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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.


Slice model structure and individual neuron dynamics. a Layer boundaries are given in µm. Subsets of soma locations from each neuron group are shown in faded black for excitatory neurons, or faded magenta for inhibitory neurons. Triangles represent pyramidal neuron somas, stars are spiny stellate cell somas, circles are basket interneuron somas and diamonds non-basket interneuron somas. One example full cell is shown for each neuron group, in solid black for excitatory neurons or solid magenta for inhibitory neurons. Compartment lengths are to scale; compartment diameters are not. Black circles are virtual electrode positions (first 8 rows shown). b Responses to step-current injections into the soma compartment of each neuron type. Spikes are detected and cut-off at Vt + 5 mV; we extend the spike trace up to +10 mV for illustrative purposes. Step-current magnitudes were 0.5 nA for the P2/3 neuron, 0.333 nA for the SS neuron, 1.0 nA for the P5 neuron, 0.75 nA for the P6 neuron, and 0.4 nA for the B and NB interneurons
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Fig5: Slice model structure and individual neuron dynamics. a Layer boundaries are given in µm. Subsets of soma locations from each neuron group are shown in faded black for excitatory neurons, or faded magenta for inhibitory neurons. Triangles represent pyramidal neuron somas, stars are spiny stellate cell somas, circles are basket interneuron somas and diamonds non-basket interneuron somas. One example full cell is shown for each neuron group, in solid black for excitatory neurons or solid magenta for inhibitory neurons. Compartment lengths are to scale; compartment diameters are not. Black circles are virtual electrode positions (first 8 rows shown). b Responses to step-current injections into the soma compartment of each neuron type. Spikes are detected and cut-off at Vt + 5 mV; we extend the spike trace up to +10 mV for illustrative purposes. Step-current magnitudes were 0.5 nA for the P2/3 neuron, 0.333 nA for the SS neuron, 1.0 nA for the P5 neuron, 0.75 nA for the P6 neuron, and 0.4 nA for the B and NB interneurons

Mentions: To demonstrate the capabilities of VERTEX for simulating LFPs in large neuron populations, we created a neocortical slice model to use in conjunction with MEA experiments in vitro (Fig. 5). The model comprises fifteen neuron groups, defined in Table 1. It is designed to contain a similar number of neurons to the comparison experimental slice. This was calculated to be 175,421 neurons, based on the slice dimensions and neuron density. The slice has clear spatial boundaries: neurons cannot be positioned outside of the slice edges, and axons cannot ‘wrap around’ these boundaries. We therefore required a connectivity model that would produce a suitable number of synapses given the large number of neurons, and that took into account each neuron’s position in relation to the slice boundaries. We used anatomical data from Binzegger et al. (2004) to specify the numbers of connections between neuron groups, and a 2D Gaussian spatial profile to model the decay in connection probability with increasing distance from a presynaptic neuron (Hellwig 2000). The standard deviation parameter of the Gaussian profile was set using axonal arborisation radius measurements reported by Blasdel et al. (1985); Fitzpatrick et al. (1985), as adapted by Izhikevich and Edelman (2008). These were different for each neuron group in each layer (see Online Resource, Table ESM4). Finally, we modelled the effect of slice cutting on connectivity by reducing the number of connections a presynaptic neuron could make by the proportion of the integral of its Gaussian connectivity profile that fell outside the slice boundaries (Eq. 3). The connectivity generation code in VERTEX implements this connectivity model automatically, though the user can also specify a uniform spatial connection probability and/or ignore slice cutting effects. VERTEX also allows users to specify specific target compartments on postsynaptic neurons that presynaptic neurons are allowed to connect to. We used this feature to incorporate known details about the dendritic regions targeted by different presynaptic neuron types—basket interneurons only make connections with pyramidal cell somas and their two adjacent compartments, for example. We used a similar pattern of connectivity to that described by Traub et al. (2005b); details are provided in the Online Resource (Supplementary Methods: Connectivity and Table ESM7). Incorporating this detail into the model is important, as the locations of synaptic inputs onto the neurons will affect the locations and sizes of the currents that contribute to the simulated LFP.Fig. 5


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)

Slice model structure and individual neuron dynamics. a Layer boundaries are given in µm. Subsets of soma locations from each neuron group are shown in faded black for excitatory neurons, or faded magenta for inhibitory neurons. Triangles represent pyramidal neuron somas, stars are spiny stellate cell somas, circles are basket interneuron somas and diamonds non-basket interneuron somas. One example full cell is shown for each neuron group, in solid black for excitatory neurons or solid magenta for inhibitory neurons. Compartment lengths are to scale; compartment diameters are not. Black circles are virtual electrode positions (first 8 rows shown). b Responses to step-current injections into the soma compartment of each neuron type. Spikes are detected and cut-off at Vt + 5 mV; we extend the spike trace up to +10 mV for illustrative purposes. Step-current magnitudes were 0.5 nA for the P2/3 neuron, 0.333 nA for the SS neuron, 1.0 nA for the P5 neuron, 0.75 nA for the P6 neuron, and 0.4 nA for the B and NB interneurons
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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Fig5: Slice model structure and individual neuron dynamics. a Layer boundaries are given in µm. Subsets of soma locations from each neuron group are shown in faded black for excitatory neurons, or faded magenta for inhibitory neurons. Triangles represent pyramidal neuron somas, stars are spiny stellate cell somas, circles are basket interneuron somas and diamonds non-basket interneuron somas. One example full cell is shown for each neuron group, in solid black for excitatory neurons or solid magenta for inhibitory neurons. Compartment lengths are to scale; compartment diameters are not. Black circles are virtual electrode positions (first 8 rows shown). b Responses to step-current injections into the soma compartment of each neuron type. Spikes are detected and cut-off at Vt + 5 mV; we extend the spike trace up to +10 mV for illustrative purposes. Step-current magnitudes were 0.5 nA for the P2/3 neuron, 0.333 nA for the SS neuron, 1.0 nA for the P5 neuron, 0.75 nA for the P6 neuron, and 0.4 nA for the B and NB interneurons
Mentions: To demonstrate the capabilities of VERTEX for simulating LFPs in large neuron populations, we created a neocortical slice model to use in conjunction with MEA experiments in vitro (Fig. 5). The model comprises fifteen neuron groups, defined in Table 1. It is designed to contain a similar number of neurons to the comparison experimental slice. This was calculated to be 175,421 neurons, based on the slice dimensions and neuron density. The slice has clear spatial boundaries: neurons cannot be positioned outside of the slice edges, and axons cannot ‘wrap around’ these boundaries. We therefore required a connectivity model that would produce a suitable number of synapses given the large number of neurons, and that took into account each neuron’s position in relation to the slice boundaries. We used anatomical data from Binzegger et al. (2004) to specify the numbers of connections between neuron groups, and a 2D Gaussian spatial profile to model the decay in connection probability with increasing distance from a presynaptic neuron (Hellwig 2000). The standard deviation parameter of the Gaussian profile was set using axonal arborisation radius measurements reported by Blasdel et al. (1985); Fitzpatrick et al. (1985), as adapted by Izhikevich and Edelman (2008). These were different for each neuron group in each layer (see Online Resource, Table ESM4). Finally, we modelled the effect of slice cutting on connectivity by reducing the number of connections a presynaptic neuron could make by the proportion of the integral of its Gaussian connectivity profile that fell outside the slice boundaries (Eq. 3). The connectivity generation code in VERTEX implements this connectivity model automatically, though the user can also specify a uniform spatial connection probability and/or ignore slice cutting effects. VERTEX also allows users to specify specific target compartments on postsynaptic neurons that presynaptic neurons are allowed to connect to. We used this feature to incorporate known details about the dendritic regions targeted by different presynaptic neuron types—basket interneurons only make connections with pyramidal cell somas and their two adjacent compartments, for example. We used a similar pattern of connectivity to that described by Traub et al. (2005b); details are provided in the Online Resource (Supplementary Methods: Connectivity and Table ESM7). Incorporating this detail into the model is important, as the locations of synaptic inputs onto the neurons will affect the locations and sizes of the currents that contribute to the simulated LFP.Fig. 5

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.