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The use of macroelectrodes in recording cellular spiking activity

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Title: The use of macroelectrodes in recording cellular spiking activity
Authors: Winestone, John S.1,2,3 john.winestone@gmail.com, Zaidel, Adam4, Bergman, Hagai3, Israel, Zvi5
Source: Journal of Neuroscience Methods. Apr2012, Vol. 206 Issue 1, p34-39. 6p.
Subjects: Electrophysiology, Brain stimulation, Electrodes, Subthalamus, Neurophysiology, Data analysis
Abstract: Abstract: Microelectrode recording (MER) is an important navigational and investigational tool, specifically with regard to deep brain stimulation (DBS) surgery. MER is often utilized when targeting the subthalamic nucleus (STN) and other deep brain nuclei in the management of Parkinson''s disease (PD), tremor, dystonia and other emerging applications. Microelectrodes are used to detect and measure cellular spiking activity while macroelectrodes are considered more suitable for measuring the collective sum of slow potentials from multiple cells near the electrode, the local field potential (LFP). Precisely how the characteristics of an electrode affect the data recorded is still unclear. Technical idiosyncrasies of some surgical cases allowed serendipitous data collection from a 250 to 6000Hz bandpassed macroelectrode recording during DBS implantation for PD. Simultaneous recording from both a microelectrode and macroelectrode were compared along the same surgical trajectory. Audio, normalized root mean square of the recorded signal, and power spectrograms were used to analyze the data. The analyses demonstrate similar results in detecting cellular spiking activity when recording with macroelectrodes compared with microelectrodes. This has important implications for the standardization of recording electrophysiological data as well as for the development of next generation closed-loop deep brain stimulation systems. [Copyright &y& Elsevier]
Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: The use of macroelectrodes in recording cellular spiking activity
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  Data: Abstract: Microelectrode recording (MER) is an important navigational and investigational tool, specifically with regard to deep brain stimulation (DBS) surgery. MER is often utilized when targeting the subthalamic nucleus (STN) and other deep brain nuclei in the management of Parkinson''s disease (PD), tremor, dystonia and other emerging applications. Microelectrodes are used to detect and measure cellular spiking activity while macroelectrodes are considered more suitable for measuring the collective sum of slow potentials from multiple cells near the electrode, the local field potential (LFP). Precisely how the characteristics of an electrode affect the data recorded is still unclear. Technical idiosyncrasies of some surgical cases allowed serendipitous data collection from a 250 to 6000Hz bandpassed macroelectrode recording during DBS implantation for PD. Simultaneous recording from both a microelectrode and macroelectrode were compared along the same surgical trajectory. Audio, normalized root mean square of the recorded signal, and power spectrograms were used to analyze the data. The analyses demonstrate similar results in detecting cellular spiking activity when recording with macroelectrodes compared with microelectrodes. This has important implications for the standardization of recording electrophysiological data as well as for the development of next generation closed-loop deep brain stimulation systems. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Apr2012
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