Sleep deprivation causes memory deficits by negatively impacting neuronal connectivity in hippocampal area CA1

Brief periods of sleep loss have long-lasting consequences such as impaired memory consolidation. Structural changes in synaptic connectivity have been proposed as a substrate of memory storage. Here, we examine the impact of brief periods of sleep deprivation on dendritic structure. In mice, we find that five hours of sleep deprivation decreases dendritic spine numbers selectively in hippocampal area CA1 and increased activity of the filamentous actin severing protein cofilin. Recovery sleep normalizes these structural alterations. Suppression of cofilin function prevents spine loss, deficits in hippocampal synaptic plasticity, and impairments in long-term memory caused by sleep deprivation. The elevated cofilin activity is caused by cAMP-degrading phosphodiesterase-4A5 (PDE4A5), which hampers cAMP-PKA-LIMK signaling. Attenuating PDE4A5 function prevents changes in cAMP-PKA-LIMK-cofilin signaling and cognitive deficits associated with sleep deprivation. Our work demonstrates the necessity of an intact cAMP-PDE4-PKA-LIMK-cofilin activation-signaling pathway for sleep deprivation-induced memory disruption and reduction in hippocampal spine density

Gravin Orchestrates Protein Kinase A and β2-Adrenergic Receptor Signaling Critical for Synaptic Plasticity and Memory

A kinase-anchoring proteins (AKAPs) organize compartmentalized pools of protein kinase A (PKA) to enable localized signaling events within neurons. However, it is unclear which of the many expressed AKAPs in neurons target PKA to signaling complexes important for long-lasting forms of synaptic plasticity and memory storage. In the forebrain, the anchoring protein gravin recruits a signaling complex containing PKA, PKC, calmodulin, and PDE4D (phosphodiesterase 4D) to the β2-adrenergic receptor. Here, we show that mice lacking the α-isoform of gravin have deficits in PKA-dependent long-lasting forms of hippocampal synaptic plasticity including β2-adrenergic receptor-mediated plasticity, and selective impairments of long-term memory storage. Furthermore, both hippocampal β2-adrenergic receptor phosphorylation by PKA, and learning-induced activation of ERK in the CA1 region of the hippocampus are attenuated in mice lacking gravin-α. We conclude that gravin compartmentalizes a significant pool of PKA that regulates learning-induced β2-adrenergic receptor signaling and ERK activation in the hippocampus in vivo, thereby organizing molecular interactions between glutamatergic and noradrenergic signaling pathways for long-lasting synaptic plasticity, and memory storage

Human Gamma Oscillations during Slow Wave Sleep

Neocortical local field potentials have shown that gamma oscillations occur spontaneously during slow-wave sleep (SWS). At the macroscopic EEG level in the human brain, no evidences were reported so far. In this study, by using simultaneous scalp and intracranial EEG recordings in 20 epileptic subjects, we examined gamma oscillations in cerebral cortex during SWS. We report that gamma oscillations in low (30–50 Hz) and high (60–120 Hz) frequency bands recurrently emerged in all investigated regions and their amplitudes coincided with specific phases of the cortical slow wave. In most of the cases, multiple oscillatory bursts in different frequency bands from 30 to 120 Hz were correlated with positive peaks of scalp slow waves (“IN-phase” pattern), confirming previous animal findings. In addition, we report another gamma pattern that appears preferentially during the negative phase of the slow wave (“ANTI-phase” pattern). This new pattern presented dominant peaks in the high gamma range and was preferentially expressed in the temporal cortex. Finally, we found that the spatial coherence between cortical sites exhibiting gamma activities was local and fell off quickly when computed between distant sites. Overall, these results provide the first human evidences that gamma oscillations can be observed in macroscopic EEG recordings during sleep. They support the concept that these high-frequency activities might be associated with phasic increases of neural activity during slow oscillations. Such patterned activity in the sleeping brain could play a role in off-line processing of cortical networks

Digital Technology and Usability and Ergonomics of Medical Devices

Trying to identify what digital medical technologies are today is a practically unresolvable task. Over the last few years, we have seen a radical change in these technologies; they have become not only extremely sophisticated and complex but also capable of maintaining evolving relationships with their users

Task-driven activity reduces the cortical activity space of the brain: experiment and whole-brain modeling

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model’s prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information.APA was supported by SEMAINE ERA-Net NEURON Project. GD was supported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), and the FP7-ICT BrainScales. PH was supported by Leenaards Foundation. BJH was supported by the Intramural Research Program of the National Institutes of Health, National Institute of Neurological Disorders and Stroke. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript