Periodic Reporting for period 4 - NEUROFISH (Whole-brain circuits controlling visuomotor behavior)
Reporting period: 2022-08-01 to 2024-07-31
Our brains constantly integrate complex streams of sensory inputs, internal states and past experience to select suitable actions and execute them at the appropriate time. A major challenge in deciphering this process is that even very simple behaviors can involve networks of neurons distributed across many different areas. Small transparent organisms, such as zebrafish larvae, allow for non-invasive optical recordings or activity manipulations from neurons throughout most of the brain. We aim to understand, from sensory input to motor output, the neural systems that allow zebrafish to respond to visual stimuli, and identify the principles behind their structural and functional organization.
In this project we have taken a systematic approach to this problem. We started by undertaking a comprehensive quantitative analysis of swim kinematics and the sensory stimuli that drive them. Building on this work, we exploited the small, transparent brain of zebrafish larvae to perform whole-brain functional imaging of genetically defined neural populations during these behaviours. We have built state-of-the-art microscope systems to allow fast volumetric brain-wide imaging as well as precise manipulations of single neurons. With these methods, we aim to reveal the neural circuit organization and activity dynamics during visuomotor behaviour. Within the context of this project, we have developed a range of computational, genetic and imaging tools to achieve these goals, and applied them in a variety of behavioral contexts from reflexive responses to motion to social interactions and escape responses.
In this project we have taken a systematic approach to this problem. We started by undertaking a comprehensive quantitative analysis of swim kinematics and the sensory stimuli that drive them. Building on this work, we exploited the small, transparent brain of zebrafish larvae to perform whole-brain functional imaging of genetically defined neural populations during these behaviours. We have built state-of-the-art microscope systems to allow fast volumetric brain-wide imaging as well as precise manipulations of single neurons. With these methods, we aim to reveal the neural circuit organization and activity dynamics during visuomotor behaviour. Within the context of this project, we have developed a range of computational, genetic and imaging tools to achieve these goals, and applied them in a variety of behavioral contexts from reflexive responses to motion to social interactions and escape responses.
We developed an unsupervised clustering method that can robustly identify different swim bout categories based on a combination of kinematic similarity and behavioral usage (Marques et al., 2018; Marques and Orger, 2019). We found that larvae show a small set of preferred swim patterns, which systematically tile the space of bout kinematics. While some patterns are used broadly across many conditions, others are used only in very specific contexts. By analyzing the sensory stimuli preceding different bout types in freely swimming fish in social or feeding contexts, we could identify the natural stimuli that trigger different bout types. This quantitative analysis of behavior provides a crucial framework for subsequent investigations of neural circuit function. While this analysis depended on characterization of high-speed kinematics based on high resolution recordings, we have shown, using machine learning methods (Jouary et al., 2024; megabouts.ai) that high-speed kinematics can be reliably inferred even from low resolution data, enabling high-throughput applications in the areas of drug screening or disease phenotyping. Using a approach based on control theory, we extending this framework to allow for more a more continuous representation of swimming kinematics, and infer the underlying dynamics and control architecture (Mullen et al. 2024)
We used the swim bout framework to investigate the effects of experience on social avoidance in zebrafish larvae (Groneberg et al., 2020). Week old larvae avoid each other at short distances, but, for larvae reared in isolation, this distance is increased. We found that isolation reared larvae use different bout types in response to social stimuli that are sensed by the lateral line. This work establishes a powerful paradigm to investigate the effect of experience on the development of social circuits.
A major challenge in neuroscience is to relate activity in large populations of neurons to behavioral features which are also high-dimensional. In collaboration with Christian Machens’ group (Feierstein et al., 2023), we applied dimensionality reduction methods to identify behavioral features represented in population activity in the hindbrain. Surprisingly we found that just two behavioural features are sufficient to explain most of this activity, and that these features define functional clusters of neurons that are distributed in a stereotyped spatial organization across individuals.
To interpret whole-brain activity maps, and propose realistic circuit models, it is critical to know not just where active neurons are, but also their morphology, projection patterns and neurotransmitter phenotypes. We have generated transgenic lines targeting different neurotransmitter classes and cell-types and passed each through an imaging pipeline to record the responses of labeled neurons to a suite of behaviorally relevant stimuli. One line identified in this screen labeled a previously uncharacterized cluster of inhibitory direction selective (DS) neurons in the rostral thalamus (see Figure). Thalamic DS neurons mostly have binocular receptive fields and show a striking topographic map of direction preference, in contrast with pretectal DS neurons. Since the anatomy and function of the thalamus in larval fish is not well understood, we undertook to more systematically map visual and motor responses throughout this region, together with in situ expression patterns of markers for subdivisions of the mammalian thalamus.
Climbing fibre projections from the inferior olive (IO) constitute one of the two major excitatory inputs to cerebellar Purkinje Cells. We set out to investigate the functional organization of this pathway in zebrafish (Felix, Markov et al., 2024). Based on stochastic single neuron labeling, inferior olive neurons can be divided into anatomical classes that differ in their dendritic morphology and axonal projection fields. We mapped the responses of IO neurons to whole-field motion stimuli, using two-photon imaging. Neurons sensitive to rotational vs. translational motion and different directions of motion, were localized to distinct regions of the olive. To understand how this organization shaped the projections to the cerebellar cortex, we used volumetric light-sheet imaging of the axon terminals and IO cell bodies simultaneously, to map the point-to-point topography of the projection.
We used the swim bout framework to investigate the effects of experience on social avoidance in zebrafish larvae (Groneberg et al., 2020). Week old larvae avoid each other at short distances, but, for larvae reared in isolation, this distance is increased. We found that isolation reared larvae use different bout types in response to social stimuli that are sensed by the lateral line. This work establishes a powerful paradigm to investigate the effect of experience on the development of social circuits.
A major challenge in neuroscience is to relate activity in large populations of neurons to behavioral features which are also high-dimensional. In collaboration with Christian Machens’ group (Feierstein et al., 2023), we applied dimensionality reduction methods to identify behavioral features represented in population activity in the hindbrain. Surprisingly we found that just two behavioural features are sufficient to explain most of this activity, and that these features define functional clusters of neurons that are distributed in a stereotyped spatial organization across individuals.
To interpret whole-brain activity maps, and propose realistic circuit models, it is critical to know not just where active neurons are, but also their morphology, projection patterns and neurotransmitter phenotypes. We have generated transgenic lines targeting different neurotransmitter classes and cell-types and passed each through an imaging pipeline to record the responses of labeled neurons to a suite of behaviorally relevant stimuli. One line identified in this screen labeled a previously uncharacterized cluster of inhibitory direction selective (DS) neurons in the rostral thalamus (see Figure). Thalamic DS neurons mostly have binocular receptive fields and show a striking topographic map of direction preference, in contrast with pretectal DS neurons. Since the anatomy and function of the thalamus in larval fish is not well understood, we undertook to more systematically map visual and motor responses throughout this region, together with in situ expression patterns of markers for subdivisions of the mammalian thalamus.
Climbing fibre projections from the inferior olive (IO) constitute one of the two major excitatory inputs to cerebellar Purkinje Cells. We set out to investigate the functional organization of this pathway in zebrafish (Felix, Markov et al., 2024). Based on stochastic single neuron labeling, inferior olive neurons can be divided into anatomical classes that differ in their dendritic morphology and axonal projection fields. We mapped the responses of IO neurons to whole-field motion stimuli, using two-photon imaging. Neurons sensitive to rotational vs. translational motion and different directions of motion, were localized to distinct regions of the olive. To understand how this organization shaped the projections to the cerebellar cortex, we used volumetric light-sheet imaging of the axon terminals and IO cell bodies simultaneously, to map the point-to-point topography of the projection.
In this project we have developed several novel, scalable methods for analyzing behavioral patterns in zebrafish larvae. Unsupervised clustering methods, for example, provide a robust categorization of zebrafish swim patterns across contexts enabling simple models that map stimulus features onto distinct behavioral responses. This establishes a foundation for the systematic investigation of neural circuits underlying behavior. The Megabouts software pipeline (Jouary et al., 2024), developed to extract behavior data consistently from diverse recording conditions, has opened the door to more accessible and standardized behavioral phenotyping. By enabling high-resolution behavior analysis from low-resolution and noisy datasets, Megabouts makes sophisticated behavioral data accessible even in high-throughput contexts, with potential applications for drug screening and disease model phenotyping.
By applying dimensionality reduction techniques (Feierstein et al., 2023) to relate population neural activity to behavioral features, we showed how it was possible to distill the complexity of the neural data, revealing a small set of distinct, functional clusters that represent key behavioral responses. While we applied this approach to visual motion responses in the hindbrain, the same challenges of high-dimensional neuronal and behavioral data exist throughout neuroscience, making this a potentially broadly applicable technique.
By applying dimensionality reduction techniques (Feierstein et al., 2023) to relate population neural activity to behavioral features, we showed how it was possible to distill the complexity of the neural data, revealing a small set of distinct, functional clusters that represent key behavioral responses. While we applied this approach to visual motion responses in the hindbrain, the same challenges of high-dimensional neuronal and behavioral data exist throughout neuroscience, making this a potentially broadly applicable technique.