When to sleep and consume caffeine to boost alertness.

STUDY OBJECTIVES: Sleep loss can cause cognitive impairments that increase the risk of mistakes and accidents. However, existing guidelines to counteract the effects of sleep loss are generic and are not designed to address individual-specific conditions, leading to sub-optimal alertness levels. Here, we developed an optimization algorithm that automatically identifies sleep schedules and caffeine-dosing strategies to minimize alertness impairment due to sleep loss for desired times of the day. METHODS: We combined our previous algorithms that separately optimize sleep or caffeine to simultaneously identify the best sleep schedules and caffeine doses that minimize alertness impairment at desired times. The optimization algorithm uses the predictions of the well-validated Unified Model of Performance to estimate the effectiveness and physiological feasibility of a large number of possible solutions and identify the best one. To assess the optimization algorithm, we used it to identify the best sleep schedules and caffeine-dosing strategies for four studies that exemplify common sleep-loss conditions and compared the predicted alertness-impairment reduction achieved by using the algorithm's recommendations against that achieved by following the U.S. Army caffeine guidelines. RESULTS: Compared to the alertness-impairment levels in the original studies, the algorithm's recommendations reduced alertness impairment on average by 63%, an improvement of 24 percentage points over the U.S. Army caffeine guidelines. CONCLUSIONS: We provide an optimization algorithm that simultaneously identifies effective and safe sleep schedules and caffeine-dosing strategies to minimize alertness impairment at user-specified times.

Individualised prediction of resilience and vulnerability to sleep loss using EEG features.

It is well established that individuals differ in their response to sleep loss. However, existing methods to predict an individual's sleep-loss phenotype are not scalable or involve effort-dependent neurobehavioural tests. To overcome these limitations, we sought to predict an individual's level of resilience or vulnerability to sleep loss using electroencephalographic (EEG) features obtained from routine night sleep. To this end, we retrospectively analysed five studies in which 96 healthy young adults (41 women) completed a laboratory baseline-sleep phase followed by a sleep-loss challenge. After classifying subjects into sleep-loss phenotypic groups, we extracted two EEG features from the first sleep cycle (median duration: 1.6 h), slow-wave activity (SWA) power and SWA rise rate, from four channels during the baseline nights. Using these data, we developed two sets of logistic regression classifiers (resilient versus not-resilient and vulnerable versus not-vulnerable) to predict the probability of sleep-loss resilience or vulnerability, respectively, and evaluated model performance using test datasets not used in model development. Consistently, the most predictive features came from the left cerebral hemisphere. For the resilient versus not-resilient classifiers, we obtained an average testing performance of 0.68 for the area under the receiver operating characteristic curve, 0.72 for accuracy, 0.50 for sensitivity, 0.84 for specificity, 0.61 for positive predictive value, and 3.59 for likelihood ratio. We obtained similar performance for the vulnerable versus not-vulnerable classifiers. These results indicate that logistic regression classifiers based on SWA power and SWA rise rate from routine night sleep can largely predict an individual's sleep-loss phenotype.

Electroencephalographic markers from routine sleep discriminate individuals who are vulnerable or resilient to sleep loss.

Sleep loss impairs cognition; however, individuals differ in their response to sleep loss. Current methods to identify an individual's vulnerability to sleep loss involve time-consuming sleep-loss challenges and neurobehavioural tests. Here, we sought to identify electroencephalographic markers of sleep-loss vulnerability obtained from routine night sleep. We retrospectively analysed four studies in which 50 healthy young adults (21 women) completed a laboratory baseline-sleep phase followed by a sleep-loss challenge. After classifying subjects as resilient or vulnerable to sleep loss, we extracted three electroencephalographic features from four channels during the baseline nights, evaluated the discriminatory power of these features using the first two studies (discovery), and assessed reproducibility of the results using the remaining two studies (reproducibility). In the discovery analysis, we found that, compared to resilient subjects, vulnerable subjects exhibited: (1) higher slow-wave activity power in channel O1 (p < 0.0042, corrected for multiple comparisons) and in channels O2 and C3 (p < 0.05, uncorrected); (2) higher slow-wave activity rise rate in channels O1 and O2 (p < 0.05, uncorrected); and (3) lower sleep spindle frequency in channels C3 and C4 (p < 0.05, uncorrected). Our reproducibility analysis confirmed the discovery results on slow-wave activity power and slow-wave activity rise rate, and for these two electroencephalographic features we observed consistent group-difference trends across all four channels in both analyses. The higher slow-wave activity power and slow-wave activity rise rate in vulnerable individuals suggest that they have a persistently higher sleep pressure under normal rested conditions.

2B-Alert App 2.0: personalized caffeine recommendations for optimal alertness.

STUDY OBJECTIVES: If properly consumed, caffeine can safely and effectively mitigate the effects of sleep loss on alertness. However, there are no tools to determine the amount and time to consume caffeine to maximize its effectiveness. Here, we extended the capabilities of the 2B-Alert app, a unique smartphone application that learns an individual's trait-like response to sleep loss, to provide personalized caffeine recommendations to optimize alertness. METHODS: We prospectively validated 2B-Alert's capabilities in a 62-hour total sleep deprivation study in which 21 participants used the app to measure their alertness throughout the study via the psychomotor vigilance test (PVT). Using PVT data collected during the first 36 hours of the sleep challenge, the app learned the participant's sleep-loss response and provided personalized caffeine recommendations so that each participant would sustain alertness at a pre-specified target level (mean response time of 270 milliseconds) during a 6-hour period starting at 44 hours of wakefulness, using the least amount of caffeine possible. Starting at 42 hours, participants consumed 0 to 800 mg of caffeine, per the app recommendation. RESULTS: 2B-Alert recommended no caffeine to five participants, 100-400 mg to 11 participants, and 500-800 mg to five participants. Regardless of the consumed amount, participants sustained the target alertness level ~80% of the time. CONCLUSIONS: 2B-Alert automatically learns an individual's phenotype and provides personalized caffeine recommendations in real time so that individuals achieve a desired alertness level regardless of their sleep-loss susceptibility.

2B-Alert Web 2.0, an Open-Access Tool for Predicting Alertness and Optimizing the Benefits of Caffeine: Utility Study.

BACKGROUND: One-third of the US population experiences sleep loss, with the potential to impair physical and cognitive performance, reduce productivity, and imperil safety during work and daily activities. Computer-based fatigue-management systems with the ability to predict the effects of sleep schedules on alertness and identify safe and effective caffeine interventions that maximize its stimulating benefits could help mitigate cognitive impairment due to limited sleep. To provide these capabilities to broad communities, we previously released 2B-Alert Web, a publicly available tool for predicting the average alertness level of a group of individuals as a function of time of day, sleep history, and caffeine consumption. OBJECTIVE: In this study, we aim to enhance the capability of the 2B-Alert Web tool by providing the means for it to automatically recommend safe and effective caffeine interventions (time and dose) that lead to optimal alertness levels at user-specified times under any sleep-loss condition. METHODS: We incorporated a recently developed caffeine-optimization algorithm into the predictive models of the original 2B-Alert Web tool, allowing the system to search for and identify viable caffeine interventions that result in user-specified alertness levels at desired times of the day. To assess the potential benefits of this new capability, we simulated four sleep-deprivation conditions (sustained operations, restricted sleep with morning or evening shift, and night shift with daytime sleep) and compared the alertness levels resulting from the algorithm's recommendations with those based on the US Army caffeine-countermeasure guidelines. In addition, we enhanced the usability of the tool by adopting a drag-and-drop graphical interface for the creation of sleep and caffeine schedules. RESULTS: For the 4 simulated conditions, the 2B-Alert Web-proposed interventions increased mean alertness by 36% to 94% and decreased peak alertness impairment by 31% to 71% while using equivalent or smaller doses of caffeine as the corresponding US Army guidelines. CONCLUSIONS: The enhanced capability of this evidence-based, publicly available tool increases the efficiency by which diverse communities of users can identify safe and effective caffeine interventions to mitigate the effects of sleep loss in the design of research studies and work and rest schedules.

Quantifying the effects of sleep loss: relative effect sizes of the psychomotor vigilance test, multiple sleep latency test, and maintenance of wakefulness test.

The psychomotor vigilance test (PVT) is a widely-used, minimally invasive, inexpensive, portable, and easy to administer behavioral measure of vigilance that is sensitive to sleep loss. We conducted analyses to determine the relative sensitivity of the PVT vs. the multiple sleep latency test (MSLT) and the maintenance of wakefulness test (MWT) during acute total sleep deprivation (TSD) and multiple days of sleep restriction (SR) in studies of healthy adults. Twenty-four studies met the criteria for inclusion. Since sleepiness countermeasures were administered in some of these studies, the relative sensitivity of the three measures to these interventions was also assessed. The difference in weighted effect size (eta-squared) was computed for each pair of sleepiness measures based on available raw test data (such as average PVT reaction time). Analyses revealed that the sleep measures were differentially sensitive to various types of sleep loss over time, with MSLT and MWT more sensitive to TSD than the PVT. However, sensitivity to SR was comparable for all three measures. The PVT and MSLT were found to be differentially sensitive to the administration of sleepiness countermeasures (drugs, sleep loss, etc.), but PVT and MWT were found to be comparably sensitive to these interventions. These findings suggest the potential utility of the PVT as a component of next-generation fatigue risk management systems.

Building a transdisciplinary expert consensus on the cognitive drivers of performance under pressure: An international multi-panel Delphi study.

Introduction: The ability to perform optimally under pressure is critical across many occupations, including the military, first responders, and competitive sport. Despite recognition that such performance depends on a range of cognitive factors, how common these factors are across performance domains remains unclear. The current study sought to integrate existing knowledge in the performance field in the form of a transdisciplinary expert consensus on the cognitive mechanisms that underlie performance under pressure. Methods: International experts were recruited from four performance domains [(i) Defense; (ii) Competitive Sport; (iii) Civilian High-stakes; and (iv) Performance Neuroscience]. Experts rated constructs from the Research Domain Criteria (RDoC) framework (and several expert-suggested constructs) across successive rounds, until all constructs reached consensus for inclusion or were eliminated. Finally, included constructs were ranked for their relative importance. Results: Sixty-eight experts completed the first Delphi round, with 94% of experts retained by the end of the Delphi process. The following 10 constructs reached consensus across all four panels (in order of overall ranking): (1) Attention; (2) Cognitive Control-Performance Monitoring; (3) Arousal and Regulatory Systems-Arousal; (4) Cognitive Control-Goal Selection, Updating, Representation, and Maintenance; (5) Cognitive Control-Response Selection and Inhibition/Suppression; (6) Working memory-Flexible Updating; (7) Working memory-Active Maintenance; (8) Perception and Understanding of Self-Self-knowledge; (9) Working memory-Interference Control, and (10) Expert-suggested-Shifting. Discussion: Our results identify a set of transdisciplinary neuroscience-informed constructs, validated through expert consensus. This expert consensus is critical to standardizing cognitive assessment and informing mechanism-targeted interventions in the broader field of human performance optimization.

Optimal sleep and work schedules to maximize alertness.

STUDY OBJECTIVES: Working outside the conventional "9-to-5" shift may lead to reduced sleep and alertness impairment. Here, we developed an optimization algorithm to identify sleep and work schedules that minimize alertness impairment during work hours, while reducing impairment during non-work hours. METHODS: The optimization algorithm searches among a large number of possible sleep and work schedules and estimates their effectiveness in mitigating alertness impairment using the Unified Model of Performance (UMP). To this end, the UMP, and its extensions to estimate sleep latency and sleep duration, predicts the time course of alertness of each potential schedule and their physiological feasibility. We assessed the algorithm by simulating four experimental studies, where we compared alertness levels during work periods for sleep schedules proposed by the algorithm against those used in the studies. In addition, in one of the studies we assessed the algorithm's ability to simultaneously optimize sleep and work schedules. RESULTS: Using the same amount of sleep as in the studies but distributing it optimally, the sleep schedules proposed by the optimization algorithm reduced alertness impairment during work periods by an average of 29%. Similarly, simultaneously optimized sleep and work schedules, for a recovery period following a chronic sleep restriction challenge, accelerated the return to baseline levels by two days when compared to the conventional 9-to-5 work schedule. CONCLUSIONS: Our work provides the first quantitative tool to optimize sleep and work schedules and extends the capabilities of existing fatigue-management tools.

2B-Alert App: A mobile application for real-time individualized prediction of alertness.

Knowing how an individual responds to sleep deprivation is a requirement for developing personalized fatigue management strategies. Here we describe and validate the 2B-Alert App, the first mobile application that progressively learns an individual's trait-like response to sleep deprivation in real time, to generate increasingly more accurate individualized predictions of alertness. We incorporated a Bayesian learning algorithm within the validated Unified Model of Performance to automatically and gradually adapt the model parameters to an individual after each psychomotor vigilance test. We implemented the resulting model and the psychomotor vigilance test as a smartphone application (2B-Alert App), and prospectively validated its performance in a 62-hr total sleep deprivation study in which 21 participants used the app to perform psychomotor vigilance tests every 3 hr and obtain real-time individualized predictions after each test. The temporal profiles of reaction times on the app-conducted psychomotor vigilance tests were well correlated with and as sensitive as those obtained with a previously characterized psychomotor vigilance test device. The app progressively learned each individual's trait-like response to sleep deprivation throughout the study, yielding increasingly more accurate predictions of alertness for the last 24 hr of total sleep deprivation as the number of psychomotor vigilance tests increased. After only 12 psychomotor vigilance tests, the accuracy of the model predictions was comparable to the peak accuracy obtained using all psychomotor vigilance tests. With the ability to make real-time individualized predictions of the effects of sleep deprivation on future alertness, the 2B-Alert App can be used to tailor personalized fatigue management strategies, facilitating self-management of alertness and safety in operational and non-operational settings.

Caffeine dosing strategies to optimize alertness during sleep loss.

Sleep loss, which affects about one-third of the US population, can severely impair physical and neurobehavioural performance. Although caffeine, the most widely used stimulant in the world, can mitigate these effects, currently there are no tools to guide the timing and amount of caffeine consumption to optimize its benefits. In this work, we provide an optimization algorithm, suited for mobile computing platforms, to determine when and how much caffeine to consume, so as to safely maximize neurobehavioural performance at the desired time of the day, under any sleep-loss condition. The algorithm is based on our previously validated Unified Model of Performance, which predicts the effect of caffeine consumption on a psychomotor vigilance task. We assessed the algorithm by comparing the caffeine-dosing strategies (timing and amount) it identified with the dosing strategies used in four experimental studies, involving total and partial sleep loss. Through computer simulations, we showed that the algorithm yielded caffeine-dosing strategies that enhanced performance of the predicted psychomotor vigilance task by up to 64% while using the same total amount of caffeine as in the original studies. In addition, the algorithm identified strategies that resulted in equivalent performance to that in the experimental studies while reducing caffeine consumption by up to 65%. Our work provides the first quantitative caffeine optimization tool for designing effective strategies to maximize neurobehavioural performance and to avoid excessive caffeine consumption during any arbitrary sleep-loss condition.

Intersubject variability in fearful face processing: the link between behavior and neural activation.

Stimuli that signal threat show considerable variability in the extents to which they enhance behavior, even among healthy individuals. However, the neural underpinning of this behavioral variability is not well understood. By manipulating expectation of threat in an fMRI study of fearful versus neutral face categorization, we uncovered a network of areas underlying variability in threat processing in healthy adults. We explicitly altered expectations by presenting face images at three different expectation levels: 80 %, 50 %, and 20 %. Subjects were instructed to report as quickly and accurately as possible whether the face was fearful (signaled threat) or not. An uninformative cue preceded each face by 4 s. By taking the difference between reaction times (RTs) to fearful and neutral faces, we quantified an overall fear RT bias (i.e., faster to fearful than to neutral faces) for each subject. This bias correlated positively with late-trial fMRI activation (8 s after the face) during unexpected-fearful-face trials in bilateral ventromedial prefrontal cortex, the left subgenual cingulate cortex, and the right caudate nucleus, and correlated negatively with early-trial fMRI activation (4 s after the cue) during expected-neutral-face trials in bilateral dorsal striatum and the right ventral striatum. These results demonstrate that the variability in threat processing among healthy adults is reflected not only in behavior, but also in the magnitude of activation in medial prefrontal and striatal regions that appear to encode affective value.

Fearful face detection sensitivity in healthy adults correlates with anxiety-related traits.

Threatening faces have a privileged status in the brain, which can be reflected in a processing advantage. However, this effect varies among individuals, even healthy adults. For example, one recent study showed that fearful face detection sensitivity correlated with trait anxiety in healthy adults (S. Japee, L. Crocker, F. Carver, L. Pessoa, & L. G. Ungerleider, 2009. Individual differences in valence modulation of face-selective M170 response. Emotion, 9, 59-69). Here, we expanded on those findings by investigating whether intersubject variability in fearful face detection is also associated with state anxiety, as well as more broadly with other traits related to anxiety. To measure fearful face detection sensitivity, we used a masked face paradigm where the target face was presented for only 33 ms and was immediately followed by a neutral face mask. Subjects then rated their confidence in detecting either fear or no fear in the target face. Fearful face detection sensitivity was calculated for each subject using signal detection theory. Replicating previous results, we found a significant positive correlation between trait anxiety and fearful face detection sensitivity. However, this behavioral advantage did not correlate with state anxiety. We also found that fearful face detection sensitivity correlated with other personality measures, including neuroticism and harm avoidance. Our data suggest that fearful face detection sensitivity varies parametrically across the healthy population, is associated broadly with personality traits related to anxiety, but remains largely unaffected by situational fluctuations in anxiety. These results underscore the important contribution of anxiety-related personality traits to threat processing in healthy adults.

Age-dependent reduction of amygdala volume in bipolar disorder.

The amygdala is hypothesized to play a critical role in mood regulation, yet its involvement in bipolar disorder remains unclear. The aim of the present study was to compare measurements of amygdala volumes in a relatively large sample of bipolar disorder patients and healthy controls ranging in age from 18 to 49 years. Subjects comprised 54 adult patients meeting DSM-IV criteria for bipolar disorder and 41 healthy controls matched for age, sex, and education. Magnetic resonance imaging (1.5 T) was performed to obtain volumetric measurements of the amygdala using a manual region-of-interest tracing method with software that allowed simultaneous visualization of the amygdala in three orthogonal planes. The anterior head of the hippocampus was removed in the sagittal plane prior to amygdala volumetry measurement. Multiple regression analysis was computed on amygdala volume measurements as a function of diagnosis, age, sex, and cerebral volume. Bipolar patients showed an age-related reduction of amygdala volume, but controls did not. Among bipolar subjects, amygdala volume was unrelated to medication history. There were no significant hemispheric or sex interactions with the main effects. Results support a role for amygdala dysfunction in bipolar disorder which appears most robustly in older relative to younger adult patients. Differential aging effects in bipolar disorder may compromise amygdala integrity and contribute to mood dysregulation.

The COMT Val158Met polymorphism and temporal lobe morphometry in healthy adults.

We examined the relationship between COMT Val158Met genotype and temporal lobe volumes, including the caudate as a control region. Thirty-one healthy subjects completed 1.5T brain MRI and genotyping. After controlling for demographics, Val158 allele homozygotes exhibited significantly smaller temporal lobe and hippocampal volumes, with a trend for smaller amygdala volumes.

Selective attention and audiovisual integration: is attending to both modalities a prerequisite for early integration?

Interactions between multisensory integration and attention were studied using a combined audiovisual streaming design and a rapid serial visual presentation paradigm. Event-related potentials (ERPs) following audiovisual objects (AV) were compared with the sum of the ERPs following auditory (A) and visual objects (V). Integration processes were expressed as the difference between these AV and (A + V) responses and were studied while attention was directed to one or both modalities or directed elsewhere. Results show that multisensory integration effects depend on the multisensory objects being fully attended--that is, when both the visual and auditory senses were attended. In this condition, a superadditive audiovisual integration effect was observed on the P50 component. When unattended, this effect was reversed; the P50 components of multisensory ERPs were smaller than the unisensory sum. Additionally, we found an enhanced late frontal negativity when subjects attended the visual component of a multisensory object. This effect, bearing a strong resemblance to the auditory processing negativity, appeared to reflect late attention-related processing that had spread to encompass the auditory component of the multisensory object. In conclusion, our results shed new light on how the brain processes multisensory auditory and visual information, including how attention modulates multisensory integration processes.

Attentional capacity for processing concurrent stimuli is larger across sensory modalities than within a modality.

One finding in attention research is that visual and auditory attention mechanisms are linked together. Such a link would predict a central, amodal capacity limit in processing visual and auditory stimuli. Here we show that this is not the case. Letter streams were accompanied by asynchronously presented streams of auditory, visual, and audiovisual objects. Either the letter streams or the visual, auditory, or audiovisual parts of the object streams were attended. Attending to various aspects of the objects resulted in modulations of the letter-stream-elicited steady-state evoked potentials (SSVEPs). SSVEPs were larger when auditory objects were attended than when either visual objects alone or when auditory and visual object stimuli were attended together. SSVEP amplitudes were the same in the latter conditions, indicating that attentional capacity between modalities is larger than attentional capacity within one and the same modality.