Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series.

ABSTRACT: Functional connectivity (FC) describes the statistical dependence between neuronal populations or brain regions in resting-state fMRI studies and is commonly estimated as the Pearson correlation of time courses. Clustering or community detection reveals densely coupled sets of regions constituting resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs but appear to fluctuate on shorter timescales. Here, we propose a new approach to reveal temporal fluctuations in neuronal time series. Unwrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair or edge, and allows tracking of fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging runs, capture individual differences, and disclose fine-scale temporal expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

SUBMITTER: Sporns O

PROVIDER: S-EPMC8233118 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series.

Project description:Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box-Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed.

| S-EPMC5175424 | biostudies-literature

Fine scale time-series RNA-Seq of shoot and root responses to Nitrogen supply

Project description:Arabidopsis plants grown in a hydroponic media with sufficient Nitrogen were given a 60mM supply of Nitrogen. Triplicate sets of plants were sampled at time points 0,5,10,15,20,30,45,60,90 and 120 minutes after supply from treated and control plants (20mM KCl). The samples were flash frozen in liquid N and used to construct RNA-Seq libraries to assay transcriptional changes in the shoots and roots, separately, in response to Nitrogen.

2018-05-11 | GSE97500 | GEO

McMurdo Ice Edge Time Series 2014-2015

Project description:McMurdo Ice Edge Time Series 2014-2015 Metagenome

| PRJNA704702 | ENA

Fine-scale time-lapse analysis of the biphasic, dynamic behaviour of the two Vibrio cholerae chromosomes.

Project description:Using fluorescent repressor-operator systems in live cells, we investigated the dynamic behaviour of chromosomal origins in Vibrio cholerae, whose genome is divided between two chromosomes. We have developed a method of analysing fine-scale motion in the curved co-ordinate system of vibrioid bacteria. Using this method, we characterized two different modes of chromosome behaviour corresponding to periods between segregation events and periods of segregation. Between segregation events, the origin positions are not fixed but rather maintained within ellipsoidal caged domains, similar to eukaryotic interphase chromosome territories. These domains are approximately 0.4 microm wide and 0.6 microm long, reflecting greater restriction in the short axis of the cell. During segregation, movement is directionally biased, speed is comparable between origins, and cell growth can account for nearly 20% of the motion observed. Furthermore, the home domain of each origin is positioned by a different mechanism. Specifically, the oriC(I) domain is maintained at a constant actual distance from the pole regardless of cell length, while the oriC(II) domain is maintained at a constant relative position. Thus the actual position of oriC(II) varies with cell length. While the gross behaviours of the two origins are distinct, their fine-scale dynamics are remarkably similar, indicating that both experience similar microenvironments.

| S-EPMC2779472 | biostudies-literature

Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series.

Project description:BackgroundComparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements.ResultsHere, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S) algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR) and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation.ConclusionsThe DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package TimeShift at http://www.picb.ac.cn/Comparative/data.html.

| S-EPMC3180390 | biostudies-literature

Reconfiguration of dynamic large-scale brain network functional connectivity in generalized tonic-clonic seizures.

Project description:An increasing number of studies in patients with generalized tonic-clonic seizures (GTCS) have reported the alteration of functional connectivity (FC) in many brain networks. However, little is known about the underlying temporal variability of FC in large-scale brain functional networks in patients. Recently, dynamic FC could provide novel insight into the physiological mechanisms in the brain. Here, we recruited 63 GTCS and 65 age- and sex-matched healthy controls. Dynamic FC approaches were used to evaluate alterations in the temporal variability of FC in patients at the region- and network-levels. In addition, two kinds of brain templates (>102 and > 103 regions) and two kinds of temporal variability FC approaches were adopted to verify the stability of the results. Patients showed increased FC variability in regions of the default mode network (DMN), ventral attention network (VAN) and motor-related areas. The DAN, VAN, and DMN illustrated enhanced FC variability at the within-network level. In addition, increased FC variabilities between networks were found between the DMN and cognition-related networks, including the VAN, dorsal attention network and frontal-parietal network in GTCS. Meanwhile, the alterations in FC variability were relatively consistent across different methods and templates. Therefore, the consistent alteration of FC variability would reflect a dynamic restructuring of the large-scale brain networks in patients with GTCS. Overly frequent information communication among cognition-related networks, especially in the DMN, might play a role in the epileptic activity and/or cognitive dysfunction in patients.

| S-EPMC7267969 | biostudies-literature

Time Series

Project description:Temporal and technical variability of human gut metagenomes

| PRJEB8347 | ENA

TIme-series

Project description:Fungal control of early-stage bacterial community development in wood

| PRJEB87091 | ENA

Generalized Dynamic Factor Models for Mixed-Measurement Time Series.

Project description:In this article, we propose generalized Bayesian dynamic factor models for jointly modeling mixed-measurement time series. The framework allows mixed-scale measurements associated with each time series, with different measurements having different distributions in the exponential family conditionally on time-varying latent factor(s). Efficient Bayesian computational algorithms are developed for posterior inference on both the latent factors and model parameters, based on a Metropolis Hastings algorithm with adaptive proposals. The algorithm relies on a Greedy Density Kernel Approximation (GDKA) and parameter expansion with latent factor normalization. We tested the framework and algorithms in simulated studies and applied them to the analysis of intertwined credit and recovery risk for Moody's rated firms from 1982-2008, illustrating the importance of jointly modeling mixed-measurement time series. The article has supplemental materials available online.

| S-EPMC4004613 | biostudies-other

A dynamic time order network for time-series gene expression data analysis.

Project description:BackgroundTypical analysis of time-series gene expression data such as clustering or graphical models cannot distinguish between early and later drug responsive gene targets in cancer cells. However, these genes would represent good candidate biomarkers.ResultsWe propose a new model - the dynamic time order network - to distinguish and connect early and later drug responsive gene targets. This network is constructed based on an integrated differential equation. Spline regression is applied for an accurate modeling of the time variation of gene expressions. Then a likelihood ratio test is implemented to infer the time order of any gene expression pair. One application of the model is the discovery of estrogen response biomarkers. For this purpose, we focused on genes whose responses are late when the breast cancer cells are treated with estradiol (E2).ConclusionsOur approach has been validated by successfully finding time order relations between genes of the cell cycle system. More notably, we found late response genes potentially interesting as biomarkers of E2 treatment.

| S-EPMC3524318 | 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