Conscious but not thinking-Mind-blanks during visuomotor tracking: An fMRI study of endogenous attention lapses.

Attention lapses (ALs) are complete lapses of responsiveness in which performance is briefly but completely disrupted and during which, as opposed to microsleeps, the eyes remain open. Although the phenomenon of ALs has been investigated by behavioural and physiological means, the underlying cause of an AL has largely remained elusive. This study aimed to investigate the underlying physiological substrates of behaviourally identified endogenous ALs during a continuous visuomotor task, primarily to answer the question: Were the ALs during this task due to extreme mind-wandering or mind-blanks? The data from two studies were combined, resulting in data from 40 healthy non-sleep-deprived subjects (20M/20F; mean age 27.1 years, 20-45). Only 17 of the 40 subjects were used in the analysis due to a need for a minimum of two ALs per subject. Subjects performed a random 2-D continuous visuomotor tracking task for 50 and 20 min in Studies 1 and 2, respectively. Tracking performance, eye-video, and functional magnetic resonance imaging (fMRI) were recorded simultaneously. A human expert visually inspected the tracking performance and eye-video recordings to identify and categorise lapses of responsiveness as microsleeps or ALs. Changes in neural activity during 85 ALs (17 subjects) relative to responsive tracking were estimated by whole-brain voxel-wise fMRI and by haemodynamic response (HR) analysis in regions of interest (ROIs) from seven key networks to reveal the neural signature of ALs. Changes in functional connectivity (FC) within and between the key ROIs were also estimated. Networks explored were the default mode network, dorsal attention network, frontoparietal network, sensorimotor network, salience network, visual network, and working memory network. Voxel-wise analysis revealed a significant increase in blood-oxygen-level-dependent activity in the overlapping dorsal anterior cingulate cortex and supplementary motor area region but no significant decreases in activity; the increased activity is considered to represent a recovery-of-responsiveness process following an AL. This increased activity was also seen in the HR of the corresponding ROI. Importantly, HR analysis revealed no trend of increased activity in the posterior cingulate of the default mode network, which has been repeatedly demonstrated to be a strong biomarker of mind-wandering. FC analysis showed decoupling of external attention, which supports the involuntary nature of ALs, in addition to the neural recovery processes. Other findings were a decrease in HR in the frontoparietal network before the onset of ALs, and a decrease in FC between default mode network and working memory network. These findings converge to our conclusion that the ALs observed during our task were involuntary mind-blanks. This is further supported behaviourally by the short duration of the ALs (mean 1.7 s), which is considered too brief to be instances of extreme mind-wandering. This is the first study to demonstrate that at least the majority of complete losses of responsiveness on a continuous visuomotor task are, if not due to microsleeps, due to involuntary mind-blanks.

Cerebral perfusion is not impaired in persons with moderate obstructive sleep apnoea when awake.

PURPOSE: It is well established that, together with a multitude of other adverse effects on health, severe obstructive sleep apnoea causes reduced cerebral perfusion and, in turn, reduced cerebral function. Less clear is the impact of moderate obstructive sleep apnoea (OSA). Our aim was to determine if cerebral blood flow is impaired in people diagnosed with moderate OSA. METHODS: Twenty-four patients diagnosed with moderate OSA (15 ≤ apnoea-hypopnea index (AHI) < 30) were recruited (aged 32-72, median 59 years, 10 female). Seven controls (aged 42-73 years, median 62 years, 4 female) with an AHI < 5 were also recruited. The OSA status of all participants was confirmed at baseline by unattended polysomnography and they had an MRI arterial-spin-labelling scan of cerebral perfusion. RESULTS: Neither global perfusion nor voxel-wise perfusion differed significantly between the moderate-OSA and control groups. We also compared the average perfusion across three regional clusters, which had been found in a previous study to have significant perfusion differences with moderate-severe OSA versus control, and found no significant difference in perfusion between the two groups. The perfusions were also very close, with means of 50.2 and 51.8 mL/100 g/min for the moderate-OSAs and controls, respectively, with a negligible effect size (Cohen's d = 0.10). CONCLUSION: We conclude that cerebral perfusion is not impaired in people with moderate OSA and that cerebral flow regulatory mechanisms can cope with the adverse effects which occur in moderate OSA. This is an important factor in clinical decisions for prescription of continuous positive airway pressure therapy (CPAP).

Increased cerebral activity during microsleeps reflects an unconscious drive to re-establish consciousness.

BACKGROUND: Microsleeps are brief instances of sleep, causing complete lapses in responsiveness and partial or total extended closure of both eyes. Microsleeps can have devastating consequences, particularly in the transportation sector. STUDY OBJECTIVES: Questions remain regarding the neural signature and underlying mechanisms of microsleeps. This study aimed to gain a better understanding of the physiological substrates of microsleeps, which might lead to a better understanding of the phenomenon. METHODS: Data from an earlier study, involving 20 healthy non-sleep-deprived subjects, were analysed. Each session lasted 50 min and required subjects to perform a 2-D continuous visuomotor tracking task. Simultaneous data collection included tracking performance, eye-video, EEG, and fMRI. A human expert visually inspected each participant's tracking performance and eye-video recordings to identify microsleeps. Our interest was in microsleeps of ≥4-s duration, leaving us with a total of 226 events from 10 subjects. The microsleep events were divided into four 2-s segments (pre, start, end, and post) (with a gap in the middle, between start and end segments, for microsleeps >4 s), then each segment was analysed relative to its prior segment by examining changes in source-reconstructed EEG power in the delta, theta, alpha, beta, and gamma bands. RESULTS: EEG power increased in the theta and alpha bands between the pre and start of microsleeps. There was also increased power in the delta, beta, and gamma bands between the start and end of microsleeps. Conversely, there was a reduction in power between the end and post of microsleeps in the delta and alpha bands. These findings support previous findings in the delta, theta, and alpha bands. However, increased power in the beta and gamma bands has not been previously reported. CONCLUSIONS: We contend that increased high-frequency activity during microsleeps reflects unconscious 'cognitive' activity aimed at re-establishing consciousness following falling asleep during an active task.

RoWDI: rolling window detection of sleep intrusions in the awake brain using fMRI.

Objective.Brief episodes of sleep can intrude into the awake human brain due to lack of sleep or fatigue-compromising the safety of critical daily tasks (i.e. driving). These intrusions can also introduce artefactual activity within functional magnetic resonance imaging (fMRI) experiments, prompting the need for an objective and effective method of removing them.Approach.We have developed a method to track sleep-like events in awake humans via rolling window detection of intrusions (RoWDI) of fMRI signal template. These events can then be used in voxel-wise event-related analysis of fMRI data. To test this approach, we generated a template of fMRI activity associated with transition to sleep via simultaneous fMRI and electroencephalogram (EEG) (N= 10). RoWDI was then used to identify sleep-like events in 20 individuals performing a cognitive task during fMRI after a night of partial sleep deprivation. This approach was further validated in an independent fMRI dataset (N= 56).Main results.Our method (RoWDI) was able to infer frequent sleep-like events during the cognitive task performed after sleep deprivation. The sleep-like events were associated with on average of 20% reduction in pupil size and prolonged response time. The blood-oxygen-level-dependent activity during the sleep-like events covered thalami-cortical regions, which although spatially distinct, co-existed with, task-related activity. These key findings were validated in the independent dataset.Significance.RoWDI can reliably detect spontaneous sleep-like events in the human brain. Thus, it may also be used as a tool to delineate and account for neural activity associated with wake-sleep transitions in both resting-state and task-related fMRI studies.

Temporal evolution of neural activity and connectivity during microsleeps when rested and following sleep restriction.

Even when it is critical to stay awake, such as when driving, sleep deprivation weakens one's ability to do so by substantially increasing the propensity for microsleeps. Microsleeps are complete lapses of consciousness but, paradoxically, are associated with transient increases in cortical activity. But do microsleeps provide a benefit in terms of attenuating the need for sleep? And is the neural response to microsleeps altered by the degree of homeostatic drive to sleep? In this study, we continuously monitored eye-video, visuomotor responsiveness, and brain activity via fMRI in 20 healthy subjects during a 20-min visuomotor tracking task following a normally-rested night and a sleep-restricted (4-h) night. As expected, sleep restriction led to an increased number of microsleeps and an increased variability in tracking error. Microsleeps exhibited transient increases in regional activity in the fronto-parietal and parahippocampal area. Network analyses revealed divergent transient changes in the right fronto-parietal, dorsal-attention, default-mode, and thalamo-cortical functional networks. In all subjects, tracking error immediately following microsleeps was improved compared to before the microsleeps. Importantly, post-microsleep recovery in tracking response speed was associated with hyperactivation in the thalamo-cortical network. The temporal evolution of functional connectivity within the frontal and posterior nodes of the default-mode network and between the right fronto-parietal and default-mode networks was associated with temporal changes in visuomotor responsiveness. These findings demonstrate distinct brain-network-level changes in brain activity during microsleeps and suggest that neural activity in the thalamo-cortical network may facilitate the transient recovery from microsleeps. The temporal pattern of evolution in brain activity and performance is indicative of dynamic changes in vigilance during the struggle to stay awake following sleep loss.

Attention lapses and behavioural microsleeps during tracking, psychomotor vigilance, and dual tasks.

This study examined the incidence of attention lapses and microsleeps under contrasting levels of task complexity during three tasks: PVT, 2-D tracking and a dual task combining the two. More attention lapses per participant (median 15vs. 3; range 1-74vs. 0-76, p=0.001), with the greatest increase with time spent-on-task (p=0.002), were evident on the more cognitively-demanding dual task than on the PVT. Conversely, fewer microsleeps (median 0vs. 0; range 0-1vs. 0-18, p=0.022) occurred during the more complex task compared to the tracking task. An increase in microsleep rate with time spent-on-task (p=0.035) was evident during the tracking task but not the dual task. These results indicate that the higher cognitive load, associated with an increase in task complexity, increased the likelihood of attention lapses, while a reduction in task complexity increased the likelihood of microsleeps.

Time-varying effective connectivity of the cortical neuroelectric activity associated with behavioural microsleeps.

An episode of complete failure to respond during an attentive task accompanied by behavioural signs of sleep is called a behavioural microsleep. We proposed a combination of high-resolution EEG and an advanced method for time-varying effective connectivity estimation for reconstructing the temporal evolution of the causal relations between cortical regions when microsleeps occur during a continuous visuomotor task. We found connectivity patterns involving left-right frontal, left-right parietal, and left-frontal/right-parietal connections commencing in the interval [-500; -250] ms prior to the onset of microsleeps and disappearing at the end of the microsleeps. Our results from global graph indices derived from effective connectivity analysis have revealed EEG-based biomarkers of all stages of microsleeps (preceding, onset, pre-recovery, recovery). In particular, this raises the possibility of being able to predict microsleeps in real-world tasks and initiate a 'wake-up' intervention to avert the microsleeps and, hence, prevent injurious and even multi-fatality accidents.

Prediction of driving ability: Are we building valid models?

The prediction of on-road driving ability using off-road measures is a key aim in driving research. The primary goal in most classification models is to determine a small number of off-road variables that predict driving ability with high accuracy. Unfortunately, classification models are often over-fitted to the study sample, leading to inflation of predictive accuracy, poor generalization to the relevant population and, thus, poor validity. Many driving studies do not report sufficient details to determine the risk of model over-fitting and few report any validation technique, which is critical to test the generalizability of a model. After reviewing the literature, we generated a model using a moderately large sample size (n=279) employing best practice techniques in the context of regression modelling. By then randomly selecting progressively smaller sample sizes we show that a low ratio of participants to independent variables can result in over-fitted models and spurious conclusions regarding model accuracy. We conclude that more stable models can be constructed by following a few guidelines.

Decreased Regional Cerebral Perfusion in Moderate-Severe Obstructive Sleep Apnoea during Wakefulness.

STUDY OBJECTIVES: To investigate gray matter volume and concentration and cerebral perfusion in people with untreated obstructive sleep apnea (OSA) while awake. DESIGN: Voxel-based morphometry to quantify gray matter concentration and volume. Arterial spin labeling perfusion imaging to quantify cerebral perfusion. SETTING: Lying supine in a 3-T magnetic resonance imaging scanner in the early afternoon. PARTICIPANTS: 19 people with OSA (6 females, 13 males; mean age 56.7 y, range 41-70; mean AHI 18.5, range 5.2-52.8) and 19 controls (13 females, 6 males; mean age: 50.0 y, range 41-81). INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: There were no differences in regional gray matter concentration or volume between participants with OSA and controls. Neither was there any difference in regional perfusion between controls and people with mild OSA (n = 11). However, compared to controls, participants with moderate-severe OSA (n = 8) had decreased perfusion (while awake) in three clusters. The largest cluster incorporated, bilaterally, the paracingulate gyrus, anterior cingulate gyrus, and subcallosal cortex, and the left putamen and left frontal orbital cortex. The second cluster was right-lateralized, incorporating the posterior temporal fusiform cortex, parahippocampal gyrus, and hippocampus. The third cluster was located in the right thalamus. CONCLUSIONS: There is decreased regional perfusion during wakefulness in participants with moderate-severe obstructive sleep apnea, and these are in brain regions which have shown decreased regional gray matter volume in previous studies in people with severe OSA. Thus, we hypothesize that cerebral perfusion changes are evident before (and possibly underlie) future structural changes.

Losing the struggle to stay awake: divergent thalamic and cortical activity during microsleeps.

Maintaining alertness is critical for safe and successful performance of most human activities. Consequently, microsleeps during continuous visuomotor tasks, such as driving, can be very serious, not only disrupting performance but sometimes leading to injury or death due to accidents. We have investigated the neural activity underlying behavioral microsleeps--brief (0.5-15 s) episodes of complete failure to respond accompanied by slow eye-closures--and EEG theta activity during drowsiness in a continuous task. Twenty healthy normally-rested participants performed a 50-min continuous tracking task while fMRI, EEG, eye-video, and responses were simultaneously recorded. Visual rating of performance and eye-video revealed that 70% of the participants had frequent microsleeps. fMRI analysis revealed a transient decrease in thalamic, posterior cingulate, and occipital cortex activity and an increase in frontal, posterior parietal, and parahippocampal activity during microsleeps. The transient activity was modulated by the duration of the microsleep. In subjects with frequent microsleeps, power in the post-central EEG theta was positively correlated with the BOLD signal in the thalamus, basal forebrain, and visual, posterior parietal, and prefrontal cortices. These results provide evidence for distinct neural changes associated with microsleeps and with EEG theta activity during drowsiness in a continuous task. They also suggest that the occurrence of microsleeps during an active task is not a global deactivation process but involves localized activation of fronto-parietal cortex, which, despite a transient loss of arousal, may constitute a mechanism by which these regions try to restore responsiveness.

Optimal EEG feature selection from average distance between events and non-events.

Biosignal classification systems often have to deal with extraneous features, highly imbalanced datasets, and a low SNR. A robust feature selection/reduction method is a crucial step in this process. Sets of artificial data were generated to test a prototype EEG-based microsleep detection system, consisting of a combination of EEG and 2-s bursts of 15-Hz sinusoids of varied signal-to-noise ratios (SNRs) ranging from 16 to 0.03. The balance between events and non-events was varied between evenly balanced and highly imbalanced (e.g., events occurring only 2% of the time). Features were spectral estimates of various EEG bands (e.g., alpha band power) or ratios between them. A total of 34 features for each of the 16 channels yielded a total of 544 features. Five minutes of EEG from a total of eight subjects were used in the generation of the artificial data. Several feature reduction and classifier structures were investigated. Taking only a single feature corresponding to the maximum of average distance between events and non-events (ADEN) on unbalanced data yielded a phi correlation of 0.94 on the mock data with an SNR of 0.3, compared with a phi coefficient of 0.00 for principal component analysis (PCA). ADEN consistently outperformed alternative system configurations, independent of the classifier utilized. While ADEN's high performance may be due to the nature of the artificial dataset, this simulation has demonstrated strong potential compared to other feature selection/reduction methods.

Efficient and regular patterns of nighttime sleep are related to increased vulnerability to microsleeps following a single night of sleep restriction.

Sleep-deprived people, or those performing extended monotonous tasks, can exhibit brief episodes in which they suspend performance and appear to fall asleep momentarily-behavioral microsleeps ("microsleeps"). In this study, microsleeps were identified using eye video and tracking response during a 20-min continuous tracking task undertaken by 16 healthy volunteers (mean age 24.9 yrs; 8 females, 8 males) in the early afternoon following a normally rested night and a night of restricted sleep (time-in-bed restricted to 4 h). Sessions were 1 wk apart and counterbalanced. Wrist actigraphy, self-reported sleepiness, and sleep quality were also recorded. We hypothesized that high microsleep rates when normally rested or after a night of sleep restriction would be related to poor sleep quality, sleep disturbance, circadian type, irregular sleep patterns, low daily sleep duration, or poor sleep efficiency. We also hypothesized that prior performance on a 10-min psychomotor vigilance task (PVT) (mean reaction time or number of PVT lapses) would be related to the number of microsleeps during the tracking task and that PVT performance could, therefore, be used as a fitness-for-duty indicator. The number of microsleeps during the tracking task increased following sleep restriction (mean 11.4 versus 27.9; p = 0.03). There were no correlations between the number of microsleeps in the normally rested session and any of the actigraphically measured or self-reported sleep measures. However, the number of microsleeps following sleep restriction was correlated with sleep efficiency (r = 0.73, p = 0.001), sleep onset latency (r = -0.57, p = 0.02), and sleep onset time-of-day standard deviation (r = -0.54, p = 0.03) over 11 normally rested nights. There was no correlation between PVT performance and the subsequent number of microsleeps during the tracking task in either session. Attributes usually associated with beneficial nighttime sleep patterns-going to sleep at a similar time each night, falling asleep quickly, and infrequent arousals-were related to greater vulnerability to microsleeps following sleep restriction. There were intercorrelations between all the sleep measures associated with microsleep rate following sleep restriction, indicating that the measures form a pattern of behaviors and are not independently related to microsleep rate. Perhaps some people maintain a regular sleep pattern because they experience sleepiness the following day when their pattern is disrupted. Conversely, people with more variation in their sleep pattern may do so because this does not substantially increase sleepiness the following day. We conclude that people with consistent sleep patterns and efficient sleep may be more prone to microsleeps than other people when their usual regular pattern is disrupted by sleep restriction.

Distinct neural correlates of time-on-task and transient errors during a visuomotor tracking task after sleep restriction.

Sleep loss leads to both time-on-task slowing of responsiveness and increased frequency of transient response errors. The consequences of such errors during real-world visuomotor tasks, such as driving, are serious and life threatening. To investigate the neuronal underpinning of time-on-task and transient errors during a visuomotor tracking task following sleep restriction, we performed fMRI on 20 healthy individuals when well-rested and when sleep-restricted while they performed a 2-D pursuit-tracking task. Sleep restriction to 4-h time-in-bed was associated with significant time-on-task decline in tracking performance and an increased number of transient tracking errors. Sleep restriction was associated with time-on-task decreases in BOLD activity in task-related areas, including the lateral occipital cortex, intraparietal cortex, and primary motor cortex. In contrast, thalamic, anterior cingulate, and medial frontal cortex areas showed overall increases irrespective of time-on-task after sleep-restriction. Furthermore, transient errors after sleep-restriction were associated with distinct transient BOLD activations in areas not involved in tracking task per se, in the right superior parietal cortex, bilateral temporal cortex, and thalamus. These results highlight the distinct cerebral underpinnings of sustained and transient modulations in alertness during increased homeostatic drive to sleep. Ability to detect neuronal changes associated with both sustained and transient changes in performance in a single task allowed us to disentangle neuronal mechanisms underlying two important aspects of sustained task performance following sleep loss.

Predicting on-road assessment pass and fail outcomes in older drivers with cognitive impairment using a battery of computerized sensory-motor and cognitive tests.

OBJECTIVES: To generate a robust model of computerized sensory-motor and cognitive test performance to predict on-road driving assessment outcomes in older persons with diagnosed or suspected cognitive impairment. DESIGN: A logistic regression model classified pass­fail outcomes of a blinded on-road driving assessment. Generalizability of the model was tested using leave-one-out cross-validation. SETTING: Three specialist clinics in New Zealand. PARTICIPANTS: Drivers (n=279; mean age 78.4, 65% male) with diagnosed or suspected dementia, mild cognitive impairment, unspecified cognitive impairment, or memory problems referred for a medical driving assessment. MEASUREMENTS: A computerized battery of sensory-motor and cognitive tests and an on-road medical driving assessment. RESULTS: One hundred fifty-five participants (55.5%) received an on-road fail score. Binary logistic regression correctly classified 75.6% of the sample into on-road pass and fail groups. The cross-validation indicated accuracy of the model of 72.0% with sensitivity for detecting on-road fails of 73.5%, specificity of 70.2%, positive predictive value of 75.5%, and negative predictive value of 68%. CONCLUSION: The off-road assessment prediction model resulted in a substantial number of people who were assessed as likely to fail despite passing an on-road assessment and vice versa. Thus, despite a large multicenter sample, the use of off-road tests previously found to be useful in other older populations, and a carefully constructed and tested prediction model, off-road measures have yet to be found that are sufficiently accurate to allow acceptable determination of on-road driving safety of cognitively impaired older drivers.

Cerebral perfusion differences between drowsy and nondrowsy individuals after acute sleep restriction.

OBJECTIVES: To investigate changes in resting cerebral blood flow (CBF) after acute sleep restriction. To investigate the extent to which changes in CBF after sleep restriction are related to drowsiness as manifested in eye-video. DESIGN: Participants were scanned for 5 min using arterial spin labeling (ASL) perfusion imaging after both sleep-restricted and rested nights. Participants were rated for visual signs of drowsiness in the eye-video recorded during the scan. SETTING: Lying supine in a 3-Tesla magnetic resonance imaging scanner. PARTICIPANTS: Twenty healthy adults (age 20-37 yr) with no history of neurologic, psychiatric, or sleep disorder, and with usual time in bed of 7.0-8.5 h. INTERVENTIONS: In the night before the sleep-restricted session, participants were restricted to 4 h time in bed. RESULTS: There was an overall reduction in CBF in the right-lateralized fronto-parietal attentional network after acute sleep restriction, although this was largely driven by participants who showed strong signs of drowsiness in the eye-video after sleep restriction. Change in CBF correlated with change in drowsiness in the basal forebrain-cingulate regions. In particular, there was a pronounced increase in CBF in the basal forebrain and anterior and posterior cingulate cortex of participants who remained alert after sleep restriction. CONCLUSIONS: The pattern of cerebral activity after acute sleep restriction is highly dependent on level of drowsiness. Nondrowsy individuals are able to increase activity in the arousal-promoting brain regions and maintain activity in attentional regions. In contrast, drowsy individuals are unable to maintain arousal and show decreased activity in both arousal-promoting and attentional regions.

Do complex models increase prediction of complex behaviours? Predicting driving ability in people with brain disorders.

Prediction of complex behavioural tasks via relatively simple modelling techniques, such as logistic regression and discriminant analysis, often has limited success. We hypothesized that to more accurately model complex behaviour, more complex models, such as kernel-based methods, would be needed. To test this hypothesis, we assessed the value of six modelling approaches for predicting driving ability based on performance on computerized sensory-motor and cognitive tests (SMCTests™) in 501 people with brain disorders. The models included three models previously used to predict driving ability (discriminant analysis, DA; binary logistic regression, BLR; and nonlinear causal resource analysis, NCRA) and three kernel methods (support vector machine, SVM; product kernel density, PK; and kernel product density, KP). At the classification level, two kernel methods were substantially more accurate at classifying on-road pass or fail (SVM 99.6%, PK 99.8%) than the other models (DA 76%, BLR 78%, NCRA 74%, KP 81%). However, accuracy decreased substantially for all of the kernel models when cross-validation techniques were used to estimate prediction of on-road pass or fail in an independent referral group (SVM 73-76%, PK 72-73%, KP 71-72%) but decreased only slightly for DA (74-75%) and BLR (75-76%). Cross-validation of NCRA was not possible. In conclusion, while kernel-based models are successful at modelling complex data at a classification level, this is likely to be due to overfitting of the data, which does not lead to an improvement in accuracy in independent data over and above the accuracy of other less complex modelling techniques.

Behavioural microsleeps in normally-rested people.

Sleep-deprived people, or those performing extended monotonous tasks, frequently have brief episodes when performance is suspended and they appear to fall asleep momentarily - behavioural microsleeps (BMs). As BM rates are highly variable between normally-rested people, this study aimed to determine whether there is a relationship between propensity for BMs and measures of sleep. Subjects undertook a continuous 50-min 2-D tracking task and BMs were identified with high temporal accuracy based on simultaneous analysis of visuomotor response, tracking speed, tracking error, vertical electrooculogram, and eye-video. BM rates and durations were correlated with measures of sleep (i.e., wrist actigraphy, Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, and Horne-Ostberg Morning-Eveningness Questionnaire). BMs occurred frequently during the task but rates were highly variable between participants (mean 79.1/h ± 66.2, range 0-226/h). There were correlations between ESS score and BM rate and duration. However, BMs were not related to other sleep measures. Thus, there is a very large variability in BM propensity in normally-rested subjects which cannot be explained by variation in sleep duration, quality, or efficiency. Propensity to fall asleep in situations in which sustained performance is required may be a trait characteristic in normally-rested people.

The relationship between behavioural microsleeps, visuomotor performance and EEG theta.

Visuomotor performance and responsiveness deteriorates with time-on-task due to drowsiness and increased propensity to sleep. Frequent episodes of behavioural microsleep (BM) are also common during extended and monotonous tasks. In this study, simultaneous recording of EEG, eye-video, and continuous visuomotor response is used to investigate visuomotor performance and EEG activity during tonic drowsiness and phasic BMs. The data were collected from 20 healthy volunteers while they performed a continuous 2-D pursuit tracking task for 50 min. We identified episodes of BMs by expert visual rating of eye-video and visuomotor response using a set of pre-defined criteria. Visuomotor performance and EEG activity were correlated with and without BM events. A moderate correlation was observed between visuomotor error and theta activity in EEG at a posterior channel (Pz) before the removal of BMs. However, when BMs were removed from the data, the correlation dropped in most subjects. Furthermore, most of the large fluctuations in performance observed during the visuomotor task disappeared after the removal of BMs. This suggests that episodic behaviours such as BMs contribute substantially to fluctuations in performance and to EEG theta activity during an extended task, and that they should be taken into account when studying tonic drowsiness.

Lapses of responsiveness: Characteristics, detection, and underlying mechanisms.

Lapses in responsiveness ('lapses'), particularly microsleeps and attention lapses, are complete disruptions in performance from approximately 0.5-15 s. They are of particular importance in the transport sector in which there is a need to maintain sustained attention for extended periods and in which lapses can lead to multiple-fatality accidents.

Different models for predicting driving performance in people with brain disorders.

Data from performance on a computerized battery of driving-related sensory-motor and cognitive tests (SMCTests™) were used to predict outcome on a blinded on-road driving assessment in 501 people with brain disorders. Six modelling approaches were assessed: discriminant analysis (DA), binary logistic regression (BLR), nonlinear causal resource analysis (NCRA), and three kernel methods (product kernel density (PK), kernel-product density (KP), and support vector machine (SVM)). At the classification level, the three kernel methods were more accurate for predicting on-road Pass or Fail (SVM 99%, PK 99%, KP 80%) than the other models (DA 75%, BLR 77%, NCRA 66%). However, accuracy decreased substantially across the kernel models when leave-one-out cross-validation was used to estimate how accurately the models would predict on-road Pass or Fail in an independent referral group (SVM 76%, PK 73%, KP 72%) but remained fairly constant for DA (74%) and BLR (76%). Cross-validation of NCRA was not possible. While kernel-based models are successful at modelling complex data at a classification level, this appears to be due to overfitting of the data which does not improve accuracy in an independent data set over and above the accuracy of other modelling techniques.