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Identification of mechanisms which increase deep sleep could lead to novel treatments which promote the restorative effects of sleep. Here, we show that knockdown of the α3 GABAA-receptor subunit from parvalbumin neurons in the thalamic reticular nucleus using CRISPR-Cas9 gene editing increased the thalamocortical delta (1.5–4 Hz) oscillations which are implicated in many health-promoting effects of sleep. Inhibitory synaptic currents in thalamic reticular parvalbumin neurons were strongly reduced in vitro. Further analysis revealed that delta power in long NREM bouts prior to NREM-REM transitions was preferentially affected by deletion of α3 subunits. Our results identify a role for GABAA receptors on thalamic reticular nucleus neurons and suggest antagonism of α3 subunits as a strategy to enhance delta activity during sleep. Uygun et al. show that deletion of GABAA receptors from the thalamic reticular nucleus using CRISPR gene editing in mice boosts the delta waves, indicating a role for GABAA receptors on thalamic reticular nucleus neurons in NREM sleep delta oscillations. [ABSTRACT FROM AUTHOR]

The brain is limited in its capacity to process all sensory stimuli present in the physical world at any point in time and relies instead on the cognitive process of attention to focus neural resources according to the contingencies of the moment. Attention can be categorized into two distinct functions: bottom-up attention, referring to attentional guidance purely by externally driven factors to stimuli that are salient because of their inherent properties relative to the background; and top-down attention, referring to internal guidance of attention based on prior knowledge, willful plans, and current goals. Over the past few years, insights on the neural circuits and mechanisms of bottom-up and top-down attention have been gained through neurophysiological experiments. Attention affects the mean neuronal firing rate as well as its variability and correlation across neurons. Although distinct processes mediate the guidance of attention based on bottom-up and top-down factors, a common neural apparatus, the frontoparietal network, is essential in both types of attentional processes. [ABSTRACT FROM PUBLISHER]

The prefrontal cortex continues to mature after puberty and into early adulthood, mirroring the time course of maturation of cognitive abilities. However, the way in which prefrontal activity changes during peri- and postpubertal cortical maturation is largely unknown. To address this question, we evaluated the developmental stage of peripubertal rhesus monkeys with a series of morphometric, hormonal, and radiographic measures, and conducted behavioral and neurophysiological tests as the monkeys performed workingmemory tasks. We compared firing rate and the strength of intrinsic functional connectivity between neurons in peripubertal vs. adult monkeys. Notably, analyses of spike train cross-correlations demonstrated that the average magnitude of functional connections measured between neurons was lower overall in the prefrontal cortex of peripubertal monkeys compared with adults. The difference resulted because negative functional connections (indicative of inhibitory interactions) were stronger and more prevalent in peripubertal compared with adult monkeys, whereas the positive connections showed similar distributions in the two groups. Our results identify changes in the intrinsic connectivity of prefrontal neurons, particularly that mediated by inhibition, as a possible substrate for peri- and postpubertal advances in cognitive capacity. [ABSTRACT FROM AUTHOR]

Sleep-wake scoring is a time-consuming, tedious but essential component of clinical and preclinical sleep research. Sleep scoring is even more laborious and challenging in rodents due to the smaller EEG amplitude differences between states and the rapid state transitions which necessitate scoring in shorter epochs. Although many automated rodent sleep scoring methods exist, they do not perform as well when scoring new datasets, especially those which involve changes in the EEG/EMG profile. Thus, manual scoring by expert scorers remains the gold standard. Here we take a different approach to this problem by using a neural network to accelerate the scoring of expert scorers. Sleep-Deep-Learner creates a bespoke deep convolution neural network model for individual electroencephalographic or local-field-potential (LFP) records via transfer learning of GoogLeNet, by learning from a small subset of manual scores of each EEG/LFP record as provided by the end-user. Sleep-Deep-Learner then automates scoring of the remainder of the EEG/LFP record. A novel REM sleep scoring correction procedure further enhanced accuracy. Sleep-Deep-Learner reliably scores EEG and LFP data and retains sleep-wake architecture in wild-type mice, in sleep induced by the hypnotic zolpidem, in a mouse model of Alzheimer's disease and in a genetic knock-down study, when compared to manual scoring. Sleep-Deep-Learner reduced manual scoring time to 1/12. Since Sleep-Deep-Learner uses transfer learning on each independent recording, it is not biased by previously scored existing datasets. Thus, we find Sleep-Deep-Learner performs well when used on signals altered by a drug, disease model, or genetic modification., (Published by Oxford University Press on behalf of Sleep Research Society 2024.)

Neurons in a distributed network of cortical and subcortical areas continue to discharge after the presentation and disappearance of stimuli, providing a neural correlate for working memory. While it is thought that the prefrontal cortex plays a central role in this network, the relative contributions of other brain areas are not as well understood. In order to compare the contributions of the dorsolateral prefrontal and posterior parietal cortex, we recorded neurophysiological activity in monkeys trained to perform two different visuo-spatial working memory tasks: a Match/Nonmatch task, and a Spatial Delayed-Match-to-Sample Task. Neurons in both areas exhibited discharges in the delay periods of the tasks that could be classified in two forms. Sustained discharges persisted after the presentation of a stimulus in the receptive field with a constant or declining rate. Anticipatory responses increased in rate during the delay period, often appearing after presentation of a stimulus out of the receptive field. Despite similarities, we uncovered distinct differences between patterns of delay period in each brain area. Only in the prefrontal cortex sustained responses related to the original stimulus survived presentation of a second stimulus, in the context of the Match/Nonmatch task. Our results provide insights on the nature of processing in two areas active during working memory, and on the unique role of the prefrontal cortex in memory maintenance.