Consolidation of temporal order in episodic memories

AbstractEven though it is known that sleep benefits declarative memory consolidation, the role of sleep in the storage of temporal sequences has rarely been examined. Thus we explored the influence of sleep on temporal order in an episodic memory task followed by sleep or sleep deprivation. Thirty-four healthy subjects (17 men) aged between 19 and 28 years participated in the randomized, counterbalanced, between-subject design. Parameters of interests were NREM/REM cycles, spindle activity and spindle-related EEG power spectra. Participants of both groups (sleep group/sleep deprivation group) performed retrieval in the evening, morning and three days after the learning night. Results revealed that performance in temporal order memory significantly deteriorated over three days only in sleep deprived participants. Furthermore our data showed a positive relationship between the ratios of the (i) first NREM/REM cycle with more REM being associated with delayed temporal order recall. Most interestingly, data additionally indicated that (ii) memory enhancers in the sleep group show more fast spindle related alpha power at frontal electrode sites possibly indicating access to a yet to be consolidated memory trace. We suggest that distinct sleep mechanisms subserve different aspects of episodic memory and are jointly involved in sleep-dependent memory consolidation

An imaging time-of-propagation system for charged particle identification at a super B factory

Super B factories that will further probe the flavor sector of the Standard Model and physics beyond will demand excellent charged particle identification (PID), particularly K/pi separation, for momenta up to 4 GeV/c, as well as the ability to operate under beam backgrounds significantly higher than current B factory experiments. We describe an Imaging Time-of-Propagation (iTOP) detector which shows significant potential to meet these requirements. Photons emitted from charged particle interactions in a Cerenkov radiator bar are internally reflected to the end of the bar, where they are collected on a compact image plane using photodetectors with fine spatial segmentation in two dimensions. Precision measurements of photon arrival time are used to enhance the two dimensional imaging, allowing the system to provide excellent PID capabilities within a reduced detector envelope. Results of the ongoing optimization of the geometric and physical properties of such a detector are presented, as well as simulated PID performance. Validation of simulations is being performed using a prototype in a cosmic ray test stand at the University of Hawaii.Comment: 3 pages, 5 figures, submitted to TIPP09 proceeding

Sigma frequency dependent motor learning in Williams syndrome

Abstract There are two basic stages of fine motor learning: performance gain might occur during practice (online learning), and improvement might take place without any further practice (offline learning). Offline learning, also called consolidation, has a sleep-dependent stage in terms of both speed and accuracy of the learned movement. Sleep spindle or sigma band characteristics affect motor learning in typically developing individuals. Here we ask whether the earlier found, altered sigma activity in a neurodevelopmental disorder (Williams syndrome, WS) predicts motor learning. TD and WS participants practiced in a sequential finger tapping (FT) task for two days. Although WS participants started out at a lower performance level, TD and WS participants had a comparable amount of online and offline learning in terms of the accuracy of movement. Spectral analysis of WS sleep EEG recordings revealed that motor accuracy improvement is intricately related to WS-specific NREM sleep EEG features in the 8–16 Hz range profiles: higher 11–13.5 Hz z-transformed power is associated with higher offline FT accuracy improvement; and higher oscillatory peak frequencies are associated with lower offline accuracy improvements. These findings indicate a fundamental relationship between sleep spindle (or sigma band) activity and motor learning in WS

Optimizing microsurgical skills with EEG neurofeedback

Background By enabling individuals to self-regulate their brainwave activity in the field of optimal performance in healthy individuals, neurofeedback has been found to improve cognitive and artistic performance. Here we assessed whether two distinct EEG neurofeedback protocols could develop surgical skill, given the important role this skill plays in medicine. Results National Health Service trainee ophthalmic microsurgeons (N = 20) were randomly assigned to either Sensory Motor Rhythm-Theta (SMR) or Alpha-Theta (AT) groups, a randomized subset of which were also part of a wait-list 'no-treatment' control group (N = 8). Neurofeedback groups received eight 30-minute sessions of EEG training. Pre-post assessment included a skills lab surgical procedure with timed measures and expert ratings from video-recordings by consultant surgeons, together with state/trait anxiety self-reports. SMR training demonstrated advantages absent in the control group, with improvements in surgical skill according to 1) the expert ratings: overall technique (d = 0.6, p < 0.03) and suture task (d = 0.9, p < 0.02) (judges' intraclass correlation coefficient = 0.85); and 2) with overall time on task (d = 0.5, p = 0.02), while everyday anxiety (trait) decreased (d = 0.5, p < 0.02). Importantly the decrease in surgical task time was strongly associated with SMR EEG training changes (p < 0.01), especially with continued reduction of theta (4–7 Hz) power. AT training produced marginal improvements in technique and overall performance time, which were accompanied by a standard error indicative of large individual differences. Notwithstanding, successful within session elevation of the theta-alpha ratio correlated positively with improvements in overall technique (r = 0.64, p = 0.047). Conclusion SMR-Theta neurofeedback training provided significant improvement in surgical technique whilst considerably reducing time on task by 26%. There was also evidence that AT training marginally reduced total surgery time, despite suboptimal training efficacies. Overall, the data set provides encouraging evidence of optimised learning of a complex medical specialty via neurofeedback training

The sleep EEG spectrum is a sexually dimorphic marker of general intelligence

The shape of the EEG spectrum in sleep relies on genetic and anatomical factors and forms an individual “EEG fingerprint”. Spectral components of EEG were shown to be connected to mental ability both in sleep and wakefulness. EEG sleep spindle correlates of intelligence, however, exhibit a sexual dimorphism, with a more pronounced association to intelligence in females than males. In a sample of 151 healthy individuals, we investigated how intelligence is related to spectral components of full-night sleep EEG, while controlling for the effects of age. A positive linear association between intelligence and REM anterior beta power was found in females but not males. Transient, spindle-like “REM beta tufts” are described in the EEG of healthy subjects, which may reflect the functioning of a recently described cingular-prefrontal emotion and motor regulation network. REM sleep frontal high delta power was a negative correlate of intelligence. NREM alpha and sigma spectral power correlations with intelligence did not unequivocally remain significant after multiple comparisons correction, but exhibited a similar sexual dimorphism. These results suggest that the neural oscillatory correlates of intelligence in sleep are sexually dimorphic, and they are not restricted to either sleep spindles or NREM sleep

The ALICE experiment at the CERN LHC

ALICE (A Large Ion Collider Experiment) is a general-purpose, heavy-ion detector at the CERN LHC which focuses on QCD, the strong-interaction sector of the Standard Model. It is designed to address the physics of strongly interacting matter and the quark-gluon plasma at extreme values of energy density and temperature in nucleus-nucleus collisions. Besides running with Pb ions, the physics programme includes collisions with lighter ions, lower energy running and dedicated proton-nucleus runs. ALICE will also take data with proton beams at the top LHC energy to collect reference data for the heavy-ion programme and to address several QCD topics for which ALICE is complementary to the other LHC detectors. The ALICE detector has been built by a collaboration including currently over 1000 physicists and engineers from 105 Institutes in 30 countries. Its overall dimensions are 161626 m3 with a total weight of approximately 10 000 t. The experiment consists of 18 different detector systems each with its own specific technology choice and design constraints, driven both by the physics requirements and the experimental conditions expected at LHC. The most stringent design constraint is to cope with the extreme particle multiplicity anticipated in central Pb-Pb collisions. The different subsystems were optimized to provide high-momentum resolution as well as excellent Particle Identification (PID) over a broad range in momentum, up to the highest multiplicities predicted for LHC. This will allow for comprehensive studies of hadrons, electrons, muons, and photons produced in the collision of heavy nuclei. Most detector systems are scheduled to be installed and ready for data taking by mid-2008 when the LHC is scheduled to start operation, with the exception of parts of the Photon Spectrometer (PHOS), Transition Radiation Detector (TRD) and Electro Magnetic Calorimeter (EMCal). These detectors will be completed for the high-luminosity ion run expected in 2010. This paper describes in detail the detector components as installed for the first data taking in the summer of 2008

Modeling of uncertainty associated with dose-response curves as applied for probabilistic risk assessment in laser safety

In laser safety, dose-response curves describe the probability for ocular injury as a function of ocular energy, and are often used to quantify the risk for ocular injury given a certain level of exposure to laser radiation. In principal, a dose-response curve describes the biological variation of the individual thresholds in a population. In laser safety, a log-normal cumulative distribution is generally assumed for the dose-response curve, for instance, when Probit analysis is performed. The log-normal distribution is defined by two parameters, the median, called ED50 and the slope. When animal experiments are performed to obtain dose-response curves for laser induced injury, ecperimental uncertainty such as focussing errors as well as variability within the group of experimental animals, such as inter-individual variability of obsorption of the ocular media, can influence the shape of the dose-response curve. We present simulations of uncertainties and cariabilities that show that the log-normal dose-response curve as obtained in a animal experiments can grossly overestimate the probability for ocular damage for small doses. It is argued that the intrinsic slope for an individual's dose response curve is rather steep, even for retinal injury, however, the dose-response curve for a group or population can be broader when there is inter-individual variability of parameters which influence the threshold. the quantitative results of the simulation of the grouping of individual dose-response curves can serve as basis to correct potentially biased dose-response curves as well as to characterize the uncertainty associated with the ED50 and the slope of the dose-response curve. A probabilistic risk analysis model, which accounts for these uncertainties by using Monte-Carlo simulation, was developed for retinal laser injuries from pulsed lasers with wavelengths from 200 nm to 20mu m, and the interpretation of the results are discussed on the basis of example calculations

Event-related activity and phase locking during a psychomotor vigilance task over the course of sleep deprivation

There is profound knowledge that sleep restriction increases tonic (event-unrelated) electroencephalographic (EEG) activity. In the present study we focused on time-locked activity by means of phasic (event-related) EEG analysis during a psychomotor vigilance task (PVT) over the course of sleep deprivation. Twenty healthy subjects (10 male; mean age ± SD: 23.45 ± 1.97 years) underwent sleep deprivation for 24 h. Subjects had to rate their sleepiness hourly (Karolinska Sleepiness Scale) and to perform a PVT while EEG was recorded simultaneously. Tonic EEG changes in the δ (1–4 Hz), θ (4–8 Hz) and α (8–12 Hz) frequency range were investigated by power spectral analyses. Single-trial (phase-locking index, PLI) and event-related potential (ERP) analyses (P1, N1) were used to examine event-related changes in EEG activity. Subjective sleepiness, PVT reaction times and tonic EEG activity (delta and theta spectral power) significantly increased over the night. In contrast, event-related EEG parameters decreased throughout sleep deprivation. Specifically, the ERP component P1 diminished in amplitude, and delta and theta PLI estimates decreased progressively over the night. It is suggested that event-related EEG measures (such as the amplitude of the P1 and especially delta/theta phase-locking) serve as a complimentary method to track the deterioration of attention and performance during sleep loss. As these measures actually reflect the impaired response to specific events rather than tonic changes during sleep deprivation they are a promising tool for future sleep research