LFPy: Multimodal Modeling of Extracellular Neuronal Recordings in Python

Hagen, Espen; Næss, Solveig; Ness, Torbjørn V.; Einevoll, Gaute T.

doi:10.1007/978-1-4614-7320-6_100681-1

Cited by 2 publications

(3 citation statements)

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“…Further, at the systems level one has methods such as electroencephalography (EEG) [61]), which measures electrical potentials at the scalp, and magnetoencephalography (MEG) [62]) which measures the magnetic field outside the head. These measures can be computed from the activity of candidate network models [63], and tools to facilitate this have been developed [31,32,64,65]. They can all be used to constrain and validate candidate network models, and used in combination they will likely be particularly powerful.…”

Section: Discussionmentioning

confidence: 99%

Estimation of neural network model parameters from local field potentials (LFPs)

et al. 2020

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Most modeling in systems neuroscience has been descriptive where neural representations such as 'receptive fields', have been found by statistically correlating neural activity to sensory input. In the traditional physics approach to modelling, hypotheses are represented by mechanistic models based on the underlying building blocks of the system, and candidate models are validated by comparing with experiments. Until now validation of mechanistic cortical network models has been based on comparison with neuronal spikes, found from the high-frequency part of extracellular electrical potentials. In this computational study we investigated to what extent the low-frequency part of the signal, the local field potential (LFP), can be used to validate and infer properties of mechanistic cortical network models. In particular, we asked the question whether the LFP can be used to accurately estimate synaptic connection weights in the underlying network. We considered the thoroughly analysed Brunel network comprising an excitatory and an inhibitory population of recurrently connected integrate-and-fire (LIF) neurons. This model exhibits a high diversity of spiking network dynamics depending on the values of only three network parameters. The LFP generated by the network was computed using a hybrid scheme where spikes computed from the point-neuron network were replayed on biophysically detailed multicompartmental neurons. We assessed how accurately the three model parameters could be estimated from power spectra of stationary 'background' LFP signals by application of convolutional neural nets (CNNs). All network parameters could be very accurately estimated, suggesting that LFPs indeed can be used for network model validation. Author summaryMost of what we have learned about brain networks in vivo has come from the measurement of spikes (action potentials) recorded by extracellular electrodes. The low-frequency part of these signals, the local field potential (LFP), contains unique information about how dendrites in neuronal populations integrate synaptic inputs, but has so far played a lesser role. To investigate whether the LFP can be used to validate network models, we computed LFP signals for a recurrent network model (the Brunel network) for which the PLOS COMPUTATIONAL BIOLOGY PLOS Computational Biology | https://doi.org/10.

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Section: Discussionmentioning

confidence: 99%

Estimation of neural network model parameters from local field potentials (LFPs)

et al. 2020

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“…In order to test the applicability of the single-dipole approximation for calculating ECoG and EEG signals, we applied the four-sphere head model [Naess et al, 2017;Hagen et al, 2018Hagen et al, , 2019.…”

Section: Single-dipole Approximation Is Justified For Eeg But Not Ecmentioning

confidence: 99%

“…Here an alternative to using full head models is to use the method of images, taking into account the discontinuity of electrical conductivity at the cortical surface [Pettersen et al, 2006;Hagen et al, 2018]. Note that while we here used LFPy 2.0 [Hagen et al, 2018[Hagen et al, , 2019, a python interface to NEURON [Carnevale and Hines, 2006], calculation of current dipole moments can easily be implemented into any framework where the transmembrane currents are available, through the simple formula given in eq. 7.…”

Section: Application Of Current Dipoles For Computing Eeg Meg and Ecmentioning

confidence: 99%

Biophysical modeling of the neural origin of EEG and MEG signals

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Halnes

Hagen

et al. 2020

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AbstractElectroencephalography (EEG) and magnetoencephalography (MEG) are among the most important techniques for non-invasively studying cognition and disease in the human brain. These signals are known to originate from cortical neural activity, typically described in terms of current dipoles. While the link between cortical current dipoles and EEG/MEG signals is relatively well understood, surprisingly little is known about the link between different kinds of neural activity and the current dipoles themselves. Detailed biophysical modeling has played an important role in exploring the neural origin of intracranial electric signals, like extracellular spikes and local field potentials. However, this approach has not yet been taken full advantage of in the context of exploring the neural origin of the cortical current dipoles that are causing EEG/MEG signals.Here, we present a method for reducing arbitrary simulated neural activity to single current dipoles. We find that the method is applicable for calculating extracranial signals, but less suited for calculating intracranial electrocorticography (ECoG) signals. We demonstrate that this approach can serve as a powerful tool for investigating the neural origin of EEG/MEG signals. This is done through example studies of the single-neuron EEG contribution, the putative EEG contribution from calcium spikes, and from calculating EEG signals from large-scale neural network simulations. We also demonstrate how the simulated current dipoles can be used directly in combination with detailed head models, allowing for simulated EEG signals with an unprecedented level of biophysical details.In conclusion, this paper presents a framework for biophysically detailed modeling of EEG and MEG signals, which can be used to better our understanding of non-inasively measured neural activity in humans.Graphical abstractHighlightsCurrent dipoles are computed from biophysically detailed simulated neuron and network activityExtracted current dipoles allow for accurate computation of EEG and MEG signals in simplified and detailed head modelsCurrent-diplole approximation generally not suitable for accurate calculations of ECoG signalsMethod provides biophysics-based link between detailed neural activity and systems-level signals

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LFPy: Multimodal Modeling of Extracellular Neuronal Recordings in Python

Cited by 2 publications

References 54 publications

Estimation of neural network model parameters from local field potentials (LFPs)

Estimation of neural network model parameters from local field potentials (LFPs)

Biophysical modeling of the neural origin of EEG and MEG signals

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