Discovering Microcircuit Secrets With Multi-Spot Imaging and Electrophysiological Recordings: The Example of Cerebellar Network Dynamics

[Network-wide effects of pallidal deep brain stimulation normalised abnormal cerebellar cortical activity in the dystonic animal model

BACKGROUND

Deep brain stimulation (DBS) targeting globus pallidus internus (GPi) is a recognised therapy for drug-refractory dystonia. However, the mechanisms underlying this effect are not fully understood. This study explores how pallidal DBS alters spatiotemporal pattern formation of neuronal dynamics within the cerebellar cortex in a dystonic animal model, the dtsz hamster.

METHODS

We conducted in vitro analysis using a high-density microelectrode array (HD-MEA) in the cerebellar cortex. For investigating the spatiotemporal pattern, mean firing rates (MFR), interspike intervals (ISI), spike amplitudes, and cerebellar connectivity among healthy control hamsters, dystonic dtsz hamsters, DBS- and sham-DBS-treated dtsz hamsters were analysed. A nonlinear data-driven method characterised the low-dimensional representation of the patterns in MEA data.

RESULTS

Our HD-MEA recordings revealed reduced MFR and spike amplitudes in the dtsz hamsters compared to healthy controls. Pallidal DBS induced network-wide effects, normalising MFR, spike amplitudes, and connectivity measures in hamsters, thereby countervailing these electrophysiological abnormalities. Additionally, network analysis showed neural activity patterns organised into communities, with higher connectivity in both healthy and DBS groups compared to dtsz.

CONCLUSIONS

These findings suggest that pallidal DBS exerts some of its therapeutic effects on dystonia by normalising neuronal activity within the cerebellar cortex. Our findings of reduced MFR and spike amplitudes in the dtsz hamsters could be a hint of a decrease in neuronal fibres and synaptic plasticity. Treatment with pallidal DBS led to cerebellar cortical activity similar to healthy controls, displaying the network-wide impact of local stimulation.

Anisotropy and Frequency Dependence of Signal Propagation in the Cerebellar Circuit Revealed by High-Density Multielectrode Array Recordings

The cerebellum is one of the most connected structures of the central nervous system and receives inputs over an extended frequency range. Nevertheless, the frequency dependence of cerebellar cortical processing remains elusive. In this work, we characterized cerebellar cortex responsiveness to mossy fibers activation at different frequencies and reconstructed the spread of activity in the sagittal and coronal planes of acute mouse cerebellar slices using a high-throughput high-density multielectrode array (HD-MEA). The enhanced spatiotemporal resolution of HD-MEA revealed the frequency dependence and spatial anisotropy of cerebellar activation. Mossy fiber inputs reached the Purkinje cell layer even at the lowest frequencies, but the efficiency of transmission increased at higher frequencies. These properties, which are likely to descend from the topographic organization of local inhibition, intrinsic electroresponsiveness, and short-term synaptic plasticity, are critical elements that have to be taken into consideration to define the computational properties of the cerebellar cortex and its pathological alterations.

Computational models of neurotransmission at cerebellar synapses unveil the impact on network computation

The neuroscientific field benefits from the conjoint evolution of experimental and computational techniques, allowing for the reconstruction and simulation of complex models of neurons and synapses. Chemical synapses are characterized by presynaptic vesicle cycling, neurotransmitter diffusion, and postsynaptic receptor activation, which eventually lead to postsynaptic currents and subsequent membrane potential changes. These mechanisms have been accurately modeled for different synapses and receptor types (AMPA, NMDA, and GABA) of the cerebellar cortical network, allowing simulation of their impact on computation. Of special relevance is short-term synaptic plasticity, which generates spatiotemporal filtering in local microcircuits and controls burst transmission and information flow through the network. Here, we present how data-driven computational models recapitulate the properties of neurotransmission at cerebellar synapses. The simulation of microcircuit models is starting to reveal how diverse synaptic mechanisms shape the spatiotemporal profiles of circuit activity and computation.