Addiction is closely linked with dysfunction of dopamine transmission in the brain circuitry of reward. Despite many decades of work, little is known about how systems-level phenomena in this pathway encode information, and how this information regulates behavior. The broad objective of this project is to introduce a new systems-level recording methodology to the arsenal of addiction and reward brain circuitry research. This study will demonstrate the feasibility of combining systems-level with molecular-level neuroscience, using implantable multi-electrodes to record extracellular single-unit activity and selective activation of neuronal subpopulations that mediate reward. Due to the highly interconnected and geometrically distributed nature of dopamine reward circuitry, it has been challenging to discern the network-wide dynamics of pathways recruited in reward-related behaviors such as addiction. To address this problem, the proposed recording instrument will deploy silicon shafts containing high-density electrodes at multiple locations in the brain. Other multi- electrode probe technologies lack the number of channels and geometry to record large numbers of neurons from deep and distributed areas required for this project. The devices proposed here will be built using nanofabrication methods to facilitate minimally invasive insertion of several multi-electrode-containing shafts throughout the mouse brain. Unlike traditional functional scanning techniques such as fMRI, the implantable devices will offer single-unit and sub-millisecond resolution. Functional control of activity in the pathway will be achieved by optogenetically activating dopaminergic neurons in the midbrain, mimicking the action of a rewarding stimulus. To test the ability to resolve systems-level activity of this dopaminergic neuron perturbation, we will implement two recording strategies. In the first approach, we will progressively scan a two-dimensional (2D) probe across an anatomical volume of interest, effectively constructing a high-resolution 3D map of action potential activity. In the second approach, we will simultaneously monitor the response of several brain areas associated with addiction, to capture the activity of up to 2,000 neurons in parallel in several distributed, but interconnected hubs in the mouse brain. In the long term the proposed devices and experimental protocols will provide a new window into the role of collective dynamic phenomena in the brain. Moreover, this technique will have broad impact on many aspects of neurophysiological and behavioral research, including reward-mediated learning and Parkinson's disease.

Public Health Relevance

Systems-level neuronal interactions are critical to understanding disorders such as addiction, but have been challenging to study in the intact brain. This project will support the development of recording instrumentation and accompanying experimental protocols for high-throughput, systems-level measurements of distributed and interconnected deep brain structures implicated in addiction and reward-mediated behaviors.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Research Project (R01)
Project #
4R01DA034178-05
Application #
9100680
Study Section
Bioengineering of Neuroscience, Vision and Low Vision Technologies Study Section (BNVT)
Program Officer
Sorensen, Roger
Project Start
2012-09-01
Project End
2017-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
5
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of California Los Angeles
Department
Neurosciences
Type
Schools of Medicine
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
Lee, Kwang; Holley, Sandra M; Shobe, Justin L et al. (2018) Parvalbumin Interneurons Modulate Striatal Output and Enhance Performance during Associative Learning. Neuron 99:239
Li, Bingzhao; Lee, Kwang; Masmanidis, Sotiris C et al. (2018) A nanofabricated optoelectronic probe for manipulating and recording neural dynamics. J Neural Eng 15:046008
Bakhurin, Konstantin I; Goudar, Vishwa; Shobe, Justin L et al. (2017) Differential Encoding of Time by Prefrontal and Striatal Network Dynamics. J Neurosci 37:854-870
Shobe, Justin L; Bakhurin, Konstantin I; Claar, Leslie D et al. (2017) Selective Modulation of Orbitofrontal Network Activity during Negative Occasion Setting. J Neurosci 37:9415-9423
Lee, Kwang; Holley, Sandra M; Shobe, Justin L et al. (2017) Parvalbumin Interneurons Modulate Striatal Output and Enhance Performance during Associative Learning. Neuron 93:1451-1463.e4
Bakhurin, Konstantin I; Mac, Victor; Golshani, Peyman et al. (2016) Temporal correlations among functionally specialized striatal neural ensembles in reward-conditioned mice. J Neurophysiol 115:1521-32
Nigam, Sunny; Shimono, Masanori; Ito, Shinya et al. (2016) Rich-Club Organization in Effective Connectivity among Cortical Neurons. J Neurosci 36:670-84
Smith, Wesley C; Rosenberg, Matthew H; Claar, Leslie D et al. (2016) Frontostriatal Circuit Dynamics Correlate with Cocaine Cue-Evoked Behavioral Arousal during Early Abstinence. eNeuro 3:
Shobe, Justin L; Claar, Leslie D; Parhami, Sepideh et al. (2015) Brain activity mapping at multiple scales with silicon microprobes containing 1,024 electrodes. J Neurophysiol 114:2043-52