Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Estimating functional connectivity in an electrically coupled interneuron network.

ABSTRACT: Even though it has been known for some time that in many mammalian brain areas interneurons are electrically coupled, a quantitative description of the network electrical connectivity and its impact on cellular passive properties is still lacking. Approaches used so far to solve this problem are limited because they do not readily distinguish junctions among direct neighbors from indirect junctions involving intermediary, multiply connected cells. In the cerebellar cortex, anatomical and functional evidence indicates electrical coupling between molecular layer interneurons (basket and stellate cells). An analysis of the capacitive currents obtained under voltage clamp in molecular layer interneurons of juvenile rats or mice reveals an exponential component with a time constant of ~20 ms, which represents capacitive loading of neighboring cells through gap junctions. These results, taken together with dual cell recording of electrical synapses, have led us to estimate the number of direct neighbors to be ~4 for rat basket cells and ~1 for rat stellate cells. The weighted number of neighbors (number of neighbors, both direct and indirect, weighted with the percentage of voltage deflection at steady state) was 1.69 in basket cells and 0.23 in stellate cells. The last numbers indicate the spread of potential changes in the network and serve to estimate the contribution of gap junctions to cellular input conductance. In conclusion the present work offers effective tools to analyze the connectivity of electrically connected interneuron networks, and it indicates that in juvenile rodents, electrical communication is stronger among basket cells than among stellate cells.

SUBMITTER: Alcami P

PROVIDER: S-EPMC3856846 | biostudies-other | 2013 Dec

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Rapid desynchronization of an electrically coupled interneuron network with sparse excitatory synaptic input.

Project description:Electrical synapses between interneurons contribute to synchronized firing and network oscillations in the brain. However, little is known about how such networks respond to excitatory synaptic input. To investigate this, we studied electrically coupled Golgi cells (GoC) in the cerebellar input layer. We show with immunohistochemistry, electron microscopy, and electrophysiology that Connexin-36 is necessary for functional gap junctions (GJs) between GoC dendrites. In the absence of coincident synaptic input, GoCs synchronize their firing. In contrast, sparse, coincident mossy fiber input triggered a mixture of excitation and inhibition of GoC firing and spike desynchronization. Inhibition is caused by propagation of the spike afterhyperpolarization through GJs. This triggers network desynchronization because heterogeneous coupling to surrounding cells causes spike-phase dispersion. Detailed network models predict that desynchronization is robust, local, and dependent on synaptic input properties. Our results show that GJ coupling can be inhibitory and either promote network synchronization or trigger rapid network desynchronization depending on the synaptic input.

| S-EPMC2954316 | biostudies-literature

Using subthreshold events to characterize the functional architecture of the electrically coupled inferior olive network.

Project description:The electrical connectivity in the inferior olive (IO) nucleus plays an important role in generating well-timed spiking activity. Here we combined electrophysiological and computational approaches to assess the functional organization of the IO nucleus in mice. Spontaneous fast and slow subthreshold events were commonly encountered during in vitro recordings. We show that whereas the fast events represent intrinsic regenerative activity, the slow events reflect the electrical connectivity between neurons ('spikelets'). Recordings from cell pairs revealed the synchronized occurrence of distinct groups of spikelets; their rate and distribution enabled an accurate estimation of the number of connected cells and is suggestive of a clustered organization. This study thus provides a new perspective on the functional and structural organization of the olivary nucleus and a novel experimental and theoretical approach to study electrically coupled networks.

| S-EPMC7012604 | biostudies-literature

Electrically coupled inhibitory interneurons constrain long-range connectivity of cortical networks.

Project description:Spontaneous infra-slow brain activity (ISA) exhibits a high degree of temporal synchrony, or correlation, between distant brain regions. The spatial organization of ISA synchrony is not explained by anatomical connections alone, suggesting that active neural processes coordinate spontaneous activity. Inhibitory interneurons (IINs) form electrically coupled connections via the gap junction protein connexin 36 (Cx36) and networks of interconnected IINs are known to influence neural synchrony over short distances. However, the role of electrically coupled IIN networks in regulating spontaneous correlation over the entire brain is unknown. In this study, we performed OIS imaging on Cx36-/- mice to examine the role of this gap junction in ISA correlation across the entire cortex. We show that Cx36 deletion increased long-distance intra-hemispheric anti-correlation and inter-hemispheric correlation in spontaneous ISA. This suggests that electrically coupled IIN networks modulate ISA synchrony over long cortical distances.

| S-EPMC7292744 | biostudies-literature

Time-varying whole-brain functional network connectivity coupled to task engagement.

Project description:Brain functional connectivity (FC), as measured by blood oxygenation level-dependent (BOLD) signal, fluctuates at the scale of 10s of seconds. It has recently been found that whole-brain dynamic FC (dFC) patterns contain sufficient information to permit identification of ongoing tasks. Here, we hypothesize that dFC patterns carry fine-grained information that allows for tracking short-term task engagement levels (i.e., 10s of seconds long). To test this hypothesis, 25 subjects were scanned continuously for 25 min while they performed and transitioned between four different tasks: working memory, visual attention, math, and rest. First, we estimated dFC patterns by using a sliding window approach. Next, we extracted two engagement-specific FC patterns representing active engagement and passive engagement by using k-means clustering. Then, we derived three metrics from whole-brain dFC patterns to track engagement level, that is, dissimilarity between dFC patterns and engagement-specific FC patterns, and the level of brainwide integration level. Finally, those engagement markers were evaluated against windowed task performance by using a linear mixed effects model. Significant relationships were observed between abovementioned metrics and windowed task performance for the working memory task only. These findings partially confirm our hypothesis and underscore the potential of whole-brain dFC to track short-term task engagement levels.

| S-EPMC6326730 | biostudies-literature

Synaptic integration in electrically coupled neurons.

Project description:Interactions among chemical and electrical synapses regulate the patterns of electrical activity of vertebrate and invertebrate neurons. In this investigation we studied how electrical coupling influences the integration of excitatory postsynaptic potentials (EPSPs). Pairs of Retzius neurons of the leech are coupled by a nonrectifying electrical synapse by which chemically induced synaptic currents flow from one neuron to the other. Results from electrophysiology and modeling suggest that chemical synaptic inputs are located on the coupled neurites, at 7.5 microm from the electrical synapses. We also showed that the space constant of the coupled neurites was 100 microm, approximately twice their length, allowing the efficient spread of synaptic currents all along both coupled neurites. Based on this cytoarchitecture, our main finding was that the degree of electrical coupling modulates the amplitude of EPSPs in the driving neurite by regulating the leak of synaptic current to the coupled neurite, so that the amplitude of EPSPs in the driving neurite was proportional to the value of the coupling resistance. In contrast, synaptic currents arriving at the coupled neurite through the electrical synapse produced EPSPs of constant amplitude. This was because the coupling resistance value had inverse effects on the amount of current arriving and on the impedance of the neurite. We propose that by modulating the amplitude of EPSPs, electrical synapses could regulate the firing frequency of neurons.

| S-EPMC1303833 | biostudies-literature

Functional network connectivity during Jazz improvisation.

Project description:One of the most complex forms of creativity is musical improvisation where new music is produced in real time. Brain behavior during music production has several dimensions depending on the conditions of the performance. The expression of creativity is suspected to be different whether novel ideas must be externalized using a musical instrument or can be imagined internally. This study explores whole brain functional network connectivity from fMRI data during jazz music improvisation compared against a baseline of prelearned score performance. Given that creativity might be affected by external execution, another dimension where musicians imagine or vocalize the music was also tested. We found improvisation was associated with a state of weak connectivity necessary for attenuated executive control network recruitment associated with a feeling of "flow" allowing unhindered musical creation. In addition, elicited connectivity for sensorimotor and executive control networks is not different whether musicians imagine or externalize (through vocalization) musical performance.

| S-EPMC8463554 | biostudies-literature

A convolutional neural network for estimating synaptic connectivity from spike trains.

Project description:The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.

| S-EPMC8187444 | biostudies-literature

The backbone network of dynamic functional connectivity.

Project description:Temporal networks have become increasingly pervasive in many real-world applications, including the functional connectivity analysis of spatially separated regions of the brain. A major challenge in analysis of such networks is the identification of noise confounds, which introduce temporal ties that are nonessential, or links that are formed by chance due to local properties of the nodes. Several approaches have been suggested in the past for static networks or temporal networks with binary weights for extracting significant ties whose likelihood cannot be reduced to the local properties of the nodes. In this work, we propose a data-driven procedure to reveal the irreducible ties in dynamic functional connectivity of resting-state fMRI data with continuous weights. This framework includes a null model that estimates the latent characteristics of the distributions of temporal links through optimization, followed by a statistical test to filter the links whose formation can be reduced to the activities and local properties of their interacting nodes. We demonstrate the benefits of this approach by applying it to a resting-state fMRI dataset, and provide further discussion on various aspects and advantages of it.

| S-EPMC8746122 | biostudies-literature

Global network influences on local functional connectivity.

Project description:A central neuroscientific pursuit is understanding neuronal interactions that support computations underlying cognition and behavior. Although neurons interact across disparate scales, from cortical columns to whole-brain networks, research has been restricted to one scale at a time. We measured local interactions through multi-neuronal recordings while accessing global networks using scalp electroencephalography (EEG) in rhesus macaques. We measured spike count correlation, an index of functional connectivity with computational relevance, and EEG oscillations, which have been linked to various cognitive functions. We found a non-monotonic relationship between EEG oscillation amplitude and spike count correlation, contrary to the intuitive expectation of a direct relationship. With a widely used network model, we replicated these findings by incorporating a private signal targeting inhibitory neurons, a common mechanism proposed for gain modulation. Finally, we found that spike count correlation explained nonlinearities in the relationship between EEG oscillations and response time in a spatial selective attention task.

| S-EPMC4641678 | biostudies-literature

Spontaneous BOLD event triggered averages for estimating functional connectivity at resting state.

Project description:Recent neuroimaging studies have demonstrated that the spontaneous brain activity reflects, to a large extent, the same activation patterns measured in response to cognitive and behavioral tasks. This correspondence between activation and rest has been explored with a large repertoire of computational methods, ranging from analysis of pairwise interactions between areas of the brain to the global brain networks yielded by independent component analysis. In this paper we describe an alternative method based on the averaging of the BOLD signal at a region of interest (target) triggered by spontaneous increments in activity at another brain area (seed). The resting BOLD event triggered averages ("rBeta") can be used to estimate functional connectivity at resting state. Using two simple examples, here we illustrate how the analysis of the average response triggered by spontaneous increases/decreases in the BOLD signal is sufficient to capture the aforementioned correspondence in a variety of circumstances. The computation of the non linear response during rest here described allows for a direct comparison with results obtained during task performance, providing an alternative measure of functional interaction between brain areas.

| S-EPMC3014405 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data