From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response.

Detailed descriptions of brain-scale sensorimotor circuits underlying vertebrate behavior remain elusive. Recent advances in zebrafish neuroscience offer new opportunities to dissect such circuits via whole-brain imaging, behavioral analysis, functional perturbations, and network modeling. Here, we harness these tools to generate a brain-scale circuit model of the optomotor response, an orienting behavior evoked by visual motion. We show that such motion is processed by diverse neural response types distributed across multiple brain regions. To transform sensory input into action, these regions sequentially integrate eye- and direction-specific sensory streams, refine representations via interhemispheric inhibition, and demix locomotor instructions to independently drive turning and forward swimming. While experiments revealed many neural response types throughout the brain, modeling identified the dimensions of functional connectivity most critical for the behavior. We thus reveal how distributed neurons collaborate to generate behavior and illustrate a paradigm for distilling functional circuit models from whole-brain data.

Geometric deep learning enables 3D kinematic profiling across species and environments.

Comprehensive descriptions of animal behavior require precise three-dimensional (3D) measurements of whole-body movements. Although two-dimensional approaches can track visible landmarks in restrictive environments, performance drops in freely moving animals, due to occlusions and appearance changes. Therefore, we designed DANNCE to robustly track anatomical landmarks in 3D across species and behaviors. DANNCE uses projective geometry to construct inputs to a convolutional neural network that leverages learned 3D geometric reasoning. We trained and benchmarked DANNCE using a dataset of nearly seven million frames that relates color videos and rodent 3D poses. In rats and mice, DANNCE robustly tracked dozens of landmarks on the head, trunk, and limbs of freely moving animals in naturalistic settings. We extended DANNCE to datasets from rat pups, marmosets, and chickadees, and demonstrate quantitative profiling of behavioral lineage during development.

Improved 3D Markerless Mouse Pose Estimation Using Temporal Semi-Supervision.

Three-dimensional markerless pose estimation from multi-view video is emerging as an exciting method for quantifying the behavior of freely moving animals. Nevertheless, scientifically precise 3D animal pose estimation remains challenging, primarily due to a lack of large training and benchmark datasets and the immaturity of algorithms tailored to the demands of animal experiments and body plans. Existing techniques employ fully supervised convolutional neural networks (CNNs) trained to predict body keypoints in individual video frames, but this demands a large collection of labeled training samples to achieve desirable 3D tracking performance. Here, we introduce a semi-supervised learning strategy that incorporates unlabeled video frames via a simple temporal constraint applied during training. In freely moving mice, our new approach improves the current state-of-the-art performance of multi-view volumetric 3D pose estimation and further enhances the temporal stability and skeletal consistency of 3D tracking.

Sociocultural determinants and patterns of healthcare utilization for epilepsy care in Uganda.

OBJECTIVE: Epilepsy is a global public health concern, with the majority of cases occurring in lower- and middle-income countries where the treatment gap remains formidable. In this study, we simultaneously explore how beliefs about epilepsy causation, perceived barriers to care, seizure disorder characteristics, and demographics influence the initial choice of healthcare for epilepsy and its impact on attaining biomedical care (BMC). METHODS: This study utilized the baseline sample (n = 626) from a prospective cohort study of people with epilepsy (PWE) attending three public hospitals in Uganda (Mulago National Referral Hospital, Butabika National Referral Mental Hospital, and Mbarara Regional Referral Hospital) for epilepsy care. Patient and household demographics, clinical seizure disorder characteristics, and sociocultural questionnaires were administered. Logistic regression and principal component analyses (PCA) were conducted to examine associations with the choice of primary seizure treatment. RESULTS: The sample was 49% female, and 24% lived in rural settings. A biomedical health facility was the first point of care for 355 (56.7%) participants, while 229 (36.6%) first sought care from a traditional healer and 42 (6.7%) from a pastoral healer. Preliminary inspection of candidate predictors using relaxed criteria for significance (p < 0.20) identified several factors potentially associated with a greater odds of seeking BMC first. Demographic predictors included older caredriver (decision-maker for the participant) age (odds ratio [OR]: 1.01, 95% confidence interval [CI]: [0.99, 1.02], p-value: 0.09), greater caredriver education level (OR = 1.21, 95% CI: [1.07, 1.37], p-value = 0.003), and lower ratio of sick to healthy family members (OR = 0.77 [0.56, 1.05], P = 0.097). For clinical predictors, none of the proposed predictors associated significantly with seeking BMC first. Self-report causation predictors associated with a greater odds of seeking BMC first included higher belief in biological causes of epilepsy (OR = 1.31 [0.92, 1.88], P = 0.133) and lower belief in socio-spiritual causes of epilepsy (OR = 0.68 [0.56, 0.84], P < 0.001). In the multivariate model, only higher caredriver education (OR = 1.19 [1.04, 1.36], P = 0.009) and lower belief in socio-spiritual causes of epilepsy (OR = 0.69 [0.56, 0.86], P < 0.01) remained as predictors of seeking BMC first. Additionally, PCA revealed a pattern which included high income with low beliefs in nonbiological causes of epilepsy as being associated with seeking BMC first (OR = 1.32 [1.12, 1.55], p = 0.001). Despite reaching some form of care faster, individuals seeking care from traditional or pastoral healers experienced a significant delay to eventual BMC (P < 0.001), with an average delay of more than two years (traditional healer: 2.53 years [1.98, 3.24]; pastoral care: 2.18 [1.21, 3.91]). CONCLUSIONS: Coupled with low economic and educational status, belief in spiritual causation of epilepsy is a dominant determinant of opting for traditional or pastoral healing over BMC, regardless of concurrent belief in biological etiologies. There is a prolonged delay to eventual BMC for PWE who begin their treatment seeking with nonallopathic providers, and although nonallopathic healers provide PWE with benefits not provided by BMC, this notable delay likely prevents earlier administration of evidence-based care with known efficacy. Based on these findings, initiatives to increase public awareness of neurobiological causes of epilepsy and effectiveness of biomedical drug treatments may be effective in preventing delays to care, as would programs designed to facilitate cooperation and referral among traditional, faith-based, and biomedical providers. This article is part of the Special Issue "The Intersection of Culture, Resources, and Disease: Epilepsy Care in Uganda".

Identification of nonvisual photomotor response cells in the vertebrate hindbrain.

Nonvisual photosensation enables animals to sense light without sight. However, the cellular and molecular mechanisms of nonvisual photobehaviors are poorly understood, especially in vertebrate animals. Here, we describe the photomotor response (PMR), a robust and reproducible series of motor behaviors in zebrafish that is elicited by visual wavelengths of light but does not require the eyes, pineal gland, or other canonical deep-brain photoreceptive organs. Unlike the relatively slow effects of canonical nonvisual pathways, motor circuits are strongly and quickly (seconds) recruited during the PMR behavior. We find that the hindbrain is both necessary and sufficient to drive these behaviors. Using in vivo calcium imaging, we identify a discrete set of neurons within the hindbrain whose responses to light mirror the PMR behavior. Pharmacological inhibition of the visual cycle blocks PMR behaviors, suggesting that opsin-based photoreceptors control this behavior. These data represent the first known light-sensing circuit in the vertebrate hindbrain.

Leaving flatland: Advances in 3D behavioral measurement.

Animals move in three dimensions (3D). Thus, 3D measurement is necessary to report the true kinematics of animal movement. Existing 3D measurement techniques draw on specialized hardware, such as motion capture or depth cameras, as well as deep multi-view and monocular computer vision. Continued advances at the intersection of deep learning and computer vision will facilitate 3D tracking across more anatomical features, with less training data, in additional species, and within more natural, occlusive environments. 3D behavioral measurement enables unique applications in phenotyping, investigating the neural basis of behavior, and designing artificial agents capable of imitating animal behavior.

Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting.

OBJECTIVE: Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of enhancing TBI triage in LMICs, we aim to develop the first deep learning model to predict outcomes after TBI and compare its performance with that of less complex algorithms. METHODS: TBI patients' data were prospectively collected in Kampala, Uganda, from 2016 to 2020. To predict good versus poor outcome at hospital discharge, we created deep neural network, shallow neural network, and elastic-net regularized logistic regression models. Predictors included 13 easily acquirable clinical variables. We assessed model performance with 5-fold cross-validation to calculate areas under both the receiver operating characteristic curve and precision-recall curve (AUPRC), in addition to standardized partial AUPRC to focus on comparisons at clinically relevant operating points. RESULTS: We included 2164 patients for model training, of which 12% had poor outcomes. The deep neural network performed best as measured by the area under the receiver operating characteristic curve (0.941) and standardized partial AUPRC in region maximizing recall (0.291), whereas the shallow neural network was best by the area under the precision-recall curve (0.770). In several other comparisons, the elastic-net regularized logistic regression was noninferior to the neural networks. CONCLUSIONS: We present the first use of deep learning for TBI prognostication, with an emphasis on LMICs, where there is great need for decision support to allocate limited resources. Optimal algorithm selection depends on the specific clinical setting; deep learning is not a panacea, though it may have a role in these efforts.

Machine Learning for Predicting In-Hospital Mortality After Traumatic Brain Injury in Both High-Income and Low- and Middle-Income Countries.

BACKGROUND: Machine learning (ML) holds promise as a tool to guide clinical decision making by predicting in-hospital mortality for patients with traumatic brain injury (TBI). Previous models such as the international mission for prognosis and clinical trials in TBI (IMPACT) and the corticosteroid randomization after significant head injury (CRASH) prognosis calculators can potentially be improved with expanded clinical features and newer ML approaches. OBJECTIVE: To develop ML models to predict in-hospital mortality for both the high-income country (HIC) and the low- and middle-income country (LMIC) settings. METHODS: We used the Duke University Medical Center National Trauma Data Bank and Mulago National Referral Hospital (MNRH) registry to predict in-hospital mortality for the HIC and LMIC settings, respectively. Six ML models were built on each data set, and the best model was chosen through nested cross-validation. The CRASH and IMPACT models were externally validated on the MNRH database. RESULTS: ML models built on National Trauma Data Bank (n = 5393, 84 predictors) demonstrated an area under the receiver operating curve (AUROC) of 0.91 (95% CI: 0.85-0.97) while models constructed on MNRH (n = 877, 31 predictors) demonstrated an AUROC of 0.89 (95% CI: 0.81-0.97). Direct comparison with CRASH and IMPACT models showed significant improvement of the proposed LMIC models regarding AUROC (P = .038). CONCLUSION: We developed high-performing well-calibrated ML models for predicting in-hospital mortality for both the HIC and LMIC settings that have the potential to influence clinical management and traumatic brain injury patient trajectories.

Machine Learning for Predicting Discharge Disposition After Traumatic Brain Injury.

BACKGROUND: Current traumatic brain injury (TBI) prognostic calculators are commonly used to predict the mortality and Glasgow Outcome Scale, but these outcomes are most relevant for severe TBI. Because mild and moderate TBI rarely reaches severe outcomes, there is a need for novel prognostic endpoints. OBJECTIVE: To generate machine learning (ML) models with a strong predictive capacity for trichotomized discharge disposition, an outcome not previously used in TBI prognostic models. The outcome can serve as a proxy for patients' functional status, even in mild and moderate patients with TBI. METHODS: Using a large data set (n = 5292) of patients with TBI from a quaternary care center and 84 predictors, including vitals, demographics, mechanism of injury, initial Glasgow Coma Scale, and comorbidities, we trained 6 different ML algorithms using a nested-stratified-cross-validation protocol. After optimizing hyperparameters and performing model selection, isotonic regression was applied to calibrate models. RESULTS: When maximizing the microaveraged area under the receiver operating characteristic curve during hyperparameter optimization, a random forest model exhibited top performance. A random forest model was also selected when maximizing the microaveraged area under the precision-recall curve. For both models, the weighted average area under the receiver operating characteristic curves was 0.84 (95% CI 0.81-0.87) and the weighted average area under the precision-recall curves was 0.85 (95% CI 0.82-0.88). CONCLUSION: Our group presents high-performing ML models to predict trichotomized discharge disposition. These models can assist in optimization of patient triage and treatment, especially in cases of mild and moderate TBI.

Machine Learning to Predict Successful Opioid Dose Reduction or Stabilization After Spinal Cord Stimulation.

BACKGROUND: Spinal cord stimulation (SCS) effectively reduces opioid usage in some patients, but preoperatively, there is no objective measure to predict who will most benefit. OBJECTIVE: To predict successful reduction or stabilization of opioid usage after SCS using machine learning models we developed and to assess if deep learning provides a significant benefit over logistic regression (LR). METHODS: We used the IBM MarketScan national databases to identify patients undergoing SCS from 2010 to 2015. Our models predict surgical success as defined by opioid dose stability or reduction 1 year after SCS. We incorporated 30 predictors, primarily regarding medication patterns and comorbidities. Two machine learning algorithms were applied: LR with recursive feature elimination and deep neural networks (DNNs). To compare model performances, we used nested 5-fold cross-validation to calculate area under the receiver operating characteristic curve (AUROC). RESULTS: The final cohort included 7022 patients, of whom 66.9% had successful surgery. Our 5-variable LR performed comparably with the full 30-variable version (AUROC difference <0.01). The DNN and 5-variable LR models demonstrated similar AUROCs of 0.740 (95% CI, 0.727-0.753) and 0.737 (95% CI, 0.728-0.746) ( P = .25), respectively. The simplified model can be accessed at SurgicalML.com . CONCLUSION: We present the first machine learning-based models for predicting reduction or stabilization of opioid usage after SCS. The DNN and 5-variable LR models demonstrated comparable performances, with the latter revealing significant associations with patients' pre-SCS pharmacologic patterns. This simplified, interpretable LR model may augment patient and surgeon decision making regarding SCS.

Gigapixel imaging with a novel multi-camera array microscope.

The dynamics of living organisms are organized across many spatial scales. However, current cost-effective imaging systems can measure only a subset of these scales at once. We have created a scalable multi-camera array microscope (MCAM) that enables comprehensive high-resolution recording from multiple spatial scales simultaneously, ranging from structures that approach the cellular scale to large-group behavioral dynamics. By collecting data from up to 96 cameras, we computationally generate gigapixel-scale images and movies with a field of view over hundreds of square centimeters at an optical resolution of 18 µm. This allows us to observe the behavior and fine anatomical features of numerous freely moving model organisms on multiple spatial scales, including larval zebrafish, fruit flies, nematodes, carpenter ants, and slime mold. Further, the MCAM architecture allows stereoscopic tracking of the z-position of organisms using the overlapping field of view from adjacent cameras. Overall, by removing the bottlenecks imposed by single-camera image acquisition systems, the MCAM provides a powerful platform for investigating detailed biological features and behavioral processes of small model organisms across a wide range of spatial scales.

Optogenetic photochemical control of designer K+ channels in mammalian neurons.

Currently available optogenetic tools, including microbial light-activated ion channels and transporters, are transforming systems neuroscience by enabling precise remote control of neuronal firing, but they tell us little about the role of indigenous ion channels in controlling neuronal function. Here, we employ a chemical-genetic strategy to engineer light sensitivity into several mammalian K(+) channels that have different gating and modulation properties. These channels provide the means for photoregulating diverse electrophysiological functions. Photosensitivity is conferred on a channel by a tethered ligand photoswitch that contains a cysteine-reactive maleimide (M), a photoisomerizable azobenzene (A), and a quaternary ammonium (Q), a K(+) channel pore blocker. Using mutagenesis, we identify the optimal extracellular cysteine attachment site where MAQ conjugation results in pore blockade when the azobenzene moiety is in the trans but not cis configuration. With this strategy, we have conferred photosensitivity on channels containing Kv1.3 subunits (which control axonal action potential repolarization), Kv3.1 subunits (which contribute to rapid-firing properties of brain neurons), Kv7.2 subunits (which underlie "M-current"), and SK2 subunits (which are Ca(2+)-activated K(+) channels that contribute to synaptic responses). These light-regulated channels may be overexpressed in genetically targeted neurons or substituted for native channels with gene knockin technology to enable precise optopharmacological manipulation of channel function.

Photochemical control of endogenous ion channels and cellular excitability.

Light-activated ion channels provide a precise and noninvasive optical means for controlling action potential firing, but the genes encoding these channels must first be delivered and expressed in target cells. Here we describe a method for bestowing light sensitivity onto endogenous ion channels that does not rely on exogenous gene expression. The method uses a synthetic photoisomerizable small molecule, or photoswitchable affinity label (PAL), that specifically targets K+ channels. PALs contain a reactive electrophile, enabling covalent attachment of the photoswitch to naturally occurring nucleophiles in K+ channels. Ion flow through PAL-modified channels is turned on or off by photoisomerizing PAL with different wavelengths of light. We showed that PAL treatment confers light sensitivity onto endogenous K+ channels in isolated rat neurons and in intact neural structures from rat and leech, allowing rapid optical regulation of excitability without genetic modification.

Continuous Whole-Body 3D Kinematic Recordings across the Rodent Behavioral Repertoire.

In mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to probe naturalistic behaviors and their neural underpinnings. To address this, we developed CAPTURE (Continuous Appendicular and Postural Tracking Using Retroreflector Embedding), a behavioral monitoring system that combines motion capture and deep learning to continuously track the 3D kinematics of a rat's head, trunk, and limbs for week-long timescales in freely behaving animals. CAPTURE realizes 10- to 100-fold gains in precision and robustness compared with existing convolutional network approaches to behavioral tracking. We demonstrate CAPTURE's ability to comprehensively profile the kinematics and sequential organization of natural rodent behavior, its variation across individuals, and its perturbation by drugs and disease, including identifying perseverative grooming states in a rat model of fragile X syndrome. CAPTURE significantly expands the range of behaviors and contexts that can be quantitatively investigated, opening the door to a new understanding of natural behavior and its neural basis.

Surgical intervention and patient factors associated with poor outcomes in patients with traumatic brain injury at a tertiary care hospital in Uganda.

OBJECTIVE: The purpose of this study was to investigate whether neurosurgical intervention for traumatic brain injury (TBI) is associated with reduced risks of death and clinical deterioration in a low-income country with a relatively high neurosurgical capacity. The authors further aimed to assess whether the association between surgical intervention and acute poor outcomes differs according to TBI severity and various patient factors. METHODS: Using TBI registry data collected from a national referral hospital in Uganda between July 2016 and April 2020, the authors performed Cox regression analyses of poor outcomes in admitted patients who did and did not undergo surgery for TBI, with surgery as a time-varying treatment variable. Patients were further stratified by TBI severity using the admission Glasgow Coma Scale (GCS) score: mild TBI (mTBI; GCS scores 13-15), moderate TBI (moTBI; GCS scores 9-12), and severe TBI (sTBI; GCS scores 3-8). Poor outcomes constituted Glasgow Outcome Scale scores 2-3, deterioration in TBI severity between admission and discharge (e.g., mTBI to sTBI), and death. Several clinical and demographic variables were included as covariates. Patients were observed for outcomes from admission through hospital day 10. RESULTS: Of 1544 patients included in the cohort, 369 (24%) had undergone surgery. Rates of poor outcomes were 4% (n = 13) for surgical patients and 12% (n = 144) among nonsurgical patients (n = 1175). Surgery was associated with a 59% reduction in the hazard for a poor outcome (HR 0.41, 95% CI 0.23-0.72). Age, pupillary nonreactivity, fall injury, and TBI severity at admission were significant covariates. In models stratifying by TBI severity at admission, patients with mTBI had an 80% reduction in the hazard for a poor outcome with surgery (HR 0.20, 95% CI 0.04-0.90), whereas those with sTBI had a 65% reduction (HR 0.35, 95% CI 0.14-0.89). Patients with moTBI had a statistically nonsignificant 56% reduction in hazard (HR 0.44, 95% CI 0.17-1.17). CONCLUSIONS: In this setting, the association between surgery and rates of poor outcomes varied with TBI severity and was influenced by several factors. Patients presenting with mTBI had the greatest reduction in the hazard for a poor outcome, followed by those presenting with sTBI. However, patients with moTBI had a nonsignificant reduction in the hazard, indicating greater variability in outcomes and underscoring the need for closer monitoring of this population. These results highlight the importance of accurate, timely clinical evaluation throughout a patient's admission and can inform decisions about whether and when to perform surgery for TBI when resources are limited.

Predicting the Individual Treatment Effect of Neurosurgery for Patients with Traumatic Brain Injury in the Low-Resource Setting: A Machine Learning Approach in Uganda.

Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these low-resource settings, effective triage of patients with TBI-including the decision of whether or not to perform neurosurgery-is critical in optimizing patient outcomes and healthcare resource utilization. Machine learning may allow for effective predictions of patient outcomes both with and without surgery. Data from patients with TBI was collected prospectively at Mulago National Referral Hospital in Kampala, Uganda, from 2016 to 2019. One linear and six non-linear machine learning models were designed to predict good versus poor outcome near hospital discharge and internally validated using nested five-fold cross-validation. The 13 predictors included clinical variables easily acquired on admission and whether or not the patient received surgery. Using an elastic-net regularized logistic regression model (GLMnet), with predictions calibrated using Platt scaling, the probability of poor outcome was calculated for each patient both with and without surgery (with the difference quantifying the "individual treatment effect," ITE). Relative ITE represents the percent reduction in chance of poor outcome, equaling this ITE divided by the probability of poor outcome with no surgery. Ultimately, 1766 patients were included. Areas under the receiver operating characteristic curve (AUROCs) ranged from 83.1% (single C5.0 ruleset) to 88.5% (random forest), with the GLMnet at 87.5%. The two variables promoting good outcomes in the GLMnet model were high Glasgow Coma Scale score and receiving surgery. For the subgroup not receiving surgery, the median relative ITE was 42.9% (interquartile range [IQR], 32.7% to 53.5%); similarly, in those receiving surgery, it was 43.2% (IQR, 32.9% to 54.3%). We provide the first machine learning-based model to predict TBI outcomes with and without surgery in LMICs, thus enabling more effective surgical decision making in the resource-limited setting. Predicted ITE similarity between surgical and non-surgical groups suggests that, currently, patients are not being chosen optimally for neurosurgical intervention. Our clinical decision aid has the potential to improve outcomes.

Correcting for physical distortions in visual stimuli improves reproducibility in zebrafish neuroscience.

Optical refraction causes light to bend at interfaces between optical media. This phenomenon can significantly distort visual stimuli presented to aquatic animals in water, yet refraction has often been ignored in the design and interpretation of visual neuroscience experiments. Here we provide a computational tool that transforms between projected and received stimuli in order to detect and control these distortions. The tool considers the most commonly encountered interface geometry, and we show that this and other common configurations produce stereotyped distortions. By correcting these distortions, we reduced discrepancies in the literature concerning stimuli that evoke escape behavior, and we expect this tool will help reconcile other confusing aspects of the literature. This tool also aids experimental design, and we illustrate the dangers that uncorrected stimuli pose to receptive field mapping experiments.

An Attitude Survey and Assessment of the Feasibility, Acceptability, and Usability of a Traumatic Brain Injury Decision Support Tool in Uganda.

BACKGROUND: Traumatic brain injury (TBI) prognostic models are potential solutions to severe human and technical shortages. Although numerous TBI prognostic models have been developed, none are widely used in clinical practice, largely because of a lack of feasibility research to inform implementation. We previously developed a prognostic model and Web-based application for in-hospital TBI care in low-resource settings. In this study, we tested the feasibility, acceptability, and usability of the application with potential end-users. METHODS: We performed our feasibility assessment with providers involved in TBI care at both a regional and national referral hospital in Uganda. We collected qualitative and quantitative data on decision support needs, application ease of use, and implementation design. RESULTS: We completed 25 questionnaires on potential uses of the app and 11 semistructured feasibility interviews. Top-cited uses were informing the decision to operate, informing the decision to send the patient to intensive care, and counseling patients and relatives. Participants affirmed the potential of the application to support difficult triage situations, particularly in the setting of limited access to diagnostics and interventions, but were hesitant to use this technology with end-of-life decisions. Although all participants were satisfied with the application and agreed that it was easy to use, several expressed a need for this technology to be accessible by smartphone and offline. CONCLUSIONS: We elucidated several potential uses for our app and important contextual factors that will support future implementation. This investigation helps address an unmet need to determine the feasibility of TBI clinical decision support systems in low-resource settings.

Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion.

In the absence of salient sensory cues to guide behavior, animals must still execute sequences of motor actions in order to forage and explore. How such successive motor actions are coordinated to form global locomotion trajectories is unknown. We mapped the structure of larval zebrafish swim trajectories in homogeneous environments and found that trajectories were characterized by alternating sequences of repeated turns to the left and to the right. Using whole-brain light-sheet imaging, we identified activity relating to the behavior in specific neural populations that we termed the anterior rhombencephalic turning region (ARTR). ARTR perturbations biased swim direction and reduced the dependence of turn direction on turn history, indicating that the ARTR is part of a network generating the temporal correlations in turn direction. We also find suggestive evidence for ARTR mutual inhibition and ARTR projections to premotor neurons. Finally, simulations suggest the observed turn sequences may underlie efficient exploration of local environments.

Neural Circuits Underlying Visually Evoked Escapes in Larval Zebrafish.

Escape behaviors deliver organisms away from imminent catastrophe. Here, we characterize behavioral responses of freely swimming larval zebrafish to looming visual stimuli simulating predators. We report that the visual system alone can recruit lateralized, rapid escape motor programs, similar to those elicited by mechanosensory modalities. Two-photon calcium imaging of retino-recipient midbrain regions isolated the optic tectum as an important center processing looming stimuli, with ensemble activity encoding the critical image size determining escape latency. Furthermore, we describe activity in retinal ganglion cell terminals and superficial inhibitory interneurons in the tectum during looming and propose a model for how temporal dynamics in tectal periventricular neurons might arise from computations between these two fundamental constituents. Finally, laser ablations of hindbrain circuitry confirmed that visual and mechanosensory modalities share the same premotor output network. We establish a circuit for the processing of aversive stimuli in the context of an innate visual behavior.