Phase of beta-frequency tACS over primary motor cortex modulates corticospinal excitability

The assessment of corticospinal excitability by means of transcranial magnetic stimulation-induced motor evoked potentials is an established diagnostic tool in neurophysiology and a widely used procedure in fundamental brain research. However, concern about low reliability of these measures has grown recently. One possible cause of high variability of MEPs under identical acquisition conditions could be the influence of oscillatory neuronal activity on corticospinal excitability. Based on research showing that transcranial alternating current stimulation can entrain neuronal oscillations we here test whether alpha or beta frequency tACS can influence corticospinal excitability in a phase-dependent manner. We applied tACS at individually calibrated alpha- and beta-band oscillation frequencies, or we applied sham tACS. Simultaneous single TMS pulses time locked to eight equidistant phases of the ongoing tACS signal evoked MEPs. To evaluate offline effects of stimulation frequency, MEP amplitudes were measured before and after tACS. To evaluate whether tACS influences MEP amplitude, we fitted one-cycle sinusoids to the average MEPs elicited at the different phase conditions of each tACS frequency. We found no frequency-specific offline effects of tACS. However, beta-frequency tACS modulation of MEPs was phase-dependent. Post hoc analyses suggested that this effect was specific to participants with low (<19 Hz) intrinsic beta frequency. In conclusion, by showing that beta tACS influences MEP amplitude in a phase-dependent manner, our results support a potential role attributed to neuronal oscillations in regulating corticospinal excitability. Moreover, our findings may be useful for the development of TMS protocols that improve the reliability of MEPs as a meaningful tool for research applications or for clinical monitoring and diagnosis. (C) 2018 Elsevier Ltd. All rights reserved

A statistical model for in vivo neuronal dynamics

Single neuron models have a long tradition in computational neuroscience. Detailed biophysical models such as the Hodgkin-Huxley model as well as simplified neuron models such as the class of integrate-and-fire models relate the input current to the membrane potential of the neuron. Those types of models have been extensively fitted to in vitro data where the input current is controlled. Those models are however of little use when it comes to characterize intracellular in vivo recordings since the input to the neuron is not known. Here we propose a novel single neuron model that characterizes the statistical properties of in vivo recordings. More specifically, we propose a stochastic process where the subthreshold membrane potential follows a Gaussian process and the spike emission intensity depends nonlinearly on the membrane potential as well as the spiking history. We first show that the model has a rich dynamical repertoire since it can capture arbitrary subthreshold autocovariance functions, firing-rate adaptations as well as arbitrary shapes of the action potential. We then show that this model can be efficiently fitted to data without overfitting. Finally, we show that this model can be used to characterize and therefore precisely compare various intracellular in vivo recordings from different animals and experimental conditions.Comment: 31 pages, 10 figure

Nanostructuring silicon probes via electrodeposition: characterization of electrode coatings for acute in vivo neural recordings

Understanding how the brain works will require tools capable of measuring neuron elec-trical activity at a network scale. However, considerable progress is still necessary to reliably increase the number of neurons that are recorded and identified simultaneously with existing mi-croelectrode arrays. This project aims to evaluate how different materials can modify the effi-ciency of signal transfer from the neural tissue to the electrode. Therefore, various coating materials (gold, PEDOT, tungsten oxide and carbon nano-tubes) are characterized in terms of their underlying electrochemical processes and recording ef-ficacy. Iridium electrodes (177-706 μm2) are coated using galvanostatic deposition under different charge densities. By performing electrochemical impedance spectroscopy in phosphate buffered saline it is determined that the impedance modulus at 1 kHz depends on the coating material and decreased up to a maximum of two orders of magnitude for PEDOT (from 1 MΩ to 25 kΩ). The electrodes are furthermore characterized by cyclic voltammetry showing that charge storage capacity is im-proved by one order of magnitude reaching a maximum of 84.1 mC/cm2 for the PEDOT: gold nanoparticles composite (38 times the capacity of the pristine). Neural recording of spontaneous activity within the cortex was performed in anesthetized rodents to evaluate electrode coating performance