Interactions between brain signals are often measured in terms of information flow . However, when solving the inverse problem -characterizing the structure behind a specific functional link- there are many possibilities with very different consequences. Excitation and inhibition, for instance, may each produce a causal dependence by coordinating neuronal populations, but inhibitory connections do not transmit information packages . Here, we introduce the concept of synaptic Granger causality (sGC), an extension of the well-known Granger causality approach that quantifies the ratio of excitatory/inhibitory projections involved in a functional link on the basis of the analysis of time series constructed using autoregressive models . The method is validated by using the membrane potential obtained from numerical simulations of neuronal motifs that mimic extracellular brain activity, similar to the experimentally measured local field potential. When excitatory and inhibitory synapses are not mixed, the method correctly predicts the type of synapses that participate in the signaling. Moreover, when both inputs project on the same neuron or population, the sGC estimates the proportion of both types of interaction . Our results suggest that sGC allows for a better inference on the anatomical structure underlying effective connectivity and its role in the information process .
The specific connectivity of a neuronal network is reflected in the dynamics of the signals recorded on its nodes. The analysis of how the activity in one node predicts the behaviour of another gives the directionality in their relationship . However, each node is composed of many different elements which define the properties of the links. For instance, excitatory and inhibitory neuronal subtypes determine the functionality of the connection. Classic indexes such as the Granger causality (GC) quantifies these interactions, but they do not infer into the mechanism behind them . Here, we introduce an extension of the well-known GC that analyses the correlation associated to the specific influence that a transmitter node has over the receiver. This way, the G-causal link has a positive or negative effect if the predicted activity follows directly or inversely, respectively, the dynamics of the sender . The method is validated in a neuronal population model, testing the paradigm that excitatory and inhibitory neurons have a differential effect in the connectivity. Our approach correctly infers the positive or negative coupling produced by different types of neurons . Our results suggest that the proposed approach provides additional information on the characterization of G-causal connections, which is potentially relevant when it comes to understanding interactions in the brain circuits .
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Interactions between brain signals are often measured in terms of information flow
The specific connectivity of a neuronal network is reflected in the dynamics of the signals recorded on its nodes. The analysis of how the activity in one node predicts the behaviour of another gives the directionality in their relationship
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when solving the inverse problem -characterizing the structure behind a specific functional link- there are many possibilities with very different consequences. Excitation and inhibition, for instance, may each produce a causal dependence by coordinating neuronal populations, but inhibitory connections do not transmit information packages
each node is composed of many different elements which define the properties of the links. For instance, excitatory and inhibitory neuronal subtypes determine the functionality of the connection. Classic indexes such as the Granger causality (GC) quantifies these interactions, but they do not infer into the mechanism behind them
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the concept of synaptic Granger causality (sGC),
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Granger causality approach that quantifies the ratio of excitatory/inhibitory projections involved in a functional link on the basis of the analysis of time series constructed using autoregressive models
GC that analyses the correlation associated to the specific influence that a transmitter node has over the receiver. This way, the G-causal link has a positive or negative effect if the predicted activity follows directly or inversely, respectively, the dynamics of the sender
R
by using the membrane potential obtained from numerical simulations of neuronal motifs that mimic extracellular brain activity, similar to the experimentally measured local field potential. When
in a neuronal population model, testing the paradigm that
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synapses are not mixed, the method correctly predicts the type of synapses that participate in the signaling. Moreover, when both inputs project on the same neuron or population, the sGC estimates the proportion of both types of interaction
neurons have a differential effect in the connectivity. Our approach correctly infers the positive or negative coupling produced by different types of neurons
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sGC allows for a better inference on the anatomical structure underlying effective connectivity and its role in the information process
the proposed approach provides additional information on the characterization of G-causal connections, which is potentially relevant when it comes to understanding interactions in the brain circuits