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Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): comparing multi-electrode recordings from simulated and biological mammalian cortical tissue.

Tomsett RJ, Ainsworth M, Thiele A, Sanayei M, Chen X, Gieselmann MA, Whittington MA, Cunningham MO, Kaiser M - Brain Struct Funct (2014)

Bottom Line: We first identified a reduced neuron model that retained the spatial and frequency filtering characteristics of extracellular potentials from neocortical neurons.A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue.We envisage that VERTEX will stimulate experimentalists, clinicians, and computational neuroscientists to use models to understand the mechanisms underlying measured brain dynamics in health and disease.

View Article: PubMed Central - PubMed

Affiliation: School of Computing Science, Newcastle University, Claremont Tower, Newcastle upon Tyne, NE1 7RU, UK, indigentmartian@gmail.com.

ABSTRACT
Local field potentials (LFPs) sampled with extracellular electrodes are frequently used as a measure of population neuronal activity. However, relating such measurements to underlying neuronal behaviour and connectivity is non-trivial. To help study this link, we developed the Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX). We first identified a reduced neuron model that retained the spatial and frequency filtering characteristics of extracellular potentials from neocortical neurons. We then developed VERTEX as an easy-to-use Matlab tool for simulating LFPs from large populations (>100,000 neurons). A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue. Our model, with virtual electrodes placed anywhere in 3D, allows direct comparisons with the in vitro recording setup. We envisage that VERTEX will stimulate experimentalists, clinicians, and computational neuroscientists to use models to understand the mechanisms underlying measured brain dynamics in health and disease.

No MeSH data available.


Changes in connectivity between neuron groups after slice cutting. a Expected number of connections from population of presynaptic neurons (columns) onto single postsynaptic neurons (rows) before slicing, based on the data from Binzegger et al. (2004). b Illustration of the effect of slice cutting on a presynaptic neuron’s (light green dot) axonal arborisation (shaded area). Figure orientation is as if looking down onto the surface of the brain, with slice boundaries indicated by the black bounding box. Connections within the green shaded area remain, but those in the grey shadedareas are removed by slicing. c Connectivity in the cortical slice model, as altered from a by slice cutting. While overall connection number decreases (note different scale bars), some connections are affected more than others because of differing axonal arborisation sizes. d Difference matrix showing the percentage change in number of synapses from slice cutting
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Fig6: Changes in connectivity between neuron groups after slice cutting. a Expected number of connections from population of presynaptic neurons (columns) onto single postsynaptic neurons (rows) before slicing, based on the data from Binzegger et al. (2004). b Illustration of the effect of slice cutting on a presynaptic neuron’s (light green dot) axonal arborisation (shaded area). Figure orientation is as if looking down onto the surface of the brain, with slice boundaries indicated by the black bounding box. Connections within the green shaded area remain, but those in the grey shadedareas are removed by slicing. c Connectivity in the cortical slice model, as altered from a by slice cutting. While overall connection number decreases (note different scale bars), some connections are affected more than others because of differing axonal arborisation sizes. d Difference matrix showing the percentage change in number of synapses from slice cutting

Mentions: Figure 6 shows the number of connections between neuron groups compared with the original numbers specified by Binzegger et al. (2004). The proportional reduction in synapses is not the same for each connection type because of the varying axonal arborisation radii. These reductions are important to consider when assessing the effect of connectivity changes on dynamics, but they illustrate that the general profile of connections between neuron groups is not substantially altered—connections from P2/3 to P2/3 and P5 neurons remain the most numerous, for example. Modelling thinner slices, or different axon arborisation profiles, could lead to the over- or under-representation of particular connections in the model.Fig. 6


Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): comparing multi-electrode recordings from simulated and biological mammalian cortical tissue.

Tomsett RJ, Ainsworth M, Thiele A, Sanayei M, Chen X, Gieselmann MA, Whittington MA, Cunningham MO, Kaiser M - Brain Struct Funct (2014)

Changes in connectivity between neuron groups after slice cutting. a Expected number of connections from population of presynaptic neurons (columns) onto single postsynaptic neurons (rows) before slicing, based on the data from Binzegger et al. (2004). b Illustration of the effect of slice cutting on a presynaptic neuron’s (light green dot) axonal arborisation (shaded area). Figure orientation is as if looking down onto the surface of the brain, with slice boundaries indicated by the black bounding box. Connections within the green shaded area remain, but those in the grey shadedareas are removed by slicing. c Connectivity in the cortical slice model, as altered from a by slice cutting. While overall connection number decreases (note different scale bars), some connections are affected more than others because of differing axonal arborisation sizes. d Difference matrix showing the percentage change in number of synapses from slice cutting
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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Fig6: Changes in connectivity between neuron groups after slice cutting. a Expected number of connections from population of presynaptic neurons (columns) onto single postsynaptic neurons (rows) before slicing, based on the data from Binzegger et al. (2004). b Illustration of the effect of slice cutting on a presynaptic neuron’s (light green dot) axonal arborisation (shaded area). Figure orientation is as if looking down onto the surface of the brain, with slice boundaries indicated by the black bounding box. Connections within the green shaded area remain, but those in the grey shadedareas are removed by slicing. c Connectivity in the cortical slice model, as altered from a by slice cutting. While overall connection number decreases (note different scale bars), some connections are affected more than others because of differing axonal arborisation sizes. d Difference matrix showing the percentage change in number of synapses from slice cutting
Mentions: Figure 6 shows the number of connections between neuron groups compared with the original numbers specified by Binzegger et al. (2004). The proportional reduction in synapses is not the same for each connection type because of the varying axonal arborisation radii. These reductions are important to consider when assessing the effect of connectivity changes on dynamics, but they illustrate that the general profile of connections between neuron groups is not substantially altered—connections from P2/3 to P2/3 and P5 neurons remain the most numerous, for example. Modelling thinner slices, or different axon arborisation profiles, could lead to the over- or under-representation of particular connections in the model.Fig. 6

Bottom Line: We first identified a reduced neuron model that retained the spatial and frequency filtering characteristics of extracellular potentials from neocortical neurons.A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue.We envisage that VERTEX will stimulate experimentalists, clinicians, and computational neuroscientists to use models to understand the mechanisms underlying measured brain dynamics in health and disease.

View Article: PubMed Central - PubMed

Affiliation: School of Computing Science, Newcastle University, Claremont Tower, Newcastle upon Tyne, NE1 7RU, UK, indigentmartian@gmail.com.

ABSTRACT
Local field potentials (LFPs) sampled with extracellular electrodes are frequently used as a measure of population neuronal activity. However, relating such measurements to underlying neuronal behaviour and connectivity is non-trivial. To help study this link, we developed the Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX). We first identified a reduced neuron model that retained the spatial and frequency filtering characteristics of extracellular potentials from neocortical neurons. We then developed VERTEX as an easy-to-use Matlab tool for simulating LFPs from large populations (>100,000 neurons). A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue. Our model, with virtual electrodes placed anywhere in 3D, allows direct comparisons with the in vitro recording setup. We envisage that VERTEX will stimulate experimentalists, clinicians, and computational neuroscientists to use models to understand the mechanisms underlying measured brain dynamics in health and disease.

No MeSH data available.