Enhancing sleep after training improves memory in Down syndrome model mice

NKCC1 inhibition improves sleep quality and EEG information content in a Down syndrome mouse model

In several brain disorders, the hyperpolarizing/inhibitory effects of GABA signaling through Cl-permeable GABAA receptors are compromised, leading to an imbalance between neuronal excitation and inhibition. For example, the Ts65Dn mouse model of Down syndrome (DS) exhibits increased expression of the Cl- importer NKCC1, leading to depolarizing gamma aminobutyric acid (GABA) signaling in the mature hippocampus and cortex. Inhibiting NKCC1 with the Food and Drug Administration (FDA)-approved diuretic bumetanide rescues inhibitory GABAergic transmission, synaptic plasticity, and cognitive functions in adult Ts65Dn mice. Given that DS individuals and Ts65Dn mice show sleep disturbances, and considering the key role of GABAergic transmission in sleep, we investigated whether NKCC1 upregulation contributes to sleep abnormalities in adult Ts65Dn mice. Chronic oral administration of bumetanide ameliorated the spectral profile of sleep, sleep architecture, and electroencephalogram (EEG) entropy/complexity, accompanied by a lower hyperactivity in trisomic mice. These results offer a potential avenue for addressing common sleep disturbances in DS.

Automated sleep scoring in hibernating and non-hibernating American black bears

Hibernating bears show remarkable metabolic suppression. Their decline in core body temperature (T b ) is moderate (from 38°C to 30-35°C), but their metabolism declines as much as 75%. To understand the role of sleep in this hypometabolic state, we recorded biotelemetrically EEG, EOG and EMG data over 3500 days from 16 captive American black bears in and out of hibernation under semi-natural conditions. This data set is too large to score manually for Wake, REM- and NREM sleep, so we tested two machine learning classifiers: (1) Somnotate trained on multiple one-day recordings, and (2) Somnivore, trained on a small subset from each recording. As automated scoring methods have not been applied to hibernating species before, a major concern is the effect changing brain temperature has on the EEG and on the machine learning based detection. Therefore, we selected reference data using consensus by 3 manual sleep scorers from each of 6 bears, two one-day recordings at the highest and lowest body temperatures during hibernation when T b was oscillating in multiday cycles, and a non-hibernating one-day recording in summer. Somnotate results were excellent when trained separately for hibernating and non-hibernating data. Training Somnotate separately for high and low T b within hibernation did not improve results further. Sleep times in hibernation were about 2x that in summer for both automated scores and manual scores (p<0.0001). There were no significant differences in occupancy of vigilance states between automated and manual scores in hibernation (p>0.05), but a small overestimate of sleep time in summer (p<0.05). Both applications yielded F-measures against manual scores in the 0.90-0.98 range. Outliers in the 0.67-0.88 range were correlated between the two applications, indicating that specific files are more challenging to annotate. We conclude that both applications have accuracies approaching that of manual scorers when trained on high quality data.

Brain circuit pathology in Down syndrome: from neurons to neural networks

Down syndrome (DS), a genetic pathology caused by triplication of chromosome 21, is characterized by brain hypotrophy and impairment of cognition starting from infancy. While studies in mouse models of DS have elucidated the major neuroanatomical and neurochemical defects of DS, comparatively fewer investigations have focused on the electrophysiology of the DS brain. Electrical activity is at the basis of brain functioning. Therefore, knowledge of the way in which brain circuits operate in DS is fundamental to understand the causes of behavioral impairment and devise targeted interventions. This review summarizes the state of the art regarding the electrical properties of the DS brain, starting from individual neurons and culminating in signal processing in whole neuronal networks. The reported evidence derives from mouse models of DS and from brain tissues and neurons derived from individuals with DS. EEG data recorded in individuals with DS are also provided as a key tool to understand the impact of brain circuit alterations on global brain activity.