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Dancing neurons, complex beats

Have you ever listened to Olodum? Imagine some 20 Brazilian kids with different types of drums and unlimited energy. Large timbales make your ears vibrate before metal drums join in with diverse intermingling beats. You will hardly be able to discern all the overlapping rhythms, yet you cannot resist dancing. In a recent issue of The Journal of Physiology, van Aerde & colleagues (2009) show that output neurons of the rat prefrontal cortex may experience something similar. Their activity is entrained by two different network oscillations with rapid transitions and overlapping phases. 
 
Why should we bother? Cortical network oscillations correlate with cognitive state, as already seen by Hans Berger in the early days of EEG recording (Berger, 1934). What Berger modestly and wisely called parallelism between psychology and electrophysiology is still a major driving force for neurophysiologists. Network oscillations bind central neurons into highly ordered patterns of activity and, hence, their analysis may eventually establish causal relationships with psychology, going beyond mere parallelism. This may be a crucial step towards a serious cellular pathophysiology of psychiatric diseases (Uhlhaas & Singer, 2006). 
 
Most brain regions express different oscillations, raising important questions. Are they mutually exclusive? How are state transitions induced? What is the underlying network architecture? How are selected cells entrained? How are the remaining cells silenced? How is excitation–inhibition balance maintained? These questions require analysis of small networks in vitro–‘small’ meaning that we still see their composition from single neurons, ‘network’ pointing towards the coherent action of neuronal assemblies. Van Aerde et al. (2009) used the advantages of brain slices which combine preserved local connectivity with high experimental control. They induced oscillations with carbachol, consistent with the dense cholinergic innervation of frontal cortices. Recording with multi-electrode arrays and cutting slices at different layers allowed them to identify two distinct oscillating microcircuits. Pharmacological interventions revealed an important role of GABAergic feedback inhibition – a common principle in oscillating networks (Mann & Paulsen, 2007). Finally, single-cell recordings showed how pyramidal cells and interneurons are entrained by synaptic potentials from both oscillating networks. Such analyses can only be performed in reduced preparations. However, methodological and conceptual links towards systems physiology are rapidly growing thanks to modern genetic and imaging techniques, including new optogenetic tools. 
 
There are different types of interactions between oscillating networks, including the strong synchronization between harmonic oscillators (with integer ratios between cycle times) and ‘nesting’ of high frequencies within slow oscillations. Non-harmonic oscillators show complex superpositions like the recently discovered ‘frequency concatenation’ in somatosensory cortex. Here, two convergent networks entrain neurons to fire at the sum of both individual cycle lengths (Roopun et al. 2008). The present pattern is different yet again: it requires synaptic interactions rather than the predominant electrical coupling in the Roopun model, and layer 5 neurons are simply driven by the prevailing oscillation at any time. This shows serious limitations of ‘canonical circuit’ models: we have to analyse each cortical region on its own, in close cooperation between physiologists, functional neuroanatomists and computational neuroscientists. There is a lot of work ahead! 
 
Present knowledge is strongly biased towards few model systems, especially single columns of primary sensory cortex. The pioneering work of van Aerde & coworkers (2009) reveals the cellular physiology of network oscillations in the prefrontal cortex. This evolutionary young association area is involved in executive control and in the pathophysiology of major psychiatric diseases. Its specific network properties are, therefore, extremely important, and the present work may already have interesting systemic consequences. The output from layer 5 goes mainly to the striatum and hypothalamus while layer 6 neurons project to sensory integration centres of the mediodorsal thalamus. The authors therefore suggest that the two patterns could differentially affect motor behaviour and arousal (layer 5) or attention towards sensory cues (layer 6). To test this, we need more data on differential recruitment of neuronal subpopulations in layers 5 and 6. Most of all, we need selective tools for interactions with both sub-networks in vivo. 
 
Along this route we may produce more and more educated guesses about mechanisms of psychiatric diseases and new therapeutic strategies. If things go well, we may reach the expertise of a good car mechanic who understands mechanisms and failures of engines and gearboxes at exactly the required macroscopic system level. In contrast to psychopharmacologists, mechanics do not treat ‘imbalances’ between different parts of the car by systemic application of cogwheels or metal powder. Analysis of cortical oscillations may help us to develop similar precisely tailored therapies for dysfunctional networks. Human beings are not machines, and the brain is no mechanical device (importantly, it is also no computer – it is a brain). Nevertheless, neuronal network mechanisms constitute important restraints and conditions for brain functions. We are just beginning to understand how interacting networks create the highly diverse, dynamically changing patterns of cortical activity. ‘Listening’ to these multiple patterns within diverse brain circuits is not as effortless and entertaining as listening to Olodum – but it is certainly worth while!