Parental age at death is associated with age at first birth in offspring.

PURPOSE: People age at different rates and the available evidence suggests that the rate of aging is partly inherited from previous generations. This heterogeneity in aging is evident already in midlife, but to what extent aging is associated with the timing of events earlier in life is not fully known. Here we aim to shed light on this topic by investigating the trade-off between reproduction and aging postulated by evolutionary theories of aging. METHODS: Drawing on the inheritance of aging we use parental age at death as a proxy for aging-rates in the offspring, and study how age at first birth depends on this variable. We use data from an almost complete Swedish birth cohort comprising 92,359 individuals. Accelerated failure time models are used to estimate the association between parental age at death and age at first birth while adjusting for parental occupational class, educational attainment, and income. RESULTS: Longer parental lifespans were consistently associated with older age at first births, both in men and women. CONCLUSION: Our findings suggest that aging-related processes may be interrelated with the processes underlying the timing of reproduction and are in general agreement with evolutionary theories of aging.

Consequences of heterogeneity in aging: parental age at death predicts midlife all-cause mortality and hospitalization in a Swedish national birth cohort.

BACKGROUND: The processes that underlie aging may advance at different rates in different individuals and an advanced biological age, relative to the chronological age, is associated with increased risk of disease and death. Here we set out to quantify the extent to which heterogeneous aging shapes health outcomes in midlife by following a Swedish birth-cohort and using parental age at death as a proxy for biological age in the offspring. METHODS: We followed a nationwide Swedish birth cohort (N = 89,688) between the ages of 39 and 66 years with respect to hospitalizations and death. Cox regressions were used to quantify the association, in the offspring, between parental age at death and all-cause mortality, as well as hospitalization for conditions belonging to the 10 most common ICD-10 chapters. RESULTS: Longer parental lifespan was consistently associated with reduced risks of hospitalization and all-cause mortality. Differences in risk were mostly evident from before the age of 50 and persisted throughout the follow-up. Each additional decade of parental survival decreased the risk of offspring all-cause mortality by 22% and risks of hospitalizations by 9 to 20% across the 10 diseases categories considered. The number of deaths and hospitalizations attributable to having parents not living until old age were 1500 (22%) and 11,000 (11%) respectively. CONCLUSIONS: Our findings highlight that increased parental lifespan is consistently associated with health benefits in the offspring across multiple outcomes and suggests that heterogeneous aging processes have clinical implications already in midlife.

Increased risk of death immediately after discharge from compulsory care for substance abuse.

BACKGROUND: In Sweden, approximately 1000 persons per year are committed to compulsory care for substance abuse for a maximum duration of six months. People admitted to compulsory care are known to suffer high mortality risks, but whether the risk of dying is further heightened immediately after discharge is not known. METHODS: Individual data from Swedish national registers were used to follow all persons discharged from a six months compulsory care episode in the period 2000-2017 (N = 7, 929). Based on a competing risks framework including re-admissions to compulsory care or imprisonment, hazard rates were estimated in five non-overlapping time windows covering the first year after discharge. RESULTS: In total, 494 persons died during follow-up, corresponding to an overall mortality rate of 7.1 per 100 person years (95% confidence interval: 6.5, 7.8). The risk was higher for men than for women and increased with age. The risk of dying during the first two weeks after discharge was higher than during the remaining follow-up period - hazard rate ratios comparing the first two weeks with subsequent time windows were between 2.6 (1.3, 5.0) and 3.7 (2.4, 5.9). This heightened risk in close proximity to discharge was only observed for deaths due to external causes, and only for people below the median age of 36 years. CONCLUSIONS: The risk of dying immediately after discharge from compulsory care is very high, especially for younger clients, and more efforts should be made to prevent these deaths.

Mortality of the COVID-19 Outbreak in Sweden in Relation to Previous Severe Disease Outbreaks.

Influenza viruses have caused disease outbreaks in human societies for a long time. Influenza often has rapid onset and relatively short duration, both in the individual and in the population. The case fatality rate varies for different strains of the virus, as do the effects on total mortality. Outbreaks related to coronavirus infections have recently become a global concern but much less is known about the dynamics of these outbreaks and their effects on mortality. In this work, disease outbreaks in Sweden, in the time period of 1860-2020, are characterized and compared to the currently ongoing COVID-19 outbreak. The focus is on outbreaks with a sharp increase in all-cause mortality. Outbreak onset is defined as the time point when death counts start to increase consistently for a period of at least 10 days. The duration of the outbreak is defined as the time period in which mortality rates are elevated. Excess mortality is estimated by standard methods. In total there were 15 outbreaks detected in the time period, the first 14 were likely caused by influenza virus infections, the last by SARS-CoV-2. The mortality dynamics of the SARS-CoV-2 outbreak is shown to be similar to outbreaks due to influenza virus, and in terms of the number of excess deaths, it is the worst outbreak in Sweden since the "Spanish flu" of 1918-1919.

A large decrease in the magnitude of seasonal fluctuations in mortality among elderly explains part of the increase in longevity in Sweden during 20th century.

BACKGROUND: Mortality rates are known to depend on the seasons and, in temperate climates, rates are highest during winter. The magnitude of these seasonal fluctuations in mortality has decreased substantially in many countries during the 20th century, but the extent to which this decrease has contributed to the concurrent increase in life expectancy is not known. Here, I describe how the seasonality of all-cause mortality among people ages 60 years or more has changed in Sweden between 1860 and 1995, and investigate how this change has contributed to the increase in life expectancy observed during the same time period. METHODS: Yearly sex-specific birth cohorts consisting of all people born in Sweden between 1800 and 1901 who reached at least 59 years of age were obtained from a genealogical database. The mortality rates for each cohort were modeled by an exponential function of age modulated by a sinusoidal function of time of year. The potential impact of seasonal fluctuations on life expectancy was investigated by a novel decomposition of the total mortality rate into a seasonal part and a part independent of the seasons. Cohort life expectancy at age 60 was used to quantify changes in lifespan during the time period. RESULTS: The magnitude of seasonal fluctuations in mortality rates decreased substantially between 1860 and 1995. For cohorts born in 1800, the risk of dying during the winter season was almost twice that of dying during summer. For cohorts born in 1900, the relative increase in winter mortality was 10%. Cohort life expectancy at age 60 increased by 4.3 years for men and 6.8 years for women, and the decrease in seasonal mortality fluctuations accounted for approximately 40% of this increase in average lifespan. CONCLUSION: By following a large number of extinct cohorts, it was possible to show how the decrease in seasonal fluctuations in mortality has contributed to an increase in life expectancy. The decomposition of total mortality introduced here might be useful to better understand the processes and mechanisms underlying the marked improvements in life expectancy seen over the last 150 years.

Exponential increase in mortality with age is a generic property of a simple model system of damage accumulation and death.

The risk of dying increases exponentially with age, in humans as well as in many other species. This increase is often attributed to the "accumulation of damage" known to occur in many biological structures and systems. The aim of this paper is to describe a generic model of damage accumulation and death in which mortality increases exponentially with age. The damage-accumulation process is modeled by a stochastic process know as a queue, and risk of dying is a function of the accumulated damage, i.e., length of the queue. The model has four parameters and the main characteristics of the model are: (i) damage occurs at random times with a constant high rate; (ii) the damage is repaired at a limited rate, and consequently damage can accumulate; (iii) the efficiency of the repair mechanism decays linearly with age; (iv) the risk of dying is a function of the accumulated damage. Using standard results from the mathematical theory of queues it is shown that there is an exponential dependence between risk of dying and age in these models, and that this dependency holds irrespective of how the damage-accumulation process is modeled. Furthermore, the ways in which this exponential dependence is shaped by the model parameters are also independent of the details of the damage accumulation process. These generic features suggest that the model could be useful when interpreting changes in the relation between age and mortality in real data. To exemplify, historical mortality data from Sweden are interpreted in the light of the model. The decrease in mortality seen between cohorts born in 1905, compared to those born in 1885, can be accounted for by higher threshold to damage. This fits well with the many advances made in public health during the 20th century.

Mortality related to methadone maintenance treatment in Stockholm, Sweden, during 2006-2013.

BACKGROUND: Methadone maintenance treatment (MMT) of opiate addiction was introduced in Sweden 50years ago. The first Swedish programs were modeled after the original Dole and Nyswander program, with strict criteria for admittance into treatment, and have been shown to have positive effects on social and health variables, including mortality. During the last 11years, there have been a number of changes in the regulations controlling MMT-programs in Sweden, and the criteria for admittance are now much less strict compared to previous ones. This study aims to characterize the current MMT-programs with respect to mortality and to compare the results to those obtained in earlier periods. METHODS: Persons entering into treatment in Stockholm county, between 2006 and 2011, were followed until September 2013 or until death occurred. Death rates for periods in treatment and out of treatment were determined and compared to rates for the general population. Proportional hazards models with treatment status as time-varying covariate were fitted to the data. A competing risk analysis was made to investigate the effects of MMT on drug-related mortality as compared to mortality from other causes. Mortality data for earlier periods were retrieved from the literature. RESULTS: A total of 441 persons entered MMT during the time period. Of these 67 died during follow-up, the death rate being almost twenty times higher than in the general population. Not being in treatment was associated with a significantly increased hazard of dying (hazard ratio: 2.1, 95% confidence interval: 1.3-3.4). The hazard ratio was mainly increased for drug-related deaths (hazard ratio: 4.4 (2.1-9.2)). CONCLUSIONS: Mortality rates among persons who entered MMT-programs in Stockholm during 2006-2011 were not increased compared to persons in treatment twenty years ago. The mortality was significantly increased during periods off treatment. Changes in regulations that minimizes the time off treatment are therefore likely to reduce the mortality rates among clients of MMT-programs.

The interest in eight new psychoactive substances before and after scheduling.

BACKGROUND: In recent years the recreational use of new psychoactive substances (NPS) has increased. NPS are considered a threat to public health and the main response to this threat is to make the selling and buying of these substances illegal. In Sweden, during the last 5 years, 62 new substances have been classified as narcotics but little is known of the effects of making a particular substance illegal. The aim of this work is to study how legal status influences the interest in NPS in Sweden. METHODS: Forty-five thousand posts made in a Swedish Internet discussion forum (Flashback Forum) related to eight NPS (MDPV, Methylone, 4-MEC, 4-HO-MET, MXE, 6-APB, AH-7921, and 3-MMC) were used to derive time-dependent measures of interest in these substances. Intervention analyses were used to investigate the effects of legal status on the forum interest. RESULTS: For all eight substances the activity on the forum (measured as number of posts per day) showed a drastic decrease around the time of classification. The statistical analysis showed that in seven of eight cases, the drop in activity could be accounted for by the legal status of the substances. CONCLUSIONS: The legal status of the substances was shown to have a substantial effect on the interest in the substances. The novel measure used to trace the interest in particular NPS could be a useful tool to follow trends in substance use in almost real-time.

Estimating the size of hidden populations from register data.

BACKGROUND: Prevalence estimates of drug use, or of its consequences, are considered important in many contexts and may have substantial influence over public policy. However, it is rarely possible to simply count the relevant individuals, in particular when the defining characteristics might be illegal, as in the drug use case. Consequently methods are needed to estimate the size of such partly 'hidden' populations, and many such methods have been developed and used within epidemiology including studies of alcohol and drug use. Here we introduce a method appropriate for estimating the size of human populations given a single source of data, for example entries in a health-care registry. METHODS: The setup is the following: during a fixed time-period, e.g. a year, individuals belonging to the target population have a non-zero probability of being "registered". Each individual might be registered multiple times and the time-points of the registrations are recorded. Assuming that the population is closed and that the probability of being registered at least once is constant, we derive a family of maximum likelihood (ML) estimators of total population size. We study the ML estimator using Monte Carlo simulations and delimit the range of cases where it is useful. In particular we investigate the effect of making the population heterogeneous with respect to probability of being registered. RESULTS: The new estimator is asymptotically unbiased and we show that high precision estimates can be obtained for samples covering as little as 25% of the total population size. However, if the total population size is small (say in the order of 500) a larger fraction needs to be sampled to achieve reliable estimates. Further we show that the estimator give reliable estimates even when individuals differ in the probability of being registered. We also compare the ML estimator to an estimator known as Chao's estimator and show that the latter can have a substantial bias when applied to epidemiological data. CONCLUSIONS: The population size estimator suggested herein complements existing methods and is less sensitive to certain types of dependencies typical in epidemiological data.

Coherent delta-band oscillations between cortical areas correlate with decision making.

Coherent oscillations in the theta-to-gamma frequency range have been proposed as a mechanism that coordinates neural activity in large-scale cortical networks in sensory, motor, and cognitive tasks. Whether this mechanism also involves coherent oscillations at delta frequencies (1-4 Hz) is not known. Rather, delta oscillations have been associated with slow-wave sleep. Here, we show coherent oscillations in the delta frequency band between parietal and frontal cortices during the decision-making component of a somatosensory discrimination task. Importantly, the magnitude of this delta-band coherence is modulated by the different decision alternatives. Furthermore, during control conditions not requiring decision making, delta-band coherences are typically much reduced. Our work indicates an important role for synchronous activity in the delta frequency band when large-scale, distant cortical networks coordinate their neural activity during decision making.

Framework to study dynamic dependencies in networks of interacting processes.

The analysis of dynamic dependencies in complex systems such as the brain helps to understand how emerging properties arise from interactions. Here we propose an information-theoretic framework to analyze the dynamic dependencies in multivariate time-evolving systems. This framework constitutes a fully multivariate extension and unification of previous approaches based on bivariate or conditional mutual information and Granger causality or transfer entropy. We define multi-information measures that allow us to study the global statistical structure of the system as a whole, the total dependence between subsystems, and the temporal statistical structure of each subsystem. We develop a stationary and a nonstationary formulation of the framework. We then examine different decompositions of these multi-information measures. The transfer entropy naturally appears as a term in some of these decompositions. This allows us to examine its properties not as an isolated measure of interdependence but in the context of the complete framework. More generally we use causal graphs to study the specificity and sensitivity of all the measures appearing in these decompositions to different sources of statistical dependence arising from the causal connections between the subsystems. We illustrate that there is no straightforward relation between the strength of specific connections and specific terms in the decompositions. Furthermore, causal and noncausal statistical dependencies are not separable. In particular, the transfer entropy can be nonmonotonic in dependence on the connectivity strength between subsystems and is also sensitive to internal changes of the subsystems, so it should not be interpreted as a measure of connectivity strength. Altogether, in comparison to an analysis based on single isolated measures of interdependence, this framework is more powerful to analyze emergent properties in multivariate systems and to characterize functionally relevant changes in the dynamics.

Reduced variability of ongoing and evoked cortical activity leads to improved behavioral performance.

Sensory responses of the brain are known to be highly variable, but the origin and functional relevance of this variability have long remained enigmatic. Using the variable foreperiod of a visual discrimination task to assess variability in the primate cerebral cortex, we report that visual evoked response variability is not only tied to variability in ongoing cortical activity, but also predicts mean response time. We used cortical local field potentials, simultaneously recorded from widespread cortical areas, to gauge both ongoing and visually evoked activity. Trial-to-trial variability of sensory evoked responses was strongly modulated by foreperiod duration and correlated both with the cortical variability before stimulus onset as well as with response times. In a separate set of experiments we probed the relation between small saccadic eye movements, foreperiod duration and manual response times. The rate of eye movements was modulated by foreperiod duration and eye position variability was positively correlated with response times. Our results indicate that when the time of a sensory stimulus is predictable, reduction in cortical variability before the stimulus can improve normal behavioral function that depends on the stimulus.

When two become one: the limits of causality analysis of brain dynamics.

Biological systems often consist of multiple interacting subsystems, the brain being a prominent example. To understand the functions of such systems it is important to analyze if and how the subsystems interact and to describe the effect of these interactions. In this work we investigate the extent to which the cause-and-effect framework is applicable to such interacting subsystems. We base our work on a standard notion of causal effects and define a new concept called natural causal effect. This new concept takes into account that when studying interactions in biological systems, one is often not interested in the effect of perturbations that alter the dynamics. The interest is instead in how the causal connections participate in the generation of the observed natural dynamics. We identify the constraints on the structure of the causal connections that determine the existence of natural causal effects. In particular, we show that the influence of the causal connections on the natural dynamics of the system often cannot be analyzed in terms of the causal effect of one subsystem on another. Only when the causing subsystem is autonomous with respect to the rest can this interpretation be made. We note that subsystems in the brain are often bidirectionally connected, which means that interactions rarely should be quantified in terms of cause-and-effect. We furthermore introduce a framework for how natural causal effects can be characterized when they exist. Our work also has important consequences for the interpretation of other approaches commonly applied to study causality in the brain. Specifically, we discuss how the notion of natural causal effects can be combined with Granger causality and Dynamic Causal Modeling (DCM). Our results are generic and the concept of natural causal effects is relevant in all areas where the effects of interactions between subsystems are of interest.

Locomotion-related oscillatory body movements at 6-12 Hz modulate the hippocampal theta rhythm.

The hippocampal theta rhythm is required for accurate navigation and spatial memory but its relation to the dynamics of locomotion is poorly understood. We used miniature accelerometers to quantify with high temporal and spatial resolution the oscillatory movements associated with running in rats. Simultaneously, we recorded local field potentials in the CA1 area of the hippocampus. We report that when rats run their heads display prominent vertical oscillations with frequencies in the same range as the hippocampal theta rhythm (i.e., 6-12 Hz). In our behavioral set-up, rats run mainly with speeds between 50 and 100 cm/s. In this range of speeds, both the amplitude and frequency of the "theta" head oscillations were increasing functions of running speed, demonstrating that the head oscillations are part of the locomotion dynamics. We found evidence that these rhythmical locomotor dynamics interact with the neuronal activity in the hippocampus. The amplitude of the hippocampal theta rhythm depended on the relative phase shift with the head oscillations, being maximal when the two signals were in phase. Despite similarity in frequency, the head movements and LFP oscillations only displayed weak phase and frequency locking. Our results are consistent with that neurons in the CA1 region receive inputs that are phase locked to the head acceleration signal and that these inputs are integrated with the ongoing theta rhythm.

A biologically plausible model of time-scale invariant interval timing.

The temporal durations between events often exert a strong influence over behavior. The details of this influence have been extensively characterized in behavioral experiments in different animal species. A remarkable feature of the data collected in these experiments is that they are often time-scale invariant. This means that response measurements obtained under intervals of different durations coincide when plotted as functions of relative time. Here we describe a biologically plausible model of an interval timing device and show that it is consistent with time-scale invariant behavior over a substantial range of interval durations. The model consists of a set of bistable units that switch from one state to the other at random times. We first use an abstract formulation of the model to derive exact expressions for some key quantities and to demonstrate time-scale invariance for any range of interval durations. We then show how the model could be implemented in the nervous system through a generic and biologically plausible mechanism. In particular, we show that any system that can display noise-driven transitions from one stable state to another can be used to implement the timing device. Our work demonstrates that a biologically plausible model can qualitatively account for a large body of data and thus provides a link between the biology and behavior of interval timing.

Effective reduced diffusion-models: a data driven approach to the analysis of neuronal dynamics.

We introduce in this paper a new method for reducing neurodynamical data to an effective diffusion equation, either experimentally or using simulations of biophysically detailed models. The dimensionality of the data is first reduced to the first principal component, and then fitted by the stationary solution of a mean-field-like one-dimensional Langevin equation, which describes the motion of a Brownian particle in a potential. The advantage of such description is that the stationary probability density of the dynamical variable can be easily derived. We applied this method to the analysis of cortical network dynamics during up and down states in an anesthetized animal. During deep anesthesia, intracellularly recorded up and down states transitions occurred with high regularity and could not be adequately described by a one-dimensional diffusion equation. Under lighter anesthesia, however, the distributions of the times spent in the up and down states were better fitted by such a model, suggesting a role for noise in determining the time spent in a particular state.

Neurobiological models of two-choice decision making can be reduced to a one-dimensional nonlinear diffusion equation.

The response behaviors in many two-alternative choice tasks are well described by so-called sequential sampling models. In these models, the evidence for each one of the two alternatives accumulates over time until it reaches a threshold, at which point a response is made. At the neurophysiological level, single neuron data recorded while monkeys are engaged in two-alternative choice tasks are well described by winner-take-all network models in which the two choices are represented in the firing rates of separate populations of neurons. Here, we show that such nonlinear network models can generally be reduced to a one-dimensional nonlinear diffusion equation, which bears functional resemblance to standard sequential sampling models of behavior. This reduction gives the functional dependence of performance and reaction-times on external inputs in the original system, irrespective of the system details. What is more, the nonlinear diffusion equation can provide excellent fits to behavioral data from two-choice decision making tasks by varying these external inputs. This suggests that changes in behavior under various experimental conditions, e.g. changes in stimulus coherence or response deadline, are driven by internal modulation of afferent inputs to putative decision making circuits in the brain. For certain model systems one can analytically derive the nonlinear diffusion equation, thereby mapping the original system parameters onto the diffusion equation coefficients. Here, we illustrate this with three model systems including coupled rate equations and a network of spiking neurons.

Large-scale visuomotor integration in the cerebral cortex.

Efficient visuomotor behavior depends on integrated processing by the visual and motor systems of the cerebral cortex. Yet, many previous cortical neurophysiology studies have examined the visual and motor modalities in isolation, largely ignoring questions of large-scale cross-modal integration. To address this issue, we analyzed event-related local field potentials simultaneously recorded from multiple visual, motor, and executive cortical sites in monkeys performing a visuomotor pattern discrimination task. The timing and cortical location of four aspects of event-related activities were examined: stimulus-evoked activation onset, stimulus-specific processing, stimulus category-specific processing, and response-specific processing. Activations appeared earliest in striate cortex and rapidly thereafter in other visual areas. Stimulus-specific processing began early in most visual cortical areas, some at activation onset. Early onset latencies were also observed in motor, premotor, and prefrontal areas, some as early as in striate cortex, but these early-activating frontal sites did not show early stimulus-specific processing. Response-specific processing began around 150 ms poststimulus in widespread cortical areas, suggesting that perceptual decision formation and response selection arose through concurrent processes of visual, motor, and executive areas. The occurrence of stimulus-specific and stimulus category-specific differences after the onset of response-specific processing suggests that sensory and motor stages of visuomotor processing overlapped in time.

Neural dynamics of cross-modal and cross-temporal associations.

We have studied a neurodynamic model of cross-modal and cross-temporal associations. We show that a network of integrate-and-fire neurons can generate spiking activity with realistic dynamics during the delay period of a paired associates task. In particular, the activity of the model resembles reported data from single-cell recordings in the prefrontal cortex.