Abstract Neuronal gap junctions, or electrical synapses, are extensively expressed in the mammalian forebrain and play a key role in synchronizing network activity. Connexin 36 (Cx36) is the primary gap junction protein in mature GABAergic neurons, but its contribution to thalamocortical oscillations and cognitive processes remains unclear. Here, we examined the effects of Cx36 deletion on sleep/wake regulation, spontaneous and evoked EEG activity, and behavior in mice. While Cx36 knockout (KO) mice displayed largely intact sleep architecture, spectral analysis revealed impaired gamma and beta band activity and reduced sigma power surges preceding NREM–REM transitions. Spindle density was preserved, but spindle amplitude and duration were reduced. Cx36KO mice exhibited blunted gamma responses to ketamine, impaired 40 Hz auditory steady-state responses, and reduced mismatch negativity with attenuated ERP amplitudes and altered evoked power. Behaviorally, Cx36KO mice showed impaired social habituation and reduced investigation-induced gamma activity. These findings demonstrate that Cx36-containing gap junctions are essential for maintaining thalamocortical synchrony and support translational EEG biomarkers relevant to schizophrenia and other psychiatric disorders. Cx36 may therefore represent a novel therapeutic target for modulating dysfunctional network activity in neuropsychiatric disease.

Sleep spindles are rhythmic electroencephalographic signatures of non-rapid-eye-movement sleep. Their dysregulation has been implicated in several neuropsychiatric illnesses. Spindles have a characteristic waxing and waning shape, but the cellular and circuit mechanisms controlling their shape are not well understood. Recent but sparse research has implied that sleep spindle shape becomes abnormal in post-traumatic stress disorder (PTSD). PTSD patients have dysfunctional GABA A receptors in midline thalamic regions, areas involved in the orchestration of sleep spindles. We modelled this GABA A dysfunction within thalamocortical (TC) neurons using localized CRISPR-Cas9 technology to test the hypothesis that GABA dysfunction would dysregulate sleep spindle shape and cause symptoms of PTSD, in mouse model behavioral evaluations. We found sleep spindles were shorter and abnormally shaped, having lost their characteristic waxing and waning shape, in mice with GABA A receptor knock-down in TC neurons (TC-α1KD). TC-α1KD mice failed to recover from learned fearful reactions following an aversive stimulus. We tested this with a contextual fear conditioning paradigm using electric foot shocks. A control group with intact GABA A receptors successfully habituated to the fear conditioned location in subsequent visits to that context without foot shocks. In contrast, TC-α1KD mice never habituated, suggesting abnormally extended fearful memories. The number of inhibitory post synaptic currents in TC neurons were significantly decreased in vitro , confirming an effective knock-down. Our results imply that abnormally shaped sleep spindles may serve as a biomarker of GABA A receptor dysfunction in TC neurons which may be involved in abnormal fear processing in PTSD. We postulate GABA A receptor dysfunction in TC neurons may be underlying pathophysiology of PTSD and our findings here may inspire the development of screens, diagnostics and objective characteristics of stress related disorders, including PTSD.

Electrical synapses are expressed ubiquitously across the brain and are crucial components of active neural circuitry and connectomes. Identification of coupled networks in living tissue is limited by technical demands of multiplexed recordings, and no dyes, fluorescent reporters, or genetic labels can currently fill the gap. We introduce a novel method of identifying and quantifying electrical synapses, opto-δL, that combines focal photostimulation of soma-targeted opsins with a spike timing-based computation for the strength of electrical synapses to rapidly measure and map electrically coupled networks in vitro. We leverage opto-δL to show that coupled networks of the mature thalamic reticular nucleus extend as far as 100 μm, synapse promiscuously across genetic subtypes of neurons, and couple 1-4 neighboring neurons to each recorded hub cell. We also demonstrate application of opto-δL to cortical networks. These results highlight the broad potential of opto-δL to interrogate the identity and roles of electrical synapses in circuitry, behavior, and cognition.

Sleep abnormalities and dysfunction of gamma band (30-80 Hz) activity generated by parvalbumin (PV) interneurons are early characteristics of Alzheimer's disease (AD) which correlate with the severity of amyloid-β deposition (Aβ) and cognitive impairment. However, the timing of these alterations in vivo with respect to disease progression is unclear. Here, in longitudinal recordings from APP/PS1/PV-cre (AD mice) from 3-6 months, we found reduced sleep slow-wave power (0.5-4 Hz) in hippocampus and medial prefrontal cortex in AD mice as young as 3 months old, compared to non-AD (PV-cre) mice, well before overt pathology. This finding was primarily due to reductions in the NREM delta range (1.5-4 Hz), a hallmark of restorative functions of sleep. In contrast, beta (15-30 Hz) power linked to insomnia was significantly higher across all sleep-wake states. Loss of deep NREM sleep was not compensated by an increase in NREM sleep time, instead NREM sleep during the dark (active) phase was slightly but significantly lower in AD mice. 40-Hz auditory steady-state responses and associated evoked calcium responses of hippocampal PV neurons recorded using fiber photometry were also impaired by 3 months old. However, Y-maze performance in 3- and 6-month-old AD mice was not significantly different from non-AD mice. These results reveal reduced deep sleep and PV-associated 40-Hz activity as very early changes amenable to early intervention occurring prior to cognitive deficits. Furthermore, they establish APP/PS1 mice as a good model to causally test the relationship between sleep, PV neuronal activity and amyloid-mediated pathology.

Neuronal gap junctions, also termed electrical synapses consisting of connexin36 (Cx36) protein, are extensively expressed in the mammalian forebrain and are suggested to play a significant role in state regulation and thalamocortical network activity. Cx36 is predominantly expressed in GABAergic neurons in the adult brain and represents a common mechanism for electrical coupling between inhibitory neurons. Specifically, the intercellular communications between the GABAergic neurons in the thalamic reticular nucleus (TRN) occur predominantly via Cx36 containing electrical synapses. We have examined the effect of a Cx36 global gene knockout (KO) and TRN-specific localized CRISPR-Cas-mediated Cx36 gene knockdown (KD) on sleep/wake state and spontaneous and evoked EEG activity. We examined the sleep/wake state, spontaneous and task-evoked EEG activity in auditory steady-state response (ASSR), mismatch negativity (MMN), and social interaction behavioral test in Cx36KO mice and in PV-Cre-Cas9 mice before and after localized Cx36 gene KD. While Cx36KO mice exhibited limited sleep/wake abnormalities, power spectral density analysis of spontaneous EEG activity revealed significant impairment in gamma and beta band activity. Interestingly, sigma band activity significantly decreased prior to NREM-REM transitions. While we observed no changes in sleep spindle density, the amplitude and duration of spindles showed statistically significant decreases in Cx36KO mice. Additionally, Cx36KO mice exhibited a blunted gamma band response to acute ketamine (15 mg/kg; IP), impaired 40 Hz ASSR, and an abnormal response in the mismatch negativity task (decreased ERP peak amplitude & evoked-power). Finally, Cx36KO mice exhibit significant impairment in social investigation-induced low-frequency gamma band activity. Preliminary observations in TRN-Cx36 KD mice showed similar impairment in ASSR and social investigation-induced gamma band activity. Our data suggests that Cx36 is involved in regulating thalamocortical network activity. Further, impairments in Cx36KO mice are consistent with abnormalities observed in neuropsychiatric disorders, including schizophrenia, suggesting Cx36 containing gap junctions as a novel therapeutic target. VA Biomedical Laboratory Research and Development Merit Award I01 BX006105(R.B.); I01 BX004500 (JMM); I01 BX006550 (DSU); IK2 BX004905 (DSU); R01 NS119227 (R.B.).

Prolonged wakefulness induces a homeostatic sleep response (HSR) with increases in non-REM sleep time and delta power. The quality of wakefulness impacts the magnitude and form of the HSR but little is known about the neural mechanisms. Opto-stimulation of basal forebrain (BF) glutamatergic neurons (stim) leads to arousal and avoidance behavior. Here we compared the HSR following 4 hr of prolonged wakefulness induced by opto-stimulation of BF glutamate neurons and compared to sleep deprivation (SD) induced by gentle handling (GH). Finally, we compared the c-Fos activation pattern of the whole brain after 4hr of opto-stimulation compared to 4hr SD induced by GH. Mice (vGlut2-Cre or C57BL6) were implanted with microdialysis/optodialysis probes targeting the BF and EEG/EMG electrodes. Unilateral opto-stimulations of BF vGluT2 (20Hz, 5s On-55s Off) neurons and SD were performed during ZT3-ZT7 with or without the administration of ionotropic glutamate receptor antagonists (DNQX + D-AP5) and allowed 3h of recovery sleep. For cFos mapping whole brains were collected after 4hr of stim or SD with time-matched undisturbed controls, and analyzed for activated cFos (Lifecanvas, Inc). BF vGluT2 stimulation caused rapid arousal and, compared to baseline day, mice stayed awake (93.3 ±1.2%. N=10) during the 4 h of stimulation, comparable to that noted for SD (~95%) by GH. Stim conditions showed a significant hourly decrease in wakefulness from 2-4h which was further decreased by optodialysis of the ionotropic glutamate receptors antagonists, effects not seen in SD condition. Unlike SD group, the recovery NREM sleep remained unaltered following BF vGluT2 stim. SD increased NREM delta (0.5-4Hz) but the stim group showed a selective increase in 2.5-3.5Hz range. The whole brain c-Fos mapping revealed different activation patterns between stimulation and SD groups for multiple brain regions including the habenula and hypothalamus. Our data suggests a differential pattern of arousal and HSR following BF vGluT2 stimulation, and resulting activation of aversive brain circuits, when compared to that of GH-induced SD. R01 NS119227 (R.B.); VA Biomedical Laboratory Research and Development Service Merit Awards I01 BX001404, I01 BX006105 (R.B.), I01 BX002774 (J.T.M.), I01 BX004673 (R.E.B.). I01 BX006550 (DSU); IK2 BX 004905 (DSU)

Sleep-wake scoring in vivo electrophysiologic signals is necessary in many basic and translational studies. Performing this manually is a burden and bottleneck. Thus, many attempts have been made to automate it. Recently, with increased access to machine learning (ML) technologies, a new wave of attempts is occurring. The overwhelmingly common strategy deployed involves leveraging large datasets to train completely novel, yet relatively simple, ML-models. However, evidence suggests this does little to help with a common problem that is ML-models can perform poorly with new or unfamiliar data. Instead, transfer-learning – re-training highly sophisticated ML models – is known to be flexible and dependable, robustly dealing with novel data. We demonstrate transfer-learning of GoogLeNet provides highly dependable sleep-wake scoring across several diverse mouse electrophysiologic datasets, and matches the scoring of the person who trained it. We term our freely-available code “Sleep-Deep-Learner”. Transfer-learning was used to retrain GoogLeNet – accessed first unmodified, from the pretrained deep-neural-nets available via MATLAB. High-level final layers were replaced to classify wavelet transforms of epochs as wakefulness, NREM sleep or REM sleep. We used F1 scores to test how closely Sleep-Deep-Learner mimics two independent expert scorers. We validated performance in wild-type EEG, EEG altered by the hypnotic agent zolpidem, LFP data, data from an Alzheimer’s disease model and even sub-cortical data (hippocampus). We also reproduced findings of a CRISPR-based study previously completed with manual scoring. We ensure accurate fine-grain sleep architecture with hypnograms and bout analyses. Automated scores were very similar to either expert scorer regardless of dataset. We estimate this reduces labor burden of scoring to one twelfth. We provide a transfer-learning based approach to automating sleep-wake scoring. This has the advantage that automated sleep-wake scores agree with those of the expert scorer using the application. This, unbiased, flexible approach of retraining before each scoring session means there is no dependence on familiarity with novel data to perform accurately. VA Biomedical Laboratory Research and Development Service CDA-2 IK2 BX004905 (D.S.U.) and Merit Awards I01BX006550 (D.S.U.) I01 BX001404 and I01 BX006105(R.B.); I01 BX004673 (R.E.B.) and NIH K01 AG068366 (FK), R01 NS119227 (R.B.)

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.

Abstract Introduction Insomnia, characterized by problems falling asleep, less sleep, and a lower quality sleep, is more prevalent among individuals with autism spectrum disorders (ASD) compared to typical development. Insomnia in ASD predicts severity of symptoms and impacts quality of life, however the mechanisms linking poor sleep with ASD are not well understood. Genetic mouse models of ASD have been essential to understanding ASD. We previously showed that mice with a deletion in exon 21 of Shank3 (Shank3ΔC), a high confidence ASD gene, recapitulate the clinical sleep-onset insomnia ASD phenotype. Sleep spindles are an important part of sleep quality thought to indicate restorative effects of sleep on information processing and cognition. Prior work in humans suggests that people with ASD have a lower spindle density. The goal of our study was to investigate spindle activity using the Shank3ΔC mouse model, to provide a valid pre-clinical model for testing future interventions. Methods Sleep phenotyping of 12-week-old adult Wild Type (WT) and Shank3ΔC male mice was performed using electroencephalography (EEG), frontal and parietal cortices bilaterally, and electromyography (EMG). Recordings consisted of 24-hours of baseline, 5-hours of sleep deprivation and 19-hours of undisturbed recovery sleep. EEG/EMG data was used to manually determine vigilance states using SleepSign for Animal and then analyzed with custom Matlab code to detect spindles. The custom Matlab code bandpass-filtered raw EEG data which was then cubed RMS-transformed to define thresholds utilized to identify spindles. Results Preliminary results suggest that Shank3ΔC mice have an overall decrease in spindle density (spindles per minute of NREM) compared to WT mice during the dark period post sleep deprivation. This decrease of spindle density in Shank3ΔC mice allows us to further explore spindle density at NREM-REM and Wake-NREM transitions and look for spindle deficits through development in a pre-clinical model of ASD. Conclusion Shank3ΔC mice are an effective pre-clinical model for understanding the mechanisms underlying poor spindle quality in ASD across development and developing targeted interventions to normalize spindle density. Support (if any) This work was supported by NIH K01NS104172 (LP), NIH R56NS124804 (LP), IK2 BX004905/BX/BLRD (DSU), K01 AG068366/AG/NIA (FK), R21 MH125242/MH/NIMH NIH (JMM), and I01 BX004500/BX/BLRD (JMM).

AbstractOptochemistry, an emerging pharmacologic approach in which light is used to selectively activate or deactivate molecules, has the potential to alleviate symptoms, cure diseases, and improve quality of life while preventing uncontrolled drug effects. The development of in-vivo applications for optochemistry to render brain cells photoresponsive without relying on genetic engineering has been progressing slowly. The nucleus accumbens (NAc) is a region for the regulation of slow-wave sleep (SWS) through the integration of motivational stimuli. Adenosine emerges as a promising candidate molecule for activating indirect pathway neurons of the NAc expressing adenosine A2A receptors (A2ARs) to induce SWS. Here, we developed a brain-permeable positive allosteric modulator of A2ARs (A2AR PAM) that can be rapidly photoactivated with visible light (λ > 400 nm) and used it optoallosterically to induce SWS in the NAc of freely behaving male mice by increasing the activity of extracellular adenosine derived from astrocytic and neuronal activity.

ABSTRACTSleep-wake scoring is a time-consuming, tedious but essential component of clinical and pre-clinical 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 data sets, 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-Net (SDN) creates a bespoke deep convolution neural network model for individual electroencephalographic or local-field-potential 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. SDN then automates scoring of the remainder of the EEG/LFP record. A novel REM scoring correction procedure further enhanced accuracy. SDN 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. SDN reduced manual scoring time to 1/12. Since SDN uses transfer learning on each independent recording, it is not biased by previously scored existing data sets. Thus, we find SDN performs well when used on signals altered by a drug, disease model or genetic modification.STATEMENT OF SIGNIFICANCESleep medicine is often critically advanced by translational research based onin vivoelectrophysiologic mouse data. A necessary but time-consuming step in this field is scoring epochs of recordings into wakefulness, non-rapid-eye-movement sleep and non-rapid-eye-movement sleep. Despite efforts to automate this, manual scoring remains the gold-standard since automatic methods poorly handle data that is not similar enough to data used during development. Here, we describe a novel automated sleep scoring method that involves retraining a deep-convolution-neural-net capable of computer vision to score sleep-wake patterns after learning from a small set of manual scores within a record. This avoids biasing the model to expect data to be the same as its training set from previous records.

Here we describe a novel group of basal forebrain (BF) neurons expressing neuronal PAS domain 1 (Npas1), a developmental transcription factor linked to neuropsychiatric disorders. Immunohistochemical staining in Npas1-cre-2A-TdTomato mice revealed BF Npas1 + neurons are distinct from well-studied parvalbumin or cholinergic neurons. Npas1 staining in GAD67-GFP knock-in mice confirmed that the vast majority of Npas1 + neurons are GABAergic, with minimal colocalization with glutamatergic neurons in vGlut1-cre-tdTomato or vGlut2-cre-tdTomato mice. The density of Npas1 + neurons was high, 5-6 times that of neighboring cholinergic, parvalbumin or glutamatergic neurons. Anterograde tracing identified prominent projections of BF Npas1 + neurons to brain regions involved in sleep-wake control, motivated behaviors and olfaction such as the lateral hypothalamus, lateral habenula, nucleus accumbens shell, ventral tegmental area and olfactory bulb. Chemogenetic activation of BF Npas1 + neurons in the light (inactive) period increased the amount of wakefulness and the latency to sleep for 2-3 hr, due to an increase in long wake bouts and short NREM sleep bouts. Non-REM slow-wave (0-1.5 Hz) and sigma (9-15 Hz) power, as well as sleep spindle density, amplitude and duration, were reduced, reminiscent of findings in several neuropsychiatric disorders. Together with previous findings implicating BF Npas1 + neurons in stress responsiveness, the anatomical projections of BF Npas1 + neurons and the effect of activating them suggest a possible role for BF Npas1 + neurons in motivationally-driven wakefulness and stress-induced insomnia. Identification of this major subpopulation of BF GABAergic neurons will facilitate studies of their role in sleep disorders, dementia and other neuropsychiatric conditions involving BF. SIGNIFICANCE STATEMENT:We characterize a group of basal forebrain (BF) neurons in the mouse expressing neuronal PAS domain 1 (Npas1), a developmental transcription factor linked to neuropsychiatric disorders. BF Npas1 + neurons are a major subset of GABAergic neurons distinct and more numerous than cholinergic, parvalbumin or glutamate neurons. BF Npas1 + neurons target brain areas involved in arousal, motivation and olfaction. Activation of BF Npas1 + neurons in the light (inactive) period increased wakefulness and the latency to sleep due to increased long wake bouts. Non-REM sleep slow waves and spindles were reduced reminiscent of findings in several neuropsychiatric disorders. Identification of this major subpopulation of BF GABAergic wake-promoting neurons will allow studies of their role in insomnia, dementia and other conditions involving BF.

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 GABA A -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 GABA A receptors on thalamic reticular nucleus neurons and suggest antagonism of α3 subunits as a strategy to enhance delta activity during sleep.

Sleep is vital and the deepest stages of sleep occur within Non-rapid-eye-movement sleep (NREM), defined by high electroencephalographic power in the delta (~0.5–4 Hz) wave frequency range. Delta waves are thought to facilitate a myriad of physical and mental health functions. This review aims to comprehensively cover the historical and recent advances in the understanding of the mechanisms orchestrating NREM delta waves. We discuss a complete neurocircuit – focusing on one leg of the circuit at a time – and delve deeply into the molecular mechanistic components that contribute to NREM delta wave regulation. We also discuss the relatively localized nature in which these mechanisms have been defined, and how likely they might generalize across distinct sensory and higher order modalities in the brain.

Poor sleep quality is associated with age-related cognitive decline, and whether reversal of these alterations is possible is unknown. In this study, we report how sleep deprivation (SD) affects hippocampal representations, sleep patterns, and memory in young and old mice. After training in a hippocampus-dependent object-place recognition (OPR) task, control animals sleep ad libitum, although experimental animals undergo 5 h of SD, followed by recovery sleep. Young controls and old SD mice exhibit successful OPR memory, whereas young SD and old control mice are impaired. Successful performance is associated with two cellular phenotypes: (1) "context" cells, which remain stable throughout training and testing, and (2) "object configuration" cells, which remap when objects are introduced to the context and during testing. Additionally, effective memory correlates with spindle counts during non-rapid eye movement (NREM)/rapid eye movement (REM) sigma transitions. These results suggest SD may serve to ameliorate age-related memory deficits and allow hippocampal representations to adapt to changing environments.

The ability to rapidly arouse from sleep is important for survival. However, increased arousals in patients with sleep apnea and other disorders prevent restful sleep and contribute to cognitive, metabolic, and physiologic dysfunction [1, 2]. Little is currently known about which neural systems mediate these brief arousals, hindering the development of treatments that restore normal sleep. The basal forebrain (BF) receives inputs from many nuclei of the ascending arousal system, including the brainstem parabrachial neurons, which promote arousal in response to elevated blood carbon dioxide levels, as seen in sleep apnea [3]. Optical inhibition of the terminals of parabrachial neurons in the BF impairs cortical arousals to hypercarbia [4], but which BF cell types mediate cortical arousals in response to hypercarbia or other sensory stimuli is unknown. Here, we tested the role of BF parvalbumin (PV) neurons in arousal using optogenetic techniques in mice. Optical stimulation of BF-PV neurons produced rapid transitions to wakefulness from non-rapid eye movement (NREM) sleep but did not affect REM-wakefulness transitions. Unlike previous studies of BF glutamatergic and cholinergic neurons, arousals induced by stimulation of BF-PV neurons were brief and only slightly increased total wake time, reminiscent of clinical findings in sleep apnea [5, 6]. Bilateral optical inhibition of BF-PV neurons increased the latency to arousal produced by exposure to hypercarbia or auditory stimuli. Thus, BF-PV neurons are an important component of the brain circuitry that generates brief arousals from sleep in response to stimuli, which may indicate physiological dysfunction or danger to the organism.

Increases in broadband cortical electroencephalogram (EEG) power in the gamma band (30–80 Hz) range have been observed in schizophrenia patients and in mouse models of schizophrenia. They are also seen in humans and animals treated with the psychotomimetic agent ketamine. However, the mechanisms which can result in increased broadband gamma power and the pathophysiological implications for cognition and behavior are poorly understood. Here we report that tonic optogenetic manipulation of an ascending arousal system bidirectionally tunes cortical broadband gamma power, allowing on-demand tests of the effect on cortical processing and behavior. Constant, low wattage optogenetic stimulation of basal forebrain (BF) neurons containing the calcium-binding protein parvalbumin (PV) increased broadband gamma frequency power, increased locomotor activity, and impaired novel object recognition. Concomitantly, task-associated gamma band oscillations induced by trains of auditory stimuli, or exposure to novel objects, were impaired, reminiscent of findings in schizophrenia patients. Conversely, tonic optogenetic inhibition of BF-PV neurons partially rescued the elevated broadband gamma power elicited by subanesthetic doses of ketamine. These results support the idea that increased cortical broadband gamma activity leads to impairments in cognition and behavior, and identify BF-PV activity as a modulator of this activity. As such, BF-PV neurons may represent a novel target for pharmacotherapy in disorders such as schizophrenia which involve aberrant increases in cortical broadband gamma activity.

Summary Age-related changes in sleep patterns have been linked to cognitive decline. Specifically, increasing age is associated with increasing fragmentation of sleep and wake cycles. However, it remains unknown if improvements in sleep architecture can ameliorate cellular and cognitive deficits. We evaluated how changes in sleep architecture following sleep restriction affected hippocampal representations and memory in young and old mice. After training in a hippocampus- dependent object/place recognition task, control animals were allowed to sleep ad libitum , while experimental animals underwent 5 hours of sleep restriction (SR). Interestingly, old SR mice exhibited successful object/place learning comparable to young control mice, whereas young SR and old control mice did not. Successful learning correlated with the presence of two hippocampal cell types: 1) “Context” cells, which remained stable throughout training and testing, and 2) “Object” cells, which shifted their preferred firing location when objects were introduced to the context and moved during testing. As expected, EEG analysis revealed more fragmented sleep and fewer spindles in old controls than young controls during the post-training sleep period. However, following the acute SR session, old animals exhibited increased consolidation of NREM and increased spindle count, while young mice only displayed changes in REM bout length. These results indicate that consolidation of NREM sleep and increases in spindle count serve to ameliorate age-related memory deficits and allow hippocampal representations to adapt to changing environments. eTORC Blurb Age-related cognitive decline is associated with poor sleep quality. This study shows that acute sleep restriction serves to improve memory, hippocampal representations, and sleep quality in old mice, having the opposite effect in young animals. These findings indicate that improving sleep quality may mitigate age-related cognitive decline. Highlights Acute sleep restriction improves memory in old mice, but adversely affects young ones Acute sleep restriction makes hippocampal representations more flexible in old mice Acute sleep restriction improves sleep quality and increases spindle count in old mice Acute sleep restriction decreases hippocampal flexibility in young mice

Abstract Introduction Neuronal gap-junctions are extensively expressed in mammalian forebrain and suggested to contribute to state-regulation and thalamocortical network activity. However, the physiological role of gap-junctions on these processes remains poorly understood. Connexin-36 (Cxn36) is highly expressed in the brain, representing a mechanism for electrical coupling of inhibitory neurons. We examined the effects of global Cnx36 deletion on sleep/wake and spontaneous and evoked EEG activity. Methods We recorded in vivo EEG/EMG in Cxn36KO mice and littermate controls. Electrodes were stereotaxically implanted above frontal cortices. We analyzed sleep/wake states and algorithmically detected sleep spindles over 24 hours. Mice underwent auditory stimulation paradigms including the auditory steady state response (ASSR; 1 second train 20-50Hz clicks, 100 reps., 85dB) and mismatch negativity (MMN; 2.5kHz standard 90%, 10kHz deviant 10%, 300ms ISI, 90dB). Social behavior and investigation-evoked EEG activity were also assessed via the social habituation task (repeated 5 min exposures to novel mouse). Results Cnx36KO mice exhibited limited sleep/wake abnormalities (n=7/group). Power spectra of EEG revealed significant impairments in spontaneous gamma-band activity (30-80Hz; All States, Light & Dark Phases), and beta activity (15-25Hz; All States, Light Phase). Sigma activity (10-15Hz) was significantly decreased (NREM and REM, Light phase). This was particularly pronounced during NREM-REM transitions. Despite no changes in spindle density, both spindle amplitude and duration were significantly decreased in Cnx36KOs. Cxn36KOs exhibited a blunted gamma-band response to acute ketamine (15mg/kg; IP), impaired 30 & 40Hz ASSR, and an abnormal response in the MMN task (decrease ERP peak amplitude & gamma). Finally, Cxn36KO mice exhibit impaired social habituation and significantly decreased investigation evoked slow gamma-band activity (30 - 55Hz). Conclusion Our data suggest Cxn36 plays a critical role in regulating thalamocortical network activity. Further, impairments in Cnx36KO mice reflect abnormalities in neuropsychiatric disorders, including schizophrenia, implicating Cnx36 containing gap junctions as a novel therapeutic target. Support Research supported by VA CDA Award BX002130 (JMM), VA Merit Awards BX004500 (JMM), BX001404 (RB), and NIMH RO1 MH39683 (Ritchie E. Brown).