The provided code is part of a computational neuroscience study that models neural connectivity and information transfer between different brain regions. This is achieved through the calculation of Transfer Entropy (TE), which is a measure of the directed information transfer between time series. Here's the biological basis of what this code is trying to model:
The code is analyzing data from three distinct Regions of Interest (ROIs) labeled as 'X', 'Y', and 'Z'. Each ROI likely represents a distinct brain area or a neuronal population where neural recordings have been obtained. In biological terms, this could reflect the activity of different cortical areas, or subcortical structures known to interact during specific cognitive tasks or physiological processes.
The term eeg, used within the data loading segment, suggests that the biological data considered here is electroencephalography (EEG) data. EEG is a non-invasive method to record electrical activity of the brain, often capturing the aggregate synaptic activity in underlying neuronal populations.
TE is a non-linear statistical measure used to identify directional information flow between neural signals, providing insights into causal interactions. In the biological sense, this could be used to discern how information is transferred from one brain area (e.g., ROI 'X') to another (e.g., ROI 'Y'), how these interactions change over time, and under different conditions.
The parameters cfgTEP.predicttime_u, cfgTEP.predicttimemax_u, and cfgTEP.predicttimemin_u specify the time intervals for prediction, reflecting the biological time scales at which these neural interactions are being assessed. This is crucial as different cognitive or sensory processes might operate at different time scales.
The use of Interaction Delay Reconstruction hints at exploring how delays in neural transmission between brain regions affect information transfer. Biologically, such delays could correspond to the time it takes for neural signals to propagate across synaptic connections or larger brain network pathways.
The cfgTESS.surrogatetype='trialshuffling' suggests the use of surrogate data techniques for validating the statistical significance of the TE findings, controlling for the role of noise and establishing that observed interactions are not due to chance.
Finally, the part concerning graph analysis (TEgraphanalysis) refers to the examination of the overall network that these ROIs form. Biologically, this could inform about functional or effective connectivity patterns, providing insights into how groupings of brain areas interact as a cohesive network, potentially relevant to cognitive functions, sensory processing, or disease states.
In summary, the code is part of a computational approach focused on understanding the directed interactions and temporal dynamics of neural signals recorded from different brain regions, providing a framework to study brain connectivity and communication pathways at a systems neuroscience level.