Entrainment of network activity by closed-loop microstimulation in healthy ambulatory rats

bioRxiv (Cold Spring Harbor Laboratory)(2020)

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摘要
Abstract As our understanding of how motor output is generated increases, it is clear that there is a need to understand the interactions of multiple distinct regions rather than just the output properties of primary motor cortex. This becomes even more imperative when trying to understand how different regions may contribute to recovery following injury. In this study we used a technique that promotes functional motor recovery after injury, activity-dependent stimulation (ADS), to determine the short- and long-term effects on network activity and neuroplasticity of intracortical connections. ADS uses recorded neural activity to trigger stimulation of the brain and may be utilized to manipulate neuronal connectivity in vivo , representing a novel technique to shape intrinsic neuroplasticity. The aim of this work was to compare the effect of ADS to randomly-generated stimulation (RS) of the somatosensory area (S1) on the single units’ patterns of activity taking place in the premotor cortex (RFA) and to investigate whether synaptic plasticity changes occur in S1 as a consequence of 21 consecutive days of stimulation. In particular, we examined both firing rate changes and correlation between spiking activity and stimuli in chronically-implanted healthy ambulatory rats during both spontaneous and evoked activity, resulting from the two stimulation paradigms. Finally, we evaluated changes in expression of synaptophysin at the end of the treatment. This experimental procedure demonstrated the ability of ADS to modulate firing properties of RFA within daily recording sessions and to promote synaptogenesis in S1, further strengthening the idea that this Hebbian-inspired protocol can be used to effectively modulate cortical connectivity and thus suggesting its translational potential for promoting recovery after brain injury.
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关键词
healthy ambulatory rats,network activity,entrainment,closed-loop
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