Number Of Time Points Research Articles

Identifying musical pieces from fMRI data using encoding and decoding models

Encoding models can reveal and decode neural representations in the visual and semantic domains. However, a thorough understanding of how distributed information in auditory cortices and temporal evolution of music contribute to model performance is still lacking in the musical domain. We measured fMRI responses during naturalistic music listening and constructed a two-stage approach that first mapped musical features in auditory cortices and then decoded novel musical pieces. We then probed the influence of stimuli duration (number of time points) and spatial extent (number of voxels) on decoding accuracy. Our approach revealed a linear increase in accuracy with duration and a point of optimal model performance for the spatial extent. We further showed that Shannon entropy is a driving factor, boosting accuracy up to 95% for music with highest information content. These findings provide key insights for future decoding and reconstruction algorithms and open new venues for possible clinical applications.

Collaborative representation-based classification of microarray gene expression data.

Microarray technology is important to simultaneously express multiple genes over a number of time points. Multiple classifier models, such as sparse representation (SR)-based method, have been developed to classify microarray gene expression data. These methods allocate the gene data points to different clusters. In this paper, we propose a novel collaborative representation (CR)-based classification with regularized least square to classify gene data. First, the CR codes a testing sample as a sparse linear combination of all training samples and then classifies the testing sample by evaluating which class leads to the minimum representation error. This CR-based classification approach is remarkably less complex than traditional classification methods but leads to very competitive classification results. In addition, compressive sensing approach is adopted to project the high-dimensional gene expression dataset to a lower-dimensional space which nearly contains the whole information. This compression without loss is beneficial to reduce the computational load. Experiments to detect subtypes of diseases, such as leukemia and autism spectrum disorders, are performed by analyzing the gene expression. The results show that the proposed CR-based algorithm exhibits significantly higher stability and accuracy than the traditional classifiers, such as support vector machine algorithm.

Number of Subjects and Time Points Needed for Multilevel Time-Series Analysis: A Simulation Study of Dynamic Structural Equation Modeling

Dynamic structural equation modeling (DSEM) is a novel, intensive longitudinal data (ILD) analysis framework. DSEM models intraindividual changes over time on Level 1 and allows the parameters of these processes to vary across individuals on Level 2 using random effects. DSEM merges time series, structural equation, multilevel, and time-varying effects models. Despite the well-known properties of these analysis areas by themselves, it is unclear how their sample size requirements and recommendations transfer to the DSEM framework. This article presents the results of a simulation study that examines the estimation quality of univariate 2-level autoregressive models of order 1, AR(1), using Bayesian analysis in Mplus Version 8. Three features are varied in the simulations: complexity of the model, number of subjects, and number of time points per subject. Samples with many subjects and few time points are shown to perform substantially better than samples with few subjects and many time points.

A 2D & 3D fluorescent toxicity pathway reporter platform for high throughput imaging-based evaluation of hepatotoxicity liabilities

Real-time dose-response curves for fructose have been non-invasively determined in primary rat hepatocyte alginate spheroids cultured in a NMR-compatible fluidized-bed bioreactor. Using 13C–labeled glucose and glycine culture medium, fructose dose was compared to glucose uptake and glycogen synthesis rate using 13C NMR spectroscopy, and to ATP and fructose-1-phosphate concentration using 31P NMR spectroscopy. A highly efficient multicoaxial perfusion system maintains high density 3-D hepatocyte cultures, permitting 13C and 31P NMR spectral time courses with 1 min time points. The perfusion system was turned off to demonstrate its efficiency and effect on the metabolites. Within 16 min, glycogen plummeted, lactate became the largest 13C–glucose metabolite via anaerobic glycolysis, while glutathione was the largest 13C–glycine metabolite. ATP depletion and fructose-1-phosphate formation demonstrated a dose response with a 3 h EC50 of 19 mM ± 8.9 mM and 17.4 mM ± 3.7 mM, respectively. Computational modeling of mass transfer corroborated experimental results and helped determine the optimal bioreactor loading densities, oxygen concentration, and perfusion rates to maintain physiologically-relevant nutrient levels. The total bioreactor plus perfusion loop has a dead volume of 2 ml, and contains 5 million hepatocytes. Due to the non-invasive measurements, there is a reduction of animal tissue by an order-of-magnitude, depending on the number of time points in an experiment. This dynamic flux approach may have generic utility for dose-response studies monitoring multiple metabolic reactions in other primary mammalian cells, such as human, that have strict oxygen demands.

Dose-response in a high density three-dimensional liver device with real-time bioenergetic and metabolic flux quantification

Real-time dose-response curves for fructose have been non-invasively determined in primary rat hepatocyte alginate spheroids cultured in a NMR-compatible fluidized-bed bioreactor. Using 13C–labeled glucose and glycine culture medium, fructose dose was compared to glucose uptake and glycogen synthesis rate using 13C NMR spectroscopy, and to ATP and fructose-1-phosphate concentration using 31P NMR spectroscopy. A highly efficient multicoaxial perfusion system maintains high density 3-D hepatocyte cultures, permitting 13C and 31P NMR spectral time courses with 1min time points. The perfusion system was turned off to demonstrate its efficiency and effect on the metabolites. Within 16min, glycogen plummeted, lactate became the largest 13C–glucose metabolite via anaerobic glycolysis, while glutathione was the largest 13C–glycine metabolite. ATP depletion and fructose-1-phosphate formation demonstrated a dose response with a 3h EC50 of 19mM±8.9mM and 17.4mM±3.7mM, respectively. Computational modeling of mass transfer corroborated experimental results and helped determine the optimal bioreactor loading densities, oxygen concentration, and perfusion rates to maintain physiologically-relevant nutrient levels. The total bioreactor plus perfusion loop has a dead volume of 2ml, and contains 5 million hepatocytes. Due to the non-invasive measurements, there is a reduction of animal tissue by an order-of-magnitude, depending on the number of time points in an experiment. This dynamic flux approach may have generic utility for dose-response studies monitoring multiple metabolic reactions in other primary mammalian cells, such as human, that have strict oxygen demands.

Neuronal Migration and Axonal Pathways Linked to Human Fetal Insular Development Revealed by Diffusion MR Tractography.

The insula is a multimodal sensory integration structure that, in addition to serving as a gateway between somatosensory areas and limbic structures, plays a crucial role in autonomic nervous system function. While anatomical studies following the development of the insula have been conducted, currently, no studies have been published in human fetuses tracking the development of neuronal migration or of white matter tracts in the cortex. In this study, we aimed to follow the neuronal migration and subsequent maturation of axons in and around the insula in human fetal ages. Using high-angular resolution diffusion magnetic resonance imaging tractography, major white matter pathways to/from the insula and its surrounding operculum were identified at a number of time points during human gestation. Pathways likely linked to neuronal migration from the ventricular zone to the inferior frontal gyrus, superior temporal region, and the insular cortex were detected in the earliest gestational age studied (15 GW). Tractography reveals neuronal migration to areas surrounding the insula occurred at different time points. These results, in addition to demonstrating key time points for neuronal migration, suggest that neurons and axonal fiber pathways underlying the insula and its surrounding gyri mature differentially despite their relationship during cortical folding.

Quantifying Selection with Pool-Seq Time Series Data.

Allele frequency time series data constitute a powerful resource for unraveling mechanisms of adaptation, because the temporal dimension captures important information about evolutionary forces. In particular, Evolve and Resequence (E&R), the whole-genome sequencing of replicated experimentally evolving populations, is becoming increasingly popular. Based on computer simulations several studies proposed experimental parameters to optimize the identification of the selection targets. No such recommendations are available for the underlying parameters selection strength and dominance. Here, we introduce a highly accurate method to estimate selection parameters from replicated time series data, which is fast enough to be applied on a genome scale. Using this new method, we evaluate how experimental parameters can be optimized to obtain the most reliable estimates for selection parameters. We show that the effective population size (Ne) and the number of replicates have the largest impact. Because the number of time points and sequencing coverage had only a minor effect, we suggest that time series analysis is feasible without major increase in sequencing costs. We anticipate that time series analysis will become routine in E&R studies.

Sparse matrix computations for dynamic network centrality.

Time sliced networks describing human-human digital interactions are typically large and sparse. This is the case, for example, with pairwise connectivity describing social media, voice call or physical proximity, when measured over seconds, minutes or hours. However, if we wish to quantify and compare the overall time-dependent centrality of the network nodes, then we should account for the global flow of information through time. Because the time-dependent edge structure typically allows information to diffuse widely around the network, a natural summary of sparse but dynamic pairwise interactions will generally take the form of a large dense matrix. For this reason, computing nodal centralities for a time-dependent network can be extremely expensive in terms of both computation and storage; much more so than for a single, static network. In this work, we focus on the case of dynamic communicability, which leads to broadcast and receive centrality measures. We derive a new algorithm for computing time-dependent centrality that works with a sparsified version of the dynamic communicability matrix. In this way, the computation and storage requirements are reduced to those of a sparse, static network at each time point. The new algorithm is justified from first principles and then tested on a large scale data set. We find that even with very stringent sparsity requirements (retaining no more than ten times the number of nonzeros in the individual time slices), the algorithm accurately reproduces the list of highly central nodes given by the underlying full system. This allows us to capture centrality over time with a minimal level of storage and with a cost that scales only linearly with the number of time points. We also describe and test three variants of the proposed algorithm that require fewer parameters and achieve a further reduction in the computational cost.

Comparing Two MLEs of The Change Point When an overline X Control Chart Is Used

To find the change point of a normal process monitored by an overline X control chart, the first and most important step is to find a good estimate of the change point. Different from all the previous studies in which the MLE of the change point is computed from a conditional likelihood function given the signal time of the overline X control chart, in this paper we also derive the MLE of the change point from the complete likelihood function. Then, the two MLEs are compared through a series of simulations based on two criteria, MSE and a new proposed criterion. The new criterion is just the average number of points in time, ordered according to their likelihood, at which the process should be examined to find the change point. The results show that our estimator is superior to the usual MLE which was used in previous studies and this superiority is shown better by our new criterion, which is more rational, when a control chart is used.

MGT-SM: A Method for Constructing Cellular Signal Transduction Networks.

A cellular signal transduction network is an important means to describe biological responses to environmental stimuli and exchange of biological signals. Constructing the cellular signal transduction network provides an important basis for the study of the biological activities, the mechanism of the diseases, drug targets and so on. The statistical approaches to network inference are popular in literature. Granger test has been used as an effective method for causality inference. Compared with bivariate granger tests, multivariate granger tests reduce the indirect causality and were used widely for the construction of cellular signal transduction networks. A multivariate Granger test requires that the number of time points in the time-series data is more than the number of nodes involved in the network. However, there are many real datasets with a few time points which are much less than the number of nodes in the network. In this study, we propose a new multivariate Granger test-based framework to construct cellular signal transduction network, called MGT-SM. Our MGT-SM uses SVD to compute the coefficient matrix from gene expression data and adopts Monte Carlo simulation to estimate the significance of directed edges in the constructed networks. We apply the proposed MGT-SM to Yeast Synthetic Network and MDA-MB-468, and evaluate its performance in terms of the recall and the AUC. The results show that MGT-SM achieves better results, compared with other popular methods (CGC2SPR, PGC, and DBN).

Survival analysis for Hispanic ELL students’ access to postsecondary schools: discrete model or Cox regression?

ABSTRACTSurvival analysis is an advanced statistical method to investigate the occurrence and the timing of an important event such as school access, dropout, and graduation in a longitudinal framework. The aim of our study is to provide practical guidelines for empirical researchers in choosing an appropriate survival analysis model. For this goal, this study chose two major survival analytical models of a discrete-time hazard model and a Cox regression model and compared analytical outcomes considering time metrics, as well as sample sizes and censoring proportions. In the analytical model, the combined specifications of varying factors using two models were adopted to analyse the Educational Longitudinal Study of 2002. We chose the college access of Hispanic English Language Learners to understand the importance of adopting a proper survival model to examine the educational outcome in the educational context. Importantly, we considered the hazard probability for the target event in the model specification which is a fundamental yet often neglected component of survival analysis. We recommended discrete models for the cases with a smaller number of time points, larger time metrics, larger sample size, and smaller proportions of censored observations.

The Case-time-control Method for Nonbinary Exposures

A popular way to reduce confounding in observational studies is to use each study participant as his or her own control. This is possible when both the exposure and the outcome are time varying and have been measured at several time points for each individual. The case-time-control method is a special case, which, under certain assumptions, allows the analyst to control for confounding by time-varying covariates, while controlling for all time-stationary characteristics of the study participants. There are two formulations of the case-time-control method. One formulation requires that the exposure be binary, and the other requires that there be no more than two time points per individual. In this article the author proposes a generalization of the case-time-control method for nonbinary exposures and an arbitrary number of time points. The author derives the asymptotic properties of the resulting estimator and assesses its finite sample properties in a simulation study.

Activation of μ-opioid receptor and Toll-like receptor 4 by plasma from morphine-treated mice

In this study, we quantified the ability of opioids present in biological samples to activate the μ-opioid receptor and TLR4 using cell-based assays. Each assay was standardised, in the presence of plasma, using morphine, its μ receptor-active metabolite morphine-6 glucuronide (M6G) and its μ receptor-inactive, but TLR4-active metabolite morphine-3 glucuronide (M3G). Specificity was verified using antagonists. Morphine- and M6G-spiked plasma samples exhibited μ receptor activation, which M3G-spiked plasma lacked. In contrast, M3G showed moderate but consistent activation of TLR-4. Plasma samples were collected at a number of time points from mice administered morphine (1 or 10mg/kg every 12h for 3days) or saline. Morphine administration led to intermittent μ receptor activation, reversed by μ receptor antagonists, and to TRL4 activation at time points where M3G is measured in plasma. Interestingly, this protocol of morphine administration also led to TLR4-independent NF-κB activation, at time points where M3G was not detected, presumably via elevation of circulating cytokines including, but not limited to, TNFα. Circulating TNFα was increased after three days of morphine administration, and TNFα mRNA elevated in the spleen of morphine-treated mice.

Alignment of time-resolved data from high throughput experiments.

To better understand the dynamics of the underlying processes in cells, it is necessary to take measurements over a time course. Modern high-throughput technologies are often used for this purpose to measure the behavior of cell products like metabolites, peptides, proteins, [Formula: see text]RNA or mRNA at different points in time. Compared to classical time series, the number of time points is usually very limited and the measurements are taken at irregular time intervals. The main reasons for this are the costs of the experiments and the fact that the dynamic behavior usually shows a strong reaction and fast changes shortly after a stimulus and then slowly converges to a certain stable state. Another reason might simply be missing values. It is common to repeat the experiments and to have replicates in order to carry out a more reliable analysis. The ideal assumptions that the initial stimulus really started exactly at the same time for all replicates and that the replicates are perfectly synchronized are seldom satisfied. Therefore, there is a need to first adjust or align the time-resolved data before further analysis is carried out. Dynamic time warping (DTW) is considered as one of the common alignment techniques for time series data with equidistant time points. In this paper, we modified the DTW algorithm so that it can align sequences with measurements at different, non-equidistant time points with large gaps in between. This type of data is usually known as time-resolved data characterized by irregular time intervals between measurements as well as non-identical time points for different replicates. This new algorithm can be easily used to align time-resolved data from high-throughput experiments and to come across existing problems such as time scarcity and existing noise in the measurements. We propose a modified method of DTW to adapt requirements imposed by time-resolved data by use of monotone cubic interpolation splines. Our presented approach provides a nonlinear alignment of two sequences that neither need to have equi-distant time points nor measurements at identical time points. The proposed method is evaluated with artificial as well as real data. The software is available as an R package tra (Time-Resolved data Alignment) which is freely available at: http://public.ostfalia.de/klawonn/tra.zip .

Performance of growth mixture models in the presence of time-varying covariates.

Growth mixture modeling is often used to identify unobserved heterogeneity in populations. Despite the usefulness of growth mixture modeling in practice, little is known about the performance of this data analysis technique in the presence of time-varying covariates. In the present simulation study, we examined the impacts of five design factors: the proportion of the total variance of the outcome explained by the time-varying covariates, the number of time points, the error structure, the sample size, and the mixing ratio. More precisely, we examined the impact of these factors on the accuracy of parameter and standard error estimates, as well ason the class enumeration accuracy. Our results showed that the consistent Akaike information criterion (CAIC), thesample-size-adjusted CAIC(SCAIC), theBayesian information criterion (BIC), and the integrated completed likelihood criterion (ICL-BIC) proved to be highly reliable indicators of the true number of latent classes in the data, across design conditions, and that the sample-size-adjusted BIC (SBIC) also proved quite accurate, especially in larger samples. In contrast, the Akaike information criterion (AIC), theentropy, thenormalized entropy criterion(NEC), and the classification likelihood criterion (CLC) proved to be unreliable indicators of the true number of latent classes in the data. Our results also showed that substantial biases in the parameter and standard error estimates tended to be associated with growth mixture models that included only four time points.

Abstract # 1805 Peripheral cutaneous inflammation induces a brain chemokine response, immune cell infiltration and functional changes

There are now compelling data linking peripheral inflammation and depression. However, the mechanisms that underlie this relationship remain unclear. Using QPCR, we measured brain, lung, liver, skin and peripheral blood leukocyte responses to a well-characterised murine model of psoriasis-like skin inflammation, Aldara (containing the TLR-7 ligand, Imiquimod), in C57BL/6 female mice. We measured tissue responses across a number of time points (4 h, 12 h, 1 d, 3 d and 5 d) and confirmed qPCR findings with protein analysis of brain tissue. Further, we measured behavioural outcomes using burrowing, a rewarding behaviour, deficits in which can be construed as anhedonic. We found significant transcriptional upregulation of a number of chemokines and pro-inflammatory cytokines (including CCL2, CCL5, CXCL10, IL-1B, IL-6 and TNF-a) in the brain and systemic organs and concurrent upregulation of protein was confirmed in the brain. However, the brain response was temporally unique in that the peak transcriptional response was observed at 3 days compared to 12 h to 1 day in the peripheral tissues. Along with chemokine and cytokine induction, we observed the infiltration of immune cells into the brain, including T cells, NK cells and monocytes, particularly at day 3 and day 5. These data indicate that the brain mounts a unique response to peripheral inflammatory stimuli, including chemokine induction that may play a role in immune cell recruitment and subsequent behavioural deficits.

SEM modeling with singular moment matrices Part III: GLS estimation

We discuss generalized least squares (GLS) and maximum likelihood (ML) estimation for structural equations models (SEM), when the sample moment matrices are possibly singular. This occurs in several instances, for example, for panel data when there are more panel waves than independent replications or for time series data where the number of time points is large, but only one unit is observed. In previous articles, it was shown that ML estimation of the SEM is possible by using a correct Gaussian likelihood function. In this article, the usual GLS fit function is modified so that it is also defined for singular sample moment matrices S. In large samples, GLS and ML estimation perform similarly, and the modified GLS approach is a good alternative when S becomes nearly singular. Both GLS approaches do not work for N = 1, since here S = 0 and the modified GLS approach yields biased estimates. In conclusion, ML estimation (and pseudo ML under misspecification) is recommended for all sample sizes including N = 1.

Multitime correlation functions in nonclassical stochastic processes

A general method is introduced for verifying multitime quantum correlations through the characteristic function of the time-dependent $P$ functional that generalizes the Glauber-Sudarshan $P$ function. Quantum correlation criteria are derived which identify quantum effects for an arbitrary number of points in time. The Magnus expansion is used to visualize the impact of the required time ordering, which becomes crucial in situations when the interaction problem is explicitly time dependent. We show that the latter affects the multi-time-characteristic function and, therefore, the temporal evolution of the nonclassicality. As an example, we apply our technique to an optical parametric process with a frequency mismatch. The resulting two-time-characteristic function yields full insight into the two-time quantum correlation properties of such a system.

Indices for the evaluation of wildfire spread simulations using contemporaneous predictions and observations of burnt area

Methods to objectively evaluate performance are critical for model development. In contrast to recent advances in wildfire simulation, there has been limited attention to evaluating fire model performance. Information to validate fire models is typically limited, commonly to a few perimeter observations at a small number of points in time. We review metrics for comparing two burnt areas at a point in time: observed and predicted. These are compared in an idealised landscape and with a case study evaluating the performance of simulations of an Australian wildfire. We assessed: Shape Deviation Index (SDI), Jaccard's coefficient, F1, Sørensen's Similarity and Area Difference Index (ADI). For decomposing fit into error components (overprediction and underprediction) we assessed the partial indices of SDI and ADI, Precision and Recall. The various metrics were evaluated for their ability to represent error and their suitability for use in model improvement frameworks.

Reliability of graph analysis of resting state fMRI using test-retest dataset from the Human Connectome Project

The exploration of brain networks with resting-state fMRI (rs-fMRI) combined with graph theoretical approaches has become popular, with the perspective of finding network graph metrics as biomarkers in the context of clinical studies. A preliminary requirement for such findings is to assess the reliability of the graph based connectivity metrics. In previous test-retest (TRT) studies, this reliability has been explored using intraclass correlation coefficient (ICC) with heterogeneous results. But the issue of sample size has not been addressed. Using the large TRT rs-fMRI dataset from the Human Connectome Project (HCP), we computed ICCs and their corresponding p-values (applying permutation and bootstrap techniques) and varied the number of subjects (from 20 to 100), the scan duration (from 400 to 1200 time points), the cost and the graph metrics, using the Anatomic-Automatic Labelling (AAL) parcellation scheme. We quantified the reliability of the graph metrics computed both at global and regional level depending, at optimal cost, on two key parameters, the sample size and the number of time points or scan duration. In the cost range between 20% to 35%, most of the global graph metrics are reliable with 40 subjects or more with long scan duration (14min 24s). In large samples (for instance, 100 subjects), most global and regional graph metrics are reliable for a minimum scan duration of 7min 14s. Finally, for 40 subjects and long scan duration (14min 24s), the reliable regions are located in the main areas of the default mode network (DMN), the motor and the visual networks.