Literature DB >> 17634356

Temporal patterning of saccadic eye movement signals.

Daniel L Kimmel1, Tirin Moore.   

Abstract

Electrical microstimulation is used widely in experimental neurophysiology to examine causal links between specific brain areas and their behavioral functions and is used clinically to treat neurological and psychiatric disorders in patients. Typically, microstimulation is applied to local brain regions as a train of equally spaced current pulses. We were interested in the sensitivity of a neural circuit to a train of variably spaced pulses, as is observed in physiological spike trains. We compared the effect of fixed, decelerating, accelerating, and randomly varying microstimulation patterns on the likelihood and metrics of eye movements evoked from the frontal eye field of monkeys, while holding the mean interpulse interval constant. Our results demonstrate that the pattern of microstimulation pulses strongly influences the probability of evoking a saccade, as well as the metrics of the saccades themselves. Specifically, the pattern most closely resembling physiological spike trains (accelerating pattern) was most effective at evoking a saccade, three times more so than the least effective decelerating pattern. A saccade-triggered average of effective random trains confirmed the positive relationship between accelerating rate and efficacy. These results have important implications for the use of electrical microstimulation in both experimental and clinical settings and suggest a means to study the role of temporal pattern in the encoding of behavioral and cognitive functions.

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Year:  2007        PMID: 17634356      PMCID: PMC6672894          DOI: 10.1523/JNEUROSCI.0386-07.2007

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


  17 in total

1.  The relative impact of microstimulation parameters on movement generation.

Authors:  Husam A Katnani; Neeraj J Gandhi
Journal:  J Neurophysiol       Date:  2012-04-25       Impact factor: 2.714

Review 2.  Insights into cortical mechanisms of behavior from microstimulation experiments.

Authors:  Mark H Histed; Amy M Ni; John H R Maunsell
Journal:  Prog Neurobiol       Date:  2012-01-28       Impact factor: 11.685

3.  Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex.

Authors:  Sungshin Kim; Thierri Callier; Gregg A Tabot; Robert A Gaunt; Francesco V Tenore; Sliman J Bensmaia
Journal:  Proc Natl Acad Sci U S A       Date:  2015-10-26       Impact factor: 11.205

4.  Pulse-pattern sensitivity in the frontal eye field of the macaque monkey.

Authors:  Pierre Pouget; Mathew J Nelson; Jeremiah Y Cohen; Richard P Heitz
Journal:  J Neurosci       Date:  2007-10-31       Impact factor: 6.167

5.  Proposing a two-level stochastic model for epileptic seizure genesis.

Authors:  F Shayegh; S Sadri; R Amirfattahi; K Ansari-Asl
Journal:  J Comput Neurosci       Date:  2013-06-04       Impact factor: 1.621

Review 6.  Probing neural circuitry and function with electrical microstimulation.

Authors:  Kelsey L Clark; Katherine M Armstrong; Tirin Moore
Journal:  Proc Biol Sci       Date:  2011-01-19       Impact factor: 5.349

7.  Microstimulation reveals limits in detecting different signals from a local cortical region.

Authors:  Amy M Ni; John H R Maunsell
Journal:  Curr Biol       Date:  2010-04-08       Impact factor: 10.834

8.  A dynamic, imperturbable link between midbrain activity and saccade velocity.

Authors:  Joshua A Seideman
Journal:  J Neurophysiol       Date:  2019-10-02       Impact factor: 2.714

9.  Direct activation of sparse, distributed populations of cortical neurons by electrical microstimulation.

Authors:  Mark H Histed; Vincent Bonin; R Clay Reid
Journal:  Neuron       Date:  2009-08-27       Impact factor: 17.173

10.  Temporally-patterned deep brain stimulation in a mouse model of multiple traumatic brain injury.

Authors:  Inna Tabansky; Amy Wells Quinkert; Nadera Rahman; Salomon Zev Muller; Jesper Lofgren; Johan Rudling; Alyssa Goodman; Yingping Wang; Donald W Pfaff
Journal:  Behav Brain Res       Date:  2014-07-27       Impact factor: 3.332

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