Search

The cerebellum takes in a great deal of sensory information from the periphery and descending signals from the cerebral cortices. It has been debated whether the paramedian lobule (PML) in the rat and its paravermal regions that project to the interpositus nucleus (IPN) are primarily involved in motor execution or motor planning. Studies that have relied on single spike recordings in behaving animals have led to conflicting conclusions regarding this issue. In this study, we tried a different approach and investigated the correlation of field potentials and multi-unit signals recorded with multi-electrode arrays from the PML cortex along with the forelimb electromyography (EMG) signals in rats during behavior. Linear regression was performed to predict the EMG signal envelopes using the PML activity for various time shifts (±25, ±50, ±100, and ± 400 ms) between the two signals to determine a causal relation. The highest correlations (~0.5 on average) between the neural and EMG envelopes were observed for zero and small (±25 ms) time shifts and decreased with larger time shifts in both directions, suggesting that paravermal PML is involved both in processing of sensory signals and motor execution in the context of forelimb reaching behavior. EMG envelopes were predicted with higher success rates when neural signals from multiple phases of the behavior were utilized for regression. The forelimb extension phase was the most difficult to predict while the releasing of the bar phase prediction was the most successful. The high frequency (>300 Hz) components of the neural signal, reflecting multi-unit activity, had a higher contribution to the EMG prediction than did the lower frequency components, corresponding to local field potentials. The results of this study suggest that the paravermal PML in the rat cerebellum is primarily involved in the execution of forelimb movements rather than the planning aspect and that the PML is more active at the initiation and termination of the behavior, rather than the progression.

Transcranial alternating current stimulation (tACS) is a non-invasive neuromodulation technique that is being tested clinically for treatment of a variety of neural disorders. Animal studies investigating the underlying mechanisms of tACS are scarce, and nearly absent in the cerebellum. In the present study, we applied 10–400 Hz alternating currents (AC) to the cerebellar cortex in ketamine/xylazine anesthetized rats. The spiking activity of cerebellar nuclear (CN) cells was transsynaptically entrained to the frequency of AC stimulation in an intensity and frequency-dependent manner. Interestingly, there was a tuning curve for modulation where the frequencies in the midrange (100 and 150 Hz) were more effective, although the stimulation frequency for maximum modulation differed for each CN cell with slight dependence on the stimulation amplitude. CN spikes were entrained with latencies of a few milliseconds with respect to the AC stimulation cycle. These short latencies and that the transsynaptic modulation of the CN cells can occur at such high frequencies strongly suggests that PC simple spike synchrony at millisecond time scales is the underlying mechanism for CN cell entrainment. These results show that subthreshold AC stimulation can induce such PC spike synchrony without resorting to supra-threshold pulse stimulation for precise timing. Transsynaptic entrainment of deep CN cells via cortical stimulation could help keep stimulation currents within safety limits in tACS applications, allowing development of tACS as an alternative treatment to deep cerebellar stimulation. Our results also provide a possible explanation for human trials of cerebellar stimulation where the functional impacts of tACS were frequency dependent.

Advanced algorithms are required to reveal the complex relations between neural and behavioral data. In this study, forelimb electromyography (EMG) signals were reconstructed from multi-unit neural signals recorded with multiple electrode arrays (MEAs) from the corticospinal tract (CST) in rats. A six-layer convolutional neural network (CNN) was compared with linear decoders for predicting the EMG signal. The network contained three session-dependent Rectified Linear Unit (ReLU) feature layers and three Gamma function layers were shared between sessions. Coefficient of determination (R2) values over 0.2 and correlations over 0.5 were achieved for reconstruction within individual sessions in multiple animals, even though the forelimb position was unconstrained for most of the behavior duration. The CNN performed visibily better than the linear decoders and model responses outlasted the activation duration of the rat neuromuscular system. These findings suggest that the CNN model implicitly predicted short-term dynamics of skilled forelimb movements from neural signals. These results are encouraging that similar problems in neural signal processing may be solved using variants of CNNs defined with simple analytical functions. Low powered firmware can be developed to house these CNN solutions in real-time applications.

A multi-electrode array (MEA) was implanted in the dorsolateral funiculus of the cervical spinal cord to record descending information during behavior in freely moving rats. Neural signals were characterized in terms of frequency and information content. Frequency analysis revealed components both at the range of local field potentials and multi-unit activity. Coherence between channels decreased steadily with inter-contact distance and frequency suggesting greater spatial selectivity for multi-unit activity compared to local field potentials. Principal component analysis (PCA) extracted multiple channels of neural activity with patterns that correlated to the behavior, indicating multiple dimensionality of the signals. Two different behaviors involving the forelimbs, face cleaning and food reaching, generated neural signals through distinctly different combination of neural channels, which suggested that these two behaviors could readily be differentiated from recordings. This preliminary data demonstrated that descending spinal cord signals recorded with MEAs can be used to extract multiple channels of command control information and potentially be utilized as a means of communication in high level spinal cord injury subjects.