Title |
Proximity of Substantia Nigra Microstimulation to Putative GABAergic Neurons Predicts Modulation of Human Reinforcement Learning
|
---|---|
Published in |
Frontiers in Human Neuroscience, May 2017
|
DOI | 10.3389/fnhum.2017.00200 |
Pubmed ID | |
Authors |
Ashwin G. Ramayya, Isaac Pedisich, Deborah Levy, Anastasia Lyalenko, Paul Wanda, Daniel Rizzuto, Gordon H. Baltuch, Michael J. Kahana |
Abstract |
Neuronal firing in the substantia nigra (SN) immediately following reward is thought to play a crucial role in human reinforcement learning. As in Ramayya et al. (2014a) we applied microstimulation in the SN of patients undergoing deep brain stimulation (DBS) for the treatment of Parkinson's disease as they engaged in a two-alternative reinforcement learning task. We obtained microelectrode recordings to assess the proximity of the electrode tip to putative dopaminergic and GABAergic SN neurons and applied stimulation to assess the functional importance of these neuronal populations for learning. We found that the proximity of SN microstimulation to putative GABAergic neurons predicted the degree of stimulation-related changes in learning. These results extend previous work by supporting a specific role for SN GABA firing in reinforcement learning. Stimulation near these neurons appears to dampen the reinforcing effect of rewarding stimuli. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 1 | 17% |
Switzerland | 1 | 17% |
Unknown | 4 | 67% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 28 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 8 | 29% |
Student > Ph. D. Student | 5 | 18% |
Student > Master | 3 | 11% |
Student > Postgraduate | 2 | 7% |
Student > Doctoral Student | 1 | 4% |
Other | 4 | 14% |
Unknown | 5 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 10 | 36% |
Computer Science | 3 | 11% |
Psychology | 3 | 11% |
Engineering | 3 | 11% |
Medicine and Dentistry | 1 | 4% |
Other | 2 | 7% |
Unknown | 6 | 21% |