Funded Awards

A thorough understanding of the complexities of the brain’s different cell types requires the sharing and integration of myriad genomic information generated from various data sources. Owen White proposes creating a Neuroscience Multi-Omic (NeMO) Archive, a cloud-based data repository for -omic data. White and his team of researchers will establish an archive for multi-omic data and metadata of the BRAIN Initiative. The group will document and archive data processing workflows to ensure standardization, as well as create resources for user engagement and data visualization. The NeMO Archive will provide an accessible community resource for raw -omics data and for other BRAIN Initiative project data, making them available for computation by the general research community.

Advances in microscopy and imaging have created new possibilities in many fields of research, but these advances have also generated large amounts of data that can overwhelm traditional data management systems. Along with collaborators at Carnegie Mellon University and the University of Pittsburgh, Alexander Ropelewski plans to establish a BRAIN Imaging Archive that takes advantages of infrastructure and personnel resources at the Pittsburgh Supercomputing Center. The Archive will include a pipeline for data submission, user access and support, and BRAIN Initiative community engagement through an online presence, workshops, and hackathons. This unique resource will provide an accessible and cost-effective way for the research community to analyze, share, and interact with large image datasets of the BRAIN Initiative.

The Human Connectome Project provides one of the largest publicly available datasets of diffusion MRI from a sample of healthy individuals. Dr. Rokem and team will create an end-to-end pipeline for analysis of human white matter connections by using “tractometry” methods to analyze the diffusion MRI dataset from the Human Connectome Project. In tractometry, tissue properties are estimated in the long-range connections between remote brain regions. This project aims to generate a normative distribution of tissue properties in the major white matter connections, develop novel statistical methods to connect the properties of white matter connections to cognitive abilities, and create visualization tools to further explore and communicate the data. These tools may create an easily accessible platform that could be applied to other important neuroscience datasets.

Connectomics describes a field of study that builds maps of the connections within the brain. Dr. Lichtman and colleagues have developed a facility for generating high-resolution, large-volume serial section electron microscopy data that can be used to generate connectomic maps. In this project, access to the facility, techniques, and analytical software will be provided to the broader neuroscience community. This will allow other research groups who may be inexperienced in these techniques to generate data in projects aimed at mapping brain circuitry, a high priority goal in the BRAIN 2025 report. By providing this resource, Dr. Lichtman and colleagues will help researchers classify the cell types within healthy and diseased brains or model systems, which will improve our understanding of brain function and neurological disorders.

The community’s need for an integrated open-source analysis platform is rapidly growing due to the increasing capacity of extracellular electrodes and the limited number of new and validated spike- sorting methods. JRCLUST, a free, open-source, standalone spike sorting software, offers a scalable, automated and well-validated spike sorting workflow for analysis of data generated by large multielectrode arrays. The software can tolerate experimental recording conditions from behaving animals, and it can handle a wide range of datasets using a set of pre-optimized parameters making it practical for wide use in the community. JRCLUST has been adopted in 20+ labs worldwide since its inception less than a year ago. Drs. Kimmel and Nathan seek to expand and maintain JRCLUST, thus empowering researchers to elucidate how functionally defined subpopulations of neurons mediate specific information-processing functions at key moments during behavior.

Polymer microelectrode arrays are electrical recording devices made of flexible materials. These materials reduce tissue damage and improve the long-term stability of the recording setup. The Meng and Song labs aim to establish a service that will manufacture and test out arrays that are customized to researchers’ needs. The service may help researchers use the latest electrical recording devices for studying a variety of neurological disorders.

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.

For a complete understanding of how the brain works, there is a need for a comprehensive parts list (all of the cells in the brain) along with knowledge of how those parts are connected. Current molecular technology has advanced the inventory of cell types in the brain, but detailed information about the circuits they form is limited. Dr. Mueller’s group will develop scalable and affordable cellular imaging and neuro-informatics tools, running preliminary experiments to connect the transcriptome to anatomy, in mice. Tools will be made available to researchers, to help accelerate the creation of detailed maps at cell resolution showing circuitry in whole brains.

Transcranial magnetic stimulation (TMS) is a noninvasive technique used to study brain function and can be used to treat some brain disorders. Researchers use computational simulations of TMS electrical fields to gain a better understanding of how TMS affects the brain. Existing simulations, however, are inherently variable due to factors that impact the accuracy and precision of the technique, such as differences in experimental set-up and coil placement. Dr. Gomez proposes to use a novel computational framework to measure and address the uncertainty and variability of TMS electrical fields. The project will increase the fidelity and reliability of TMS simulations. The results will benefit researchers and clinicians by enabling more precise control of the technique and may help improve medical uses of TMS.

Non-invasive brain stimulation methods, like transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and transcranial alternating current stimulation (tACS), modulate brain activity in a safe and painless manner. Several BRAIN Initiative projects are combining these non-invasive brain stimulation methods with neuroimaging methods. Dr. Opitz and team will develop a computational tool that integrates empirically validated finite element method (FEM) models with imaging data, by extending and scaling the SimNIBS (Simulation of Non-Invasive Brain Stimulation) software platform. The SimNIBS is the leading open source software platform for generating FEM-based models of the electric field distribution produced by TMS, tDCS, and tACS. The resultant analysis package will allow rapid visualization and analysis of non-invasive brain stimulation (NIBS) BRAIN Initiative data, which could be more broadly used by the general neuroscience community.

Deep brain stimulation (DBS), a method of modulating brain circuit function, is FDA-approved for certain brain disorders such as Parkinson’s Disease. The NIH BRAIN Initiative aims to launch neurotechnological developments that include new ways of directly affecting brain circuit function. Use of these novel interventions warrants careful consideration about ways in which brain stimulation may affect personal identity, autonomy, authenticity and, more generally, agency. In this project, Dr. Roskies and her team will develop an assessment tool to measure changes in agency due to direct brain interventions, and establish a database to catalogue these changes in agency in various patient populations receiving DBS. These efforts have the potential to facilitate improvements in therapeutic approaches and informed consent and will be used to develop a framework for further neuroethical thought about brain interventions, allowing us to better identify, articulate, and measure effects on agency.

Changes occur to the central nervous system (CNS) throughout life due to activity-dependent plasticity as we learn new behaviors. Adaptive neurotechnologies are systems that can interact with the CNS in ways that produce beneficial neural plasticity. The group led by Schalk and Brunner has created a software platform, BCI2000, that can be used in a variety of adaptive neurotechnology applications. However, the current BCI2000 system requires considerable expertise in programming for successful implementation. This project will produce a configuration of BCI2000 that can be more easily used for adaptive neurotechnology experiments, as well as an introductory course and online training for scientists, engineers, and clinicians using the system. These new resources will allow deployed neurotechnologies to be more quickly used in the study, diagnosis, and treatment of brain disorders.

The proliferation and heterogeneity of magnetic resonance imaging (MRI) experiments, data analysis pipelines, and statistical modeling procedures presents a challenge for effective data sharing and collaboration. Russell Poldrack and colleagues propose expansion of the Brain Imaging Data Structure (BIDS), which standardizes the description and collection of imaging data/metadata for MRI, with development plans for other neuroimaging types as well. Under BIDS, the group will develop standards for pre-processing data pipelines, computational modeling results, and statistical modeling, using quick validation of any implemented standard so that researchers can assess whether their data fit within BIDS guidelines. These standardization goals will facilitate sharing of data, modeling, and results, ensuring their usability and engaging the greater research community in developing highly useable data standards.

Technological advancements in high-resolution imaging of brain volumes permits the accumulation of huge quantities of data that requires solution for storage and archiving. Dr. Brock’s project develops an open, accessible, and cloud-based data archive for electron microscopy and X-ray microtomography data by leveraging the proven architecture of the existing BossDB database. Allowing for petabyte scale data storage, curation, sharing, visualization and analysis, the archive is scalable and allows for a fast in- memory spatial data store, seamless migration of data between low cost and durable object storage (i.e. S3), and rapid access to the enormous datasets. The system enables computing data quality metrics on large datasets and metadata stores through a standardized interface. The archive is developed through an agile process that actively folds in community stakeholders for regular reviews and continuous opportunities for design input.

Dr. Makeig et al. have identified the need for the creation of data archives and standards for specifying, identifying, and annotating the data deposited. Specifically, they aim to create a gateway from the OpenNeuro.org archive for human neuroelectromagnetic data such as EEG and MEG data. This gateway will also provide tools to users for quality evaluation and data visualization. This resource will further allow machine learning methods to be applied to human brain activity data.

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.

AFNI (Analysis of Functional NeuroImages) is an open-source software package for neuroimaging analysis and visualization of both functional and structural MRI as well as other modalities. Drs. Cox and Nielson propose to extend this widely used software package by offering containerization, cloud accessibility and web-accessible visualization. The software extension could support evolving BRAIN Initiative standards for human neuroimaging data organization and experiment specification. The project makes it possible for public integration testing of the software package, thus enabling end-user feedback and wider adoption and dissemination within the neuroimaging community.

The mammalian brain has unique abilities to process various sensory inputs from the environment, based on efficient and complex brain network structures and dynamics. However, the precise connections between the brain’s intricate wiring and its rich network dynamics are unclear, as is our understanding of how the brain’s connectivity and dynamics relate to underlying neural coding principles. Leveraging the Allen Institute’s Mouse Brain Connectivity Atlas, Dr. Choi will explore these issues, ultimately aiming to help close the gap between brain biophysiology and neural coding principles, with a focus on predictive coding theory using data-driven mathematical models.

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.

Novel neuromodulation, recording, and imaging techniques applied to human and non- human primate brains generate datasets that require tools for organizing, processing and analyzing data that are widely available and easy to use. Drs. Milham and Craddock plan to extend C-PAC (Configurable Pipeline for the Analysis of Connectomes), building a configurable data analysis pipeline that incorporates various statistical analysis, machine learning, and network analytic techniques. In addition to adapting methods used in human imaging for non-human primate data, the project will implement a toolbox for alignment of electrophysiological data with brain imaging data. The resulting software enables high- throughput, semiautomated and end-to-end processing and analysis of structural and functional MRI data that are accessed locally or via the cloud.

To gain a complete understanding of how certain cell types work, researchers must be able to access those specific cells. Despite advances in single-cell transcriptomics, commonly used genetic tools and methodologies have variable expression, can lack specificity, and often take a long time to create. Dr. Tasic’s group aims to develop a set of adeno-associated viruses containing enhancers to target specific cell types following administration via an injection behind the eye to access the whole brain, in mice. The enhancers will be defined via RNA and epigenetic sequencing, before being screened for their specificity. Once made publicly available, this toolkit could offer a more efficient method to access specific cell classes, facilitating investigations into their roles in circuit function.

Alzheimer’s disease, epilepsy and other neurological disorders can cause problems with how the brain learns. In this project, researchers in the Disterhoft and Voss labs will perform experiments on rodents and human subjects designed to understand the cellular effects of electrical stimulation on the hippocampus, a region known to be involved in learning and memory. As part of this project, they will examine the relationship between stimulation and hippocampal neuronal synchrony during theta activity rhythms. Their results may help researchers understand the underlying circuitry associated with the memory problems common in many brain disorders.

Typically, we experience the world as a continuous sequence of events; but when recalling memories, we remember segmented episodes. The hippocampus and prefrontal cortex (PFC) play a role in segmenting, linking, and retrieving memories of associated events, but the neural circuit mechanism of this process is not well understood. Dr. Jafarpour aims to use intracranial encephalography (iEEG) to identify the neural dynamics in the hippocampal-PFC circuit that support the encoding of sequences of words. By combining iEEG with advanced analytical techniques, natural language processing models, and research with patients with hippocampal lesions, this project will offer insights into the neural basis of speech encoding and memory formation. The work may inform the development of neural prosthetics for use by patients with memory impairments.

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.

Many traumatic brain injury (TBI) patients often experience chronic cognitive impairments that disrupt functioning and can interfere with societal reentry – current efforts aim to rapidly restore cognitive function to TBI patients. In doing so, a thorough understanding of the opportunities and challenges posed by rapid cognitive restoration is critical. To address this need, Dr. Joseph Fins and his team will interview patients and family members before implantation of thalamic deep brain stimulation (DBS) devices. These interviews will collect perspectives on risks and benefits, expectations and fears, as well as factors that are weighed during decision making. After implantation, interviews will collect perspectives on the impact of cognitive impairment and restoration. The project aims to develop legal theory that supports social reentry for TBI subjects who have achieved cognitive restoration, paving the way for maximizing patient-centered benefits of any therapeutic advance.

Recent tissue-clearing techniques and advances in microscopy have made it possible to produce 3D images of intact brains. to help ensure consistency in data collection and analysis, Dr. Hamilton and her team will develop a set of standards for3D imaging of whole brains for the neuroscience research community.. Dr. Hamilton’s group will convene a Working Group of experts who will work through a consensus process to establish standards that will be distributed to the research community. These standards should help improve the efficiency of imaging research and allow comparisons across studies.

Working memory is crucial to many human functions, and a better understanding of its underlying mechanisms could improve our understanding of memory abnormalities in advanced age. Current hypotheses suggest that working memory is maintained by a hierarchy of interconnected cortical areas, but more work is needed to understand the unique role of each area. Dr. Resulaj aims to study visual working memory at the systems and cellular level in the mouse. Utilizing novel behavior assays, a laser scanning galvo system, and optogenetics, this work ultimately will develop a computational model of visual working memory in the mouse cortex.

Activity in the striatum is known to be linked to perceptual decision-making processes orchestrated by the cortex. Dr. Sun proposes to use genetically-encoded calcium indicators to examine cortex-wide activity patterns in mice performing perceptual decision tasks. This will be combined with wide-field calcium imaging in order to correlate behavior with changes in neural activity. The research will enhance our understanding of how activity in the cortico-striatal pathway supports decision-making.

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.

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.

<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>

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Dr. Ghosh and colleagues propose to develop a new computer infrastructure for scientists to share, collaborate, and process neurophysiological data. The data will be open to both scientists and also be used to engage high school and college students. The team will also develop tools to facilitate data submission and access and to encourage adoption of the Neurodata Without Borders data standard.

This project develops DABI (Data Archive for the Brain Initiative) to aid the dissemination of human neurophysiological data generated through the BRAIN Initiative. Incorporating infrastructure from a pre- existing hub for delivering effective informatics and analytics solutions for major projects in the study of neurological diseases, Drs. Toga, Duncan, and Pouratian will aggregate data related to human electrophysiology, making the data broadly available and accessible to the research community. The group plans to incorporate analysis tools with user interfaces, implement tools for data management and use, and link metadata across different data modalities. The overarching goal of this project is to secure, link, and disseminate BRAIN Initiative data with all pertinent recording and imaging parameters coming from participating sites

Many labs develop unique software to manage and interpret their findings, but those programs are often specific for certain types of datasets, making them difficult to share among researchers. Dr. Van Hooser’s team plans to create an interface standard that establishes a common set of processes for accessing neurophysiological and imaging data. The standard will be tested, and revised accordingly, based on feedback from graduate students and postdoctoral researchers during data access events, or “hack-a-thons.” The interface standard will help increase the speed of research and make data widely available, allowing individuals outside of the neuroscience and research communities to make discoveries.

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.

Olfaction is an extremely important sensory modality across species, and it is one of the first to be disrupted in many neurodegenerative diseases. Dr. Doyle will conduct a full census of all cell types in the mouse olfactory bulb, to better understand information integration and processing in the olfactory system. This approach will utilize transcriptomic (single cell RNA-sequencing) and epigenomic (single nucleus methylome- and single nucleus ATAC-sequencing) data generated by members of the BRAIN Initiative Cell Census Network (BICCN). Overall, this will allow for a fuller understanding of the roles of cell diversity, regulatory elements, and transcription factors in the mouse olfactory system.

While it is known that proprioception plays an important role in movement, how the brain translates proprioceptive information into information for movement in the somatosensory cortex (S1) is poorly understood. Dr. Blum aims to study how active and passive movements of limbs elicit different responses from muscle spindles and Golgi tendon organs, especially as reflected in area 3a of S1. Using electrophysiological data, arm kinematic measurements, and EMGs in monkeys trained to perform an arm-reaching task, this research aims to model afferent proprioceptive signals and motor outputs in a neural network model. These results will improve our understand of the neural control of movement.

Optogenetics and chemo-optogenetics are emerging tools for neuroscience research, providing ways to modulate cell function with light. However, current limitations for these tools include limited ability to target cells deep in the brain, the relatively small effects they elicit, and safety concerns over long-term use. Dr. Lam proposes to develop new chemo-optogenetic tools to address these issues, based on the TRPA1 and TRPV1 receptor channels. Further, she will develop this technology for in vivo use, testing its utility in behavioral assays of zebrafish. The work will improve our ability to understand brain circuit function, and will create a platform for the discovery of other novel chemo-optogenetic approaches that mimic natural cell activity.

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.

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.

Various improvements are being considered to create next generation biophysical models of blood oxygenation and fMRI measurements in brain. Dr. Sencan plans to quantify spatial and temporal diversity in cortical metabolism in order to inform these scientific advancements. By measuring processes of cortical oxygenation, metabolic rate, and blood flow responses at microvascular scales, she aims to understand how these processes contribute to brain function and vary across age, region, and depth in the awake, mouse brain. To achieve these goals, she will use novel experimental techniques, related to faster, deeper, and more realistic imaging of oxygen concentration. This work may support better interpretation of clinical results derived through imaging technologies like fMRI.

Microstimulation electrodes help neurologists examine the brains of patients suffering from epileptic seizures and other neural circuit problems. The Cui team aims to develop standardized in vivo and in vitro model systems for testing the safety and efficiency of novel microstimulation devices made with newly designed t materials. Experiments in mice and cultured neurons will examine the short- and long-term effects of stimulation on the health of the tissue and the integrity of the electrodes. This system may help researchers develop better microstimulation devices for treating a variety of neurological disorders.

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.

Determining how brain cells communicate with each other bolsters understanding of the causes of disorders. The new Neuropixels probe allows for highly detailed recordings of neurons communicating; however, sophisticated software is required to use this tool and to understand the data it generates, which include nearly 1000 recording sites from a single probe. For other labs to use this system, a support network is needed to address user requests, maintain the software code, and improve documentation. Through this project, Dr. Siegle and colleagues will support other labs that wish to use Neuropixel probes to better understand the underpinnings of neurological and mental disorders.

The brain’s inferior colliculus is described to have a patch- or matrix-like “modular” anatomical organization, with various nuclei and distinct chemical boundaries. However, the functional implications of this organization are unknown. Dr. Lesicko’s experiments aim to determine whether this modularity underlies distinct streams of information processing in the inferior colliculus. Using a combination of two-photon calcium imaging, clustering analysis, optogenetics, and behavioral assays in mice performing a behavioral task, the work will examine distinctions in neural activity and mechanisms for auditory and somatosensory signaling in the inferior colliculus.

Different laboratories use different behavioral systems, hardware, and software, which makes it difficult to standardize, communicate, and replicate experiments. Dr. Kepecs et al. have led the creation of a laboratory consortium to address this problem by developing requirements for behavioral data formats and an open source software suite for editing, executing, and visualizing behavioral tasks. They will also engage the scientific community to promote the adoption of these standards and tools.

Brain-computer interfaces (BCI) show promising potential in treating symptoms of various brain diseases. A collaborative group at the University of California, San Diego, has developed a high-density array of microgrid electrodes that are thin, non-destructive, and sit lightly on the surface of the brain, for improved decoding and resolution of neural activity. This study will implement this technology to study cortical physiology and organization in the rat somatosensory cortex. Dr. Cleary aims to test the capabilities of these surface microgrids for discriminating cortical columns in the rat “barrel cortex,” representing innervation of the whiskers. He also will study how surface stimulation of the cortex affects neural activity in different layers of the cortex, initially in rats and subsequently in human neurosurgical patients. This work may improve BCI technology, providing additional treatment strategies for various brain diseases.

Recent tools have led to advances in 3-D imaging of whole brains, opening the door for proteomic imaging that can increase our understanding of how the structure and activity in cells lead to brain functions. Unfortunately, proteomic imaging is limited by antibodies currently available for this type of research. Dr. Chung’s team aims to develop a comprehensive, open-source toolbox of antibodies generated against 300 proteins that are critical for understanding how the brain works. These antibodies should be more cost-effective, targeted, and compatible with a variety of tissue processing techniques.

Agency, our ability to act and experience a sense of responsibility for our actions, is central to individual identity and societal conceptions of moral responsibility. Neural devices are currently used to treat some brain disorders, such as Parkinson’s disease, and are being developed to treat others such as depression and obsessive-compulsive disorder, yet their use raises important ethical concerns about potential effects on agency. Dr. Goering, Dr. Klein and their team will investigate agency in individuals receiving brain computer interface devices for sensory, motor, communication, and psychiatric indications. They aim to build a user-centered neural agency framework, and, ultimately, to enhance the informed consent process by developing a communication tool that patient participants might use to better understand and discuss potential changes in agency associated with use of neural devices.

Molecular and cellular neuroscientists often lack the training in computer programming to fully explore “-omics” data common in the BRAIN Initiative. Drs. Hertzano and White will implement analytic software for visualization and interactive genome browsing of gene expression and RNA-seq data, including simple and complex cross-dataset analysis. These tools will be made available in the BRAIN Initiative funded Neuroscience Multi-Omic Data Archive (NeMO) which hosts multi-omic data. The software will provide an easy-to-use web-based work environment for visualization and analysis of multi-modality and multi- omic data, interrogation of relationships between epigenomic signatures and gene expression, and integration of analytical techniques for multivariate analysis, gene co- expression and other analyses.

More than 500,000 children in the US and Canada suffer from epilepsy and 30% of these children continue to experience seizures despite being treated with anti-seizure medication. Unmanaged, epilepsy can result in cognitive decline, social isolation, and poor quality of life, and has substantial economic impact on families and society. Novel approaches for treating epilepsy such as vagal nerve stimulation and responsive neurostimulation are being developed, but this work has been conducted predominately in adults and the outcomes of these trials are often not clearly generalizable to children. In this project, Drs. Illes and McDonald will explore ethical issues confronting families and clinicians when considering new treatment options for drug-resistant epilepsy in children. They aim to develop, evaluate, and deliver patient-directed resources in the form of infographics and informational materials and videos, and clinician resources for family decision-making, clinician counseling, and care.

In an effort to improve our understanding of how synaptic plasticity contributes to social memory, Dr. Phillips aim to create a tool to modulate synaptic plasticity in specific circuits during behavior. Using advanced imaging techniques and various innovative biochemical assays, she plans to create a photo-activatable inhibitor of presynaptic plasticity and will use this tool to understand the circuitry of social memory. The researcher will develop constructs of a photo-activatable PKA inhibitor and screen the constructs using 2-photon fluorescence lifetime imaging in HeLa cells. The optimized inhibitor constructs will then be expressed in the dCA2-vCA1, vCA1-PL, and PL-dPAG projections in mice and used to block presynaptic plasticity during social learning in either aggressive or neutral contexts.  Successful development of these tools will enable investigation of neural circuitry to better our understanding of behavior and neuropsychiatric diseases.

Electrical stimulation devices are used for neuroscience research and the treatment of brain disorders. To make the procedures more predictable, researchers in the Halgren and Bazhenov labs aim to develop a novel computational approach for predicting which neuron type will be activated by stimulation. Models will be based on neuron type, shape, brain location, and connectivity. The validity of these models will be tested in mice and humans using advanced imaging and microscopic techniques that will help the researchers observe neuronal responses.

Neurodevelopmental disorders can arise from impairments in sensory-dependent refinement or strengthening of synapses during postnatal brain development. However, treatment strategies focused on addressing such impairments lack an advanced understanding of the cellular and molecular mechanisms of this process. Building on prior results showing that expression of the gene Fn14 is driven by visual experience and encodes a receptor required for synaptic refinement, Dr. Cheadle aims to use slice electrophysiology and bioinformatics to study how microglia-to-neuron signaling is involved in refinement via examining the role of TWEAK, an Fn14 receptor ligand, in healthy and neurodevelopmentally-impaired mice. The results may offer insights into the development of treatments for neurodevelopmental disorders.

Hippocampal microcircuits include excitatory pyramidal cells, which integrate information and signal to downstream brain regions, and inhibitory interneurons, which function locally to regulate pyramidal cell activity. Circuit dysfunction in the ventral hippocampus (vHipp) has been associated with various brain disorders. Dr. Donegan will use techniques including mammalian GFP reconstitution across synaptic partners (mGRASP), fiber photometry, in vivo electrophysiology, and optogenetics, to test the hypothesis that different types of interneurons in the vHipp differentially regulate the function of ventral hippocampus pyramidal cells depending on their projection target. She will also test whether vHipp microcircuit anatomy and function are altered by chronic stress, shedding light on the potential link between circuit dysfunction in the hippocampus and brain disorders.

Magnetogenetics refers to a promising new method for wirelessly stimulating brain circuits. Initial reports suggest that neurons can be genetically modified to express magnetically sensitive ion channels, allowing the neurons to be activated by exposure to electromagnetic fields. The Liu group aims to systematically explore the full potential and limitations of this technology. Their results may help neuroscientists noninvasively examine the role of brain circuits in a variety of behaviors and disorders.

Brain-computer interfaces and neuroprosthetics have provided a significant benefit to patients with cervical spinal cord injuries. However, current technology is limited in its abilities to allow the user to control how much force is exerted by the prosthesis and to provide sensory feedback from the prosthetic hand. In a public-private collaboration with Blackrock Microsystems, Dr. Boninger and colleagues are looking to improve the dexterity of neuroprostheses by incorporating microstimulation of the somatosensory cortex. This stimulation could provide tactile feedback to the user and hopefully allow the user to better control the force applied. Ultimately, this approach will improve the dexterity and control of prosthetic limbs used by patients with spinal cord injuries.

Transcranial magnetic stimulation (TMS) has the potential to non-invasively treat brain circuit disorders. Nevertheless, scientists do not completely understand how it may work. Researchers in the Falchier, Opitz, and Vlachos labs plan to use electrical and optical recording techniques to examine the molecular and cellular effects of TMS on neurons in brain slices from rodents and nonhuman primates. Their goal is to develop computational models that will help researchers understand and predict the effects of TMS. Ultimately, the results may help researchers devise improved TMS based treatments for a variety of neurological disorders.

To help improve nerve stimulation devices, the Fried team will explore the fundamental neurophysiological principles that guide how retinal and cortical neurons respond to artificial electrical and magnetic stimulation. Neural responses to stimulation will be recorded in individual retinal ganglion cells from rodents or nonhuman primates. The scientists will then map the sensitivity of different parts of each neuron. The results may be used to create models of neuronal activity that may inform the development of more efficient nerve stimulation devices for treating neurological disorders.

The NIH BRAIN Initiative aims to catalyze novel tools and technologies to modulate brain circuit function, paving the way for new treatment options for brain disorders. However, such interventions also have the potential to cause unintended changes in aspects of cognition, behavior, and emotion. These changes, in turn, raise concerns regarding autonomy, personal identity, and capacity for informed consent. In this study, Dr. Cabrera Trujillo and her team will study ethical concerns, beliefs, and attitudes about the use of novel bioelectric approaches among clinicians, patients, and the broader public. The work will provide stakeholder perspectives that will be valuable for informing the responsible development and use of these novel neurotechnologies.

Individuals with severe, disabling, chronic mental illness, such as treatment-refractory schizophrenia/schizoaffective disorder (TRS), have often been excluded from research, leading to challenges in developing treatment for their illnesses as well as access to that treatment. Here, Dr. Judith Gault will examine the ethical issues that will lay a foundation for conducting clinical research in TRS patients who have traditionally been excluded in studies. These ethical principles will include transparency, accessibility and safety of clinical trials testing neurosurgical intervention in TRS patients who are in urgent need of effective novel interventions. The team will explore the ethical implications of excluding individuals based on their capacity to consent, surgical risks, and severity of symptoms for a planned clinical trial to treat TRS. Successful completion of the project could revolutionize our understanding of how to overcome research disparities among severely disabled individuals by improving transparency, accessibility, and safety of clinical trials.

Small, tightly-packed granule cells (GrCs) in the cerebellum have been associated with sensory and motor events, but their specific roles remain unclear due to their close packing.  Dr. Broussard aims to precisely record from and perturb GrCs and measure the resulting effects on neural firing patterns and activity in mice. The researcher will develop a spike-counting method for genetically encoded indicators by optimizing the kinetics of the calcium sensor GCaMP. After developing a cleared skull preparation to access the full posterior cerebellum in mice, the new indicator will be used to map the neocortical drivers of GrC activity across the cerebellum, and rodent behavioral tasks will be used to disambiguate sensory, motor and internal-state contributions to granule cell activity patterns. Better knowledge of GrCs activity will further our understanding of neural circuits that guide sensory processing and plays roles in neurological disorders.

A variety of cell types across the cerebral cortex develop from a small number of radial glia neural stem cells, though current tools lack the ability to connect this lineage to genetic mutations underlying developmental perturbations in a scalable manner. Dr. Nowakowski’s team will combine developmental lineage tracing and spatial transcriptomic technologies into molecular barcodes that trace the origin of cortical cells in mice. In addition to potentially increasing understanding of brain development, these studies may introduce a scalable framework to integrate lineage tracing technologies with spatial tissue mapping.

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.

High frequency biphasic stimulation (HFBS) of the spinal cord is a new and highly effective method for treating lower back pain, but little is known about why it works. Here the Tai group will perform experiments in animals to explore how HFBS blocks nerve signal conduction along spinal cord axons. They will focus on changes in axonal ion gradients, ion channels, and ion pumps in response to stimulation. Their results may help researchers not only understand how HFBS works but also refine the way it is used to treat pain.

To process information about the world, the nervous system must distinguish external stimuli from internally generated sensory stimuli, but the mechanisms underlying the cellular basis of internally generated sensation and movement are not well understood. Here, Dr. Suver plans to develop a small circuit model of this process using the antennae of the fruit fly as a model system. Using electrophysiological recordings, optogenetics, and immunohistochemistry, she will develop the neural circuits controlling and sensing antennal movement as a cellular model for studying principles of active sensing. The insights gained from a tractable genetic circuit model may improve understanding of active sensation, as well as how these mechanisms might fail in disease.

A central goal of the NIH BRAIN Initiative is to develop new and improved methods for modulating the activity of specific neural cells and circuits, including those of the spinal cord. Dr. Heckman and his team will study the effect of dorsal electrical stimulation (DES) on motor circuits of the lumbar spinal cord. Specifically, they will investigate how DES affects two functions of descending inputs from the brain to the spinal cord – the generation of movements and the control of spinal neuron excitability. This work will help define the potential of DES for selective control of spinal motor circuits and may inform efforts to restore movement after spinal cord injury via DES.

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.

Evidence suggests that both spontaneous and learned behaviors engage activity in various regions of the cortex. However, for perceptual decision-making, more complex and demanding tasks involve decorrelated cortical activity as a more distributed, widespread process throughout the cortex. Dr. Pinto aims to understand this phenomenon, using cutting-edge technology to study how distributed cortical acitivity mediates complex decision-making and the mechanisms of task-induced changes in large-scale cortical dynamics. The research will utilize two-photon calcium imaging, virtual reality behavioral tasks, pharmacogenetics, optogenetics, and modelling, increasing our understanding of the neural basis of decision-making.

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.

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.

Despite the ability of scientists to record from and modulate the activity of individual neurons and neuronal circuits, it has been difficult to translate those observations into a readout of behavior. Dr. Datta and colleagues have developed a technology called Motion Sequencing (MoSeq) that captures, in three dimensions, the behavior of a freely moving mouse and then, using machine learning, turns that data into standard “syllables” of behavior. While MoSeq could have broad applications for several research projects, the complex math makes it difficult for non-expert users to implement. This project will adapt the MoSeq system to make it more accessible for researchers and will provide a training course on how to set up and use MoSeq. This will enable a wider range of research projects to use the technology, which could improve our ability to study the effects of drugs or genetic and neuronal changes on behavior.

A thorough understanding of brain function requires the integration of neuroscience data across species and scales. While current software can verify data quality within one or a handful of data sources, reproducibility across multiple data sources is limited. Xenophon Papademetris and colleagues are developing software tools with cross-scale, cross-species reproducibility analysis in mind. By leveraging data created by two other BRAIN Initiative projects at Yale University, Papademetris will extend current software algorithms to incorporate data from multiple sources, design the software to be cross-platform compatible, validate the software through rigorous testing, and finally, distribute it to the community. The potential for a set of software tools to reliably and reproducibly analyze multiple heterogeneous neuroscience data types will help to break down data barriers for the greater neuroscience community.

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.

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.

The ability to learn, think, and make decisions in complex environments is extremely important to critical life functions across species, but the neural correlates of external and internal representative states are largely unstudied. Recent studies suggest activity in the amygdala and orbitofrontal cortex correlate to state representation, but their exact roles are unknown. Dr. Barack aims to investigate how monkeys learn these state representations, using a complex, sequential decision-making task. During this novel behavioral task, similar to the boardgame “Battleship,” the team with elucidate learning in a complex environment, measuring associated neural mechanisms of circuits in the amygdala and orbitofrontal cortex activated in learning and decision-making. These results could promote the creation of computational models that may provide insights about animal and human interactions with the world.

Despite physical therapy, about 50% of stroke survivors must live with impaired hand function, impacting daily activities for the remainder of their life. However, recent studies in rats have suggested that low-frequency cortical stimulation may help alleviate such symptoms. In order to translate this work to humans, Dr. Khanna aims to use a multiscale model of electrophysiological recording to monitor motor and somatosensory activity during dexterous control, comparing affected and unaffected hemispheres in non-human primates (NHP) recovering from a stroke. The results may inform development of therapeutic techniques for stroke-related motor dysfunction, while improving our understanding of neuromodulation.

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.

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.

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.

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.

Neurophysiology research, which focuses on recording brain cell activity, produces enormous amounts of complex data that are difficult to manage. Drs. Rubel and Ng will build upon the Neurodata Without Borders: Neurophysiology project to create a system that will allow for standardizing, sharing, and reusing neurophysiological data. The team will design an open source software system; develop methods to establish a consistent vocabulary for defining cell types, measurements, and behavioral tasks; and create tools to help the community adopt these new resources and standards. The proposed system will help accelerate neurophysiological discoveries as well as reproducibility studies.

To leverage the public investment in the BRAIN Initiative, the sharing of data produced by its myriad projects is paramount. Dr. Podrack’s project extends the recently released OpenNeuro, which was developed based on the well-established and successful OpenfMRI, for an archive of neuroimaging data. The extended archive encompasses a broader range of neuroimaging data including EEG, MEG, diffusion MRI and others. The archive also implements easy-to-use data submission, semi-automated curation and advanced data processing workflows, which run directly on the cloud platform. The archive allows to share the results alongside the data, federate with other relevant repositories, and accessible to all researchers.

Deep brain stimulation (DBS) is currently used in children with dystonia, epilepsy, and Tourette Syndrome, and this use is expanding to other neuropsychiatric conditions. In doing so, there are several challenging ethical issues, and no consistent guidance on the use of DBS in pediatric populations. To address this challenge, Dr. Gabriel Lazaro-Munoz and team will examine neuroethics issues and decisional and informational needs of families by conducting interviews with pDBS stakeholders (minors, caregivers, and clinicians). These interviews will inform the development of a decision aid for caregivers considering DBS for dystonia, the most common use of pDBS. This project will provide key information about the ethical issues facing families, minors, and clinicians alike when considering pDBS, as well as develop a clinical decision aid for making informed, patient-centered decisions surrounding the clinical use of invasive neuromodulation in minors.

Many orofacial behaviors critical for survival, such as chewing, drinking, and breathing, require motor command modulation based on information from proprioceptors. However, since many previous studies of proprioceptors were not performed in awake, behaving animals, our understanding of this system is lacking. Dr. Olson aims to target and record from mouse jaw-innervating proprioceptors in the mensencephalic trigeminal (MeV) nucleus, while the animals perform orofacial behaviors. Using advancements in in vivo physiology, behavioral analysis, and computational techniques, the study will entail recording from these neurons and mapping the functional organization of the area. The findings may improve our understanding of how the brain controls jaw movements in real-time.

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.

Electrocorticography (ECOG) allows the direct recording of a small population of neurons in human subjects, generating vast amounts of data. Dr. Beauchamp plans to develop the software RAVE (R Analysis and Visualization of Electrocorticography data) to help researchers explore such datasets. Incorporating established and successful informatics approaches that enable standardization, sharing, and re-use of neurophysiology data and analyses, RAVE includes rigorous statistical methodologies and seamless integration with existing analysis platforms. To facilitate user adoption and maximize impact, the developers plan to release RAVE 1.0 to the entire ECOG community within 6 months of the project start.

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.

Studying how the brain works from the molecular to the circuit level is crucial for improving our understanding of how the brain functions normally, and what goes wrong in various disorders. Antibody probe techniques are effective tools that work at both of those levels. This project will develop a collection of validated, recombinant antibodies that are also highly renewable. In addition, antibodies will be miniaturized to increase binding efficiency and improve labeling precision. These resources will provide a cutting-edge, validated set of research tools to enable neuroscience research across a variety of resolutions from the intracellular to the neuronal network level.

fMRI can be a powerful tool to measure activity throughout the entire brain. But, because it is an indirect measure of activity through changes in blood oxygenation, it is subject to error based on local microstructural and microvascular properties of the cortex. Thus, Dr. Blazejewska intends to remove these biases in fMRI results, providing a framework for a more accurate representation of neural activity. Using histology of human brain specimens with advanced ex vivo and in vivo imaging, the team will map between tissue microarchitecture and fMRI results, correcting errors due to microstructure. These results should allow for a more accurate measurement of functional activity across all cortical layers, promoting a better understanding of the brain’s circuitry.

Two-photon microscopy has emerged as a key technique for studying the activity of living neural networks. However, little optimization has been performed for the associated fluorescent activity probes and sensors. Dr. Drobizhev’s research group will create a resource at Montana State University to characterize the properties of two-photon probes and make that service available to the broader research community. Given increased used of this type of resource by BRAIN Initiative investigators, the research group will also organize meetings to help other labs develop their own characterization processes.

GABAergic interneurons have a key role in regulating circuits in the brain, and their dysfunction is linked to neurodevelopmental diseases. It is known that diversity exists between classes of interneurons, but the extent and mechanisms of this diversity are unknown. Dr. Crow will explore the transcriptional, epigenetic, and developmental mechanisms that govern interneuron diversity, in a cross-species analysis. The work will leverage computational and phylogenetic models, machine learning, and advances in single cell RNA-sequencing technology to shed light on interneuron identity.

Neuroimaging techniques are advancing at a rapid rate, resulting in high resolution images of brain tissue and large datasets that can be difficult to manage. William Gray Roncal’s team propose an integration framework called SABER: Scalable Analytics for Brain Exploration Research. With a focus on techniques (e.g., electron microscopy) that produce high resolution brain images, SABER will create a unified framework by which these data types are accessible to a broader audience, can be processed in a reproducible, portable way, and can be scaled from small data volumes to large datasets. SABER has the potential to enable discoveries from high-resolution imaging techniques, making the parsing of entire brains a new reality.

Using currently available imaging technology, capturing a comprehensive 3D molecular snapshot of a single mouse brain takes approximately 3 years. And, to create such a map of one single human brain is estimated to potentially take 6,000 years of imaging time!  Dr. Regev’s team will develop tools based on compressed sensing, a method that uses small amounts of data to model larger images and datasets, to help accelerate the imaging timeline for brain maps. To test the tools, Dr. Regev’s group will use them to create a detailed atlas of specific areas of mouse brain, and will share all protocols and codes to help speed brain mapping across the research community.

Genetic isoforms are different types of RNA products that come from the same portion of a gene and can affect genetic activity individually or in complex units. It is unknown whether brain-region and development-specific isoforms are generated in specific cell populations. Dr. Tilgner’s group will develop novel genetic tools to closely examine distinct isoforms in 5 regions of the mouse brain at different developmental times, specifically identifying those that occur in broadly from those that appear in specific cell populations. This work could reveal distinct mechanisms for differential RNA-isoform expression, the location and timing of which may be relevant to neurodevelopmental disease.

The characterization of single neurons is a major theme of research in the BRAIN Initiative Cell Census Network. Dr. Mukamel and team develop Single Neuron Analyzer (SNA), a cloud-based computer tool for interactive data exploration, analysis, and validation for single cell molecular data generated by the BRAIN Initiative. The software specifically implements machine learning and cross-validation techniques to improve the reproducibility of neuronal cell type identification.

A research team led by Drs. Carp, Wang, and Wolpaw plans to explore the underlying reasons why electrical stimulation of the cerebral cortex can produce distant changes in the spinal cord. In a rat model system, the researchers will use chemogenetic and neuron tracing techniques to examine how different cortical stimulation parameters may rewire spinal cord circuits. Their results may ultimately help researchers design new treatments for people with movement impairments following neurological injury.

Dopamine (DA) inputs in the midbrain influence striatal activity in the basal ganglia, and dysfunction in this system is associated with many neurological and psychiatric illnesses. Dr. Vu aims to measure dopamine release onto the dendrites of single striatal neurons in real-time using 2-photon microscopy, and map functionally-relevant dopamine release onto striatal dendrites during different behaviors in mice. This work will inform our understanding of the spatial organization of dopamine inputs onto single neurons in the striatum, as well as how these inputs affect post-synaptic cell firing.

The NIH BRAIN Initiative aims to facilitate development of new approaches for precisely measuring and modulating brain circuit function. The high tissue penetrability of ultrasound waves presents untapped opportunities for accessing neural circuits throughout the mammalian brain and offers the possibility of transforming our ability to map brain circuit activity, test new models of brain function, and ultimately, to diagnose and treat brain diseases and disorders. This project, led by Drs. Shoham, Froemke, and Kimmel, aims to elucidate the fundamental mechanisms of ultrasound stimulation, via mathematical analyses, computational modeling, and experimental validation in a mouse model. A thorough characterization of how ultrasound affects neural cells and circuits is an essential step forward in basic neuroscience research, and may enable further development of ultrasound as a tool for both neuroscientists and clinicians.

Organoids, grown in laboratory settings to resemble parts of the developing human brain, hold great potential for shedding light on human brain function and disease. Researchers are working to achieve key bioengineering advancements, including successful vascularization of brain organoids, generating the full complement of cell types present in a human brain, and recording and modulating neural activity in organoids. These anticipated advances in bioengineered human brain modeling research may raise ethical questions about the moral status of large, complex human brain organoids and ethical boundaries on manipulating increasingly realistic engineered brain models. In this project, Dr. Hyun will lead proactive ethical discussions among ethicists and the neuroscientists conducting this cutting-edge work to develop greater awareness and understanding of these ethical implications and to inform future management of ethical issues that may be unique to this novel area of brain research.

Recently, researchers in the Wang, Worrell, and Wu labs showed that electrical stimulation may cause non-neuronal cells, astrocytes, to release or secrete chemical-filled vesicles. In this project, the researchers aim to understand whether this astrocyte signaling may play a role in how the brain reacts to electrical stimulation. They will use cultured astrocytes to examine the molecular mechanism behind astrocytic vesicular release and the effect it has on neighboring brain cells. The results may provide fundamental and novel insights into the role astrocytes play in healthy and diseased brain signaling.

Many organisms are capable of interval timing, as it is essential to many tasks important for survival. However, no research has determined a neural mechanism for this process.  Dr. Smart will study the neural mechanisms of interval timing in a coordinated network throughout the brain, and how these estimates of time affect behavior. The work will leverage behavioral assays, whole-brain neural activity imaging, and genetic techniques in the Drosophila brain. These findings will improve our understanding of how the brain keeps time.

Inconsistent terminologies of neuroimaging metadata prevent the precise description of the design and intent of an experiment, experimental subject characteristics, and the data acquired. Dr. Keator and colleagues aim to address this problem by developing human neuroimaging domain-specific controlled vocabularies. This will greatly improve our ability to search across datasets; to reuse, compare and integrate data across studies and sites; and to replicate neuroscience findings. They also aim to promote the adoption of the controlled vocabularies through community engagement.

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.

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.

Recent advances in sequencing technology are revealing diverse cell types across the human and mouse brains, with some transient progenitor cell types found to exist only during development. In addition to this diversity of cell types, early evidence points to diversity in lipid expression and function throughout the brain. Dr. Bhaduri aims to characterize the developing human cortex across multiple regions, linking differences in transcriptomic and lipidomic data in order to understand how metabolism and transcriptional regulation interact to determine cell fate decisions in cortical progenitors. The work will offer insight into how the human brain develops.

Habituation is a form of learning by which organisms become less responsive to repetitive stimuli. While often impaired in disorders with complex cognitive symptoms, and thus has relevance to human disease, there are significant gaps in the understanding of habituation learning. To address this challenge, Dr. Nelson will study the critical role of protein modifications in habituation learning, using larval zebrafish as a model system. The project will involve training in protein modification and in vivo electrophysiology, integrating these approaches into a well-rounded system to examine learning across genes, circuits, and behavior. Success of the project may reveal how post-translational protein modifications influence synaptic plasticity within defined neural circuits as we learn.

The possibility of harnessing ultrasound to modulate nerve function has interested scientists for many years. Though recent work has demonstrated the feasibility of using ultrasound to stimulate the central and peripheral nervous systems, it remains unclear whether ultrasound directly affects neuronal excitability or acts indirectly on the connections between neurons at the synaptic or circuit level. In this project, Drs. Cheng, Han and colleagues will explore these questions, using cutting-edge approaches to achieve high spatial resolution of stimulation, and high temporal resolution of recording the resultant neuronal effects. This work will inform future design of ultrasound neuro-stimulators for basic neuroscience research and possible novel therapies for neurological disorders.

Traumatic events can result in anxiety disorders, including post-traumatic stress disorder (PTSD), a hallmark of which is amplified expression of fear and resistance to behavioral therapies. While the ventromedial prefrontal cortex (vmPFC) is thought to have a fear-inhibiting role, activity in its subregions is also elevated in many PTSD patients. To understand this functional dichotomy, Dr. Cummings will investigate the mechanisms by which vmPFC subregions encode learned fear by using a range of approaches, including calcium imaging, circuit tracing, in vivo optogenetic manipulation, and ex vivo electrophysiology in mice. Success of the project should result in characterization of the role of vmPFC in the regulation of fear memory, which may inform existing models of fear circuitry and help elucidate the mechanisms that are recruited in fear learning.