Biology subjects

Ness, T. V.

Publications and source records attributed to Ness, T. V..

15 recordsLinked to original sources

bioRxiv2026

Can we trust subthalamic local field potential? Geometrical and dynamical factors constraining the interpretability of extracellular recordings

Local field potentials (LFPs) are widely interpreted as readouts of population synaptic activity, an assumption derived almost entirely from cortical recordings. Whether these principles extend to subcortical structures remains unclear. We address this question in the subthalamic nucleus (STN), where LFPs are routinely recorded and used to guide adaptive deep brain stimulation for Parkinsons disease, using a biophysically detailed population model benchmarked against patient microelectrode recordings. As in the cortex, STN extracellular potentials were dominated by synaptic currents. Differently from the cortex, however, LFPs could not be reliably predicted from these currents or other average population quantities. This dissociation arises from the STN symmetric neuronal morphology and lack of recurrent connectivity, which promote destructive interference among single-neuron contributions, decoupling the LFP from population-level dynamics. This decoupling was not absolute: pathological beta synchrony restored a robust synapse-LFP relationship by consistent underlying dynamics, while the aperiodic slope of the power spectral density tracked STN neuronal morphology, firing rate, and excitatory-inhibitory balance. Together, these findings challenge the prevailing view of LFPs as universal readouts of population activity. Our results show that the interpretability of extracellular signals depends critically on neuronal morphology and synchronization state, and provide a mechanistic framework for the use of STN LFPs as biomarkers in adaptive deep brain stimulation for Parkinsons disease.

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bioRxiv2026

Cell-type dependence of effects from transcranial electric brain stimulation

There is an urgent need for better treatment options for many neurological conditions, including Alzheimers disease, Parkinsons disease, depression, and epilepsy. Transcranial electrical stimulation (tES) is a non-invasive, safe, inexpensive, and promising method that could address some of this unmet need. The therapeutic value of tES has been well demonstrated, but the effect is highly variable. To enable tES to reach its full potential requires a better understanding of how tES modulates neural activity so that tES treatments can be tailored to specific neurological conditions and individual patients. The neural response to tES is, however, highly complex, and the parameter space involved in optimizing tES treatments is daunting. This has made it difficult to obtain general insights into how tES modulates neural activity, and a central challenge lies in the cell-type-specific and frequency-dependent nature of these responses. In this study, we investigate cell-type-specific neuronal responses to tES over a broad frequency range, using a large database of biophysically detailed neuron models. We find that pyramidal cells respond strongly to low-frequency tES, but their responses drop sharply with frequency. In contrast, inhibitory neurons show a smaller reduction and, on average, become more responsive than pyramidal cells above [~] 60 Hz. By leveraging a reciprocity theorem we demonstrate that the effect of tES on a given cell-type is proportional to the frequency-dependent current-dipole moment that determines the EEG-signal contribution of this cell-type. We further identified the dendritic asymmetry as key in determining tES responses across the frequency spectrum. Counterintuitively, we also found that while total cell length increases tES sensitivity at low frequencies, it can have the opposite effect at high frequencies. Furthermore, we derived an analytical formula for idealized neuron models which can approximately predict the tES sensitivity of different cell types at any given frequency. By characterizing the role of morphology and stimulation frequency in determining tES responses of single cells, this is an important step towards a better understanding of tES at the fundamental level. These results also provide an efficient and accurate method for characterizing and comparing the tES responses of different neural populations across the frequency spectrum, which facilitates optimizing tES for cell-type specific targeting. Author summary

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bioRxiv2025

Computational modelling of schizophrenia-associated alterations of ion-channel-encoding gene expression predicts a decrease in delta power

Schizophrenia presents with a wide range of phenotypes that can help to understand the the mechanisms of the disease. Among these, alterations in delta oscillations are especially amenable to experimental investigation, yet the mechanisms underlying these changes remain insufficiently understood. Biophysically detailed computational modeling offers a powerful approach to investigate these phenomena, as it enables multi-scale integration of genetic and electrophysiological data. In this study, we developed a minimal network model composed of biophysically detailed, multicompartmental neurons to replicate experimental data on the effects of pharmacological blockage of gabaergic neurotransmission on delta-band power. We inserted post-mortem RNA expression data from the anterior cingulate and prefrontal cortices of individuals with schizophrenia and matched controls into the model to study the effects of schizophrenia-associated alterations of ion-channel expression on delta-oscillation power. Our simulations revealed a significant reduction in delta-band power in schizophrenia, driven by altered expression of calcium channel genes in pyramidal neurons. These results provide insights into the genetic contributions to oscillatory disruptions observed in schizophrenia, and our modelling framework can help to develop stratification strategies that bridge genetics and in vivo electrophysiology.

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bioRxiv2024

Understanding Electric Brain Stimulation Through the Reciprocity Theorem

Electrical stimulation of nervous tissue is grounded in well-established biophysical principles, yet understanding its precise effect on neural activity remains challenging. This represents a major obstacle to both scientific and clinical applications of electrical brain stimulation. We here show that the reciprocity theorem of electromagnetism can be applied more broadly than previously acknowledged, providing a framework for elucidating the effects of electrical brain stimulation on neurons. Through this new perspective, we account for the observed weak frequency-dependence of extracellular electrical stimulation-induced membrane potential changes, and demonstrate a 1/r falloff for nearby and 1/r2 falloff for distant neurons. We also show that for transcranial electrical stimulation, the susceptibility of a neuron to the stimulation is directly proportional to the size of the current dipole moment resulting from somatic current input. The induced somatic membrane potential changes are small and exhibit weak spatial but strong orientation dependency across human neocortex. The reciprocity-based approach to electrical stimulation reproduces a large body of previous experimental data, and provides new insights into how ES affects neurons.

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bioRxiv2024

On the validity of electric brain signal predictions based on population firing rates

Neural activity at the population level is commonly studied experimentally through measurements of electric brain signals like local field potentials (LFPs), or electroencephalography (EEG) signals. To allow for comparison between observed and simulated neural activity it is therefore important that simulations of neural activity can accurately predict these brain signals. Simulations of neural activity at the population level often rely on point-neuron network models or firing-rate models. While these simplified representations of neural activity are computationally efficient, they lack the explicit spatial information needed for calculating LFP/EEG signals. Different heuristic approaches have been suggested for overcoming this limitation, but the accuracy of these approaches has not fully been assessed. One such heuristic approach, the so-called kernel method, has previously been applied with promising results and has the additional advantage of being well-grounded in the bio-physics underlying electric brain signal generation. It is based on calculating rate-to-LFP/EEG kernels for each synaptic pathway in a network model, after which LFP/EEG signals can be obtained directly from population firing rates. This amounts to a massive reduction in the computational effort of calculating brain signals because the brain signals are calculated for each population instead of for each neuron. Here, we investigate how and when the kernel method can be expected to work, and present a theoretical framework for predicting its accuracy. We show that the relative error of the brain signal predictions is a function of the single-cell kernel heterogeneity and the spike-train correlations. Finally, we demonstrate that the kernel method is most accurate for the dominating brain signal contributions. We thereby further establish the kernel method as a promising approach for calculating electric brain signals from large-scale neural simulations.

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bioRxiv2023

Multitask Learning of Biophysically-Detailed Neuron Models

The human brain operates at multiple levels, from molecules to circuits, and understanding these complex processes requires integrated research efforts. Simulating biophysically-detailed neuron models is a computationally expensive but effective method for studying local neural circuits. Recent innovations have shown that artificial neural networks (ANNs) can accurately predict the behaviour of these detailed models in terms of spikes, electrical potentials, and optical readouts. While these methods have the potential to accelerate large network simulations by several orders of magnitude compared to conventional differential equation based modelling, they currently only predict voltage outputs for the soma or a select few neuron compartments. Our novel approach, based on enhanced state-of-the-art architectures for multitask learning (MTL), allows for the simultaneous prediction of membrane potentials in each compartment of a neuron model, at a speed of up to two orders of magnitude faster than classical simulation methods. By predicting all membrane potentials together, our approach not only allows for comparison of model output with a wider range of experimental recordings (patch-electrode, voltage-sensitive dye imaging), it also provides the first stepping stone towards predicting local field potentials (LFPs), electroencephalogram (EEG) signals, and magnetoencephalography (MEG) signals from ANN-based simulations. It further presents a challenging benchmark for MTL architectures due to the large amount of data involved, the presence of correlations between neighbouring compartments, and the non-Gaussian distribution of membrane potentials.

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bioRxiv2023

Interplay of CaHVA and Ca2+-activated K+ channels affecting firing rate in perineuronal net disruption

Perineuronal nets (PNNs) are extracellular matrix structures consisting of proteoglycans crosslinked to hyaluronan. They wrap around subgroups of individual neurons in the brain, primarily parvalbumin positive inhibitory neurons. The nets have been found to affect conductances and activation curves of certain ion channels, including Ca2+-activated K+ and high-voltage-activated Ca2+ channels. We studied how PNN related parameters affected the firing rate of one-compartment neuron models. We found that the direct effect of the CaHVA current on firing rate was small, while it had a much larger indirect effect on firing rate through initiation of the SK current. Upregulation of the SK conductance similarly had a pronounced effect on the firing rate. The SK currents therefore acted as the main determinant of firing rate out of these two mechanisms. We shifted the CaHVA channel activation by 14.5 mV and increased the SK conductance by a factor of 3.337, consistent with experimental findings on PNN breakdown in the literature. We studied this in nine different models and found a reduction in firing rate in some, but not all, of these models.

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bioRxiv2022

Imaging through Windansee electrode arrays reveals a small fraction of local neurons following surface MUA

Prior studies have shown that neuronal spikes can be recorded with microelectrode arrays placed on the cortical surface. However, the etiology of these spikes remains unclear. Because the top cortical layer (layer 1) contains very few neuronal cell bodies, it has been proposed that these spikes originate from neurons with cell bodies in layer 2. To address this question, we combined two-photon calcium imaging with electrophysiological recordings from the cortical surface in awake mice using chronically implanted PEDOT:PSS electrode arrays on transparent parylene C substrate. Our electrode arrays (termed Windansee) were integrated with cortical windows offering see-through optical access while also providing measurements of local field potentials (LFP) and multiunit activity (MUA) from the cortical surface. To enable longitudinal data acquisition, we have developed a mechanical solution for installation, connectorization, and protection of Windansee devices aiming for an unhindered access for high numerical aperture microscope objectives and a lifetime of several months while worn by a mouse. Contrary to the common notion, our measurements revealed that only a small fraction of layer 2 neurons from the sampled pool (~13%) faithfully followed MUA recorded from the surface above the imaging field-of-view. Surprised by this result, we turned to computational modeling for an alternative explanation of the MUA signal. Using realistic modeling of neurons with back-propagating dendritic properties, we computed the extracellular action potential at the cortical surface due to firing of local cortical neurons and compared the result to that due to axonal inputs to layer 1. Assuming the literature values for the cell/axon density and firing rates, our modeling results show that surface MUA due to axonal inputs is over an order of magnitude larger than that due to firing of layer 2 pyramidal neurons. Thus, a combination of surface MUA recordings with two-photon calcium imaging can provide complementary information about the input to a cortical column and the local circuit response. Cortical layer I plays an important role in integration of a broad range of cortico-cortical, thalamocortical and neuromodulatory inputs. Therefore, detecting their activity as MUA while combining electrode recording with two-photon imaging using optically transparent surface electrode arrays would facilitate studies of the input/output relationship in cortical circuits, inform computational circuit models, and improve the accuracy of the next generation brain-machine interfaces.

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bioRxiv2022

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

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 ([lsim] 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 ({gtrsim} 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. Author summaryUnderstanding the brains function and activity in healthy and pathological states across spatial scales and times spanning entire lives is one of humanitys great undertakings. In experimental and clinical work probing the brains activity, a variety of electric and magnetic measurement techniques are routinely applied. However interpreting the extracellularly measured signals remains arduous due to multiple factors, mainly the large number of neurons contributing to the signals and complex interactions occurring in recurrently connected neuronal circuits. To understand how neurons give rise to such signals, mechanistic modeling combined with forward models derived using volume conductor theory has proven to be successful, but this approach currently does not scale to the systems level (encompassing millions of neurons or more) where simplified or abstract neuron representations typically are used. Motivated by experimental findings implying approximately linear relationships between times of neuronal action potentials and extracellular population signals, we provide a biophysics-based method for computing causal filters relating spikes and extracellular signals that can be applied with spike times or rates of large-scale neuronal network models for predictions of population signals without relying on ad hoc approximations.

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bioRxiv2021

A multimodal 3D neuro-microphysiological system with neurite-trapping microelectrodes

Three-dimensional cell technologies as pre-clinical models are emerging tools for mimicking the structural and functional complexity of the nervous system. The accurate exploration of phenotypes in engineered 3D neuronal cultures, however, demands morphological, molecular and especially functional measurements. Particularly crucial is measurement of electrical activity of individual neurons with millisecond resolution. Current techniques rely on customized electrophysiological recording set-ups, characterized by limited throughput and poor integration with other readout modalities. Here we describe a novel approach, using multiwell glass microfluidic microelectrode arrays, allowing non-invasive electrical recording from engineered 3D neural tissues. We demonstrate parallelized studies with reference compounds, calcium imaging and optogenetic stimulation. Additionally, we show how microplate compatibility allows automated handling and high-content analysis of human induced pluripotent stem cell-derived neurons. This microphysiological platform opens up new avenues for high-throughput studies on the functional, morphological and molecular details of neurological diseases and their potential treatment by therapeutic compounds.

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bioRxiv2020

Computation of the electroencephalogram (EEG) from network models of point neurons

The electroencephalogram (EEG) is one of the main tools for non-invasively studying brain function and dysfunction. To better interpret EEGs in terms of neural mechanisms, it is important to compare experimentally recorded EEGs with the output of neural network models. Most current neural network models use networks of simple point neurons. They capture important properties of cortical dynamics, and are numerically or analytically tractable. However, point neuron networks cannot directly generate an EEG, since EEGs are generated by spatially separated transmembrane currents. Here, we explored how to compute an accurate approximation of the EEG with a combination of quantities defined in point-neuron network models. We constructed several different candidate approximations (or proxies) of the EEG that can be computed from networks of leaky integrate-and-fire (LIF) point neurons, such as firing rates, membrane potentials, and specific combinations of synaptic currents. We then evaluated how well each proxy reconstructed a realistic ground-truth EEG obtained when the synaptic input currents of the LIF network were fed into a three-dimensional (3D) network model of multi-compartmental neurons with realistic cell morphologies. We found that a new class of proxies, based on an optimized linear combination of time-shifted AMPA and GABA currents, provided the most accurate estimate of the EEG over a wide range of network states of the LIF point-neuron network. The new linear proxies explained most of the variance (85-95%) of the ground-truth EEG for a wide range of cell morphologies, distributions of presynaptic inputs, and position of the recording electrode. Non-linear proxies, obtained using a convolutional neural network (CNN) to predict the EEG from synaptic currents, increased proxy performance by a further 2-8%. Our proxies can be used to easily calculate a biologically realistic EEG signal directly from point-neuron simulations and thereby allow a quantitative comparison between computational models and experimental EEG recordings. Author summaryNetworks of point neurons are widely used to model neural dynamics. Their output, however, cannot be directly compared to the electroencephalogram (EEG), which is one of the most used tools to non-invasively measure brain activity. To allow a direct integration between neural network theory and empirical EEG data, here we derived a new mathematical expression, termed EEG proxy, which estimates with high accuracy the EEG based simply on the variables available from simulations of point-neuron network models. To compare and validate these EEG proxies, we computed a realistic ground-truth EEG produced by a network of simulated neurons with realistic 3D morphologies that receive the same spikes of the simpler network of point neurons. The new obtained EEG proxies outperformed previous approaches and worked well under a wide range of simulated configurations of cell morphologies, distribution of presynaptic inputs, and position of the recording electrode. The new proxies approximated well both EEG spectra and EEG evoked potentials. Our work provides important mathematical tools that allow a better interpretation of experimentally measured EEGs in terms of neural models of brain function.

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bioRxiv2020

Biophysical modeling of the neural origin of EEG and MEG signals

Electroencephalography (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 abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/181875v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@47f029org.highwire.dtl.DTLVardef@e8244dorg.highwire.dtl.DTLVardef@a765c1org.highwire.dtl.DTLVardef@96550_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LICurrent dipoles are computed from biophysically detailed simulated neuron and network activity C_LIO_LIExtracted current dipoles allow for accurate computation of EEG and MEG signals in simplified and detailed head models C_LIO_LICurrent-diplole approximation generally not suitable for accurate calculations of ECoG signals C_LIO_LIMethod provides biophysics-based link between detailed neural activity and systems-level signals C_LI

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bioRxiv2019

LFPy - multimodal modeling of extracellular neuronal recordings in Python

1.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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bioRxiv2019

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

Most modeling in systems neuroscience has been descriptive where neural representations, that is, 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 infer properties of the neuronal network. 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 synaptic weight 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. Significance statementMost of what we have learned about brain networks in vivo have 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 ground-truth parameters are known. By application of convolutional neural nets (CNNs) we found that the synaptic weights indeed could be accurately estimated from background LFP signals, suggesting a future key role for LFP in development of network models.

neuroscience↗

bioRxiv2018

Multimodal modeling of neural network activity: computing LFP, ECoG, EEG and MEG signals with LFPy2.0

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.\n\nLFPy (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.\n\nWe 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.\n\nThe 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.

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