Study Objectives: Stressful events can directly produce significant alterations in subsequent sleep, in particular rapid eye movement sleep (REM); however, the neural mechanisms underlying the process are not fully known. Here, we investigated the role of the basolateral nuclei of the amygdala (BLA) in regulating the effects of stressful experience on sleep. Methods: We used optogenetics to briefly inhibit glutamatergic cells in BLA during the presentation of inescapable footshock (IS) and assessed effects on sleep, the acute stress response, and fear memory. c-Fos expression was also assessed in the amygdala and the medial prefrontal cortex (mPFC), both regions involved in coping with stress, and in brain stem regions implicated in the regulation of REM. Results: Compared to control mice, peri-shock inhibition of BLA attenuated an immediate reduction in REM after IS and produced a significant overall increase in REM. Moreover, upon exposure to the shock context alone, mice receiving peri-shock inhibition of BLA during training showed increased REM without altered freezing (an index of fear memory) or stress-induced hyperthermia (an index of acute stress response). Inhibition of BLA during REM under freely sleeping conditions enhanced REM only when body temperature was high, suggesting the effect was influenced by stress. Peri-shock inhibition of BLA also led to elevated c-Fos expression in the central nucleus of the amygdala and mPFC and differentially altered c-Fos activity in the selected brain stem regions. Conclusions: Glutamatergic cells in BLA can modulate the effects of stress on REM and can mediate effects of fear memory on sleep that can be independent of behavioral fear.

Introduction:The amygdala, via the CNA, has direct projections to the LC and other regions (e.g., LDT, PPT, DRN, and subcoeruleus) in the mesopontine brainstem that play significant roles in regulating both sleep and arousal and the stress response. Here we used optogenetics to assess how CNA regulates neural activity in LC in anesthetized rats. Methods: To specifically target CNA projections to LC, CNA was infected with AAV-EF1a-DIO-hChR2(H134R)-EYFP and/or AAVEF1a-DIO-eNpHR3.0-EYFP and the LC with AAV-EF1a-mCherry-IRES-WGA-Cre that mediates bicistronic expression of mCherry and WGA-Cre. Vectors in CNA coded for double floxed and inverted open reading frame opsins, hChR2 or eNpHR3.0. When a neuron in the LC/ peri-LC zone synapses with neurons in CNA and expressed WGA-Cre, it is transneurally transferred to CNA neurons, where Cre activity flips the opsin gene(s) into its correct orientation thereby allowing its translation. Subsequent expression of the opsin(s) gene enables excitation of ChR2 expressing neurons/projections by blue light or inhibition of the NpHR expressing neurons/projections from CNA by yellow light. For recording, the rats were anesthetized with isoflurane, and optic fibers and recording electrodes were stereotaxically lowered into place. When stable recordings of single neurons were obtained, either CNA or terminal fibers were presented with blue or yellow light for stimulation or inhibition, respectively. Results: Ninety-six cells were recorded in seven rats. Responses of ≥25% change in firing rates were found in 58 neurons that received stimulation. Blue light stimulation of different durations suppressed firing in neurons in the vicinity of LC (1 sec (n=3), 2 sec (n=12) and 5 sec, (n=13)). By comparison, yellow light stimulation of 30 sec enhanced, firing in neurons in vicinity of LC (n=30).Conclusion: These data demonstrate that CNA can influence activity in LC/peri-LC zone with potential relevance for its role in sleep, arousal and the stress response. Support (If Any): MH1057701, MH64827. AMYGDALAR REGULATION OF PONTINE REM REGULATORY REGIONS: EFFECTS OF SLEEPWilliams BL, Sutton AM, Fitzpatrick ME, Machida M, Wellman LL, Lonart G, Sanford LD Eastern Virginia Medical School, Norfolk, VA Introduction: The central nucleus of the amygdala (CNA) projects to brainstem regions that generate and regulate REM. However, synaptic mapping of the circuitry and the actual influence of the CNA on these regions has not been determined. In this study, we used optogenetic methods to assess the influence of CNA inputs into the oral pontine reticular nucleus (PnO), the pedunculo-pontine tegmentum (PPT) and the nucleus subcoeruleus (SubC) on REM. Methods: Twelve male Wistar strain rats were stereotaxically injected with an excitatory optogenetic construct (AAV5-EF1a-DIO -hChR2(H134R)-EYFP) into the CNA and with AAV5-EF1a-mCherry-IRES-WGA-Cre into PnO (n=4), PPT (n=4), or SubC (n=4). These constructs were designed such that only those CNA neurons that were synaptically connected to the specific brainstem reg...

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Introduction:To date, no validated, computer-based tools exist to measure, predict, and optimize neurobehavioral performance due to sleep loss at both individual and group-average levels, while also accounting for the effects of caffeine. We addressed this gap by developing and validating a predictive mathematical model of performance [the unified model of performance (UMP)], and instantiating it into two tools: 1) 2B-Alert App, a smartphone application for real-time, individualized performance prediction and 2) 2B-Alert Web, a Webbased software for designing sleep/wake and caffeine schedules to optimize group-average performance. Methods: We developed and validated the UMP on psychomotor vigilance task (PVT) performance data from 14 different studies (in laboratory and field conditions), encompassing >500 subjects and including a wide range of sleep/wake schedules and caffeine doses. We then developed and validated an automated method to customize the UMP to an individual's sleep-loss phenotype based on the individual's PVT measurements. Finally, we embedded these capabilities into a smartphone app (Android and iPhone) and a Web tool, which allow users to enter sleep/wake schedules and caffeine consumption (doses and times), and obtain individual-specific or group-average performance predictions. Results: The UMP predicted group-average PVT performance across 26 different sleep/wake schedules (from partial to total sleep loss) and 6 different caffeine doses (ranging from repeated 200 mg doses to single 600 mg dose) with errors ranging from 6% to 36%. Accounting for the effects of caffeine in the model improved prediction accuracy by up to 70%. Individualized UMP models improved prediction accuracy by up to 50% compared to a group-average model. The Web tool is freely available at: , and the 2B-Alert App is expected to be available by summer of 2017. Conclusion: The 2B-Alert App and Web provide practical means for personal fatigue management and for optimizing work/rest schedules.

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