Oscillatory synchrony

In the last decades, oscillatory synchronization between single neurons as well as neuronal populations has been found in many studies, suggesting oscillatory synchronization as an important mechanism involved in dynamic network coordination (Engel et al., 2001; Fries, 2009; Engel and Fries, 2010; Buzsáki and Wang, 2012). Oscillatory synchronization in

neuronal populations has been described in different distinct frequency bands, such as delta (1-4 Hz), theta (4-8 Hz), alpha (8-15Hz), beta (18-35Hz), and gamma (40-100Hz) (Engel and Fries, 2010), raising the question of whether these different frequency bands are coupled to distinct perceptual, cognitive, or motoric functions and whether they have different

anatomical origins.

The first specific oscillatory synchronization processes in the gamma-band (40-120Hz) were described in a series of anesthetized cat experiments, while animals were passively observing different visual stimuli and neuronal activity was recorded in V1. Gamma-band synchronizations between neurons as well as neuronal populations were found to be stimulus specific (Gray and Singer, 1989). A few years later, long range synchronizations (>2mm) between neurons in V1 of one hemisphere as well as between the two hemispheres were found to be almost always in the gamma-band (König et al., 1995). Experiments

conducted on awake monkeys that had to attend one of two visual stimuli on a monitor showed that neurons recorded in V4 within the receptive field of the attended stimulus showed increased gamma-band synchronization with their surrounding population (Fries et al., 2001). Interestingly, lower frequency synchronizations (< 17Hz) were also present, showing modulation in the opposite direction. In another study, the same modulation of gamma-band synchrony was found between FEF and V4 (Gregoriou et al., 2009). Recent experiments with monkeys performing a similar task revealed that populations of neurons in V1 within the receptive field of the attended stimulus were selectively synchronized in the gamma-band with populations in V4, while populations in V1 within the receptive field of the non-attended were not synchronized with V4 (Bosman et al., 2012). By using Granger causality, they could show that the direction of the synchronization was mainly from V1 to V4 and not the other way around, suggesting gamma-band synchronization as a bottom-up coordination mechanism in the visual system. The electrocorticogram grid arrays used in this study actually spanned large parts of the visual system, including parietal and frontal areas, allowing for a more systematic assessment of the information coordination across cortex.

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Granger causality analyses of the directed functional connectivity across 8 areas revealed that bottom-up information flow is coordinated by gamma-band and theta-band

synchronization, while the top-down information flow is coordinated by beta-band

synchronization, with V1 as the lowest and parietal area 7a as the highest in the hierarchy (Bastos et al., 2015).

The importance of beta-band synchronization originating from parietal areas was in fact shown many years earlier in a study were monkeys had to press and hold a hand

leverfor variable amounts of time. The investigators showed directed functional connectivity via Granger causality from several parietal areas to motor areas (Brovelli et al., 2004). In a study where monkeys had to perform a mixed delayed center-out reach and saccade task while neuronal activity was recorded from PRR, two important findings regarding beta-band synchronization were established (Scherberger et al., 2005). First, the level of beta-band synchronization of neurons with their surrounding population was selective for the

preparation of reach compared to saccade movements and, secondly, the level of beta-band synchronization was predictive of the task period, suggesting beta-band synchrony to be involved in intention or movement preparation coordination. These findings are well in line with the described results in the decision making section showing that beta-band

synchronization of neuronal populations selectively reflected the decision outcome or intention, which is presumably the same as movement preparation (Pesaran et al., 2008;

Haegens et al., 2011), as mentioned before. Also, findings from more recent studies where monkeys had to perform coordinated reach and saccade movements while single neuron and LFP activity were recorded simultaneously in PRR and LIP are in accordance with the idea that beta-band synchrony is involved in the coordination of movement intention or preparation (Dean et al., 2012; Wong et al., 2016). They found that only neurons

synchronized with the larger populations in both areas were predictive of the movement initiation of coordinated reach and saccade movements. However, low frequency

synchronizations of populations across areas seem to be involved in movement intention coordination as well (Nácher et al., 2013), as described before in the decision section.

It is important to state that there are many more studies describing selective coordination mechanisms by oscillatory synchronization. Many experiments have been conducted on rats performing a vast assortment of different tasks while activity in the hippocampus, the entorhinal cortex, and different cortical regions was recorded, with

findings corroborating a coordinative role of gamma-band and theta-band oscillatory synchrony (Buzsáki, 2010; Buzsáki and Wang, 2012; Schomburg, 2015). The studies presented here were selected with regard to coordination of information across cortex spanning perceptual processing, decision making, and behavior generation. A possible interpretation of all presented results is that gamma-band synchronization and possibly theta-band synchronization coordinate the bottom-up attention control originating from the visual areas. In contrast, beta-band and possibly low-frequency synchronization could serve as coordinative mechanisms for intention or top-down control of the information flow with beta-band synchronizations originating from parietal areas. Additionally, beta-band

synchronization could possibly be the coordinative mechanism of a putative distributed consensus across cortex, as suggested for decision making (Cisek, 2012).

Two important questions remain unanswered. What is the advantage of oscillatory synchronization as a coordinate mechanism? And, how is the information flow coordinated by this synchronization? It is important to stress that so far, no common agreement or causal proof exists to answer these two questions. However, a convincing answer to the first

question is the idea of feedforward coincidence detection (Fries, 2009). The number of synaptic inputs to a neuron is large (1000- 10000) and the postsynaptic potentials triggered by spikes are known to decrease rapidly after initiation, which effectively leaves only a few milliseconds for arriving spikes to be integrated to elicit a spike from the target neuron. If neurons are oscillatory synchronized to each other, then their spikes have on average a greater impact on their targets. The advantage of such a mechanism is not only a reduction of energy cost and an increase in spike efficiency, but also a rhythmic gain modulation. A rhythmic, synchronized activation of a population of neurons results in phases of high excitability when all neurons fire and phases of low excitability in between. As a

consequence, the amount of excitation necessary to elicit spikes from the target neurons is rhythmically modulated or, in other words, the gain is modulated. This allows for a selective amplification of inputs from one group of neurons to another group of neurons, by simply changing the phase of synchrony of the target neural population to be in phase with one group of neurons and out of phase with the other group. This highly flexible mechanism of selective communication, which results in a coordination of information flow, is called communication through coherence and is a possible answer to the second question posed above (Fries, 2005).

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Most studies to date have analyzed the kind of synchronization thought to

coordinate information flow between pairs of neurons, areas, or local populations. Yet, the brain or brain areas are a strong interconnected network on the anatomical as well as functional scale (Berger et al., 2007; Bullmore and Sporns, 2009; Markov et al., 2014), which makes it essential to analyze the functional network structure to understand the

coordination of information flow. However, due to the above-mentioned possibility that aspects of the communication can average out at the level of population signals, it is essential to analyze functional interaction on the level of single neurons to understand the formation of potential ensembles.