Wei-Chung Allen Lee - US grants

DESCRIPTION (provided by applicant): Understanding of the role of GABAergic inhibitory interneurons is central to any model of brain function. Interneurons are critical to normal cortical processing and their dysfunction is thought to produce an array of disorders ranging from schizophrenia to epilepsy. Our goal is to understand the role of different classes of interneurons in the functional organization of layers 2/3 of the mouse visual cortex. A better understanding of the functional properties of distinct classes of interneurons may allow targeting of potential therapeutic treatments to specifically ameliorate disease processes. We will apply new techniques to study the physiology of virtually every neuron in a volume of cortex in mice in which distinct subclasses of interneurons are labeled. By combining two-photon calcium imaging and transgenic mouse technology we will address the following Specific Aims: Aim1: We will examine the visual responses of neuronal populations in mouse primary visual cortex. Orientation selectivity, ocular dominance and retinotopy will be measured for thousands of neurons in a volume of layer 2/3. From past work, we expect to find good orientation tuning, but neurons that respond to different orientations will be mixed together. The salt-and-pepper arrangement of orientation implies that there are specific connections among neurons in the local circuit. I propose to study one important aspect of this topic, the role of inhibitory interneurons in such a network. Aim 2: We will examine the receptive-field properties of interneuron subclasses in mouse visual cortex. Two-photon calcium imaging will be applied to existing lines of transgenic mice in which distinct subpopulations of interneurons are labeled. We hypothesize that one class of interneurons (fast-spiking, parvalbumin-positive cells) will be strongly responsive, but non-selective for orientation. Too little is known of other classes, such as low-threshold spiking, somatostatin-positive cells, to make specific predictions about them. But, it is likely that some classes of inhibitory interneurons will have strong stimulus selectivity. The ability to sample large populations of neurons will help characterize rare cell types. More generally, combining comprehensive local circuit physiology with mouse genetics will accelerate our understanding how circuits and their components underlie function and behavior.

Project Summary/Abstract Understanding how information is processed in neuronal circuits is a central question in neuroscience and key to understanding how the brain works. A neuron's function is fundamentally dependent on how it is connected within its network. Therefore, understanding the relationship between network connectivity - circuit structure - and cellular function will help us to understand how neurons and networks transform information to bring about perception and behavior. Recent advances in large-scale electron microscopy have allowed us to begin detailed mapping of neural network anatomy. The olfactory circuit of Drosophila melanogaster is an excellent model system for this purpose because it contains a relatively small number of neurons in a small volume, existing tools allow genetically-targeted labeling, and our knowledge about functional properties of neurons in this system is rapidly expanding. We will exploit these key advantages to understand the rules underlying olfactory network organization. This proposal would generate for the first time, large-scale EM datasets in which long-range neuronal connectivity can be reconstructed and corresponded to identified cells whose in vivo physiology is known or can be examined in different animals. Specifically, we will examine the convergence and divergence of projection neuron connecitivity to higher-order neurons in two target regions of the protocerebrum, the lateral horn and the mushroom body. We hypothesize that lateral horn neurons receive stereotyped and convergent connectivity from projection neurons of the same type. Interestingly, neurons in the lateral horn have recently been implicated in innate olfactory behaviors. In contrast, we hypothesize that mushroom body neurons, known to be crucial for olfactory learning, have more random and less stereotyped connectivity with projection neurons. These are fundamental questions about the structure and function of neuronal networks that are uniquely approachable in the olfactory system of the fly. Finally, by understanding the basic principles of neuronal network connectivity we will be poised to compare diseased and healthy brains to assess how circuit structure is affected in models of neurodegenerative disorders to rationally design treatment strategies.

Project Summary/Abstract Our brains contain billions of neurons, each with thousands of synapses. Together, they form a functional neural network with trillions of connections. Its scale and complexity is daunting, but from this complexity emerges perception and behavior. How do we understand the organization of such an immense and complex network? A key path forward is investigating the relationship between structure and function in neuronal circuits. The function of a neuron is fundamentally dependent on how it is connected. Therefore, understanding the relationship between circuit structure - connectivity - and cellular function will help us understand how neurons and networks process information. Unfortunately, detailed connectivity mapping remains difficult. One critical barrier is data throughput. Recently, high-throughput transmission electron microscopy (TEM) has increased the speed of imaging, but continues to rely on humans for laborious manual sample collection and handling. Current methods of automated sectioning can collect thousands of electron microscopy (EM) samples on a tape substrate, but are incompatible with fast TEM imaging because the tape prevents transmission of an electron beam. Serial sections collected in this manner are currently imaged using scanning EM, which is typically slower. This proposal aims to develop novel technologies that synergistically bridge automated sample collection and high-speed TEM imaging to transcend the throughput barrier. We will generate a novel tape substrate for sample collection that is compatible with TEM imaging and use it to collect thousands of serial thin sections. Additionally, we will engineer and build a sample stage for continuous TEM imaging of tape-collected samples. These methods would allow high-quality EM imaging of local mammalian cortical circuits to be completed in ~1 year compared to more than 100 years with conventional approaches. We expect that the routine generation of larger, high-quality datasets using these novel techniques will also accelerate advances in their analyses. We will immediately use this approach to increase our understanding of the fundamental principles underlying cortical processing and organization. Furthermore, with higher- throughput EM imaging, we will finally be poised to compare diseased and healthy brains to assess how circuit connectivity is altered, thereby directing intelligent treatment strategies.

Project Summary/Abstract A fundamental goal in neuroscience is to understand how information is processed in neuronal circuits. Ultimately, we would like to understand the relationship between circuit structure and network function. However, the immense complexity of most brain networks has been a significant barrier to progress. Neurons are a primary computational component of networks in the brain, yet we do not have a comprehensive list of their types for even the simplest mammalian neuronal circuit. Moreover, a neuron?s function is fundamentally dependent on how it is connected within its network, yet mammalian neuronal networks consist of billions of cells with trillions of connections. How can we get a handle on such a complex computational machine? Recent advances in large-scale electron microscopy (EM) and molecular genetic tools have allowed us to begin detailed mapping of neural network anatomy and cellular physiology. The cerebellum is an excellent system to validate our novel platform as part of a systematic effort to reverse engineer a functional neural circuit that is involved in motor control and social behavior. Its basic structure is well ordered, relatively simple and sufficiently described to have inspired computational models that capture aspects of cerebellar function. However, even the most advanced models are limited by an incomplete characterization of the cell types and connectivity within the cerebellum. Here, we propose to validate our next-generation large-scale EM platform and provide a comprehensive characterization of cerebellar circuitry. We will use tools recently developed in our lab to a circuit that offers the advantages of relative simplicity and a strong starting foundation. These studies will allow us to understand principles of cerebellar circuit organization and may help us determine the role of specific circuit elements in neurodegenerative disorders.

Project Summary During perceptual decision-making, populations of neurons, arranged in highly interconnected microcircuits, work together to encode sensory stimuli and to transform sensory perception into appropriate behavioral choices. A fundamental gap in our knowledge about perceptual decision-making is understanding how the connectivity in cortical microcircuits shapes dynamics and information codes in populations of neurons. This gap has arisen because anatomical connectivity and activity have generally been studied separately, and because a computational framework to understand structure-function relationships in cortical microcircuits is missing. Here, we will assemble a team of researchers with complementary skills to tackle this problem. We will combine approaches to study population coding and dynamics using two-photon calcium imaging during a novel and complex decision task for mice, with measurements of connectivity in the imaged neurons using electron microscopy (EM)-based connectomics. Furthermore, we will use our activity and connectivity data to develop a data-driven model to explore structure-function relationships across cortical microcircuits. We will apply our new approach to investigate how population codes, microcircuit connectivity, and structure- function relationships differ across cortex to perform distinct computational tasks during perceptual decision- making. Although it is well established that sensory and association cortices perform different functions, little is known about the mechanisms underlying these different roles, including distinctions in microcircuit connectivity and population coding schemes. In a first aim, we will compare population codes and microcircuit connectivity for sensory stimuli and behavioral choices in visual cortex (V1; sensory cortex) and posterior parietal cortex (PPC; association cortex). We will use computational tools to examine how distinct coding schemes provide functional benefits. We will use EM connectomics in V1 and PPC for neurons imaged during a perceptual decision task to probe structure-function relationships for stimulus and choice codes. We will develop a data- driven recurrent neural network model to relate connectivity and population activity. In a second aim, we will investigate how neuronal populations transform sensory information into behavioral choices using microcircuit connectivity. We will develop a new statistical concept ? intersection information ? to identify activity patterns in V1 and PPC that carry sensory information that informs behavioral choices. Using EM connectomics, we will reconstruct the microcircuit connectivity between cells to test hypotheses about sensory-to-choice information flow. Our work will be some of the first to compare population coding and microcircuit connectivity across cortical regions and to explore structure-function relationships for perceptual decision-making.

Project Summary The goal of this proposal is to develop a widely adoptable, high-throughput, functional connectomics platform to semi-automatically reconstruct and analyze the synaptic connectivity of functionally characterized neuronal microcircuits. We will develop this pipeline in the context of understanding neural microcircuits that control walking, using the Drosophila ventral nerve cord (VNC) as a model system. The VNC constitutes over a third of the fly central nervous system, and functions like the spinal cord to control movement and sensation of the limbs. It has a tractable number of neurons (~60,000), many of which are genetically identified and anatomically stereotyped, making it an excellent brain region for developing a comprehensive approach to characterize neuronal circuits. Our approach will integrate several technologies from our labs, including in vivo two-photon calcium imaging in walking flies, high-throughput transmission electron microscopy (EM), automated, deep- learning based connectomic reconstruction, and cell morphology-based analytics. Using this pipeline we will generate the first dense functional connectomes of the VNC, including the sensorimotor leg circuitry from multiple male and female flies. We will use these data to test the hypothesis that there exist specific patterns of synaptic connectivity between functional subtypes of leg proprioceptors and motor neurons. By creating the first connectomes of a microcircuit that orchestrates walking behavior, we anticipate this project will provide new and fundamental insight into the circuit basis of sensorimotor transformation and motor control. It will also create an important resource for the Drosophila and neuroscience communities, through publicly available tools and datasets that integrate connectivity, morphology, and cellular physiology data. Moreover, the technical approaches we develop for high-throughput functional connectomics can be readily applied to other brain regions and across species.

Project Summary A fundamental gap in our knowledge of the nervous system is understanding how variations in wiring and connectivity of neuronal circuits relate to variability in neural computations and behavior. This gap has arisen because anatomical connectivity and function are typically studied separately. Here, we will assemble a team of researchers with complementary skills to tackle this problem. We will combine several technologies developed in our labs, including in vivo calcium imaging during behavior to study neuronal population activity during perceptually-guided behaviors and high-throughput electron microscopy (EM) to extract the connectivity of an underlying network essential for that behavior. To do so, we will use Drosophila melanogaster as a model system because it has a powerful genetic toolkit, tractable number of neurons, is amenable to large-scale behavioral screens, and is a realistic target for comparative whole-brain connectomics. This makes the fly an excellent model to develop a comprehensive approach to characterize neuronal circuits. We will apply our new approach to investigate how population codes, network connectivity, and structure-function relationships differ between individuals. Although it is well known that individuals, as well as males and females, exhibit variable behaviors, little is understood about how variations in neuronal wiring and connectivity relate to variations in neural computation and ultimately behavior. In our first aim, we will compare population codes, wiring, and connectivity between multiple isogenic individuals that exhibit differences in visually-guided approach behavior. In a second aim, we will apply similar approaches to investigate differences in odor preference behavior. We will test how stochastic brain asymmetry, weighting of sensory signals, and repertoire of local interneurons influence computations within individual brains. Analyzing structure-function relationships across individuals will examine the tradeoff between neuronal circuit precision and variability, and reveal how specific variations shape information processing and behavior. We will generate models predicting neuronal function and behavior from circuit wiring and neuronal structure. Our work will be among the first to compare whole-brain, synaptic-resolution connectomes of multiple individuals to reveal fundamental constraints on functional network organization and discover how circuit variability supports individuality.