Abstract
This review considers state-of-the-art analyses of functional integration in neuronal macrocircuits. We focus on detecting and estimating directed connectivity in neuronal networks using Granger causality (GC) and dynamic causal modelling (DCM). These approaches are considered in the context of functional segregation and integration and--within functional integration--the distinction between functional and effective connectivity. We review recent developments that have enjoyed a rapid uptake in the discovery and quantification of functional brain architectures. GC and DCM have distinct and complementary ambitions that are usefully considered in relation to the detection of functional connectivity and the identification of models of effective connectivity. We highlight the basic ideas upon which they are grounded, provide a comparative evaluation and point to some outstanding issues.
Highlights
Several dichotomies have proved useful in thinking about analytic approaches to functional brain architectures
Functional connectivity refers to the statistical dependence or mutual information between two neuronal systems, while effective connectivity refers to the influence that one neural system exerts over another [2,3]
In conclusion, Granger causality (GC) and dynamic causal modelling (DCM) are complementary: both model neural interactions and both are concerned with directed causal interactions
Summary
Several dichotomies have proved useful in thinking about analytic approaches to functional brain architectures. Electrophysiological measurements support richer models of neuronal dynamics in DCM that comprise sources with laminar specific mixtures of neuronal populations These have evolved from kernel-based models [7] that use postsynaptic convolution operators to describe responses at excitatory and inhibitory synapses to conductance-based models, where particular ion channels can be modelled and identified [39]. Pros and cons Clearly, GC and DCM have complementary aims and strengths: GC can be applied directly to any given timeseries to detect the coupling among empirically sampled neuronal systems This can provide useful insights into the system’s dynamical behaviour in different conditions or in spontaneously active ‘resting’ states. In DCM for electrophysiological data, the models will potentially allow the characterisation of receptor-specific contributions to brain connectivity, which may be important in a pharmacological and clinical setting [11]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
