Impairment in time perception, a critical component of decision-making, represents a risk factor for psychiatric conditions including substance abuse. A therapeutic that ameliorates this impairment could be advantageous in the treatment of impulsivity and decision-making disorders.

Recent studies have reported the existence of hippocampal "time cells," neurons that fire at particular moments during periods when behavior and location are relatively constant. However, an alternative explanation of apparent time coding is that hippocampal neurons "path integrate" to encode the distance an animal has traveled. Here, we examined hippocampal neuronal firing patterns as rats ran in place on a treadmill, thus "clamping" behavior and location, while we varied the treadmill speed to distinguish time elapsed from distance traveled. Hippocampal neurons were strongly influenced by time and distance, and less so by minor variations in location. Furthermore, the activity of different neurons reflected integration over time and distance to varying extents, with most neurons strongly influenced by both factors and some significantly influenced by only time or distance. Thus, hippocampal neuronal networks captured both the organization of time and distance in a situation where these dimensions dominated an ongoing experience.

Advances in neuroanatomy and computational power are leading to the construction of new digital brain atlases. Atlases are rising as indispensable tools for comparing anatomical data as well as being stimulators of new hypotheses and experimental designs. Brain atlases describe nervous systems which are inherently plastic and variable. Thus, the levels of brain plasticity and stereotypy would be important to evaluate as limiting factors in the context of static brain atlases. In this review, we discuss the extent of structural changes which neurons undergo over time, and how these changes would impact the static nature of atlases. We describe the anatomical stereotypy between neurons of the same type, highlighting the differences between invertebrates and vertebrates. We review some recent experimental advances in our understanding of anatomical dynamics in adult neural circuits, and how these are modulated by the organism's experience. In this respect, we discuss some analogies between brain atlases and the sequenced genome and the emerging epigenome. We argue that variability and plasticity of neurons are substantially high, and should thus be considered as integral features of high-resolution digital brain atlases.

The purpose of the present study was to evaluate the effect of time (season, surgical starting time in the daytime, preoperative waiting time) on patients with gastric cancer.

The study utilizes the Chinese version of the Zimbardo Time Perspective Inventory (ZTPI-C) and a novelty intertemporal prosocial discounting paradigm to explore the preferences of individuals with the Present Impulsive Time Perspective (PITP) and the Future Time Perspective (FTP) in intertemporal prosocial choices, and uncovers the cognitive mechanisms underpinning intertemporal altruism from the personality traits. The findings revealed: (1) The donation behaviors of both groups decreased as time delay rose, aligning with the hyperbolic model. (2) PITP individuals had significantly higher discount rates than those with FTP, and the scores of FTP individuals on the "Future" dimension of the ZTPI-C were positively correlated with the amount of money they were willing to forgo. These results suggest that time perspective, as a stable personality trait, can predict individuals' intertemporal prosocial preferences. Our research enriches the theory of intertemporal choices and extends the Perceived-time-based model (PTBM) to the domain of intertemporal social preferences.

Real-time functional magnetic resonance imaging (fMRI) enables one to monitor a subject's brain activity during an ongoing session. The availability of online information about brain activity is essential for developing and refining interactive fMRI paradigms in research and clinical trials and for neurofeedback applications. Data analysis for real-time fMRI has traditionally been based on hypothesis-driven processing methods. Off-line data analysis, conversely, may be usefully complemented by data-driven approaches, such as independent component analysis (ICA), which can identify brain activity without a priori temporal assumptions on brain activity. However, ICA is commonly considered a time-consuming procedure and thus unsuitable to process the high flux of fMRI data while they are acquired. Here, by specific choices regarding the implementation, we exported the ICA framework and implemented it into real-time fMRI data analysis. We show that, reducing the ICA input to a few points within a time-series in a sliding-window approach, computational times become compatible with real-time settings. Our technique produced accurate dynamic readouts of brain activity as well as a precise spatiotemporal history of quasistationary patterns in the form of cumulative activation maps and time courses. Results from real and simulated motor activation data show comparable performances for the proposed ICA implementation and standard linear regression analysis applied either in a sliding-window or in a cumulative mode. Furthermore, we demonstrate the possibility of monitoring transient or unexpected neural activities and suggest that real-time ICA may provide the fMRI researcher with a better understanding and control of subjects' behaviors and performances.

Individuals with a balanced time perspective, which includes good thoughts about the past, awareness of present constraints and adaptive planning for a positive future, are more likely to report optimal wellbeing. However, people who have had traumas such as adverse childhood experiences (ACEs) are likely to have less balanced time perspectives and lower overall wellbeing when compared to those with fewer or no ACEs. Time perspective can be improved via time-travel narratives that support people in feeling connected to a wise and loving future version of themselves, an approach that has until now only been provided in counseling contexts. Our team used an iterative inclusive design process to shape a scalable time-travel narrative tool - a responsive and progressive web application called Time Machine. Among other functionalities, Time Machine allowed people to record and listen to messages as if they were from and to their past and future selves. Using pre-planned as well as post-hoc analyses, we analyzed quantitative and qualitative data from 96 paid design partners (participants) who were taken through a 26-day pilot study of the technology. Among other effects, the results revealed: (1) high engagement throughout the design process, (2) improvements in self-reported time perspective and overall wellbeing scores that were greater for those using Time Machine during an optional-use period, (3) twice as much improvement in overall wellbeing scores for design partners with high ACEs (16%) versus low ACEs (8%), and (4) feelings of unconditional love apparently mediating the relationship between scores on time perspective and overall wellbeing measures. We discuss the limitations of these results as well as implications for the future role of spiritually informed scalable time-travel narrative technologies in healthcare and wellness.

The most common reported epidemic time series in epidemiological surveillance are the daily or weekly incidence of new cases, the hospital admission count, the ICU admission count, and the death toll, which played such a prominent role in the struggle to monitor the Covid-19 pandemic. We show that pairs of such curves are related to each other by a generalized renewal equation depending on a smooth time varying delay and a smooth ratio generalizing the reproduction number. Such a functional relation is also explored for pairs of simultaneous curves measuring the same indicator in two neighboring countries. Given two such simultaneous time series, we develop, based on a signal processing approach, an efficient numerical method for computing their time varying delay and ratio curves, and we verify that its results are consistent. Indeed, they experimentally verify symmetry and transitivity requirements and we also show, using realistic simulated data, that the method faithfully recovers time delays and ratios. We discuss several real examples where the method seems to display interpretable time delays and ratios. The proposed method generalizes and unifies many recent related attempts to take advantage of the plurality of these health data across regions or countries and time, providing a better understanding of the relationship between them. An implementation of the method is publicly available at the EpiInvert CRAN package.

Recent evidence suggests a two-way interaction between the immune and circadian systems. Circadian control of immune factors, as well as the effect of immunological variables on circadian rhythms, might be key elements in both physiological and pathological responses to the environment. Among these relevant factors, galectin-1 is a member of a family of evolutionarily-conserved glycan-binding proteins with both extracellular and intracellular effects, playing important roles in immune cell processes and inflammatory responses. Many of these actions have been studied through the use of mice with a null mutation in the galectin-1 (Lgals1) gene. To further analyze the role of endogenous galectin-1 in vivo, we aimed to characterize the circadian behavior of galectin-1 null (Lgals1-/-) mice.

In clinical or epidemiological follow-up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods, the RMST is easier to interpret and does not require the proportional hazard assumption. To date, regression models based on the RMST are indirect or direct models of the RMST and baseline covariates. However, time-dependent covariates are becoming increasingly common in follow-up studies. Based on the inverse probability of censoring weighting (IPCW) method, we developed a regression model of the RMST and time-dependent covariates. Through Monte Carlo simulation, we verified the estimation performance of the regression parameters of the proposed model. Compared with the time-dependent Cox model and the fixed (baseline) covariate RMST model, the time-dependent RMST model has a better prediction ability. Finally, an example of heart transplantation was used to verify the above conclusions.

To describe the pharmacists' workflow, including tasks and time spent, to better understand their work capacity.

Studies of prefrontal cortex (PFC) lesion patients suggest that information conveying high immediacy, certainty, or tangibility engages the more posterior part of the PFC, whereas information that is more abstract or complex engages the anterior part. We examined whether the anterior and posterior subdivisions of the PFC have distinct roles in processing temporal information during decision making in healthy individuals. We hypothesized that the more the locus of activation is in the posterior (as opposed to anterior) PFC, the more the decision maker will be affected by recent information at the expense of past outcomes. Participants performed a complex decision task while their PFC activity was monitored using fMRI. Results indicate that individual differences in the effect of recent outcomes correspond to differences in the locus of activation, with elevated recency associated with more posterior loci of activation.

The gendered division of labour contributes to differences in the way time is spent and experienced by women and men. Time spent in paid and unpaid labour is associated with sleep outcomes, therefore, we examined (i) the relationships between time use and time pressure, and sleep, and (ii) whether these relationships were modified by gender.

A wide variety of approaches to estimate the degree of synchrony between two or more spike trains have been proposed. One of the most recent methods is the ISI-distance which extracts information from the interspike intervals (ISIs) by evaluating the ratio of the instantaneous firing rates. In contrast to most previously proposed measures it is parameter free and time-scale independent. However, it is not well suited to track changes in synchrony that are based on spike coincidences. Here we propose the SPIKE-distance, a complementary measure which is sensitive to spike coincidences but still shares the fundamental advantages of the ISI-distance. In particular, it is easy to visualize in a time-resolved manner and can be extended to a method that is also applicable to larger sets of spike trains. We show the merit of the SPIKE-distance using both simulated and real data.

To quantify the major environmental drivers of stream bacterial population dynamics, we modelled temporal differences in stream bacterial communities to quantify community shifts, including those relating to cyclical seasonal variation and more sporadic bloom events. We applied Illumina MiSeq 16S rRNA bacterial gene sequencing of 892 stream biofilm samples, collected monthly for 36-months from six streams. The streams were located a maximum of 118 km apart and drained three different catchment types (forest, urban and rural land uses). We identified repeatable seasonal patterns among bacterial taxa, allowing their separation into three ecological groupings, those following linear, bloom/trough and repeated, seasonal trends. Various physicochemical parameters (light, water and air temperature, pH, dissolved oxygen, nutrients) were linked to temporal community changes. Our models indicate that bloom events and seasonal episodes modify biofilm bacterial populations, suggesting that distinct microbial taxa thrive during these events including non-cyanobacterial community members. These models could aid in determining how temporal environmental changes affect community assembly and guide the selection of appropriate statistical models to capture future community responses to environmental change.

This study aimed to assess the effects of isotemporal replacement of sitting time (SIT) with standing (STA) on cardiometabolic biomarkers. In this cross-sectional study, male adolescents wore the GT3X+ activity monitor for 7 days to measure the SIT and STA. Moderate-to-vigorous physical activity (MVPA) was estimated by a youth-specific cut-off point. An isotemporal substitution approach was used to examine the effects of replacing different periods of SIT (15, 30, 60, and 120 min) with STA on cardiometabolic biomarkers [total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), non-HDL-c, low-density lipoprotein cholesterol (LDL-c), triglycerides (TG), glucose, insulin, HOMA2-β, HOMA2-S, and HOMA2-IR]. Analysis of covariance (ANCOVA) with a post-hoc Bonferroni test was used to compare the adjusted means between the four subgroups that were clustered according to SIT and STA amount. Adolescents (n = 84; age, 16.7 ± 0.9 years) wore GT3X+ for 15.2 ± 2.3 h, for 6.7 ± 0.6 days. Isotemporal substitution of SIT with STA was associated with TC, non-HDL-c, LDL-c, and TG. ANCOVA results showed a statistically significant difference for TC, non-HDL-c, and LDL-c. These findings showed that for male adolescents, sitting less and standing more may be an effective alternative to reduce cardiometabolic biomarker levels related to lipid metabolism, regardless of MVPA.

Computer vision approaches have made significant inroads into offline tracking of behavior and estimating animal poses. In particular, because of their versatility, deep-learning approaches have been gaining attention in behavioral tracking without any markers. Here, we developed an approach using DeepLabCut for real-time estimation of movement. We trained a deep-neural network (DNN) offline with high-speed video data of a mouse whisking, then transferred the trained network to work with the same mouse, whisking in real-time. With this approach, we tracked the tips of three whiskers in an arc and converted positions into a TTL output within behavioral time scales, i.e., 10.5 ms. With this approach, it is possible to trigger output based on movement of individual whiskers, or on the distance between adjacent whiskers. Flexible closed-loop systems like the one we have deployed here can complement optogenetic approaches and can be used to directly manipulate the relationship between movement and neural activity.

The production of movement in a simple reaction time task can be separated into two time periods: the foreperiod, which is thought to include preparatory processes, and the reaction time interval, which includes initiation processes. To better understand these processes, transcranial magnetic stimulation has been used to probe corticospinal excitability at various time points during response preparation and initiation. Previous research has shown that excitability decreases prior to the "go" stimulus and increases following the "go"; however these two time frames have been examined independently. The purpose of this study was to measure changes in CE during both the foreperiod and reaction time interval in a single experiment, relative to a resting baseline level. Participants performed a button press movement in a simple reaction time task and excitability was measured during rest, the foreperiod, and the reaction time interval. Results indicated that during the foreperiod, excitability levels quickly increased from baseline with the presentation of the warning signal, followed by a period of stable excitability leading up to the "go" signal, and finally a rapid increase in excitability during the reaction time interval. This excitability time course is consistent with neural activation models that describe movement preparation and response initiation.

Optogenetic tools, such as channelrhodopsin2 (ChR2), have enabled the behavior of whole organisms by light-mediated manipulation of neuronal activities. Fluorescent indicators have been used to aid in the understanding of what is happening in living cells. To date, optogenetic stimulation and imaging acquisition were sequentially performed during detector "live time." However, there is a problem with interrupting acquisition time sequences because such stimulation invades the time territory of fluorescent imaging. Here, our purpose was to show that optogenetic stimulation can be performed within the "dead time" of the charge-coupled device camera, the short interval of data transfer between frames. We show the kinetic measurement of Ca(2+) dynamics in neuron-like cells upon ChR2 stimulation, by which we reveal the biphasic property of the Ca(2+) increase in response to optical stimulation.

Individual differences in delay-discounting correlate with important real world outcomes, for example education, income, drug use, and criminality. As such, delay-discounting has been extensively studied by economists, psychologists and neuroscientists to reveal its behavioral and biological mechanisms in both human and non-human animal models. However, two major methodological differences hinder comparing results across species. Human studies present long time-horizon options verbally, whereas animal studies employ experiential cues and short delays. To bridge these divides, we developed a novel language-free experiential task inspired by animal decision-making studies. We found that the ranks of subjects' time-preferences were reliable across both verbal/experiential and second/day differences. Yet, discount factors scaled dramatically across the tasks, indicating a strong effect of temporal context. Taken together, this indicates that individuals have a stable, but context-dependent, time-preference that can be reliably assessed using different methods, providing a foundation to bridge studies of time-preferences across species.