Abstract
Two main approaches in exploring causal relationships in biological systems using time-series data are the application of Dynamic Causal model (DCM) and Granger Causal model (GCM). These have been extensively applied to brain imaging data and are also readily applicable to a wide range of temporal changes involving genes, proteins or metabolic pathways. However, these two approaches have always been considered to be radically different from each other and therefore used independently. Here we present a novel approach which is an extension of Granger Causal model and also shares the features of the bilinear approximation of Dynamic Causal model. We have first tested the efficacy of the extended GCM by applying it extensively in toy models in both time and frequency domains and then applied it to local field potential recording data collected from in vivo multi-electrode array experiments. We demonstrate face discrimination learning-induced changes in inter- and intra-hemispheric connectivity and in the hemispheric predominance of theta and gamma frequency oscillations in sheep inferotemporal cortex. The results provide the first evidence for connectivity changes between and within left and right inferotemporal cortexes as a result of face recognition learning.
Highlights
In order to exploit the full potential of high-throughput data in biology we have to be able to convert them into the most appropriate framework for contributing to knowledge about how the biological system generating them is functioning
Analysis of the relative causal contributions of low and high frequency oscillations reveals that prior to learning, theta activity is more predominant in right than in left hemisphere processing, and that learning reduces this so that high frequency oscillations gain more control
The results are obtained based upon a way to combine aspects of both the Granger and Dynamic Causal Models, which can be used to establish significant causal relations in both time and frequency domains and applied to local field potential recordings from multiple (64 channel) electrodes implanted in the inferotemporal cortex of both sides of the brain in sheep in order to establish changes in causal connections within and between the two hemispheres as a result of learning to discriminate visually between pairs of faces
Summary
In order to exploit the full potential of high-throughput data in biology we have to be able to convert them into the most appropriate framework for contributing to knowledge about how the biological system generating them is functioning This process is best conceptualized as first building a nodal network derived from empirically derived knowledge of the biological structures and molecules involved (nodes) and secondly to use computational-based steps to discover the nature, dynamics and directionality of connections (directed edges) between them. A typical problem cited is the so called ‘‘Simpson paradox’’ in which the successes of groups seem reversed when the groups are combined This demonstrates the ambiguity that can result in determining causal relationships based only on frequency data. There is still no accepted unified way to tackle this issue
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