Joint model with latent state for longitudinal and multistate data

In many chronic diseases, the patient's health status is followed up by quantitative markers. The evolution is often characterized by a 2-phase degradation process, that is, a normal phase followed by a pathological degradation phase preceding the disease diagnosis. We propose a joint multistate model with latent state for the joint modeling of repeated measures of a quantitative marker, time-to-illness and time-to-death. Using data from the PAQUID cohort on cognitive aging, we jointly studied cognitive decline, dementia risk, and death risk. We estimated the mean evolution of cognitive scores given age at dementia for subjects alive and demented, the mean evolution of cognitive scores for subjects alive and nondemented, in addition to age at acceleration of cognitive decline and duration of the pre-dementia phase.     

Joint Modeling for Cognitive Trajectory and Risk of Dementia in the Presence of Death

Summary .  Dementia is characterized by accelerated cognitive decline before and after diagnosis as compared to normal aging. It has been known that cognitive impairment occurs long before the diagnosis of dementia. For individuals who develop dementia, it is important to determine the time when the rate of cognitive decline begins to accelerate and the subsequent gap time to dementia diagnosis. For normal aging individuals, it is also useful to understand the trajectory of cognitive function until their death. A Bayesian change-point model is proposed to fit the trajectory of cognitive function for individuals who develop dementia. In real life, people in older ages are subject to two competing risks, e.g., dementia and dementia-free death. Because the majority of people do not develop dementia, a mixture model is used for survival data with competing risks, which consists of dementia onset time after the change point of cognitive function decline for demented individuals and death time for nondemented individuals. The cognitive trajectories and the survival process are modeled jointly and the parameters are estimated using the Markov chain Monte Carlo method. Using data from the Honolulu Asia Aging Study, we show the trajectories of cognitive function and the effect of education, apolipoprotein E 4 genotype, and hypertension on cognitive decline and the risk of dementia.     

Toward a stochastic formulation of microbial growth in relation to bioreactor performances: case study of an E. coli fed-batch process

A stochastic microbial growth model has been elaborated in the case of the culture of E. coli in fed-batch and scale-down reactors. This model is based on the stochastic determination of the generation time of the microbial cells. The determination of generation time is determined by choosing the appropriate value on a log-normal distribution. The appropriateness of such distribution is discussed and growth curves are obtained that show good agreement compared with the experimental results. The mean and the standard deviation of the log-normal distribution can be considered to be constant during the batch phase of the culture, but they vary when the fed-batch mode is started. It has been shown that the parameters related to the log-normal distribution are submitted to an exponential evolution. The aim of this study is to explore the bioreactor hydrodynamic effect on microbial growth. Thus, in a second time, the stochastic growth model has been reinforced by data coming from a previous stochastic bioreactor mixing model (1). The connection of these hydrodynamic data with the actual stochastic growth model has allowed us to explain the scale-down effect associated with the glucose concentration fluctuations. It is important to point out that the scale-down effect is induced differently according to the feeding strategy involved in the fed-batch experiments.     

General session: III. Heredity counseling

ABSTRACT

Aging is a major risk factor for both normal and pathological cognitive decline. However, individuals vary in their rate of age-related decline. We developed an easily interpretable composite measure of cognitive age, and related both the level of cognitive age and cognitive slope to sociodemographic, genetic, and disease indicators and examined its prediction of dementia transition. Using a sample of 19,594 participants from the Health and Retirement Study, cognitive age was derived from a set of performance tests administered at each wave. Our findings reveal different conclusions as they relate to levels versus slopes of cognitive age, with more pronounced differences by sex and race/ethnicity for absolute levels of cognitive decline rather than for rates of declines. We also find that both level and slope of cognitive age are inversely related to education, as well as increased for persons with APOE ?4 and/or diabetes. Finally, results show that the slope in cognitive age predicts subsequent dementia among non-demented older adults. Overall, our study suggests that this measure is applicable to cross-sectional and longitudinal studies on cognitive aging, decline, and dementia with the goal of better understanding individual differences in cognitive decline.     

The Influence of Multimorbidity on Clinical Progression of Dementia in a Population-Based Cohort

Introduction

Co-occurrence with other chronic diseases may influence the progression of dementia, especially in case of multiple chronic diseases. We aimed to verify whether multimorbidity influenced cognitive and daily functioning during nine years after dementia diagnosis compared with the influence in persons without dementia.

Methods

In the Kungsholmen Project, a population-based cohort study, we followed 310 persons with incident dementia longitudinally. We compared their trajectories with those of 679 persons without dementia. Progression was studied for cognition and activities of daily life (ADLs), measured by MMSE and Katz Index respectively. The effect of multimorbidity and its interaction with dementia status was studied using individual growth models.

Results

The mean (SD) follow-up time was 4.7 (2.3) years. As expected, dementia related to both the decline in cognitive and daily functioning. Irrespective of dementia status, persons with more diseases had significantly worse baseline daily functioning. In dementia patients having more diseases also related to a significantly faster decline in daily functioning. Due to the combination of lower functioning in ADLs at baseline and faster decline, dementia patients with multimorbidity were about one to two years ahead of the decline of dementia patients without any co-morbidity. In persons without dementia, no significant decline in ADLs over time was present, nor was multimorbidity related to the decline rate. Cognitive decline measured with MMSE remained unrelated to the number of diseases present at baseline.

Conclusion

Multimorbidity was related to baseline daily function in both persons with and without dementia, and with accelerated decline in people with dementia but not in non-demented individuals. No relationship of multimorbidity with cognitive functioning was established. These findings imply a strong interconnection between physical and mental health, where the greatest disablement occurs when both somatic and mental disorders are present.     

Selenium and cognitive impairment: a brief-review based on results from the EVA study

Preventing cognitive impairment and dementia in the elderly is a major public health challenge for our century and all hypotheses should be explored. Selenium (Se) is one of the factors that may affect the risk of cognitive decline. Its importance in the health and aging process has been documented. Because of the potential of selenoproteins to protect against oxidative stress, Se raises significant expectations for the prevention of chronic diseases including cancer, cardiovascular disease, and type 2 diabetes conditions commonly associated with oxidative stress. Thus, the relationships between Se and cognitive impairment or dementia can be examined through vascular risk factors for dementia, with particular interest in diabetes and dyslipidemia. In addition, in cases of Se deficiency, the brain is the organ that remains Se replete the longest suggesting that Se plays an important role in brain functions. This article presents results obtained in the frame of a longitudinal study on Se and cognitive impairment. They are consistent with the hypothesis that low Se status is a risk factor for cognitive decline even after taking into account vascular risk factors. The concomitant evolution between plasma Se decrease over a 9-year period and cognitive decline suggested that optimal Se status is potentially important to maintain neuropsychological functions in aging people. However, as our understanding of Se biology is incomplete, epidemiological studies are needed to define the groups of population that could benefit from Se supplementation.     

Translational cognitive neuroscience of dementia with touchscreen operant chambers

Translational cognitive neuroscience of dementia involves mainly two areas: the validation of newly developed dementia animal models and the preclinical assessment of novel drug candidates in such model animals. To validate new animal models, a multidomain panel (battery) approach is essential in that dementia is, by definition, not merely a memory disorder but rather a multidomain cognitive/behavior disorder: animal modeling with a certain type of dementia would develop cognitive impairments in multiple (two at minimum) domains in a specific order according to unique spreading patterns of its neuropathology. In new drug development, the availability of highly sensitive tools assessing animal cognition is crucial to the detection of cognitive decline at the earliest stage of the disease, which may be an optimal time point to test a drug candidate. Using interspecies translatable (analogous) cognitive tasks would also be necessary to successfully predict the efficacy of drug candidates in subsequent clinical trials. Currently, this translational prediction is seriously limited given discrepancies in behavioral assessment methods between animals and humans in the preclinical and clinical trials, respectively. Since neurodegenerative diseases are often accompanied by not only cognitive but also affective and movement disorders, simultaneous assessment of task-relevant locomotor behavior and motivation is also important to rule out the effects of potential confounders. The touchscreen operant platform may satisfy these needs by offering several advantages over conventional methodology. In this review, we discuss the touchscreen operant chamber system and highlight some of its qualities as a promising and desirable tool for translational research of dementia.     

Leisure activities, cognition and dementia

Accumulated evidence shows that leisure activities have a positive impact on cognitive function and dementia. This review aimed to systematically summarize the current evidence on this topic taking into account the limitations of the studies and biological plausibility for the underlying mechanisms linking cognition, dementia and leisure activities, with special attention on mental, physical and social activities. We included only longitudinal studies, with a follow-up time of at least 2 years, published in English from 1991 to March 2011 on leisure activities and cognition (n = 29) or dementia (n = 23) and provided some evidence from intervention studies on the topic. A protective effect of mental activity on cognitive function has been consistently reported in both observational and interventional studies. The association of mental activity with the risk of dementia was robust in observational studies but inconsistent in clinical trials. The protective effect of physical activity on the risk of cognitive decline and dementia has been reported in most observational studies, but has been less evident in interventional studies. Current evidence concerning the beneficial effect of other types of leisure activities on the risk of dementia is still limited and results are inconsistent. For future studies it is imperative that the assessment of leisure activities is standardized, for example, the frequency, intensity, duration and the type of activity; and also that the cognitive test batteries and the definition of cognitive decline are harmonized/standardized. Further, well designed studies with long follow-up times are necessary. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease.     

Cardiovascular Disease Risk Models and Longitudinal Changes in Cognition: A Systematic Review

Background

Cardiovascular disease and its risk factors have consistently been associated with poor cognitive function and incident dementia. Whether cardiovascular disease prediction models, developed to predict an individual''s risk of future cardiovascular disease or stroke, are also informative for predicting risk of cognitive decline and dementia is not known.

Objective

The objective of this systematic review was to compare cohort studies examining the association between cardiovascular disease risk models and longitudinal changes in cognitive function or risk of incident cognitive impairment or dementia.

Materials and Methods

Medline, PsychINFO, and Embase were searched from inception to March 28, 2014. From 3,413 records initially screened, 21 were included.

Results

The association between numerous different cardiovascular disease risk models and cognitive outcomes has been tested, including Framingham and non-Framingham risk models. Five studies examined dementia as an outcome; fourteen studies examined cognitive decline or incident cognitive impairment as an outcome; and two studies examined both dementia and cognitive changes as outcomes. In all studies, higher cardiovascular disease risk scores were associated with cognitive changes or risk of dementia. Only four studies reported model prognostic performance indices, such as Area Under the Curve (AUC), for predicting incident dementia or cognitive impairment and these studies all examined non-Framingham Risk models (AUC range: 0.74 to 0.78).

Conclusions

Cardiovascular risk prediction models are associated with cognitive changes over time and risk of dementia. Such models are easily obtainable in clinical and research settings and may be useful for identifying individuals at high risk of future cognitive decline and dementia.     

Lichte Cognitieve Stoornissen (MCI): Prodromen van dementie?

Mild cognitive impairment: a prodromal phase of dementia? Cognitive decline without dementia is common among older persons. A variety of clinical concepts have been introduced in the past 30 years, in order to describe these cognitive deficits arising in older persons. The most frequently used concept is Mild Cognitive Impairment (MCI). MCI is generally seen as a prodromal phase of Alzheimer disease (AD). Several concepts are described, with the neuropsychiatric features and predictors of conversion to dementia c.q. AD. Finally, consequences of preclinically diagnoses for health care are clarified. Tijdschr Gerontol Geriatr 2007; 38:115-121     

Multi-stage transitional models with random effects and their application to the Einstein aging study

Longitudinal studies of aging often gather repeated observations of cognitive status to describe the development of dementia and to assess the influence of risk factors. Clinical progression to dementia is often conceptualized by a multi-stage model of several transitions that synthesizes time-varying effects. In this study, we assess the influence of risk factors on the transitions among three cognitive status: cognitive stability (normal cognition for age), memory impairment, and clinical dementia. We have developed a shared random effects model that not only links the propensity of transitions and to the probability of informative missingness due to death, but also incorporates heterogeneous transition between subjects. We evaluate four approaches using generalized logit and four using proportional odds models to the first-order Markov transition probabilities as a function of covariates. Random effects were incorporated into these models to account for within-subject correlations. Data from the Einstein Aging Study are used to evaluate the goodness-of-fit of these models using the Akaike information criterion. The best fitting model for each type (generalized logit and proportional odds) is recommended and their results are discussed in more details.     

A prospective study of cognitive health in the elderly (Oregon Brain Aging Study): effects of family history and apolipoprotein E genotype.

The oldest old are the fastest-growing segment of our population and have the highest prevalence of dementia. Little is known about the genetics of cognitive health in the very old. The aim of this study was to determine whether the genetic risk factors for Alzheimer disease (AD)--namely, apolipoprotein E (APOE) epsilon4 allele and a family history of dementia-continue to be important factors in the cognitive health of the very old. Case-control studies suggest that the effect of genetic factors diminishes at age >75 years. The present prospective study provided evidence to the contrary. We studied 114 Caucasian subjects who were physically healthy and cognitively intact at age 75 years and who were followed, for an average of 4 years, with neurological, psychometric, and neuroimaging examinations. Excellent health at entry did not protect against cognitive decline. Incidence of cognitive decline rose sharply with age. epsilon4 and a family history of dementia (independent of epsilon4) were associated with an earlier age at onset of dementia. Subjects who had epsilon4 or a family history of dementia had a ninefold-higher age-specific risk for dementia than did those who had neither epsilon4 nor a family history of dementia. These observations suggest that the rate of cognitive decline increases with age and that APOE and other familial/genetic factors influence the onset age throughout life.     

Genetic background modifies CNS‐mediated sensorimotor decline in the AD‐BXD mouse model of genetic diversity in Alzheimer's disease

Many patients with Alzheimer's dementia (AD) also exhibit noncognitive symptoms such as sensorimotor deficits, which can precede the hallmark cognitive deficits and significantly impact daily activities and an individual's ability to live independently. However, the mechanisms underlying sensorimotor dysfunction in AD and their relationship with cognitive decline remains poorly understood, due in part to a lack of translationally relevant animal models. To address this, we recently developed a novel model of genetic diversity in Alzheimer's disease, the AD‐BXD genetic reference panel. In this study, we investigated sensorimotor deficits in the AD‐BXDs and the relationship to cognitive decline in these mice. We found that age‐ and AD‐related declines in coordination, balance and vestibular function vary significantly across the panel, indicating genetic background strongly influences the expressivity of the familial AD mutations used in the AD‐BXD panel and their impact on motor function. Although young males and females perform comparably regardless of genotype on narrow beam and inclined screen tasks, there were significant sex differences in aging‐ and AD‐related decline, with females exhibiting worse decline than males of the same age and transgene status. Finally, we found that AD motor decline is not correlated with cognitive decline, suggesting that sensorimotor deficits in AD may occur through distinct mechanisms. Overall, our results suggest that AD‐related sensorimotor decline is strongly dependent on background genetics and is independent of dementia and cognitive deficits, suggesting that effective therapeutics for the entire spectrum of AD symptoms will likely require interventions targeting each distinct domain involved in the disease.     

The research of constructing dynamic cognition model based on brain network

Estimating the functional interactions and connections between brain regions to corresponding process in cognitive, behavioral and psychiatric domains is a central pursuit for understanding the human connectome. Few studies have examined the effects of dynamic evolution on cognitive processing and brain activation using brain network model in scalp electroencephalography (EEG) data. Aim of this study was to investigate the brain functional connectivity and construct dynamic programing model from EEG data and to evaluate a possible correlation between topological characteristics of the brain connectivity and cognitive evolution processing. Here, functional connectivity between brain regions is defined as the statistical dependence between EEG signals in different brain areas and is typically determined by calculating the relationship between regional time series using wavelet coherence. We present an accelerated dynamic programing algorithm to construct dynamic cognitive model that we found that spatially distributed regions coherence connection difference, the topologic characteristics with which they can transfer information, producing temporary network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time after variation audio stimulation, dynamic programing model gives the dynamic evolution processing at different time and frequency. In this paper, by applying a new construct approach to understand whole brain network dynamics, firstly, brain network is constructed by wavelet coherence, secondly, different time active brain regions are selected by network topological characteristics and minimum spanning tree. Finally, dynamic evolution model is constructed to understand cognitive process by dynamic programing algorithm, this model is applied to the auditory experiment, results showed that, quantitatively, more correlation was observed after variation audio stimulation, the EEG function connection dynamic evolution model on cognitive processing is feasible with wavelet coherence EEG recording.     

Tests for character displacement

Character displacement is an important concept in ecology which has been surrounded by controversy due largely to a lack of clearly stated hypotheses and statistical tests. Existing tests implicity assume random species sizes estimated without error--a random-effects model. We introduce the log-uniform distribution for species sizes and show that it has properties of direct relevance to character displacement. We present tests which assume uniform and log-normal species sizes and have the log-uniform distribution as an alternative. The tests have low power for sample sizes typically encountered in ecology. The effect of estimating species sizes is small. The results exemplify the shortcomings of the traditional random-effects model for species sizes.     

Effect of APOE and a polygenic risk score on incident dementia and cognitive decline in a healthy older population

Few studies have measured the effect of genetic factors on dementia and cognitive decline in healthy older individuals followed prospectively. We studied cumulative incidence of dementia and cognitive decline, stratified by APOE genotypes and polygenic risk score (PRS) tertiles, in 12,978 participants of the ASPirin in Reducing Events in the Elderly (ASPREE) trial. At enrolment, participants had no history of diagnosed dementia, cardiovascular disease, physical disability or cognitive impairment. Dementia (adjudicated trial endpoint) and cognitive decline, defined as a >1.5 standard deviation decline in test score for either global cognition, episodic memory, language/executive function or psychomotor speed, versus baseline scores. Cumulative incidence for all‐cause dementia and cognitive decline was calculated with mortality as a competing event, stratified by APOE genotypes and tertiles of a PRS based on 23 common non‐APOE variants. During a median 4.5 years of follow‐up, 324 participants developed dementia, 503 died. Cumulative incidence of dementia to age 85 years was 7.4% in all participants, 12.6% in APOE ε3/ε4 and 26.6% in ε4/ε4. APOE ε4 heterozygosity/homozygosity was associated with a 2.5/6.3‐fold increased dementia risk and 1.4/1.8‐fold cognitive decline risk, versus ε3/ε3 (p < 0.001 for both). High PRS tertile was associated with a 1.4‐fold dementia risk versus low (CI 1.04–1.76, p = 0.02), but was not associated with cognitive decline (CI 0.96–1.22, p = 0.18). Incidence of dementia among healthy older individuals is low across all genotypes; however, APOE ε4 and high PRS increase relative risk. APOE ε4 is associated with cognitive decline, but PRS is not.     

Periodontitis and Cognitive Decline in Alzheimer’s Disease

Periodontitis is common in the elderly and may become more common in Alzheimer’s disease because of a reduced ability to take care of oral hygiene as the disease progresses. Elevated antibodies to periodontal bacteria are associated with an increased systemic pro-inflammatory state. Elsewhere raised serum pro-inflammatory cytokines have been associated with an increased rate of cognitive decline in Alzheimer’s disease. We hypothesized that periodontitis would be associated with increased dementia severity and a more rapid cognitive decline in Alzheimer’s disease. We aimed to determine if periodontitis in Alzheimer’s disease is associated with both increased dementia severity and cognitive decline, and an increased systemic pro inflammatory state. In a six month observational cohort study 60 community dwelling participants with mild to moderate Alzheimer’s Disease were cognitively assessed and a blood sample taken for systemic inflammatory markers. Dental health was assessed by a dental hygienist, blind to cognitive outcomes. All assessments were repeated at six months. The presence of periodontitis at baseline was not related to baseline cognitive state but was associated with a six fold increase in the rate of cognitive decline as assessed by the ADAS-cog over a six month follow up period. Periodontitis at baseline was associated with a relative increase in the pro-inflammatory state over the six month follow up period. Our data showed that periodontitis is associated with an increase in cognitive decline in Alzheimer’s Disease, independent to baseline cognitive state, which may be mediated through effects on systemic inflammation.     

Beta-Amyloid Monomer and Insulin/IGF-1 Signaling in Alzheimer's Disease

Alzheimer's disease is the most common form of dementia among older people and is still untreatable. While ??-amyloid protein is recognized as the disease determinant with a pivotal role in inducing neuronal loss and dementia, an impaired brain insulin signaling seems to account in part for the cognitive deficit associated with the disease. The origin of this defective signaling is uncertain. Accumulating toxic species of ??-amyloid, the so-called oligomers, has been proposed to be responsible for downregulation of neuronal insulin receptors. We have found that the nontoxic form of ??-amyloid, the monomer, is able to activate insulin/insulin-like growth factor-1 (IGF-1) receptor signaling and thus behaves as a neuroprotectant agent. Our suggestion is that depletion of ??-amyloid monomers, occurring in the preclinical phase of Alzheimer's disease, might be the cause of early insulin/IGF-1 signaling disturbances that anticipate cognitive decline.     

长白山不同演替阶段针阔混交林群落物种多度分布格局

为解释长白山温带森林群落构建和物种多度格局的形成过程, 该文以不同演替阶段的针阔混交林监测样地数据为基础, 采用中性理论模型、生物统计模型(对数正态分布模型)和生态位模型(Zifp模型、分割线段模型、生态位优先模型)拟合森林群落物种多度分布, 并用χ ²检验、Kolmogorov-Smirnov (K-S)检验和赤池信息准则(AIC)选择最佳拟合模型。结果显示: 中性模型能很好地预测长白山温带森林不同演替阶段植物群落的物种多度分布。在10 m × 10 m尺度上, 5种模型均可被χ ²检验和K-S检验接受, 但中性模型拟合效果不如对数正态分布模型、Zifp模型、分割线段模型和生态位优先模型, 表明小尺度上中性过程和生态位过程均能解释群落物种多度分布, 但生态位过程的解释能力相对较大。而在中大尺度上(30 m × 30 m、60 m × 60 m和90 m × 90 m), 中性模型为最优拟合模型, 并且随着研究尺度增加, 生态位模型和生物统计模型逐渐被χ ²检验拒绝, 表明中性过程在长白山针阔混交林群落物种多度分布格局形成中的作用随着研究尺度增加而逐渐增大。该文证实了中性过程在长白山温带针阔混交林群落结构形成中具有重要作用, 但未否认生态位机制在群落构建中的贡献。因此, 温带森林群落构建过程中中性理论和生态位理论并非相互矛盾, 而是相互融合的。在研究森林群落物种多度分布时, 应重视取样尺度和演替阶段的影响, 并采用多种模型进行拟合。     

The feeling of understanding: an exploration with neural models

There exists a dynamic interaction between the world of information and the world of concepts, which is seen as a quintessential byproduct of the cultural evolution of individuals as well as of human communities. The feeling of understanding (FU) is that subjective experience that encompasses all the emotional and intellectual processes we undergo in the process of gathering evidence to achieve an understanding of an event. This experience is part of every person that has dedicated substantial efforts in scientific areas under constant research progress. The FU may have an initial growth followed by a quasi-stable regime and a possible decay when accumulated data exceeds the capacity of an individual to integrate them into an appropriate conceptual scheme. We propose a neural representation of FU based on the postulate that all cognitive activities are mapped onto dynamic neural vectors. Two models are presented that incorporate the mutual interactions among data and concepts. The first one shows how in the short time scale, FU can rise, reach a temporary steady state and subsequently decline. The second model, operating over longer scales of time, shows how a reorganization and compactification of data into global categories initiated by conceptual syntheses can yield random cycles of growth, decline and recovery of FU.