Literature DB >> 23884944

Differences in adaptation rates after virtual surgeries provide direct evidence for modularity.

Denise J Berger1, Reinhard Gentner, Timothy Edmunds, Dinesh K Pai, Andrea d'Avella.   

Abstract

Whether the nervous system relies on modularity to simplify acquisition and control of complex motor skills remains controversial. To date, evidence for modularity has been indirect, based on statistical regularities in the motor commands captured by muscle synergies. Here we provide direct evidence by testing the prediction that in a truly modular controller it must be harder to adapt to perturbations that are incompatible with the modules. We investigated a reaching task in which human subjects used myoelectric control to move a mass in a virtual environment. In this environment we could perturb the normal muscle-to-force mapping, as in a complex surgical rearrangement of the tendons, by altering the mapping between recorded muscle activity and simulated force applied on the mass. After identifying muscle synergies, we performed two types of virtual surgeries. After compatible virtual surgeries, a full range of movements could still be achieved recombining the synergies, whereas after incompatible virtual surgeries, new or modified synergies would be required. Adaptation rates after the two types of surgery were compared. If synergies were only a parsimonious description of the regularities in the muscle patterns generated by a nonmodular controller, we would expect adaptation rates to be similar, as both types of surgeries could be compensated with similar changes in the muscle patterns. In contrast, as predicted by modularity, we found strikingly faster adaptation after compatible surgeries than after incompatible ones. These results indicate that muscle synergies are key elements of a modular architecture underlying motor control and adaptation.

Entities:  

Mesh:

Year:  2013        PMID: 23884944      PMCID: PMC6618678          DOI: 10.1523/JNEUROSCI.0122-13.2013

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  60 in total

1.  Long-term training modifies the modular structure and organization of walking balance control.

Authors:  Andrew Sawers; Jessica L Allen; Lena H Ting
Journal:  J Neurophysiol       Date:  2015-10-14       Impact factor: 2.714

2.  Learning new gait patterns: Exploratory muscle activity during motor learning is not predicted by motor modules.

Authors:  Rajiv Ranganathan; Chandramouli Krishnan; Yasin Y Dhaher; William Z Rymer
Journal:  J Biomech       Date:  2016-02-10       Impact factor: 2.712

3.  Speech function of the oropharyngeal isthmus: A modeling study.

Authors:  Bryan Gick; Peter Anderson; Hui Chen; Chenhao Chiu; Ho Beom Kwon; Ian Stavness; Ling Tsou; Sidney Fels
Journal:  Comput Methods Biomech Biomed Eng Imaging Vis       Date:  2014

4.  Distinct types of neural reorganization during long-term learning.

Authors:  Xiao Zhou; Rex N Tien; Sadhana Ravikumar; Steven M Chase
Journal:  J Neurophysiol       Date:  2019-02-06       Impact factor: 2.714

5.  Neural basis for hand muscle synergies in the primate spinal cord.

Authors:  Tomohiko Takei; Joachim Confais; Saeka Tomatsu; Tomomichi Oya; Kazuhiko Seki
Journal:  Proc Natl Acad Sci U S A       Date:  2017-07-24       Impact factor: 11.205

6.  Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry.

Authors:  Shota Hagio; Motoki Kouzaki
Journal:  J R Soc Interface       Date:  2018-10-10       Impact factor: 4.118

7.  Adaptive neuron-to-EMG decoder training for FES neuroprostheses.

Authors:  Christian Ethier; Daniel Acuna; Sara A Solla; Lee E Miller
Journal:  J Neural Eng       Date:  2016-06-01       Impact factor: 5.379

8.  The effects of motor modularity on performance, learning and generalizability in upper-extremity reaching: a computational analysis.

Authors:  Mazen Al Borno; Jennifer L Hicks; Scott L Delp
Journal:  J R Soc Interface       Date:  2020-06-03       Impact factor: 4.118

9.  When 90% of the variance is not enough: residual EMG from muscle synergy extraction influences task performance.

Authors:  Victor R Barradas; Jason J Kutch; Toshihiro Kawase; Yasuharu Koike; Nicolas Schweighofer
Journal:  J Neurophysiol       Date:  2020-04-08       Impact factor: 2.714

Review 10.  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

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