Calcium Activation of Parvalbumin Neurons Induced by Electrical Motor Cortex Stimulation.

Electrical motor cortex stimulation (EMCS) has been used for Parkinson's Disease (PD) treatment. Some studies found that distinct cell types might lead to selective effects. As the largest subgroup of interneurons, Parvalbumin (PV) neurons have been reported to be involved in the mechanisms of therapeutic efficacy for PD treatment. However, little is known about their responses to the EMCS. In this study, we used in-vivo two-photon imaging to record calcium activities of PV neurons (specific type) and all neurons (non-specific type) in layer 2/3 primary motor cortex (MI) during EMCS with various stimulus parameters. We found PV neurons displayed different profiles of activation property compared to all neurons. The cathodal polarity preference of PV neurons decreased at a high-frequency stimulus. The calcium transients of PV neurons generated by EMCS trended to be with large amplitude and short active duration. The optimal activation frequency of PV neurons is higher than that of all neurons. These results improved our understanding of the selective effects of EMCS on specific cell types, which could bring more effective stimulation protocols for PD treatment.

Online Decoding System with Calcium Image From Mice Primary Motor Cortex.

With the development of calcium imaging, neuroscientists have been able to study neural activity with a higher spatial resolution. However, the real-time processing of calcium imaging is still a big challenge for future experiments and applications. Most neuroscientists have to process their imaging data offline due to the time-consuming of most existing calcium imaging analysis methods. We proposed a novel online neural signal processing framework for calcium imaging and established an Optical Brain-Computer Interface System (OBCIs) for decoding neural signals in real-time. We tested and evaluated this system by classifying the calcium signals obtained from the primary motor cortex of mice when the mice were performing a lever-pressing task. The performance of our online system could achieve above 80% in the average decoding accuracy. Our preliminary results show that the online neural processing framework could be applied to future closed-loop OBCIs studies.