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

PLoS Computational Biology, vol. 18, pp. e1010353

Abstract

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 efficiency 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.

Authors 8

1. Espen Hagen corresponding

   Norwegian University of Life Sciences

   Affiliation as printed

   Department of Data Science, Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway

2. Steinn Hauser Magnússon

   University of Oslo

   Affiliation as printed

   Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway

3. Torbjørn V. Ness

   Norwegian University of Life Sciences

   Affiliation as printed

   Department of Physics, Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway

4. Geir Halnes

   Norwegian University of Life Sciences

   Affiliation as printed

   Department of Physics, Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway

5. Pooja N. Babu

   Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Simulation & Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), Jülich Research Centre, Jülich, Germany

6. Charl Linssen

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre

   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6); Computational and Systems Neuroscience & Institute for Advanced Simulation (IAS-6); Theoretical Neuroscience & JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre and JARA, Jülich, Germany

   Simulation & Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), Jülich Research Centre, Jülich, Germany

   Computational and Systems Neuroscience & Institute for Advanced Simulation (IAS-6)

   Institute of Neuroscience and Medicine (INM-6)

   Theoretical Neuroscience & JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre and JARA, Jülich, Germany

7. Abigail Morrison Aachen Software Engineering Department of Computer Science 3

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre · RWTH Aachen University

   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6); Computational and Systems Neuroscience & Institute for Advanced Simulation (IAS-6); Theoretical Neuroscience & JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre and JARA, Jülich, Germany

   Simulation & Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), Jülich Research Centre, Jülich, Germany

   Software Engineering, Department of Computer Science 3, RWTH Aachen University, Aachen, Germany

   Computational and Systems Neuroscience & Institute for Advanced Simulation (IAS-6)

   Institute of Neuroscience and Medicine (INM-6)

   Theoretical Neuroscience & JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre and JARA, Jülich, Germany

8. Gaute T. Einevoll corresponding

   University of Oslo · Norwegian University of Life Sciences

   Affiliation as printed

   Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway

   Department of Physics, Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway