wilming N, Hernandez Bocanegra DC, Prat
Ortega G & Donner TH (2021). Adaptive circuit dynamics across human cortex during
evidence accumulation in changing environments. Nature
Neuroscience. Online ahead of print.
Each "*source_reconstructions*" .zip contains files for trial onset-aligned epochs (full-length trials composed of 12 evidence samples only), separately for low (1-35 Hz in steps of 1 Hz; "LF") and high (36-160 Hz in steps of 4 Hz; "HF") frequency TFR decompositions. Furthermore, each session is spread over a number of files that contain 100 trials each. Files from one epoch type can be safely concatenated in pandas.
Individual files can be read by using `pandas.read_hdf`. This will return a table that contains individual ROIs as columns and a multi-index that labels each data point. Specifically, the index contains a trial identifier ('trial'), a time identifier ('time', seconds relative to trial onset), an identifier for the TFR settings ('est_key') and a frequency identifier ('est_val').
See https://github.com/DonnerLab/2021_Murphy_Adaptive-Circuit-Dynamics-Across-Human-Cortex/tree/main/source_reconstruct/pymeg for code that makes and further processes datasets of this form. They are made with lcmv_peter.py and an example of further processing is sr_agg_parallel.py (in this case, aggregation of reconstructed over vertices within specified ROIs).
Each “*sr_behav.zip” contains behavioural (‘choices’), task (sample
locations: ‘stimIn’; change-point positions: ‘pswitch’; generative
distributions at end of each trial: ‘fdist’; and generative distributions per
sample position: ‘distseq’) and minimal eye-tracking data (‘pupil’, ‘Xgaze’, ‘Ygaze’,
all from only 0.57 s following sample onset) from the same trials in the source
reconstructed datasets. Use the ‘trialID’ variable in combination with the ‘trial’
identifier in the source reconstructed datasets to align trials.