Abnormal patterns of sleep and EEG power distribution during non-rapid eye movement sleep in the sheep model of Huntington's disease.

Sleep disruption is a common invisible symptom of neurological dysfunction in Huntington's disease (HD) that takes an insidious toll on well-being of patients. Here we used electroencephalography (EEG) to examine sleep in 6 year old OVT73 transgenic sheep (Ovis aries) that we used as a presymptomatic model of HD. We hypothesized that despite the lack of overt symptoms of HD at this age, early alterations of the sleep-wake pattern and EEG powers may already be present. We recorded EEG from female transgenic and normal sheep (5/group) during two undisturbed 'baseline' nights with different lighting conditions. We then recorded continuously through a night of sleep disruption and the following 24 h (recovery day and night). On baseline nights, regardless of whether the lights were on or off, transgenic sheep spent more time awake than normal sheep particularly at the beginning of the night. Furthermore, there were significant differences between transgenic and normal sheep in both EEG power and its pattern of distribution during non-rapid eye movement (NREM) sleep. In particular, there was a significant decrease in delta (0.5-4 Hz) power across the night in transgenic compared to normal sheep, and the distributions of delta, theta and alpha oscillations that typically dominate the EEG in the first half of the night of normal sheep were skewed so they were predominant in the second, rather than the first half of the night in transgenic sheep. Interestingly, the effect of sleep disruption on normal sheep was also to skew the pattern of distribution of EEG powers so they looked more like that of transgenic sheep under baseline conditions. Thus it is possible that transgenic sheep exist in a state that resemble a chronic state of physiological sleep deprivation. During the sleep recovery period, normal sheep showed a significant 'rebound' increase in delta power with frontal dominance. A similar rebound was not seen in transgenic sheep, suggesting that their homeostatic response to sleep deprivation is abnormal. Although sleep abnormalities in early stage HD patients are subtle, with patients often unaware of their existence, they may contribute to impairment of neurological function that herald the onset of disease. A better understanding of the mechanisms underlying EEG abnormalities in early stage HD would give insight into how, and when, they progress into the sleep disorder. The transgenic sheep model is ideally positioned for studies of the earliest phase of disease when sleep abnormalities first emerge.

Dynamics of a memory trace: effects of sleep on consolidation.

BACKGROUND: There is evidence that sleep is important for memory consolidation, but the underlying neuronal changes are not well understood. We studied the effect of sleep modulation on memory and on neuronal activity in a memory system of the domestic chick brain after the learning process of imprinting. Neurons in this system become, through imprinting, selectively responsive to a training (imprinting) stimulus and so possess the properties of a memory trace. RESULTS: The proportion of neurons responsive to the training stimulus reaches a maximum the day after training. We demonstrate that sleep is necessary for this maximum to be achieved, that sleep stabilizes the initially unstable, selective responses of neurons to the imprinting stimulus, and that for sleep to be effective, it must occur during a particular period of time after training. During this period, there is a time-dependent increase in EEG activity in the 5-6 Hz band, that is, in the lower range of the theta bandwidth. The effects of sleep disturbance on consolidation cannot be attributed to fatigue or to stress. CONCLUSIONS: We establish that long-term trace consolidation requires sleep within a restricted period shortly after learning. Undisturbed sleep is necessary for the stabilization of long-term memory, measured at the behavioral and neuronal levels, and of long-term but not short-term neuronal responsiveness to the training stimulus.

Learning alters theta amplitude, theta-gamma coupling and neuronal synchronization in inferotemporal cortex.

BACKGROUND: How oscillatory brain rhythms alone, or in combination, influence cortical information processing to support learning has yet to be fully established. Local field potential and multi-unit neuronal activity recordings were made from 64-electrode arrays in the inferotemporal cortex of conscious sheep during and after visual discrimination learning of face or object pairs. A neural network model has been developed to simulate and aid functional interpretation of learning-evoked changes. RESULTS: Following learning the amplitude of theta (4-8 Hz), but not gamma (30-70 Hz) oscillations was increased, as was the ratio of theta to gamma. Over 75% of electrodes showed significant coupling between theta phase and gamma amplitude (theta-nested gamma). The strength of this coupling was also increased following learning and this was not simply a consequence of increased theta amplitude. Actual discrimination performance was significantly correlated with theta and theta-gamma coupling changes. Neuronal activity was phase-locked with theta but learning had no effect on firing rates or the magnitude or latencies of visual evoked potentials during stimuli. The neural network model developed showed that a combination of fast and slow inhibitory interneurons could generate theta-nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity in the model similar changes were produced as in inferotemporal cortex after learning. The model showed that these changes could potentiate the firing of downstream neurons by a temporal desynchronization of excitatory neuron output without increasing the firing frequencies of the latter. This desynchronization effect was confirmed in IT neuronal activity following learning and its magnitude was correlated with discrimination performance. CONCLUSIONS: Face discrimination learning produces significant increases in both theta amplitude and the strength of theta-gamma coupling in the inferotemporal cortex which are correlated with behavioral performance. A network model which can reproduce these changes suggests that a key function of such learning-evoked alterations in theta and theta-nested gamma activity may be increased temporal desynchronization in neuronal firing leading to optimal timing of inputs to downstream neural networks potentiating their responses. In this way learning can produce potentiation in neural networks simply through altering the temporal pattern of their inputs.

Abnormally abrupt transitions from sleep-to-wake in Huntington's disease sheep (Ovis aries) are revealed by automated analysis of sleep/wake transition dynamics.

Sleep disturbance is a common and disruptive symptom of neurodegenerative diseases such as Alzheimer's and Huntington's disease (HD). In HD patients, sleep fragmentation appears at an early stage of disease, although features of the earliest sleep abnormalities in presymptomatic HD are not fully established. Here we used novel automated analysis of quantitative electroencephalography to study transitions between wake and non-rapid eye movement sleep in a sheep model of presymptomatic HD. We found that while the number of transitions between sleep and wake were similar in normal and HD sheep, the dynamics of transitions from sleep-to-wake differed markedly between genotypes. Rather than the gradual changes in EEG power that occurs during transitioning from sleep-to-wake in normal sheep, transition into wake was abrupt in HD sheep. Furthermore, transitions to wake in normal sheep were preceded by a significant reduction in slow wave power, whereas in HD sheep this prior reduction in slow wave power was far less pronounced. This suggests an impaired ability to prepare for waking in HD sheep. The abruptness of awakenings may also have potential to disrupt sleep-dependent processes if they are interrupted in an untimely and disjointed manner. We propose that not only could these abnormal dynamics of sleep transitions be useful as an early biomarker of HD, but also that our novel methodology would be useful for studying transition dynamics in other sleep disorders.

Characterizing Sleep Spindles in Sheep.

Sleep spindles are distinctive transient patterns of brain activity that typically occur during non-rapid eye movement (NREM) sleep in humans and other mammals. Thought to be important for the consolidation of learning, they may also be useful for indicating the progression of aging and neurodegenerative diseases. The aim of this study was to characterize sleep spindles in sheep (Ovis aries). We recorded electroencephalographs wirelessly from six sheep over a continuous period containing 2 nights and a day. We detected and characterized spindles using an automated algorithm. We found that sheep sleep spindles fell within the classical range seen in humans (10-16 Hz), but we did not see a further separation into fast and slow bands. Spindles were detected predominantly during NREM sleep. Spindle characteristics (frequency, duration, density, topography) varied between individuals, but were similar within individuals between nights. Spindles that occurred during NREM sleep in daytime were indistinguishable from those found during NREM sleep at night. Surprisingly, we also detected numerous spindle-like events during unequivocal periods of wake during the day. These events were mainly local (detected at single sites), and their characteristics differed from spindles detected during sleep. These "wake spindles" are likely to be events that are commonly categorized as "spontaneous alpha activity" during wake. We speculate that wake and sleep spindles are generated via different mechanisms, and that wake spindles play a role in cognitive processes that occur during the daytime.

The correlation determinant in tests for synchronization in neuronal spike data.

We present a statistical approach to the identification of correlated activity in multineuron spike data, based on the value of the correlation determinant. This approach is not compromised by the lack of independence often encountered in this kind of data. We illustrate our method by applying it both to simulated data and to data recorded from neurons in a forebrain region (intermediate medial mesopallium, IMM) of the behaving domestic chick and simultaneously from the corresponding contralateral region. There is no direct anatomical connection between the two sites, and the validity of this technique is strongly supported by the observation that when the test indicates significantly correlated activity for neurons within either hemisphere, this correlation is greatly reduced, and ultimately obliterated, by serial incorporation of activity from neurons in the opposite hemisphere. Since the value of individual correlation coefficients allied to the Bonferroni correction is often used as a diagnostic tool, we also present comparisons of that approach with our correlation determinant approach.

Automated detection and characterisation of rumination in sheep using in vivo electrophysiology.

Rumination is a precisely timed process that occupies a large part of a sheep's day. The complex motor coordination required to chew and swallow means that quantification of rumination may provide a surrogate marker for effective motor function. Here, data from 24h in vivo electrophysiological recordings, collected as part of an earlier study, were reanalysed for chewing- and swallowing-related activity. The electroencephalographic (EEG) and electromyographic (EMG) data were collected from sheep with surgically-implanted electrodes. An algorithm was designed to detect coordinated, rhythmic muscle activity. This could distinguish episodes of eating from those of rumination. Normal sheep spent ~29% of their time ruminating. Rumination comprised ~40s bouts of regular (~1.7s(-1)) chewing interspersed by ~6.5s intervals during which time no chewing took place. Eating was significantly less regular than rumination, with quicker chewing (~2.7s(-1)). Biomarkers for measuring progression of disease would be invaluable for studying neurodegenerative disease such as Huntington's disease (HD). To test the feasibility of using rumination as such a biomarker, we also made recordings from two neurologically impaired sheep. These showed deviations from the pattern of rumination and eating seen in normal sheep. This validates not only our use of rumination as a measure of normal motor function, but also as a surrogate biomarker for measuring motor dysfunction in impaired sheep.

Memory-related brain lateralisation in birds and humans.

Visual imprinting in chicks and song learning in songbirds are prominent model systems for the study of the neural mechanisms of memory. In both systems, neural lateralisation has been found to be involved in memory formation. Although many processes in the human brain are lateralised--spatial memory and musical processing involves mostly right hemisphere dominance, whilst language is mostly left hemisphere dominant--it is unclear what the function of lateralisation is. It might enhance brain capacity, make processing more efficient, or prevent occurrence of conflicting signals. In both avian paradigms we find memory-related lateralisation. We will discuss avian lateralisation findings and propose that birds provide a strong model for studying neural mechanisms of memory-related lateralisation.

Complex spike patterns in olfactory bulb neuronal networks.

BACKGROUND: T-pattern analysis is a procedure developed for detecting non-randomly recurring hierarchical and multiordinal real-time sequential patterns (T-patterns). NEW METHOD: We have inquired whether such patterns of action potentials (spikes) can be extracted from extracellular activity sampled simultaneously from many neurons across the mitral cell layer of the olfactory bulb (OB). Spikes were sampled from urethane-anaesthetized rats over a 6h recording session, or a period lasting as long as permitted by the physiological condition of the animal. Breathing was recorded to mark peak inhalation and exhalation. RESULTS: Complex T-patterns of up to ∼20 elements were identified with functional connections often spanning the full extent of the array. A considerable proportion of these sequences incorporated breathing. COMPARISON WITH EXISTING METHODS: In contrast to sequence detection by synfire, the incidence of sequences detected in our real data is very much greater than in the same data when randomized either by shuffling, or an alternative procedure preserving the interval structure of each spike train, and so more conservative. Further, when recordings were terminated before completion of the full recording session, the relative pattern detection in real and randomized data was a strong indicator of physiological condition-in recordings leading up to the preparation becoming physiologically unstable, the number of patterns detected in real data approached that in the randomized data. CONCLUSIONS: We conclude that such sequences are an important physiological property of the neural system studied, and suggest that they may form a basis for encoding sensory information.

Olfactory bulb encoding during learning under anesthesia.

Neural plasticity changes within the olfactory bulb are important for olfactory learning, although how neural encoding changes support new associations with specific odors and whether they can be investigated under anesthesia, remain unclear. Using the social transmission of food preference olfactory learning paradigm in mice in conjunction with in vivo microdialysis sampling we have shown firstly that a learned preference for a scented food odor smelled on the breath of a demonstrator animal occurs under isofluorane anesthesia. Furthermore, subsequent exposure to this cued odor under anesthesia promotes the same pattern of increased release of glutamate and gamma-aminobutyric acid (GABA) in the olfactory bulb as previously found in conscious animals following olfactory learning, and evoked GABA release was positively correlated with the amount of scented food eaten. In a second experiment, multiarray (24 electrodes) electrophysiological recordings were made from olfactory bulb mitral cells under isofluorane anesthesia before, during and after a novel scented food odor was paired with carbon disulfide. Results showed significant increases in overall firing frequency to the cued-odor during and after learning and decreases in response to an uncued odor. Analysis of patterns of changes in individual neurons revealed that a substantial proportion (>50%) of them significantly changed their response profiles during and after learning with most of those previously inhibited becoming excited. A large number of cells exhibiting no response to the odors prior to learning were either excited or inhibited afterwards. With the uncued odor many previously responsive cells became unresponsive or inhibited. Learning associated changes only occurred in the posterior part of the olfactory bulb. Thus olfactory learning under anesthesia promotes extensive, but spatially distinct, changes in mitral cell networks to both cued and uncued odors as well as in evoked glutamate and GABA release.