Explore the vast range of titles available.

Competitive circuits in the amygdala of mice drive either freezing or flight behaviour in response to threat, and involve distinct neuronal subtypes. Freeze or flee — choosing the best response to danger The appropriate selection of either a passive or an active fear response when faced with a threat is critical to an animal's survival, but how that decision is made remains poorly understood. Here, Andreas Lüthi and colleagues describe competitive circuits in the amygdala that involve distinct neuronal subtypes and drive either the freezing or the flight behaviour. When faced with threat, the survival of an organism is contingent upon the selection of appropriate active or passive behavioural responses 1 , 2 , 3 . Freezing is an evolutionarily conserved passive fear response that has been used extensively to study the neuronal mechanisms of fear and fear conditioning in rodents 4 . However, rodents also exhibit active responses such as flight under natural conditions 2 . The central amygdala (CEA) is a forebrain structure vital for the acquisition and expression of conditioned fear responses, and the role of specific neuronal sub-populations of the CEA in freezing behaviour is well-established 1 , 5 , 6 , 7 . Whether the CEA is also involved in flight behaviour, and how neuronal circuits for active and passive fear behaviour interact within the CEA, are not yet understood. Here, using in vivo optogenetics and extracellular recordings of identified cell types in a behavioural model in which mice switch between conditioned freezing and flight, we show that active and passive fear responses are mediated by distinct and mutually inhibitory CEA neurons. Cells expressing corticotropin-releasing factor (CRF + ) mediate conditioned flight, and activation of somatostatin-positive (SOM + ) neurons initiates passive freezing behaviour. Moreover, we find that the balance between conditioned flight and freezing behaviour is regulated by means of local inhibitory connections between CRF + and SOM + neurons, indicating that the selection of appropriate behavioural responses to threat is based on competitive interactions between two defined populations of inhibitory neurons, a circuit motif allowing for rapid and flexible action selection.

Learning causes a change in how information is processed by neuronal circuits. Whereas synaptic plasticity, an important cellular mechanism, has been studied in great detail, we know much less about how learning is implemented at the level of neuronal circuits and, in particular, how interactions between distinct types of neurons within local networks contribute to the process of learning. Here we show that acquisition of associative fear memories depends on the recruitment of a disinhibitory microcircuit in the mouse auditory cortex. Fear-conditioning-associated disinhibition in auditory cortex is driven by foot-shock-mediated cholinergic activation of layer 1 interneurons, in turn generating inhibition of layer 2/3 parvalbumin-positive interneurons. Importantly, pharmacological or optogenetic block of pyramidal neuron disinhibition abolishes fear learning. Together, these data demonstrate that stimulus convergence in the auditory cortex is necessary for associative fear learning to complex tones, define the circuit elements mediating this convergence and suggest that layer-1-mediated disinhibition is an important mechanism underlying learning and information processing in neocortical circuits. Stimulus convergence and concomitant auditory cortex disinhibition are essential for fear learning. Sounds like fear It is generally recognized that learned behavioural responses, such as those associated with sound, involve changes within specific neural circuits. However, we are only beginning to understand how those changes are implemented and what interactions between different types of neurons within the circuits contribute to the learning process. Using classical sound-based fear-conditioning in mice as a model system, Andreas Lüthi and colleagues identify a distinct disinhibition-based circuit that is critical to learning. The neural circuit involved is not specific to auditory cortex, and may represent a general mechanism through which cholinergic neuromodulation gates cortical activity.

Bioimages frequently exhibit low signal-to-noise ratios due to experimental conditions, specimen characteristics, and imaging trade-offs. Reliable segmentation of such ambiguous images is difficult and laborious. Here we introduce deepflash2, a deep learning-enabled segmentation tool for bioimage analysis. The tool addresses typical challenges that may arise during the training, evaluation, and application of deep learning models on ambiguous data. The tool’s training and evaluation pipeline uses multiple expert annotations and deep model ensembles to achieve accurate results. The application pipeline supports various use-cases for expert annotations and includes a quality assurance mechanism in the form of uncertainty measures. Benchmarked against other tools, deepflash2 offers both high predictive accuracy and efficient computational resource usage. The tool is built upon established deep learning libraries and enables sharing of trained model ensembles with the research community. deepflash2 aims to simplify the integration of deep learning into bioimage analysis projects while improving accuracy and reliability. The signal-to-noise ratio in bioimages is often low, which is problematic for segmentation. Here the authors report a deep learning method, deepflash2, to facilitate the segmentation of ambiguous bioimages through multi-expert annotations and integrated quality assurance.

Philip Tovote describes the 1980 paper in which Michael Fanselow systematically investigated freezing as a defensive response in rodents

Fear extinction is an adaptive process whereby defensive responses are attenuated following repeated experience of prior fear-related stimuli without harm. The formation of extinction memories involves interactions between various corticolimbic structures, resulting in reduced central amygdala (CEA) output. Recent studies show, however, the CEA is not merely an output relay of fear responses but contains multiple neuronal subpopulations that interact to calibrate levels of fear responding. Here, by integrating behavioural, in vivo electrophysiological, anatomical and optogenetic approaches in mice we demonstrate that fear extinction produces reversible, stimulus- and context-specific changes in neuronal responses to conditioned stimuli in functionally and genetically defined cell types in the lateral (CEl) and medial (CEm) CEA. Moreover, we show these alterations are absent when extinction is deficient and that selective silencing of protein kinase C delta-expressing (PKCδ) CEl neurons impairs fear extinction. Our findings identify CEA inhibitory microcircuits that act as critical elements within the brain networks mediating fear extinction. The central amygdala inhibitory microcircuits mediate fear extinction by reversible, stimulus- and context-specific changes in neuronal responses. These alterations are absent when extinction is deficient and selective silencing of PKCδ neurons impairs fear extinction.

Brain-wide neural circuits enable bi- and quadrupeds to express adaptive locomotor behaviors in a context- and state-dependent manner, e.g., in response to threats or rewards. These behaviors include dynamic transitions between initiation, maintenance and termination of locomotion. Advances within the last decade have revealed an intricate coordination of these individual locomotion phases by complex interaction of multiple brain circuits. This review provides an overview of the neural basis of state-dependent modulation of locomotion initiation, maintenance and termination, with a focus on insights from circuit-centered studies in rodents. The reviewed evidence indicates that a brain-wide network involving excitatory circuit elements connecting cortex, midbrain and medullary areas appears to be the common substrate for the initiation of locomotion across different higher-order states. Specific network elements within motor cortex and the mesencephalic locomotor region drive the initial postural adjustment and the initiation of locomotion. Microcircuits of the basal ganglia, by implementing action-selection computations, trigger goal-directed locomotion. The initiation of locomotion is regulated by neuromodulatory circuits residing in the basal forebrain, the hypothalamus, and medullary regions such as locus coeruleus. The maintenance of locomotion requires the interaction of an even larger neuronal network involving motor, sensory and associative cortical elements, as well as defined circuits within the superior colliculus, the cerebellum, the periaqueductal gray, the mesencephalic locomotor region and the medullary reticular formation. Finally, locomotor arrest as an important component of defensive emotional states, such as acute anxiety, is mediated via a network of survival circuits involving hypothalamus, amygdala, periaqueductal gray and medullary premotor centers. By moving beyond the organizational principle of functional brain regions, this review promotes a circuit-centered perspective of locomotor regulation by higher-order states, and emphasizes the importance of individual network elements such as cell types and projection pathways. The realization that dysfunction within smaller, identifiable circuit elements can affect the larger network function supports more mechanistic and targeted therapeutic intervention in the treatment of motor network disorders.

BackgroundMigraine is a disabling neurological disorder, characterized by recurrent headaches. During migraine attacks, individuals often experience sensory symptoms such as cutaneous allodynia which indicates the presence of central sensitization. This sensitization is prevented by oral administration of propranolol, a common first-line medication for migraine prophylaxis, that also normalized the activation of the locus coeruleus (LC), considered as the main origin of descending noradrenergic pain controls. We hypothesized that the basal modulation of trigeminal sensory processing by the locus coeruleus is shifted towards more facilitation in migraineurs and that prophylactic action of propranolol may be attributed to a direct action in LC through beta-adrenergic receptors.MethodsWe used simultaneous in vivo extracellular recordings from the trigeminocervical complex (TCC) and LC of male Sprague–Dawley rats to characterize the relationship between these two areas following repeated meningeal inflammatory soup infusions. Von Frey Hairs and air-puff were used to test periorbital mechanical allodynia. RNAscope and patch-clamp recordings allowed us to examine the action mechanism of propranolol.ResultsWe found a strong synchronization between TCC and LC spontaneous activities, with a precession of the LC, suggesting the LC drives TCC excitability. Following repeated dural-evoked trigeminal activations, we observed a disruption in coupling of activity within LC and TCC. This suggested an involvement of the two regions’ interactions in the development of sensitization. Furthermore, we showed the co-expression of alpha-2A and beta-2 adrenergic receptors within LC neurons. Finally propranolol microinjections into the LC prevented trigeminal sensitization by desynchronizing and decreasing LC neuronal activity.ConclusionsAltogether these results suggest that trigemino-coerulean coupling plays a pivotal role in migraine progression, and that propranolol’s prophylactic effects involve, to some extent, the modulation of LC activity through beta-2 adrenergic receptors. This insight reveals new mechanistic aspects of LC control over sensory processing.

The defense response to threat involves complex behavioral and autonomic adjustments. We identified integrated, short-lasting microstates and long-lasting macrostates evoked by threat, consisting of patterned behavioral and cardiac responses, which are dynamically interrelated, dependent on environmental threat levels, and controlled by neurons in the midbrain periaqueductal gray region.

Key Points Newly developed technologies enable us to gain novel insights into how the brain generates fear and anxiety states, based on the identification and manipulation of neuronal circuits within and among individual brain regions. Fear is mediated by a brain-wide distributed network involving long-range projection pathways and local connectivity. The disinhibitory microcircuit is a common motif in the basolateral amygdala (BLA), central amygdala and the prelimbic region of the medial prefrontal cortex, and is instrumental in fear acquisition and expression. Encoding of fear extinction involves many of the same brain areas that are involved in fear acquisition and expression; however, different circuits within the amygdala and prefrontal cortex are involved. Indeed, fear extinction circuits may in fact inhibit fear circuits to dampen fearful responding. As with fear and fear extinction, a brain-wide neuronal network underlies anxiety, with identified local microcircuits within the bed nucleus of the stria terminalis, the lateral septum, the ventral tegmental area (VTA) and the BLA. Importantly, there is potential overlap between fear and anxiety circuits. There is overlap of neuronal circuits that mediate negative and positive valence in areas such as the VTA. Understanding the interplay between these circuits is of vital importance for understanding adaptive behavioural states. Recent methodological progress has greatly facilitated the determination of the connectivity and functional characterization of complex neural circuits. In this Review, Tovote, Fadok and Lüthi examine studies that have adopted circuit-based approaches to gain insight into how the brain governs fear and anxiety. Decades of research has identified the brain areas that are involved in fear, fear extinction, anxiety and related defensive behaviours. Newly developed genetic and viral tools, optogenetics and advanced in vivo imaging techniques have now made it possible to characterize the activity, connectivity and function of specific cell types within complex neuronal circuits. Recent findings that have been made using these tools and techniques have provided mechanistic insights into the exquisite organization of the circuitry underlying internal defensive states. This Review focuses on studies that have used circuit-based approaches to gain a more detailed, and also more comprehensive and integrated, view on how the brain governs fear and anxiety and how it orchestrates adaptive defensive behaviours.