Andrea Plano

Abnormal Cognition, Sleep, EEG and Brain Metabolism in a Novel Knock-In Alzheimer Mouse, PLB1

Late-stage neuropathological hallmarks of Alzheimers disease (AD) are β-amyloid (βA) and hyperphosphorylated tau peptides, aggregated into plaques and tangles, respectively. Corresponding phenotypes have been mimicked in existing transgenic mice, however, the translational value of aggressive over-expression has recently been questioned. As controlled gene expression may offer animal models with better predictive validity, we set out to design a transgenic mouse model that circumvents complications arising from pronuclear injection and massive over-expression, by targeted insertion of human mutated amyloid and tau transgenes, under the forebrain- and neurone-specific CaMKIIα promoter, termed PLB1Double. Crossing with an existing presenilin 1 line resulted in PLB1Triple mice. PLB1Triple mice presented with stable gene expression and age-related pathology of intra-neuronal amyloid and hyperphosphorylated tau in hippocampus and cortex from 6 months onwards. At this early stage, pre-clinical 18FDG PET/CT imaging revealed cortical hypometabolism with increased metabolic activity in basal forebrain and ventral midbrain. Quantitative EEG analyses yielded heightened delta power during wakefulness and REM sleep, and time in wakefulness was already reliably enhanced at 6 months of age. These anomalies were paralleled by impairments in long-term and short-term hippocampal plasticity and preceded cognitive deficits in recognition memory, spatial learning, and sleep fragmentation all emerging at ∼12 months. These data suggest that prodromal AD phenotypes can be successfully modelled in transgenic mice devoid of fibrillary plaque or tangle development. PLB1Triple mice progress from a mild (MCI-like) state to a more comprehensive AD-relevant phenotype, which are accessible using translational tools such as wireless EEG and microPET/CT.

EEG, activity, and sleep architecture in a transgenic AβPPswe/PSEN1A246E Alzheimer's disease mouse.

Since sleep and electroencephalogram (EEG) disturbances are endophenotypes of Alzheimers disease (AD) patients alongside cognitive dysfunction, we here characterized these parameters in transgenic mice carrying transgenes for amyloid-β protein precursor (AβPPswe) and presenilin 1 (PSEN1A246E) at 5 (pre-plaque) and 20 months, relative to PSEN1 and wild-type (WT) mice, using a novel wireless microchip device. While circadian rhythms were not affected, we obtained significantly higher overall activity at 5 months in the AβPP/PSEN1 strain (p < 0.001) compared to both PSEN1 and WT animals. Vigilance staging revealed that AβPP/PSEN1 animals present with an age-independent increase in wakefulness (p < 0.001) and a decrease in non rapid-eye movement (NREM) sleep (p < 0.01). These changes were age- and genotype-dependent only during the light phase, while dark phase activity pattern were equally affected at both ages. In all genotypes, the amount of REM sleep was lower at 20 months indicating a general age-related profile. Spectral power of qEEG changed in AβPP/PSEN1 mice at 5 months during wakefulness and REM sleep; during wakefulness hippocampal delta (0.5-5 Hz) was reduced and theta (5-9 Hz) power enhanced. By contrast, NREM EEG spectra were affected by age and genotype. Interestingly, PSEN1 animals also showed spectral EEG changes, these differed from both WT and AβPP/PSEN1 animals. Our results indicate that AβPP/PSEN1 mice exhibit abnormalities in activity and sleep architecture preceding amyloid plaque deposition as well as age-related changes in cortical EEG power. Though not fully recapitulating the profile of AD patients, this suggests activity and EEG recordings as sensitive and translational biomarkers in murine models.

Inference of Granger causal time-dependent influences in noisy multivariate time series.

Inferring Granger-causal interactions between processes promises deeper insights into mechanisms underlying network phenomena, e.g. in the neurosciences where the level of connectivity in neural networks is of particular interest. Renormalized partial directed coherence has been introduced as a means to investigate Granger causality in such multivariate systems. A major challenge in estimating respective coherences is a reliable parameter estimation of vector autoregressive processes. We discuss two shortcomings typical in relevant applications, i.e. non-stationarity of the processes generating the time series and contamination with observational noise. To overcome both, we present a new approach by combining renormalized partial directed coherence with state space modeling. A numerical efficient way to perform both the estimation as well as the statistical inference will be presented.

Long-term home cage activity scans reveal lowered exploratory behaviour in symptomatic female Rett mice

Highlights • Mecp2Stop mutant female mice develop Rett-like symptoms late in life (>6 months).• Symptoms include anomalies in motor, activity and anxiety profiles and were assessed in various behavioural tasks.• Deficits occurred in ambulatory activity during novelty exploration and habituation to a novel environment.• Circadian rhythms and anxiety were not affected, but food intake was higher and global activity lower in mutant mice.

Progressive age-related changes in sleep and EEG profiles in the PLB1Triple mouse model of Alzheimer’s disease

Sleep disturbances are common in Alzheimers disease (AD) and now assumed to contribute to disease onset and progression. Here, we investigated whether activity, sleep/wake pattern, and electroencephalogram (EEG) profiles are altered in the knock-in PLB1Triple mouse model from 5 to 21 months of age. PLB1Triple mice displayed a progressive increase in wakefulness and non-rapid eye movement sleep fragmentation from 9 months onward, whereas PLB1WT wild type controls showed such deterioration only at 21 months. Impaired habituation to spatial novelty was also detected in PLB1Triple mice. Hippocampal power spectra of transgenic mice revealed progressive, vigilance stage-, brain region-, and age-specific changes. Age had an impact on EEG spectra in both cohorts but led to accelerated genotype-dependent differences, ultimately affecting all bands at 21 months. Overall, although PLB1Triple animals display only subtle amyloid and tau pathologies, robust sleep-wake and EEG abnormalities emerged. We hypothesize that such endophenotypes are sensitive, noninvasive, and reliable biomarker to identify onset and progression of AD.

Modulation of food consumption and sleep-wake cycle in mice by the neutral CB1 antagonist ABD459

The brain endocannabinoid system is a potential target for the treatment of psychiatric and metabolic conditions. Here, a novel CB1 receptor antagonist (ABD459) was synthesized and assayed for pharmacological efficacy in vitro and for modulation of food consumption, vigilance staging and cortical electroencephalography in the mouse. ABD459 completely displaced the CB1 agonist CP99540 at a Ki of 8.6 nmol/l, and did not affect basal, but antagonized CP55940-induced GTP&ggr;S binding with a KB of 7.7 nmol/l. Acute ABD459 (3–20 mg/kg) reliably inhibited food consumption in nonfasted mice, without affecting motor activity. Active food seeking was reduced for 5–6 h postdrug, with no rebound after washout. Epidural recording of electroencephalogram confirmed that ABD459 (3 mg/kg) robustly reduced rapid eye movement (REM) sleep, with no alterations of wakefulness or non-REM sleep. Effects were strongest during 3 h postdrug, followed by a progressive washout period. The CB1 antagonist AM251 (3 mg/kg) and agonist WIN-55,212-2 (WIN-2: 3 mg/kg) also reduced REM, but variously affected other vigilance stages. WIN-2 caused a global suppression of normalized spectral power. AM251 and ABD459 lowered delta power and increased power in the theta band in the hippocampus, but not the prefrontal cortex. The neutral antagonist ABD459 thus showed a specific role of endocannabinoid release in attention and arousal, possibly through modulation of cholinergic activity.

A Pilot Study into the Effects of the CB1 Cannabinoid Receptor Agonist WIN55,212-2 or the Antagonist/Inverse Agonist AM251 on Sleep in Rats

The plant cannabinoid Δ9-tetrahydrocannabinol and the endocannabinoid anandamide increase the amount of sleep via a CB1 receptor mediated mechanism. Here, we explored the use of a novel electroencephalogram (EEG) recording device based on wireless EEG microchip technology (Neurologger) in freely-moving rats, and its utility in experiments of cannabinoids-induced alterations of EEG/vigilance stages. EEG was recorded through epidural electrodes placed above pre-frontal and parietal cortex (overlaying the dorsal hippocampus). As cannabinoids, we acutely administered the full synthetic CB1 receptor agonist, WIN55,212-2 (1 mg/kg), and the antagonist/inverse agonist, AM251 (2 mg/kg), either alone or together through the intraperitoneal route. WIN55,212-2 increased the total amount of NREM sleep and the length of each NREM bout, but this was unlikely due to CB1 receptor activation since it was not prevented by AM251. However, WIN55,212-2 also lowered overall EEG spectral power especially in theta and alpha frequency bands during wakefulness and NREM sleep, and this effect was reversed by AM251. The antagonist/inverse agonist caused no sleep alterations by itself and moderately increased spectral power in Theta, alpha and beta frequency bands during NREM sleep when administered on its own. Implications of endocannabinoid modulation of the sleep-wake cycle and its possible interactions with other transmitter systems are considered.

On the identification of sleep stages in mouse electroencephalography time-series

The automatic identification of sleep stages in electroencephalography (EEG) time-series is a long desired goal for researchers concerned with the study of sleep disorders. This paper presents advances towards achieving this goal, with particular application to EEG time-series recorded from mice. Approaches in the literature apply supervised learning classifiers, however, these do not reach the performance levels required for use within a laboratory. In this paper, detection reliability is increased, most notably in the case of REM stage identification, by naturally decomposing the problem and applying a support vector machine (SVM) based classifier to each of the EEG channels. Their outputs are integrated within a multiple classifier system. Furthermore, there exists no general consensus on the ideal choice of parameter values in such systems. Therefore, an investigation into the effects upon the classification performance is presented by varying parameters such as the epoch length; features size; number of training samples; and the method for calculating the power spectral density estimate. Finally, the results of these investigations are brought together to demonstrate the performance of the proposed classification algorithm in two cases: intra-animal classification and inter-animal classification. It is shown that, within a dataset of 10 EEG recordings, and using less than 1% of an EEG as training data, a mean classification errors of Awake 6.45%, NREM 5.82%, and REM 6.65% (with standard deviations less than 0.6%) are achieved in intra-animal analysis and, when using the equivalent of 7% of one EEG as training data, Awake 10.19%, NREM 7.75%, and REM 17.43% are achieved in inter-animal analysis (with mean standard deviations of 6.42%, 2.89%, and 9.69% respectively). A software package implementing the proposed approach will be made available through Cybula Ltd.

Multivariate analysis of dynamical processes with applications to the neurosciences

Nowadays, data are recorded with increasing spatial and temporal resolution. Commonly these data are analyzed using univariate or bivariate approaches. Most of the analysis techniques assume stationarity of the underlying dynamical processes. Here, we present an approach that is capable of analyzing multivariate data, the so-called renormalized partial directed coherence. It utilizes the concept of Granger causality and is applicable to non-stationary data. We discuss its abilities and limitations, and demonstrate its usefulness in an application to murine electroencephalography (EEG) data during sleep transitions.