Overall Discussion and Perspectives

Based on the three experiments I can conclude that muscle activity as a representative measure for motor action depends on theta and beta band activity in the cortical motor action network (as has been suggested by Battaglia-Mayer et al. 2003; Fagg and Arbib 1998; Rizzolatti and Luppino 2001) comprising posterior region (BA 7 and BA 4), M1 and premotor regions (BA 6).

All three studies point to the fact that theta and beta band activity probably reflect the excitability state of the network where particularly beta band power increase is probably related to a more inhibited state of of local areas within the network, whereas theta band oscillations mainly reflect long-range communication here quantified through coherence. Theta band coherence (either ITC or CMC) could represent whether the network at that time is more open or more closed for long-range communication and action.

Given the fact that corticomuscular coherence and obvious motor behaviour is directly linked to the excitability state of the motor action initiation network, either represented through phase or through amplitude, one can conclude that the actual brain state is important for the regulations of behaviour. This has a lot of implications for everyone’s daily life. First this means that voluntary and induced motor activity is more likely when the motor action network is in an excited state where corticomuscular coherence is increased, whereas it is less likely when it is in a more “inhibited” state when

corticomuscular coherence is decreased. This not only goes well in line with

and complements findings in other modalities, but also gives further support for the fact that the “gating by inhibition” framework (Jensen and Mazaheri 2010), and the “communication through coherence” hypothesis (Fries 2005) is also well suited to the sensorimotor system.

But there are still a lot of open questions.

As I showed before, theta band seems to be relevant both for communication between muscle and brain but also for the integration of the different areas probably through an intrinsically entrainment mechanism. Thus, the first thing, which would be nice to find out in future study is, if theta band activity is generated by a pacemaker region.

To be brought into consideration are, for example, the hippocampus, or the cerebellum. It might be helpful to go to animal models where LFP data can be obtained and we can measure deeper sources. Addtionally, experiments with EcoG data where, for example, theta phase is brought together with

corticomuscular coherence resting state data would give a hint of the underlying structures obtained in my results. Secondly, to get a deeper understanding of how the integration of networks is built, it would be good to see if the interplay between local beta band power, theta band phase in the motor action network and corticomuscular coherence gets stronger when learning new movements, which would bring us a better understanding of where the “shaping” of the architecture originates. For example, whether the error rate of a newly learned movement is stronger if there is less inhibitory activity or the phase dependence is not yet as finely tuned as in well learned behaviours as we measured them. Thus showing whether the learned

responses differ in network integration and communication compared to

non-learned responses. And lastly we could try to entrain rhythms such as theta band rhythm via TMS so that we get a better feeling of how phase influences behaviour or beta band rhythms to see how induced power changes influence behaviour and coherence. And how inhibitory pulses can be used for fine-tuning.

Another interesting question which we could not answer within this thesis is whether theta beta cross frequency coupling is something which applies more generally to the cortex or how cross frequency coupling with oscillation of more “inhibitory nature” takes place in general (cross frequency coupling having often been done between gamma and theta band which is more excitatory).

If we could get to know more about how these networks are built and integrated we would first of all understand the gating mechanism better.

Beyond that it would be helpful for therapeutic interventions. For example for Parkinson disease where an elevation in beta band power leads to movement poverty symptoms, if we could somehow influence this elevation by

counteracting the elevated inhibitory activity, for example by influencing theta phase through TMS it might be possible to reduce symptoms.

On a more general level beyond motor action related networks, it would also be useful to find out more about network formation and integration. For

example, in psychiatry there are probably a lot of diseases, which probably go along with weaker inhibitory activity and with malfunctioning networks. First it would be helpful to find out how networks are dysfunctionally differentiated and how it comes to that differentiation. And whether this could be prevented

in the first place. Or whether it can be therapeutically handled.

By regulating the brain state within a network, for example by improving methods such as neurofeedback or computer trainings. Generally I think that the focus of psychotherapeutic interventions should get more into improving methods, which shape or tune functional networks. For example by reducing the conversational amount in therapy lessons and coming back to more exercising and action related elements, such as fear exposition in behavioural therapy. Additionally these findings also limit therapeutic interventions such as TMS at the moment, particularly TMS without neuronavigation because they are imprecise in their location and we cannot completely exclude that there are also conditioned responses to the tone, which again somehow disturbs network tuning. Improving therapeutically used TMS should then go more into directions, as has been suggested by Weisz et al. where a

differentiation or fine tuning of a network is made a priori to Stimulation.