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The neural correlates of intraindividual response variability were investigated in a serial choice reaction time (CRT) task. Reaction times (RTs) from the faster and slower portions of the RT distribution for the task were separately aggregated and associated P300 event-related potentials computed. Independent behavioral measures of executive function and IQ were also recorded. Across frontal, fronto-central, central, centro-parietal and parietal scalp regions, P300 amplitudes were significantly greater for faster relative to slower behavioral responses. However, P300 peak amplitude latencies did not differ according to the speed of the behavioral RT. Importantly, controlling for select independent measures of executive function attenuated shared variance in P300 amplitude for faster and slower trials. The findings suggest that P300 amplitude rather than latency is associated with the speed of behavioral RTs, and the possibility that fluctuations in executive control underlie variability in speeded responding.

Intraindividual variability (IIV) in sleep may be a risk factor for disease above the influence of mean sleep. Associations between IIV in sleep and risk for a comprehensive set of common medical and mental health conditions have not been assessed in a representative sample. This study examined mean and IIV in total sleep time (TST), sleep quality (SQ), sleep efficiency (SE), and circadian midpoint (CM) in 771 adults recruited for an epidemiological study. Participants completed 14 days of sleep diaries to assess TST, SQ, SE, and CM, after which they reported on medical conditions and mental health symptoms. Data were analyzed using logistic regression, and models controlled for gender, body mass index, age, and race. Lower mean TST, SQ, and SE were related to increased odds of having gastrointestinal problems, depression, and anxiety. IIV in TST was related to increased odds of having neurological, breathing, and gastrointestinal problems, as well as pain and depression; all results held controlling for mean sleep and adjusting for false discovery rate. IIV in SQ and SE was not associated with odds of having any medical or mental health conditions after adjusting for false discovery rate, nor was IIV in CM or mean CM. Confirming previous research, mean TST, SQ, and SE are related to risk for gastrointestinal problems, depression, and anxiety. IIV in TST may be a unique facet of disturbed sleep that is associated with increased risk for a diverse cluster of medical and mental health conditions.

Increased intraindividual variability (IIV) is a hallmark of disorders of attention. Recent work has linked these disorders to abnormalities in a “default mode” network, comprising brain regions routinely deactivated during goal-directed cognitive tasks. Findings from a study of the neural basis of attentional lapses suggest that a competitive relationship between the “task-negative” default mode network and regions of a “task-positive” attentional network is a potential locus of dysfunction in individuals with increased IIV. Resting state studies have shown that this competitive relationship is intrinsically represented in the brain, in the form of a negative correlation or antiphase relationship between spontaneous activity occurring in the two networks. We quantified the negative correlation between these two networks in 26 subjects, during active (Eriksen flanker task) and resting state scans. We hypothesized that the strength of the negative correlation is an index of the degree of regulation of activity in the default mode and task-positive networks and would be positively related to consistent behavioral performance. We found that the strength of the correlation between the two networks varies across individuals. These individual differences appear to be behaviorally relevant, as interindividual variation in the strength of the correlation was significantly related to individual differences in response time variability: the stronger the negative correlation (i.e., the closer to 180° antiphase), the less variable the behavioral performance. This relationship was moderately consistent across resting and task conditions, suggesting that the measure indexes moderately stable individual differences in the integrity of functional brain networks. We discuss the implications of these findings for our understanding of the behavioral significance of spontaneous brain activity, in both healthy and clinical populations.

Behavioral and neural indices of performance monitoring are key to understanding behavioral adaptation during task performance. However, associations between performance monitoring event‐related potentials (ERPs) and task behavior have been inconsistent. This inconsistency may partly reflect reliance on single‐subject averages that obscure trial‐by‐trial changes in ERPs and behavior, and a tendency to examine only one or two ERP indices at a time. Our objective was to uncover how neural variability during performance monitoring contributes to behavioral adaptation, revealing variability as a functional signature of cognitive control. We investigated whether current‐trial response times (RTs) and accuracy can be predicted from previous‐ and current‐trial congruency and accuracy and ERP indices of performance monitoring (N2, P3, error‐related negativity [ERN], error positivity, [Pe]). Flanker data from 291 healthy participants (54% female) were analyzed using multilevel location‐scale modeling. This modeling framework facilitates simultaneous examination of mean and variance relationships of single‐trial data. Previous‐ and current‐trial ERP amplitudes uniquely predict current‐trial RTs and accuracy, beyond previous‐ and current‐trial congruency and accuracy effects. Previous‐ and current‐trial N2, P3, ERN, and Pe were concurrently related to the mean and variance of RTs and to accuracy. The observed within‐person changes in the relationship between performance‐monitoring ERPs and task behavior indicate that trial‐by‐trial neural fluctuations reflect dynamic adjustments in cognitive control across successive actions. These findings demonstrate the value of modeling intraindividual variability in neurophysiological measures to understand adaptive behavior. Summary Points Moment‐to‐moment changes in brain activity predict how behavior adjusts from one trial to the next. Analyzing both average and variability patterns shows that neural fluctuations meaningfully track adaptive control. Variability in brain signals reflects adaptive control of behavior, not random noise. Trial‐by‐trial modeling revealed that neural variability in performance‐monitoring ERPs (N2, P3, ERN, Pe) predicts both the mean and variability of response times and mean accuracy in a flanker task. These findings show that fluctuations in brain activity reflect dynamic adjustments in cognitive control, linking neural variability to adaptive behavior.

Longitudinal cohort studies of cognitive aging must confront several sources of within-person variability in scores. In this article, we compare several neuropsychological measures in terms of longitudinal error variance and relationships with biomarker-assessed brain amyloidosis (Aβ). Analyses used data from the Wisconsin Registry for Alzheimer's Prevention. We quantified within-person longitudinal variability and age-related trajectories for several global and domain-specific composites and their constituent scores. For a subset with cerebrospinal fluid or amyloid positron emission tomography measures, we examined how Aβ modified cognitive trajectories. Global and theoretically derived composites exhibited lower intraindividual variability and stronger age × Aβ interactions than did empirically derived composites or raw scores from single tests. For example, the theoretical executive function outperformed other executive function scores on both metrics. These results reinforce the need for careful selection of cognitive outcomes in study design, and support the emerging consensus favoring composites over single-test measures. •Identifying early cognitive change requires tests with low error variance.•In a middle-aged sample, composites were less noisy than single tests.•Global and theory-driven composites outperformed data-driven composites.

Abstract Study Objectives Variable daily sleep (ie, higher intraindividual variability; IIV) is associated with negative health consequences, but potential physiological mechanisms are poorly understood. This study examined how the IIV of sleep timing, duration, and quality is associated with physiological dysregulation, with diurnal cortisol trajectories as a proximal outcome and allostatic load (AL) as a multisystem distal outcome. Methods Participants are 436 adults (M age ± standard deviation = 54.1 ± 11.7, 60.3% women) from the Midlife in the United States study. Sleep was objectively assessed using 7-day actigraphy. Diurnal cortisol was measured via saliva samples (four/day for 4 consecutive days). AL was measured using 23 biomarkers from seven systems (inflammatory, hypothalamic–pituitary–adrenal axis, metabolic glucose and lipid, cardiovascular, parasympathetic, sympathetic) using a validated bifactor model. Linear and quadratic effects of sleep IIV were estimated using a validated Bayesian model. Results Controlling for covariates, more variable sleep timing (p = .04 for risetime, p = .097 for bedtime) and total sleep time (TST; p = .02), but not mean sleep variables, were associated with flatter cortisol diurnal slope. More variable sleep onset latency and wake after sleep onset, later average bedtime, and shorter TST were associated with higher AL adjusting for age and sex (p-values < .05); after controlling for all covariates, however, only later mean bedtime remained significantly associated with higher AL (p = .04). Conclusions In a community sample of adults, more variable sleep patterns were associated with blunted diurnal cortisol trajectories but not with higher multisystem physiological dysregulation. The associations between sleep IIV and overall health are likely complex, including multiple biopsychosocial determinants and require further investigation.

Purpose The purpose of this paper is to investigate the influence of employees’ trait rumination on the variability of their state rumination and the continuing influence on their negative affect at home. Design/methodology/approach A time-lagged experience sampling method was used for the data collection from full-time employees in the hotel industry. The hypotheses were tested with multilevel modeling using a random coefficient modeling approach. Findings Hotel employees who are high in trait rumination generally show high levels of state rumination and greater within-person variability in state rumination over time. Additionally, the negative effects of workplace state rumination can last until employees come home and the next day before going to work. Furthermore, employees who are high in trait rumination are more likely to be influenced by state rumination, as they experience more negative affect after arriving home. Practical implications Rumination has been shown to decrease hotel employee overall well-being. The findings of this study provide suggestions for remedial measures that can be taken by hotel organizations to help employees address ruminative thinking. Originality/value Drawing on response styles and work/family border theories, this study contributes to the rumination literature by considering both trait rumination and state rumination in a broader context. For a comprehensive understanding of the dynamic temporal characteristics of state rumination, this study considers the net intraindividual variability of state rumination as the outcome of trait rumination.

Abstract Introduction: Research has extensively examined the relationship between adolescents’ mental health and average sleep duration/quality. Using rigorous methodology, this study characterized adolescents’ objective sleep intraindividual variability (IIV) and examined its role on mood beyond the effects of their respective individual mean (IIM) values. Aims and Methods: One hundred forty-six community-dwelling adolescents (47.3% male) aged 16.2 ± 1.0 (M ± SD) years wore an actigraph that assessed bedtime, risetime, time-in-bed (TIB), and sleep onset latency (SOL) throughout a 15-day vacation with relatively unconstrained sleep opportunity. Self-report sleep quality (SSQ), negative mood (MOOD), and other covariates were assessed using questionnaires. For each sleep variable, individuals’ mean values (IIM) and IIV were used to simultaneously predict MOOD with SSQ as a mediator. Models were estimated in a Bayesian IIV framework; both linear and quadratic effects of the IIM and IIV were examined. Results: Longer and more variable TIB, as well as more variable SOL (but not mean SOL), were associated with poorer SSQ (ps < .01), which in turn, was associated with more negative MOOD (ps < .05). The indirect effect of SOL IIV was curvilinear, such that as SOL became more variable, the deteriorating effect of high SOL IIV accelerated. Neither bedtime nor risetime IIV was significantly associated with SSQ or MOOD. Conclusions: During relatively unconstrained sleep opportunity, more variable TIB and SOL were associated with more negative mood, mediated by poorer perceived sleep quality. Significant effects of IIV were over and above that of mean values, suggesting that unique aspects of sleep IIV are relevant to how adolescents perceive sleep quality and their mood.

Background and Objective: Within-person variability in cognitive performance has emerged as a promising indicator of cognitive health with potential to distinguish normative and pathological cognitive aging. We use a smartphone-based digital health approach with ecological momentary assessments (EMA) to examine differences in variability in performance among older adults with mild cognitive impairment (MCI) and those who were cognitively unimpaired (CU). Method: A sample of 311 systematically recruited, community-dwelling older adults from the Einstein Aging Study (Mean age = 77.46 years, SD = 4.86, Range = 70–90; 67% Female; 45% Non-Hispanic White, 40% Non-Hispanic Black) completed neuropsychological testing, neurological assessments, and self-reported questionnaires. One hundred individuals met Jak/Bondi criteria for MCI. All participants performed mobile cognitive tests of processing speed, visual short-term memory binding, and spatial working memory on a smartphone device up to six times daily for 16 days, yielding up to 96 assessments per person. We employed heterogeneous variance multilevel models using log-linear prediction of residual variance to simultaneously assess cognitive status differences in mean performance, within-day variability, and day-to-day variability. We further tested whether these differences were robust to the influence of environmental contexts under which assessments were performed. Results: Individuals with MCI exhibited greater within-day variability than those who were CU on ambulatory assessments that measure processing speed ( p < 0.001) and visual short-term memory binding ( p < 0.001) performance but not spatial working memory. Cognitive status differences in day-to-day variability were present only for the measure of processing speed. Associations between cognitive status and within-day variability in performance were robust to adjustment for sociodemographic and contextual variables. Conclusion: Our smartphone-based digital health approach facilitates the ambulatory assessment of cognitive performance in older adults and the capacity to differentiate individuals with MCI from those who were CU. Results suggest variability in mobile cognitive performance is sensitive to MCI and exhibits dissociative patterns by timescale and cognitive domain. Variability in processing speed and visual short-term memory binding performance may provide specific detection of MCI. The 16-day smartphone-based EMA measurement burst offers novel opportunity to leverage digital technology to measure performance variability across frequent assessments for studying cognitive health and identifying early clinical manifestations of cognitive impairment.

Reaction time variability (RTV) has been estimated using Gaussian, ex-Gaussian, and diffusion model (DM) indices. Rarely have studies examined interrelationships among these performance indices in childhood, and the use of reaction time (RT) computational models has been slow to take hold in the developmental psychopathology literature. Here, we extend prior work in adults by examining the interrelationships among different model parameters in the ABCD sample and demonstrate how computational models of RT can clarify mechanisms of time-on-task effects and sex differences in RTs. This study utilized trial-level data from the stop signal task from 8916 children (9-10 years old) to examine Gaussian, ex-Gaussian, and DM indicators of RTV. In addition to describing RTV patterns, we examined interrelations among these indicators, temporal patterns, and sex differences. There was no one-to-one correspondence between DM and ex-Gaussian parameters. Nonetheless, drift rate was most strongly associated with standard deviation of RT and tau, while nondecisional processes were most strongly associated with RT, mu, and sigma. Performance worsened across time with changes driven primarily by decreasing drift rate. Boys were faster and less variable than girls, likely attributable to girls' wide boundary separation. Intercorrelations among model parameters are similar in children as has been observed in adults. Computational approaches play a crucial role in understanding performance changes over time and can also clarify mechanisms of group differences. For example, standard RT models may incorrectly suggest slowed processing speed in girls that is actually attributable to other factors.