Literature DB >> 24259547

A framework for using signal, noise, and variation to determine whether the brain controls movement synergies or single muscles.

Mati Joshua1, Stephen G Lisberger.   

Abstract

We have used an analysis of signal and variation in motor behavior to elucidate the organization of the cerebellar and brain stem circuits that control smooth pursuit eye movements. We recorded from the abducens nucleus and identified floccular target neurons (FTNs) and other, non-FTN vestibular neurons. First, we assessed neuron-behavior correlations, defined as the trial-by-trial correlation between the variation in neural firing and eye movement, in brain stem neurons. In agreement with prior data from the cerebellum, neuron-behavior correlations during pursuit initiation were large in all neurons. Second, we asked whether movement variation arises upstream from, in parallel to, or downstream from a given site of recording. We developed a model that highlighted two measures: the ratio of the SDs of neural firing rate and eye movement ("SDratio") and the neuron-behavior correlation. The relationship between these measures defines possible sources of variation. During pursuit initiation, SDratio was approximately equal to neuron-behavior correlation, meaning that the source of signal and variation is upstream from the brain stem. During steady-state pursuit, neuron-behavior correlation became somewhat smaller than SDratio for FTNs, meaning that some variation may arise downstream in the brain stem. The data contradicted the model's predictions for sources of variation in pathways that run parallel to the site of recording. Because signal and noise are tightly linked in motor control, we take the source of variation as a proxy for the source of signal, leading us to conclude that the brain controls movement synergies rather than single muscles for eye movements.

Keywords:  brain stem; electrophysiology; motor noise; motor synergies; variability

Mesh:

Year:  2013        PMID: 24259547      PMCID: PMC3921394          DOI: 10.1152/jn.00510.2013

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  33 in total

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Journal:  J Neurosci       Date:  1996-02-15       Impact factor: 6.167

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Authors:  W Zhou; W M King
Journal:  Nature       Date:  1998-06-18       Impact factor: 49.962

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Journal:  J Neurophysiol       Date:  1994-08       Impact factor: 2.714

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  15 in total

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Authors:  Stephen G Lisberger; Javier F Medina
Journal:  Curr Opin Neurobiol       Date:  2015-04-02       Impact factor: 6.627

2.  Shared sensory estimates for human motion perception and pursuit eye movements.

Authors:  Trishna Mukherjee; Matthew Battifarano; Claudio Simoncini; Leslie C Osborne
Journal:  J Neurosci       Date:  2015-06-03       Impact factor: 6.167

3.  Responses of Purkinje cells in the oculomotor vermis of monkeys during smooth pursuit eye movements and saccades: comparison with floccular complex.

Authors:  Ramanujan T Raghavan; Stephen G Lisberger
Journal:  J Neurophysiol       Date:  2017-05-17       Impact factor: 2.714

4.  Encoding of Reward and Decoding Movement from the Frontal Eye Field during Smooth Pursuit Eye Movements.

Authors:  Adi Lixenberg; Mati Joshua
Journal:  J Neurosci       Date:  2018-10-24       Impact factor: 6.167

5.  Signal, Noise, and Variation in Neural and Sensory-Motor Latency.

Authors:  Joonyeol Lee; Mati Joshua; Javier F Medina; Stephen G Lisberger
Journal:  Neuron       Date:  2016-03-10       Impact factor: 17.173

Review 6.  Neuromechanical principles underlying movement modularity and their implications for rehabilitation.

Authors:  Lena H Ting; Hillel J Chiel; Randy D Trumbower; Jessica L Allen; J Lucas McKay; Madeleine E Hackney; Trisha M Kesar
Journal:  Neuron       Date:  2015-04-08       Impact factor: 17.173

7.  Dissecting patterns of preparatory activity in the frontal eye fields during pursuit target selection.

Authors:  Ramanujan T Raghavan; Mati Joshua
Journal:  J Neurophysiol       Date:  2017-07-19       Impact factor: 2.714

8.  Encoding of eye movements explains reward-related activity in cerebellar simple spikes.

Authors:  Adi Lixenberg; Merav Yarkoni; Yehudit Botschko; Mati Joshua
Journal:  J Neurophysiol       Date:  2020-01-15       Impact factor: 2.714

9.  Cortical activity predicts good variation in human motor output.

Authors:  Sarine Babikian; Eva Kanso; Jason J Kutch
Journal:  Exp Brain Res       Date:  2017-02-04       Impact factor: 1.972

10.  Emergence of an Adaptive Command for Orienting Behavior in Premotor Brainstem Neurons of Barn Owls.

Authors:  Fanny Cazettes; Brian J Fischer; Michael V Beckert; Jose L Pena
Journal:  J Neurosci       Date:  2018-07-16       Impact factor: 6.167

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