Alexei Koulakov - US grants

[unreadable] DESCRIPTION (provided by applicant): Our goal is to understand the quantitative principles whereby neural networks in the brain are formed during development. An ideal candidate to study these principles is the topographic projection from retina to optic tectum or mammalian superior colliculus. Axons of neighboring retinal cells terminate proximally in the superior colliculus thus forming a topographically precise representation of the visual world called topographic or retinotopic map. Coordinate axes are encoded in retina and in the target through graded expression of molecular labels, such as Eph receptor tyrosine kinases and their ligands, ephrins. Additional sharpening of projections is facilitated by correlated neural activity. In this proposal we will combine various developmental mechanisms in a single quantitative model and show how their interactions provide required precision of topographic mapping. The specific aims of this proposal include: i) How can one combine activity-dependent and activity-independent factors in the same model? ii) Why is the dynamics of axons and dendrites different during development? iii) What is the role of synaptic maturation in the axon and dendrite branch dynamics? Our project will help to understand the mechanisms whereby genetic program in the form of molecular labels and environmental information conveyed by correlated neural activity shape the developing neuronal connectivity. Our study will therefore provide insights on neurological conditions characterized by abnormal development of sensory function, including a possible effect of disruption of Eph/ ephrin pathways on impairment of visual processing in humans. Our model will aid in addressing developmental disabilities and neuro-degenerative diseases linked to defects in circuitry. All studies will be carried out in close collaboration with experimental groups. Expert advice will be solicited on all stages of the project. [unreadable] [unreadable]

DESCRIPTION (provided by applicant): Adult brain retains the ability to produce new neurons throughout life. New neurons are generated from stem cells through a cascade of events which include symmetric and asymmetric divisions and continuous changes of cell morphology. The cascade culminates with the young neurons establishing connections with other cells and becoming integrated into the pre-existing neuronal circuitry. Persistent neurogenesis is observed only in a number of the adult brain regions. In one of them, the hippocampal dentate gyrus, new neurons may be important for learning, memory, and mood. Adult neurogenesis is a dynamic process that responds to a wide range of stimuli which can enhance or suppress its output and may affect any step of the differentiation cascade. For instance, an antidepressant drug fluoxetine (Prozac) enhances, whereas aging decreases, hippocampal neurogenesis. However, the steps of the differentiation cascade affected by pro- or anti-neurogenic factors and the mechanisms of their action are not known. The main goal of this collaborative project is to develop a computational model of adult neurogenesis. Towards this goal, we will develop a novel research method that integrates computational and experimental techniques for a quantitative investigation of the steps that define adult neurogenesis. A new approach developed in Enikolopov lab (experimental collaborator) uses a set of genetically encoded markers to monitor the progression of progenitor cells through the differentiation cascade. This approach allows to determine the abundances of different cell types as a function of time as the cells divide and differentiate. The abundances are evaluated only for cells that were dividing in the beginning of the experiment and correspond to the pair wise correlation function for different cell types. To convert the abundances as a function of time into the rates of division and differentiation of various cell types we will use the computational model developed by the group of Dr. Koulakov. This computational model will also be used to determine the effects of aging and antidepressants on the parameters of division and differentiation cascade. We will investigate the changes occurring in the genome-wide gene expression profiles as a function of stage of the differentiation cascade. We will monitor the dependence of gene expression on both aging and antidepressants and will elucidate the underlying gene regulation dynamics. Finally, we will study theoretically the putative computational properties of the adult neurogenesis. The specific aims (SA) of this proposal include: SA 1: To develop a computational model for the stem cell division and differentiation cascade in the adult hippocampus. This aim will allow inferring the division diagram and transition rates from the experimental data and will allow to study the changes induced by aging and antidepressants. SA 2: To dissect the transcription regulation network controlling adult hippocampal neurogenesis. We will investigate changes of gene expression associated with aging and antidepressant drugs and will uncover potential regulatory network mechanisms. SA 3: To study the unique computational properties of neural stem cells. Here we will calculate the rate of learning as a function of sparseness of representation and will argue that cell-based learning rules adopt to new stimuli faster than conventional synapse based Hebb rules. Intellectual merit: The proposed research will contribute to systems biology on two levels. First, we will elucidate the mechanisms of neural stem differentiation leading to the production of new neurons. Second, we will develop methods for determining division and differentiation rates from time-dependent data of cell abundances. This computational framework may become standard in the studies of stem cell differentiation in other fields of biology. Broader impacts: This project is based on the synergy between theoretical sciences, novel computational methods, and cutting-edge experiments in neurobiology. The award will provide a unique crossdisciplinary environment for training of young neuroscientists. We expect that two postdoctoral fellows, specializing in theoretical and in experimental approaches, will receive training through this award. To broader society: Our studies will help to elucidate the mechanisms of cognitive decline associated with aging and to determine the targets of antidepressant drug therapies. Because the lifetime incidence of depression in the US is more than 12% in men and 20% in women, our studies may substantially contribute to public health.

DESCRIPTION (provided by applicant): The brain is an extremely complex network consisting of billions of neurons linked by trillions of synapses. Neuronal function depends on how these neurons are connected within this network. A wide range of brain functions, including sensory perception, learning, memory, decision making, cognition, reasoning, and communication, is therefore related to the details of this neuronal connectivity. Many neuropsychiatric and neurodegenerative disorders, including schizophrenia, autism spectrum disorders, Alzheimer's and Parkinson's diseases, are linked to abnormal changes in neuronal connectivity. Understanding neuronal circuitry is therefore a task of enormous importance. Despite the progress made by using microscopic and electrophysiological approaches, especially for small networks, understanding neuronal connectivity has been stalled by astronomical complexity of this task. Here we propose to provide the computational and theoretical foundations for a novel technology which will dramatically accelerate our capacity to determine neuronal connectivity with single-neuron resolution. We are adapting the techniques of high-throughput next-generation DNA sequencing for the purposes of obtaining the structure of neuronal connectivity. We argue that because the cost of DNA sequencing has dropped precipitously over the last few years and the efficiency of these techniques is undergoing explosive growth, obtaining connectivity of sufficiently large networks is now feasible at sufficiently low cost. In our proposl, for example, we present preliminary data on the reconstruction of connectivity within a network of cultured mouse neurons containing about 1200 network nodes, which is the largest neuronal network reconstructed to date. To accomplish this task, we introduce unique short sequences of DNA into every neuron in the network. Because these short sequences uniquely label individual cells, we call them genetic barcodes. Using specifically designed viruses, we made these barcodes jump across synaptic junctions. Using enzymes called DNA recombinases, we connect barcodes from the host cell to the invader barcodes that travel across synapses into pairs. The barcode pairs carry information about network connectivity that can be obtained by DNA sequencing. Reconstructing neuronal connections from sequencing results presents unique computational and theoretical challenges that have never been dealt with before. In this project, we will build mathematical models that describe the underlying biological processes, test these models against experimental data, and use the resulting expertise to design accurate and efficient computational algorithms. Because of the need for feedback between theory, computational algorithms, and experiments, our project is intensely collaborative. The specific aims (SA) of this proposal include: SA 1: To develop a method of generating random barcodes in genomic DNA using the shufflon system. Here we will study, both theoretically and experimentally, the method of generating a large ensemble of barcode sequences using Rci recombinase that can shuffle DNA as a deck of cards. SA 2: To develop a biophysically realistic model for barcode processing within cells. In this SA we will study the mathematical models of barcodes jumping across synapses and their post-processing with the goal of identifying potential artifacts. The results of these models will be used for error correction in SA3. SA 3: To develop the computational pipeline for the reconstruction of connectivity from sequencing data. Here we will build a set of algorithms for efficient reconstruction of neuronal circuits from barcode pairs. Intellectual merit. The proposed research will contribute to biology on several levels. First, we will develop a novel set of technologies that will allow assaying neuronal connections with the single-neuron resolution. Second, we will build descriptive models for biophysics and combinatorics of DNA recombination that can be used in neuroscience and beyond. Finally, we will design a set of bioinformatics algorithms that are specific for the task o reconstructing neuronal connectivity. Broader impact. This project is based on the synergy between theoretical sciences, novel computational methods, and cutting-edge experiments in cellular neurobiology. The award will provide a unique cross-disciplinary environment for training of young neuroscientists. We expect that two postdoctoral fellows, specializing in theoretical and in experimental approaches, will receive training through this award. To broader society: Reconstructing neural circuits has significance for both fundamental studies of the brain and the studies of abnormalities of brain function. It is hard, if not impossible, to identify a medical condition involving the nervous system that would not affect neuronal connections.

PROJECT SUMMARY We propose to develop a strategy for understanding olfactory coding by linking the molecular identity of odorant receptors (OR) to their odor sensitivities in vivo and discovering the logic of neural circuits that process smell. Unlike for other sensory modalities (i.e. vision, audition), it is not understood what properties of odorants are important for olfaction, how these properties are processed by olfactory neural circuits, and how odorant receptor genes have evolved to optimize such an encoding. The relationship between odorant structure (chemical space), the sequence of odorant receptors, the underlying spatial-temporal patterns of activity in the brain (neuronal space) and the perceived odor quality (perceptual space) has been elusive. An efficient method for connecting the olfactory sensory spaces will have a paradigm-shifting effect on olfactory research. Our multidisciplinary approach will use cutting edge next-generation sequencing technologies (FISSEQ, MAPseq and RNAseq) together with functional widefield fluorescence and two photon imaging in vivo to define the functional properties of olfactory sensory neurons that express defined odorant receptors (ORs), to discover their connections to individual glomeruli olfactory bulb (OB), second order OB output (mitral/tufted) cells and map their projection statistics to the downstream olfactory processing brain areas. Using these tools, we will map the identity and spatial layout of all 3,500 glomeruli in the mouse olfactory bulb according to the OR types from which they receive inputs. We will further link the molecular identity of ORs to their glomerular responses to hundreds of odorants (>500) in the form of OR/odorant binding affinity matrices across hundreds of glomeruli (~500) that are optically accessible in vivo. In the same samples, we will track thousands (>1,000/experiment) of individual olfactory bulb projections to their input glomeruli and their target brain areas by RNA-barcoding in relation to their tuning to odorants via multiphoton imaging in vivo. Our approach will bridge the gap between the molecular biology of ORs and neurophysiology and will usher in a new era of understanding the functional basis of olfaction. It will allow unprecedented resolution and throughput for determining OR-ligand interactions across hundreds of odorants, and the connectivity of tens of thousands of single neurons at once in a single specimen. The data obtained will enable the study of OR- ligand interactions, relate the chemical identity of odorants to olfactory perception, and the construction of artificial nose devices for immediate biomedical applications, including disease diagnostics.

SUMMARY To date, fundamental understanding of which features of odorants are decoded by the brain, and how information about these features is channeled through the olfactory system is still lacking. Odorants are sensed by the olfactory sensory neurons (OSNs) in the olfactory epithelium expressing specialized odorant receptors (ORs). Each OSN expresses one OR gene out of a species-dependent complement of hundreds of OR genes. OSN axons expressing the same OR type converge onto the same glomerulus on the olfactory bulb (OB) surface, forming a 2D map which is approximately stereotypical across individuals. Mitral/tufted cells (MTCs), the principal neurons of the OB are driven by inputs from glomeruli, as well as lateral and top-down signals. MTCs send their axons to higher olfactory processing centers, forming what is commonly assumed to be, highly distributed and largely random projection patterns. Computational models of olfactory processing are sensitive to the structure of the MTC projectome, with different models relying on different statistics of connections. Determining the structure of MTC connectivity is therefore of utmost importance for understanding the computational principles underlying olfactory information processing. However, to date, the structure of these projections across individuals remains uncharted territory, and information on the statistics of projections for ensembles of single MT neurons per individual is very limited, especially, in mammals. This is due to the low yield of imaging-based anatomical reconstruction strategies via sparse labeling of a small number of individual neurons per brain. To understand the logic and specificity of the MTC projectome, in this project, we will leverage the high throughput of state-of-the-art sequencing technologies, such as fluorescence in situ sequencing (FISSEQ) and a novel RNA barcoding sequencing-based method (MAPseq) in conjunction with in vivo functional imaging, modern computational technologies and theoretical tools. Preliminary data comprising of the brain-wide projections of hundreds of individual neurons supports the existence of specialized, non-random projection motifs that can be compared between animals. We will further investigate the structure of the brain-wide MTC projections and relate it to the MTC responses to large sets of odorants. We will share this data with the broader olfaction community and incorporate it into a computational network model of olfactory processing. The Specific Aims (SAs) of this project are: SA1. To determine the logic and specificity of individual mitral and tufted cells projections across the major target brain regions of the olfactory bulb. SA2. To investigate the structure of mitral and tufted cells' projectome within individual OB target brain regions. SA3. To understand the relationship between the bulb projectome and the odor responses of mitral and tufted cells.

The overall goal of this project is to develop a reinforcement learning (RL) theory of motivation, understood here as motivational salience, and to test the conclusions of this theory using experimental observations obtained in the ventral pallidum (VP). Animals' actions depend on the shifting values of internal demands determined by physiological or behavioral conditions, such as thirst, hunger, addiction, specific nutrient deficiency, etc. These need-based modulations of the perceived values of reinforcements (reward or punishment} are described by a mathematical variable called motivational salience or, simply, motivation. Including motivation adds a new level of complexity to RL theory, and allows it to generate flexible ongoing behaviors. Here, we will investigate how motivation can be learned by neuronal networks to generate complex adaptive behaviors and compare the conclusions of our theory with the VP circuits. Previous studies indicate that the VP plays an important role in a variety of behaviors, potentially, by influencing motivational salience. In vivo recordings suggest that VP neuron firing correlates with motivational states. Lesions, pharmacological and optogenetic manipulations in VP cause profound changes in behaviors motivated by natural rewards or drugs of addiction. Dysfunction of this structure is linked to depression and drug addiction in humans. Our theoretical results suggest that distinct classes of neurons in the VP should play essential roles in representing either positive or negative motivational states. We further hypothesize that the functional interactions locally within the VP are critical for generating such signals that guide motivated behaviors. Consistent with predictions of RL theory, in our preliminary studies, we found that individual VP neurons could be classified as either positive or negative 'motivation neurons', as the activities of these neurons represented both expected values of outcomes and motivational states. When population activity is considered, representations of outcome expectation can be distinguished from representations of motivation fluctuating according to the animals' physiological states. Based on the preliminary data, we devised an integrated approach, combining studies in computational analysis and theory (Koulakov lab) with advanced molecular genetic tools, optogenetics, chemogenetics, electrophysiology, and imaging in behaving mice (Li lab), to test our hypotheses through the following Aims: Aim 1. To develop methods for identifying motivation in the population activity of VP neurons. Here we will use novel behavioral and computational methods to disambiguate representations of motivation and outcome expectation in neuronal responses. Aim 2. To develop reinforcement learning theory of motivation and to test its predictions using responses of VP neurons. Here we will develop the Q-learning theory of motivation and compare networks trained using this theory to responses of VP neurons. Aim 3. To identify the circuit basis of representations of motivation in VP neuronal populations. We will identify the network structure in Q-learning networks with motivation, and test predictions using opto- and chemogenetic manipulations in VP. RELEVANCE (See instructions): The neural mechanisms of motivated behaviors remain unclear. In the proposed research program, we will determine the precise circuit mechanisms and computations by which neurons in the ventral pallidum participate in modulating motivated behaviors. Findings from this project will have important clinical implications, as impairments in motivational processes are core features of depression and drug addiction.