A new perspective for the training assessment: Machine learning-based neurometric for augmented user's evaluation

Inappropriate training assessment might have either high social costs and economic impacts, especially in high risks categories, such as Pilots, Air Traffic Controllers, or Surgeons. One of the current limitations of the standard training assessment procedures is the lack of information about the amount of cognitive resources requested by the user for the correct execution of the proposed task. In fact, even if the task is accomplished achieving the maximum performance, by the standard training assessment methods, it would not be possible to gather and evaluate information about cognitive resources available for dealing with unexpected events or emergency conditions. Therefore, a metric based on the brain activity (neurometric) able to provide the Instructor such a kind of information should be very important. As a first step in this direction, the Electroencephalogram (EEG) and the performance of 10 participants were collected along a training period of 3 weeks, while learning the execution of a new task. Specific indexes have been estimated from the behavioral and EEG signal to objectively assess the users' training progress. Furthermore, we proposed a neurometric based on a machine learning algorithm to quantify the user's training level within each session by considering the level of task execution, and both the behavioral and cognitive stabilities between consecutive sessions. The results demonstrated that the proposed methodology and neurometric could quantify and track the users' progresses, and provide the Instructor information for a more objective evaluation and better tailoring of training programs. © 2017 Borghini, Aricò, Di Flumeri, Sciaraffa, Colosimo, Herrero, Bezerianos, Thakor and Babiloni

Loss of consciousness is related to hyper-1 correlated gamma-band activity in anesthetized macaques and sleeping humans

Loss of consciousness can result from a wide range of causes, including natural sleep and pharmacologically induced anesthesia. Important insights might thus come from identifying neuronal mechanisms of loss and re-emergence of consciousness independent of a specific manipulation. Therefore, to seek neuronal signatures of loss of consciousness common to sleep and anesthesia we analyzed spontaneous electrophysiological activity recorded in two experiments. First, electrocorticography (ECoG) acquired from 4 macaque monkeys anesthetized with different anesthetic agents (ketamine, medetomidine, propofol) and, second, stereo-electroencephalography (sEEG) from 10 epilepsy patients in different wake-sleep stages (wakefulness, NREM, REM). Specifically, we investigated co-activation patterns among brain areas, defined as correlations between local amplitudes of gamma-band activity. We found that resting wakefulness was associated with intermediate levels of gamma-band coupling, indicating neither complete dependence, nor full independence among brain regions. In contrast, loss of consciousness during NREM sleep and propofol anesthesia was associated with excessively correlated brain activity, as indicated by a robust increase of number and strength of positive correlations. However, such excessively correlated brain signals were not observed during REM sleep, and were present only to a limited extent during ketamine anesthesia. This might be related to the fact that, despite suppression of behavioral responsiveness, REM sleep and ketamine anesthesia often involve presence of dream-like conscious experiences. We conclude that hyper-correlated gamma-band activity might be a signature of loss of consciousness common across various manipulations and independent of behavioral responsiveness

Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease

Complexity analysis of electroencephalogram (EEG) signals has emerged as a valuable tool for characterizing Parkinson’s disease (PD). Fractal dimension (FD) is a widely employed method for measuring the complexity of shapes with many applications in neurodegenerative disorders. Nevertheless, very little is known on the fractal characteristics of EEG in PD measured by FD. In this study we performed a spatio-temporal analysis of EEG in PD using FD in four dimensions (4DFD).We analyzed 42 resting-state EEG recordings comprising two groups: 27 PD patients without dementia and 15 healthy control subjects (HC). From the original resting-state EEG we derived the cortical activations defined by a source reconstruction at each time sample, generating point clouds in three dimensions. Then, a sliding window of one second (the fourth dimension) was used to compute the value of 4DFD by means of the box-counting algorithm. Our results showed a significantly higher value of 4DFD in the PD group (p < 0.001). Moreover, as a diagnostic classifier of PD, 4DFD obtained an area under curve value of 0.97 for a receiver operating characteristic curve analysis. These results suggest that 4DFD could be a promising method for characterizing the specific changes in the brain dynamics associated with PDProject PID2019-105145RB-I00Spanish Government (MCIN/AEI/10.13039/501100011033)Italian Ministry of Health—(Ricerca Corrente 2022–2024)National PhD in Artificial Intelligence, XXXVII cycleHealth and life sciences, organized by Università Campus Bio-Medico di Rom

EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers

Several models defining different types of cognitive human behaviour are available. For this work, we have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools able to assess at which level of cognitive control the operator is dealing with the considered task, that is if he/she is performing the task as an automated routine (skill level), as procedures-based activity (rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such behaviours have been evaluated from a neurophysiological point of view, for example, by considering brain activity variations across the different SRK levels. Therefore, the proposed study aimed to investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers, demonstrated that specific brain features could characterize and discriminate the different SRK levels, therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic setting

Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients

Patients surviving severe brain injury may regain consciousness without recovering their ability to understand, move and communicate. Recently, electrophysiological and neuroimaging approaches, employing simple sensory stimulations or verbal commands, have proven useful in detecting higher order processing and, in some cases, in establishing some degree of communication in brain-injured subjects with severe impairment of motor function. To complement these approaches, it would be useful to develop methods to detect recovery of consciousness in ways that do not depend on the integrity of sensory pathways or on the subject's ability to comprehend or carry out instructions. As suggested by theoretical and experimental work, a key requirement for consciousness is that multiple, specialized cortical areas can engage in rapid causal interactions (effective connectivity). Here, we employ transcranial magnetic stimulation together with high-density electroencephalography to evaluate effective connectivity at the bedside of severely brain injured, non-communicating subjects. In patients in a vegetative state, who were open-eyed, behaviourally awake but unresponsive, transcranial magnetic stimulation triggered a simple, local response indicating a breakdown of effective connectivity, similar to the one previously observed in unconscious sleeping or anaesthetized subjects. In contrast, in minimally conscious patients, who showed fluctuating signs of non-reflexive behaviour, transcranial magnetic stimulation invariably triggered complex activations that sequentially involved distant cortical areas ipsi- and contralateral to the site of stimulation, similar to activations we recorded in locked-in, conscious patients. Longitudinal measurements performed in patients who gradually recovered consciousness revealed that this clear-cut change in effective connectivity could occur at an early stage, before reliable communication was established with the subject and before the spontaneous electroencephalogram showed significant modifications. Measurements of effective connectivity by means of transcranial magnetic stimulation combined with electroencephalography can be performed at the bedside while by-passing subcortical afferent and efferent pathways, and without requiring active participation of subjects or language comprehension; hence, they offer an effective way to detect and track recovery of consciousness in brain-injured patients who are unable to exchange information with the external environment

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

Stratification of unresponsive patients by an independently validated index of brain complexity.

OBJECTIVE: Validating objective, brain-based indices of consciousness in behaviorally unresponsive patients represents a challenge due to the impossibility of obtaining independent evidence through subjective reports. Here we address this problem by first validating a promising metric of consciousness-the Perturbational Complexity Index (PCI)-in a benchmark population who could confirm the presence or absence of consciousness through subjective reports, and then applying the same index to patients with disorders of consciousness (DOCs). METHODS: The benchmark population encompassed 150 healthy controls and communicative brain-injured subjects in various states of conscious wakefulness, disconnected consciousness, and unconsciousness. Receiver operating characteristic curve analysis was performed to define an optimal cutoff for discriminating between the conscious and unconscious conditions. This cutoff was then applied to a cohort of noncommunicative DOC patients (38 in a minimally conscious state [MCS] and 43 in a vegetative state [VS]). RESULTS: We found an empirical cutoff that discriminated with 100% sensitivity and specificity between the conscious and the unconscious conditions in the benchmark population. This cutoff resulted in a sensitivity of 94.7% in detecting MCS and allowed the identification of a number of unresponsive VS patients (9 of 43) with high values of PCI, overlapping with the distribution of the benchmark conscious condition. INTERPRETATION: Given its high sensitivity and specificity in the benchmark and MCS population, PCI offers a reliable, independently validated stratification of unresponsive patients that has important physiopathological and therapeutic implications. In particular, the high-PCI subgroup of VS patients may retain a capacity for consciousness that is not expressed in behavior

Exploring the Brain Responses to Driving Fatigue through Simultaneous EEG and fNIRS Measurements

© 2020 World Scientific Publishing Company. Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO2) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO2 decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO2 oscillations suggest that activations in HbO2 are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain's responses as evidence of state changes during fatigue driving