Pyramidal cell types drive functionally distinct cortical activity patterns during decision-making

Understanding how cortical circuits generate complex behavior requires investigating the cell types that comprise them. Functional differences across pyramidal neuron (PyN) types have been observed within cortical areas, but it is not known whether these local differences extend throughout the cortex, nor whether additional differences emerge when larger-scale dynamics are considered. We used genetic and retrograde labeling to target pyramidal tract, intratelencephalic and corticostriatal projection neurons and measured their cortex-wide activity. Each PyN type drove unique neural dynamics, both at the local and cortex-wide scales. Cortical activity and optogenetic inactivation during an auditory decision task revealed distinct functional roles. All PyNs in parietal cortex were recruited during perception of the auditory stimulus, but, surprisingly, pyramidal tract neurons had the largest causal role. In frontal cortex, all PyNs were required for accurate choices but showed distinct choice tuning. Our results reveal that rich, cell-type-specific cortical dynamics shape perceptual decisions.


Statistics
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Data analysis
Data analysis code was developed by B. Saenz and J. DeAngelo using Python 3.9. Full code available via Zenodo: https://doi.org/10.5281/ zenodo.7262015. Details: Code for Random Forest analysis was modified from open-source "Random Forest in Python" tutorial by Towards Data Science. Distance to the optimal sinking point was calculated using a weighted distance transform (path-finding algorithm, modified from code by Omar Richardson (2020). Citation: Richardson, O. weighted_distance_transform, <https://github.com/0mar/weighted-distance-transform> (2020).
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Data
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March 2021
Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection. All studies must disclose on these points even when the disclosure is negative.

Study description
We developed a technoeconomic model of costs and net emissions associated with farming seaweed for climate benefits at scales relevant to the global carbon budget. Given the large uncertainty in the technoeconomic model parameters, we performed a Monte Carlo analysis, sampling uniformly across parameter ranges to produce 5,000 unique simulations of cost per ton of CO2 sequestered or avoided in any given location for each seaweed nutrient scenario. We then assessed the 5th, 25th, 50th, 75th, and 95th percentile Monte Carlo results globally, as well as the lowest-cost 1% areas for each scenario. Finally, we performed a LightGBM analysis of model parameters for the lowest-cost regions to determine variable importance across the Monte Carlo simulations.  (2021) interpolated to our 1/12-degree grid resolution. * Distance to nearest port: We use the Distance from Port V1 dataset from Global Fishing Watch (https://globalfishingwatch.org/ data-download/datasets/public-distance-from-port-v1) interpolated to our 1/12-degree grid resolution. * Significant wave height: We use data for annually-averaged significant wave height from the European Center for Medium-range Weather Forecasts (ECMWF) interpolated to our 1/12-degree grid resolution. * Ocean depth: We use data from the General Bathymetric Chart of the Oceans (GEBCO). * Shipping lanes: We use data of Automatic Identification System (AIS) signal count per ocean grid cell, interpolated to our 1/12degree grid resolution. We define a major shipping lane grid cell as any cell with >2.25 x 10^8 AIS signals, a threshold that encompasses most major trans-Pacific and trans-Atlantic shipping lanes as well as major shipping lanes in the Indian Ocean, North Sea, and coastal routes worldwide. * Marine Protected Areas (MPAs): We use data from the World Database on Protected Areas (WDPA) and define a MPA as any ocean WDPA >20 km^2.

Sampling strategy
Due to the lack of existing data for the technoecomoic variables in our model, we assumed a uniform distribtion across uncertainty ranges and sampled randomly across those ranges for each variable. Seaweed yield was sampled from a normal distribution according to uncertainty analysis from Arzeno-Soltero et al.

Data collection
Data files were recorded and saved as netCDF files throughout model runs, and statistical metadata was saved as .csv files. Data was compiled and analyzed by J. DeAngelo, using code developed by B. Saenz and J. DeAngelo (Python version 3.9).
Timing and spatial scale We produced 5,000 simulations for each seaweed nutrient scenario for our Monte Carlo analysis. Each simulation represents the potential cost per ton of carbon sequestered or avoided by either growing and sinking seaweed or using seaweed to replace emissions-intensive products. These costs assume that the maximum seaweed biomass could be grown annually in the G-MACMODS seaweed growth model (Arzeno-Soltero et al., https://doi.org:https://doi.org/10.31223/X52P8Z). G-MACMODS is a global, spatiallyexplicit seaweed growth model; our technoeconomic model is also global and spatially-explicit in assessing the costs and net emissions associated with farming the seaweed represented by G-MACMODS.