LFP and oscillations-what do they tell us?

Abstract

This review surveys recent trends in the use of local field potentials-and their non-invasive counterparts-to address the principles of functional brain architectures. In particular, we treat oscillations as the (observable) signature of context-sensitive changes in synaptic efficacy that underlie coordinated dynamics and message-passing in the brain. This rich source of information is now being exploited by various procedures-like dynamic causal modelling-to test hypotheses about neuronal circuits in health and disease. Furthermore, the roles played by neuromodulatory mechanisms can be addressed directly through their effects on oscillatory phenomena. These neuromodulatory or gain control processes are central to many theories of normal brain function (e.g. attention) and the pathophysiology of several neuropsychiatric conditions (e.g. Parkinson's disease).

Figure 1

This schematic illustrates the link between the parameters of a dynamic causal model—such as effective connectivity or synaptic efficacy—and the spectral signatures of these coupling parameters. Left panel: state space or dynamic causal model of neuronal states x generating observed data y. The equations at the top represent the equations of motion and (static) observer function generating data. These dynamics are driven by random fluctuations v, where w represents measurement noise. The example shown here is perhaps the simplest; with recurrently and reciprocally (and linearly) coupled excitatory (black) and inhibitory (red) neuronal populations. Right panel: this illustrates the corresponding spectral behaviours expressed in terms of spectral densities. The top equation shows that the observed spectral density g(ω) is a mixture of signal generated by applying transfer functions K(ω) to the spectral density of the random fluctuations (assumed to be the identity matrix here for simplicity) plus a component due to measurement noise. Crucially, the transfer functions and ensuing spectral density are determined by the eigenvalues of the model's connectivity (shown on the lower left). In turn, the eigenvalues are relatively simple functions of the connectivity. The resulting (Lorentzian) spectral density is centred on the imaginary part of the eigenvalue and corresponds to the connection strength of reciprocal connections. The dispersion (full width half maximum) of the spectral peak is determined by the recurrent connectivity. This example shows how connectivity parameters can be expressed directly and intuitively in measured spectra. Furthermore, peristimulus time-dependent changes in the spectral peak disclose stimulus-induced changes in the strength of reciprocal connectivity (i.e. short-term changes in synaptic efficacy of the sort that could be mediated by NMDA receptors)—as illustrated on the lower right. In practice, dynamic causal models are much more complicated than the above example; they usually consider distributed networks of sources with multiple populations within each source and multiple states within each population—with non-linear coupling.