Image_3_A Novel 3D-Printed Multi-Drive System for Synchronous Electrophysiological Recording in Multiple Brain Regions.TIF
Extracellular electrophysiology has been widely applied in neural network studies. Local field potentials and single-unit activities can be recorded with high-density electrodes, which facilitate the decoding of neural codes. However, the chronic multi-regional recording is still a challenging task for achieving high placement accuracy and long-term stability. Here, we present a novel electrode design with low-cost 3D-printed parts and custom printed circuits boards. This new design could facilitate precise electrode placement in multiple brain regions simultaneously and reduce the working time for surgical procedures as well. In this paper, the design and fabrication of the 3D printed multi-channel microdrive are explained in detail. We also show the result of high-quality electrophysiological recordings in eight pain-related areas from rats and the electrode placement accuracy. This novel 3D-printed multi-drive system could achieve synchronous electrophysiological recording in multiple brain regions and facilitate future neural network research.
History
References
- https://doi.org//10.1002/hbm.23310
- https://doi.org//10.1016/j.jneumeth.2008.12.024
- https://doi.org//10.1152/jn.00785.2013
- https://doi.org//10.1523/ENEURO.0261-18.2018
- https://doi.org//10.3791/51675
- https://doi.org//10.1016/0306-4522(89)90423-5
- https://doi.org//10.1002/hipo.22488
- https://doi.org//10.1038/nrn3241
- https://doi.org//10.1016/j.neuron.2015.01.028
- https://doi.org//10.1073/pnas.1309729110
- https://doi.org//10.1146/annurev.neuro.15.1.353
- https://doi.org//10.1016/j.jneumeth.2010.11.014
- https://doi.org//10.1088/1741-2560/13/6/066013
- https://doi.org//10.1021/ac403397r
- https://doi.org//10.1016/j.jneumeth.2006.01.017
- https://doi.org//10.1152/jn.00955.2014
- https://doi.org//10.1016/0013-4694(70)90105-7
- https://doi.org//10.1038/nature24636
- https://doi.org//10.1162/NECO_a_00661
- https://doi.org//10.1016/s0165-0270(02)00115-2
- https://doi.org//10.1038/nn.3905
- https://doi.org//10.1016/j.jneumeth.2006.12.016
- https://doi.org//10.3389/fncir.2017.00008
- https://doi.org//10.1016/j.matdes.2004.02.009
- https://doi.org//10.1016/j.neuron.2009.08.037
- https://doi.org//10.3791/1098
- https://doi.org//10.1073/pnas.1934665100
- https://doi.org//10.1016/s0896-6273(00)80295-0
- https://doi.org//10.1016/0014-4886(69)90086-7
- https://doi.org//10.1523/JNEUROSCI.2036-13.2013
- https://doi.org//10.1016/j.tics.2016.12.001
- https://doi.org//10.1088/1741-2560/9/5/056015
- https://doi.org//10.1109/memb.2005.1511501
- https://doi.org//%2010.1126/science.127.3296.469
- https://doi.org//10.1016/s0165-0270(00)00362-9
- https://doi.org//10.1016/s1385-299x(99)00034-3
- https://doi.org//10.1088/1741-2552/ab05b6
Usage metrics
Read the peer-reviewed publication
Categories
- Radiology and Organ Imaging
- Decision Making
- Clinical Nursing: Tertiary (Rehabilitative)
- Image Processing
- Autonomic Nervous System
- Cellular Nervous System
- Biological Engineering
- Sensory Systems
- Central Nervous System
- Neuroscience
- Endocrinology
- Artificial Intelligence and Image Processing
- Signal Processing
- Rehabilitation Engineering
- Biomedical Engineering not elsewhere classified
- Stem Cells
- Neurogenetics
- Developmental Biology