Project code for extracting neural population timescale from field potential data (LFP, ECoG, etc).
Paper is now published in eLife (here.)
Gao, R., van den Brink, R. L., Pfeffer, T., & Voytek, B. (2020). Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture. eLife, 9, e61277.
Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture.
In this project, we developed a method for measuring neuronal timescales from neural field potential data via spectral parameterization, and apply it to invasive ECoG data from humans and macaques. We find a gradient of neuronal timescales that increase from sensory/motor towards association brain regions, and further combine several other brain-wide structural, gene expression, and behavioral datasets to dissect the physiological factors that underly variations in timescale across the brain, as well as its change during behavior and aging.
./echo_utils.py contains all the python helper functions used for subsequent analyses and visualizations.
./scripts/ contains analysis scripts that compute and parameterize the PSDs in each ECoG database.
./data/ contains intermediate data tables and diagnostic plots.
./notebook/ contains Jupyter notebook that explains the project and paper in its entirety, and produces the figures seen in the publication. See Table 2 in the paper for the notebook-figure correspondence.
Surface projection of T1w/T2w and gene expression data is done using Rudy's repository here.