Neural population dynamics in motor cortex are different for reach and grasp
- University of Chicago, United States
Abstract
Low-dimensional linear dynamics are observed in neuronal population activity in primary motor cortex (M1) when monkeys make reaching movements. This population-level behavior is consistent with a role for M1 as an autonomous pattern generator that drives muscles to give rise to movement. In the present study, we examine whether similar dynamics are also observed during grasping movements, which involve fundamentally different patterns of kinematics and muscle activations. Using a variety of analytical approaches, we show that M1 does not exhibit such dynamics during grasping movements. Rather, the grasp-related neuronal dynamics in M1 are similar to their counterparts in somatosensory cortex, whose activity is driven primarily by afferent inputs rather than by intrinsic dynamics. The basic structure of the neuronal activity underlying hand control is thus fundamentally different from that underlying arm control.
Data availability
The data that support the findings of this study have been deposited in Dryad, accessible at https://doi.org/10.5061/dryad.xsj3tx9cm
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Neural population dynamics in motor cortex are different for reach and graspDryad Digital Repository, doi:10.5061/dryad.xsj3tx9cm.
Article and author information
Author details
Elizaveta V Okorokova
Funding
National Institute of Neurological Disorders and Stroke (NS082865)
- Nicholas G Hatsopoulos
- Sliman J Bensmaia
National Institute of Neurological Disorders and Stroke (NS096952)
- Aneesha K Suresh
National Institute of Neurological Disorders and Stroke (NS045853)
- Nicholas G Hatsopoulos
National Institute of Neurological Disorders and Stroke (NS111982)
- Nicholas G Hatsopoulos
National Institute of Neurological Disorders and Stroke (NS101325)
- Sliman J Bensmaia
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All surgical, behavioral, and experimental procedures conformed to the guidelines of the National Institutes of Health and were approved by the University of Chicago Institutional Animal Care and Use Committee (#72042).
Copyright
© 2020, Suresh et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Neural population dynamics in motor cortex are different for reach and grasp
eLife 9:e58848.
https://doi.org/10.7554/eLife.58848
Further reading
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Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters – the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations – all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.
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