Synapse-Specific Trapping of SNARE Machinery Proteins in the Anesthetized Drosophila Brain.

General anesthetics disrupt brain network dynamics through multiple pathways, in part through postsynaptic potentiation of inhibitory ion channels as well as presynaptic inhibition of neuroexocytosis. Common clinical general anesthetic drugs, such as propofol and isoflurane, have been shown to interact and interfere with core components of the exocytic release machinery to cause impaired neurotransmitter release. Recent studies however suggest that these drugs do not affect all synapse subtypes equally. We investigated the role of the presynaptic release machinery in multiple neurotransmitter systems under isoflurane general anesthesia in the adult female Drosophila brain using live-cell super-resolution microscopy and optogenetic readouts of exocytosis and neural excitability. We activated neurotransmitter-specific mushroom body output neurons and imaged presynaptic function under isoflurane anesthesia. We found that isoflurane impaired synaptic release and presynaptic protein dynamics in excitatory cholinergic synapses. In contrast, isoflurane had little to no effect on inhibitory GABAergic or glutamatergic synapses. These results present a distinct inhibitory mechanism for general anesthesia, whereby neuroexocytosis is selectively impaired at excitatory synapses, while inhibitory synapses remain functional. This suggests a presynaptic inhibitory mechanism that complements the other inhibitory effects of these drugs.

Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies.

Identifying different sleep stages in humans and other mammals has traditionally relied on electroencephalograms. Such an approach is not feasible in certain animals such as invertebrates, although these animals could also be sleeping in stages. Here, we perform long-term multichannel local field potential recordings in the brains of behaving flies undergoing spontaneous sleep bouts. We acquired consistent spatial recordings of local field potentials across multiple flies, allowing us to compare brain activity across awake and sleep periods. Using machine learning, we uncover distinct temporal stages of sleep and explore the associated spatial and spectral features across the fly brain. Further, we analyze the electrophysiological correlates of microbehaviors associated with certain sleep stages. We confirm the existence of a distinct sleep stage associated with rhythmic proboscis extensions and show that spectral features of this sleep-related behavior differ significantly from those associated with the same behavior during wakefulness, indicating a dissociation between behavior and the brain states wherein these behaviors reside.

Whole-Brain Calcium Imaging in Drosophila during Sleep and Wake.

Genetically encoded calcium indicators (GECIs) allow for the noninvasive evaluation of neuronal activity in vivo, and imaging GECIs in Drosophila has become commonplace for understanding neural functions and connectivity in this system. GECIs can also be used as read-outs for studying sleep in this model organism. Here, we describe a methodology for tracking the activity of neurons in the fly brain using a two-photon (2p) microscopy system. This method can be adapted to perform functional studies of neural activity in Drosophila under both spontaneous and evoked conditions, as well as during spontaneous or induced sleep. We first describe a tethering and surgical procedure that allows survival under the microscopy conditions required for long-term recordings. We then outline the steps and reagents required for optogenetic activation of sleep-promoting neurons while simultaneously recording neural activity from the fly brain. We also describe the procedure for recording from two different locations-namely, the top of the head (e.g., to record mushroom body calyx activity) or the back of the head (e.g., to record central complex activity). We also provide different strategies for recording from GECIs confined to the cell body versus the entire neuron. Finally, we describe the steps required for analyzing the multidimensional data that can be acquired. In all, this protocol shows how to perform calcium imaging experiments in tethered flies, with a focus on acquiring spontaneous and induced sleep data.

Whole-Brain Electrophysiology in Drosophila during Sleep and Wake.

Sleep studies in Drosophila melanogaster rely mostly on behavioral read-outs to support molecular or circuit-level investigations in this model. Electrophysiology can provide an additional level of understanding in these studies to, for example, investigate changes in brain activity associated with sleep manipulations. In this protocol, we describe a procedure for performing multichannel local field potential (LFP) recordings in the fruit fly, with a flexible system that can be adapted to different experimental paradigms and situations. The approach uses electrodes containing multiple recording sites (16), allowing the acquisition of large amounts of neuronal activity data from a transect through the brain while flies are still able to sleep. The approach starts by tethering the fly, followed by positioning it on an air-supported ball. A multichannel silicon probe is then inserted laterally into the fly brain via one eye, allowing for recording of electrical signals from the retina through to the central brain. These recordings can be acquired under spontaneous conditions or in the presence of visual stimuli, and the minimal surgery promotes long-term recordings (e.g., overnight). Sleep and wake can be tracked using infrared cameras, which allow for the measurement of locomotive activity as well as microbehaviors such as proboscis extensions during sleep. The protocol has been optimized to promote subject survivability, which is an important factor when performing long-term (∼16-h) recordings. The approach described here uses specific recording probes, data acquisition devices, and analysis tools. Although it is expected that some of these items might need to be adapted to the equipment available in different laboratories, the overall aim is to provide an overview on how to record electrical activity across the brain of behaving (and sleeping) flies using this kind of approach and technology.

Whole-Brain Electrophysiology and Calcium Imaging in Drosophila during Sleep and Wake.

Sleep is likely a whole-brain phenomenon, with most of the brain probably benefiting from this state of decreased arousal. Recent advances in our understanding of some potential sleep functions, such as metabolite clearance and synaptic homeostasis, make it evident why the whole brain is likely impacted by sleep: All neurons have synapses, and all neurons produce waste metabolites. Sleep experiments in the fly Drosophila melanogaster suggest that diverse sleep functions appear to be conserved across all animals. Studies of brain activity during sleep in humans typically involve multidimensional data sets, such as those acquired by electroencephalograms (EEGs) or functional magnetic resonance imaging (fMRI), and these whole-brain read-outs often reveal important qualities of different sleep stages, such as changes in frequency dynamics or connectivity. Recently, various techniques have been developed that allow for the recording of neural activity simultaneously across multiple regions of the fly brain. These whole-brain-recording approaches will be important for better understanding sleep physiology and function, as they provide a more comprehensive view of neural dynamics during sleep and wake in a relevant model system. Here, we present a brief summary of some of the findings derived from sleep activity recording studies in sleeping Drosophila flies and discuss the value of electrophysiological versus calcium imaging techniques. Although these involve very different preparations, they both highlight the value of multidimensional data for studying sleep in this model system, like the use of both EEG and fMRI in humans.

Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies.

Sleep is observed in most animals, which suggests it subserves a fundamental process associated with adaptive biological functions. However, the evidence to directly associate sleep with a specific function is lacking, in part because sleep is not a single process in many animals. In humans and other mammals, different sleep stages have traditionally been identified using electroencephalograms (EEGs), but such an approach is not feasible in different animals such as insects. Here, we perform long-term multichannel local field potential (LFP) recordings in the brains of behaving flies undergoing spontaneous sleep bouts. We developed protocols to allow for consistent spatial recordings of LFPs across multiple flies, allowing us to compare the LFP activity across awake and sleep periods and further compare the same to induced sleep. Using machine learning, we uncover the existence of distinct temporal stages of sleep and explore the associated spatial and spectral features across the fly brain. Further, we analyze the electrophysiological correlates of micro-behaviours associated with certain sleep stages. We confirm the existence of a distinct sleep stage associated with rhythmic proboscis extensions and show that spectral features of this sleep-related behavior differ significantly from those associated with the same behavior during wakefulness, indicating a dissociation between behavior and the brain states wherein these behaviors reside.

Balancing Prediction and Surprise: A Role for Active Sleep at the Dawn of Consciousness?

The brain is a prediction machine. Yet the world is never entirely predictable, for any animal. Unexpected events are surprising, and this typically evokes prediction error signatures in mammalian brains. In humans such mismatched expectations are often associated with an emotional response as well, and emotional dysregulation can lead to cognitive disorders such as depression or schizophrenia. Emotional responses are understood to be important for memory consolidation, suggesting that positive or negative 'valence' cues more generally constitute an ancient mechanism designed to potently refine and generalize internal models of the world and thereby minimize prediction errors. On the other hand, abolishing error detection and surprise entirely (as could happen by generalization or habituation) is probably maladaptive, as this might undermine the very mechanism that brains use to become better prediction machines. This paradoxical view of brain function as an ongoing balance between prediction and surprise suggests a compelling approach to study and understand the evolution of consciousness in animals. In particular, this view may provide insight into the function and evolution of 'active' sleep. Here, we propose that active sleep - when animals are behaviorally asleep but their brain seems awake - is widespread beyond mammals and birds, and may have evolved as a mechanism for optimizing predictive processing in motile creatures confronted with constantly changing environments. To explore our hypothesis, we progress from humans to invertebrates, investigating how a potential role for rapid eye movement (REM) sleep in emotional regulation in humans could be re-examined as a conserved sleep function that co-evolved alongside selective attention to maintain an adaptive balance between prediction and surprise. This view of active sleep has some interesting implications for the evolution of subjective awareness and consciousness in animals.

Innate visual preferences and behavioral flexibility in Drosophila.

Visual decision making in animals is influenced by innate preferences as well as experience. Interaction between hard-wired responses and changing motivational states determines whether a visual stimulus is attractive, aversive or neutral. It is, however, difficult to separate the relative contribution of nature versus nurture in experimental paradigms, especially for more complex visual parameters such as the shape of objects. We used a closed-loop virtual reality paradigm for walking Drosophila to uncover innate visual preferences for the shape and size of objects, in a recursive choice scenario allowing the flies to reveal their visual preferences over time. We found that Drosophila melanogaster display a robust attraction/repulsion profile for a range of object sizes in this paradigm, and that this visual preference profile remains evident under a variety of conditions and persists into old age. We also demonstrate a level of flexibility in this behavior: innate repulsion to certain objects could be transiently overridden if these were novel, although this effect was only evident in younger flies. Finally, we show that a neuromodulatory circuit in the fly brain, Drosophila neuropeptide F (dNPF), can be recruited to guide visual decision making. Optogenetic activation of dNPF-expressing neurons converted a visually repulsive object into a more attractive object. This suggests that dNPF activity in the Drosophila brain guides ongoing visual choices, to override innate preferences and thereby provide a necessary level of behavioral flexibility in visual decision making.

Spontaneous Activity in the Zebrafish Tectum Reorganizes over Development and Is Influenced by Visual Experience.

Spontaneous patterns of activity in the developing visual system may play an important role in shaping the brain for function. During the period 4-9 dpf (days post-fertilization), larval zebrafish learn to hunt prey, a behavior that is critically dependent on the optic tectum. However, how spontaneous activity develops in the tectum over this period and the effect of visual experience are unknown. Here we performed two-photon calcium imaging of GCaMP6s zebrafish larvae at all days from 4 to 9 dpf. Using recently developed graph theoretic techniques, we found significant changes in both single-cell and population activity characteristics over development. In particular, we identified days 5-6 as a critical moment in the reorganization of the underlying functional network. Altering visual experience early in development altered the statistics of tectal activity, and dark rearing also caused a long-lasting deficit in the ability to capture prey. Thus, tectal development is shaped by both intrinsic factors and visual experience.

Using an abstract geometry in virtual reality to explore choice behaviour: visual flicker preferences in honeybees.

Closed-loop paradigms provide an effective approach for studying visual choice behaviour and attention in small animals. Different flying and walking paradigms have been developed to investigate behavioural and neuronal responses to competing stimuli in insects such as bees and flies. However, the variety of stimulus choices that can be presented over one experiment is often limited. Current choice paradigms are mostly constrained as single binary choice scenarios that are influenced by the linear structure of classical conditioning paradigms. Here, we present a novel behavioural choice paradigm that allows animals to explore a closed geometry of interconnected binary choices by repeatedly selecting among competing objects, thereby revealing stimulus preferences in an historical context. We used our novel paradigm to investigate visual flicker preferences in honeybees (Apis mellifera) and found significant preferences for 20-25 Hz flicker and avoidance of higher (50-100 Hz) and lower (2-4 Hz) flicker frequencies. Similar results were found when bees were presented with three simultaneous choices instead of two, and when they were given the chance to select previously rejected choices. Our results show that honeybees can discriminate among different flicker frequencies and that their visual preferences are persistent even under different experimental conditions. Interestingly, avoided stimuli were more attractive if they were novel, suggesting that novelty salience can override innate preferences. Our recursive virtual reality environment provides a new approach to studying visual discrimination and choice behaviour in animals.