Diazepam-induced changes in sleep: Role of the α1 GABAA receptor subtype

Ligands acting at the benzodiazepine (BZ) site of γ-aminobutyric acid type A (GABAA) receptors currently are the most widely used hypnotics. BZs such as diazepam (Dz) potentiate GABAA receptor activation. To determine the GABAA receptor subtypes that mediate the hypnotic action of Dz wild-type mice and mice that harbor Dz-insensitive α1 GABAA receptors [α1 (H101R) mice] were compared. Sleep latency and the amount of sleep after Dz treatment were not affected by the point mutation. An initial reduction of rapid eye movement (REM) sleep also occurred equally in both genotypes. Furthermore, the Dz-induced changes in the sleep and waking electroencephalogram (EEG) spectra, the increase in power density above 21 Hz in non-REM sleep and waking, and the suppression of slow-wave activity (SWA; EEG power in the 0.75- to 4.0-Hz band) in non-REM sleep were present in both genotypes. Surprisingly, these effects were even more pronounced in α1(H101R) mice and sleep continuity was enhanced by Dz only in the mutants. Interestingly, Dz did not affect the initial surge of SWA at the transitions to sleep, indicating that the SWA-generating mechanisms are not impaired by the BZ. We conclude that the REM sleep inhibiting action of Dz and its effect on the EEG spectra in sleep and waking are mediated by GABAA receptors other than α1, i.e., α2, α3, or α5 GABAA receptors. Because α1 GABAA receptors mediate the sedative action of Dz, our results provide evidence that the hypnotic effect of Dz and its EEG “fingerprint” can be dissociated from its sedative action.

Grafts of adenosine-releasing cells suppress seizures in kindling epilepsy

Adenosine is an inhibitor of neuronal activity in the brain. The local release of adenosine from grafted cells was evaluated as an ex vivo gene therapy approach to suppress synchronous discharges and epileptic seizures. Fibroblasts were engineered to release adenosine by inactivating the adenosine-metabolizing enzymes adenosine kinase and adenosine deaminase. After encapsulation into semipermeable polymers, the cells were grafted into the brain ventricles of electrically kindled rats, a model of partial epilepsy. Grafted rats provided a nearly complete protection from behavioral seizures and a near-complete suppression of afterdischarges in electroencephalogram recordings, whereas the full tonic–clonic convulsions in control rats remained unaltered. Thus, the local release of adenosine resulting in adenosine concentrations <25 nM at the site of action is sufficient to suppress seizure activity and, therefore, provides a potential therapeutic principle for the treatment of drug-resistant partial epilepsies.

Induction of a physiological memory in the cerebral cortex by stimulation of the nucleus basalis.

Auditory cortical receptive field plasticity produced during behavioral learning may be considered to constitute "physiological memory" because it has major characteristics of behavioral memory: associativity, specificity, rapid acquisition, and long-term retention. To investigate basal forebrain mechanisms in receptive field plasticity, we paired a tone with stimulation of the nucleus basalis, the main subcortical source of cortical acetylcholine, in the adult guinea pig. Nucleus basalis stimulation produced electroencephalogram desynchronization that was blocked by systemic and cortical atropine. Paired tone/nucleus basalis stimulation, but not unpaired stimulation, induced receptive field plasticity similar to that produced by behavioral learning. Thus paired activation of the nucleus basalis is sufficient to induce receptive field plasticity, possibly via cholinergic actions in the cortex.

Intracortical and corticothalamic coherency of fast spontaneous oscillations.

We report that fast (mainly 30- to 40-Hz) coherent electric field oscillations appear spontaneously during brain activation, as expressed by electroencephalogram (EEG) rhythms, and they outlast the stimulation of mesopontine cholinergic nuclei in acutely prepared cats. The fast oscillations also appear during the sleep-like EEG patterns of ketamine/xylazine anesthesia, but they are selectively suppressed during the prolonged phase of the slow (<1-Hz) sleep oscillation that is associated with hyperpolarization of cortical neurons. The fast (30- to 40-Hz) rhythms are synchronized intracortically within vertical columns, among closely located cortical foci, and through reciprocal corticothalamic networks. The fast oscillations do not reverse throughout the depth of the cortex. This aspect stands in contrast with the conventional depth profile of evoked potentials and slow sleep oscillations that display opposite polarity at the surface and midlayers. Current-source-density analyses reveal that the fast oscillations are associated with alternating microsinks and microsources across the cortex, while the evoked potentials and the slow oscillation display a massive current sink in midlayers, confined by two sources in superficial and deep layers. The synchronization of fast rhythms and their high amplitudes indicate that the term "EEG desynchronization," used to designate brain-aroused states, is incorrect and should be replaced with the original term, "EEG activation" [Moruzzi, G. & Magoun, H.W. (1949) Electroencephalogr. Clin. Neurophysiol. 1, 455-473].

Temporal fluctuations in coherence of brain waves.

As a measure of dynamical structure, short-term fluctuations of coherence between 0.3 and 100 Hz in the electroencephalogram (EEG) of humans were studied from recordings made by chronic subdural macroelectrodes 5-10 mm apart, on temporal, frontal, and parietal lobes, and from intracranial probes deep in the temporal lobe, including the hippocampus, during sleep, alert, and seizure states. The time series of coherence between adjacent sites calculated every second or less often varies widely in stability over time; sometimes it is stable for half a minute or more. Within 2-min samples, coherence commonly fluctuates by a factor up to 2-3, in all bands, within the time scale of seconds to tens of seconds. The power spectrum of the time series of these fluctuations is broad, extending to 0.02 Hz or slower, and is weighted toward the slower frequencies; little power is faster than 0.5 Hz. Some records show conspicuous swings with a preferred duration of 5-15s, either irregularly or quasirhythmically with a broad peak around 0.1 Hz. Periodicity is not statistically significant in most records. In our sampling, we have not found a consistent difference between lobes of the brain, subdural and depth electrodes, or sleeping and waking states. Seizures generally raise the mean coherence in all frequencies and may reduce the fluctuations by a ceiling effect. The coherence time series of different bands is positively correlated (0.45 overall); significant nonindependence extends for at least two octaves. Coherence fluctuations are quite local; the time series of adjacent electrodes is correlated with that of the nearest neighbor pairs (10 mm) to a coefficient averaging approximately 0.4, falling to approximately 0.2 for neighbors-but-one (20 mm) and to < 0.1 for neighbors-but-two (30 mm). The evidence indicates fine structure in time and space, a dynamic and local determination of this measure of cooperativity. Widely separated frequencies tending to fluctuate together exclude independent oscillators as the general or usual basis of the EEG, although a few rhythms are well known under special conditions. Broad-band events may be the more usual generators. Loci only a few millimeters apart can fluctuate widely in seconds, either in parallel or independently. Scalp EEG coherence cannot be predicted from subdural or deep recordings, or vice versa, and intracortical microelectrodes show still greater coherence fluctuation in space and time. Widely used computations of chaos and dimensionality made upon data from scalp or even subdural or depth electrodes, even when reproducible in successive samples, cannot be considered representative of the brain or the given structure or brain state but only of the scale or view (receptive field) of the electrodes used. Relevant to the evolution of more complex brains, which is an outstanding fact of animal evolution, we believe that measures of cooperativity are likely to be among the dynamic features by which major evolutionary grades of brains differ.

Rastreamento do olhar na avaliação de presença em atividades mediadas por vídeo.

A percepção de presença (PP), evolução do conceito de telepresença, pode ser definida como ilusão perceptiva de não mediação e/ou a percepção ilusória da realidade. O método mais utilizado para a avaliação da PP faz uso de questionários aplicados aos sujeitos, após sua participação numa experiência. Além de não fornecer informações em tempo real esse método sofre muitas interferências advindas tanto dos sujeitos submetidos ao experimento como dos avaliadores dos questionários. Os métodos que poderiam ser mais efetivos para a avaliação da PP, em tempo real, fazem uso de sinais fisiológicos que variam independentemente da vontade dos sujeitos, como batimento cardíaco, eletrocardiograma, eletroencefalograma, resistividade e umidade da pele. Os sinais fisiológicos, no entanto, só variam de forma significativa em situações de estresse, inviabilizando sua utilização em atividades normais, sem estresse. Outra forma de avaliar a PP é utilizar sistemas de rastreamento do olhar. Estudados e desenvolvidos desde o século 19, os sistemas de rastreamento do olhar fornecem um mapeamento do movimento dos olhos. Além de indicar para onde os sujeitos estão olhando, podem também monitorar a dilatação da pupila e as piscadas. Atualmente existem sistemas de rastreamento do olhar comerciais de baixo custo, que apesar de terem menos precisão e frequência que os equipamentos de alto custo são mais práticos e possuem software de plataforma aberta. No futuro serão tão comuns e simples de usar como são hoje as câmeras em dispositivos móveis e computadores, o que viabilizará a aplicação das técnicas e métodos aqui propostos em larga escala, principalmente para monitorar a atenção e envolvimento de atividades mediadas por vídeo. É apresentada uma ferramenta que faz uso do rastreamento do olhar para avaliar a percepção de presença em atividades mediadas por vídeo (com estímulos sonoros). Dois experimentos foram realizados para validar as hipóteses da pesquisa e a ferramenta. Um terceiro experimento foi executado para verificar a capacidade da ferramenta em avaliar a percepção de presença em atividades não estressantes mediadas por vídeo.

Effects of hours of service regularity of schedules, and cargo loading on truck and bus driver fatigue. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Road tests of alertness variables. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

The High Frequency Component of Heart Rate Variability and Delta EEG Power during Sleep in Healthy Women and Women with Irritable Bowel Syndrome

Thesis (Ph.D.)--University of Washington, 2016-06

Comparison of the distributions of classical and adaptively aligned EEG power spectra

The distributions of eyes-closed resting electroencephalography (EEG) power spectra and their residuals were described and compared using classically averaged and adaptively aligned averaged spectra. Four minutes of eyes-closed resting EEG was available from 69 participants. Spectra were calculated with 0.5-Hz resolution and were analyzed at this level. It was shown that power in the individual 0.5 Hz frequency bins can be considered normally distributed when as few as three or four 2-second epochs of EEG are used in the average. A similar result holds for the residuals. Power at the peak Alpha frequency has quite different statistical behaviour to power at other frequencies and it is considered that power at peak Alpha represents a relatively individuated process that is best measured through aligned averaging. Previous analyses of contrasts in upper and lower alpha bands may be explained in terms of the variability or distribution of the peak Alpha frequency itself.

Left-right information flow in the brain during EEG arousals

In disorders such as sleep apnea, sleep is fragmented with frequent EEG-arousal (EEGA) as determined via changes in the sleep-electroencephalogram. EEGA is a poorly understood, complicated phenomenon which is critically important in studying the mysteries of sleep. In this paper we study the information flow between the left and right hemispheres of the brain during the EEGA as manifested through inter-hemispheric asynchrony (IHA) of the surface EEG. EEG data (using electrodes A1/C4 and A2/C3 of international 10-20 system) was collected from 5 subjects undergoing routine polysomnography (PSG). Spectral correlation coefficient (R) was computed between EEG data from two hemispheres for delta-delta(0.5-4 Hz), theta-thetas(4.1-8 Hz), alpha-alpha(8.1-12 Hz) & beta-beta(12.1-25 Hz) frequency bands, during EEGA events. EEGA were graded in 3 levels as (i) micro arousals (3-6 s), (ii) short arousals (6.1-10 s), & (iii) long arousals (10.1-15 s). Our results revealed that in beta band, IHA increases above the baseline after the onset of EEGA and returns to the baseline after the conclusion of event. Results indicated that the duration of EEGA events has a direct influence on the onset of IHA. The latency (L) between the onset of arousals and IHA were found to be L=2plusmn0.5 s (for micro arousals), 4plusmn2.2 s (short arousals) and 6.5plusmn3.6 s (long arousals)

Monitoring Infants By Automatic Video Processing

This work has, as its objective, the development of non-invasive and low-cost systems for monitoring and automatic diagnosing specific neonatal diseases by means of the analysis of suitable video signals. We focus on monitoring infants potentially at risk of diseases characterized by the presence or absence of rhythmic movements of one or more body parts. Seizures and respiratory diseases are specifically considered, but the approach is general. Seizures are defined as sudden neurological and behavioural alterations. They are age-dependent phenomena and the most common sign of central nervous system dysfunction. Neonatal seizures have onset within the 28th day of life in newborns at term and within the 44th week of conceptional age in preterm infants. Their main causes are hypoxic-ischaemic encephalopathy, intracranial haemorrhage, and sepsis. Studies indicate an incidence rate of neonatal seizures of 0.2% live births, 1.1% for preterm neonates, and 1.3% for infants weighing less than 2500 g at birth. Neonatal seizures can be classified into four main categories: clonic, tonic, myoclonic, and subtle. Seizures in newborns have to be promptly and accurately recognized in order to establish timely treatments that could avoid an increase of the underlying brain damage. Respiratory diseases related to the occurrence of apnoea episodes may be caused by cerebrovascular events. Among the wide range of causes of apnoea, besides seizures, a relevant one is Congenital Central Hypoventilation Syndrome (CCHS) \cite{Healy}. With a reported prevalence of 1 in 200,000 live births, CCHS, formerly known as Ondine's curse, is a rare life-threatening disorder characterized by a failure of the automatic control of breathing, caused by mutations in a gene classified as PHOX2B. CCHS manifests itself, in the neonatal period, with episodes of cyanosis or apnoea, especially during quiet sleep. The reported mortality rates range from 8% to 38% of newborn with genetically confirmed CCHS. Nowadays, CCHS is considered a disorder of autonomic regulation, with related risk of sudden infant death syndrome (SIDS). Currently, the standard method of diagnosis, for both diseases, is based on polysomnography, a set of sensors such as ElectroEncephaloGram (EEG) sensors, ElectroMyoGraphy (EMG) sensors, ElectroCardioGraphy (ECG) sensors, elastic belt sensors, pulse-oximeter and nasal flow-meters. This monitoring system is very expensive, time-consuming, moderately invasive and requires particularly skilled medical personnel, not always available in a Neonatal Intensive Care Unit (NICU). Therefore, automatic, real-time and non-invasive monitoring equipments able to reliably recognize these diseases would be of significant value in the NICU. A very appealing monitoring tool to automatically detect neonatal seizures or breathing disorders may be based on acquiring, through a network of sensors, e.g., a set of video cameras, the movements of the newborn's body (e.g., limbs, chest) and properly processing the relevant signals. An automatic multi-sensor system could be used to permanently monitor every patient in the NICU or specific patients at home. Furthermore, a wire-free technique may be more user-friendly and highly desirable when used with infants, in particular with newborns. This work has focused on a reliable method to estimate the periodicity in pathological movements based on the use of the Maximum Likelihood (ML) criterion. In particular, average differential luminance signals from multiple Red, Green and Blue (RGB) cameras or depth-sensor devices are extracted and the presence or absence of a significant periodicity is analysed in order to detect possible pathological conditions. The efficacy of this monitoring system has been measured on the basis of video recordings provided by the Department of Neurosciences of the University of Parma. Concerning clonic seizures, a kinematic analysis was performed to establish a relationship between neonatal seizures and human inborn pattern of quadrupedal locomotion. Moreover, we have decided to realize simulators able to replicate the symptomatic movements characteristic of the diseases under consideration. The reasons is, essentially, the opportunity to have, at any time, a 'subject' on which to test the continuously evolving detection algorithms. Finally, we have developed a smartphone App, called 'Smartphone based contactless epilepsy detector' (SmartCED), able to detect neonatal clonic seizures and warn the user about the occurrence in real-time.

Watermarking biomedical time series data

This thesis addresses the problem of information hiding in low dimensional digital data focussing on issues of privacy and security in Electronic Patient Health Records (EPHRs). The thesis proposes a new security protocol based on data hiding techniques for EPHRs. This thesis contends that embedding of sensitive patient information inside the EPHR is the most appropriate solution currently available to resolve the issues of security in EPHRs. Watermarking techniques are applied to one-dimensional time series data such as the electroencephalogram (EEG) to show that they add a level of confidence (in terms of privacy and security) in an individual’s diverse bio-profile (the digital fingerprint of an individual’s medical history), ensure belief that the data being analysed does indeed belong to the correct person, and also that it is not being accessed by unauthorised personnel. Embedding information inside single channel biomedical time series data is more difficult than the standard application for images due to the reduced redundancy. A data hiding approach which has an in built capability to protect against illegal data snooping is developed. The capability of this secure method is enhanced by embedding not just a single message but multiple messages into an example one-dimensional EEG signal. Embedding multiple messages of similar characteristics, for example identities of clinicians accessing the medical record helps in creating a log of access while embedding multiple messages of dissimilar characteristics into an EPHR enhances confidence in the use of the EPHR. The novel method of embedding multiple messages of both similar and dissimilar characteristics into a single channel EEG demonstrated in this thesis shows how this embedding of data boosts the implementation and use of the EPHR securely.

Towards a unified understanding of event-related changes in the EEG:the Firefly model of synchronization through cross-frequency phase modulation

Although event-related potentials (ERPs) are widely used to study sensory, perceptual and cognitive processes, it remains unknown whether they are phase-locked signals superimposed upon the ongoing electroencephalogram (EEG) or result from phase-alignment of the EEG. Previous attempts to discriminate between these hypotheses have been unsuccessful but here a new test is presented based on the prediction that ERPs generated by phase-alignment will be associated with event-related changes in frequency whereas evoked-ERPs will not. Using empirical mode decomposition (EMD), which allows measurement of narrow-band changes in the EEG without predefining frequency bands, evidence was found for transient frequency slowing in recognition memory ERPs but not in simulated data derived from the evoked model. Furthermore, the timing of phase-alignment was frequency dependent with the earliest alignment occurring at high frequencies. Based on these findings, the Firefly model was developed, which proposes that both evoked and induced power changes derive from frequency-dependent phase-alignment of the ongoing EEG. Simulated data derived from the Firefly model provided a close match with empirical data and the model was able to account for i) the shape and timing of ERPs at different scalp sites, ii) the event-related desynchronization in alpha and synchronization in theta, and iii) changes in the power density spectrum from the pre-stimulus baseline to the post-stimulus period. The Firefly Model, therefore, provides not only a unifying account of event-related changes in the EEG but also a possible mechanism for cross-frequency information processing.