Somatostatin+/nNOS+ neurons are involved in delta electroencephalogram activity and cortical-dependent recognition memory

Microglial TNFα controls daily changes in synaptic GABAARs and sleep slow waves

Microglia sense the changes in their environment. How microglia actively translate these changes into suitable cues to adapt brain physiology is unknown. We reveal an activity-dependent regulation of cortical inhibitory synapses by microglia, driven by purinergic signaling acting on P2RX7 and mediated by microglia-derived TNFα. We demonstrate that sleep induces microglia-dependent synaptic enrichment of GABAARs in a manner dependent on microglial TNFα and P2RX7. We further show that microglia-specific depletion of TNFα alters slow waves during NREM sleep and blunt memory consolidation in sleep-dependent learning tasks. Together, our results reveal that microglia orchestrate sleep-intrinsic plasticity of synaptic GABAARs, sculpt sleep slow waves, and support memory consolidation.

Somatostatin and the pathophysiology of Alzheimer's disease

Among the central features of Alzheimer's disease (AD) progression are altered levels of the neuropeptide somatostatin (SST), and the colocalisation of SST-positive interneurons (SST-INs) with amyloid-β plaques, leading to cell death. In this theoretical review, I propose a molecular model for the pathogenesis of AD based on SST-IN hypofunction and hyperactivity. Namely, hypofunctional and hyperactive SST-INs struggle to control hyperactivity in medial regions in early stages, leading to axonal Aβ production through excessive presynaptic GABAB inhibition, GABAB1a/APP complex downregulation and internalisation. Concomitantly, excessive SST-14 release accumulates near SST-INs in the form of amyloids, which bind to Aβ to form toxic mixed oligomers. This leads to differential SST-IN death through excitotoxicity, further disinhibition, SST deficits, and increased Aβ release, fibrillation and plaque formation. Aβ plaques, hyperactive networks and SST-IN distributions thereby tightly overlap in the brain. Conversely, chronic stimulation of postsynaptic SST2/4 on gulutamatergic neurons by hyperactive SST-INs promotes intense Mitogen-Activated Protein Kinase (MAPK) p38 activity, leading to somatodendritic p-tau staining and apoptosis/neurodegeneration - in agreement with a near complete overlap between p38 and neurofibrillary tangles. This model is suitable to explain some of the principal risk factors and markers of AD progression, including mitochondrial dysfunction, APOE4 genotype, sex-dependent vulnerability, overactive glial cells, dystrophic neurites, synaptic/spine losses, inter alia. Finally, the model can also shed light on qualitative aspects of AD neuropsychology, especially within the domains of spatial and declarative (episodic, semantic) memory, under an overlying pattern of contextual indiscrimination, ensemble instability, interference and generalisation.

Cortical somatostatin long-range projection neurons and interneurons exhibit divergent developmental trajectories

The mammalian cerebral cortex contains an extraordinary diversity of cell types that emerge by implementing different developmental programs. Delineating when and how cellular diversification occurs is particularly challenging for cortical inhibitory neurons because they represent a small proportion of all cortical cells and have a protracted development. Here, we combine single-cell RNA sequencing and spatial transcriptomics to characterize the emergence of neuronal diversity among somatostatin-expressing (SST+) cells in mice. We found that SST+ inhibitory neurons segregate during embryonic stages into long-range projection (LRP) neurons and two types of interneurons, Martinotti cells and non-Martinotti cells, following distinct developmental trajectories. Two main subtypes of LRP neurons and several subtypes of interneurons are readily distinguishable in the embryo, although interneuron diversity is likely refined during early postnatal life. Our results suggest that the timing for cellular diversification is unique for different subtypes of SST+ neurons and particularly divergent for LRP neurons and interneurons.

Sleep and wake in a model of the thalamocortical system with Martinotti cells

The mechanisms leading to the alternation between active (UP) and silent (DOWN) states during sleep slow waves (SWs) remain poorly understood. Previous models have explained the transition to the DOWN state by a progressive failure of excitation because of the build-up of adaptation currents or synaptic depression. However, these models are at odds with recent studies suggesting a role for presynaptic inhibition by Martinotti cells (MaCs) in generating SWs. Here, we update a classical large-scale model of sleep SWs to include MaCs and propose a different mechanism for the generation of SWs. In the wake mode, the network exhibits irregular and selective activity with low firing rates (FRs). Following an increase in the strength of background inputs and a modulation of synaptic strength and potassium leak potential mimicking the reduced effect of acetylcholine during sleep, the network enters a sleep-like regime in which local increases of network activity trigger bursts of MaC activity, resulting in strong disfacilitation of the local network via presynaptic GABAB1a -type inhibition. This model replicates findings on slow wave activity (SWA) during sleep that challenge previous models, including low and skewed FRs that are comparable between the wake and sleep modes, higher synchrony of transitions to DOWN states than to UP states, the possibility of triggering SWs by optogenetic stimulation of MaCs, and the local dependence of SWA on synaptic strength. Overall, this work points to a role for presynaptic inhibition by MaCs in the generation of DOWN states during sleep.

Neural circuit plasticity for complex non-declarative sensorimotor memory consolidation during sleep

Evidence is accumulating that the brain actively consolidates long-term memory during sleep. Motor skill memory is a form of non-declarative procedural memory and can be coordinated with multi-sensory processing such as visual, tactile, and, auditory. Conversely, perception is affected by body movement signal from motor brain regions. Although both cortical and subcortical brain regions are involved in memory consolidation, cerebral cortex activity can be recorded and manipulated noninvasively or minimally invasively in humans and animals. NREM sleep, which is important for non-declarative memory consolidation, is characterized by slow and spindle waves representing thalamo-cortical population activity. In animals, electrophysiological recording, optical imaging, and manipulation approaches have revealed multi-scale cortical dynamics across learning and sleep. In the sleeping cortex, neural activity is affected by prior learning and neural circuits are continually reorganized. Here I outline how sensorimotor coordination is formed through awake learning and subsequent sleep.

Alterations of sleep oscillations in Alzheimer's disease: A potential role for GABAergic neurons in the cortex, hippocampus, and thalamus

Sleep abnormalities are widely reported in patients with Alzheimer's disease (AD) and are linked to cognitive impairments. Sleep abnormalities could be potential biomarkers to detect AD since they are often observed at the preclinical stage. Moreover, sleep could be a target for early intervention to prevent or slow AD progression. Thus, here we review changes in brain oscillations observed during sleep, their connection to AD pathophysiology and the role of specific brain circuits. Slow oscillations (0.1-1 Hz), sleep spindles (8-15 Hz) and their coupling during non-REM sleep are consistently reduced in studies of patients and in AD mouse models although the timing and magnitude of these alterations depends on the pathophysiological changes and the animal model studied. Changes in delta (1-4 Hz) activity are more variable. Animal studies suggest that hippocampal sharp-wave ripples (100-250 Hz) are also affected. Reductions in REM sleep amount and slower oscillations during REM are seen in patients but less consistently in animal models. Thus, changes in a variety of sleep oscillations could impact sleep-dependent memory consolidation or restorative functions of sleep. Recent mechanistic studies suggest that alterations in the activity of GABAergic neurons in the cortex, hippocampus and thalamic reticular nucleus mediate sleep oscillatory changes in AD and represent a potential target for intervention. Longitudinal studies of the timing of AD-related sleep abnormalities with respect to pathology and dysfunction of specific neural networks are needed to identify translationally relevant biomarkers and guide early intervention strategies to prevent or delay AD progression.

Neuroinflammation, Sleep, and Circadian Rhythms

Molecules involved in innate immunity affect sleep and circadian oscillators and vice versa. Sleep-inducing inflammatory molecules are activated by increased waking activity and pathogens. Pathologies that alter inflammatory molecules, such as traumatic brain injury, cancer, cardiovascular disease, and stroke often are associated with disturbed sleep and electroencephalogram power spectra. Moreover, sleep disorders, such as insomnia and sleep disordered breathing, are associated with increased dysregulation of inflammatory processes. Inflammatory molecules in both the central nervous system and periphery can alter sleep. Inflammation can also modulate cerebral vascular hemodynamics which is associated with alterations in electroencephalogram power spectra. However, further research is needed to determine the interactions of sleep regulatory inflammatory molecules and circadian clocks. The purpose of this review is to: 1) describe the role of the inflammatory cytokines interleukin-1 beta and tumor necrosis factor-alpha and nucleotide-binding domain and leucine-rich repeat protein-3 inflammasomes in sleep regulation, 2) to discuss the relationship between the vagus nerve in translating inflammatory signals between the periphery and central nervous system to alter sleep, and 3) to present information about the relationship between cerebral vascular hemodynamics and the electroencephalogram during sleep.

The Engram's Dark Horse: How Interneurons Regulate State-Dependent Memory Processing and Plasticity

Brain states such as arousal and sleep play critical roles in memory encoding, storage, and recall. Recent studies have highlighted the role of engram neurons-populations of neurons activated during learning-in subsequent memory consolidation and recall. These engram populations are generally assumed to be glutamatergic, and the vast majority of data regarding the function of engram neurons have focused on glutamatergic pyramidal or granule cell populations in either the hippocampus, amygdala, or neocortex. Recent data suggest that sleep and wake states differentially regulate the activity and temporal dynamics of engram neurons. Two potential mechanisms for this regulation are either via direct regulation of glutamatergic engram neuron excitability and firing, or via state-dependent effects on interneuron populations-which in turn modulate the activity of glutamatergic engram neurons. Here, we will discuss recent findings related to the roles of interneurons in state-regulated memory processes and synaptic plasticity, and the potential therapeutic implications of understanding these mechanisms.

Interneuron Types and Their Circuits in the Basolateral Amygdala

The basolateral amygdala (BLA) is a cortical structure based on its cell types, connectivity features, and developmental characteristics. This part of the amygdala is considered to be the main entry site of processed and multisensory information delivered via cortical and thalamic afferents. Although GABAergic inhibitory cells in the BLA comprise only 20% of the entire neuronal population, they provide essential control over proper network operation. Previous studies have uncovered that GABAergic cells in the basolateral amygdala are as diverse as those present in other cortical regions, including the hippocampus and neocortex. To understand the role of inhibitory cells in various amygdala functions, we need to reveal the connectivity and input-output features of the different types of GABAergic cells. Here, I review the recent achievements in uncovering the diversity of GABAergic cells in the basolateral amygdala with a specific focus on the microcircuit organization of these inhibitory cells.

Sleep down state-active ID2/Nkx2.1 interneurons in the neocortex

Pyramidal cells and GABAergic interneurons fire together in balanced cortical networks. In contrast to this general rule, we describe a distinct neuron type in mice and rats whose spiking activity is anti-correlated with all principal cells and interneurons in all brain states but, most prevalently, during the down state of non-REM (NREM) sleep. We identify these down state-active (DSA) neurons as deep-layer neocortical neurogliaform cells that express ID2 and Nkx2.1 and are weakly immunoreactive to neuronal nitric oxide synthase. DSA neurons are weakly excited by deep-layer pyramidal cells and strongly inhibited by several other GABAergic cell types. Spiking of DSA neurons modified the sequential firing order of other neurons at down-up transitions. Optogenetic activation of ID2⁺Nkx2.1⁺ interneurons in the posterior parietal cortex during NREM sleep, but not during waking, interfered with consolidation of cue discrimination memory. Despite their sparsity, DSA neurons perform critical physiological functions.

Screening and identification of potential biomarkers for obstructive sleep apnea via microarray analysis

Obstructive sleep apnea (OSA) is a common chronic disease and increases the risk of cardiovascular disease, metabolic and neuropsychiatric disorders, resulting in a considerable socioeconomic burden. This study aimed to identify potential key genes influence the mechanisms and consequences of OSA.Gene expression profiles related to OSA were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in subcutaneous adipose tissues from OSA compared with normal tissues were screened using R software, followed by gene ontology (GO) and pathway enrichment analyses. Subsequently, a protein-protein interaction (PPI) network for these DEGs was constructed by STRING, and key hub genes were extracted from the network with plugins in Cytoscape. The hub genes were further validated in another GEO dataset and assessed by receiver operating characteristic (ROC) analysis and Pearson correlation analysis.There were 373 DEGs in OSA samples in relative to normal controls, which were mainly associated with olfactory receptor activity and olfactory transduction. Upon analyses of the PPI network, GDNF, SLC2A2, PRL, and SST were identified as key hub genes. Decreased expression of the hub genes was association with OSA occurrence, and exhibited good performance in distinguishing OSA from normal samples based on ROC analysis. Besides, the Pearson method revealed a strong correlation between hub genes, which indicates that they may act in synergy, contributing to OSA and related disorders.This bioinformatics research identified 4 hub genes, including GDNF, SLC2A2, PRL, and SST which may be new potential biomarkers for OSA and related disorders.

A roadmap for development of neuro-oscillations as translational biomarkers for treatment development in neuropsychopharmacology

New treatment development for psychiatric disorders depends critically upon the development of physiological measures that can accurately translate between preclinical animal models and clinical human studies. Such measures can be used both as stratification biomarkers to define pathophysiologically homogeneous patient populations and as target engagement biomarkers to verify similarity of effects across preclinical and clinical intervention. Traditional "time-domain" event-related potentials (ERP) have been used translationally to date but are limited by the significant differences in timing and distribution across rodent, monkey and human studies. By contrast, neuro-oscillatory responses, analyzed within the "time-frequency" domain, are relatively preserved across species permitting more precise translational comparisons. Moreover, neuro-oscillatory responses are increasingly being mapped to local circuit mechanisms and may be useful for investigating effects of both pharmacological and neuromodulatory interventions on excitatory/inhibitory balance. The present paper provides a roadmap for development of neuro-oscillatory responses as translational biomarkers in neuropsychiatric treatment development.

Slow Wave Sleep Is a Promising Intervention Target for Alzheimer's Disease

Alzheimer's disease (AD) is the major cause of dementia, characterized by the presence of amyloid-beta plaques and neurofibrillary tau tangles. Plaques and tangles are associated with sleep-wake cycle disruptions, including the disruptions in non-rapid eye movement (NREM) slow wave sleep (SWS). Alzheimer's patients spend less time in NREM sleep and exhibit decreased slow wave activity (SWA). Consistent with the critical role of SWS in memory consolidation, reduced SWA is associated with impaired memory consolidation in AD patients. The aberrant SWA can be modeled in transgenic mouse models of amyloidosis and tauopathy. Animal models exhibited slow wave impairments early in the disease progression, prior to the deposition of amyloid-beta plaques, however, in the presence of abundant oligomeric amyloid-beta. Optogenetic rescue of SWA successfully halted the amyloid accumulation and restored intraneuronal calcium levels in mice. On the other hand, optogenetic acceleration of slow wave frequency exacerbated amyloid deposition and disrupted neuronal calcium homeostasis. In this review, we summarize the evidence and the mechanisms underlying the existence of a positive feedback loop between amyloid/tau pathology and SWA disruptions that lead to further accumulations of amyloid and tau in AD. Moreover, since SWA disruptions occur prior to the plaque deposition, SWA disruptions may provide an early biomarker for AD. Finally, we propose that therapeutic targeting of SWA in AD might lead to an effective treatment for Alzheimer's patients.