Neuroimaging of endogenous lapses of responsiveness,

Attention lapses (ALs) and microsleeps (MSs) are complete lapses of responsiveness in which performance is completely disrupted for a short period of time, but consciousness is retained in the case of ALs. ALs are behaviourally different from MSs, as in an AL the eyes remain open whereas in a MS eyes are partially or completely closed. Both ALs and MSs can result in catastrophic consequences, especially in the transportation sector. Research over the past two decades has investigated the AL and MS phenomena using behavioural and physiological means. However, both ALs and MSs need further investigation to separate the different types of ALs physiologically, and to explore the neural signature of MSs in relation to normal sleep and drowsiness. Hence, the objective of this project was to understand the underlying physiological substrates of endogenous (internal) ALs and MSs which could potentially result in differentiating types of ALs and provide more understanding of MSs. Data from two previous Christchurch Neurotechnology Research Programme (NeuroTech™) studies (C and D) were combined resulting in a total of 40 subjects. During each session, subjects performed a 2-D continuous visuomotor tracking (CVT) task for 50 min (Study C) and 20 min (Study D). For each participant, tracking performance, eye-video, EEG, and fMRI were simultaneously collected. A human expert visually inspected the tracking performance and eye-video recordings to identify and categorize lapses of responsiveness for each participant. Participants performed the 2-D CVT task without interruptions. The repetitive nature of the task and the lack of a motivational factor made the task monotonous and fatiguing. As a result, it was more likely to introduce boredom leading to task-unrelated thoughts (TUTs), which divides attention between the task and the internal thoughts unrelated to the task, also fatigue which will introduce a trend of vigilance decrement over time. The project had hypotheses focusing on the changes in the brain’s activity compared to the baseline of good responsiveness tracking. We expected a decrease in dorsal attention network (DAN) activity during ALs due to a decoupling of attention from the external environment. Furthermore, we hypothesized that the ALs were due to involuntary mind-blanks. As such, we expected no change in default mode network (DMN) activity, as would have otherwise been expected if the ALs were due to mind-wandering. Functional connectivity (FC) of the brain was also investigated between the networks of interest which were the DMN, DAN, frontoparietal network (FPN), sensorimotor network (SMN), visual network (VSN), salience network (SN), eye-movement network (EMN), and working memory network (WMN), by analysing data from fMRI. EEG data were also used to perform analysis on ALs and MSs, by analysing changes in power in the delta, theta, alpha, beta, and gamma bands. Voxel-wise fMRI throughout the whole brain, group-ICA, haemodynamic response (HR) over the regions of interest (ROIs), and FC analyses were performed to reveal the neural signature during ALs. In voxel-wise analysis, a significant increase in activity was found in two regions: the dorsal anterior cingulate cortex (dACC) and the supplementary motor area (SMA). The group-ICA analysis did not show any significant results but did show a trend of increased activity in an independent component (IC) that was spatially correlated with SMN. Dynamic HR analysis was performed to further investigate findings from the voxel-wise analysis. Our results were not significant but there were strong trends of change. There was a trend of increased HR 7.5 s after the onset of the AL in the left intraparietal sulcus (IPS) of the DAN. There was also a decrease of 2.5 s before the onset of the AL in the right posterior parietal cortex (PPC) of the FPN. There was also an increase in the HR 5 s after the onset of the AL in the dACC of the SN. Finally, an increase in the HR 15 s before the onset of ALs in the left inferior parietal lobule (IPL) of the DMN is a major finding, as it is an indication that a lapse is about to happen. The HR analysis provided consistent findings with the voxel-wise analysis. FC analysis showed increases in FC within all networks of interest during the ALs. On looking at FC between networks, there was an increase in FC between the DMN and the FPN, no change between the DAN and the FPN, a decrease in FC between the SMN and the FPN, and an increase in FC between the FPN and the VSN. The EMN had an increased FC with the DMN, while it had both increases and decreases in FC with the DAN. There was also an increase in FC between the SN and the DAN, and no change between the SN and the DMN. Finally, a decrease in FC was found between the WMN and the DMN. These findings indicate an overlap between decoupling due to ALs and the process of recovery from ALs. The EEG analysis showed no significant change in the relative difference between average spectral power during ALs and their average baselines for any band of interest for ALs. During MSs, there was a significant increase in power relative to responsive baselines in the delta, theta, alpha, beta, and gamma bands. However, we could not be completely sure that all motion-related artefacts had been removed. Hence, we investigated this further by removing the effect of the global signal, which left only an increase in gamma activity, in addition to a trend of decreased activity in the alpha band. The significant increase in BOLD seen in the voxel-wise analysis is considered to represent the recovery of responsiveness following ALs. This was also seen in trends in group ICA and HR analyses. Overall, findings from the FC analysis show that, in addition to decoupling during ALs, and recovery from ALs, it is highly likely that the ALs during the 2-D CVT task were due to involuntary mind-blanks. This is supported by three major findings: (1) no significant increase in DMN activity in both voxel-wise and HR analyses, (2) the decrease in the HR in the FPN prior to the onset of the AL, and (3) the decrease in FC between the DMN and the WMN. This is further supported behaviourally by the short average duration of ALs (~ 1.7 s), in contrast to what would be likely during mind-wandering. Finally, the significant results from the EEG analysis of MSs, agreed with the literature in delta, theta, and alpha bands. However, increased power in beta and gamma bands was an important finding. We consider this increased high-frequency activity reflects unconscious ‘cognitive’ activity during a MS aimed at restoring consciousness after having fallen asleep during an active task. This highlights a key behavioural and physiological difference between MSs and sleep. Even after removing the effect of the global signal, we still believe that MSs and sleep are physiologically different in the recovery process. To summarize our key findings: (1) this is the first study to demonstrate that ALs during a continuous task are likely to be due to involuntary mind-blanks, (2) the increase in the HR in the DMN 15 s before the onset of AL could be a predictive signature of these lapses, and finally (3) MSs are physiologically different from sleep in terms of the recovery process. This project has improved our understanding of endogenous ALs and MSs and taken us a step closer to accurate detection/prediction systems which can increase prevention of fatal accidents

Microsleep Predicting Comparison Between LSTM and ANN Based on the Analysis of Time Series EEG Signal

A microsleep is an unintentional, transient loss of consciousness correlated with sleep that lasts up to fifteen seconds. Electroencephalogram (EEG), recordings have been extensively utilized to diagnose and study various neurological disorders. This study analyzes time series EEG signals to predict microsleep employing two deep learning models: Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN). The findings show that the ANN model achieves outstanding metrics in microsleep prediction, outperforming the LSTM in key performance metrics. The model demonstrated exceptional performance, as demonstrated by the outcomes of the Scatter Plot, R2 Score, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Between the two models, the ANN model achieved the most significant R2, MAE, MSE, and RMSE values (0.84, 1.10, 1.90, and 1.38) compared to the LSTM model. The critical contribution of this study lies in its development of comprehensive and effective methods for accurately predicting microsleep events from EEG signals

EEG theta oscillations during sleep deprivation

Brain oscillations of different frequencies characterize the electroencephalogram (EEG) during distinct cognitive and vigilant states. Theta oscillations (4-8 Hz) are unusual because they have been found in the near-opposite conditions of sleepiness and alert cognitive control. Most neuroscience research fo-cuses exclusively on the latter, leaving this paradox unresolved. With this thesis, I focus instead on the-ta during sleep deprivation (sdTheta), which has been hypothesized to reflect intrusions of local slow wave sleep on wake, based on a study in rats. The goal was to determine whether sdTheta in humans could also be considered a form of local sleep in wake, or if it was a manifestation of more typical cog-nition-related theta. I collected high-density EEG data from young healthy adults undergoing sleep deprivation to observe how sdTheta is affected by time awake, time of day, different tasks, and condi-tions. To independently track the effects of sleep deprivation, I also conducted extensive questionnaires and collected pupillometry data. I found that sdTheta can be widespread across the brain, although the specific sources depend on the ongoing task. Curiously, theta mostly originated from areas not critical for the task. I found that sdTheta occurs in bursts, making it unlike the isolated theta events thought to reflect local sleep. Furthermore, I found that independently from changes in the occurrences of such bursts, wake oscillation amplitudes increase with time awake, following a homeostatic trajectory. This supports the hypothesis that neuronal connectivity increases with time awake, which is what underlies sleep need. Unexpectedly, I found that the wake maintenance zone, a time before habitual bedtime when it is difficult to fall asleep, can mask these homeostatic changes in oscillation amplitudes. How-ever, the wake maintenance zone only minimally affects the presence of sdTheta bursts. Finally, I could not find any evidence that theta bursts were the cause of behavioral lapses nor compensating for sleep loss, supporting the previous finding of sdTheta originating from task-unrelated areas. Therefore, I ten-tatively propose that sdTheta bursts are a manifestation of unneeded parts of the brain at rest, alt-hough not necessarily “local sleep.” If this means that sdTheta is a different type of oscillation from theta involved in cognition, then care will be needed to dissociate the two types. Regardless of what it does, theta makes for a robust marker of sleep need and can have many clinical diagnostic applications, especially when analyzed effectively

Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data

The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices

Attention in the Brain Under Conditions of Sub-Optimal Alertness: Neurobiological Effects and Individual Differences

Sleep deprivation (SD) is a prevalent problem in modern society, and one that can have serious adverse consequences for health and safety. Critically, even short periods of SD can lead to relatively large decrements in attention, which may in turn cause an individual to neglect important environmental stimuli. In this thesis, I report the results of three experiments designed to investigate the neural bases of attentional declines under conditions of sleep loss and mental fatigue. In two experiments using arterial spin labeled fMRI, a technique that enables the quantification of absolute levels of cerebral blood flow (CBF), it was found that CBF patterns in the resting brain differed significantly based on arousal levels (Study #1) and prior cognitive workload (Study #2). These findings are a departure from prior neuroimaging studies, which have typically taken neural activity during non-task periods as static and inseparable baseline. In a test of sustained attention, performance declines were observed both following SD (Study #1) and when performing the task for an extended period of time while well-rested (Study #2). These decrements were primarily mediated by hypoactivation in a fronto-parietal attentional circuit. Furthermore, resting baseline levels of cerebral blood flow in the thalamus and prefrontal cortex before the start of the task were predictive of interindividual differences in subsequent performance decline (Study #2). In Study #3, an experiment using standard BOLD fMRI, it was found that performance declines in a test of selective attention following SD were accompanied by reduced functional connectivity between top-down control areas and regions of ventral visual cortex, as well as reductions in activation to targets in object-selective areas. Taken together, these results further our understanding of the neural basis of attention under conditions when this system is taxed beyond its normal limits

SLEEPING WHILE AWAKE: A NEUROPHYSIOLOGICAL INVESTIGATION ON SLEEP DURING WAKEFULNESS.

Il sonno e la veglia vengono comunemente considerati come due stati distinti. L\u2019alternanza tra essi, la cui presenza \ue8 stata dimostrata in ogni specie animale studiata fino ad oggi, sembra essere una delle caratteristiche che definisce la nostra vita. Allo stesso tempo, per\uf2, le scoperte portate alla luce negli ultimi decenni hanno offuscato i confini tra questi due stati. I meccanismi del sonno hanno sempre affascinato i neurofisiologi, che infatti, nell\u2019ultimo secolo, li hanno caratterizzati in dettaglio: ora sappiamo che all\u2019attivit\ue0 del sonno sottost\ue0 una specifica attivit\ue0 neuronale chiamata slow oscillation. La slow oscillation, che \ue8 costituita da (ancora una volta) un\u2019alternanza tra periodi di attivit\ue0 e periodi di iperpolarizzazione e silenzio neuronale (OFF-periods), \ue8 la modalit\ue0 base di attivazione del cervello dormiente. Questa alternanza \ue8 dovuta alla tendenza dei neuroni surante lo stato di sonno, di passare ad un periodo silente dopo un\u2019attivazione iniziale, una tendenza a cui viene dato il nome di bistabilit\ue0 neuronale. Molti studi hanno dimostrato come la bistabilit\ue0 neuronale tipica del sonno ed i relativi OFF-periods, possano accadere anche durante la veglia in particolari condizioni patologiche, nelle transizioni del sonno e durante le deprivazioni di sonno. Per questo motivo, se accettassimo che la bistabilit\ue0 neuronale e gli OFF-periods rappresentino una caratteristica fondamentale del sonno, allora dovremmo ammettere che stiamo assistendo ad un cambio di paradigma: da una prospettiva neurofisiologica il sonno pu\uf2 intrudere nella veglia. In questa tesi ho analizzato i nuovi -fluidi- confini tra sonno e veglia e le possibili implicazioni di questi nel problema della persistenza personale attraverso il tempo. Inoltre, ho studiato le implicazioni cliniche dell\u2019intrusione di sonno nella veglia in pazienti con lesioni cerebrali focali di natura ischemica. In particolare, i miei obiettivi sono stati: 1) Dimostrare come la bistabilit\ue0 neuronale possa essere responsabile della perdita di funzione nei pazienti affetti da ischemia cerebrale e come questo potrebbe avere implicazioni nello studio della patofisiologia dell\u2019ischemia cerebrale e nella sua terapia; 2) Stabilire le basi per un modello di sonno locale presente nella vita di tutti i giorni: la sensazione di sonnolenza. Infatti, essa potrebbe riflettere la presenza di porzioni di corteccia in stato di sonno, ma durante lo stato di veglia; 3) Difendere il criterio biologico di identit\ue0, che troverebbe nell\u2019attivit\ue0 cerebrale la continuit\ue0 necessaria al mantenimento della nostra identit\ue0 nel tempo.Sleep and wakefulness are considered two mutually exclusive states. The alternation between those two states seems to be a defining characteristic of our life, a ubiquitous phenomenon demonstrated in every animal species investigated so far. However, during the last decade, advances in neurophysiology have blurred the boundaries between those states. The mechanisms of sleep have always intrigued neurophysiologists and great advances have been made over the last century in understanding them: we now know that the defining characteristic underlying sleep activity is a specific pattern of neuronal activity, namely the slow oscillation. The slow oscillation, which is characterized by the periodic alternation between periods of activity (ON-periods) and periods of hyperpolarization and neuronal silence (OFF-periods) is the default mode of activity of the sleeping cortex. This alternation is due to the tendency of neurons to fall into a silent period after an initial activation; such tendency is known as \u201cbistability\u201d. There is accumulating evidence that sleep-like bistability, and the ensuing OFF-periods, may occur locally in the awake human brain in some pathological conditions, in sleep transition, as well as after sleep deprivation. Therefore, to the extent that bistability and OFF periods represents the basic neuronal features of sleep, a paradigm shift is in place: from a neurophysiological perspective sleep can intrude into wakefulness. In this thesis, I explore the fluid boundaries between sleep and wakefulness and investigate their possible implications on the problem of personal persistence over time. Moreover, I study the clinical implications of the intrusion of sleep into wakefulness in patients with focal brain injury due to stroke. Specifically, I aim to: 1) show how the sleep-like bistability can be responsible for the loss of function in stroke patients. This may have implications for understanding the pathophysiology of stroke and helping to foster recovery; 2) establish the basis for a model of local sleep that might be present in the everyday life, id est the sensation of sleepiness. Indeed, sleepiness could reflect islands of sleep during wakefulness; 3) advocate the biological criterion of identity, in which the continuity necessary for maintaining ourselves over time could be represented by never resting activity in the brain

EEG COHERENCE BIOMARKERS FOR DIFFERENTIATING ATTENTION STATES

Ph.DDOCTOR OF PHILOSOPH

ENIGMA-Sleep:Challenges, opportunities, and the road map

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine

Detecting fatigue in car drivers and aircraft pilots by using eye-motion metrics

Fatigue is widely recognised for risking the safety of aviation and ground transportation. To enhance transport safety, fatigue detection systems based on psychophysiological measures have been under development for many years. However, a reliable and robust fatigue detection system is still missing. This thesis starts with a literature review of fatigue concepts in the transportation field and the current psychophysiological measures to fatigue, and narrows down the focus to improving fatigue detection systems using eye-motion measures. A research gap was identified between current fatigue systems only focusing on part of sleepy symptoms and a comprehensive fatigue detection system including mental fatigue needed. To address this gap, four studies were conducted to reshape the understanding of fatigue in transportation and explore effective eye-motion metrics for indicating fatigue considering different causal factors. Studies 1 and 2 investigated the influence of two types of task-related fatigue on eye movement. Twenty participants completed a vigilance task before and after a 1-h simulator-based drive with a secondary task. Forty participants, divided equally into two groups, finished the same task before and after a 1-h and 1.5-h monotonous driving task. The results demonstrated that two types of task-related fatigue caused by cognitive overload and prolonged underload induced different physiological responses to eye-motion metrics. The results also proved that the increased mental fatigue decreased driver’s vigilance. Studies 3 and 4 simulated two hazardous fatigue scenarios for pilots. Study 3 explored the relationship between eye-motion metrics and pilot fatigue in an underload flight condition with sleep deprivation (low workload and sleep pressure). Study 4 explored the effective eye-motion metrics to estimate pilot’s cognitive fatigue imposed by time on task and high workload. The results suggested different eye-motion metrics to indicate sleepiness and mental fatigue. In addition, based on the sleepiness and mental fatigue indicators in Studies 3 and 4, several classifiers were built and evaluated to accurately detect sleepiness and mental fatigue. These findings show that considering casual factors such as sleep pressure, time on task and workload when using eye-motion metrics to detect fatigue can improve the accuracy and face validity of the current fatigue detection systems