Browsing by Subject "Calcium imaging"
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Item Open Access General Anesthetics Activate a Central Pain-Suppression Circuit in the Amygdala(2020) Hua, ThuyGeneral anesthesia (GA) can produce analgesia (loss of pain) independent of inducing loss of consciousness, but the underlying mechanisms remain unclear. We hypothesized that GA suppresses pain in part by activating supraspinal analgesic circuits. We discovered a distinct population of GABAergic neurons activated by GA in the mouse central amygdala (CeAGA neurons). In vivo calcium imaging revealed that different GA drugs activate a shared ensemble of CeAGA neurons. CeAGA neurons also possess basal activity that mostly reflect animals’ internal state rather than external stimuli. Optogenetic activation of CeAGA potently suppressed both pain-elicited reflexive and self-recuperating behaviors across sensory modalities, and abolished neuropathic pain-induced mechanical (hyper-)sensitivity. Conversely, inhibition of CeAGA activity exacerbated pain, produced strong aversion, and cancelled the analgesic effect of low-dose ketamine. CeAGA neurons have widespread inhibitory projections to numerous affective pain-processing centers. Our study points to CeAGA as a potential powerful therapeutic target for alleviating chronic pain.
Item Open Access Neural Dynamics in the Basal Ganglia Underlying Birdsong Practice and Performance(2021) Singh Alvarado, JonnathanSkilled movements are typically more variable during practice, promoting exploration, yet highly stereotyped during performance, favoring exploitation. How neurons encode and dynamically regulate motor variability across practice and performance states remains unknown. Songbirds sing more variable songs when practicing alone and highly stereotyped songs when performing to a female, providing a powerful system to explore how neural ensembles regulate motor variability. Here, I used this system to identify neural mechanisms underlying practice and performance. First, I used deep brain imaging techniques to demonstrate that spiny neurons (SNs) in the basal ganglia (BG) encode vocal variability during solo practice, and that SN activity is strongly suppressed to enable stereotyped song performance towards a female. Second, I showed that optogenetically inhibiting SNs reduces pitch variability to female-directed levels. Third, I collaborated with Dr. John Pearson’s lab to uncover a coding scheme whereby specific patterns of SN activity map onto distinct spectral variants of syllables during vocal practice. Lastly, I use photometry, anatomical tracing, molecular profiling, and ex vivo physiology to establish that adrenergic signaling in the BG regulates vocal variability by directly suppressing SN activity. I conclude that SN ensembles encode and drive vocal exploration during practice, and the social context-dependent noradrenergic regulation of SN activity enables stereotyped and highly precise vocal performance.
Item Embargo Neural Network Approaches for Cortical Circuit Dissection and Calcium Imaging Data Analysis(2023) Baker, Casey MichelleThe brain encodes diverse cognitive functions through the coordinated activity of interacting neural circuits. Neural ensembles are groups of coactive neurons in these circuits that respond to similar stimuli. Neural ensembles are found throughout the brain and have been associated with many cognitive processes including memory, motor control, and perception. However, a key goal of systems neuroscience is to establish a functional link between neural activity and behavior and these previous studies established only a correlation between ensembles and behavior. Demonstrating a functional link between ensembles and behavior requires precise manipulation of ensemble activity. Manipulating ensemble activity allows neuroscientists to determine the patterns of neural activity that are necessary and sufficient to drive behavior. Additionally, recording and analyzing the activity of hundreds to thousands of neurons simultaneously allows neuroscientists to elucidate the patterns of neural activity underlying behavior. In this dissertation, we developed novel computational tools to help scientists selectively activate ensembles and analyze large-scale neural activity with single-cell resolution.One method to precisely activate cortical ensembles while limiting off-target effects is to stimulate pattern completion neurons. Pattern completion neurons are subsets of neurons in an ensemble that, when activated, can trigger the activation of the rest of the ensemble. However, scientists currently lack methods to reliably identify pattern completion neurons. The first project in this dissertation used computational modeling to identify characteristics of pattern completion neurons in cortical ensembles. We developed a realistic spiking model of layer 2/3 of the mouse visual cortex. We then identified ensembles in the network and quantified the pattern completion capability of different neuron pairs in an ensemble. We analyzed the relationship between structural and dynamic parameters and pattern completion capability. We found that multiple graph theory parameters, and degree in particular, could predict the pattern completion capability of a neuron pair. Additionally, we found that neurons that fired earlier in an ensemble recall event were more likely to have pattern completion properties than neurons that fired later. Lastly, we showed that we can measure this temporal latency in vivo with modern calcium indicators. The later projects in this dissertation used deep learning to improve calcium imaging analysis. First, we developed a semi-supervised pipeline for neuron segmentation to reduce the burden of manual labeling. We compensated for the low number of ground truth labels in two ways. First, we augmented the training data with pseudolabels generated with ensemble learning. Next, we used domain-specific knowledge to predict optimal hyperparameters from the limited ground truth labels. Our pipeline achieved state-of-the-art accuracy when trained on only 25% the number of manual labels as supervised methods. Lastly, we developed a spatiotemporal deep learning pipeline to predict the underlying electrical activity from calcium imaging videos. Calcium imaging provides only an indirect measurement of spiking neural activity, and various spike inference pipelines have attempted to accurately recover spiking timing and rate. Our pipeline improved the detection of single-spike events and improved spike rate prediction throughout the video. This improved performance will help scientists reconstruct neural circuits and study single-cell responses to stimuli. Overall, the tools developed in this dissertation will help systems neuroscience researchers establish a causal link between neural activity and behavior and will help determine the precise patterns of neural activity underlying these behaviors.
Item Open Access Striatal circuit and microcircuit mechanisms for habitual behavior(2017) O'Hare, JustinHabit formation is a behavioral adaptation that automates routine actions. This automation preserves cognitive resources that would otherwise be used to monitor action-outcome relationships. The dorsolateral striatum (DLS), which serves as the brain’s conduit into the basal ganglia, has been implicated in habit formation. However, it was not known whether and how the local DLS circuitry adapts to facilitate habitual behavior. By imaging DLS input-output computations of mice trained in a lever pressing task, I identified pathway-specific features of DLS output that strongly predicted the expression and suppression of habitual behavior. These results demonstrated that DLS actively contributes to the habit memory. To understand how these circuit-level adaptations arise, I then performed a series of ex vivo and in vivo experiments probing the local striatal microcircuitry in the context of habits. I found that a single class of interneuron, the striatal fast-spiking interneuron (FSI), was responsible for these habit-predictive changes in DLS output. I further found that FSIs undergo experience-dependent plasticity with habit formation and that their activity in DLS is required for the expression of habitual behavior. Surprisingly, FSIs also appeared to paradoxically excite physiologically distinct subsets of projection neurons in vivo. Taken together, this body of work outlines a circuit- and microcircuit-level mechanism whereby DLS provides a necessary contribution to the neurobiological underpinnings of habit.