Datasets and scripts for "Attractor dynamics of a whole-cortex network model predicts emergence and structure of fMRI co-activation patterns in the mouse brain"

Description

We provide datasets and scripts for reproducing the results of "Attractor dynamics of a whole-cortex network model predicts emergence and structure of fMRI co-activation patterns in the mouse brain". The datasets include the 34 x 34 anatomical connectivity of the mouse brain between excitatory populations, the empirical fMRI time series of 15 mice, and their organization into 6 co-activation patterns (CAPs). The Python script “Empirical_VS_Model.py” implements 3 whole-cortex models with their corresponding best-fit parameters (our full model with directed connectivity and non-linear neural dynamics, an undirected model with non-linear dynamics but undirected anatomical connections, and a linear model with directed anatomical connections but linear activation function). The script uses those models to calculate network statistics averaged over long-time scales (e.g. the functional connectivity matrix), and it finally compares them to the corresponding empirical statistics of the real mouse brain. The script also plots the figures showing the balance between the excitatory and inhibitory currents in our full model. To conclude, the Python script “Empirical_VS_Model.py” implements our full best-fit model with directed connectivity and non-linear neural dynamics, and it uses the model to calculate the topography and probability of occupancy of its spiking-activity attractors. The script also contains our mapping algorithm, which reconstructs the probability of occupancy of the attractors from the empirical data and compares it with the model distribution. Then the script uses linear combinations of the model attractors to reconstruct the topography of CAPs, and it finally compares the model topography to the empirical one obtained from the real mouse brain.

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