A major goal in neuroscience is to understand the computations performed by local brain circuits. A large obstacle to achieving this goal is that - at least in mammals - we currently cannot observe the spiking activity of most neurons within a circuit. A key reason is that standard electrodes are just too big, and provoke too much damage to brain tissue. If placed with high enough density to sample a majority of neurons, they would destroy the very circuit they are intended to monitor. Another important obstacle to understanding local brain computations is that circuit dynamics are rapidly and dramatically altered by chemical neuromodulators, which normally go unobserved. Real-time monitoring of critical modulators such as dopamine can be achieved using fast-scan cyclic voltammetry, but this method has not yet been effectively combined with large-scale circuit recordings. The proposed work would make important progress towards overcoming these obstacles, using ultra-dense arrays of 8m carbon thread electrodes. These are stiff enough to insert deep into the brain, yet small enough to avoid a destructive immune response. By using an 80m distance between electrodes, the great majority of neurons within a cortical layer would be within recording range. Furthermore, carbon thread electrodes are well-suited for chemical sensing using voltammetry. This proposal is to construct advanced new tools for neuroscientific investigation in a series of modular steps, culminating in 1024-channel, combined electrophysiological and electrochemical recording in freely-behaving rats.
Aim 1 involves the development and testing of silicon frameworks that allow assembly of ultra-dense arrays, together with updated headstages that allow hundreds of channels to be monitored simultaneously.
Aim 2 will exploit the ability of carbon thread electrodes to be sliced in situ during histological processing. This greatly facilitates the ability to localize individual recordig sites within microcircuit architecture, and to identify individual recorded neurons.
Aim 3 involves further optimization of carbon thread electrodes for chemical sensing, and joint single-unit recording and fast-scan cyclic voltammetry across many electrodes simultaneously. Overall this project combines expertise in electrical engineering, neurophysiology, and neurochemistry to create innovative, powerful devices that will be widely disseminated and may have transformational impact for our understanding of how our brains work.

Public Health Relevance

The proposed project would develop advanced new devices for investigating how brain circuits work, and how they are perturbed in neurological and psychiatric disorders. In addition to supporting basic and preclinical scientific research, these devices may eventually be used to enhance human health as diagnostic tools and components of neural prostheses.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project--Cooperative Agreements (U01)
Project #
3U01NS094375-02S1
Application #
9269029
Study Section
Special Emphasis Panel (ZNS1 (02))
Program Officer
Aguel, Felipe
Project Start
2015-09-30
Project End
2018-07-31
Budget Start
2016-08-01
Budget End
2017-07-31
Support Year
2
Fiscal Year
2016
Total Cost
$154,997
Indirect Cost
$54,999
Name
University of Michigan Ann Arbor
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Hill, Mackenna; Rios, Estefania; Sudhakar, Shyam Kumar et al. (2018) Quantitative simulation of extracellular single unit recording from the surface of cortex. J Neural Eng 15:056007
Angulo-Garcia, David; Berke, Joshua D; Torcini, Alessandro (2016) Cell Assembly Dynamics of Sparsely-Connected Inhibitory Networks: A Simple Model for the Collective Activity of Striatal Projection Neurons. PLoS Comput Biol 12:e1004778
Patel, Paras R; Zhang, Huanan; Robbins, Matthew T et al. (2016) Chronic in vivo stability assessment of carbon fiber microelectrode arrays. J Neural Eng 13:066002