Literature DB >> 34623400

Volitional control of individual neurons in the human brain.

Kramay Patel1,2,3,4, Chaim N Katz1,2,4, Suneil K Kalia1,4,5, Milos R Popovic1,2,4,6, Taufik A Valiante1,2,4,5,6,7,8.   

Abstract

Brain-machine interfaces allow neuroscientists to causally link specific neural activity patterns to a particular behaviour. Thus, in addition to their current clinical applications, brain-machine interfaces can also be used as a tool to investigate neural mechanisms of learning and plasticity in the brain. Decades of research using such brain-machine interfaces have shown that animals (non-human primates and rodents) can be operantly conditioned to self-regulate neural activity in various motor-related structures of the brain. Here, we ask whether the human brain, a complex interconnected structure of over 80 billion neurons, can learn to control itself at the most elemental scale-a single neuron. We used the unique opportunity to record single units in 11 individuals with epilepsy to explore whether the firing rate of a single (direct) neuron in limbic and other memory-related brain structures can be brought under volitional control. To do this, we developed a visual neurofeedback task in which participants were trained to move a block on a screen by modulating the activity of an arbitrarily selected neuron from their brain. Remarkably, participants were able to volitionally modulate the firing rate of the direct neuron in these previously uninvestigated structures. We found that a subset of participants (learners), were able to improve their performance within a single training session. Successful learning was characterized by (i) highly specific modulation of the direct neuron (demonstrated by significantly increased firing rates and burst frequency); (ii) a simultaneous decorrelation of the activity of the direct neuron from the neighbouring neurons; and (iii) robust phase-locking of the direct neuron to local alpha/beta-frequency oscillations, which may provide some insights in to the potential neural mechanisms that facilitate this type of learning. Volitional control of neuronal activity in mnemonic structures may provide new ways of probing the function and plasticity of human memory without exogenous stimulation. Furthermore, self-regulation of neural activity in these brain regions may provide an avenue for the development of novel neuroprosthetics for the treatment of neurological conditions that are commonly associated with pathological activity in these brain structures, such as medically refractory epilepsy.
© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  brain–machine interfaces; epilepsy; instrumental conditioning; limbic; neurofeedback

Mesh:

Year:  2021        PMID: 34623400      PMCID: PMC8719845          DOI: 10.1093/brain/awab370

Source DB:  PubMed          Journal:  Brain        ISSN: 0006-8950            Impact factor:   15.255


  73 in total

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Authors:  Ranganatha Sitaram; Tomas Ros; Luke Stoeckel; Sven Haller; Frank Scharnowski; Jarrod Lewis-Peacock; Nikolaus Weiskopf; Maria Laura Blefari; Mohit Rana; Ethan Oblak; Niels Birbaumer; James Sulzer
Journal:  Nat Rev Neurosci       Date:  2016-12-22       Impact factor: 34.870

4.  Operant conditioning of neural activity in freely behaving monkeys with intracranial reinforcement.

Authors:  Ryan W Eaton; Tyler Libey; Eberhard E Fetz
Journal:  J Neurophysiol       Date:  2016-12-28       Impact factor: 2.714

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Journal:  Neuroscience       Date:  1989       Impact factor: 3.590

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Authors:  E E Fetz
Journal:  Science       Date:  1969-02-28       Impact factor: 47.728

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Authors:  Vivek R Athalye; Fernando J Santos; Jose M Carmena; Rui M Costa
Journal:  Science       Date:  2018-03-02       Impact factor: 47.728

8.  Organization of the output of the ventral striatopallidal system in the rat: ventral pallidal efferents.

Authors:  H J Groenewegen; H W Berendse; S N Haber
Journal:  Neuroscience       Date:  1993-11       Impact factor: 3.590

9.  Human memory strength is predicted by theta-frequency phase-locking of single neurons.

Authors:  Ueli Rutishauser; Ian B Ross; Adam N Mamelak; Erin M Schuman
Journal:  Nature       Date:  2010-03-24       Impact factor: 49.962

10.  Dominant frequencies of resting human brain activity as measured by the electrocorticogram.

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Journal:  Neuroimage       Date:  2013-04-30       Impact factor: 6.556

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  1 in total

1.  Cognitive Control as a Multivariate Optimization Problem.

Authors:  Harrison Ritz; Xiamin Leng; Amitai Shenhav
Journal:  J Cogn Neurosci       Date:  2022-03-05       Impact factor: 3.225

  1 in total

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