Funded Awards

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Title Investigator Institute Fiscal Year FOA Number Status Project Number Priority Area Summary
A 5-dimensional connectomics approach to the neural basis of behavior KATZ, PAUL UNIVERSITY OF MASSACHUSETTS AMHERST 2018 Active
  • Integrated Approaches

The brain is constantly assessing information that guides decision making, which can be a matter of life or death. For example, animals can choose to go to a place filled with food or an area filled with predators. Dr. Katz and his team will examine how neural circuits allow the mollusk Berghia stephaniaedecide where to go, implementing this common decision behavior with fundamental, reductionist neural mechanisms. The group will start by creating a complete map of the Berghia nervous system, which will detail connections between neurons and sensorimotor structures, as well as gene expression in the cells, before exploring the cells and circuits involved in decision making related to navigation. This project will provide a new animal model for studying the nervous system in fundamental simplicity and will offer a broader understanding of the decision-making processes in more complex brain structures.

A Brain Circuit Program for Understanding the Sensorimotor Basis of Behavior Clandinin, Thomas Robert Dickinson, Michael H (contact) Druckmann, Shaul Mann, Richard S Murray, Richard M Tuthill, John Comber Wilson, Rachel California Institute Of Technology 2017 Active
  • Integrated Approaches
The coordination amongst components of the central nervous system to guide sensorimotor behavior requires an understanding of exactly how these modules interact, from low-level transmissions guiding individual muscles, to high-level communications for complex behavior. Michael Dickinson and a multi-disciplinary team of experts will develop a theory of Drosophila fruit fly behavior that incorporates neural processes and feedback across hierarchical levels, using methods developed from their prior BRAIN effort. Here, the team plans to use synergistic approaches – genetics, electrophysiology, imaging, biomechanics, behavior analysis, and computational methods – to understand feedback and the flow of information within and across different processing stages in the awake, intact fly brain. By investigating these hierarchical levels with parallel approaches, this project has the potential to provide a fundamental synthesis of how the central nervous system generates behavior.
A genetically Encoded Method to Trace Neuronal Circuits in the Zebrafish Brain Lois, Carlos Prober, David Aaron (contact) California Institute Of Technology 2019 Active
  • Integrated Approaches

A goal of the BRAIN Initiative is to help researchers map out complete wiring diagrams of neural circuits. Recently the Prober and Lois group developed an approach called TRACT (Transcellular control of transcription) which allows researchers to map out and genetically manipulate the activity of neural circuits in Drosophila. However, it is difficult to study complex behaviors in these animals. In this project, the Prober and Lois groups plan to tailor the TRACT system for zebrafish, a vertebrates and that is easier for scientists to work with when studying complex behaviors. Their results may help scientists study, in great detail, how neural circuits control behavior under healthy and disease conditions.

A Neural Systems Approach to Understanding the Dynamic Computations Underlying our Sense of Direction Taube, Jeffrey Steven (contact) Van Der Meer, Matthijs Dartmouth College 2019 Active
  • Integrated Approaches

How do you find your way around? Navigation relies on a variety of information points such as self-motion and visual landmarks, which help with our sense of direction. Using large-scale recordings, live calcium imaging, and modeling data, Dr. Taube’s group will examine where direction-related information comes from and how the brain puts it all together to determine where an animal is and where it needs to go. This project will improve understanding of how the brain collects information from the eyes, head, and body and combines it with visual landmarks to determine head direction.

A unified cognitive network model of language Crone, Nathan E Tandon, Nitin (contact) University Of Texas Hlth Sci Ctr Houston 2016 Active
  • Human Neuroscience
  • Integrated Approaches
  • Interventional Tools
  • Monitor Neural Activity
Current non-invasive methodologies limit our ability to understand the neural basis of cognitive processes due to poor temporal or spatial resolution, and typical intracranial EEG (icEEG) approaches provide fragmentary information. To address these limitations, Drs. Tandon and Crone will study human language function, working with epilepsy patients who have intracranial electrodes in place. The group will then modulate activity at identified nodes of brain activity using closed-loop direct cortical stimulation. This project could provide insight into language processing and organization in the brain using a novel method of modeling neural computation, and provide insight into the language impairments that can affect patients with a range of neurologic and psychiatric illnesses.
A unified framework to study history dependence in the nervous system Santamaria, Fidel University Of Texas San Antonio 2019 Active
  • Integrated Approaches
  • Theory & Data Analysis Tools

A central property of the nervous system is history dependence: its ability to change reaction rates based on previous activity. Though the phenomenon is prevalent across scales of neuronal organization, sensory modalities, and species, there is no unified theory for history dependence. For this project, Dr. Fidel Santamaria and team will apply mathematical approaches to history dependence, validate the significance of that approach, and establish collaborations to test the hypothesis across species and scales. Overall, this project aims to provide a unified theoretical framework and in doing so, pave the way toward applications to study, analyze, and design experiments of history-dependent neuronal activity across multiple scales, from synaptic plasticity to complex spiking patterns in neural networks.

Bayesian estimation of network connectivity and motifs Ringach, Dario L University Of California Los Angeles 2016 Active
  • Integrated Approaches
  • Theory & Data Analysis Tools
Learning how emergent behavior arises from single neurons is a key challenge in modern neuroscience. Ringach and his colleagues plan to create sophisticated algorithms and methodologies to derive the functional connectivity of neurons based on activity patterns at the single-cell level and then identify collections of neurons, or network motifs, that play important computational roles in network functions. The researchers will then validate their algorithms against a database combining functional calcium imaging data with “ground truth” estimates of direct synaptic connectivity. These tools and validation data will enable the investigation of how network motifs differ in both health and disease states.
Behavioral readout of spatiotemporal codes dissected by holographic optogenetics Rinberg, Dmitry (contact) Shoham, Shy New York University School Of Medicine 2014 Complete
  • Cell Type
  • Circuit Diagrams
  • Integrated Approaches
  • Interventional Tools
  • Monitor Neural Activity
  • Theory & Data Analysis Tools
Dr. Rinberg's team aims to understand how the brain turns odors into nerve signals by activating and recording neurons in the olfactory bulbs of mice as they detect a variety of odors.
Behavioral state modulation of sensorimotor processing in cerebellar microcircuits Heiney, Shane A Baylor College Of Medicine 2017 Active
  • Integrated Approaches
Behavioral states affect sensorimotor processing, as sensory signals are converted into motor commands. Because these transformations are often distributed throughout the brain, it is challenging to understand the contributions of individual brain areas. Shane Heiney and colleagues are investigating how locomotion and arousal – two well-characterized behavioral states – subsequently affect cerebellar processing in mice. Using a combination of psychophysics, large-scale multiphoton imaging, and electrophysiology, Heiney plans to develop a quantitative framework for interpreting effects of behavior on cerebellar circuitry, and to study the impact of behavior on skilled movements at multiple stages of sensorimotor processing. These experiments have the potential to illuminate how a neural system and behavioral state are dynamically modulated in time.
Beyond Diagnostic Classification of Autism: Neuroanatomical, Functional, and Behavioral Phenotypes Fletcher, Preston Thomas University Of Utah 2016 Active
  • Integrated Approaches
  • Theory & Data Analysis Tools
A major barrier to creating effective treatments for autism spectrum disorder (ASD), a lifelong neurological disorder characterized by stereotyped behavior and difficulties in social interactions, is the lack of understanding of the underlying brain mechanisms. Fletcher and his team propose to develop novel statistical methods for integrating the analyses of neuroimaging data (functional and structural MRI) with behavioral assessments. The resulting set of open-source tools will help relate brain networks to specific ASD behaviors, as well as those observed in other neuropsychological disorders.
BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain Chung, Moo K University Of Wisconsin-madison 2016 Complete
  • Integrated Approaches
  • Theory & Data Analysis Tools

To what extents are structural and functional brain networks the product of heritability? That is the question that Chung and his colleagues will address with their proposal to develop tools to analyze in detail brain imaging scans (MRI, functional MRI, diffusion tensor imaging) they have collected from 200 pairs of monozygotic and same-sex dizygotic twins. The tools will be part of a new open-source suite of algorithms for analyzing their enormous cache of neuroimaging data, which the researchers will use to establish a baseline map for the genetic influences on brain network development in both health and disease.

Building a Complete, Predictive, Data-Driven Model of Action Selection During Olfactory Navigation Louis, Matthieu R. P. J. C. G. University Of California Santa Barbara 2019 Active
  • Integrated Approaches

We still do not understand fully how animals process sensory inputs from a noisy environment, yet this is a crucial function that the nervous system must perform to make correct behavioral decisions. Dr. Louis and colleagues propose to create a model of how the nervous system converts noisy sensory information into something that can be used to navigate the environment. Specifically, they are studying the behavior of fly larvae and their response to olfactory inputs,  i.e. their attraction to food odors, to study how environmental inputs are processed in the brain. This information will then be tested to uncover how stimuli from a noisy environment is translated into behavioral decisions.

Building analysis tools and a theory framework for inferring principles of neural computation from multi-scale organization in brain recordings Sommer, Friedrich T University Of California Berkeley 2018 Active
  • Integrated Approaches
  • Theory & Data Analysis Tools

Innovative recording techniques have uncovered interactions between individual neurons and cell populations that comprise complex and poorly- defined neural dynamics underlying computations and brain functions. Dr. Sommer proposes combining new tools to analyze this neuronal activity with a theoretical framework of the associated computations. After decoding behavior in mice from hippocampal recordings during exploration and replay and local field potentials from visual cortex, the group will extract “place components” or the position of the animal from the activity data. Subsequently, the team will establish a theoretical framework that, at the computational level, will describe computations underlying brain function in terms of high-dimensional representations, and at the mechanistic level will describe how the operations and representations are mapped onto biological mechanisms. Future users will be able to use this framework to design computations, explore multiple potential mechanisms, create a simulation of an experiment, and compare simulation data to a real experiment.

Canonical computations for motor learning by the cerebellar cortex micro-circuit Brunel, Nicolas Hull, Court A Lisberger, Stephen G (contact) Medina, Javier F Duke University 2019 Active
  • Integrated Approaches

The cerebellum plays critical roles in learning and performing coordinated, well-timed movements. The majority of research on the cerebellum has focused on Purkinje cells. This project aims to use optogenetics, machine-learning, electrophysiology, imaging, and computer modeling to investigate the entire cerebellar circuit including all relevant cell types to gain a better understanding of how the cerebellum functions to support motor learning.

Causal mapping of emotion networks with concurrent electrical stimulation and fMRI Adolphs, Ralph (contact) Howard, Matthew A. Poldrack, Russell A California Institute Of Technology 2018 Active
  • Human Neuroscience
  • Integrated Approaches
  • Interventional Tools
  • Monitor Neural Activity
Limited treatment options exist for emotional disorders because we do not understand the neural systems by which emotions are processed. Adolphs and colleagues will study how emotion is caused  by activity in brain networks. They will electrically stimulate emotion-related brain regions, such as the amygdala, in awake neurosurgical patients, and use concurrent fMRI to image the whole-brain networks engaged by the stimulated structures. Psychophysiological, behavioral, and self-report measures of emotion will be collected to quantify how the stimulation-induced activation patterns associate with specific components of emotion. This work could inform interventions to treat mood disorders through deep-brain stimulation.
Circuit and Synaptic Mechanisms of Visual Spatial Attention Haider, Bilal GEORGIA INSTITUTE OF TECHNOLOGY 2018 Active
  • Integrated Approaches

The role of attention in sensory perception is an important question in neuroscience, especially when trying to understand and create better treatments for disorders like schizophrenia, autism spectrum disorders, and attention deficit disorders. Dr. Haider and team will utilize transgenic mice and combine high-density local field potential and neural activity recordings in the visual cortex, patch-clamp recordings from cortical and thalamic synaptic connections, cell-type specific optogenetics, and a well-characterized spatial attention task to elucidate the neural mechanisms of attention at multiple levels: specific cells, synapses, and circuits. 

Circuit mechanisms for encoding naturalistic motion in the mammalian retina Wei, Wei UNIVERSITY OF CHICAGO 2018 Active
  • Integrated Approaches

Understanding how sensory information is extracted by anatomically and functionally defined neural circuits exemplifies one of the many remaining questions surrounding neural circuit function. Using the visual direction-selective circuit in the mouse retina, Dr. Wei and colleagues will perform circuit analyses incorporating a variety of approaches: synapse-specific circuit manipulation, multiphoton calcium imaging, patch clamp electrophysiology, connectomic circuit tracing, and theoretical analysis of information encoding. Results from this work may have broad implications in understanding fundamental principles of neural computation by a well-defined neural circuit.

Circuit mechanisms underlying learned changes in persistent neural activity Aksay, Emre (contact) Goldman, Mark S Seung, Hyunjune Sebastian Weill Medical Coll Of Cornell Univ 2018 Active
  • Integrated Approaches
Understanding how brain circuit-level changes mediate behavioral changes requires detailed knowledge of circuit-wide activity patterns before, during, and after learning. Aksay’s team will study the dynamics of learning by revealing the changes in circuit activity patterns underlying a newly learned behavior. Specifically, they will study the adaptive tuning of the persistent neural activity underlying visual gaze-holding behavior in the zebrafish oculomotor system. The researchers will simultaneously record throughout the oculomotor brainstem and cerebellum during learning, perform anatomical reconstructions at electron microscopic resolution of the imaged circuits, incorporate these data into computational models to make predictions for sites of plasticity, and test those predictions through optical perturbations and electrophysiology. This work could serve as a blueprint for understanding cerebellar involvement in numerous behaviors.
Circuit principles of demotivation in the decision to switch behaviors Crickmore, Michael A Boston Children's Hospital 2019 Active
  • Integrated Approaches

How is a decision made whether or not to switch behaviors? This project aims to use experimental and computational approaches to study how information from competing motivations is processed and integrated to decide whether or not to switch behavior, i.e., to stop one behavior and start another. This work conducted in the Crickmore lab will make use of the Drosophila model to study motivational regulation. The findings will be used to generate circuit and computational models that can provide better insight into motivation.

Circuitry underlying response summation in mouse and primate: Theory and experiment REYNOLDS, JOHN H et al. SALK INSTITUTE FOR BIOLOGICAL STUDIES 2018 Active
  • Integrated Approaches

Each cortical neuron in the brain receives inputs from, potentially, thousands of other cells but produces only one collective response. It is unknown how neurons combine assorted inputs, which often come from many sources -- including sensory stimuli -- into a single response.  Drs. Brunel, Miller, and Reynolds will use visual and experimental optogenetic stimulation to compare responses in the visual cortexes of mice and monkeys as the neurons receive a variety of inputs. The team will also examine how inputs from specific types of neurons influence responses elicited in the cells with which they are communicating.  These findings may increase our understanding of brain circuit function in healthy brains and may provide clues to disorders in which critical circuits are disrupted.

Coarse-graining approaches to networks, learning, and behavior Bialek, William Palmer, Stephanie E (contact) Schwab, David Jason University Of Chicago 2018 Active
  • Integrated Approaches
  • Theory & Data Analysis Tools

Behavioral neuroscience research produces large quantities of high- dimensional data requiring complicated interrogations. To uncover simpler underpinnings of complex neural recordings, Drs. Palmer, Bialek, and Schwab propose incorporating renormalization group (RG) techniques to a wide range of multi-unit, neural data. The statistical algorithms of their theoretical framework will be freely available and disseminated, as they should be relatively straightforward to apply regardless of discipline. This project could support tractable, efficient analysis of large datasets by enhancing future users’ ability to discern specific properties of neuronal populations critical to behaviors.

Comprehensive Analysis of a Decision Circuit Pehlevan, Cengiz Samuel, Aravinthan D. Sternberg, Paul Warren (contact) Zhen, Mei California Institute Of Technology 2019 Active
  • Integrated Approaches

Past experiences often drive how an animal develops. How does the brain draw on those experiences to guide developmental choices? Dr. Sternberg’s team will take a detailed look at the C. elegans decision-making circuit during development, using state-of-the-art molecular tools, computational modeling, and functional imaging, to examine how sensory inputs influence the developmental choices an animal makes. With the help of advanced technology, Dr. Sternberg’s colleagues will see how brain circuits change before, during, and after decision making.

Computational and circuit mechanisms for information transmission in the brain Eden, Uri Tzvi Frank, Loren M Ganguli, Surya Kepecs, Adam (contact) Kramer, Mark Alan Machens, Christian Tolosa, Vanessa Cold Spring Harbor Laboratory 2015 Complete
  • Cell Type
  • Circuit Diagrams
  • Integrated Approaches
  • Interventional Tools
  • Monitor Neural Activity
  • Theory & Data Analysis Tools
Dr. Kepecs and colleagues are investigating how information is integrated into decision making, and then further transformed into behavior. This project focuses on understanding information flow across specific regions of the brain in trained rats. By performing parallel, large-scale, simultaneous electrical recordings of neural activity in these different brain regions while the animals perform two different types of decision-making tasks, these researchers hope to observe how activity in one area influences activity in a downstream area. In addition, there are plans to identify and manipulate the activity of neurons that connect these brain areas to understand the causal relationships governing information flow among these regions. Gaining such mechanistic insights into how the brain processes information will provide insights into how both the normal and disordered brain operates.
Computational and circuit mechanisms of decision making Shadlen, Michael Neil Columbia University Health Sciences 2019 Active
  • Integrated Approaches

Many cognitive functions rely on brain mechanisms involved in decision-making. Dr. Shadlen and colleagues aim to better understand how the brain makes increasingly complex decisions by using visual processing as a model. By combining complex behavioral tasks and multichannel neural recordings from multiple brain regions in nonhuman primates, the team will explore context-dependent interactions between brain regions; changes that occur during decision-making; and the timing at which an organism must make two distinct decisions about a single object.

Computational and circuit mechanisms underlying motor control Costa, Rui M. (contact) Jessell, Thomas M. Columbia University Health Sciences 2017 Active
  • Integrated Approaches
The mechanisms by which the nervous system produces controlled movements involve interactions between cortical and subcortical regions in the brain, the spinal cord, and muscle, but a clear understanding of these interactions remains elusive. Rui Costa, Thomas Jessell, and colleagues are planning to study the functional and computational logic of connectivity between these motor centers to characterize the role of specific corticospinal neurons during movements. When investigating motor control through cell-type-specific connectivity from brain to spinal cord, the team will use optogenetic manipulations and computational modeling to obtain a clear understanding of these circuit mechanisms. This project – in addition to the use of innovative methods – will also provide an understanding of how these systems are preserved across rodent and nonhuman primate species.
Computational and Circuit Mechanisms Underlying Rapid Learning Buffalo, Elizabeth A University Of Washington 2018 Active
  • Integrated Approaches

The circuit mechanisms underlying memory consolidation allow for detailed memory formation. Impairments in these circuits negatively impact patients dramatically with myriad neurological disorders. Dr. Buffalo’s project will study the neural circuits underlying rapid learning, using single-unit and field recordings in human and nonhuman primates (NHP) during the execution of learning- dependent tasks. Alongside electrophysiological recordings in both species during naturalistic and learning task performance and during sleep, the group will perform neural network modeling and state- space analyses. The project could reveal how abstract sensorimotor representations in this circuitry enable “learning to learn” new associations to form memories in humans and NHP.

Context-dependent processing in sensorimotor cortex Collinger, Jennifer UNIVERSITY OF PITTSBURGH AT PITTSBURGH 2018 Active
  • Human Neuroscience
  • Integrated Approaches
  • Interventional Tools
  • Monitor Neural Activity

When you reach for a beverage, the way you pick up the drink depends on whether it is in a sturdy mug or a delicate champagne flute, as well as your reach configuration. Dr. Collinger and her colleagues plan to investigate the way environmental context affects motor cortex activity as the brain plans movements, such as grasping an object. Two individuals with tetraplegia will receive implants in their motor cortex to record activity while they use brain signals to control a robotic prothesis in a variety of tasks including grasping an object or grasping into empty space, picking up objects of various sizes and materials, and picking up objects for different goals. A better understanding of how the brain prepares these movements may lead to improved devices and therapies for those with sensory or motor problems. 

Controlling the spatial extent of light-based monitoring and manipulation of neural activity in vivo Sabatini, Bernardo HARVARD MEDICAL SCHOOL 2018 Active
  • Interventional Tools

Optogenetics has dramatically advanced neuroscience, allowing the manipulation and monitoring of activity in genetically-defined neurons in the brain using light. However, while deeper brain structures can be accessed using optical fibers, standard fibers only illuminate tissue near their tip and are invasive in small animals. The team will develop light-delivery tools—tapered fiber optics—that allow precise, flexible control of spatially separated groups of neurons. Coupling these optical devices with electrical stimulators, the group plans to interrogate the same neurons using both methods simultaneously and incorporate novel viral preparations to enable genetic change in the neurons through the device. This toolset should expand our ability to manipulate and record neuronal circuits in a less invasive manner.

Cortical circuits and information flow during memory-guided perceptual decisions Sur, Mriganka Massachusetts Institute Of Technology 2014 Complete
  • Cell Type
  • Circuit Diagrams
  • Integrated Approaches
  • Interventional Tools
  • Monitor Neural Activity
  • Theory & Data Analysis Tools
Dr. Sur and his team will combine a number of cutting-edge, large-scale imaging and computational techniques to determine the exact brain circuits involved in generating short term memories that influence decisions.
Cortical Interactions Underlying Sensory Representations Chen, Jerry BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) 2018 Active
  • Integrated Approaches

Sensory perception involves the transformation of sensory input into mnemonic representations, likely through interactions within and between cortical areas. However, a challenge for neuroscientists has been to distinguish information that is processed locally versus information that is transferred to and from other cortical areas. Using whisker-based paired association tasks in the mouse, Dr. Chen will apply two-photon calcium imaging and optogenetic manipulations to provide insight into these cortical circuits and evaluate predictive models that have been proposed to explain important aspects of perception. Taken together, these efforts could broaden the understanding of sensory representations that undergird perception.

Cortical Signature and Modulation of Pain WANG, FAN et al. DUKE UNIVERSITY 2018 Active
  • Integrated Approaches

There are two components of pain perception: sensory-signal-dependent and affective-cognitive aspects. The primary somatosensory cortex (S1) has been implicated in the affective-cognitive aspect of pain. In certain chronic neuropathic pain conditions, light touch can trigger intense feelings of pain – a hypersensitivity known as mechanical allodynia. Drs. Wang and He will test the hypothesis that S1 neurons that project directly back to the spinal cord facilitate mechanical hypersensitivity, whereas S1 neurons that project intra-cortically to motor cortex suppress this hypersensitivity. The team will use viral-genetic labeling of cortical neurons, in vivo calcium imaging and electrophysiological recordings in mice, optogenetic-assisted slice physiology, trans-synaptic tracing, and computational analyses to study the sensory- and motor-cortical modulation of pain.

Cortical Spatial Processing for Solving the Cocktail Party Problem Han, Xue Sen, Kamal K (contact) Boston University (charles River Campus) 2019 Active
  • Integrated Approaches

The cocktail party syndrome describes the process by which the brain tunes into one sound while hearing many others in the background. This often happens when two people have a conversation during a noisy cocktail party. Understanding the neural basis of this cocktail party effect has been a challenge for the auditory research field. To address this, the Sen and Han labs plans to explore how the circuits of the auditory cortex of the mouse brain may play a role in controlling the cocktail party syndrome.  to use novel tools and test hypotheses involving the neural circuits of the auditory cortex, Their results may help researchers understand the circuitry behind a variety of psychological and hearing disorders that disrupt this process.

Cracking the Olfactory Code Rinberg, Dmitry New York University School Of Medicine 2019 Active
  • Integrated Approaches

Although olfaction is an important sense used by most animals to interact with their environment, there is a lack of empirical understanding of olfactory processing. Dr. Rinberg and team will collect the first system-wide dataset of neural and perceptual responses to a large, principled set of odorants and leverage recent technical advances in molecular genetics, neural imaging, electrophysiology, opto- and chemo-genetics, human psychophysics, and machine learning to reveal the computational logic of olfaction. The goal of this project is to uncover the rules that determine how various chemical features of an odorant are represented as neural activity, how this neural activity gets transformed as it propagates from the olfactory bulb to the piriform cortex, and the relevance of various features of these olfactory bulb neural responses to eliciting behavior and perception. These experiments will create a community-wide resource with potentially wide-ranging implications for general understanding of sensory information neural processes.

CRCNS Research Proposal: Cortico-amygdalar substrates of adaptive learningRecent advances in computational psychiatry have revealed failures in using models of the reward environment to flexibly change undesired behavior in individuals with substance use SOLTANI, ALIREZA (contact); IZQUIERDO, ALICIA DARTMOUTH COLLEGE 2018 Active
  • Theory & Data Analysis Tools

Recent advances in computational psychiatry have revealed failures in using models of the reward environment to flexibly change undesired behavior in individuals with substance use disorders (SUDs). Drs. Soltani and Izquierdo will inhibit precise brain regions and simultaneously perform calcium imaging in rodents performing an adaptive learning task to explore circuitry between the cortex and amygdala. Results from this project could lead to improved systems-level understanding of behavioral inflexibility in people with SUDs and of the precise roles of involved brain areas for better, more effective therapeutic targeting in the future.

CRCNS: Advancing Computational Methods to Reveal Human Thalamocortical Dynamics JONES, STEPHANIE RUGGIANO (contact); HAMALAINEN, MATTI BROWN UNIVERSITY 2018 Active
  • Theory & Data Analysis Tools

Advancing methods to image and interpret neural activity in humans on fine temporal-spatial scales is critical to understanding how the brain works in health and disease. However, the ability to record non-invasively from deep in the human brain with current technology is lacking. To address this issue, Drs. Hamalainen and Jones will integrate magneto-/electroencephalography (MEG/EEG), computational neural modeling, and invasive electrophysiological recording in humans to optimize methods to localize distributed deep and shallow brain sources, and to develop a computational tool to interpret the underlying cellular events. In addition to developing free open source software that will advance the ability to non-invasively study subcortical interactions in humans with MEG/EEG, this approach will provide novel insight into distributed subcortical activity that is not possible with one method alone.

CRCNS: An Integrative Study of Hippocampal-Neocortical Memory Coding during Sleep CHEN, ZHE (contact); WILSON, MATTHEW A NEW YORK UNIVERSITY SCHOOL OF MEDICINE 2018 Active
  • Theory & Data Analysis Tools

<p>Sleep is critical to memory and learning, and deciphering the neural codes underlying hippocampal and sensory cortical circuits would reveal important mechanisms of memory consolidations. Therefore, the study of hippocampal-neocortical memory coding during sleep is aimed at identifying a more complete answer to the "where", "what" and "when" questions related to memory processing, where a complete understanding is currently lacking. Drs. Chen and Wilson will combine electrophysiology, population-decoding methods, optogenetics and closed-loop neural interface to uncover sleep-associated memory contents of neural codes in the hippocampus and visual cortex and to dissect the circuit mechanisms of hippocampal-neocortical interaction and memory consolidation during various stages of sleep. The proposed project will provide valuable insight into targeted memory reactivation during sleep for memory enhancement or therapeutic applications.</p>

 

CRCNS: Cholinergic contribution to hippocampal information processing CANAVIER, CARMEN CASTRO (contact); GASPARINI, SONIA LSU HEALTH SCIENCES CENTER 2018 Active
  • Theory & Data Analysis Tools

Neuromodulation in the hippocampus is thought to guide learning and memory processes, and a thorough knowledge of the mechanisms underlying encoding and retrieval is critical towards informing clinical interventions for cognitive disorders. Drs. Canavier and Gasparini will investigate how the neurotransmitter acetylcholine controls routing in areas CA1 and CA3 of the hippocampus. Their approach uses both computational modeling and experiments to better understand the neural basis of how different oscillation frequencies can be used to route information and how acetylcholine could control this routing. The resultant improvement in understanding how information is processed and stored in the hippocampus may eventually guide therapeutic strategies for cognitive disorders.

CRCNS: CLOSED-LOOP COMPUTATIONAL NEUROSCIENCE FOR CAUSALLY DISSECTING CIRCUITS ROZELL, CHRISTOPHER JOHN GEORGIA INSTITUTE OF TECHNOLOGY 2019 Active
  • Theory & Data Analysis Tools

The neural pathways underlying sensory perception are richly structured systems, with multilayered recurrent connections between cell types within a cortical layer, between cortical laminae, between areas within a sensory pathway, and between different brain regions. Despite substantial progress characterizing neural responses, it is particularly challenging to determine causal interactions within recurrently connected circuits due to the confounding influence of the interconnections. Drs. Rozell and Stanley plan to design a closed-loop stimulation system to decouple recurrently connected elements and clamp neural ensemble activity. Notably, the system will work in real-time by analyzing neural activity and feedback to guide stimulation on a scale of milliseconds. The team proposes to test this system in the rodent whisker barrel cortex – though applications in other brain areas, as well as the development of new medical devise based on the controller architecture, could likely result.

CRCNS: Collaboration toward an experimentally validated multiscale model of rTMS QUEISSER, GILLIAN TEMPLE UNIV OF THE COMMONWEALTH 2018 Active
  • Theory & Data Analysis Tools

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique that relies on electromagnetic induction. Though studied clinically for the treatment of various disorders, effective repetitive TMS (rTMS) therapies remain elusive, hampered by technical limitations and a complex parameter space. To better understand the mechanisms underlying rTMS, Dr. Queisser aims to bridge modeling and basic neuroscience to build a multi-scale computational model which combines field simulations, network/single-cell plasticity modeling, and molecular-level calcium simulations. The proposed project is a first important step towards biology-driven, computer-assisted personalized rTMS therapies to promote beneficial neural plasticity. Moreover, this molecular approach provides the perspective in testing synergistic effects of pharmacological interventions and rTMS-based therapies, which may be instrumental in informing future clinical trials to tackle mental health disease.

CRCNS: Common algorithmic strategies used by the brain for labeling points in high-dimensional space NAVLAKHA, SAKET SALK INSTITUTE FOR BIOLOGICAL STUDIES 2018 Active
  • Theory & Data Analysis Tools

Sensory systems in simple model organisms, like olfaction in the fruit fly, are well understood but must be translated to higher level vertebrates and expanded to include computational models for full comprehension. Dr. Navlakha hopes to understand what computations are used by the mammalian olfactory system using a mouse model and extending to develop a computer algorithm for application across species. The group plans to learn what circuit mechanisms are used in the mouse olfactory system, which may help identify how disruption of these mechanisms causes circuit malfunction. Using these data to improve computational processing performance, they could uncover insights into how the brain computes more broadly in health and disease.

CRCNS: Community-supported open-source software for computational neuroanatomy GARYFALLIDIS, ELEFTHERIOS INDIANA UNIVERSITY BLOOMINGTON 2018 Active
  • Theory & Data Analysis Tools

Diffusion-weighted Magnetic Resonance Imaging (dMRI) is the only currently available, non-invasive, method to measure the properties connections in living human brains. Widely used in clinical tests for a variety of brain disorders, dMRI helps researchers understand networks involved in perception in cognition. Dr. Garyfallidis plans to implement novel algorithms for dMRI data analysis, share benchmark data sets, and support development of cloud-computing software tools. Computational methods proposed could accelerate research using dMRI for clinical application and increase our ability to make inferences from dMRI data.

CRCNS: Computational Approach to Assess Replicability of Neurobehavior Phenotypes BOGUE, MOLLY A JACKSON LABORATORY 2018 Active
  • Theory & Data Analysis Tools

The scientific community and general public have become increasingly concerned about a lack of replicability among published discoveries, particularly in behavioral science, but extending to many areas of pre-clinical research. Dr. Bogue proposes a practical approach to the challenge of research replicability that will help circumvent extensive and costly efforts and delays in the initial reporting of important findings, while facilitating changes in how scientists evaluate and communicate research. This project will provide an approach, guidelines and publicly available data resources to reduce the number of irreproducible studies that are published and improperly used as foundational research, increasing the public health impact of NIH-funded research and ultimately restoring confidence in the public's investment in research through timely, cost-effective improvements in the scientific process.

CRCNS: Computational neuroimaging of the human RESS, DAVID B BAYLOR COLLEGE OF MEDICINE 2018 Active
  • Theory & Data Analysis Tools

The human brainstem plays a critical role in brain function, both in health and disease, yet remarkably little is known about this critical brain region. While functional magnetic resonance imaging (fMRI) of the brain has provided tremendous insight into the cerebral cortex, the depth and small size of brainstem structures, such as the superior colliculus, has made imaging of the brainstem challenging. Dr. Ress proposes to build a set of methods and modeling that will enable the use of ultra-high-field fMRI to study the brainstem. The group will demonstrate validity by performing visual response experiments in the superior colliculus of humans and, if successful, could obtain much higher resolution data that could be transformative for basic research and clinical studies alike.

CRCNS: Decision Making in Changing Environments GOLD, JOSHUA I (contact); JOSIC, KRESIMIR ; KILPATRICK, ZACHARY PETER UNIVERSITY OF PENNSYLVANIA 2018 Active
  • Theory & Data Analysis Tools

Decisions are often deliberative processes that depend on the ability to accumulate uncertain information over time, but sometimes, new information requires dynamic updates. While research has begun to examine decision-making under dynamic conditions, no studies have identified representations of this adaptive decision variable that can flexibly accumulate information to guide behavior. Dr. Gold and collaborators plan to use theoretical and experimental approaches to understand how and where the brain encodes these decision variables. Specifically, they test whether brain circuits that integrate evidence under static conditions can also implement adaptive processes under dynamic conditions. This integrated computational, behavioral, and neurophysiological approach will provide novel insights into many aspects of higher brain function and complex behaviors that depend on dynamic processing of information.

CRCNS: Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation RICHARDSON, ROBERT MARK (contact); TURNER, ROBERT STERLING UNIVERSITY OF PITTSBURGH AT PITTSBURGH 2018 Active
  • Theory & Data Analysis Tools

Deep brain stimulation (DBS) represents one of the major clinical breakthroughs in the age of translational neuroscience, though harnessing the full therapeutic potential of adaptive DBS remains a challenge. Drs. Richardson and Turner will employ artificial intelligence strategies to further elevate the therapeutic potential of DBS. The concurrent use of research electrocorticography (ECoG) during DBS surgery recently has enabled basic neuroscience investigation of human cortical-subcortical network dynamics. Therefore, the researchers will develop a computational framework for deep learning-based multi-feature decoding of behavioral and disease states from ECoG, in order to advance the evolution of aDBS. By employing artificial intelligence strategies to innovate in the field of translational, personalized, medicine, this work will inform the design of novel strategies for biomarker-responsive brain stimulation.

CRCNS: Dynamical Constraints on Neural Population Activity YU, BYRON M (contact); BATISTA, AARON PAUL CARNEGIE-MELLON UNIVERSITY 2018 Active
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Cognitive and behavioral processes that unfold over time reflect, at least in part, dynamical constraints imposed by neural circuitry. Understanding these dynamics requires finely perturbing neural activity in varied ways. Drs. Batista and Yu will employ a brain-computer interface (BCI) paradigm to study neural dynamics. BCI enables perturbation of neural activity by harnessing an animal's volitional control to drive the activity of a population of neurons into specified configurations, allowing causal tests of dynamical constraints and their relation to behavior. By recording multi-neuronal activity in the motor cortex of macaque monkeys, the researchers will have a deeper insight into how movements are prepared and executed, which holds therapeutic implications for movement disorders (e.g., Parkinson’s), as well as the potential to improve the performance of BCIs that assist paralyzed patients and amputees.

CRCNS: Dynamical mechanisms of oscillation transitions in the olfactory system KAY, LESLIE M (contact); CLELAND, THOMAS A UNIVERSITY OF CHICAGO 2018 Active
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The olfactory system is an excellent model for studying the role of neural oscillations within experimentally accessible tissues, but there lacks a thorough, multi-level understanding of dynamical flexibility of the cortical circuits underlying olfaction. Drs. Kay and Cleland will establish a mechanistic model of oscillations and synchronization in the mammalian olfactory system, combining electrophysiology from awake/behaving rats with recordings from acute mouse slices of the olfactory bulb. Integrating these datasets into a common network model will explicate the construction and utility of these systemwide dynamics based on their underlying cellular and network mechanisms. The proposed work takes a fairly well-characterized network and, via computational modeling, combines studies across different levels of analysis to build a mechanistic model of a complex dynamical system.

CRCNS: Dynamics of Gain Recalibration in the Hippocampal-Entorhinal Path Integration SystemThe striking organization of hippocampal place cells and grid cells have provided unique insights into how the brain constructs and uses representations of the envi KNIERIM, JAMES J (contact); COWAN, NOAH JOHN; HEDRICK, KATHRYN ; ZHANG, KECHEN JOHNS HOPKINS UNIVERSITY 2018 Active
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The striking organization of hippocampal place cells and grid cells have provided unique insights into how the brain constructs and uses representations of the environment to guide behavior. These spatially selective cells are influenced by both internal signals and external stimuli. How do these two sets of information re-calibrate when positions in the environment change? Drs. Cowan, Hedrick, Knierim, and Zhang propose that visual feedback guides these updates. They will conduct a set of interactive computational and experimental studies to investigate in detail the computational mechanisms underlying this novel phenomenon. This project, combining electrophysiology, engineering, and modeling, will propel the theory forward to explain the network dynamics underlying path integration, with implications for mental health illness characterized by an inability to appropriately react to external information about the world.

CRCNS: Geometry-based Brain Connectome Analysis DUNSON, DAVID BRIAN (contact); ZHANG, ZHENGWU DUKE UNIVERSITY 2018 Active
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Increasing evidence suggests that an individual's brain connectome plays a fundamental role in cognitive functioning and the risk of developing mental disorders. However, large gaps between image acquisition and in connectome construction and data analysis have limited progress in understanding the relationships between brain connectome structure and phenotypes. Drs. Dunson and Zhang will develop transformative tools to enhance understanding of how the brain connectome varies according to individual differences. The toolbox will be applied to the Human Connectome Project and UK Biobank datasets and rigorously validated. By reducing measurement errors in connectome construction, and improving the inference of relationships between connectome structure and an individual's mental health and substance use, this project can revolutionize mechanistic understanding and clinical practice in prevention and treatment of mental health disorders.

CRCNS: Improving Bioelectronic Selectivity with Intrafascicular Stimulation JUNG, RANU (contact); ABBAS, JAMES J FLORIDA INTERNATIONAL UNIVERSITY 2018 Active
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Electrical stimulation technology for activating small groups of peripheral nerve fibers could form the foundation of bioelectronic systems to influence metabolic processes, enhance immune system function, regulate gastrointestinal activity, or treat a variety of medical conditions. Drs. Jung and Abbas propose to enhance the clinical viability of these techniques by developing stimulation strategies that can selectively activate small groups of fibers that produce the desired clinical effect without producing undesirable side effects. The longitudinal intrafascicular electrodes (LIFEs) produced in this international collaboration will have multiple points of contact on nerve fibers and stimulation pulse flexibility for targeted activation in anesthetized rabbits.

CRCNS: Joint coding of shape and texture in the primate brain PASUPATHY, ANITHA UNIVERSITY OF WASHINGTON 2018 Active
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A fundamental capacity of the primate visual system is its ability to process both the form and texture of visual stimuli. Using a combination of primate neurophysiology experiments, behavior and computational modeling, Dr. Pasupathy hopes to achieve a new level of understanding about how the non-human primate brain integrates visual information about form and surface properties. Shared stimuli and computational approaches will permit combining the groups' electrophysiological and computational investigations in primate visual cortex with data from Japanese collaborators who perform psychophysical studies in humans. These findings could bring researchers closer to devising strategies to alleviate and treat brain disorders of impaired form and texture processing resulting from dysfunctions in the occipito-temporal pathway.

 

CRCNS: Modeling the nanophysiology of dendritic spines YUSTE, RAFAEL COLUMBIA UNIV NEW YORK MORNINGSIDE 2018 Active
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Dendritic spines mediate essentially all excitatory connections and are thus critical elements in the brain, but their function is still poorly understood. In particular, a key question is whether or not they are electrical compartments. Dr. Rafael Yuste will explore the application of a broad theory to accurately model the constraints that the nanostructure of dendritic spines places on electrical current flow. Specifically, his team will combine modeling approaches to extract features from data, and experimental approaches to study how the geometry and composition of a dendritic spine affect the electrical and ionic fluxes and the coupling between the synapse and the dendrite. The work will help understand how synaptic voltages are shaped by dendritic spines, a phenomenon that is affected in many mental and neurological diseases.

CRCNS: Modeling the role of auditory feedback in speech motor control HOUDE, JOHN FRANCIS (contact); NAGARAJAN, SRIKANTAN S UNIVERSITY OF CALIFORNIA, SAN FRANCISCO 2018 Active
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The importance of auditory feedback in speaking is underscored by the many diseases with speech disorders whose etiology have been wholly or partially ascribed to underlying deficits in auditory feedback processing, including autism, stuttering, schizophrenia, dementia, and Parkinson's disease. Drs. Houde and Nagarajan propose to investigate a computational model of speech that assumes state-feedback control by the auditory system. This project could lead to better understanding of the role of auditory feedback, which may lead to improved diagnosis and treatment for these speech impairments.

CRCNS: Modulating Neural Population Interactions between Cortical Areas YU, BYRON M (contact); SMITH, MATTHEW A CARNEGIE-MELLON UNIVERSITY 2018 Active
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The brain networks underlying visual attention remain poorly understood, in particular how populations of neurons communicate across regions to facilitate attention. Causal interventions, such as micro-stimulation, are a critically important way to test theories of communication between brain regions as well as to develop potential therapies. The overarching goal of Drs. Smith and Yu's project is to identify and optimize patterns of micro-stimulation in one brain region that influence another brain region, and in turn behavior. Their approach combines advanced physiological methods for simultaneous recording in multiple brain areas, a rigorous quantitative approach to understanding neuronal communication, and a novel optimization approach to using micro-stimulation to modulate neuronal activity and behavior. The implications of this work have extremely broad scope and may reveal fundamental principles by which inter-area communication supports myriad perceptual and cognitive abilities.

CRCNS: MOVE!-MOdeling of fast Movement for Enhancement via neuroprosthetics SARMA, SRIDEVI V JOHNS HOPKINS UNIVERSITY 2018 Active
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Tracking fast unpredictable movements is a valuable skill, applicable in many situations (e.g., chasing prey). The sensorimotor control system (SCS) is responsible for such actions and its performance depends on neurons, communication between brains and muscles, and muscle dynamics whose contributions have not been explicitly quantified. Dr. Sridevi Sarma and a team of investigators will build upon new theory developed using feedback control principles and an appropriately simplified model of the SCS to identify how neural computing, delays, and muscles interact during the generation of fast movements. In doing so, the group will seek to restore motor performance, and more importantly restore fast and agile movements, in patients with movement disorders via neuroprosthetic devices that are designed using a validated model of the sensorimotor control system and modern control theory.

CRCNS: Multi-scale models of proprioceptive encoding for sensorimotor control TING, LENA H EMORY UNIVERSITY 2018 Active
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Proprioception, or one’s relative sense of body position and strength during movement, is guided by muscle spindle sensory afferents. While altered muscle spindle function is implicated in a wide range of sensorimotor impairments and neurological disorders, the basic mechanisms of muscle spindle sensory encoding are not well understood. To address this issue, Dr. Ting will develop a novel, mechanistic model of muscle spindle sensory encoding to that will test hypotheses about the role of molecular, cellular, and circuit level mechanisms on sensorimotor control in healthy and impaired humans and animals. The model will be a useful platform to integrate classical and new findings of muscle spindle function spanning multiple levels. Importantly, the model will improve our basic understanding of how sensory impairments alter both sensing and moving, and to drive the development of new treatments.

CRCNS: Neural Basis of Planning LEE, DAEYEOL (contact); MA, WHEE KY YALE UNIVERSITY 2018 Active
  • Theory & Data Analysis Tools

Strategic planning is important for humans and other animals during learning and decision making. While mechanisms for reinforcement learning have been well studied, how the brain utilizes knowledge of the environment to plan sequential actions remains unexplored. To address this issue, Drs. Lee and Ma, PIs with complementary expertise will investigate how different subdivisions of the primate prefrontal cortex contribute to the evaluation and arbitration of different learning algorithms during strategic planning in primates. By taking advantage of recent advances in machine learning and decision neuroscience, the proposed studies will elucidate how multiple learning algorithms are simultaneously implemented and coordinated via specific patterns of activity in the prefrontal cortex. The results from these studies will transform the behavioral and analytical paradigms used to study high-order planning and their neural underpinnings in humans and animals.

CRCNS: Neural signals that maintain/refresh LTP and memory GRIFFITH, LESLIE C BRANDEIS UNIVERSITY 2018 Active
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Understanding the molecular basis of memory storage through long-term potentiation (LTP) has major implications for memory disorders and stroke. Neural signals maintain and refresh LTP and require low levels of calcium, but whether achievement of this level is dependent on spontaneous neural activity is not known. To address this issue, Dr. Griffith will use acute hippocampal slices, behavioral observations in Drosophila, and computational modeling to test the role of spontaneous neural signals in memory refresh and maintenance. This project has the potential to bear importantly on the fundamental question of whether refresh reactions are mediated by spontaneous activity, providing important information towards understanding and treatment of memory disorders.

CRCNS: NEUROCOMPUTATIONAL STUDY OF REWARD-RELATED DECISION-MAKING & UNCERTAINTY YU, ANGELA UNIVERSITY OF CALIFORNIA, SAN DIEGO 2019 Active
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While important advances have been made in understanding human learning and decision-making, there is still a lack of understanding of the different motivational factors that influence decision-making. Motivational factors may include immediate and long-term rewards, as well as the idea of reducing uncertainties that inevitably and invariably arise during daily real-life navigation of the world, due to a lack of (or change in) data and information. Dr. Yu and team will use a combination of cognitive modeling, innovative behavioral experiments, fMRI data, physiological (pupillometry, cardiac, and respiratory) data, and psychiatric measures (questionnaires addressing depressiveness, anxiety, anhedonia, locus of control, pessimism, and drug abuse) to address how two forms of uncertainty – expected and unexpected – act as distinctive motivational factors in human decision making.

CRCNS: OPTIMIZATION OF CLOSED-LOOP CONTROL OF GAMMA OSCILLATIONS NAIR, SATISH S UNIVERSITY OF MISSOURI-COLUMBIA 2019 Active
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Neuronal oscillations are thought to support numerous cognitive functions, with gamma oscillations in particular supporting communication between brain regions. Gamma oscillations are expressed ubiquitously across cortical and subcortical areas, including the basolateral nucleus of the amygdala (BL), an important regulator of emotional behaviors. Dr. Nair and colleagues will use real-time local field potential (LFP) decoding of gamma oscillations and high-speed optogenetic neuromodulation in a novel and promising closed loop paradigm. The goals of this study are to develop a full-scale anatomically and physiologically constrained biophysical model of the rodent BL, and together with new signal processing routines, develop in vivo gamma modulation algorithms that are customized and optimized for each individual subject.

CRCNS: PROCESSING SPEED IN THE HUMAN CONNECTOME ACROSS THE LIFESPAN HERMES, DORA MAYO CLINIC ROCHESTER 2019 Active
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Many anatomical and neuroimaging studies have shown that the white matter pathways between brain regions – the connectome – change with development, maturation, and aging.  How these developmental changes affect the speed and variability of neural communication at the milliseconds scale is not well understood. Dr. Hermes and team plan to create a large, freely available database of cortico-cortical evoked potentials (CCEP) recorded from pre-surgical epilepsy patients. These valuable data are rarely shared, and currently, there are no standards to improve the consistency of analysis of data from different research sites. The development of a database of CCEP data in the Brain Imaging Data Structure (BIDS) format for intracranial EEG (iEEG) (a standard structure for iEEG data containing metadata that are both human and machine-readable), along with the tools to analyze it, will be an impactful resource for the BIDS developer community as well as the broader neuroscience community.

CRCNS: Real-time neural decoding for calcium imaging CHEN, RONG (contact); BHATTACHARYYA, SHUVRA S UNIVERSITY OF MARYLAND BALTIMORE 2018 Active
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Real-time neural decoding predicts behavior based on neural data, provided it can do so at the same pace with which the behavior is being monitored. While miniature cellular imaging is fast becoming a powerful way to study neural circuits by recording activity with cellular spatial resolution and sub-second temporal resolution, it also generates massive amounts of high-dimensional spatiotemporal data, with which real-time neural decoding has yet to keep apace. Drs. Bhattacharyya and Chen propose to develop a software platform, called RNDC-Lab (Real-time Neural Decoding for Cellular imaging Laboratory), that will provide integrated capabilities for design of and experimentation with novel real-time neural decoding systems for miniature cellular imaging. RNDC-Lab will provide a framework and platform for cost-efficient, real-time signal processing, the success of this project carries therapeutic implications for improving precise neuromodulation systems.

CRCNS: REWARD AND MOTIVATION IN NEURAL NETWORKS KOULAKOV, ALEXEI COLD SPRING HARBOR LABORATORY 2019 Active
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Animal behaviors are learned and guided toward goals defined by the values of rewards and aversive events, as described by reinforcement learning (RL) theory. On the other hand, the values are not absolute but shifted largely by internal demands, such as thirst and hunger, and the intensity of behaviors are regulated accordingly. This issue has not been successfully addressed in standard RL theories. Drs. Koulakov and Li will test the hypothesis that the neuronal interactions within the ventral pallidum (VP) – a key brain region in the reward circuit – are critical for such processes that guide motivated behaviors. They will test their hypothesis using an integrated approach that combines molecular genetic tools, optogenetics, chemogenetics, electrophysiology and imaging in behaving mice, and advanced computational analysis and modeling.

CRCNS: Rhythm generation in rodent spinal cord DOUGHERTY, KIMBERLY J DREXEL UNIVERSITY 2018 Active
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Understanding the rhythm-generating mechanisms that give rise to locomotion are critical to inform therapeutic interventions following injury or motor disorders. Spinal circuitry orchestrating the rhythm and patterning of locomotion are located in the lumbar spinal cord. In a collaborative project, Dr. Dougherty will use state-of-art experimental studies of spinal neurons and neural circuits in combination with computational modeling to dissect the organization and operating mechanisms of the spinal locomotor central pattern generator. The identification of rhythm generating mechanisms and the organization of spinal flexor and extensor circuitries will provide essential insights that can be applied to treatments and recovery of function following spinal cord injury or other motor disorders involving abnormal spinal locomotor processing.

CRCNS: Sparse odor coding in the olfactory bulb RINBERG, DMITRY (contact); KOULAKOV, ALEXEI NEW YORK UNIVERSITY SCHOOL OF MEDICINE 2018 Active
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Animals learn about their environment through their sensory systems, and the mammalian olfactory system is ideal to understand the computations in brain areas that format this incoming information for easy and flexible extraction by downstream brain areas. Drs. Koulakov and Rinberg will utilize recently developed theoretical frameworks, new optical methods for stimulus control, and multi-neuron recordings, to carry out a collaborative project that tests the basic principles of sensory processing in the olfactory system. This project will help elucidate the general principles of olfactory information processing by demonstrating how sensory representations can be dynamically tuned to reflect particular tasks faced by the organism. Because about 1-2% of people in North America experience a smell disorder and loss in sense of smell can negatively affect quality of life, this work holds important implications for clinical and therapeutic interventions.

CRCNS: Theory and Experiments to Elucidate Neural Coding in the Reward Circuit WITTEN, DANIELA (contact); WITTEN, ILANA UNIVERSITY OF WASHINGTON 2018 Active
  • Theory & Data Analysis Tools

Dopamine neurons are implicated in a wide range of normal behavioral functions, as well as a wide range of neuropsychiatric diseases, including addiction. Dr. Witten's group will perform two-photon imaging in the midbrain of mice while they learn a complex decision-making task and incorporate a suite of statistical tools to address challenges in analyzing the activity and behavioral data. The identification of sub-populations of dopamine neurons with different functional properties could provide much-needed insight into how dopamine neurons contribute to the neurobiology of addiction.

CRCNS: Theory-guided studies of cortical mechanisms of multi-input integration MILLER, KENNETH D (contact); VAN HOOSER, STEPHEN D COLUMBIA UNIVERSITY HEALTH SCIENCES 2018 Active
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Processing in cortical circuitry is critical to healthy development, underlies features of intelligence, and malfunctions during disease. Drs. Miller and Van Hooser will test the predictions of a powerful framework for understanding how the sensory cortex globally integrates multiple sources of input, bottom-up and top-down, to produce neuronal responses, and ultimately, perception. Combining a novel theory on neural responses, the stabilized supralinear network, with optical and genetic manipulations of visual cortical circuits in awake ferrets, the group will probe how the visual cortex responds to various natural stimuli. Understanding such global integration occurring in the cortex could lead to the improvement of prosthetic devices that interface with the brain to treat blindness and other disorders.

CRCNS: US-France Modeling & Predicting BCI Learning from Dynamic Networks BASSETT, DANIELLE SMITH UNIVERSITY OF PENNSYLVANIA 2018 Active
  • Theory & Data Analysis Tools

Brain-computer interfaces (BCIs) are increasingly used for control and communication, and for treatment of neurological disorders, yet despite their societal and clinical impact, many engineering challenges remain. In particular, voluntarily modulating brain activity to control a BCI requires several weeks or months to reach high performance, affecting the user’s daily life. To characterize the neural mechanisms of BCI learning and predict future performance, Dr. Danielle Bassett and a collaborative international team will leverage experimental data and interdisciplinary theoretical techniques. They will characterize brain networks at multiple scales, developing models to predict the ability to control the BCI, as well as methods to engineer BCI frameworks for adapting to neural plasticity. This project will enable a comprehensive understanding of the neural mechanisms of BCI learning, fostering the design of viable BCI frameworks that improve usability and performance.

CRCNS: US-French Research Proposal: Neurovascular coupling-democracy or oligarchy? DREW, PATRICK JAMES PENNSYLVANIA STATE UNIVERSITY-UNIV PARK 2018 Active
  • Theory & Data Analysis Tools

Hemodynamic signals, such as those measured by functional magnetic resonance imaging (fMRI), are used to non-invasively image brain activity, but it is not known whether changes in blood flow are governed by average neural activity, or the activity of the most active neurons. Drs. Drew and Charpak, along with an international collaborative team, will use in vivo two-photon imaging, in close coordination with computational analysis methods, to investigate how neural activity is coupled to changes in blood flow. The combination of these two approaches will yield a quantitative understanding of how blood flow changes relate to neural activity, and a determination of the mechanisms underlying neurovascular coupling. A deeper understanding of the conversion of these hemodynamic signals into neural activity will inform the interpretation of human imaging studies, with clinical and therapeutic implications.

CRCNS: US-Japan Research Proposal: The Computational Principles of a Neural Face Processing System FREIWALD, WINRICH ROCKEFELLER UNIVERSITY 2018 Active
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A fundamental gap remains in the understanding of how neural circuits represent complex objects like faces and permit facial recognition. The neural mechanisms of face processing are essential to human social life, and altered social perception is characteristic of many pervasive neurodevelopmental disorders. Dr. Freiwald plans to identify the neural mechanisms and computational principles underlying face recognition circuitry and explore how alterations to these circuits impair function. Integrating functional magnetic resonance imaging with electrophysiological recordings in the targeted brain regions of non-human primates, the group could uncover details of more general visual object recognition as well as advancing understanding of the circuit mechanisms for social perception.

CRCNS:Navigation Through A Memory Space in the Rodent Hippocampus HOWARD, MARC W BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) 2018 Active
  • Theory & Data Analysis Tools

One primary function of memory is to remember the past in order to anticipate and make decisions about the future. Neurophysiological findings show that the rodent hippocampus stores representations of past events, and that hippocampal theta oscillations may provide a mechanism to imagine future paths through space. Dr. Marc Howard and collaborators will use a combination of empirical work, advanced data analyses and computational modeling to develop a hypothesis for how the hippocampus and frontal cortex cooperate to navigate memory space and inform future behavior. By bridging levels of description from behavior, to an abstract mathematical framework, to systems neuroscience, this work may shed new light on fundamental mechanisms underlying memory in the hippocampus, paving the way towards treatment of memory dysfunction in a myriad of neurological disorders.

Crowd coding in the brain:3D imaging and control of collective neuronal dynamics Kanold, Patrick O (contact) Losert, Wolfgang Plenz, Dietmar Univ Of Maryland, College Park 2014 Complete
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Dr. Kanold and his team propose cutting edge methods to stimulate neurons at different depths in the auditory cortex, and will use new computational methods to understand complex interactions between neurons in mice while testing their ability to hear different sounds.
Data-driven analysis for neuronal dynamic modeling Mishne, Gal Yale University 2018 Active
  • Integrated Approaches
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The communications and interactions between neurons across the sensory-motor system require additional investigations with novel methodologies to understand dynamic activity patterns underlying behavior. Dr. Mishne will develop modular mathematical tools to automatically analyze massive amounts of high-resolution, spatiotemporal, neuronal activity data gathered from mice performing a reaching task. The proposed calcium imaging data will be processed in three modules that: develop methods for ROI (region of interest) extraction, use tensors and non-linear tools for multi-modal integration of neuronal activity with behavior, and predict future behavioral responses using a recurrent neural network approach. These methods for automated analysis, organization, and modeling of calcium imaging data gathered during behavioral tasks will be available for use by the entire community.

Defining the anatomical, molecular and functional logic of internal copy circuits involved in dexterous forelimb behaviors Azim, Eiman Salk Institute For Biological Studies 2019 Active
  • Integrated Approaches

Skilled control of forelimbs is one of the most important roles of the mammalian motor system and requires a circuitry in the nervous system that allow for rapid adjustments. Using molecular, anatomical, and behavioral tools, this project aims to study how a component of that circuitry, the propriospinal neurons in the spinal cord, relays information to and from the brain.

DELINEATING CELL-SPECIFIC OUTPUT PATHWAYS OF THE GPe THAT SUPPORT LONG-LASTING BEHAVIORAL RECOVERY IN DOPAMINE DEPLETED MICE Gittis, Aryn Hilary Carnegie-mellon University 2018 Active
  • Integrated Approaches
Deep brain stimulation in the basal ganglia system, a treatment for Parkinson’s disease, provides only transient relief of motor symptoms. Gittis and colleagues will identify which neuronal subpopulations in the external globus pallidus (GPe) within the basal ganglia are required to induce long-lasting motor rescue in dopamine-depleted mice. Optogenetics and in vivo recordings will be used to assess the impact of modulating specific neuronal subpopulations on GPe circuit dynamics and on behavior. Virally-targeted circuit mapping will elucidate the pathways through which GPe neuronal subpopulations mediate their motor effects. If successful, this work will advance the current understanding of basal ganglia circuitry, and potentially lead to better treatments for motor dysfunction.
Dendritic Computation and Representation of Head Direction in Retrosplenial Cortex Harnett, Mark Thomas Massachusetts Institute Of Technology 2019 Active
  • Integrated Approaches

How do individual neurons in the mammalian cortex integrate multiple streams of input to guide behavior? In this project scientists will image the dendrites and cell bodies of neurons to determine how head direction and visual information is combined by neurons in a region of the brain called the retrosplenial cortex during navigational behaviors. This research will enhance our understanding of associative cortical function and provide new insights into cellular- and circuit-level mechanisms of navigation.

Development of predictive coding networks for spatial navigation Dragoi, George Yale University 2018 Active
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Sequential neuronal attractors (i.e., neural network patterns with stable functional dynamics) have mainly been studied in adult animals, which accumulate spatial experience during development. Therefore, the early-life development of sequential neuronal attractors for encoding future navigation experiences (i.e., predictive coding) has remained mysterious. George Dragoi and colleagues seek to elucidate the roles of innate versus experiential factors in the emergence of internally-generated (hippocampus-mediated) representations of the world. While controlling prior spatial experience, they will record chronically from hippocampal neuron ensembles in developing, freely-behaving and sleeping rats, and will identify and analyze predictive coding network properties. This project could aid the study of neuronal ensemble pattern disruptions, with implications for disorders with developmental etiologies like schizophrenia and autism.
Dexterous BMIs for tetraplegic humans utilizing somatosensory cortex stimulation Andersen, Richard A California Institute Of Technology 2016 Active
  • Human Neuroscience
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As of 2016, approximately 160,000 Americans are living with partial or complete tetraplegia, a severe form of paralysis in which patients lose partial or total function and sensation in all four limbs. Many of these patients have sufficiently intact brain circuits to plan movements, but are unable to act on those plans due to paralysis at the spinal level. In this project, Andersen and his team will work with tetraplegic patients implanted with a brain machine interface (BMI) to record from and stimulate brain circuits. Their goal is to understand how the brain encodes the ability to reach for and grasp an object. They also propose to stimulate somatosensory cortex to restore sensory cues the hands would normally receive when grasping an object, and to combine these recording and stimulating efforts to design bi-directional BMIs. This work could lead to improved quality of life for patients with tetraplegia, and could inform treatment of motor impairments due to other causes including stroke and neurodegenerative diseases.
Diagnosis of Alzheimer's Disease Using Dynamic High-Order Brain Networks Shen, Dinggang (contact) Yap, Pew-thian Univ Of North Carolina Chapel Hill 2016 Active
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Despite being the most common form of dementia, Alzheimer’s disease (AD) has no known cure and current clinical diagnosis relies on subjective neuropsychological and neurobehavioral assessments. Shen and his team plan to create machine learning-based algorithms that will hone in on changes to the functional connectivity of brain networks over time—as measured by neuroimaging techniques such as diffusion MRI—as possible indicators of mild cognitive impairment (MCI), which generally occurs well before AD symptoms. The researchers will design their diagnostic tools with the flexibility to also improve the success of the early detection of other neurological disorders, including schizophrenia, autism, and multiple sclerosis.
Discovering dynamic computations from large-scale neural activity recordings Engel, Tatiana Cold Spring Harbor Laboratory 2018 Active
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Dynamic neuronal activity patterns underlie behavioral and cognitive functions in healthy and disordered brains, but large-scale recordings of this activity produce massive amounts of data requiring complex computations. Dr. Engel’s project provides a novel theoretical framework for analytically modeling the process by which temporally diverse responses of single neurons contribute to population activity during decision making. The group will validate unbiased, computational methods to examine dynamic activity in primate and mouse cortices and incorporate this framework into their freely available “BrainFlow” software and visualization tools.

Dissecting circuits for local and long-range competitive inhibition in the mouse superior colliculus Mysore, Shreesh P Johns Hopkins University 2019 Active
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The superior colliculus is an area found deep inside the brain that guides the brain’s reaction to competing stimuli and is involved in spatial attention control. In this project, the Myshore group will examine the role local and long-range inhibitory neural circuits play in controlling these behaviors. Initial experiments will be performed on mice shown visual stimuli. The researchers will use optogenetics to manipulate inhibitory neurons and endoscopic calcium imaging to record any resulting changes in excitatory neuronal activity. The results may help researchers better understand the role inhibitory circuits play in behavior and brain diseases.

Dissecting distributed representations by advanced population activity analysis methods and modeling Druckmann, Shaul Stanford University 2019 Active
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Sensory information passes through multiple brain areas to ultimately lead to behavior, but there is no clear understanding of how these multiple brain areas interact. Dr. Shaul Druckmann and team will develop analytical techniques that will facilitate interpreting the interactions among brain areas by recording brain activity and studying strategic perturbations of the data patterns. Working with experimental collaborators, they will use three high-quality datasets to design analytical approaches to interpret this data. They will first develop statistical metrics, then validate dimensionality reduction approaches that capture complex data in more interpretable forms. Finally, they will adapt modeling approaches to create mechanistic models of how circuit structure supports the activity dynamics of sensory information. These approaches and tools will have the potential to reveal how brain areas interface with each other and how these interactions shape behavior.

Dissecting the dual role of dopamine in context-dependent and learned behaviors Ruta, Vanessa Rockefeller University 2019 Active
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Dysfunctions in dopamine signaling underlie a number of neuropsychiatric conditions; however, the diversity of roles dopamine plays in the brain has made it difficult to study. Using Drosophila as a model, Dr. Ruta and colleagues propose to use cutting-edge techniques to study how reward and locomotor signals are translated to different patterns of dopamine release and how they engage distinct dopamine receptor signaling cascades. Specifically, the team will study the fly’s mushroom body, which is involved in olfactory processing, learning, memory, and reward. This region is especially amenable to these experiments due to its relatively simple structure and available genetic tools. The results may shed light on the role of dopamine signaling in modulating behavior.

Dynamic network computations for foraging in an uncertain environment Angelaki, Dora (contact) Dragoi, Valentin Pitkow, Zachary Samuel Schrater, Paul R Baylor College Of Medicine 2015 Complete
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The computational strategies and underlying mechanisms the brain uses to enable animals to interact flexibly with their environment are poorly understood. These researchers will use large-scale, wireless, electrical recordings from six relevant, interconnected brain regions in freely-behaving monkeys to record neuronal activity while the animals engage in foraging behavior-a natural task that involves sensory integration, spatial navigation, memory, and complex decision-making. The research team will use theoretical models of decision-making to interpret the neural activity data gathered as the animals interact with their environment, with the ambitious goal of understanding how brains create and use internal models of the world.
Dynamic Neural Mechanisms of Audiovisual Speech Perception Beauchamp, Michael S (contact) Schroeder, Charles E Baylor College Of Medicine 2019 Active
  • Human Neuroscience
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  • Monitor Neural Activity

Speech perception often lies at the heart of our interactions with other people. By nature, it is multisensory, combining auditory information from the voice with visual information from the face. However, there is a large gap in our knowledge about this critical cognitive skill because most experimental techniques available in humans have poor spatiotemporal resolution. In this strategic opportunity to study patients undergoing clinically-indicated brain surgery, Dr. Michael Beauchamp and his team will use intracranial recording (iEEG) in humans to study the neural mechanisms of speech perception. In addition to high-resolution intracranial electrode grids, the group will also leverage non-penetrating electrodes that are safely placed on the cortical surface of the brain. This multi-pronged approach will enable the group to study the organization and operation of the brain during audiovisual speech perception, providing a better understanding of this important human skill.

Dynamic Neural Mechanisms of Audiovisual Speech Perception Schroeder, Charles E Columbia University Health Sciences 2016 Active
  • Human Neuroscience
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Limitations in spatial and temporal resolution with current non-invasive brain imaging technologies prevent a thorough understanding of the mechanisms of speech perception – from audio-visual (AV) integration, to encoding, and cognitive interpretation. Dr. Charles Schroeder proposes directly recording from neurons in epilepsy patients while they process AV speech using electrocorticographic (ECoG) techniques to determine how oscillations in neuronal excitability influence processing and encoding. Not only could this project improve our ability to treat neurological disorders affecting speech and language processing, but it may allow a more comprehensive investigation into the functional interactions between brain circuits and perception.
Dynamics and Causal Functions of Large-Scale Cortical and Subcortical Networks SCHALK, GERWIN WADSWORTH CENTER 2018 Active
  • Human Neuroscience
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To produce a behavior, brain areas need to talk to each other. This communication has been difficult to study in humans, but novel tools provide a window into these conversations. Dr. Schalk and his colleagues plan to establish a consortium that will bring together a large cohort of study subjects and experts across scientific disciplines. They will record from state-of-the-art brain implants to investigate which regions are involved in speech, language, and music awareness; to measure how stimulating certain areas affects speech and language; and to explore how areas talk to one another during changing speech perception. These results should increase understanding of how brain regions interact, which may provide insights to treating neurological and psychiatric disorders.

EFFECTIVE CONNECTIVITY IN BRAIN NETWORKS: Discovering Latent Structure, Network Complexity and Recurrence. Hanson, Stephen Jose Rutgers The State Univ Of Nj Newark 2016 Active
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A longstanding goal of neuroscience has been matching specific functions to local brain structure and neural activity. Despite success in identifying brain areas associated with cognitive tasks such as memory, attention, and language, many areas engaged during cognitive tasks are often considered “secondary” and are consequently ignored. One weakness in current methods to associate brain regions with specific functions has been the reliance on direct correlation between increased neural activity and task performance. To identify and assess how secondary areas contribute to important cognitive tasks, Hanson and his colleagues plan to extend IMaGES and develop new functional brain imaging analysis software to search for brain areas with less intuitive, but still relevant, connections to certain tasks. This project will advance efforts to analyze information flow in the brain and determine how neural pathways are altered in both health and disease.
Efficient resource allocation and information retention in working memory circuits Ching, Shinung (contact) Snyder, Lawrence H Washington University 2019 Active
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Working memory - our ability to temporarily hold information in mind - is important for many aspects of normal human cognition, but there are critical gaps in our knowledge of how the brain remembers and stores multiple items. Through a combination of behavioral tests and computational modeling, Dr. ShiNung Ching and his team will test the validity of their theory that the organization of networks optimizes efficient resource allocation. Key to this project is the integration of experimental and computation methods to tightly link observed behavioral phenomena, theory, and underlying neural mechanisms. Because dysfunction of working memory occurs in numerous neuropsychiatric illnesses, the success of this project could inform improvements in diagnosing and treating these disorders.

Elucidating the Wiring and Rewiring of Poly-synaptic Memory Circuits by Directed Stepwise Trans-neuronal Tracing Xu, Wei Ut Southwestern Medical Center 2018 Active
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Elucidating the organization of long-range poly-synaptic neuronal pathways is essential to understanding brain functions and the pathogenesis of brain disorders. Xu’s team will develop and utilize technologies to observe hypothesized circuit rewiring during learning and memory. Modified viral vectors will enable controlled, stepwise trans-neuronal tracing, which will be used to define distinct neuronal subpopulations in the hippocampus based on their poly-synaptic inputs/outputs. The team will then manipulate specific subpopulations to determine if different neuronal groups convey distinctly sensory information and, in turn, adjust different aspects of behavior. Lastly, the connectivity of neurons of interest will be traced—before and after a learning process—to examine if learning and memory alters connectivity. This work could deepen our understanding of the neurobiology of memory in health and disease.
Embedded Ensemble Encoding Antic, Srdjan D Lytton, William W (contact) Suny Downstate Medical Center 2016 Active
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The enormous complexity of brain interactions provides numerous challenges in understanding and treating brain diseases such as autism, schizophrenia, and Alzheimer’s disease. A large part of this complexity lies in “the neural code,” which describes how cells in the brain communicate with one another. Lytton and his colleagues propose the development of a novel embedded-ensemble encoding theory for understanding the creation of ensembles of neurons that are believed to generate thoughts, perceptions, and actions. The heart of this theory states that temporary neuronal ensembles form among groups of neurons across the brain whose activity becomes synchronized. The ultimate goal of this project is to bridge the gap between single neurons and neural networks and derive fundamental insights into cortical function that may advance the understanding of a variety of neurological diseases.
Emergent dynamics from network connectivity: a minimal model Curto, Carina Pennsylvania State University-univ Park 2016 Active
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Many networks in the brain exhibit emergent dynamics: that is, they display patterns of neural activity that are shaped by the intrinsic structure of the network, rather than modified by an external input. Such dynamics are believed to underlie central pattern generators for locomotion, oscillatory activity in cortex and hippocampus, and the complex interplay between sensory-driven responses and ongoing spontaneous activity. The goal of this research by Curto and her colleague is to develop a theory of how emergent dynamics can arise solely from the structure of connectivity between neurons. Having a deeper understanding of the dynamics of neural circuits is critical for studying diseases in which those dynamics are thought to be disrupted, such as Parkinson's disease, schizophrenia, and epilepsy.
Ensemble neural dynamics in the medial prefrontal cortex underlying cognitive flexibility and reinforcement learning Ganguli, Surya Schnitzer, Mark J (contact) Stanford University 2017 Active
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The prefrontal cortex plays a critical role in cognitive flexibility and decision-making, but the neural circuits underlying these processes remain unclear. Mark Schnitzer and Surya Ganguli are applying reinforcement learning theory (i.e., how to select optimal future actions based on past actions) to understand how neural ensembles in prefrontal cortex guide behavior. With an innovative mini-microscope for neural calcium imaging in active mice, the team plans to use this method to acquire stable, long-term recordings of neural ensemble dynamics, then create a neural network model that tests how these dynamics affect an animal’s actions. A clear understanding of this important neural circuit has the potential to inform clinical applications for psychiatric conditions for which cognitive flexibility is compromised.
Filtered Point Process Inference Framework for Modeling Neural Data Brown, Emery N. Massachusetts General Hospital 2016 Active
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Spikes are key elements of neural computation and methods to improve the extraction of spike data from calcium imaging and other similar imaging methods are much in demand. Existing techniques are either extremely slow or susceptible to noise. Brown and his colleagues plan to develop a mathematical framework for analyzing neuronal spikes, and to apply it to the analysis of calcium imaging data in behaving mice and to neuroendocrine data related to the secretion of hormones in humans. This framework will shed light on sensory encoding in the rodent brain. It will also aid our understanding of pathological neuroendocrine states and improve the efficacy of treatments of hormonal disorders, including diabetes, obesity and osteoporosis.
From microscale structure to population coding of normal and learned behavior Debello, Wiliam Mcintyre Ellisman, Mark H Fischer, Brian J Pena, Jose L (contact) Albert Einstein College Of Medicine 2017 Active
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The mechanisms underlying how neuron populations execute auditory-driven animal behavior (i.e., sound localization), and how experience sculpts the behavior and the underlying neural representation of auditory space, are currently unknown. To better understand the relationship between activity patterns across neural populations and behavior, Jose Pena and colleagues will study the sound-driven, head-orienting responses of barn owls. The team will combine electrophysiological, anatomical, and behavioral analyses to map neuronal population activities upon presentation of sounds. They will investigate the network architecture supporting the activity patterns, as well as how the network changes with learning. The main goal of this project is to envision a complete understanding of auditory localization, from the microcircuit to population coding to behavior.
Functional Architecture of Speech Motor Cortex Chang, Edward University Of California, San Francisco 2016 Active
  • Human Neuroscience
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  • Monitor Neural Activity
Speaking is one example of a complex behavior that most humans can perform effortlessly, but scientists do not fully understand how the brain is able to drive speech production. Building on their prior work on the neural representation of articulatory and acoustic feature representations of speech, Chang and his team will conduct ultra high-density electrocorticography in epilepsy patients to study how the ventral sensorimotor cortex encodes the movements that produce speech, and how the prefrontal cortex is able to exert inhibitory control over speech. This work will advance our understanding of communication disorders, and refine the ability of clinicians to map speech areas of the brain in their patients.
Functional Dissection of Neural Circuitry Underlying Parenting Behavior Hong, Weizhe University Of California Los Angeles 2019 Active
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It remains unclear what accounts for differences in parenting behavior in different sexes and physiological states. Parenting behaviors in mice have been found to be governed in part by GABAergic neurons within the medial amygdala. This project will take advantage of cutting-edge functional manipulation and imaging techniques to determine the neural mechanisms and circuitry underlying these differences in parenting behaviors.

Functional dissection of thalamocortical interactions through genetically-defined TRN subnetworks Feng, Guoping (contact) Halassa, Michael M Massachusetts Institute Of Technology 2019 Active
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The thalamic reticular nucleus (TRN) is a part of the brain that is important for many activities such as sensory processing, arousal, and cognition. Disrupted TRN function could underlie behavioral deficits seen in disorders such as schizophrenia, autism, and ADHD. However, little is understood about how the circuitry in the TRN contributes to these functions. Using molecular tools, anatomical tracing, and in vivo recordings, the team for this project will study how organization of the TRN circuitry gives rise to function, specifically how distinct subgroups of TRN neurons form subnetworks that contribute to different aspects of sensory processing, arousal, and cognition.

GABAergic circuit interactions within the behaving mouse dLGN Bickford, Martha E (contact) Guido, William University Of Louisville 2017 Active
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The flow of visual information from the retina to the dorsal lateral geniculate nucleus (dLGN) in the brain is regulated by behavior, but the dynamic neural circuits governing these interactions have yet to be studied in awake, behaving animals. Martha Bickford and team are determining how inhibitory elements of the dLGN coordinate in behaving animals to modulate visual responsiveness and firing mode. They plan to use both optogenetic and chemogenetic techniques to target specific activation or inactivation of inhibitory circuits in dLGN, observing both dLGN neuron responses and measures of behavioral state in the mice. By developing these methods in vivo, the group aims to develop a novel approach to answering a wide variety of questions regarding thalamic function.
Graph theoretical analysis of the effect of brain tumors on functional MRI networks Holodny, Andrei I Makse, Hernan (contact) City College Of New York 2016 Active
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Individuals with brain tumors often recover function after the brain has adapted to the tumor. It is difficult, however, to predict which patients will recover based solely on the location of the tumor. Makse and his colleagues propose to develop a software tool to analyze a neuroimaging database of 1500 patients with glial tumors in order to discover the relationship between brain disease states and tumor location. This project will extend and test their theoretical model of how the brain adapts to recover lost functions in the presence of a brain tumor. The researchers’ new software tool will also aid in the understanding, diagnosis, and treatment of brain disorders thought to be due to disruptions of brain connectivity, including Alzheimer's disease, ADHD, stoke and traumatic brain injury.
Human Neocortical Neurosolver Hamalainen, Matti Hines, Michael L Jones, Stephanie Ruggiano (contact) Brown University 2016 Active
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Magnetoencephalography (MEG) and electroencephalography (EEG) are the leading non-invasive methods for recording human brain activity with millisecond resolution. However, it is still extremely difficult to interpret the underlying cellular and circuit-level sources of these large-scale signals without simultaneous invasive recordings. This challenge limits the use of MEG and EEG in the development of treatments for neural disorders. Jones and her colleagues propose a new software tool, called the Human Neocortical Neurosolver (HNN), that allows researchers to develop and test hypotheses about the origin of non-invasively measured human brain signals obtained with MEG and EEG. The insights obtained with the HNN tool will be helpful in understanding the underpinnings of neurological and psychiatric diseases, such as autism and schizophrenia.
Identifying, manipulating, and studying a complete sensory-to-motor model behavior circuit STOWERS, LISA SCRIPPS RESEARCH INSTITUTE 2018 Active
  • Integrated Approaches

Sensory stimuli can elicit many types of behaviors, yet it remains unclear how this occurs. Dr. Stowers’ project aims to improve understanding of the link between sensory input and behavioral changes. Her team will look at a well-defined behavioral response in mice and determine the complete neural circuit responsible from olfactory input to muscle. Once the circuit is identified, Dr. Stowers’ group will study the circuit’s neuronal activity patterns to determine how behavioral information is coded within the brain. This project will help advance our understanding of how the brain converts stimuli from the environment into behavioral changes.

Impact of cortical feedback on odor concentration change coding Shusterman, Roman Smear, Matthew C (contact) University Of Oregon 2017 Active
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The brain uses both feedforward and feedback connections across many of its neural systems, but the computational role of feedback in these circuits is often unclear. Matthew Smear and Roman Shusterman are investigating cortical feedback neurons in the olfactory system through novel optogenetic strategies that can identify, record, and silence these neurons. After first determining the feedback signals that an olfactory cortical area sends to the olfactory bulb in awake mice, the team will investigate the necessity of that feedback in odor sensitivity. In the long-term, the optogenetic silencing method proposed here has the potential to facilitate a greater understanding of the role of top-down feedback in neuronal computation.
Integrative Analysis of Long-range Top-down Cortical Circuit for Attentional Behavior Morishita, Hirofumi Icahn School Of Medicine At Mount Sinai 2017 Active
  • Integrated Approaches
Thought to be driven by regions in frontal cortex, attentional behavior underlies many core cognitive behaviors, yet its precise neural circuit mechanisms remain poorly understood. Hirofumi Morishita and collaborators plan to investigate the role of cortical circuits between the frontal and sensory cortex while dynamically modulating attentional behavior in mice. Through an innovative combination of technologies including viral mapping, electrophysiology, fiber photometry, miniscope imaging, and optogenetics, the team plans to identify when these circuits are activated, how they affect attentional behavior, and whether modulation of these circuits can improve attentional behavior. Having a strong basis for the causal role of this frontal-sensory cortical circuit will pave the way for analysis of other circuits in the brain, as well as potential examination of this circuit in pre-clinical applications.
Integrative Functional Mapping of Sensory-Motor Pathways Dickinson, Michael H (contact) Holmes, Philip J Mann, Richard S Wilson, Rachel California Institute Of Technology 2014 Complete
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Dr. Dickinson will lead an interdisciplinary team to study how the brain uses sensory information to guide movements, by recording the activity of individual neurons from across the brain in fruit flies, as they walk on a treadmill and see and smell a variety of sights and odors.
Intraoperative studies of flexible decision-making Baltuch, Gordon H (contact) Gold, Joshua I University Of Pennsylvania 2017 Active
  • Human Neuroscience
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  • Monitor Neural Activity
Even relatively simple sensory-motor decisions, such as goal-directed eye movements, exhibit sufficient flexibility and nuance to be considered a “window on cognition.” Gordon Baltuch’s team will leverage the unique opportunity provided by surgical treatment of Parkinson’s disease using deep brain stimulation, to study decision-making in the human brain at the single-neuron level. The team will simultaneously measure behavioral response time and accuracy (by asking neurosurgical patients to select a visual stimulus via eye movements) while performing brain electrophysiology. Additionally, they will conduct parallel monkey and human studies that, unlike Parkinson’s studies alone, will distinguish normal versus disrupted mechanisms in the Parkinson’s -affected brain. This project may yield a sustainable research program that probes not only neural mechanisms of decision-making, but also potential causes of, and remedies to, cognitive side effects associated with deep brain stimulation.
Invasive Approach to Model Human Cortex-Basal Ganglia Action-Regulating Networks Pouratian, Nader University Of California Los Angeles 2016 Active
  • Human Neuroscience
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  • Interventional Tools
  • Monitor Neural Activity
Circuits between the frontal cortex and basal ganglia (BG) may support the ability to suppress actions once additional information becomes available to indicate the most appropriate decision, but few studies provide the necessary spatial and temporal resolution to investigate this mechanistically. Dr. Pouratian’s group will utilize deep brain stimulation (DBS) electrodes in Parkinson’s patients to record from cortical and BG regions in multiple action-suppression tasks. In addition to investigating unit and local field potential activity during tasks, the group will use DBS coupled with functional imaging to stimulate the circuits and measure effects on brain activity, eventually developing a computational model of action suppression. Aside from informing the basic science of this circuitry, this project could expand upon how DBS influences brain networks for action, which could improve therapeutic use in various disorders.
Investigating information processing in parallel circuits that link external chemical signals to social behavior Meeks, Julian P Ut Southwestern Medical Center 2017 Active
  • Integrated Approaches
Understanding the contributions of sensory circuits to perception, emotions, and behavior is a critical task in neuroscience, but for the accessory olfactory system in mice – an ideal-model sensory circuit – technical barriers have prevented a thorough investigation. Julian Meeks’ team aims to overcome these barriers by expanding the capacity to measure olfactory chemosensory encoding and integration ex vivo through stereolithography and volumetric imaging methods. They also plan to evaluate how the accessory olfactory system sorts information in the olfactory bulb and its immediate downstream targets through retrograde labeling and multi-site multi-electrode recordings. Success with this ambitious project will improve our understanding of the mechanisms by which mammalian neural circuits decode environmental information and use that information to guide behaviors.
Investigating the neurocircuitry of sleep duration regulation Fu, Ying-hui University Of California, San Francisco 2018 Active
  • Integrated Approaches
Gene variations have been identified (called ADRB1 and DEC2) that enable individuals expressing these variations to sleep fewer hours per day without health detriments. Fu’s team seeks to demonstrate that there are unique neurocircuits for sleep duration/efficiency, separate from sleep/wake-promoting circuits. First, the team will systematically search for ADRB1-positive and DEC2-positve cells in the brains of transgenic mice. To confirm that ADRB1/DEC2-expressing networks are critical for regulating sleep duration/efficiency, they will pharmacogenetically and optogenetically manipulate ADRB1/DEC2-expressing neurons/circuits and evaluate the resulting effects on sleep. To understand mechanistic relationships, they will examine ADRB1/DEC2-positive cell activities while monitoring sleep state with EEG/EMG recording. This project may lead to a better understanding of the neurocircuitry of sleep regulation.
Lagging or Leading? Linking Substantia Nigra Activity to Spontaneous Motor Sequences Adams, Ryan Prescott Datta, Sandeep R Sabatini, Bernardo L (contact) Harvard Medical School 2015 Complete
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One of the goals of the BRAIN Initiative is to understand how the brain generates behaviors. These researchers are utilizing a novel 3D machine vision technology to automate classification of spontaneous behavior when freely-moving mice are confronted with stimuli; they are then correlating that information with dense recordings of neural activity in key regions of the brain implicated in movement disorders. Researchers are then manipulating the activity of specific neurons in this brain region with light to test their role in the animal’s behavior. Dr. Sabatini and colleagues offer an innovative ‘grammatical’ structure to understanding how the brain produces complex, systematic behavior.
Large-scale Network Modeling for Brain Dynamics: Statistical Learning and Optimization Luo, Xi Brown University 2016 Active
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Functional MRI (fMRI) is a useful technique for examining brain-wide networks involved in tasks that involve perceiving stimuli and behaviorally responding to those stimuli. Luo and his colleagues are applying machine learning approaches for modeling whole-brain networks using fMRI and behavioral response data captured during the performance of specific tasks. The algorithms the team develops will facilitate the analysis of a wide array of neuroimaging and behavioral data, which may lead to the discovery of pharmacological targets for treating neurological and psychiatric disorders.
Large-scale recording of population activity during social cognition in freely moving non-human primates DRAGOI, VALENTIN et al. UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON 2018 Active
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Humans are social creatures. Positive interactions with others, such as cooperation and altruism, are important for our species’ health and survival, but not much is known about the mechanisms underlying these behaviors. Drs. Aazhang, Dragoi, and Wright plan to identify specific brain regions involved in social cognition and investigate how brain activity changes as animals determine whether to cooperate with each other. The team will record brain activity in freely interacting monkeys as they participate in social cognition tasks, including working with one another to obtain food. Increased knowledge about complex social behaviors may help understand collective interactions among individuals and could improve treatments for certain mental health disorders.

Learning spatio-temporal statistics from the environment in recurrent networks Brunel, Nicolas Shouval, Harel Zeev (contact) University Of Texas Hlth Sci Ctr Houston 2016 Active
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Learning new tasks and interacting with new environments leads to changes in the dynamics of brain circuits. The ability of animals to incorporate the statistical properties of the environment into decision-making brain circuits is necessary for survival. Shouval and his colleagues plan to develop a theoretical model for how brain circuits implement these statistics. This model may provide novel insights into the basis of a variety of neurophysiological processes, including learning and memory.
Linking Plasticity of Hippocampal Representation across the Single Neuron and Circuit Levels BASU, JAYEETA et al. NEW YORK UNIVERSITY SCHOOL OF MEDICINE 2018 Active
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<p>Functional circuits between the entorhinal cortex and hippocampus are known to play a major role in spatial navigation and episodic memories. To develop a theoretical model of studying neural plasticity at both the single cell and circuit levels, Drs. Basu and Clopath will target specific cortico-hippocampal circuits using&nbsp;in vivo&nbsp;two-photon calcium imaging, slice electrophysiology, and optogenetic manipulation, in newly-developed transgenic mice. The knowledge gained from these experiments will aid in the understanding of neural circuits of functional memory and may influence treatments for diseases with neurological dysfunctional states, such as Alzheimer’s disease.&nbsp;</p>

MACHINE LEARNING APPROACHES FOR ELECTROPHYSIOLOGICAL CELL CLASSIFICATION Barth, Alison L Carnegie-mellon University 2017 Active
  • Integrated Approaches
Recordings of neural spike activity produce high-density, temporally precise patterns of neural firing that researchers aim to deconstruct into meaningful accounts of information processing in the brain. As the amount of generated data increases, neuroscientists need to decode more information than neuronal firing – they must be able to identify which specific cell-types are firing. Alison Barth is teaming up with computer scientists to develop a machine-learning classifier that can differentiate between inhibitory neuron subtypes in somatosensory cortex by using information from features such as rate of spontaneous firing, response to stimulation, and covariance of activity. By developing algorithms for cell identification that can identify specific neuron subtypes from spike train data in vivo, this project has the potential to build bridges between local circuit computations and cognitive processes.
Manifold-valued statistical models for longitudinal morphometic analysis in preclinical Alzheimer's disease (AD) Johnson, Sterling C Singh, Vikas (contact) University Of Wisconsin-madison 2016 Active
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In light of the profound public health issues Alzheimer’s disease (AD) represents, there is a tremendous need for methods to identify the onset of the disease as early as possible. Singh and his colleague propose to develop novel methods for analyzing Cauchy deformation tensors (CDTs) in brain images. These methods will enable the identification of structural changes in healthy midlife adults that are predictive of AD onset. The proposed analysis will be conducted on the largest preclinical AD cohort assembled to date and will help inform how telltale clinical biomarkers of AD emerge in asymptomatic individuals at risk for the disease. These preclinical biomarkers may be used in the design of clinical trials for new therapies.
Mapping of spatiotemporal code features to neural and perceptual spaces RINBERG, DMITRY et al. NEW YORK UNIVERSITY SCHOOL OF MEDICINE 2018 Active
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Sensory systems neuroscience investigates how stimuli are represented by the activity of populations of neurons, as well as how neural circuits process this information, resulting in behavioral outcomes. After varying patterns of optogenetic stimulation and recording both neural activity and behavioral output, Drs. Rinberg and Panzeri will develop a mathematical model of neural coding for odor discrimination in mice. Their model may determine the specialized spatiotemporal neural code involved in olfactory processing which could be further applied to other neural circuits.

Measuring, Modeling, and Modulating Cross-Frequency Coupling Eden, Uri Tzvi Kramer, Mark Alan (contact) Boston University (charles River Campus) 2018 Active
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Cross-frequency coupling (CFC) is a phenomenon through which brain rhythms of different frequencies (fast vs. slow oscillations) coordinate to enable efficient communication between and among neural networks. Current methods measure a single type of CFC related to a given research question, but do not necessarily account for different interactions or combinations between phase and amplitude in fast and slow frequency bands. Drs. Kramer and Eden will develop a more general statistical inferential framework to estimate CFC in rats by creating a method to acquire real-time phase and amplitude data for estimation of CFC to accommodate dynamic manipulations. The team will incorporate computational modeling studies to simulate CFC between the amygdala and the frontal cortex and test via in vivo experiments. This framework will allow future users to explore the basis of network communication in the brain and evaluate the causal role of cross-frequency coupling.

Mechanisms of Information Routing in Primate Fronto-striatal Circuits Womelsdorf, Thilo Vanderbilt University 2019 Active
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The brain can rapidly reconfigure its activity to switch between goals and tasks, but the neural mechanisms underlying this complex process are not well understood. Based on evidence that changes in routing are driven by quick bursts of neural activity, Dr. Thilo Womelsdorf and his team will develop new analysis tools to investigate the mechanisms of routing information in neural circuits during goal-directed behavior. After first detecting the bursts, the group will characterize how they coordinate within one functional network, then address the impact of that coordination on information flow. The development of these analytical tools to study complex cognition within a rigorous framework has the potential to create a seamless pipeline for translation between experimental findings and analytical solutions.

Mechanisms of neural circuit dynamics in working memory Bialek, William Brody, Carlos D (contact) Seung, Hyunjune Sebastian Tank, David W Wang, Samuel Sheng-hung Witten, Ilana Princeton University 2014 Complete
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Dr. Brody and his colleagues will study the underlying neuronal circuitry that contributes to short-term "working" memory, using tools to record circuit activity across many brain areas simultaneously while rodents run on a track-ball through virtual mazes projected onto a screen.
Mechanisms of neural circuit dynamics in working memory anddecision-making Brody, Carlos D (contact) Pillow, Jonathan William Seung, Hyunjune Sebastian Tank, David W Wang, Samuel Sheng-hung Witten, Ilana Princeton University 2017 Active
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Intense research efforts have focused on understanding working memory and decision-making, but technical and theoretical limitations have prevented a thorough understanding of these cognitive processes. Building on a previous BRAIN award, Carlos Brody and a team of experts are now aiming to outline a multi-brain-region, biophysical circuit model of the mechanisms that underlie working memory and decision-making. While mice complete a working memory task, the group will employ a variety of advanced imaging methods and optogenetic inactivation approaches to inform computational methods of incorporating these data into an integrative circuit model of the central nervous system. This combination of innovative methods can provide a mechanistic understanding of how the brain works with information.
Mechanisms of Rapid, Flexible Cognitive Control in Human Prefrontal Cortex Sheth, Sameer BAYLOR COLLEGE OF MEDICINE 2018 Active
  • Human Neuroscience
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The human brain can quickly “program” itself to adapt to novel situations, such as figuring out how to drive a rental car through a new city. Dr. Sheth and his colleagues plan to investigate how the brain assembles pieces of information into plans that help us manage new circumstances, and then develops a computational model of this learning. They will record from the brain’s dorso-lateral prefrontal cortex in patients with deep brain stimulation who are performing tasks to understand what information is being encoded and how it is processed. The project offers to provide a computational understanding of complex cognition. This may improve our understanding of cortical brain function and of neurological disorders that interfere with complex thinking.

Memory consolidation during sleep studied by direct neuronal recording and stimulation inside human brain FRIED, ITZHAK UNIVERSITY OF CALIFORNIA LOS ANGELES 2018 Active
  • Human Neuroscience
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  • Monitor Neural Activity

Sleep is important for learning and memory, but the exact mechanisms of this process are not known. Dr. Fried and his team will examine the role of sleep in memory formation in humans by recording brain activity during sleep following learning tasks. Dr. Fried’s group will identify the sleep events, such as sleep stage or changes in firing activity, that show the strongest association with memory consolidation. They will also examine whether electrical or auditory stimulation during sleep improves memory performance compared to undisturbed sleep. Greater knowledge of these mechanisms may help in the development of treatments for people suffering from memory and/or sleep disorders. 

Mental, measurement, and model complexity in neuroscience Balasubramanian, Vijay Gold, Joshua I (contact) University Of Pennsylvania 2018 Active
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Three specific challenges for neuroscience data include: 1) identifying the relevant spatial, temporal, and computational scales in which the underlying information-processing dynamics are best understood, 2) identifying the best ways to design and select models to account for these dynamics, and 3) inferring what the data tells us about how the brain itself processes complex information. Drs. Gold and Balasubramanian will develop theoretical tools for understanding how the brain integrates information across large temporal and spatial scales, using definitions of complexity to facilitate the analysis and interpretation of complex neural and behavioral data sets. This formal, mathematical assessment of data complexity could be used by the community for other data-driven model building and for comparisons of existing neuroscience models.

Methodologically-Integrated Approaches Linking Cell Types to Neural Circuits and Function Callaway, Edward M Salk Institute For Biological Studies 2017 Active
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Cortical circuits in the mouse are relatively well understood, but the extent they generalize to phylogenetically higher species remains unclear. Edward Callaway and colleagues are developing a suite of methods that will record neurons in non-human primate cortex, to better understand principles and functions of neural circuits in this model organism. Through molecular, genetic, viral, and large scale optical and electrical tools - including high-density electrode arrays and two-photon calcium imaging, Callaway’s team will investigate the levels of selectivity at which visually-evoked activity can be linked to circuits in terms of their cell types, connections, and functions. This novel approach in macaque monkeys has the potential to characterize large ensembles of simultaneously recorded neurons in important ways, a critical step in understanding neural circuitry across species.
Methods from Computational Topology and Geometry for Analysing Neuronal Tree and Graph Data Mitra, Partha Pratim (contact) Wang, Yusu Cold Spring Harbor Laboratory 2016 Active
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The complex tree shapes of neurons are important for their role in neuronal circuitry, but they are mathematically challenging to characterize and analyze. Mitra and his colleagues are applying advanced methods from computational topology and geometry to classify neuronal structure and its role in the function of circuits. The resulting tools will be made available to neuroscientists studying normal and diseased brain circuitry.
Models and Methods for Calcium Imaging Data with Application to the Allen Brain Observatory Buice, Michael Witten, Daniela (contact) University Of Washington 2018 Active
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Though calcium imaging permits single-cell observations in behaving animals, variation between trials and complexities in activity-dependent calcium dynamics and fluorescent read-out create challenging data analyses. Motivated by a large-scale, publicly-available repository of calcium imaging data obtained from mouse models at the Allen Brain Observatory, Drs. Witten and Buice will develop novel statistical models, methods, and software to improve analysis techniques comparing extracellular electrophysiology and calcium imaging recordings in the context of behavior. Creating new, open, online algorithms to interpret fluorescent traces of firing neurons and building models that account for variations in neuronal activity, could improve researchers’ ability to draw rigorous and replicable conclusions on the basis of calcium imaging data.

Multi-region 'Network of Networks' Recurrent Neural Network Models of Adaptive and Maladaptive Learning Rajan, Kanaka Icahn School Of Medicine At Mount Sinai 2019 Active
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Transformative new technologies can now capture neural activity from thousands of single neurons at high resolution. However, they produce large-scale datasets whose analysis and interpretation represent a bottleneck for computational models and theories, which must account for brain areas as interacting, intercommunicating neural circuits. For this project, Dr. Kanaka Rajan and her team will develop powerful, scalable, multi-region, neural network models and analysis tools for addressing this bottleneck.  The group will model the computations occurring across multiple brain regions during complex behavior, first using zebrafish as a test case before scaling up and expanding the range of their models. Success of this project will open up new avenues for probing circuit mechanisms within and between brain regions in both health and disease.

Multi-regional neural circuit dynamics underlying short-term memory Druckmann, Shaul Li, Nuo (contact) Baylor College Of Medicine 2017 Active
  • Integrated Approaches
Short-term memory is involved in many core cognitive behaviors, but it remains unclear whether its neural circuitry is mediated by a single distributed circuit or by many distinct parallel representations, and whether causal relations exist between regions. Nuo Lui and Shaul Druckmann are using optogenetic approaches to selectively perturb brain regions, observing whether this disruption in persistent activity in mouse frontal cortex also results in transient and/or lasting disruptions in behavior. By developing new analysis and modeling techniques to convert neural recordings and perturbations into circuit models, this work could provide a comprehensive investigation of the multi-region circuits that mediate short-term memory.
Multi-regional neural circuit dynamics underlying short-term memory Druckmann, Shaul Li, Nuo (contact) Baylor College Of Medicine 2019 Active
  • Integrated Approaches

Short term memory (STM) is essential for reasoning, decision-making, and flexible behavior, yet the neural substrate for STM is poorly understood. This project will use advanced techniques to first map and record circuits involved in STM and then use that information to selectively perturb individual circuits during specific tasks to better understand how these circuits and brain-wide activity dynamics generate memory-supported behaviors.

Multilevel Analysis of Neuronal Computations Underlying the Robust Encoding of Sensory Information in the Mammalian Olfactory System Arenkiel, Benjamin R Pfaffinger, Paul (contact) Baylor College Of Medicine 2019 Active
  • Integrated Approaches

In many ways, the brain is a computer, performing complex calculations that result in behavior, but those exact mechanisms are unknown. Dr. Pfaffinger’s team will use live imaging, electrophysiology, computational modeling, and genetic approaches to comprehensively look at the olfactory system, including its circuitry, how the brain responds to odors, and how that changes during learning. These studies will help inform general circuitry principles as well as how the brain processes sensory information. 

Multimodal modeling framework for fusing structural and functional connectome data Nagarajan, Srikantan S. Raj, Ashish (contact) Weill Medical Coll Of Cornell Univ 2016 Active
  • Integrated Approaches
  • Theory & Data Analysis Tools
Recent advances in the development of imaging tools are allowing researchers to both measure brain function (i.e., EEG, fMRI, PET) and the underlying structure of brain connections (i.e., diffusion MRI). Integrating functional brain activity data across imaging platforms, each of which provide unique information, has been tricky, as has been combining that data with structural connectivity data. Raj and his team are developing sophisticated modeling programs that combine this wealth of data across multiple spatial scales. These programs will provide insight into the relationship between brain function and structure and how the relationship is altered in cases of injury and disease.
Multiplex imaging of neuronal activity and signaling dynamics underlying learning in discrete amygdala circuits of behaving mice. Li, Bo Mao, Tianyi Zhong, Haining (contact) Oregon Health & Science University 2018 Active
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Dysfunction in the amygdala circuitry has large ramifications for myriad actions including those driven by threat or reward, and is essential for both learned behaviors - and for mood. How individual learning tasks differentially change this circuit to produce different behaviors remains largely unknown. Haining Zhong’s team will perform two-photon, multiplex imaging using a tiny GRIN lens, which allows optical access to deep brain structures, to image calcium activity as a proxy for neuronal firing in the amygdalae of behaving mice. Simultaneously, they will image the activity dynamics of the biochemical cAMP/PKA signaling pathway, as a readout for stress-/reward-induced neuromodulation. The team aims to discover and characterize, with cell-type specificity, functional subdivisions of the amygdala. This work may improve understanding of neuropsychiatric diseases associated with amygdala dysfunction.
Multiscale analysis of how the basal ganglia impact cortical processing in behaving mice Jaeger, Dieter Emory University 2019 Active
  • Integrated Approaches

Questions remain as to how output from the basal ganglia affects cerebral cortical activity during the processes of decision making, motor planning, and movement. Using modern molecular, imaging, and behavior approaches, this project will address questions of how basal ganglia output leads to cortical activation at the level of individual neurons and networks. Based on the information gathered, the researchers will build a thalamo-cortical network model to test which basal ganglia outputs and stimulus patterns are responsible for specific behaviors.

MULTISCALE ANALYSIS OF SENSORY-MOTOR CORTICAL GATING IN BEHAVING MICE Jaeger, Dieter (contact) Stanley, Garrett B. Emory University 2015 Complete
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The neural circuitry underlying how animals make motor decisions, especially in response to sensory or environmental cues, is not well understood. Many motor disorders, including Parkinson’s and Huntington’s disease, are linked to faulty circuits in a region of the brain called the basal ganglia. Researchers will use a variety of advanced methods to image, record, and manipulate the activity of neurons in this area as well as in the areas of the brain involved in sensory perception and movement. By employing these methods at multiple scales – from the individual neuron to neuronal networks – and then correlating these data with the behavior of awake, behaving mice, researchers hope to reveal how sensory information is integrated with input from the basal ganglia to result in the decision to initiate or suppress movement.
Multiscale Imaging of Spontaneous Activity in Cortex: Mechanisms, Development and Function Constable, R. Todd Crair, Michael (contact) Yale University 2015 Complete
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Being able to observe the activity of a single neuron while simultaneously observing the activity of entire brain regions is a critical step in bridging the gap in understanding of how a collection of nerve cells ultimately generates an organized behavior. Dr. Crair and colleagues will develop and use two different imaging techniques to measure the activity of individual neurons, regions of the brain, and the whole brain, during different behavior states, such as REM and non-REM sleep, in developing mice. Bridging their analyses and insights between and within scales will allow these researchers to examine neural circuits and networks in different brain states and determine how they are modulated through development.
Network basis of action selection Komiyama, Takaki Kreitzer, Anatol (contact) Lim, Byungkook J. David Gladstone Institutes 2015 Complete
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Three separate research groups are collaborating to understand in detail how three distinct areas of the brain function and work together to enable learning and decision-making behaviors. Drs. Kreitzer, Komiyama, and Lim are leveraging an impressive set of technologies to monitor and perturb different cell types in each brain region while the mice perform learning and decision-making tasks. By applying multiple recording methods across these brain regions at both the level of a single neuron and entire subpopulations of neurons, while the animals perform the same set of tasks, researchers hope to develop a single model of how vertebrate animals make choices about what to do next.

Network Connectivity Modeling of Heterogeneous Brain Data to Examine Ensembles of Activity Across Two Levels of Dimensionality Gates, Kathleen Univ Of North Carolina Chapel Hill 2016 Active
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Functional MRI (fMRI) is currently the most ubiquitous imaging technique for measuring whole brain activity in humans. The usefulness of fMRI in both research and clinical settings, however, has been limited by the availability of computational tools for analyzing the data. Most tools allow researchers to track activity in brain regions within a known network, without the ability to simultaneously examine connections between various networks. Gates and her colleagues have proposed a set of software tools that enable the simultaneous analysis of within- and between-network connectivity. The tools will also make it easier to combine fMRI data across individuals in order to learn more about how whole brain activity differs across people in both health and disease.
Neural circuits for spatial navigation Maimon, Gaby Rockefeller University 2018 Active
  • Integrated Approaches
A circuit-level understanding of how brains perform quantitative, navigation-related computations would be a major advance for neuroscience. Gaby Maimon’s team will study how brains construct navigational signals and how these signals guide behavior. Using physiological recordings in active fruit flies, they seek to identify a circuit by which sensory information arrives at the central brain (where neurons respond to sensory-motor signals) to update the head-direction when flies turn in darkness. To investigate whether the fly’s internal heading/compass signal is needed for them to keep a straight bearing, the researchers will take recordings after impairing this system. Finally, they will test whether flies have forward speed-sensitive neurons that enable 2D navigation. Such discoveries could elucidate how model brain machinery can perform day-to-day navigation tasks, and how to approach conditions in which these abilities are impaired, such as Alzheimer’s disease.
Neural circuits in zebrafish: form, function and plasticity Cepko, Constance L Engert, Florian (contact) Lichtman, Jeff W Sompolinsky, Haim Harvard University 2014 Complete
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Dr. Engert's team will combine a wide array of cutting-edge neuroscience techniques to watch the entire brain activity of a see-through fish while it swims, and to make detailed maps of its brain circuitry.
Neural circuits underlying thirst and satiety regulation Oka, Yuki CALIFORNIA INSTITUTE OF TECHNOLOGY 2018 Active
  • Integrated Approaches

The neural circuits involved in the regulation of thirst and satiety remain poorly understood. Classical models postulate that water deficits drive appetite, which is sated when the internal environment is rehydrated. Using viral tracing studies, in vivo optical recording in mice, retrograde labeling, and single-cell RNA-seq analysis, Dr. Oka and team will test the hypothesis that ingestive behavior itself directly modulates appetite in the brain before the body is satiated. This work will bring new understanding to the role of thirst circuits and drinking behavior.

Neural Computation for Innate Behaviors in the Superior Colliculus Meister, Markus California Institute Of Technology 2019 Active
  • Integrated Approaches

The brain receives a significant amount of sensory data all the time but is able to filter data to make informed decisions. Dr. Meister and colleagues are studying an area of the brain, the superior colliculus (SC), that links visual input from the eye to motor decisions. Using a previously developed computational model for SC processing, Dr. Meister’s team aims to understand the anatomy of the SC; determine how the SC processes specific visual inputs; and determine the role the SC plays in visual memory.

Neural Computations Underlying Vocal Sensorimotor Transformations Long, Michael A New York University School Of Medicine 2019 Active
  • Integrated Approaches

This project aims to investigate the circuit mechanisms that underlie sensorimotor transformations. Using behavioral, molecular, and electrophysiological approaches, this project will characterize the neural basis of rapid vocal exchanges using the singing mouse as a model system that engages in “countersinging,” a socially modulated behavior. 

Neural ensembles underlying natural tracking behavior Fiete, Ila R. Huk, Alexander C Priebe, Nicholas J. (contact) University Of Texas, Austin 2015 Complete
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Animals move their eyes to track the movement of objects around them. These researchers will measure and manipulate the activity of populations of identified neurons in marmosets during pursuit eye movements. This work will allow a detailed understanding of how the pursuit circuit integrates information from a large number regions is a critical step in bridging the gap in understanding of how a collection of nerve cells ultimately generates an organized behavior. Dr. Crair and colleagues will develop and use two different imaging techniques to measure the activity of individual neurons, regions of the brain, and the whole brain, during different behavior states, such as REM and non-REM sleep, in developing mice. Bridging their analyses and insights between and within scales will allow these researchers to examine neural circuits and networks in different brain states and determine how they are modulated through development.
Neural mechanisms and behavioral consequences of non-Gaussian likelihoods in sensorimotor learning Nemenman, Ilya M. (contact) Sober, Samuel Emory University 2016 Active
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A central goal of neuroscience is to understand how learning is implemented by the nervous system. However, despite years of studies in animals and humans, our knowledge of both the computational basis of learning and its implementation by the brain is still rudimentary. This project by Nemenman and his colleagues will spawn a unified mathematical theory explaining how the brain learns complex skills. The researchers will validate their theory in songbirds, with the goal of understanding sensorimotor learning of a single acoustic parameter, pitch, which is precisely regulated by the songbird brain. A better understanding of the mechanisms underlying sensorimotor learning could guide rehabilitative strategies that exploit the plasticity of complex behavior.
Neural mechanisms of active avoidance behavior Castro-alamancos, Manuel A Drexel University 2018 Active
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Maladaptive, active avoidance behavior is present in most forms of pathological anxiety. To better understand this process, Castro-Alamancos and colleagues will study freely-behaving, genetically-modified mice performing active avoidance (e.g., withdrawing from aversive noise or mild electrical foot shocks). They will employ behavioral, electrophysiological, optogenetic, chemogenetic, pharmacological, and histological procedures to test some of specific hypotheses: 1) The substantia nigra pars reticulata (SNr) mediates active avoidance behavior via projections to midbrain locomotor regions. and 2) Specific regions of the striatum control SNr activity during avoidance via connections projecting from the striatum to the substantia nigra. This project will provide understanding about the neural circuits responsible for active avoidance behavior.
Neural representation of mating partners by male C. elegans Linderman, Scott Warren Samuel, Aravinthan D. Sternberg, Paul Warren (contact) California Institute Of Technology 2019 Active
  • Integrated Approaches

Understanding the function of neural circuits requires knowledge about neural connectivity and activity in response to stimuli. Dr. Sternberg and colleagues aim to elucidate the full set of sensorimotor events that organize the mating behavior in C. elegans, taking advantage of its complete connectome along with computational modeling and cell-specific manipulations. They will catalog the sensorimotor events that organize mating behavior; model the circuits involved in the mating process; and use genetic tools to characterize each type of sensory neuron involved. This research may give insight into precise circuit control of a specific behavior – the type of understanding that helps neuroscientists better understand how the brain works.

Neural sequences for planning and production of learned vocalizations Cooper, Brenton G. Hahnloser, Richard Roberts, Todd F (contact) Ut Southwestern Medical Center 2018 Active
  • Integrated Approaches

To understand how the brain controls voluntary movements via sequences of neuronal activity, Dr. Roberts and colleagues intend to study how the brains of songbirds control singing, a natural behavior. For this project they will use calcium imaging, electrophysiological recordings, and optogenetic manipulations in a cell-type specific manner to investigate how specific circuits help songbirds plan, prepare, and sing their songs. The work could improve our understanding of how patterns of neuronal activity integrate to allow voluntary, skilled actions, which may help researchers understand how the breakdown of these circuits can cause movement disorders, like Parkinson’s disease.

Neuromodulation of Brain States Luo, Liqun Stanford University 2018 Active
  • Integrated Approaches
Abnormalities of the serotonin neuromodulatory system contribute to mood disorders. In rodents, Luo’s team will use their recently-developed viral-genetic tools to dissect the complexities of the serotonin system into specific sub-systems. They will anatomically characterize the organization of the dorsal raphe (DR)-serotonin sub-systems, identifying how each sub-system divides up the projections of the entire DR-serotonin system, as well as the input-output relationship for each sub-system. Sub-system behavioral functions will be determined by manipulating and recording serotonin neuron subtypes in anxiety- and depression-like states. Finally, the team will explore the circuit and cellular mechanisms by which serotonin regulates thirst-motivated behavior, using a technique to genetically manipulate thirst-activated neurons. This work could elucidate how serotonin modulates diverse physiological functions and behaviors.
Neuromodulatory control of collective circuit dynamics in C. elegans Flavell, Steven Willem Massachusetts Institute Of Technology 2017 Active
  • Integrated Approaches
The neural mechanisms that allow animals to initiate, maintain, and terminate long-lasting behavioral states (e.g., sleep/wake, emotional, and cognitive states) are unknown. Steven Flavell’s team aims to identify, in freely-moving C. elegans, the circuit-wide neural dynamics that define roaming and dwelling behavioral states, and to examine how specific neuromodulators coordinate the activity of their target neurons to organize whole-circuit activity patterns over long stretches of time. The team will combine their own newly-developed calcium imaging technology, which can simultaneously monitor every neuron in a circuit, with genetic/optogenetic manipulations (e.g., neuromodulator deletions and inter-neuron functional connectivity perturbations) and with novel analysis/modeling methods. These studies could help reveal how neuromodulators orchestrate whole-circuit changes in activity to influence animal behavior.
Neuronal circuits for context-driven bias in auditory categorization Cohen, Yale E Geffen, Maria Neimark (contact) Kording, Konrad P. University Of Pennsylvania 2019 Active
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The sensory signals and our brain’s response to them is noisy, which means that we must perform important cognitive tasks based on uncertain information. Dr. Geffen and colleagues aim to generate a computational framework and identify the circuits involved in one type of these cognitive tasks, auditory characterization. Using mice trained in forced choice tasks prompted by auditory stimuli, they will gather data from the auditory and posterior parietal cortices to gain insights that can be applied to hearing dysfunction, perception, and cognition. These findings will be important in developing clinical approaches to alleviating hearing difficulties in complex acoustic environments as well as potentially helping patients with deficits in cognitive tasks such as those with schizophrenia or other psychiatric disorders.

Neuronal mechanisms of human episodic memory Mamelak, Adam Nathaniel Rutishauser, Ueli (contact) Cedars-sinai Medical Center 2017 Active
  • Human Neuroscience
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No meaningful therapies for memory disorders exist, partially due to a lack of mechanistic knowledge about human memory. Ueli Rutishauser’s multi-institutional, multi-disciplinary team will study how memories of facts and events are formed and used in the human brain. The team will use electrophysiological methods to record single neurons, simultaneously in multiple brain areas, in awake patients who are implanted with electrodes to localize epileptic seizures. This work will combine single-neuron physiology, behavioral testing, electrical stimulation, and computational modeling, to address three questions: (i) how persistent activity supports memory formation, (ii) what mechanisms translate memories into decisions and judgments, and (iii) how memories are formed and recalled over time. A circuit-level understanding of memory may enable development of new treatments for memory disorders.
Neuronal population dynamics within and across cortical areas Doiron, Brent UNIVERSITY OF PITTSBURGH AT PITTSBURGH 2018 Active
  • Integrated Approaches
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Drs. Doiron, Smith, and Yu will develop a method that combines large-scale network modeling, large-scale neural recordings, and neural population analyses to understand the key network principles that drive behavior. The team proposes to validate their methods using data recorded from macaque prefrontal cortex to the visual area, V4. A toolkit called Balance BEAM (Brains, Experiments, Analysis and Models), implemented in Matlab, will include a graphical user interface for designing balanced, optimized network models. If successful, Balance BEAM will allow future users to better research how neural circuits give rise to transient activity, steady-state activity, and neural variability.

Neurostimulation and Recording of Real World Spatial Navigation in Humans Suthana, Nanthia A University Of California Los Angeles 2017 Active
  • Human Neuroscience
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  • Monitor Neural Activity
Spatial memory is thought to involve neurons in the medial temporal lobe that exhibit increased firing rates when an animal is in a specific location during spatial navigation. However, human single-neuron studies have been limited to immobile subjects viewing 2-dimensional navigational tasks. Nanthia Suthana’s team will use intracranial single-neuron and local field potential recordings, combined with deep brain stimulation (DBS), in epilepsy patients performing freely-moving spatial navigation memory tasks using state-of-the-art virtual reality headset technology and full-body motion capture. The team will record from medial temporal lobe subregions, to determine the role of single neurons and oscillations during navigation and memory, and how these neurophysiological mechanisms can be enhanced by deep brain stimulation. This work may yield insights into the neuronal correlates of real-world spatial navigation and memory.
New methods and theories to interrogate organizational principles from single cell to neuronal networks Dierssen, Mara Ye, Bing (contact) University Of Michigan At Ann Arbor 2019 Active
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Understanding how individual neurons contribute to network functions is fundamental to neuroscience, but the link between neuronal structure and network connectivity is unclear. Dr. Bing Ye and his team will develop and validate a user-friendly toolset for discovering the rules that link neuronal morphology to network connectivity, allowing them to make predictions about neural network properties based on the structure of single neurons. This open-source computational tool will incorporate visualization and analysis of neuronal populations derived from imaging data, as well as models for interrogating the organizational principles underlying brain network architecture. The successful development of these novel computational tools will enable researchers to investigate how the shapes of individual cells contribute to the connectivity of the nervous system in both health and disease.

Next-Generation Calcium Imaging Analysis Methods Paninski, Liam M Columbia Univ New York Morningside 2016 Active
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Tracking the flow of calcium ions into and out of neurons is a good way to monitor the simultaneous activity of many neurons with single-cell resolution. The proliferation of high-resolution, high-throughput calcium imaging equipment is generating enormous 2D and 3D datasets that are challenging to interpret. Paninski and his colleagues are working on innovative statistical and computational approaches to make sense of those imaging data and to combine them with other types of data, such as those from multielectrode arrays. The proposed analytical methods will be scalable, modular, and extensible, providing flexibility to users and developers.
Nonlinear Causal Analysis of Neural Signals Sejnowski, Terrence J University Of California, San Diego 2018 Active
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Understanding how neural signals from different parts of the brain impact each other over time could help reveal differences in information flow during typical and abnormal cortical states. Dr. Sejnowski proposes to develop a new algorithm for the causal analysis of time-series data called cross-dynamical Delay Differential Analysis (CD-DDA), for modeling time- series data. The work should extend CD-DDA to identify causally- connected brain network phenomena, using simulated Hodgkin-Huxley network models and electrocorticography recordings from epilepsy patients. Though this project uses electrocorticography data to validate CD- DDA, the tool can be applied to calcium recordings from single neurons, voltage sensitive dye recordings, local field potentials, EEG, MEG, and fMRI data. These analytical methods could help future users uncover the influence of information flow across cortical areas on activity of different neuronal populations.

Norepinephrine modulation of neocortex during flexible behavior Cohen, Jeremiah Yaacov (contact) O'connor, Daniel Hans Johns Hopkins University 2018 Active
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Flexible behavior requires that animals explore their environment and respond to changes, while exploiting features of the environment that have known value. Cohen and colleagues will test a theory that the neurotransmitter norepinephrine facilitates behavioral flexibility by modulating neocortical activity. During reward-based behavioral tasks, the team will record and manipulate the activity of norepinephrine-releasing neurons in the locus coeruleus (the primary source of forebrain norepinephrine), as well as their targets in the sensory and prefrontal cortex of mice. Understanding how norepinephrine modulates the neocortex in the context of behavior will be necessary to understand disorders of attention and mood that rely on norepinephrine signaling.
Novel Bayesian linear dynamical systems-based methods for discovering human brain circuit dynamics in health and disease Menon, Vinod Stanford University 2016 Active
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It is currently not known how dynamic patterns of brain activity are transformed into cognition, emotion, perception and action in both health and disease in humans. Menon and his colleagues plan to develop novel algorithms for identifying dynamic functional networks in the brain and characterizing network interactions between brain regions involved in cognitive tasks, translating their studies from in vivo rodent data to humans. The researchers’ algorithms will facilitate rigorous investigations of brain dynamics that support critical cognitive functions and significantly advance the understanding of dynamic processes underlying human brain function and dysfunction. As an example, one of the major goals of the project will be using the newly created algorithms to investigate aberrant functional circuits associated with cognitive impairments in Parkinson’s disease.

Oxytocin Modulation of Neural Circuit Function and Behavior Tsien, Richard W New York University School Of Medicine 2018 Active
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While oxytocin hormonal signaling has been studied in maternal behavior and implicated in several brain disorders, we know little about how variations in its release modulates the circuits that regulate social behaviors. Tsien’s group will develop new tools and cutting-edge techniques with large-scale methods to tackle the oxytocin system from both the source (oxytocin neurons) and the receiving ends (oxytocin receptor-expressing neurons). From the source, they will address the connectivity, behavioral influence, in vivo responses, release, and experience-dependent changes of the oxytocin neurons. From the receiving ends, they will dive into detailed cellular, synaptic, and microcircuit mechanisms that mediate oxytocin actions. With these data, they plan to explore state-dependent changes in aggression due to oxytocin. A better understanding of the endogenous action of oxytocin is key to unleashing its therapeutic potential.

Population Neural Activity Mediating Sensory Perception Across Modalities CLANDININ, THOMAS ROBERT et al. STANFORD UNIVERSITY 2018 Active
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Understanding how the brain processes sensory information to result in selected behaviors remains a challenge in the field of neuroscience. Drs. Clandinin, Ganguli, Murthy, and Scott will utilize the fruit fly model to examine how different sensory pathways and timescales interact at the brain-wide level. Their project will involve synthetic and naturalistic sensory stimuli of three modalities (vision, taste, and mechanosensation) alongside state-of-the-art in vivo two-photon calcium imaging. By incorporating brain-wide results across sensory modalities in Drosophila, along with circuit algorithms and cell-type specific aspects of sensory integration these studies can help inform neural circuit function of sensory processing in more complex systems. 

Predictive models of brain dynamics during decision making and their validation using distributed optogenetic stimulation Pesaran, Bijan New York University 2017 Active
  • Integrated Approaches
Distributed across the frontal and parietal cortices are circuits through which our brains combine sensory information and experience to select spatial goals for movement. Different areas in the frontal-parietal circuit have specialized functional roles, but the neural activity patterns within those areas remain unclear. Bijan Pesaran’s team will develop and validate predictive models of neuronal dynamics underlying visual-saccadic decision-making. They will use electrophysiology and optogenetic stimulation in non-human primates performing a decision task. While monitoring activity and behavior, the team will assign functional roles to neural activity patterns by precisely perturbing the circuit (via temporally-patterned optogenetic stimulation) to achieve targeted neural activity states predicted by the numerical model. This project’s success may offer new ways to delineate the contributions of different cortical areas to attentional selection and decision-making.
Quantifying causality for neuroscience Kording, Konrad P. University Of Pennsylvania 2019 Active
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Understanding causality is central to neuroscience, both in how the action of one neuron affects another, as well as in medical approaches that aim to produce causal effects. For this project, Dr. Konrad Kording and his team will develop a set of computational techniques that will allow neuroscientists to quantify how neurons causally influence one another. To do so, they will utilize approaches popular in econometrics, wherein the observation of variables that approximate random system perturbations will allow for the discovery of causal relations. The interdisciplinary team will apply these techniques to problems in neuroscience through a combination of machine learning and engineering, paving the way for important advances toward understanding and quantifying causality in both basic and clinical applications.

Quantitative mapping of oxygenation around neural interfaces using novel PISTOL MR imaging MUTHUSWAMY, JITENDRAN et al. ARIZONA STATE UNIVERSITY-TEMPE CAMPUS 2018 Active
  • Interventional Tools

Current and emerging neural implants disrupt the local blood-brain barrier during placement into the brain, potentially leading to oxidative stress in brain tissue along the path of the implant. Muthuswamy, Kodibagkar and Sridharan will develop a novel magnetic resonance imaging technique with a siloxane contrast agent for measuring oxygen levels and potential oxidative stress. The technique will allow simultaneous monitoring of single-neuron electrophysiology and quantitative spatiotemporal mapping of oxygen levels around neural interfaces and will be scaled for use at multiple sites in the rodent brain. This technology represents an important step, as more sensitive measures of oxygen levels during typical neuronal function and after electrode placement are necessary to understand the long-term efficacy of neural implants.

Readout and control of spatiotemporal neuronal codes for behavior Babadi, Behtash Chialvo, Dante R Fellin, Tommaso Histed, Mark H Kanold, Patrick O Losert, Wolfgang Maunsell, John Hr (contact) Panzeri, Stefano Vt Plenz, Dietmar Rinberg, Dmitry Shoham, Shy University Of Chicago 2018 Active
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Organisms must accurately represent stimuli in the outside world and use that representation to generate behavioral actions. While it is understood that neuronal population responses carry information about specific external stimuli, it is unclear whether the brain “reads” this information to form sensory perceptions. Maunsell and colleagues have developed a patterned neuronal stimulation technology, previously funded via the BRAIN Initiative, and will apply it to answer long-standing questions about neural coding and readout in the visual, olfactory, and auditory systems. Using rodents, they will determine which neurons within a network are encoding behaviorally relevant information, and also determine the extent to which temporal patterns of those neurons’ activity are being used to guide behavior. A better understanding of neuronal mechanisms related to sensation and action will advance a theoretical framework for understanding neural codes, which could improve diagnosis for many neurological disorders.

Real-time statistical algorithms for controlling neural dynamics and behavior Park, Il Memming (contact) Pillow, Jonathan William State University New York Stony Brook 2018 Active
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High-throughput technologies help researchers understand how neural systems perform the computations that underlie perception, cognition, and behavior, but data obtained through simultaneous recordings from large groups of neurons and fine-grained behavioral tasks create a major bottleneck in data collection and analysis. Drs. Park and Pillow intend to develop statistical tools for tracking internal states of the brain that are not directly measurable from observation of behavior and neural signals. To uncover neuronal computations required for behavioral learning, the group will develop methods for tracking and enhancing the evolution of internal brain states during learning, generating optimal stimuli corresponding to those states to perturb or correct behavior. Subsequent evolution of this work could improve clinical tracking and intervention of neurological disorders with a behavioral component, like Parkinson’s disease. The effort will result in a real-time tool for tracking internal brain and behavioral states, applicable for both basic neuroscience research and clinical applications.

Revealing neural computations through combined optical and electrical recordings Field, Gregory Darin (contact) Sher, Alexander Duke University 2019 Active
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The retina is a thin layer of eye tissue that turns what we see into signals that are sent to the brain. It remains a challenge to understand how information is processed across the multiple layers of the highly interconnected neurons in the retina. To address this, the Field and Sher groups will develop a technology that combines large-scale optical and electrical recordings and visual stimulation to determine how diverse cell types in the retina shape its function. The results may help researchers understand the circuit dynamics behind vision and blindness and provide clues to universal principles that govern how circuits throughout the nervous system process information.

Revealing the connectivity and functionality of brain stem circuits Berg, Darwin K Deschenes, Martin Freund, Yoav Shai Goulding, Martyn D Kleinfeld, David (contact) Knutsen, Per M University Of California San Diego 2014 Complete
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Dr. Kleinfeld and his colleagues will use a variety of tools and techniques to create detailed maps of circuits in the brainstem, the region that regulates many life-sustaining functions such as breathing and swallowing, and match the circuits to actions they control.
Reverse Engineering the Brain Stem Circuits that Govern Exploratory Behavior Deschenes, Martin Freund, Yoav Shai Golomb, David Kleinfeld, David (contact) Mitra, Partha Pratim Wang, Fan University Of California, San Diego 2018 Active
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An overarching question in neuroscience is how motor actions are coordinated to form different behaviors. Orofacial motor actions in rodents (e.g., head bobbing, vibrissa whisking, and licking with the tongue) coordinate to simultaneously serve exploration without conflicting with life-supporting motor actions like breathing and swallowing. However, the neural basis for orofacial motor coordination remains elusive. Kleinfeld and colleagues aim to reverse engineer brainstem circuits that guide orofacial motor actions. The team will advance computational models and various neuroscience and machine learning tools to add informative labels to individual brainstem neurons, place these cells within circuits, connect circuits with motor actions, and coordinate different actions into behaviors. If successful, this project will create a publicly available 3D atlas of orofacial brainstem neurons, yielding lessons about the nature vital brain functions and of neuronal computation.

Sensorimotor processing, decision making, and internal states: towards a realistic multiscale circuit model of the larval zebrafish brain Engert, Florian (contact) Lichtman, Jeff W Sompolinsky, Haim Harvard University 2017 Active
  • Integrated Approaches
Understanding animal behavior requires a comprehensive look at the neural mechanisms that govern behaviors across spatial and temporal scales. Florian Engert and a team of experts are planning to generate a multi-level circuit model of the larval zebrafish brain by building on previous BRAIN work in which they integrated behavioral assays with whole-brain imaging and electron microscopy. Here, they plan to use these methods to understand additional relevant behaviors, including phototaxis, rheotaxis, escape, and hunting. Then, they will investigate how these behaviors interact in the presence of conflicting stimuli. Finally, they will study how internal states (e.g., hunger, stress or loneliness) can modulate each specific behavior, as well as their interaction. Together, this work will provide a thorough look at how complex behavior arises from the synapse to the whole brain level.
Sensory recruitment by working memory: neuronal basis and neural circuitry Noudoost, Behrad University Of Utah 2019 Active
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Maintaining working memory is crucial for performing everyday tasks, but we still do not understand the interactions between the prefrontal and visual cortices that are key for this process. Dr. Noudoost and colleagues will combine neurophysiological recordings, microstimulation and pharmacological manipulation, and computational modelling to study how the prefrontal cortex drives changes in the activity of visual areas during working memory tasks and how those changes affect behavior.

Spatiotemporal Coding in the Pain Circuit Along the Spine-brain Continuum Borton, David Allenson Saab, Carl Y (contact) Rhode Island Hospital 2018 Active
  • Integrated Approaches

Neuropathic pain is a national health challenge for which there are limited effective therapeutics, and understanding the mechanisms of pain circuity is a critical step towards developing clinical interventions. Drs. Carl Saab and David Borton will develop electrophysiology and imaging tools that permit long-term investigation of spinal cord pain circuits in awake, behaving mice. In particular, these tools will pair real-time recording from inhibitory neurons in the spine-brain continuum with behavioral tasks to assess the link between sensory thalamocortical rhythms and pain stimuli. By identifying dynamic and functional connectivity in brain-spine neural circuits in the context of behavioral tasks, this work could enhance our understanding of how circuits create and contribute to pain perception and behavior, ultimately paving the way towards treatment.

Spatiotemporal control of dendritic inhibition by a family of diverse somatostatin-expressing interneurons Rudy, Bernardo NEW YORK UNIVERSITY SCHOOL OF MEDICINE 2018 Active
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Due to technical challenges, studying the circuits that mediate cortical computations has been restricted to the superficial layers of the cortex. Recently, Dr. Bernardo and team have utilized a novel method, known as channelrhodopsin-assisted patching or ChAP, that permits&nbsp;in vivo&nbsp;recording and labeling of genetically-tagged neurons throughout the brain, including entire cortical columns. The team will use ChAP recordings in awake head-restrained mice, as well as dynamic calcium imaging and electrophysiology, to study inhibitory neuronal circuits in layer 5 pyramidal cells in the somatosensory barrel cortex. The researchers will use paired recordings, optogenetic manipulations, and morphological analysis in acute slices to create circuit maps. Furthermore, they intend to utilize the experimental results to develop models of these cortical networks, which could be applied to understanding of other brain areas.

 

Spinal Circuits for the Control of Dextrous Movement Goulding, Martyn D Salk Institute For Biological Studies 2019 Active
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Motor circuits in the cervical spinal cord control many essential behaviors, such as skilled reaching and grasping. However, the cervical spinal cord remains an understudied region of the central nervous system. Dr. Goulding and team (the Spinal Cord Circuit Team, TeamSCC) will generate a novel atlas to study circuit dynamics and functional connectome of premotor and motor neurons of cervical spinal circuits controlling forelimb movements in rodents. This searchable, web-based portal will contain 3D visualization tools linked to molecular, electrophysiological, functional, and network model databases. These studies will inform ongoing research of motor control and brain function, leading to a better understanding of the motor system and how it is affected by neurological diseases and spinal injuries. 

Striatal Plasticity in Habit Formation as a Platform to Deconstruct Adaptive Learning CALAKOS, NICOLE DUKE UNIVERSITY 2018 Active
  • Integrated Approaches

The striatum receives direct input from many cortical and thalamic areas and is a major contributor to behaviors involving movement and learning. The striatum and its connections are implicated in diseases and disorders like Parkinson’s disease, Huntington’s disease, addiction, and compulsion. Whereas traditional forms of striatal plasticity involve the activity of direct and indirect medium spiny neurons, Dr. Calakos and team recently discovered that an increase in the excitability of local fast-spiking inhibitory interneurons plays an important role in habit learning – a paradigm that they have termed dviLP (direct vs indirect Latency Plasticity). Their novel tools – namely DISCO (Dual-pathway Imaging of Striatal Circuit Output) and DART (Drugs Acutely Restricted by Tethering) – should allow them to map, model, and manipulate striatal functional circuits during habit learning.

Structural and functional connectivity of the social decision-making network Smith, Adam Steven University Of Kansas Lawrence 2019 Active
  • Integrated Approaches

The formation of social relationships requires displays of commitment from social partners, yet we understand little of how neural networks function to regulate these context-appropriate social behaviors. Dr. Smith et al. aim to use the socially monogamous prairie vole as a model organism to study the neural mechanisms underlying social decision-making. The study will involve simultaneous fast measurement and manipulation of neural activity through real-time recordings and the alteration of neurochemical inputs within social decision-making networks. The work may help scientists better understand the brain circuitry that underlies social behavior.

Structural, single-cell transcriptomic, and functional 3-photon mapping of spinal pain circuits Kara, Prakash Kodandaramaiah, Suhasa B Vulchanova, Lyudmila H (contact) University Of Minnesota 2019 Active
  • Integrated Approaches

The Vulchanova, Kara, and Kodandaramaiah labs aim to study networks of neurons in the spinal cord that control transmission of pain signals to the brain. Their multi-disciplinary approach involves advanced genetic tracing techniques to map the cell’s wiring diagrams; calcium imaging to monitor circuit activity during pain; and single cell transcriptomics to analyze the gene activity of the cells in the circuits. Their results may help researchers understand how the nervous system process pain.

Studying perceptual decision-making across cortex by combining population imaging, connectomics, and computational modeling Harvey, Christopher D (contact) Lee, Wei-chung Allen Panzeri, Stefano Vt Harvard Medical School 2018 Active
  • Integrated Approaches

Perceptual decision-making involves groups of neurons working together in microcircuits to encode and transform sensory information into behavioral choices. However, the neural dynamics that allow for sensory-to-choice transformations remain poorly understood. Drs. Christopher Harvey, Wei-Chung Lee, Stefano Panzeri and team will perform two-photon calcium imaging of the mouse visual cortex (V1; sensory cortex) and posterior parietal cortex (PPC; association cortex) during a newly developed virtual- reality navigation task and build a computational network model to identify neurons that contribute to the sensory-to-choice transformations. Connectomics datasets comparing V1 and PPC, calcium imaging data from large populations during decisional tasks, and new computational frameworks of cortical microcircuits will allow for a better understanding of the neural mechanisms involved in perceptual decision-making.

Subiculm circuits for cortical feedback regulation of spatial mapping and learning Nitz, Douglas Arthur Xu, Xiangmin (contact) University Of California-irvine 2018 Active
  • Integrated Approaches
Degeneration of the subiculum, a sub-region of the hippocampal formation, has been implicated in Alzheimer's disease progression. In freely-moving rodents, Xu’s team will study the unique subiculum circuits that mediate cortical regulation of hippocampus-associated spatial navigation and learning. Using advanced approaches for genetic targeting of specific cell populations (i.e., sub-types of subiculum neurons), and molecular and viral tracing techniques, the team will generate a detailed map of the synaptic circuit organization for the pathway from the retrosplenial cortex, to the subiculum, and to the hippocampal CA1 region. This research may help improve understanding of the neural circuit mechanisms underlying Alzheimer's disease.
Subthalamic and corticosubthalamic coding of speech production Richardson, Robert Mark University Of Pittsburgh At Pittsburgh 2016 Active
  • Human Neuroscience
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Evidence points to an important role for the basal ganglia (BG) in speech. For instance, deep brain stimulation (DBS) of the subthalamic nucleus (STN) within the BG can improve motor symptoms for patients with Parkinson’s disease, but often does not improve speech impairments and in fact can disrupt language function. Richardson proposes to develop a model for how the BG helps drive speech production by recording activity of individual neurons within the STN along with STN and cortical local field potentials, in patients with Parkinson’s disease undergoing surgery to implant a DBS device. This work could lead to improved treatment for speech impairments in movement disorders, and reduced speech-related side effects of DBS therapy.
Thalamocortical and corticocortical mechanisms for sleep-dependent visual learning Aton, Sara J University Of Michigan 2018 Active
  • Integrated Approaches
It remains unclear how sleep-associated changes in the activity of specific brain circuits contribute to consolidation of transient sensory experiences into long-lasting memories. Using freely-behaving mice, Aton’s team will test the necessity and sufficiency of sleep-associated thalamocortical activity patterns in consolidating a simple form of experience-dependent plasticity. With cutting-edge optogenetic strategies, in vivo electrophysiology, and novel computational tools, the researchers will characterize and selectively manipulate state-specific interactions between neurons, and evaluate how these interactions (and the network dynamics they regulate) drive sensory plasticity and learning. This work has the potential to lead to better understanding of psychiatric disorders where sleep and cognition are affected, including schizophrenia, depression, autism, and dementia.
Thalamocortical state control of tactile sensing: Mechanisms, Models, and Behavior Stanley, Garrett B. Georgia Institute Of Technology 2018 Active
  • Integrated Approaches
The complex circuit interconnecting the brain’s thalamus and cortex (etymology: connecting the inner chamber to the rind) is continuously controlled by inputs that fundamentally shape information processing necessary for perception and behavior. However, the precise link between thalamic state and the resulting sensory representations in the cortex remains an unanswered question in neuroscience. Using the vibrissa system (facial whiskers) in awake mice, Garrett Stanley and colleagues will optogenetically modulate thalamic activity and, using electrophysiological and optical measurements, explore the downstream impact on cortical representations and subsequent perception, as well as measure sensory behavioral tasks. This project’s success may help us understand nervous system disorders in which individuals exhibit loss of sensitivity and the ability to adapt to changes in the sensory environment.
The cerebro-cerebellar-basal-gangliar network for visuomotor learning Fusi, Stefano Goldberg, Michael E. (contact) Strick, Peter Columbia University Health Sciences 2019 Active
  • Integrated Approaches

Learning that a particular object cues a particular action, such as a red light cueing us to stop, is critical to our ability to function in the world. This project aims to use physiological, computational, and anatomical methods to investigate how brain regions (prefrontal cortex, basal ganglia, and cerebellum) work as a network to assign symbols to a particular movement or behavior. This will be done by mapping the various components of the network, perturbing individual nodes of the network to tease apart individual roles in the process, and developing f computational models to analyze the activity of all regions simultaneously. This work will advance our understanding of the circuit basis of visuomotor learning.

The diversity of dopamine neurons: from connectivity and activity to functions. Uchida, Naoshige HARVARD UNIVERSITY 2018 Active
  • Integrated Approaches

Dysfunction in the dopamine system is implicated in several neurological disorders, including addiction, depression, and schizophrenia. Dr. Uchida and team will investigate the diversity of dopamine neurons, examining their connectivity and activity patterns, along with their functions. The experiments will focus on dopamine neurons projecting to the posterior ‘tail’ of the striatum and those projecting to the ventral striatum during a variety of behavioral tasks in mice, including classical conditioning, nose-poke choice behavior based on outcomes, and novel object exploration. Their techniques, including optogenetics, fiber photometry, electrophysiology, and calcium imaging, may help build a computational and theoretical framework for understanding the scope of dopamine neuron function.

THE DYNAMICS OF LONG RANGE CORRELATIONS IN CORTEX: SINGLE UNITS AND OXYGEN Snyder, Lawrence H Washington University 2017 Active
  • Integrated Approaches
The brain is always active. Intrinsic, on-going correlated neural activity interacts with on-demand, task-specific processing, but it remains unclear how neuronal activity gives rise to resting state network dynamics. Lawrence Snyder and colleagues are utilizing miniaturized, high-channel-count carbon fiber arrays that are implanted in the awake monkey to simultaneously record oxygen level, spikes, and local field potentials. This novel methodological approach in hardware will permit the researchers to investigate which cells give rise to intrinsic activity, and how that activity differs when the animal is at rest or performing a task. By examining the cellular contributions to the brain in these states, this work has the potential to formulate a detailed characterization of the underlying dynamics of neural activity.
The role of patterned activity in neuronal codes for behavior Maunsell, John Hr University Of Chicago 2014 Complete
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Dr. Maunsell's team will explore how large populations of neurons process visual information, using a newly developed light stimulation technique to induce brain cell activity in the visual cortex of mice.
the self-tuning brain: cellular and circuit mechanisms of behavioral resilience Fairhall, Adrienne L Gardner, Timothy James Lois, Carlos (contact) California Institute Of Technology 2017 Active
  • Integrated Approaches
In some instances, the brain can adapt to functional losses after neurological disease or injury, and recover normal behavior. Carlos Lois and colleagues will explore, in songbirds, the neuronal mechanisms by which the brain maintains stable behaviors after perturbation of function using gene delivery, optogenetics, in vivo functional imaging, electrophysiology, behavioral analysis, and computational modelling. They will introduce a variety of perturbations (including real-time optogenetic perturbations as well as permanent genetic perturbations) to high vocal center neurons, and study behavioral and circuit responses, as well as cellular and circuit properties of song restoration. They will then generate quantitative theoretical models to account for the behavioral resilience. This research is relevant to the pursuit of new avenues for treating neurological diseases.
Tools for modeling state-dependent sensory encoding by neural populations across spatial and temporal scales David, Stephen V (contact) Mesgarani, Nima Oregon Health & Science University 2019 Active
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Humans and other animals adapt their hearing in noisy environments, but the mechanisms underlying this central auditory process are not well understood. Here, Dr. Stephen David and his team will develop computational tools to understand how the neural populations in the healthy brain represent complex natural sounds. The group will build a software library that can model the functional relationship between auditory stimuli and corresponding neural responses, for which there are few existing models that effectively measure comparisons between the two. The system will also support machine learning methods that will be valuable for large-scale signal processing problems. These experiments will provide novel insight into how the brain solves problems that will allow engineering of new devices and treatments for conditions involving auditory dysfunction.

Toward a Theory for Macroscopic Neural Computation Based on Laplace Transform Howard, Marc W Boston University (charles River Campus) 2016 Active
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Behavioral and cognitive experiments suggest that a number of sensory processes follow Weber-Fechner scaling, which states that as the intensity of an actual stimulus increases linearly, the intensity of our perception increases only logarithmically. Yet, how the brain transforms linear changes in stimuli intensity to logarithmic changes in perception is not entirely understood. Nor is it known whether other cognitive processes like memory also follow some form of Weber-Fechner scaling. Howard and his colleagues plan to develop a theoretical means for understanding logarithmic sampling by sensory systems in the interpretation of neural data. The hope is that this understanding will provide insight into basic neurophysiological processes and how the brain both represents the past and predicts the future, which can be relevant to diseases involving memory loss and disturbances in health-related decision making.
Towards a Complete Description of the Circuitry Underlying Memory replay. Soltesz, Ivan University Of California-irvine 2014 Complete
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Dr. Soltesz's team will combine computer brain modeling and large-scale recordings of hundreds of neurons to understand how the brain generates sharp-wave-ripples, a neuronal activity pattern essential for learning and memory.
Towards a Complete Description of the Circuitry Underlying Sharp Wave-Mediated Memory Replay Buzsaki, Gyorgy Lisman, John E Losonczy, Attila Schnitzer, Mark J Soltesz, Ivan (contact) Stanford University 2017 Active
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In mammals, it has been difficult to address how neurons function as a network to produce cognition, and there are no circuit mechanisms of mammalian brain signals that are understood to the same degree as in simple systems such as invertebrates. Ivan Soltesz and a team of experts are using large-scale recordings and optical monitoring to elucidate subcellular events and probe the sharp-wave ripple (a hippocampal signal associated with memory consolidation) in awake, behaving animals. As part of this project, the team will use supercomputers to generate a full-scale computational model that links sharp-wave ripples to memory replay. This project has the potential to provide a detailed look at the principles by which neurons coordinate signals to produce cognitive function.
Towards a unified framework for dopamine signaling in the striatum Sabatini, Bernardo L (contact); Assad, John ; Datta, Sandeep R; Gershman, Samuel J; Linderman, Scott Warren; Uchida, Naoshige ; Wilbrecht, Linda E Harvard Medical School 2019 Active
  • Integrated Approaches

Dopamine has many important functions in the brain, particularly those involving movement and reward. Dopaminergic neurons and the activity of dopamine have been proposed to reinforce the actions and sensory context associated with reward, trigger the execution of motor actions, and control the vigor, strength, and timing of motor actions. Dr. Sabatini will lead an exceptional team of investigators to develop a quantitative model of dopaminergic circuits and dopamine function. They aim to determine what features of the environment, reward history, reward expectation, and motor action are encoded in the activity of ventral tegmental area (VTA) and substantia nigra pars compacta (SNpc) dopaminergic neurons – as well as the effects on downstream targets in the striatum – in mice. The project will develop an efficient and comprehensive framework to readily share data, protocols, instrumentation designs, code, and biological resources with the neuroscience community.  

Uncovering Population-Level Cellular Relationships to Behavior via Mesoscale Networks Carlson, David E Duke University 2019 Active
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In complex neural circuitry, it is not clearly understood how the activity of individual neurons coordinate with larger networks to ultimately give rise to behavior. Here, Dr. David Carlson and his team will study how neurons’ action potentials, long considered to be a fundamental unit of information, relate to whole-brain spatiotemporal voltage patterns and behavior. To uncover this relationship, they will develop novel computational methods capable of creating generalizable maps that relate voltage signals from multiple brain regions based upon machine learning approaches. These network maps then will be used to classify neurons and employ statistical approaches to uncover meaningful relationships across scales of neural activity and behavior with relevance to health and disease.

Understanding the logic of the brain-wide olfactory bulb projectome Albeanu, Dinu Florentin (contact) Koulakov, Alexei Cold Spring Harbor Laboratory 2019 Active
  • Integrated Approaches

It remains unclear how information from odors are processed by the brain regions that make up our olfactory system. Using high-throughput sequencing methods, in vivo functional imaging, and anatomical and computational tools, this project aims to study how cells in the olfactory bulb extract information from odors and then send this information to other parts of the brain for processing.

Understanding the Neural Basis of Volitional State through Continuous Recordings in Humans Cash, Sydney S Massachusetts General Hospital 2016 Active
  • Human Neuroscience
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  • Interventional Tools
  • Monitor Neural Activity
Every day, humans make many cognitive shifts of their own volition. Examples are as diverse as changes in wakefulness to planning complex movements. Current research often explores only neural activity that is associated with behavior using fixed, externally-driven models. Dr. Sydney Cash’s team will capitalize on data from patients who already have implanted electrodes to investigate the neural basis for voluntary cognitive shifts by first examining activity during directed versus spontaneous motor acts, and then moving into language processing. The group plans to simultaneously improve and expand upon human neuronal recording technologies to enable more continuous, real-time studies, which has implications for our understanding of fundamental mechanisms underlying cognitive neuroscience, as well as various neuropsychiatric disorders and brain-machine interfaces.
Understanding V1 circuit dynamics and computations Miller, Kenneth D (contact) Scanziani, Massimo Columbia University Health Sciences 2018 Active
  • Integrated Approaches
The primary visual cortex (V1) is an extensively studied cortical area, yet current models poorly capture how V1 neurons respond to complex stimuli, such as natural scenes. Miller, Scanziani, and colleagues will combine new technologies for genetically identifying cell types in mouse V1, as well as monitoring and manipulating V1 neural circuits using tools like multi-photon holographic optogenetics. The team will pursue the necessary experimental data (i.e., synaptic connectivity and physiological responses of all V1 cell types) to build predictive models of how V1 dynamics form the basis of vision. Moreover, they aim to establish a generalizable paradigm for understanding any cortical area. If successful, this collaborative effort of experimentalists and theorists will achieve new insights into visual cortical function and dynamics that could help in understanding the origins of various neurological disorders.
Using Direct Brain Stimulation to Study Cognitive Electrophysiology Kahana, Michael Jacob University Of Pennsylvania 2019 Active
  • Human Neuroscience
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Diseases of memory and cognition are among the most devastating for patients and families, but the complexity of dynamic neural circuits that give rise to cognition creates challenges for effective therapeutic and clinical interventions. Dr. Michael Kahana and his team propose a multi-site, collaborative effort to uncover the neural processes underlying different forms of memory. They aim to use both direct intracranial stimulation and dynamical modeling to definitively identify brain activity that is causally linked to cognition. In a safe and ethically sound recruitment of patients undergoing otherwise therapeutic surgery, the success of this project will inform a theoretical framework for simulating the interaction between direct electrical stimulation and human neurophysiology, as well as insights and specific targets for future cognitive therapies.

Using functionally-defined glomeruli to probe circuit function in the mammalian olfactory bulb WACHOWIAK, DALE UNIVERSITY OF UTAH 2018 Active
  • Integrated Approaches

Olfactory information is first processed in the olfactory bulb (OB), where the primary olfactory sensory neurons project. Using transgenic mice, improved calcium imaging techniques, and a novel method for rapidly and flexibly presenting large numbers of odorants, Dr. Wachowiak and team will define functional maps of sensory neuron inputs to OB glomeruli to understand how OB circuits shape glomeruli output, as well as how odor experiences shape the glomeruli circuits. Furthermore, results from these experiments will lead the team to develop modeling frameworks to understand the organization and behavior of olfactory circuits.

Vertically integrated approach to visual neuroscience: microcircuits to behavior Euler, Thomas Huberman, Andrew D Meister, Markus Seung, Hyunjune Sebastian (contact) Wong, Rachel O Princeton University 2014 Complete
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  • Circuit Diagrams
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Dr. Seung and colleagues Thomas Euler (U Tübingen), Andrew Huberman (UC San Diego), Markus Meister (Caltech), and Rachel Wong (UW Seattle) will use state-of-the-art genetic, electrophysiological, and imaging tools to map the connectivity of the retina, the light-sensing tissue in the eye. The goal is to delineate all the retina's neural circuits and define their specific roles in visual perception and behavior.
Viral Strategies for Functional Connectomics in the Visual System Reid, R Clay Allen Institute 2017 Active
  • Integrated Approaches
A fundamental unanswered question in neuroscience is how specific connections between neurons underlie information processing. Clay Reid and colleagues will study the functional logic of wiring within three cortical areas: the primary visual cortex, and the anterolateral and posteromedial visual areas, before examining the functional logic of connections between these areas. Inter-neuron connections will be determined by using a modified virus that specifically labels ensembles of neurons, all of which connect with a single pre-designated target neuron. The team will then use two-photon calcium imaging to make movies of each neuron's activity in response to carefully-chosen visual stimuli. The approaches developed here may improve researchers’ ability to study the relationship between altered inter-neuron connections and neurological and psychiatric functional deficits.
Visual coding in freely moving behavior Niell, Cristopher M University Of Oregon 2019 Active
  • Integrated Approaches

Studying how the brain processes visual information in a natural environment can be tricky because researchers cannot exactly see what a freely moving subject is seeing. The Niell group aims to address this problem by developing a head-mounted, dual miniature camera system that aims to simultaneously capture an animal subject’s visual scenery while also recording the subject’s corresponding eye positions.. Combining this data may help the researchers approximate what an animal saw so that they can compare the scenery to corresponding neural activity in the brain. The Niell group plans to test this system on freely moving and head fixed animals. The results may help researchers understand how a brain encodes visual information under more natural conditions.