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

2018 ◽  
Author(s):  
Espen Hagen ◽  
Solveig Næss ◽  
Torbjørn V. Ness ◽  
Gaute T. Einevoll
Keyword(s):  
Neuronal Activity ◽  
Message Passing Interface ◽  
Dipole Moments ◽  
Volume Conductor ◽  
Network Activity ◽  
Head Model ◽  
Current Dipole ◽  
Link Type ◽  
Transmembrane Currents ◽  
Extracellular Potentials

AbstractRecordings 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.

scholarly journals Aη-α and Aη-β peptides impair LTP ex vivo within the low nanomolar range and impact neuronal activity in vivo

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Maria Mensch ◽  
Jade Dunot ◽  
Sandy M. Yishan ◽  
Samuel S. Harris ◽  
Aline Blistein ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Ex Vivo ◽  
Long Term Potentiation ◽  
Network Activity ◽  
Strongly Correlated ◽  
App Processing ◽  
Term Potentiation ◽  
Hippocampal Network

Abstract Background Amyloid precursor protein (APP) processing is central to Alzheimer’s disease (AD) etiology. As early cognitive alterations in AD are strongly correlated to abnormal information processing due to increasing synaptic impairment, it is crucial to characterize how peptides generated through APP cleavage modulate synapse function. We previously described a novel APP processing pathway producing η-secretase-derived peptides (Aη) and revealed that Aη–α, the longest form of Aη produced by η-secretase and α-secretase cleavage, impaired hippocampal long-term potentiation (LTP) ex vivo and neuronal activity in vivo. Methods With the intention of going beyond this initial observation, we performed a comprehensive analysis to further characterize the effects of both Aη-α and the shorter Aη-β peptide on hippocampus function using ex vivo field electrophysiology, in vivo multiphoton calcium imaging, and in vivo electrophysiology. Results We demonstrate that both synthetic peptides acutely impair LTP at low nanomolar concentrations ex vivo and reveal the N-terminus to be a primary site of activity. We further show that Aη-β, like Aη–α, inhibits neuronal activity in vivo and provide confirmation of LTP impairment by Aη–α in vivo. Conclusions These results provide novel insights into the functional role of the recently discovered η-secretase-derived products and suggest that Aη peptides represent important, pathophysiologically relevant, modulators of hippocampal network activity, with profound implications for APP-targeting therapeutic strategies in AD.

scholarly journals Epileptogenesis due to glia-mediated synaptic scaling

2008 ◽  
Vol 6 (37) ◽  
pp. 655-668 ◽  
Author(s):  
Cristina Savin ◽  
Jochen Triesch ◽  
Michael Meyer-Hermann
Keyword(s):  
Immune System ◽  
Neuronal Activity ◽  
Activity Patterns ◽  
Brain Inflammation ◽  
Network Activity ◽  
Homeostatic Regulation ◽  
Synaptic Scaling ◽  
Tnf Α ◽  
Factor Α ◽  
Potential Interactions

Homeostatic regulation of neuronal activity is fundamental for the stable functioning of the cerebral cortex. One form of homeostatic synaptic scaling has been recently shown to be mediated by glial cells that interact with neurons through the diffusible messenger tumour necrosis factor-α (TNF-α). Interestingly, TNF-α is also used by the immune system as a pro-inflammatory messenger, suggesting potential interactions between immune system signalling and the homeostatic regulation of neuronal activity. We present the first computational model of neuron–glia interaction in TNF-α-mediated synaptic scaling. The model shows how under normal conditions the homeostatic mechanism is effective in balancing network activity. After chronic immune activation or TNF-α overexpression by glia, however, the network develops seizure-like activity patterns. This may explain why under certain conditions brain inflammation increases the risk of seizures. Additionally, the model shows that TNF-α diffusion may be responsible for epileptogenesis after localized brain lesions.

scholarly journals Multiscale Modeling of EEG/MEG Response of a Compact Cluster of Tightly Spaced Pyramidal Neocortical Neurons

2020 ◽  
pp. 195-211
Author(s):  
Sergey N. Makarov ◽  
Jyrki Ahveninen ◽  
Matti Hämäläinen ◽  
Yoshio Okada ◽  
Gregory M. Noetscher ◽  
...  
Keyword(s):  
High Resolution ◽  
Boundary Element ◽  
Multiscale Modeling ◽  
Cluster Size ◽  
Fast Multipole Method ◽  
Head Model ◽  
Current Dipole ◽  
Neocortical Neurons ◽  
Dipole Sources ◽  
Compact Cluster

AbstractIn this study, the boundary element fast multipole method or BEM-FMM is applied to model compact clusters of tightly spaced pyramidal neocortical neurons firing simultaneously and coupled with a high-resolution macroscopic head model. The algorithm is capable of processing a very large number of surface-based unknowns along with a virtually unlimited number of elementary microscopic current dipole sources distributed within the neuronal arbor.The realistic cluster size may be as large as 10,000 individual neurons, while the overall computation times do not exceed several minutes on a standard server. Using this approach, we attempt to establish how well the conventional lumped-dipole model used in electroencephalography/magnetoencephalography (EEG/MEG) analysis approximates a compact cluster of realistic neurons situated either in a gyrus (EEG response dominance) or in a sulcus (MEG response dominance).

Magnetoencephalographic accuracy profiles for the detection of auditory pathway sources

2015 ◽  
Vol 60 (2) ◽  
Author(s):  
Martin Bauer ◽  
Lutz Trahms ◽  
Tilmann Sander
Keyword(s):  
Brain Stem ◽  
Electric Dipole ◽  
Brain Research ◽  
Dipole Moments ◽  
Simulated Data ◽  
Auditory Pathway ◽  
Dipole Strength ◽  
Current Dipole ◽  
Limited Region ◽  
The Brain

AbstractThe detection limits for cortical and brain stem sources associated with the auditory pathway are examined in order to analyse brain responses at the limits of the audible frequency range. The results obtained from this study are also relevant to other issues of auditory brain research. A complementary approach consisting of recordings of magnetoencephalographic (MEG) data and simulations of magnetic field distributions is presented in this work. A biomagnetic phantom consisting of a spherical volume filled with a saline solution and four current dipoles is built. The magnetic fields outside of the phantom generated by the current dipoles are then measured for a range of applied electric dipole moments with a planar multichannel SQUID magnetometer device and a helmet MEG gradiometer device. The inclusion of a magnetometer system is expected to be more sensitive to brain stem sources compared with a gradiometer system. The same electrical and geometrical configuration is simulated in a forward calculation. From both the measured and the simulated data, the dipole positions are estimated using an inverse calculation. Results are obtained for the reconstruction accuracy as a function of applied electric dipole moment and depth of the current dipole. We found that both systems can localize cortical and subcortical sources at physiological dipole strength even for brain stem sources. Further, we found that a planar magnetometer system is more suitable if the position of the brain source can be restricted in a limited region of the brain. If this is not the case, a helmet-shaped sensor system offers more accurate source estimation.

scholarly journals mGlu1 receptor mediates homeostatic control of intrinsic excitability through Ih in cerebellar Purkinje cells

2016 ◽  
Vol 115 (5) ◽  
pp. 2446-2455 ◽  
Author(s):  
Hyun Geun Shim ◽  
Sung-Soo Jang ◽  
Dong Cheol Jang ◽  
Yunju Jin ◽  
Wonseok Chang ◽  
...  
Keyword(s):  
Purkinje Cells ◽  
Neuronal Activity ◽  
Metabotropic Glutamate Receptor ◽  
Activity Level ◽  
Network Activity ◽  
Firing Rates ◽  
Metabotropic Glutamate ◽  
Gated Channel ◽  
Intrinsic Plasticity ◽  
Cerebellar Purkinje Cells

Homeostatic intrinsic plasticity is a cellular mechanism for maintaining a stable neuronal activity level in response to developmental or activity-dependent changes. Type 1 metabotropic glutamate receptor (mGlu1 receptor) has been widely known to monitor neuronal activity, which plays a role as a modulator of intrinsic and synaptic plasticity of neurons. Whether mGlu1 receptor contributes to the compensatory adjustment of Purkinje cells (PCs), the sole output of the cerebellar cortex, in response to chronic changes in excitability remains unclear. Here, we demonstrate that the mGlu1 receptor is involved in homeostatic intrinsic plasticity through the upregulation of the hyperpolarization-activated current ( Ih) in cerebellar PCs. This plasticity was prevented by inhibiting the mGlu1 receptor with Bay 36–7620, an mGlu1 receptor inverse agonist, but not with CPCCOEt, a neutral antagonist. Chronic inactivation with tetrodotoxin (TTX) increased the components of Ih in the PCs, and ZD 7288, a hyperpolarization-activated cyclic nucleotide-gated channel selective inhibitor, fully restored reduction of firing rates in the deprived neurons. The homeostatic elevation of Ih was also prevented by BAY 36–7620, but not CPCCOEt. Furthermore, KT 5720, a blocker of protein kinase A (PKA), prevented the effect of TTX reducing the evoked firing rates, indicating the reduction in excitability of PCs due to PKA activation. Our study shows that both the mGlu1 receptor and the PKA pathway are involved in the homeostatic intrinsic plasticity of PCs after chronic blockade of the network activity, which provides a novel understanding on how cerebellar PCs can preserve the homeostatic state under activity-deprived conditions.

scholarly journals Imaging of respiratory-related population activity with single-cell resolution

2007 ◽  
Vol 292 (1) ◽  
pp. C508-C516 ◽  
Author(s):  
Frank Funke ◽  
Mathias Dutschmann ◽  
Michael Müller
Keyword(s):  
Single Cell ◽  
Neuronal Activity ◽  
Single Cells ◽  
Activity Patterns ◽  
Respiratory Rhythm ◽  
Network Activity ◽  
Respiratory Neurons ◽  
Ventrolateral Medulla ◽  
Population Activity ◽  
Respiratory Network

The pre-Bötzinger complex (PBC) in the rostral ventrolateral medulla contains a kernel involved in respiratory rhythm generation. So far, its respiratory activity has been analyzed predominantly by electrophysiological approaches. Recent advances in fluorescence imaging now allow for the visualization of neuronal population activity in rhythmogenic networks. In the respiratory network, voltage-sensitive dyes have been used mainly, so far, but their low sensitivity prevents an analysis of activity patterns of single neurons during rhythmogenesis. We now have succeeded in using more sensitive Ca2+ imaging to study respiratory neurons in rhythmically active brain stem slices of neonatal rats. For the visualization of neuronal activity, fluo-3 was suited best in terms of neuronal specificity, minimized background fluorescence, and response magnitude. The tissue penetration of fluo-3 was improved by hyperosmolar treatment (100 mM mannitol) during dye loading. Rhythmic population activity was imaged with single-cell resolution using a sensitive charge-coupled device camera and a ×20 objective, and it was correlated with extracellularly recorded mass activity of the contralateral PBC. Correlated optical neuronal activity was obvious online in 29% of slices. Rhythmic neurons located deeper became detectable during offline image processing. Based on their activity patterns, 74% of rhythmic neurons were classified as inspiratory and 26% as expiratory neurons. Our approach is well suited to visualize and correlate the activity of several single cells with respiratory network activity. We demonstrate that neuronal synchronization and possibly even network configurations can be analyzed in a noninvasive approach with single-cell resolution and at frame rates currently not reached by most scanning-based imaging techniques.

scholarly journals Astrocytes derived from ASD patients alter behavior and destabilize neuronal activity through aberrant Ca2+ signaling

2021 ◽  
Author(s):  
Megan Allen ◽  
Ben S. Huang ◽  
Michael J. Notaras ◽  
Aiman Lodhi ◽  
Estibaliz Barrio Alonso ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Hippocampal Neurons ◽  
Repetitive Behavior ◽  
Long Term Potentiation ◽  
Autism Spectrum ◽  
Network Activity ◽  
Postmortem Brain ◽  
Primary Role ◽  
Neuronal Network Activity ◽  
Postmortem Brain Tissue

AbstractThe cellular mechanisms of Autism Spectrum Disorder (ASD) are poorly understood. Cumulative evidence suggests that abnormal synapse function underlies many features of this disease. Astrocytes play in several key neuronal processes, including the formation of synapses and the modulation of synaptic plasticity. Astrocyte abnormalities have also been identified in the postmortem brain tissue of ASD patients. However, it remains unclear whether astrocyte pathology plays a mechanistic role in ASD, as opposed to a compensatory response. To address this, we strategically combined stem cell culturing with transplantation techniques to determine disease specific properties inherent to patient derived astrocytes. We demonstrate that ASD astrocytes induce repetitive behavior as well as impair memory and long-term potentiation when transplanted into the healthy mouse brain. These in vivo phenotypes were accompanied by reduced neuronal network activity and spine density caused by ASD astrocytes in hippocampal neurons in vitro. Transplanted ASD astrocytes also exhibit exaggerated Ca2+ fluctuations in chimeric brains. Genetic modulation of evoked Ca2+ responses in ASD astrocytes modulates behavior and neuronal activity deficits. Thus, we determine that ASD patient astrocytes are sufficient to induce repetitive behavior as well as cognitive deficit, suggesting a previously unrecognized primary role for astrocytes in ASD.

scholarly journals BigMPI4py: Python module for parallelization of Big Data objects

2019 ◽  
Author(s):  
Alex M. Ascension ◽  
Marcos J. Araúzo-Bravo
Keyword(s):  
Big Data ◽  
Message Passing ◽  
Message Passing Interface ◽  
Parallel Implementation ◽  
Germ Layers ◽  
Link Type ◽  
Big Data Applications ◽  
Genome Bisulfite Sequencing ◽  
Data Objects ◽  
Parallel Evaluation

AbstractBig Data analysis is a discipline with a growing number of areas where huge amounts of data is extracted and analyzed. Parallelization in Python integrates Message Passing Interface via mpi4py module. Since mpi4py does not support parallelization of objects greater than 231 bytes, we developed BigMPI4py, a Python module that wraps mpi4py, supporting object sizes beyond this boundary. BigMPI4py automatically determines the optimal object distribution strategy, and also uses vectorized methods, achieving higher parallelization efficiency. BigMPI4py facilitates the implementation of Python for Big Data applications in multicore workstations and HPC systems. We validated BigMPI4py on whole genome bisulfite sequencing (WGBS) DNA methylation ENCODE data of 59 samples from 27 human tissues. We categorized them on the three germ layers and developed a parallel implementation of the Kruskall-Wallis test to find CpGs with differential methylation across germ layers. We observed a differentiation of the germ layers, and a set of hypermethylated genes in ectoderm and mesoderm-related tissues, and another set in endoderm-related tissues. The parallel evaluation of the significance of 55 million CpG achieved a 22x speedup with 25 cores. BigMPI4py is available at https://gitlab.com/alexmascension/bigmpi4py and the Jupyter Notebook with WGBS analysis at https://gitlab.com/alexmascension/wgbs-analysis

scholarly journals Respiration competes with theta for modulating parietal cortex neurons

2019 ◽  
Author(s):  
Felix Jung ◽  
Yevgenij Yanovsky ◽  
Jurij Brankačk ◽  
Adriano BL Tort ◽  
Andreas Draguhn
Keyword(s):  
Neuronal Activity ◽  
Parietal Cortex ◽  
Field Potential ◽  
Anterior Cingulate ◽  
Theta Oscillations ◽  
Network Activity ◽  
Coupled Oscillations ◽  
Neuronal Network Activity ◽  
Posterior Parietal ◽  
The Brain

AbstractRecent work has shown that nasal respiration entrains local field potential (LFP) and neuronal activity in widespread regions of the brain. This includes non-olfactory regions where respiration-coupled oscillations have been described in different mammals, such as rodents, cats and humans. They may, thus, constitute a global signal aiding interregional communication. Nevertheless, the brain produces other widespread slow rhythms, such as theta oscillations, which also mediate long-range synchronization of neuronal activity. It is completely unknown how these different signals interact to control neuronal network activity. In this work, we characterized respiration- and theta-coupled activity in the posterior parietal cortex of mice. Our results show that respiration-coupled and theta oscillations have different laminar profiles, in which respiration preferentially entrains LFPs and units in more superficial layers, whereas theta modulation does not differ across the parietal cortex. Interestingly, we find that the percentage of theta-modulated units increases in the absence of respiration-coupled oscillations, suggesting that both rhythms compete for modulating parietal cortex neurons. We further show through intracellular recordings that synaptic inhibition is likely to play a role in generating respiration-coupled oscillations at the membrane potential level. Finally, we provide anatomical and electrophysiological evidence of reciprocal monosynaptic connections between the anterior cingulate and posterior parietal cortices, suggesting a possible source of respiration-coupled activity in the parietal cortex.

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