Literature DB >> 16190906

Frontal and parietal cortical ensembles predict single-trial muscle activity during reaching movements in primates.

David M Santucci1, Jerald D Kralik, Mikhail A Lebedev, Miguel A L Nicolelis.   

Abstract

Previously we have shown that the kinematic parameters of reaching movements can be extracted from the activity of cortical ensembles. Here we used cortical ensemble activity to predict electromyographic (EMG) signals of four arm muscles in New World monkeys. The overall shape of the EMG envelope was predicted, as well as trial-to-trial variations in the amplitude and timing of bursts of muscle activity. Predictions of EMG patterns exhibited during reaching movements could be obtained not only from primary motor cortex, but also from dorsal premotor, primary somatosensory and posterior parietal cortices. These results suggest that these areas represent signals correlated to EMGs of arm muscles in a distributed manner, and that the larger the population sampled, the more reliable the predictions. We propose that, in the future, recordings from multiple cortical areas and the extraction of muscle patterns from these recordings will help to restore limb mobility in paralysed patients.

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Year:  2005        PMID: 16190906     DOI: 10.1111/j.1460-9568.2005.04320.x

Source DB:  PubMed          Journal:  Eur J Neurosci        ISSN: 0953-816X            Impact factor:   3.386


  21 in total

1.  Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials.

Authors:  Arjun K Bansal; Wilson Truccolo; Carlos E Vargas-Irwin; John P Donoghue
Journal:  J Neurophysiol       Date:  2011-12-07       Impact factor: 2.714

2.  Direct comparison of the task-dependent discharge of M1 in hand space and muscle space.

Authors:  M M Morrow; L R Jordan; L E Miller
Journal:  J Neurophysiol       Date:  2006-11-22       Impact factor: 2.714

3.  Neuronal correlates of signal detection in the posterior parietal cortex of rats performing a sustained attention task.

Authors:  J Broussard; M Sarter; B Givens
Journal:  Neuroscience       Date:  2006-10-11       Impact factor: 3.590

4.  Prediction of upper limb muscle activity from motor cortical discharge during reaching.

Authors:  Eric A Pohlmeyer; Sara A Solla; Eric J Perreault; Lee E Miller
Journal:  J Neural Eng       Date:  2007-11-12       Impact factor: 5.379

5.  Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms.

Authors:  Katie Z Zhuang; Mikhail A Lebedev; Miguel A L Nicolelis
Journal:  J Neurophysiol       Date:  2014-09-10       Impact factor: 2.714

6.  Spatially dynamic recurrent information flow across long-range dorsal motor network encodes selective motor goals.

Authors:  Peter E Yoo; Maureen A Hagan; Sam E John; Nicholas L Opie; Roger J Ordidge; Terence J O'Brien; Thomas J Oxley; Bradford A Moffat; Yan T Wong
Journal:  Hum Brain Mapp       Date:  2018-03-08       Impact factor: 5.038

7.  A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces.

Authors:  John P Cunningham; Paul Nuyujukian; Vikash Gilja; Cindy A Chestek; Stephen I Ryu; Krishna V Shenoy
Journal:  J Neurophysiol       Date:  2010-10-13       Impact factor: 2.714

8.  A brain-machine interface enables bimanual arm movements in monkeys.

Authors:  Peter J Ifft; Solaiman Shokur; Zheng Li; Mikhail A Lebedev; Miguel A L Nicolelis
Journal:  Sci Transl Med       Date:  2013-11-06       Impact factor: 17.956

Review 9.  The science of neural interface systems.

Authors:  Nicholas G Hatsopoulos; John P Donoghue
Journal:  Annu Rev Neurosci       Date:  2009       Impact factor: 12.449

Review 10.  Restoring sensorimotor function through intracortical interfaces: progress and looming challenges.

Authors:  Sliman J Bensmaia; Lee E Miller
Journal:  Nat Rev Neurosci       Date:  2014-05       Impact factor: 34.870

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