Global perceived stress predicts cognitive change among older adults.
ABSTRACT: Research on stress and cognitive aging has primarily focused on examining the effects of biological and psychosocial indicators of stress, with little attention provided to examining the association between perceived stress and cognitive aging. We examined the longitudinal association between global perceived stress (GPS) and cognitive change among 116 older adults (M(age) = 80, SD = 6.40, range = 67-96) in a repeated measurement burst design. Bursts of 6 daily cognitive assessments were repeated every 6 months over a 2-year period, with self-reported GPS assessed at the start of every burst. Using a double-exponential learning model, 2 parameters were estimated: (a) asymptotic level (peak performance), and (b) asymptotic change (the rate at which peak performance changed across bursts). We hypothesized that greater GPS would predict slowed performance in tasks of attention, working memory, and speed of processing and that increases in GPS across time would predict cognitive slowing. Results from latent growth curve analyses were consistent with our first hypothesis and indicated that level of GPS predicted cognitive slowing across time. Changes in GPS did not predict cognitive slowing. This study extends previous findings by demonstrating a prospective association between level of GPS and cognitive slowing across a 2-year period, highlighting the role of psychological stress as a risk factor for poor cognitive function.
Project description:<h4>Background</h4>Neural oscillations represent synchronous neuronal activation and are ubiquitous throughout the brain. Oscillatory activity often includes brief high-amplitude bursts in addition to background oscillations, and burst activity may predict performance on working memory, motor, and comprehension tasks.<h4>Objective</h4>We evaluated beta burst activity as a possible biomarker for motor symptoms in Parkinson's disease (PD). The relationship between beta amplitude dynamics and motor symptoms is critical for adaptive DBS for treatment of PD.<h4>Methods</h4>We applied threshold-based and support vector machine (SVM) analyses of burst parameters to a defined on/off oscillator and to intraoperative recordings of local field potentials from the subthalamic nucleus of 16 awake patients with PD.<h4>Results</h4>Filtering and time-frequency analysis techniques critically influenced the accuracy of identifying burst activity. Threshold-based analysis lead to biased results in the presence of changes in long-term beta amplitude and accurate quantification of bursts with thresholds required unknowable a priori knowledge of the time in bursts. We therefore implemented an SVM analysis, and we did not observe changes in burst fraction, rate, or duration with the application of cDBS in the participant data, even though SVM analysis was able to correctly identify bursts of the defined on/off oscillator.<h4>Conclusion</h4>Our results suggest that cDBS of the STN may not change beta burst activity. Additionally, threshold-based analysis can bias the fraction of time spent in bursts. Improved analysis strategies for continuous and adaptive DBS may achieve improved symptom control and reduce side-effects.
Project description:It is now possible to create individual-specific genetic scores, called genome-wide polygenic scores (GPS). We used a GPS for years of education (<i>EduYears</i>) to predict reading performance assessed at UK National Curriculum Key Stages 1 (age 7), 2 (age 12) and 3 (age 14) and on reading tests administered at ages 7 and 12 in a UK sample of 5,825 unrelated individuals. <i>EduYears</i> GPS accounts for up to 5% of the variance in reading performance at age 14. GPS predictions remained significant after accounting for general cognitive ability and family socioeconomic status. Reading performance of children in the lowest and highest 12.5% of the <i>EduYears</i> GPS distribution differed by a mean growth in reading ability of approximately two school years. It seems certain that polygenic scores will be used to predict strengths and weaknesses in education.
Project description:Recent theorizing differentiates key constraints on cognition, including one's current range of processing efficiency (i.e., flexibility or inconsistency) as well as the capacity to expand flexibility over time (i.e., plasticity). The present study uses intensive assessment of response time data to examine the interplay between markers of intraindividual variability (inconsistency) and gains across biweekly retest sessions (plasticity) in relation to age-related cognitive function.Participants included 304 adults (aged 64 to 92 years: M = 74.02, SD = 5.95) from Project MIND, a longitudinal burst design study assessing performance across micro and macro intervals (response latency trials, weekly bursts, annual retests). For two reaction time (RT) measures (choice RT and one-back choice RT), baseline measures of RT inconsistency (intraindividual standard deviation, ISD, across trials at the first testing session) and plasticity (within-person performance gains in average RT across the 5 biweekly burst sessions) were computed and were then employed in linear mixed models as predictors of individual differences in cognitive function and longitudinal (6-year) rates of cognitive change.Independent of chronological age and years of education, higher RT inconsistency was associated uniformly with poorer cognitive function at baseline and with increased cognitive decline for measures of episodic memory and crystallized verbal ability. In contrast, predictive associations for plasticity were more modest for baseline cognitive function and were absent for 6-year cognitive change.These findings underscore the potential utility of response times for articulating inconsistency and plasticity as dynamic predictors of cognitive function in older adults.
Project description:Hippocampal pyramidal cells encode an animal's location by single action potentials and complex spike bursts. These elementary signals are believed to play distinct roles in memory consolidation. The timing of single spikes and bursts is determined by intrinsic excitability and theta oscillations (5-10 Hz). Yet contributions of these dynamics to place fields remain elusive due to the lack of methods for specific modification of burst discharge. In mice lacking Kcnq3-containing M-type K<sup>+</sup> channels, we find that pyramidal cell bursts are less coordinated by the theta rhythm than in controls during spatial navigation, but not alert immobility. Less modulated bursts are followed by an intact post-burst pause of single spike firing, resulting in a temporal discoordination of network oscillatory and intrinsic excitability. Place fields of single spikes in one- and two-dimensional environments are smaller in the mutant. Optogenetic manipulations of upstream signals reveal that neither medial septal GABA-ergic nor cholinergic inputs alone, but rather their joint activity, is required for entrainment of bursts. Our results suggest that altered representations by bursts and single spikes may contribute to deficits underlying cognitive disabilities associated with KCNQ3-mutations in humans.
Project description:Confinement of molecules in specific small volumes and areas within a cell is likely to be a general strategy that is developed during evolution for regulating the interactions and functions of biomolecules. The cellular plasma membrane, which is the outermost membrane that surrounds the entire cell, was considered to be a continuous two-dimensional liquid, but it is becoming clear that it consists of numerous nano-meso-scale domains with various lifetimes, such as raft domains and cytoskeleton-induced compartments, and membrane molecules are dynamically trapped in these domains. In this article, we give a theoretical account on the effects of molecular confinement on reversible bimolecular reactions in a partitioned surface such as the plasma membrane. By performing simulations based on a lattice-based model of diffusion and reaction, we found that in the presence of membrane partitioning, bimolecular reactions that occur in each compartment proceed in bursts during which the reaction rate is sharply and briefly increased even though the asymptotic reaction rate remains the same. We characterized the time between reaction bursts and the burst amplitude as a function of the model parameters, and discussed the biological significance of the reaction bursts in the presence of strong inhibitor activity.
Project description:The burst coding hypothesis posits that the occurrence of sudden high-frequency patterns of action potentials constitutes a salient syllable of the neural code. Many neurons, however, do not produce clearly demarcated bursts, an observation invoked to rule out the pervasiveness of this coding scheme across brain areas and cell types. Here we ask how detrimental ambiguous spike patterns, those that are neither clearly bursts nor isolated spikes, are for neuronal information transfer. We addressed this question using information theory and computational simulations. By quantifying how information transmission depends on firing statistics, we found that the information transmitted is not strongly influenced by the presence of clearly demarcated modes in the interspike interval distribution, a feature often used to identify the presence of burst coding. Instead, we found that neurons having unimodal interval distributions were still able to ascribe different meanings to bursts and isolated spikes. In this regime, information transmission depends on dynamical properties of the synapses as well as the length and relative frequency of bursts. Furthermore, we found that common metrics used to quantify burstiness were unable to predict the degree with which bursts could be used to carry information. Our results provide guiding principles for the implementation of coding strategies based on spike-timing patterns, and show that even unimodal firing statistics can be consistent with a bivariate neural code.
Project description:Burst suppression patterns in the electroencephalogram are a reliable marker of recent severe brain insult. Here we analyze statistical properties of bursts occurring in 20 electroencephalographic recordings acquired from hypothermic asphyxic newborns in the hours immediately following birth. We show that the distributions of burst area and duration in these acute data predict later clinical outcome in both structural neuroimaging and neurodevelopment. Our findings indicate the first early electroencephalographic metrics that offer outcome prediction in asphyxic neonates undergoing hypothermia treatment.
Project description:Burst contention is a major problem in the Optical Burst Switching (OBS) networks. Due to inadequate contention resolution techniques, the burst loss is prominent in OBS. In order to resolve contention fiber delay lines, wavelength converters, deflection routing, burst segmentation, and retransmission are used. Each one has its own limitations. In this paper, a new hybrid scheme is proposed which combines buffering and retransmission, which increases the mean number of bursts processed in the system. In this hybrid method, retransmission with controllable arrival and uncontrollable arrival is analyzed. Normally all the bursts reach the first hop and few of them go for second hop to reach destination. After all the bursts reach the destination the server may go for maintenance activity or wait for the arrival of next burst. We model it as a batch arrival single server retrial queue with buffer. Numerical results are analyzed to show the mean number of bursts processed in the system with uncontrollable arrival and controllable arrivals.
Project description:In this manuscript we propose a spline-based sieve nonparametric maximum likelihood estimation method for joint distribution function with bivariate interval-censored data. We study the asymptotic behavior of the proposed estimator by proving the consistency and deriving the rate of convergence. Based on the sieve estimate of the joint distribution, we also develop an efficient nonparametric test for making inference about the dependence between two interval-censored event times and establish its asymptotic normality. We conduct simulation studies to examine the finite sample performance of the proposed methodology. Finally we apply the method to assess the association between two subtypes of mild cognitive impairment (MCI): amnestic MCI and non-amnestic MCI, for Huntington disease (HD) using data from a 12-year observational cohort study on premanifest HD individuals, PREDICT-HD.
Project description:Cognitive deficits are a core feature of psychiatric disorders like schizophrenia and bipolar disorder. Evidence supports a genome-wide polygenic score (GPS) for educational attainment (GPS<sub>EDU</sub>) can be used to explain variability in cognitive performance. We aimed to identify different cognitive domains associated with GPS<sub>EDU</sub> in a transdiagnostic clinical cohort of chronic psychiatric patients with known cognitive deficits. Bipolar and schizophrenia patients from the PsyCourse cohort (N?=?730; 43% female) were used. Likewise, we tested whether GPSs for schizophrenia (GPS<sub>SZ</sub>) and bipolar disorder (GPS<sub>BD</sub>) were associated with cognitive outcomes. GPS<sub>EDU</sub> explained 1.5% of variance in the backward verbal digit span, 1.9% in the number of correctly recalled words of the Verbal Learning and Memory Test, and 1.1% in crystallized intelligence. These effects were robust to the influences of treatment and diagnosis. No significant associations between GPS<sub>SZ</sub> or GPS<sub>BD</sub> with cognitive outcomes were found. Furthermore, these risk scores did not confound the effect of GPS<sub>EDU</sub> on cognitive outcomes. GPS<sub>EDU</sub> explains a small fraction of cognitive performance in adults with psychiatric disorders, specifically for domains related to linguistic learning and working memory. Investigating such a proxy-phenotype longitudinally, could give intriguing insight into the disease course, highlighting at what time genes play a more influential role on cognitive performance. Better understanding the origin of these deficits might help identify those patients at risk for lower levels of functioning and poor social outcomes. Polygenic estimates may in the future be part of predictive models for more personalized interventions.