Computing Extracellular Electric Potentials from Neuronal Simulations

Ness, Torbjørn V.; Halnes, Geir; Næss, Solveig; Pettersen, Klas H.; Einevoll, Gaute T.

doi:10.1007/978-3-030-89439-9_8

Cited by 8 publications

(13 citation statements)

References 105 publications

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“…The dominance of inhibitory over excitatory contributions in the LFP is in agreement with previous reports (e.g., [ 18 , 68 ]). It should, however, be noted that our choices of synaptic density shape functions for each pathway ( L YX ( z ) defined in Table 2 ) may significantly affect the corresponding kernel appearances—inhomogeneous synapse densities may result in much stronger responses than homogeneous densities [ 41 , 60 , 69 ]. Furthermore, the direct contribution by evoked transmembrane currents on population ‘I’ can be expected to be minor, in part explained by the smaller spatial extents of the neurons and low cell count.…”

Section: Resultsmentioning

confidence: 99%

Brain signal predictions from multi-scale networks using a linearized framework

Hagen

Magnusson²,

Ness³

et al. 2022

PLoS Comput Biol

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Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (≲ 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models (≳ 106 neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials (‘spikes’) or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin. Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for. The work also constitutes a major advance in computational efficacy of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework.

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Section: Resultsmentioning

confidence: 99%

Brain signal predictions from multi-scale networks using a linearized framework

Hagen

Magnusson²,

Ness³

et al. 2022

PLoS Comput Biol

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show abstract

“…The dominance of inhibitory over excitatory contributions in the LFP is in agreement with previous reports (e.g., [18, 68]). It should, however, be noted that our choices of synaptic density shape functions for each pathway ( L Y X ( z ) defined in Table 2) may significantly affect the corresponding kernel appearances – inhomogeneous synapse densities may result in much stronger responses than homogeneous densities [41, 60, 69]. Furthermore, the direct contribution by evoked transmembrane currents on population ‘I’ can be expected to be minor, in part explained by the smaller spatial extents of the neurons and low cell count.…”

Section: Resultsmentioning

confidence: 99%

Brain signal predictions from multi-scale networks using a linearized framework

Hagen

Magnusson

Ness

et al. 2022

Preprint

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Simulations of neural activity at different levels of detail have become ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular electric measurements of brain signals that reflect transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (<300Hz or so) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large scale neuronal network models (>1E6 neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials ('spikes') or spike rates, and corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin. Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay and volume conductor model allows for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for. The work also constitutes a major advance in computational efficacy of accurate, biophysics-based signal predictions from large scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework.

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“…The improvements in experimental capabilities in the last decade have finally allowed spatial information to be drawn from FP sources (e.g., multisite recordings: Benito et al, 2014 ; Herreras et al, 2015 ; Whitmore and Lin, 2016 ), which is rapidly changing the erroneous idea that they reflect nearby activities in individual populations. It has become evident that in addition to resolving a number of genuine questions about the FPs ( Bédard et al, 2004 ; Reimann et al, 2013 ; Anastassiou and Koch, 2015 ; Herreras et al, 2022 ; Ness et al, 2022 ), some general concepts need to be fleshed out to bring them closer to the physical principles at play. As such, the recent boost to computational capabilities and the advances in simulation environments has made it possible to explicitly formulate a major problem, that of scaling micro to mesoscopic sources, which along with the dipolar nature of neuronal currents, will define the 3D geometry of FPs ( Varona et al, 2000 ; López-Aguado et al, 2002 ; Fernández-Ruiz et al, 2013 ; Reimann et al, 2013 ; Torres et al, 2019 ; Herreras et al, 2022 ).…”

Section: Space Finallymentioning

confidence: 99%

“…How the different electrical properties of tissues potentially influence the electrical fields within them has been considered on several occasions and some significant deviations of FP spread from expected in purely resistive media have been found ( Elul, 1971 ; López-Aguado et al, 2001 ; Bédard et al, 2004 , 2022 ; Holcman and Yuste, 2015 ; Savtchenko et al, 2017 ). However, it is widely accepted that the spatial extent of the FPs is not differentially affected within the most common frequency band of interest (0.1−200 Hz) ( Nunez and Srinivasan, 2006 ; Ranta et al, 2017 ; Ness et al, 2022 ). Resistivity is not fully homogeneous in the brain and there are significant deviations from a uniform propagation of electric fields in regions where cellular elements of a particular geometry are grouped together (e.g., anisotropy in large fiber bundles), or where there are tissue heterogeneities (e.g., in layers with tight soma packing or near the ventricles: Li et al, 1968 ; Okada et al, 1994 ; López-Aguado et al, 2001 ; McCaan et al, 2019 ; Torres et al, 2019 ).…”

Section: Space Finallymentioning

confidence: 99%

Theoretical considerations and supporting evidence for the primary role of source geometry on field potential amplitude and spatial extent

Herreras

Torres

Makarov

et al. 2023

Front. Cell. Neurosci.

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Field potential (FP) recording is an accessible means to capture the shifts in the activity of neuron populations. However, the spatial and composite nature of these signals has largely been ignored, at least until it became technically possible to separate activities from co-activated sources in different structures or those that overlap in a volume. The pathway-specificity of mesoscopic sources has provided an anatomical reference that facilitates transcending from theoretical analysis to the exploration of real brain structures. We review computational and experimental findings that indicate how prioritizing the spatial geometry and density of sources, as opposed to the distance to the recording site, better defines the amplitudes and spatial reach of FPs. The role of geometry is enhanced by considering that zones of the active populations that act as sources or sinks of current may arrange differently with respect to each other, and have different geometry and densities. Thus, observations that seem counterintuitive in the scheme of distance-based logic alone can now be explained. For example, geometric factors explain why some structures produce FPs and others do not, why different FP motifs generated in the same structure extend far while others remain local, why factors like the size of an active population or the strong synchronicity of its neurons may fail to affect FPs, or why the rate of FP decay varies in different directions. These considerations are exemplified in large structures like the cortex and hippocampus, in which the role of geometrical elements and regional activation in shaping well-known FP oscillations generally go unnoticed. Discovering the geometry of the sources in play will decrease the risk of population or pathway misassignments based solely on the FP amplitude or temporal pattern.

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Computing Extracellular Electric Potentials from Neuronal Simulations

Cited by 8 publications

References 105 publications

Brain signal predictions from multi-scale networks using a linearized framework

Brain signal predictions from multi-scale networks using a linearized framework

Brain signal predictions from multi-scale networks using a linearized framework

Theoretical considerations and supporting evidence for the primary role of source geometry on field potential amplitude and spatial extent

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