Reversible Block of Cerebellar Outflow Reveals Cortical Circuitry for Motor Coordination

Nashef, Abdulraheem; Cohen, Oren S.; Harel, Ran; Israel, Zvi; Prut, Yifat

doi:10.1016/j.celrep.2019.04.100

Cited by 37 publications

(68 citation statements)

References 55 publications

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“…We previously posited that the excitation/inhibition interplay triggered by the CTC input plays a role in generating the firing transient at the onset of movement (Nashef et al, 2018a). Consistent with this hypothesis, blocking the cerebellar outflow by dentate cooling (Hore and Flament, 1988) or high frequency stimulation in the SCP (Nashef et al, 2019) abolishes this transient, particularly in cortical cells that integrate CTC input. The loss of this transient was accompanied by ataxic movements.…”

Section: Discussionmentioning

confidence: 71%

“…We implanted stimulating electrodes in the SCP (Figure 1) of monkeys (n=3) that were trained to wear an exoskeleton (Kinarm system) and perform a center-out reaching task ( Figure 1A-B). The behavioral task used here was designed to encourage the monkeys to move as fast as possible by predicting the time of the "go" signal (Nashef et al, 2019). Neural activity was recorded from arm-related motor cortical areas using multiple single electrodes in 3 monkeys ( Figure 1C); in two of these animals, neurons were also tested using a 3-barrel recording pipette (Thiele et al, 2006).…”

Section: Resultsmentioning

confidence: 99%

“…Stimulus-evoked responses of single neurons. We identified SCP-responsive cortical neurons according to a previously published method (Nashef et al, 2019;Nashef et al, 2018a). Simulation of a feed-forward inhibition (FFI) circuit.…”

Section: Discussionmentioning

confidence: 99%

“…A bipolar concentric electrode (NSEX100, David Kopf Instruments, impedance range of 30-60 kΩ) was positioned in the peduncle through the chamber. Evoked intracortical responses to stimulation through the electrode were used to verify its location before gluing the electrode to the chamber (Nashef et al, 2019;Nashef et al, 2018a;Nashef et al, 2018b;Ruach et al, 2015).…”

Section: Insertion Of Stimulating Electrode Into the Superior Cerebelmentioning

confidence: 99%

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Cortical inhibitory network selects cerebellar signals for movement initiation

Nashef

Cohen

Perlmutter

et al. 2020

Preprint

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The onset of voluntary movements is driven by coordinated firing across a large population of motor cortical neurons. This pattern of activity is determined by both local interactions and long-range corticocortical and subcortical inputs. The way remote areas of the brain communicate to effectively drive movement is still unclear. We addressed this question by studying an important pathway through which the cerebellum communicates, via the motor thalamus, with the motor cortex. We found that similar to the sensory cortices, thalamic input to the motor cortex triggers feedforward inhibition by directly contacting inhibitory cells via particularly effective GluR2- lacking AMPA receptors blocked by NASPM. Based on these results, we constructed a classifier for SCP-responsive cortical cells to identify pyramidal and PV interneurons and study their role in controlling movements. The findings indicate that PV and pyramidal cells are co-driven by TC input in response to activation of the CTC pathway. During task performance, PV and pyramidal cells had comparable relations to movement parameters (directional tuning and movement duration). However, PV interneurons exhibited stronger movement-related activity that preceded the firing of pyramidal cells. This seemingly counterintuitive sequence of events where inhibitory cells are recruited more strongly and before excitatory cells may in fact enhance the signal-to-noise ratio of cerebellar signals by suppressing other inputs and prioritizing the excitatory synchronized volley from the TC system which occurs at the right time to overcome the inhibitory signal. In this manner, the CTC system can shape cortical activity in a way that exceeds its sheer synaptic efficacy.

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Section: Discussionmentioning

confidence: 71%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Insertion Of Stimulating Electrode Into the Superior Cerebelmentioning

confidence: 99%

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Cortical inhibitory network selects cerebellar signals for movement initiation

Nashef

Cohen

Perlmutter

et al. 2020

Preprint

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“…Further, the cerebellum receives information from the primary motor and premotor cortex through the pons. Recent studies have demonstrated that the cerebellarthalamo-cortical system plays an important role in motor control (Viaro et al, 2017;Nashef et al, 2018Nashef et al, , 2019 and motor learning (Tanaka et al, 2018). A recent study also demonstrated that the cortico-cerebellar loop contributes to cognitive processes (Gao et al, 2018).…”

Section: Toward Building a Whole-brain Network Modelmentioning

confidence: 99%

Simulation of a Human-Scale Cerebellar Network Model on the K Computer

Yamaura

Igarashi

Yamazaki

2020

Front. Neuroinform.

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Computer simulation of the human brain at an individual neuron resolution is an ultimate goal of computational neuroscience. The Japanese flagship supercomputer, K, provides unprecedented computational capability toward this goal. The cerebellum contains 80% of the neurons in the whole brain. Therefore, computer simulation of the human-scale cerebellum will be a challenge for modern supercomputers. In this study, we built a human-scale spiking network model of the cerebellum, composed of 68 billion spiking neurons, on the K computer. As a benchmark, we performed a computer simulation of a cerebellum-dependent eye movement task known as the optokinetic response. We succeeded in reproducing plausible neuronal activity patterns that are observed experimentally in animals. The model was built on dedicated neural network simulation software called MONET (Millefeuille-like Organization NEural neTwork), which calculates layered sheet types of neural networks with parallelization by tile partitioning. To examine the scalability of the MONET simulator, we repeatedly performed simulations while changing the number of compute nodes from 1,024 to 82,944 and measured the computational time. We observed a good weak-scaling property for our cerebellar network model. Using all 82,944 nodes, we succeeded in simulating a human-scale cerebellum for the first time, although the simulation was 578 times slower than the wall clock time. These results suggest that the K computer is already capable of creating a simulation of a human-scale cerebellar model with the aid of the MONET simulator.

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