Abstract
Effective connectivity measures the pattern of causal interactions between brain regions. Traditionally, these patterns of causality are inferred from brain recordings using either non-parametric, i.e., model-free, or parametric, i.e., model-based, approaches. The latter approaches, when based on biophysically plausible models, have the advantage that they may facilitate the interpretation of causality in terms of underlying neural mechanisms. Recent biophysically plausible neural network models of recurrent microcircuits have shown the ability to reproduce well the characteristics of real neural activity and can be applied to model interacting cortical circuits. Unfortunately, however, it is challenging to invert these models in order to estimate effective connectivity from observed data. Here, we propose to use a classification-based method to approximate the result of such complex model inversion. The classifier predicts the pattern of causal interactions given a multivariate timeseries as input. The classifier is trained on a large number of pairs of multivariate timeseries and the respective pattern of causal interactions, which are generated by simulation from the neural network model. In simulated experiments, we show that the proposed method is much more accurate in detecting the causal structure of timeseries than current best practice methods. Additionally, we present further results to characterize the validity of the neural network model and the ability of the classifier to adapt to the generative model of the data.
Highlights
To understand how the human brain works, it is fundamental to study the interactions among its regions and not just their individual behavior
In the remaining part of the article, we first describe the proposed neural network model derived from Mazzoni et al (2008), that we extended by adding connections between microcircuits rather than studying single isolated microcircuits as in the original model
In order to show the ability of the proposed supervised method to adapt to the given generative model, we train the classifier on the multivariate autoregressive (MAR) dataset and show that, in this case, it behaves to Granger Causality Analysis (GCA)
Summary
To understand how the human brain works, it is fundamental to study the interactions among its regions and not just their individual behavior (see Bullmore and Sporns, 2009). The pattern of causal interactions between the temporal behavior of brain regions is called effective connectivity (see Sporns, 2007; Friston, 2011). Inferring such pattern from observations, e.g., from functional neuroimaging data, is a challenging task. Model-based methods define a generative model of the dynamics of the neural system and estimate the parameters of the model, e.g., the pattern of causal interactions, from. The most well-known among such models are the multivariate autoregressive (MAR) model (Granger, 1969) and the dynamic causal model (DCM) for functional MRI data (Friston et al, 2003)
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