Awakening from anesthesia switches cortical wave modes.

Spontaneous cortical activity exhibits diverse spatiotemporal dynamics across brain states, but the organizational principles during state transitions remain unclear. In a recent study combining cortex-wide voltage imaging and neural modeling, Liang and colleagues revealed that global-local competition and long-range connections underlie the emergence of complex cortical wave patterns during awakening from anesthesia.

Cortical regulation of two-stage rapid eye movement sleep.

Rapid eye movement (REM) sleep is a sleep state characterized by skeletal muscle paralysis and cerebral cortical activation. Yet, global cortical dynamics and their role in regulating REM sleep remain unclear. Here we show that in mice, REM sleep is accompanied by highly patterned cortical activity waves, with the retrosplenial cortex (RSC) as a major initiation site. Two-photon imaging of layer 2/3 pyramidal neurons of the RSC revealed two distinct patterns of population activities during REM sleep. These activities encoded two sequential REM sleep substages, characterized by contrasting facial movement and autonomic activity and by distinguishable electroencephalogram theta oscillations. Closed-loop optogenetic inactivation of RSC during REM sleep altered cortical activity dynamics and shortened REM sleep duration via inhibition of the REM substage transition. These results highlight an important role for the RSC in dictating cortical dynamics and regulating REM sleep progression.

Cardiovascular baroreflex circuit moonlights in sleep control.

Sleep disturbances are strongly associated with cardiovascular diseases. Baroreflex, a basic cardiovascular regulation mechanism, is modulated by sleep-wake states. Here, we show that neurons at key stages of baroreflex pathways also promote sleep. Using activity-dependent genetic labeling, we tagged neurons in the nucleus of the solitary tract (NST) activated by blood pressure elevation and confirmed their barosensitivity with optrode recording and calcium imaging. Chemogenetic or optogenetic activation of these neurons promoted non-REM sleep in addition to decreasing blood pressure and heart rate. GABAergic neurons in the caudal ventrolateral medulla (CVLM)-a downstream target of the NST for vasomotor baroreflex-also promote non-REM sleep, partly by inhibiting the sympathoexcitatory and wake-promoting adrenergic neurons in the rostral ventrolateral medulla (RVLM). Cholinergic neurons in the nucleus ambiguous-a target of the NST for cardiac baroreflex-promoted non-REM sleep as well. Thus, key components of the cardiovascular baroreflex circuit are also integral to sleep-wake brain-state regulation.

Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data.

Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demanding but still highly informative. We thus developed a region-CNN-based deep learning method to identify, segment, and reconstruct synapses and mitochondria to explore the structural plasticity of synapses and mitochondria in the auditory cortex of mice subjected to fear conditioning. Upon reconstructing over 135,000 mitochondria and 160,000 synapses, we find that fear conditioning significantly increases the number of mitochondria but decreases their size and promotes formation of multi-contact synapses, comprising a single axonal bouton and multiple postsynaptic sites from different dendrites. Modeling indicates that such multi-contact configuration increases the information storage capacity of new synapses by over 50%. With high accuracy and speed in reconstruction, our method yields structural and functional insight into cellular plasticity associated with fear learning.

A common hub for sleep and motor control in the substantia nigra.

The arousal state of the brain covaries with the motor state of the animal. How these state changes are coordinated remains unclear. We discovered that sleep-wake brain states and motor behaviors are coregulated by shared neurons in the substantia nigra pars reticulata (SNr). Analysis of mouse home-cage behavior identified four states with different levels of brain arousal and motor activity: locomotion, nonlocomotor movement, quiet wakefulness, and sleep; transitions occurred not randomly but primarily between neighboring states. The glutamic acid decarboxylase 2 but not the parvalbumin subset of SNr γ-aminobutyric acid (GABA)-releasing (GABAergic) neurons was preferentially active in states of low motor activity and arousal. Their activation or inactivation biased the direction of natural behavioral transitions and promoted or suppressed sleep, respectively. These GABAergic neurons integrate wide-ranging inputs and innervate multiple arousal-promoting and motor-control circuits through extensive collateral projections.

Robust, automated sleep scoring by a compact neural network with distributional shift correction.

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring.

Control of Non-REM Sleep by Midbrain Neurotensinergic Neurons.

The periaqueductal gray (PAG) in the midbrain is known to coordinate behavioral and autonomic responses to threat and injury through its descending projections to the brainstem. Here, we show that neurotensin (NTS)-expressing glutamatergic neurons in the ventrolateral PAG (vlPAG) powerfully promote non-rapid eye movement (NREM) sleep partly through their projection to the caudal medulla. Optogenetic and chemogenetic activation of vlPAG NTS neurons strongly enhanced NREM sleep, whereas their inactivation increased wakefulness. Calcium imaging and optrode recording showed that they are preferentially active during NREM sleep. The NREM-promoting effect of vlPAG NTS neurons is partly mediated by their projection to the caudal ventromedial medulla, where they excite GABAergic neurons. Bidirectional optogenetic and chemogenetic manipulations showed that the medullary GABAergic neurons also promote NREM sleep, and they innervate multiple monoaminergic populations. Together, these findings reveal a novel pathway for NREM sleep generation, in which glutamatergic neurons drive broad GABAergic inhibition of wake-promoting neuronal populations.

Sleep Regulation by Neurotensinergic Neurons in a Thalamo-Amygdala Circuit.

A crucial step in understanding the sleep-control mechanism is to identify sleep neurons. Through systematic anatomical screening followed by functional testing, we identified two sleep-promoting neuronal populations along a thalamo-amygdala pathway, both expressing neurotensin (NTS). Rabies-mediated monosynaptic retrograde tracing identified the central nucleus of amygdala (CeA) as a major source of GABAergic inputs to multiple wake-promoting populations; gene profiling revealed NTS as a prominent marker for these CeA neurons. Optogenetic activation and inactivation of NTS-expressing CeA neurons promoted and suppressed non-REM (NREM) sleep, respectively, and optrode recording showed they are sleep active. Further tracing showed that CeA GABAergic NTS neurons are innervated by glutamatergic NTS neurons in a posterior thalamic region, which also promote NREM sleep. CRISPR/Cas9-mediated NTS knockdown in either the thalamic or CeA neurons greatly reduced their sleep-promoting effect. These results reveal a novel thalamo-amygdala circuit for sleep generation in which NTS signaling is essential for both the upstream glutamatergic and downstream GABAergic neurons.

A Motor Theory of Sleep-Wake Control: Arousal-Action Circuit.

Wakefulness, rapid eye movement (REM) sleep, and non-rapid eye movement (NREM) sleep are characterized by distinct electroencephalogram (EEG), electromyogram (EMG), and autonomic profiles. The circuit mechanism coordinating these changes during sleep-wake transitions remains poorly understood. The past few years have witnessed rapid progress in the identification of REM and NREM sleep neurons, which constitute highly distributed networks spanning the forebrain, midbrain, and hindbrain. Here we propose an arousal-action circuit for sleep-wake control in which wakefulness is supported by separate arousal and action neurons, while REM and NREM sleep neurons are part of the central somatic and autonomic motor circuits. This model is well supported by the currently known sleep and wake neurons. It can also account for the EEG, EMG, and autonomic profiles of wake, REM, and NREM states and several key features of their transitions. The intimate association between the sleep and autonomic/somatic motor control circuits suggests that a primary function of sleep is to suppress motor activity.

Author Correction: Selective synaptic remodeling of amygdalocortical connections associated with fear memory.

In the version of this article initially published, Fig. 7f purported to show an example of a multi-synapse spine. However, the structure in question included a mitochondrion and microtubules, meaning that it was actually a segment of dendritic shaft. A new image showing an example of a bona fide spine has been substituted. The legend has been changed to state that the image shows two boutons rather than three. The error has been corrected in the HTML and PDF versions of the article. The original and corrected figures are shown in the accompanying Author Correction.

An automated pipeline for bouton, spine, and synapse detection of in vivo two-photon images.

BACKGROUND: In the nervous system, the neurons communicate through synapses. The size, morphology, and connectivity of these synapses are significant in determining the functional properties of the neural network. Therefore, they have always been a major focus of neuroscience research. Two-photon laser scanning microscopy allows the visualization of synaptic structures in vivo, leading to many important findings. However, the identification and quantification of structural imaging data currently rely heavily on manual annotation, a method that is both time-consuming and prone to bias. RESULTS: We present an automated approach for the identification of synaptic structures in two-photon images. Axon boutons and dendritic spines are structurally distinct. They can be detected automatically using this image processing method. Then, synapses can be identified by integrating information from adjacent axon boutons and dendritic spines. In this study, we first detected the axonal boutons and dendritic spines respectively, and then identified synapses based on these results. Experimental results were validated manually, and the effectiveness of our proposed method was demonstrated. CONCLUSIONS: This approach will helpful for neuroscientists to automatically analyze and quantify the formation, elimination and destabilization of the axonal boutons, dendritic spines and synapses.

Selective synaptic remodeling of amygdalocortical connections associated with fear memory.

Neural circuits underlying auditory fear conditioning have been extensively studied. Here we identified a previously unexplored pathway from the lateral amygdala (LA) to the auditory cortex (ACx) and found that selective silencing of this pathway using chemo- and optogenetic approaches impaired fear memory retrieval. Dual-color in vivo two-photon imaging of mouse ACx showed pathway-specific increases in the formation of LA axon boutons, dendritic spines of ACx layer 5 pyramidal cells, and putative LA-ACx synaptic pairs after auditory fear conditioning. Furthermore, joint imaging of pre- and postsynaptic structures showed that essentially all new synaptic contacts were made by adding new partners to existing synaptic elements. Together, these findings identify an amygdalocortical projection that is important to fear memory expression and is selectively modified by associative fear learning, and unravel a distinct architectural rule for synapse formation in the adult brain.