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Neurons in the basolateral amygdala projecting to canonical fear or reward circuits undergo opposing changes in synaptic strength following fear or reward conditioning, and selectively activating these projection-target-defined neural populations causes either negative or positive reinforcement, respectively. Positive and negative associations hard-wired in the brain The amygdala is part of the brain important for emotional processing, handling stimuli that have either positive or negative associations — the good and the bad. Little is known about how amygdala neurons differentiate or compartmentalize these distinctions. Here, Kay Tye and colleagues identify the basolateral amygdala (BLA) as a site of divergence for circuits mediating positive and negative emotional or motivational responses. In studies in mice they find that neurons in the BLA projecting to fear or reward circuits undergo opposing changes in synaptic strength following fear or reward conditioning. Selective activation of neural populations causes, respectively, either negative or positive reinforcement. Transcriptome analysis reveals candidate genes that may mediate these functional differences. The ability to differentiate stimuli predicting positive or negative outcomes is critical for survival, and perturbations of emotional processing underlie many psychiatric disease states. Synaptic plasticity in the basolateral amygdala complex (BLA) mediates the acquisition of associative memories, both positive 1 , 2 and negative 3 , 4 , 5 , 6 , 7 . Different populations of BLA neurons may encode fearful or rewarding associations 8 , 9 , 10 , but the identifying features of these populations and the synaptic mechanisms of differentiating positive and negative emotional valence have remained unknown. Here we show that BLA neurons projecting to the nucleus accumbens (NAc projectors) or the centromedial amygdala (CeM projectors) undergo opposing synaptic changes following fear or reward conditioning. We find that photostimulation of NAc projectors supports positive reinforcement while photostimulation of CeM projectors mediates negative reinforcement. Photoinhibition of CeM projectors impairs fear conditioning and enhances reward conditioning. We characterize these functionally distinct neuronal populations by comparing their electrophysiological, morphological and genetic features. Overall, we provide a mechanistic explanation for the representation of positive and negative associations within the amygdala.

Little is known about the mechanisms underlying the orchestration of competing motivational drives. During the simultaneous presentation of cues associated with shock or sucrose, when rats may engage in fear- or reward-related behaviors, amygdala neurons projecting to prefrontal cortex more accurately predict behavioral output and bias animals toward fear-related behavior. Orchestrating appropriate behavioral responses in the face of competing signals that predict either rewards or threats in the environment is crucial for survival. The basolateral nucleus of the amygdala (BLA) and prelimbic (PL) medial prefrontal cortex have been implicated in reward-seeking and fear-related responses, but how information flows between these reciprocally connected structures to coordinate behavior is unknown. We recorded neuronal activity from the BLA and PL while rats performed a task wherein competing shock- and sucrose-predictive cues were simultaneously presented. The correlated firing primarily displayed a BLA→PL directionality during the shock-associated cue. Furthermore, BLA neurons optogenetically identified as projecting to PL more accurately predicted behavioral responses during competition than unidentified BLA neurons. Finally photostimulation of the BLA→PL projection increased freezing, whereas both chemogenetic and optogenetic inhibition reduced freezing. Therefore, the BLA→PL circuit is critical in governing the selection of behavioral responses in the face of competing signals.

Insights into the general nature of motor skill could fundamentally change how we develop movement abilities, with implications for musculoskeletal well-being and injury. Here, we sought to identify indicators of general motor skill—those shared by experts across disciplines (e.g., squash, ballet, volleyball) during non-specialized movements (e.g., reaching for water). Identifying such general indicators of motor skill has remained elusive. Using ultrasound imaging with deep learning and optical flow analysis, we tracked elastic tissues (muscles and associated connective tissues) during a simple reaching task performed similarly by world-class athletes and regional-level athletes drawn from diverse disciplines, as well as untrained non-experts. We analyzed two types of inefficient tissue motions that do not contribute to the net work done by the muscles to actuate joints. These are transverse muscle movements orthogonal to the muscle fiber direction and physiological tremors. We discovered that world-class experts minimize both of these inefficient motions compared to regional-level athletes and non-experts. While regional-level athletes surprisingly showed similar inefficiencies to non-experts, they used elastic tissues more effectively, achieving equivalent arm movements with smaller actuation-related tissue motions. We establish elastic tissue motion as a key indicator of general motor skill, expanding our understanding of elastic mechanisms and their role in general aspects of motor skill.

Tracking points in ultrasound (US) videos can be especially useful to characterize tissues in motion. Tracking algorithms that analyze successive video frames, such as variations of Optical Flow and Lucas–Kanade (LK), exploit frame-to-frame temporal information to track regions of interest. In contrast, convolutional neural-network (CNN) models process each video frame independently of neighboring frames. In this paper, we show that frame-to-frame trackers accumulate error over time. We propose three interpolation-like methods to combat error accumulation and show that all three methods reduce tracking errors in frame-to-frame trackers. On the neural-network end, we show that a CNN-based tracker, DeepLabCut (DLC), outperforms all four frame-to-frame trackers when tracking tissues in motion. DLC is more accurate than the frame-to-frame trackers and less sensitive to variations in types of tissue movement. The only caveat found with DLC comes from its non-temporal tracking strategy, leading to jitter between consecutive frames. Overall, when tracking points in videos of moving tissue, we recommend using DLC when prioritizing accuracy and robustness across movements in videos, and using LK with the proposed error-correction methods for small movements when tracking jitter is unacceptable.

Cumulative fatigue during repetitive work is associated with occupational risk and productivity reduction. Usually, subjective measures or muscle activity are used for a cumulative evaluation; however, Industry 4.0 wearables allow overcoming the challenges observed in those methods. Thus, the aim of this study is to analyze alterations in respiratory inductance plethysmography (RIP) to measure the asynchrony between thorax and abdomen walls during repetitive work and its relationship with local fatigue. A total of 22 healthy participants (age: 27.0 ± 8.3 yrs; height: 1.72 ± 0.09 m; mass: 63.4 ± 12.9 kg) were recruited to perform a task that includes grabbing, moving, and placing a box in an upper and lower shelf. This task was repeated for 10 min in three trials with a fatigue protocol between them. Significant main effects were found from Baseline trial to the Fatigue trials (p < 0.001) for both RIP correlation and phase synchrony. Similar results were found for the activation amplitude of agonist muscle (p < 0.001), and to the muscle acting mainly as a joint stabilizer (p < 0.001). The latter showed a significant effect in predicting both RIP correlation and phase synchronization. Both RIP correlation and phase synchronization can be used for an overall fatigue assessment during repetitive work.

In order to thrive, animals must be able to recognize aversive and appetitive stimuli within the environment and subsequently initiate appropriate behavioral responses. This assignment of positive or negative valence to a stimulus is a key feature of emotional processing, the neural substrates of which have been a topic of study for several decades. Until recently, the result of this work has been the identification of specific brain regions, such as the basolateral amygdala (BLA) and nucleus accumbens (NAc), as important to valence encoding. The advent of modern tools in neuroscience has allowed further dissection of these regions to identify specific populations of neurons signaling the valence of environmental stimuli. In this review, we focus upon recent work examining the mechanisms of valence encoding, and provide a model for the systematic investigation of valence within anatomically-, genetically-, and functionally defined populations of neurons.

The ability to associate temporally segregated information and assign positive or negative valence to environmental cues is paramount for survival. Studies have shown that different projections from the basolateral amygdala (BLA) are potentiated following reward or punishment learning 1 – 7 . However, we do not yet understand how valence-specific information is routed to the BLA neurons with the appropriate downstream projections, nor do we understand how to reconcile the sub-second timescales of synaptic plasticity 8 – 11 with the longer timescales separating the predictive cues from their outcomes. Here we demonstrate that neurotensin (NT)-expressing neurons in the paraventricular nucleus of the thalamus (PVT) projecting to the BLA (PVT-BLA:NT) mediate valence assignment by exerting NT concentration-dependent modulation in BLA during associative learning. We found that optogenetic activation of the PVT-BLA:NT projection promotes reward learning, whereas PVT-BLA projection-specific knockout of the NT gene ( Nt s) augments punishment learning. Using genetically encoded calcium and NT sensors, we further revealed that both calcium dynamics within the PVT-BLA:NT projection and NT concentrations in the BLA are enhanced after reward learning and reduced after punishment learning. Finally, we showed that CRISPR-mediated knockout of the Nts gene in the PVT-BLA pathway blunts BLA neural dynamics and attenuates the preference for active behavioural strategies to reward and punishment predictive cues. In sum, we have identified NT as a neuropeptide that signals valence in the BLA, and showed that NT is a critical neuromodulator that orchestrates positive and negative valence assignment in amygdala neurons by extending valence-specific plasticity to behaviourally relevant timescales. In mouse brain, neurotensin released into the basolateral amygdala by neurons in the paraventricular nucleus of the thalamus assigns positive or negative valence during associative learning.

Tissue motions within body segments, such as the relative movements of muscles, fascia, and bone, remain largely unexplored despite their relevance to movement dysfunction, force transmission, and motor skill. Here, we present a time-synchronized multimodal dataset that bridges this gap by capturing both internal tissue dynamics and conventional biomechanical measurements during arm reaching. Thirty-six participants across three expertise levels (world-class athletes, regional athletes, and untrained individuals) performed slow, rhythmic reaching movements while we recorded data using B-mode ultrasound imaging, motion capture, electromyography, and accelerometry. The dataset includes processed signals, derived parameters (segmented reach events, tissue boundary motion, arm kinematics, tremor events, and muscle activation levels), and metadata. Notably, using the DUSTrack point-tracking workflow, we provide trajectories for 11 points across approximately 300,000 ultrasound frames from the upper arm. This resource enables at least three primary applications: (1) supervised training and benchmarking of deep learning models for point tracking in ultrasound videos, (2) development of ultrasound-based metrics for characterizing soft tissue mechanics, and (3) biomechanical investigation of how tissue-level dynamics support motor performance. All data, processing code, and tutorials are provided in accessible formats with documentation.

Rationale The basolateral amygdala (BLA) and medial geniculate nucleus of the thalamus (MGN) have both been shown to be necessary for the formation of associative learning. While the role that the BLA plays in this process has long been emphasized, the MGN has been less well-studied and surrounded by debate regarding whether the relay of sensory information is active or passive. Objectives We seek to understand the role the MGN has within the thalamoamgydala circuit in the formation of associative learning. Methods Here, we use optogenetics and in vivo electrophysiological recordings to dissect the MGN-BLA circuit and explore the specific subpopulations for evidence of learning and synthesis of information that could impact downstream BLA encoding. We employ various machine learning techniques to investigate function within neural subpopulations. We introduce a novel method to investigate tonic changes across trial-by-trial structure, which offers an alternative approach to traditional trial-averaging techniques. Results We find that the MGN appears to encode arousal but not valence, unlike the BLA which encodes for both. We find that the MGN and the BLA appear to react differently to expected and unexpected outcomes; the BLA biased responses toward reward prediction error and the MGN focused on anticipated punishment. We uncover evidence of tonic changes by visualizing changes across trials during inter-trial intervals (baseline epochs) for a subset of cells. Conclusion We conclude that the MGN-BLA projector population acts as both filter and transferer of information by relaying information about the salience of cues to the amygdala, but these signals are not valence-specified.

Dopamine modulates medial prefrontal cortex (mPFC) activity to mediate diverse behavioural functions 1 , 2 ; however, the precise circuit computations remain unknown. One potentially unifying model by which dopamine may underlie a diversity of functions is by modulating the signal-to-noise ratio in subpopulations of mPFC neurons 3 – 6 , where neural activity conveying sensory information (signal) is amplified relative to spontaneous firing (noise). Here we demonstrate that dopamine increases the signal-to-noise ratio of responses to aversive stimuli in mPFC neurons projecting to the dorsal periaqueductal grey (dPAG). Using an electrochemical approach, we reveal the precise time course of pinch-evoked dopamine release in the mPFC, and show that mPFC dopamine biases behavioural responses to aversive stimuli. Activation of mPFC–dPAG neurons is sufficient to drive place avoidance and defensive behaviours. mPFC–dPAG neurons display robust shock-induced excitations, as visualized by single-cell, projection-defined microendoscopic calcium imaging. Finally, photostimulation of dopamine terminals in the mPFC reveals an increase in the signal-to-noise ratio in mPFC–dPAG responses to aversive stimuli. Together, these data highlight how dopamine in the mPFC can selectively route sensory information to specific downstream circuits, representing a potential circuit mechanism for valence processing. Dopamine in the medial prefrontal cortex modulates behavioural responses to aversive stimuli by increasing the signal-to-noise ratio of neurons projecting to the dorsal periaqueductal grey.