Luc Berthouze | bioRxiv Science

bioRxiv2016

The effect of local inter-inhibitory connectivity on the dynamics of an activity-dependent neuronal network growth model

The balance between excitation and inhibition in a neuronal network is considered to be an important predictor of neural excitability. Various processes are thought to maintain this balance across a range of stimuli/conditions. However, the developmental formation of this balance remains an open question, especially regarding the interplay between network blue-print (the spatial arrangement of excitatory and inhibitory nodes) and homeostatic processes. In this paper, we use a published model of activity-dependent growth to show that the E/I ratio alone cannot accurately predict system behaviour but rather it is the combination of this ratio and the underlying spatial arrangement of neurones that predict both activity in, and structure of, the resulting network. In particular, we highlight the particular role of clustered inter-inhibitory connectivity. We develop a measure that allows us to determine the relationship between inter-inhibitory connectivity clustering and system behaviour in an exhaustive list of spatial arrangements with a given fixed number of excitatory and inhibitory neurones. Our results reveal that, for a given E/I ratio, networks with high levels of inter-inhibitory clustering are more likely to experience oscillatory behaviour than networks with low levels, and we investigate the network attributes which characterise each global behaviour type produced by the model. We identify possible approaches for extensions of the current work, and discuss the implications these results may have on future modelling studies in this field.

Neuroscience↗

bioRxiv2015

Resting state MEG oscillations show long-range temporal correlations of phase synchrony that break down during finger-tapping

The capacity of the human brain to interpret and respond to multiple temporal scales in its surroundings suggests that its internal interactions must also be able to operate over a broad temporal range. In this paper, we utilise a recently introduced method for characterising the rate of change of the phase difference between MEG signals and use it to study the temporal structure of the phase interactions between MEG recordings from the left and right motor cortices during rest and during a finger-tapping task. We use the Hilbert transform to estimate moment-to-moment fluctuations of the phase difference between signals. After confirming the presence of scale-invariance we estimate the Hurst exponent using detrended fluctuation analysis (DFA). An exponent of >0.5 is indicative of long-range temporal correlations (LRTCs) in the signal. We find that LRTCs are present in the / and {beta} frequency bands of resting state MEG data. We demonstrate that finger movement disrupts LRTCs correlations, producing a phase relationship with a structure similar to that of Gaussian white noise. The results are validated by applying the same analysis to data with Gaussian white noise phase difference, recordings from an empty scanner and phase-shuffled time series. We interpret the findings through comparison of the results with those we obtained from an earlier study during which we adopted this method to characterise phase relationships within a Kuramoto model of oscillators in its sub-critical, critical and super-critical synchronisation states. We find that the resting state MEG from left and right motor cortices shows moment-to-moment fluctuations of phase difference with a similar temporal structure to that of a system of Kuramoto oscillators just prior to its critical level of coupling, and that finger tapping moves the system away from this pre-critical state towards a more random state.

Neuroscience↗