Literature DB >> 26133797

Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics.

Sagi Perel1, Patrick T Sadtler2, Emily R Oby2, Stephen I Ryu3, Elizabeth C Tyler-Kabara4, Aaron P Batista2, Steven M Chase5.   

Abstract

A diversity of signals can be recorded with extracellular electrodes. It remains unclear whether different signal types convey similar or different information and whether they capture the same or different underlying neural phenomena. Some researchers focus on spiking activity, while others examine local field potentials, and still others posit that these are fundamentally the same signals. We examined the similarities and differences in the information contained in four signal types recorded simultaneously from multielectrode arrays implanted in primary motor cortex: well-isolated action potentials from putative single units, multiunit threshold crossings, and local field potentials (LFPs) at two distinct frequency bands. We quantified the tuning of these signal types to kinematic parameters of reaching movements. We found 1) threshold crossing activity is not a proxy for single-unit activity; 2) when examined on individual electrodes, threshold crossing activity more closely resembles LFP activity at frequencies between 100 and 300 Hz than it does single-unit activity; 3) when examined across multiple electrodes, threshold crossing activity and LFP integrate neural activity at different spatial scales; and 4) LFP power in the "beta band" (between 10 and 40 Hz) is a reliable indicator of movement onset but does not encode kinematic features on an instant-by-instant basis. These results show that the diverse signals recorded from extracellular electrodes provide somewhat distinct and complementary information. It may be that these signal types arise from biological phenomena that are partially distinct. These results also have practical implications for harnessing richer signals to improve brain-machine interface control.
Copyright © 2015 the American Physiological Society.

Keywords:  LFP; encoding; motor cortex; single units; threshold crossings

Mesh:

Year:  2015        PMID: 26133797      PMCID: PMC4556850          DOI: 10.1152/jn.00293.2014

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


  66 in total

1.  Recursive bayesian decoding of motor cortical signals by particle filtering.

Authors:  A E Brockwell; A L Rojas; R E Kass
Journal:  J Neurophysiol       Date:  2004-04       Impact factor: 2.714

2.  An extensible infrastructure for fully automated spike sorting during online experiments.

Authors:  Gopal Santhanam; Maneesh Sahani; Stephen Ryu; Krishna Shenoy
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2004

3.  Large-scale organization of preferred directions in the motor cortex. II. Analysis of local distributions.

Authors:  Thomas Naselaris; Hugo Merchant; Bagrat Amirikian; Apostolos P Georgopoulos
Journal:  J Neurophysiol       Date:  2006-09-13       Impact factor: 2.714

4.  Real-time decoding of nonstationary neural activity in motor cortex.

Authors:  Wei Wu; Nicholas G Hatsopoulos
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2008-06       Impact factor: 3.802

5.  Dependence of the activity of interpositus and red nucleus neurons on sensory input data generated by movement.

Authors:  J E Burton; N Onoda
Journal:  Brain Res       Date:  1978-08-18       Impact factor: 3.252

6.  The utility of multichannel local field potentials for brain-machine interfaces.

Authors:  Eun Jung Hwang; Richard A Andersen
Journal:  J Neural Eng       Date:  2013-06-07       Impact factor: 5.379

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.  Combining Wireless Neural Recording and Video Capture for the Analysis of Natural Gait.

Authors:  Justin D Foster; Oren Freifeld; Paul Nuyujukian; Stephen I Ryu; Michael J Black; Krishna V Shenoy
Journal:  Int IEEE EMBS Conf Neural Eng       Date:  2011 Apr-May

9.  Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.

Authors:  Julie Dethier; Paul Nuyujukian; Stephen I Ryu; Krishna V Shenoy; Kwabena Boahen
Journal:  J Neural Eng       Date:  2013-04-10       Impact factor: 5.379

10.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.

Authors:  Leigh R Hochberg; Daniel Bacher; Beata Jarosiewicz; Nicolas Y Masse; John D Simeral; Joern Vogel; Sami Haddadin; Jie Liu; Sydney S Cash; Patrick van der Smagt; John P Donoghue
Journal:  Nature       Date:  2012-05-16       Impact factor: 49.962

View more
  20 in total

1.  Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface.

Authors:  Nicholas A Sachs; Ricardo Ruiz-Torres; Eric J Perreault; Lee E Miller
Journal:  J Neural Eng       Date:  2015-12-11       Impact factor: 5.379

2.  Evaluation of Decoding Algorithms for Estimating Bladder Pressure from Dorsal Root Ganglia Neural Recordings.

Authors:  Shani E Ross; Zhonghua Ouyang; Sai Rajagopalan; Tim M Bruns
Journal:  Ann Biomed Eng       Date:  2017-11-27       Impact factor: 3.934

3.  The critical stability task: quantifying sensory-motor control during ongoing movement in nonhuman primates.

Authors:  Kristin M Quick; Jessica L Mischel; Patrick J Loughlin; Aaron P Batista
Journal:  J Neurophysiol       Date:  2018-06-27       Impact factor: 2.714

4.  Continuous decoding of human grasp kinematics using epidural and subdural signals.

Authors:  Robert D Flint; Joshua M Rosenow; Matthew C Tate; Marc W Slutzky
Journal:  J Neural Eng       Date:  2016-11-30       Impact factor: 5.379

5.  Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts' law.

Authors:  Francis R Willett; Brian A Murphy; William D Memberg; Christine H Blabe; Chethan Pandarinath; Benjamin L Walter; Jennifer A Sweet; Jonathan P Miller; Jaimie M Henderson; Krishna V Shenoy; Leigh R Hochberg; Robert F Kirsch; A Bolu Ajiboye
Journal:  J Neural Eng       Date:  2017-02-08       Impact factor: 5.379

6.  Accurate Estimation of Neural Population Dynamics without Spike Sorting.

Authors:  Eric M Trautmann; Sergey D Stavisky; Subhaneil Lahiri; Katherine C Ames; Matthew T Kaufman; Daniel J O'Shea; Saurabh Vyas; Xulu Sun; Stephen I Ryu; Surya Ganguli; Krishna V Shenoy
Journal:  Neuron       Date:  2019-06-03       Impact factor: 17.173

7.  Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner.

Authors:  Cecilia Gallego-Carracedo; Matthew G Perich; Raeed H Chowdhury; Lee E Miller; Juan Álvaro Gallego
Journal:  Elife       Date:  2022-08-15       Impact factor: 8.713

8.  Power-saving design opportunities for wireless intracortical brain-computer interfaces.

Authors:  Nir Even-Chen; Dante G Muratore; Sergey D Stavisky; Leigh R Hochberg; Jaimie M Henderson; Boris Murmann; Krishna V Shenoy
Journal:  Nat Biomed Eng       Date:  2020-08-03       Impact factor: 25.671

9.  Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters.

Authors:  Emily R Oby; Sagi Perel; Patrick T Sadtler; Douglas A Ruff; Jessica L Mischel; David F Montez; Marlene R Cohen; Aaron P Batista; Steven M Chase
Journal:  J Neural Eng       Date:  2016-04-21       Impact factor: 5.379

10.  Making brain-machine interfaces robust to future neural variability.

Authors:  David Sussillo; Sergey D Stavisky; Jonathan C Kao; Stephen I Ryu; Krishna V Shenoy
Journal:  Nat Commun       Date:  2016-12-13       Impact factor: 14.919

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.