Solveig Næss

• Mäki-Marttunen, Tuomo; Kaufmann, Tobias; Elvsåshagen, Torbjørn; Devor, Anna; Djurovic, Srdjan; Westlye, Lars Tjelta; Linne, Marja-Leena; Rietschel, Marcella; Schubert, Dirk; Borgwardt, Stefan; Efrim-Budisteanu, Magdalena; Bettella, Francesco; Halnes, Geir; Hagen, Espen; Næss, Solveig; Ness, Torbjørn V; Moberget, Torgeir; Metzner, Christoph; Edwards, Andrew G.; Fyhn, Marianne; Dale, Anders M.; Einevoll, Gaute T. & Andreassen, Ole Andreas (2019). Biophysical Psychiatry—How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders.. Frontiers in Psychiatry.  ISSN 1664-0640.  10(534) . doi: doi: 10.3389/fpsyt.2019.00534
• Mäki-Marttunen, Tuomo; Krull, Florian; Bettella, Francesco; Hagen, Espen; Næss, Solveig; Ness, Torbjørn V; Moberget, Torgeir; Elvsåshagen, Torbjørn; Metzner, Christoph; Devor, Anna; Edwards, Andrew G.; Fyhn, Marianne; Djurovic, Srdjan; Dale, Anders; Andreassen, Ole Andreas & Einevoll, Gaute (2019). Alterations in schizophrenia-associated genes can lead to increased power in delta oscillations. Cerebral Cortex.  ISSN 1047-3211.  29(2), s 875- 891 . doi: 10.1093/cercor/bhy291
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2018). Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0. Frontiers in Neuroinformatics.  ISSN 1662-5196.  12(92), s 1- 36 . doi: 10.3389/fninf.2018.00092 Full text in Research Archive. Show summary

  Recordings of extracellular electrical, and later also magnetic, brain signals have been the dominant technique for measuring brain activity for decades. The interpretation of such signals is however nontrivial, as the measured signals result from both local and distant neuronal activity. In volume-conductor theory the extracellular potentials can be calculated from a distance-weighted sum of contributions from transmembrane currents of neurons. Given the same transmembrane currents, the contributions to the magnetic field recorded both inside and outside the brain can also be computed. This allows for the development of computational tools implementing forward models grounded in the biophysics underlying electrical and magnetic measurement modalities. LFPy (LFPy.readthedocs.io) incorporated a well-established scheme for predicting extracellular potentials of individual neurons with arbitrary levels of biological detail. It relies on NEURON (neuron.yale.edu) to compute transmembrane currents of multicompartment neurons which is then used in combination with an electrostatic forward model. Its functionality is now extended to allow for modeling of networks of multicompartment neurons with concurrent calculations of extracellular potentials and current dipole moments. The current dipole moments are then, in combination with suitable volume-conductor head models, used to compute non-invasive measures of neuronal activity, like scalp potentials (electroencephalographic recordings; EEG) and magnetic fields outside the head (magnetoencephalographic recordings; MEG). One such built-in head model is the four-sphere head model incorporating the different electric conductivities of brain, cerebrospinal fluid, skull and scalp. We demonstrate the new functionality of the software by constructing a network of biophysically detailed multicompartment neuron models from the Neocortical Microcircuit Collaboration (NMC) Portal (bbp.epfl.ch/nmc-portal) with corresponding statistics of connections and synapses, and compute in vivo-like extracellular potentials (local field potentials, LFP; electrocorticographical signals, ECoG) and corresponding current dipole moments. From the current dipole moments we estimate corresponding EEG and MEG signals using the four-sphere head model. We also show strong scaling performance of LFPy with different numbers of message-passing interface (MPI) processes, and for different network sizes with different density of connections. The open-source software LFPy is equally suitable for execution on laptops and in parallel on high-performance computing (HPC) facilities and is publicly available on GitHub.com.

• Næss, Solveig; Chintaluri, Chaitanya; Ness, Torbjørn Vefferstad; Dale, Anders; Einevoll, Gaute & Wójcik, Daniel K. (2017). Corrected four-sphere head model for EEG signals. Frontiers in Human Neuroscience.  ISSN 1662-5161.  11, s 1- 7 . doi: 10.3389/fnhum.2017.00490 Full text in Research Archive. Show summary

  The EEG signal is generated by electrical brain cell activity, often described in terms of current dipoles. By applying EEG forward models we can compute the contribution from such dipoles to the electrical potential recorded by EEG electrodes. Forward models are key both for generating understanding and intuition about the neural origin of EEG signals as well as inverse modeling, i.e., the estimation of the underlying dipole sources from recorded EEG signals. Different models of varying complexity and biological detail are used in the field. One such analytical model is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. While conceptually clear, the mathematical expression for the electric potentials in the four-sphere model is cumbersome, and we observed that the formulas presented in the literature contain errors. Here, we derive and present the correct analytical formulas with a detailed derivation. A useful application of the analytical four-sphere model is that it can serve as ground truth to test the accuracy of numerical schemes such as the Finite Element Method (FEM). We performed FEM simulations of the four-sphere head model and showed that they were consistent with the corrected analytical formulas. For future reference we provide scripts for computing EEG potentials with the four-sphere model, both by means of the correct analytical formulas and numerical FEM simulations.

• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2013). LFPy: Multimodal Modeling of Extracellular Neuronal Recordings in Python, In Dieter Jaeger & Ranu Jung (ed.),  Encyclopedia of Computational Neuroscience.  Springer Science+Business Media B.V..  ISBN 978-1461466741.  LFPy: Multimodal Modeling of Extracellular Neuronal Recordings in Python..
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2013). Multimodal Modeling of Extracellular Neuronal Recordings in Python, In Dieter Jaeger & Ranu Jung (ed.),  Encyclopedia of Computational Neuroscience.  Springer Science+Business Media B.V..  ISBN 978-1461466741.  0. Show summary

  LFPy is an open-source tool for calculating brain signals such as extracellular potentials and mag- netic signals from simulated activity in multi- compartment neuron models, ranging from single cells to large neuronal networks. The tool is provided as a Python package and relies on the NEURON simulation environment.

• Hagen, Espen; Næss, Solveig; Ness, Torbjørn Vefferstad & Einevoll, Gaute (2013). LFPy: Multimodal Modeling of Extracellular Neuronal Recordings in Python, In Dieter Jaeger & Ranu Jung (ed.),  Encyclopedia of Computational Neuroscience.  Springer Science+Business Media B.V..  ISBN 978-1461466741.  0. Show summary

  LFPy is an open-source tool for calculating brain signals such as extracellular potentials and magnetic signals from simulated activity in multicompartment neuron models, ranging from single cells to large neuronal networks. The tool is provided as a Python package and relies on the NEURON simulation environment.

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• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2018). Multimodal modeling of neural network activity: computing LFP, ECoG, EEG and MEG signals with LFPy 2.0.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2018). Multimodal modeling of neural network activity: computing LFP, ECoG, EEG and MEG signals with LFPy2.0.
• Næss, Solveig (2018). Hva skjer i hjernen når vi tenker?.
• Næss, Solveig; Ness, Torbjørn V; Hagen, Espen & Einevoll, Gaute (2018). Biophysical modelling of EEG signals from large neural populations.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2017). Computing Brain Signals (CBra): Concurrent simulation of network activity, extracellular electric potentials and magnetic fields.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2017). Computing Brain Signals: concurrent simulation of network activity, extracellular electric potentials and magnetic fields.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2017). Computing brain signals (CBra): Concurrent simulation of network activity, extracellular electric potentials and magnetic fields.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn Vefferstad & Einevoll, Gaute (2017). Computing Brain Signals: Concurrent simulation of network activity, extracellular electric poten- tials and magnetic fields..
• Næss, Solveig (2017). Biophysical Modeling of Single Neuron Contributions to ECoG and EEG signals.
• Næss, Solveig; Ness, Torbjørn V; Halnes, Geir; Halgren, Eric; Dale, Anders M. & Einevoll, Gaute (2017). Biophysical modeling of single neuron contributions to ECoG and EEG signals.
• Næss, Solveig; Ness, Torbjørn V; Halnes, Geir; Halgren, Eric; Dale, Anders M. & Einevoll, Gaute (2017). Biophysical modeling of single-neuron contributions to ECoG and EEG signals.
• Næss, Solveig; Ness, Torbjørn V; Halnes, Geir; Halgren, Eric; Dale, Anders & Einevoll, Gaute (2017). Biophysical modeling of single-neuron contributions toECoG, EEG and MEG signals.
• Buccino, Alessio Paolo; Stöber, Tristan Manfred; Næss, Solveig; Cauwenberghs, Gert & Häfliger, Philipp (2016). Extracellular single neuron stimulation with high-density multi-electrode array.
• Næss, Solveig & Einevoll, Gaute (2016). Modeling Extracellular Potentials from Neurons in the Brain.
• Næss, Solveig; Ness, Torbjørn Vefferstad; Dale, Anders M. & Einevoll, Gaute (2016). Understanding EEG with Biophysical Modeling.
• Næss, Solveig; Ness, Torbjørn Vefferstad; Halnes, Geir; Halgren, Eric; Dale, Anders M. & Einevoll, Gaute (2016). Biophysical modeling of single-neuron contributions to EEG and ECoG signals.

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