Literature DB >> 22131376

Physical model of coherent potentials measured with different electrode recording site sizes.

Matthew J Nelson1, Pierre Pouget.   

Abstract

A question that still complicates interpretation of local field potentials (LFPs) is how electrode properties like impedance, size, and shape affect recorded LFPs. In addition, how any such effects should be considered when comparing LFP, electroencephalogram (EEG), or electrocorticogram (ECoG) data has not been clearly described. A generally accepted concrete physical model describes that an electrode records the spatial average of the voltage across its uninsulated tip, yet the effects of this spatial averaging on recorded coherence have never been modeled. Using simulations based on this physical model, we show here that for any effects to occur, a spatial voltage gradient on a scale smaller than an electrode's recording site must exist over the site's surface. When this occurs, larger electrodes on average report higher coherence between locations, with the effect continuously increasing as the voltage profile over the extent of the recording site is increasingly nonuniform. We quantitatively compared published coherence estimates of LFP, ECoG, and EEG data across a range of studies and found a possible modest effect of electrode size in published ECoG data only. We used the model to quantify the expected coherence for any electrode size in relation to any given spatial frequency of a voltage profile. From this and existing estimates of the spread of voltages underlying each of these data types, our simulations quantitatively agree with the published data and importantly suggest that LFP coherence will be independent of recording site size within the range of microelectrodes typically used for extracellular recordings.

Mesh:

Year:  2011        PMID: 22131376     DOI: 10.1152/jn.00177.2011

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  6 in total

1.  Cellular Classes in the Human Brain Revealed In Vivo by Heartbeat-Related Modulation of the Extracellular Action Potential Waveform.

Authors:  Clayton P Mosher; Yina Wei; Jan Kamiński; Anirban Nandi; Adam N Mamelak; Costas A Anastassiou; Ueli Rutishauser
Journal:  Cell Rep       Date:  2020-03-10       Impact factor: 9.423

2.  Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study.

Authors:  C G Bénar; C Grova; V K Jirsa; J M Lina
Journal:  J Comput Neurosci       Date:  2019-07-11       Impact factor: 1.621

3.  Fast Oscillatory Commands from the Motor Cortex Can Be Decoded by the Spinal Cord for Force Control.

Authors:  Renato N Watanabe; Andre F Kohn
Journal:  J Neurosci       Date:  2015-10-07       Impact factor: 6.167

4.  A multi-channel, flex-rigid ECoG microelectrode array for visual cortical interfacing.

Authors:  Elena Tolstosheeva; Víctor Gordillo-González; Volker Biefeld; Ludger Kempen; Sunita Mandon; Andreas K Kreiter; Walter Lang
Journal:  Sensors (Basel)       Date:  2015-01-06       Impact factor: 3.576

5.  Brain rhythms define distinct interaction networks with differential dependence on anatomy.

Authors:  Julien Vezoli; Martin Vinck; Conrado Arturo Bosman; André Moraes Bastos; Christopher Murphy Lewis; Henry Kennedy; Pascal Fries
Journal:  Neuron       Date:  2021-10-20       Impact factor: 17.173

6.  Consequences of EEG electrode position error on ultimate beamformer source reconstruction performance.

Authors:  Sarang S Dalal; Stefan Rampp; Florian Willomitzer; Svenja Ettl
Journal:  Front Neurosci       Date:  2014-03-11       Impact factor: 4.677

  6 in total

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