Funded Awards

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Title Investigator Institute Fiscal Year FOA Number Status Project Number Priority Area Summary
A BRAIN Initiative Resource: The Neuroscience Multi-omic Data Archive White, Owen R University Of Maryland Baltimore 2017 RFA-MH-17-255 Active
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  • Human Neuroscience
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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.

A Community Resource for Single Cell Data in the Brain Gee, James C Hawrylycz, Michael (contact) Martone, Maryann E Ng, Lydia Lup-ming Philippakis, Anthony Allen Institute 2017 RFA-MH-17-215 Active
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One major technical challenge for the BRAIN Initiative is the storage and dissemination of large amounts of data collected by different project teams. Hawrylycz and colleagues will support the cell census efforts of the BRAIN Initiative by hosting the BRAIN Cell Data Center (BCDC). Through the BCDC, they will store single-cell data on genetics, histology, electrophysiology, morphology, anatomical location, and synaptic connections from multiple species in a standardized manner. They will also develop and provide training for web-based tools to ease data visualization and analysis efforts. This will facilitate the integration of multiple data streams to better identify and characterize the different cell types in the brain.
A Confocal Fluorescence Microscopy Brain Data Archive Bruchez, Marcel P Ropelewski, Alexander J (contact) Watkins, Simon C Carnegie-mellon University 2017 RFA-MH-17-255 Active
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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.

A Facility to Generate Connectomics Information Lichtman, Jeff HARVARD UNIVERSITY 2018 RFA-NS-18-005 Active
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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.

A Fast, Accurate and Cloud-based Data Processing Pipeline for High-Density, High-Site-Count Electrophysiology Kimmel, Bruce VIDRIO TECHNOLOGIES, LLC 2018 RFA-MH-17-257 Active
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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.

A high-speed volumetric multiphoton microscope for the study of developing neural circuits in retina Feller, Marla University Of California Berkeley 2016 RFA-MH-16-725 Complete
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Spontaneous neuronal activity plays a role in the wiring of retinal circuits during development. Current imaging techniques are unable to capture such activity accurately. Dr. Feller’s team will assemble a system containing a resonant scanner-based two-photon microscope with the ability to achieve three-dimensional imaging of a single spontaneous firing event in vivo. Her team will utilize this high-speed volumetric two-photon imaging during visual stimulation to study the formation of functional neuronal circuits in the developing mouse retina.
Achieving ethical integration in the development of novel neurotechnologies Chiong, Winston University Of California, San Francisco 2017 RFA-MH-17-260 Active
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Novel neurotechnologies hold promise for treating neuropsychiatric disorders, but also raise profound neuroethics issues including self-ownership of our thoughts, emotions, and actions. Engaging patients and researchers in the early stages of neurotechnology research and clinical translation can help ensure ethical development of the field. This research study will be embedded in one of two projects funded by the DARPA BRAIN Initiative to develop implantable brain stimulation devices that both monitor and adaptively stimulate brain areas involved in mood and behavior regulation. Dr. Chiong and an interdisciplinary team with expertise in neuroscience, clinical care, law, philosophy, and social science will assess neuroethics issues associated with the DARPA-funded brain stimulation project. The overall goal is to enable acceptability and adoption of new treatments for neuropsychiatric disorders, by recognizing and incorporating the perspectives of patients, researchers, and other stakeholders into the design of these novel neurotechnological therapies.
All-Optical Methods for Studying Sequential Motor Behaviors Roberts, Todd F Ut Southwestern Medical Center 2016 RFA-MH-16-725 Complete
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The execution of learned sequential motor behaviors is thought to be supported by precise sequences of neuronal activity in the brain. Dr. Roberts seeks to identify brain circuits important for learning vocal behaviors, and has pioneered several techniques in songbirds, including viral vector methods, two-photon microscopy, optogenetic studies, and in vivo calcium imaging. The Roberts Lab will employ a newly developed two-photon digital holographic system for optogenetic stimulation, along with targeted whole-cell recordings, to map the functional organization of circuits. This all-optical interrogation of circuits involved in generating precisely timed sequential vocal behaviors could be used to identify how sequences of neuronal activity underlying complex learned behaviors are generated in the brain.
Assessing the Effects of Deep Brain Stimulation on Agency Roskies, Adina L Dartmouth College 2018 RFA-MH-18-500 Active
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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.

Bayesian estimation of network connectivity and motifs Ringach, Dario L University Of California Los Angeles 2016 RFA-EB-15-006 Active
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Learning how emergent behavior arises from single neurons is a key challenge in modern neuroscience. Ringach and his colleagues plan to create sophisticated algorithms and methodologies to derive the functional connectivity of neurons based on activity patterns at the single-cell level and then identify collections of neurons, or network motifs, that play important computational roles in network functions. The researchers will then validate their algorithms against a database combining functional calcium imaging data with “ground truth” estimates of direct synaptic connectivity. These tools and validation data will enable the investigation of how network motifs differ in both health and disease states.
Behavioral readout of spatiotemporal codes dissected by holographic optogenetics Rinberg, Dmitry (contact) Shoham, Shy New York University School Of Medicine 2014 RFA-NS-14-009 Complete
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Dr. Rinberg's team aims to understand how the brain turns odors into nerve signals by activating and recording neurons in the olfactory bulbs of mice as they detect a variety of odors.
Berkeley Course on Mining and Modeling of Neuroscience Data Sommer, Friedrich T University Of California Berkeley 2015 RFA-MH-15-215 Complete
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In their quest to understand the brain, neuroscientists continue to improve techniques for recording simultaneously from increasingly large numbers of neurons. This generates enormously large data sets. Analysis of these data sets will require new algorithms to understand how coordinated neural activity correlates to cognitive function. The goal of the course proposed by Sommer and colleagues is to identify, teach, and disseminate the best available methods for the analysis of large-scale neuroscience data sets. Their course will build on an existing course, "Mining and modeling of neuroscience data," and addresses a critical need by bringing individuals with quantitative backgrounds into the field of neuroscience.
Beyond Diagnostic Classification of Autism: Neuroanatomical, Functional, and Behavioral Phenotypes Fletcher, Preston Thomas University Of Utah 2016 RFA-EB-15-006 Active
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A major barrier to creating effective treatments for autism spectrum disorder (ASD), a lifelong neurological disorder characterized by stereotyped behavior and difficulties in social interactions, is the lack of understanding of the underlying brain mechanisms. Fletcher and his team propose to develop novel statistical methods for integrating the analyses of neuroimaging data (functional and structural MRI) with behavioral assessments. The resulting set of open-source tools will help relate brain networks to specific ASD behaviors, as well as those observed in other neuropsychological disorders.
BIDS-Derivatives: A data standard for derived data and models in the BRAIN Initiative Poldrack, Russell A Stanford University 2017 RFA-MH-17-256 Active
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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.

Boss: A cloud-based data archive for electron microscopy and x-ray microtomography Wester, Brock A. Johns Hopkins University 2018 RFA-MH-17-255 Active
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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.

BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain Chung, Moo K University Of Wisconsin-madison 2016 RFA-EB-15-006 Complete
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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.

BRAIN power: expanding reproducibility, quality control, and visualization in AFNI/SUMA COX, ROBERT WILLIAM (contact); NIELSON, DYLAN MILES U.S. NATIONAL INSTITUTE OF MENTAL HEALTH 2018 RFA-MH-17-257 Active
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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.

Building analysis tools and a theory framework for inferring principles of neural computation from multi-scale organization in brain recordings Sommer, Friedrich T University Of California Berkeley 2018 RFA-EB-17-005 Active
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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.

C-PAC: A configurable, compute-optimized, cloud-enabled neuroimaging analysis software for reproducible translational and comparative Craddock, Richard Cameron Milham, Michael Peter (contact) Child Mind Institute, Inc. 2018 RFA-MH-17-257 Active
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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.

Circuit mechanisms of evidence accumulation during decision-making Luo, Zhihao Princeton University 2017 RFA-MH-17-250 Active
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Dr. Luo will use optogenetic tools to inactivate specific brain structures while simultaneously recording neuronal activity across other brain areas in the rat during evidence accumulation tasks. This research could uncover the neural circuits that support the gradual accumulation of evidence during decision making.
Coarse-graining approaches to networks, learning, and behavior Bialek, William Palmer, Stephanie E (contact) Schwab, David Jason University Of Chicago 2018 RFA-EB-17-005 Active
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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.

Collaborative Standards for Brain Microscopy Hamilton, Carol M Research Triangle Institute 2018 RFA-MH-17-256 Active
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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.

Computational and circuit mechanisms for information transmission in the brain Eden, Uri Tzvi Frank, Loren M Ganguli, Surya Kepecs, Adam (contact) Kramer, Mark Alan Machens, Christian Tolosa, Vanessa Cold Spring Harbor Laboratory 2015 RFA-NS-15-005 Complete
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Dr. Kepecs and colleagues are investigating how information is integrated into decision making, and then further transformed into behavior. This project focuses on understanding information flow across specific regions of the brain in trained rats. By performing parallel, large-scale, simultaneous electrical recordings of neural activity in these different brain regions while the animals perform two different types of decision-making tasks, these researchers hope to observe how activity in one area influences activity in a downstream area. In addition, there are plans to identify and manipulate the activity of neurons that connect these brain areas to understand the causal relationships governing information flow among these regions. Gaining such mechanistic insights into how the brain processes information will provide insights into how both the normal and disordered brain operates.
Computational Modeling of Deep Brain Stimulation of the Ventral Striatum Dougherty, Darin D Widge, Alik S (contact) Massachusetts General Hospital 2016 RFA-MH-16-725 Complete
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Deep brain stimulation (DBS) targeting the ventral internal capsule/ventral striatum (VC/VS) is being used as a treatment for those with obsessive compulsive disorder (OCD), but with inconsistent clinical results. As a tool to examine mechanisms underlying this process, Dr. Widge and colleagues will adapt the recently developed "StimVision" software suite to model DBS electrical fields that activate brain tissue, in collaboration with the McIntyre lab (Case Western Reserve University). Using data from their DBS patient cohort, the team will integrate novel algorithms to improve modeling of neural mechanisms underlying the effects of DBS. This research, using StimVision with a specific DBS patient group, will improve understanding of cortical circuits underlying the behavioral effects of DBS, potentially enhancing circuit-oriented therapies.
Cortical circuits and information flow during memory-guided perceptual decisions Sur, Mriganka Massachusetts Institute Of Technology 2014 RFA-NS-14-009 Complete
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Dr. Sur and his team will combine a number of cutting-edge, large-scale imaging and computational techniques to determine the exact brain circuits involved in generating short term memories that influence decisions.
CoSMo - Summer School in Computational Sensory-Motor Neuroscience Schrater, Paul R University Of Minnesota 2015 RFA-MH-15-215 Complete
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Schrater and his team will offer a two-week short course, covering cross-disciplinary training in mathematical modelling techniques to understand brain function related to sensorimotor control and dysfunction. The course aims to increase participants' understanding of the brain's working principles in health and disease, via lectures accompanied by hands-on modelling and simulation tutorials. Course participants will be better positioned to contribute successfully to the overall goals of the BRAIN Initiative.
CRCNS Research Proposal: Cortico-amygdalar substrates of adaptive learningRecent advances in computational psychiatry have revealed failures in using models of the reward environment to flexibly change undesired behavior in individuals with substance use SOLTANI, ALIREZA (contact); IZQUIERDO, ALICIA DARTMOUTH COLLEGE 2018 PAR-18-804 Active
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Recent advances in computational psychiatry have revealed failures in using models of the reward environment to flexibly change undesired behavior in individuals with substance use disorders (SUDs). Drs. Soltani and Izquierdo will inhibit precise brain regions and simultaneously perform calcium imaging in rodents performing an adaptive learning task to explore circuitry between the cortex and amygdala. Results from this project could lead to improved systems-level understanding of behavioral inflexibility in people with SUDs and of the precise roles of involved brain areas for better, more effective therapeutic targeting in the future.

CRCNS: Advancing Computational Methods to Reveal Human Thalamocortical Dynamics JONES, STEPHANIE RUGGIANO (contact); HAMALAINEN, MATTI BROWN UNIVERSITY 2018 PAR-18-804 Active
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Advancing methods to image and interpret neural activity in humans on fine temporal-spatial scales is critical to understanding how the brain works in health and disease. However, the ability to record non-invasively from deep in the human brain with current technology is lacking. To address this issue, Drs. Hamalainen and Jones will integrate magneto-/electroencephalography (MEG/EEG), computational neural modeling, and invasive electrophysiological recording in humans to optimize methods to localize distributed deep and shallow brain sources, and to develop a computational tool to interpret the underlying cellular events. In addition to developing free open source software that will advance the ability to non-invasively study subcortical interactions in humans with MEG/EEG, this approach will provide novel insight into distributed subcortical activity that is not possible with one method alone.

CRCNS: An Integrative Study of Hippocampal-Neocortical Memory Coding during Sleep CHEN, ZHE (contact); WILSON, MATTHEW A NEW YORK UNIVERSITY SCHOOL OF MEDICINE 2018 PAR-18-804 Active
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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.

CRCNS: Cholinergic contribution to hippocampal information processing CANAVIER, CARMEN CASTRO (contact); GASPARINI, SONIA LSU HEALTH SCIENCES CENTER 2018 PAR-17-804 Active
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Neuromodulation in the hippocampus is thought to guide learning and memory processes, and a thorough knowledge of the mechanisms underlying encoding and retrieval is critical towards informing clinical interventions for cognitive disorders. Drs. Canavier and Gasparini will investigate how the neurotransmitter acetylcholine controls routing in areas CA1 and CA3 of the hippocampus. Their approach uses both computational modeling and experiments to better understand the neural basis of how different oscillation frequencies can be used to route information and how acetylcholine could control this routing. The resultant improvement in understanding how information is processed and stored in the hippocampus may eventually guide therapeutic strategies for cognitive disorders.

CRCNS: Collaboration toward an experimentally validated multiscale model of rTMS QUEISSER, GILLIAN TEMPLE UNIV OF THE COMMONWEALTH 2018 PAR-18-804 Active
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Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique that relies on electromagnetic induction. Though studied clinically for the treatment of various disorders, effective repetitive TMS (rTMS) therapies remain elusive, hampered by technical limitations and a complex parameter space. To better understand the mechanisms underlying rTMS, Dr. Queisser aims to bridge modeling and basic neuroscience to build a multi-scale computational model which combines field simulations, network/single-cell plasticity modeling, and molecular-level calcium simulations. The proposed project is a first important step towards biology-driven, computer-assisted personalized rTMS therapies to promote beneficial neural plasticity. Moreover, this molecular approach provides the perspective in testing synergistic effects of pharmacological interventions and rTMS-based therapies, which may be instrumental in informing future clinical trials to tackle mental health disease.

CRCNS: Common algorithmic strategies used by the brain for labeling points in high-dimensional space NAVLAKHA, SAKET SALK INSTITUTE FOR BIOLOGICAL STUDIES 2018 PAR-18-804 Active
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Sensory systems in simple model organisms, like olfaction in the fruit fly, are well understood but must be translated to higher level vertebrates and expanded to include computational models for full comprehension. Dr. Navlakha hopes to understand what computations are used by the mammalian olfactory system using a mouse model and extending to develop a computer algorithm for application across species. The group plans to learn what circuit mechanisms are used in the mouse olfactory system, which may help identify how disruption of these mechanisms causes circuit malfunction. Using these data to improve computational processing performance, they could uncover insights into how the brain computes more broadly in health and disease.

CRCNS: Community-supported open-source software for computational neuroanatomy GARYFALLIDIS, ELEFTHERIOS INDIANA UNIVERSITY BLOOMINGTON 2018 PAR-18-804 Active
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Diffusion-weighted Magnetic Resonance Imaging (dMRI) is the only currently available, non-invasive, method to measure the properties connections in living human brains. Widely used in clinical tests for a variety of brain disorders, dMRI helps researchers understand networks involved in perception in cognition. Dr. Garyfallidis plans to implement novel algorithms for dMRI data analysis, share benchmark data sets, and support development of cloud-computing software tools. Computational methods proposed could accelerate research using dMRI for clinical application and increase our ability to make inferences from dMRI data.

CRCNS: Computational Approach to Assess Replicability of Neurobehavior Phenotypes BOGUE, MOLLY A JACKSON LABORATORY 2018 PAR-17-804 Active
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The scientific community and general public have become increasingly concerned about a lack of replicability among published discoveries, particularly in behavioral science, but extending to many areas of pre-clinical research. Dr. Bogue proposes a practical approach to the challenge of research replicability that will help circumvent extensive and costly efforts and delays in the initial reporting of important findings, while facilitating changes in how scientists evaluate and communicate research. This project will provide an approach, guidelines and publicly available data resources to reduce the number of irreproducible studies that are published and improperly used as foundational research, increasing the public health impact of NIH-funded research and ultimately restoring confidence in the public's investment in research through timely, cost-effective improvements in the scientific process.

CRCNS: Computational neuroimaging of the human RESS, DAVID B BAYLOR COLLEGE OF MEDICINE 2018 PAR-18-804 Active
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The human brainstem plays a critical role in brain function, both in health and disease, yet remarkably little is known about this critical brain region. While functional magnetic resonance imaging (fMRI) of the brain has provided tremendous insight into the cerebral cortex, the depth and small size of brainstem structures, such as the superior colliculus, has made imaging of the brainstem challenging. Dr. Ress proposes to build a set of methods and modeling that will enable the use of ultra-high-field fMRI to study the brainstem. The group will demonstrate validity by performing visual response experiments in the superior colliculus of humans and, if successful, could obtain much higher resolution data that could be transformative for basic research and clinical studies alike.

CRCNS: Decision Making in Changing Environments GOLD, JOSHUA I (contact); JOSIC, KRESIMIR ; KILPATRICK, ZACHARY PETER UNIVERSITY OF PENNSYLVANIA 2018 PAR-17-804 Active
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Decisions are often deliberative processes that depend on the ability to accumulate uncertain information over time, but sometimes, new information requires dynamic updates. While research has begun to examine decision-making under dynamic conditions, no studies have identified representations of this adaptive decision variable that can flexibly accumulate information to guide behavior. Dr. Gold and collaborators plan to use theoretical and experimental approaches to understand how and where the brain encodes these decision variables. Specifically, they test whether brain circuits that integrate evidence under static conditions can also implement adaptive processes under dynamic conditions. This integrated computational, behavioral, and neurophysiological approach will provide novel insights into many aspects of higher brain function and complex behaviors that depend on dynamic processing of information.

CRCNS: Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation RICHARDSON, ROBERT MARK (contact); TURNER, ROBERT STERLING UNIVERSITY OF PITTSBURGH AT PITTSBURGH 2018 PAR-18-804 Active
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Deep brain stimulation (DBS) represents one of the major clinical breakthroughs in the age of translational neuroscience, though harnessing the full therapeutic potential of adaptive DBS remains a challenge. Drs. Richardson and Turner will employ artificial intelligence strategies to further elevate the therapeutic potential of DBS. The concurrent use of research electrocorticography (ECoG) during DBS surgery recently has enabled basic neuroscience investigation of human cortical-subcortical network dynamics. Therefore, the researchers will develop a computational framework for deep learning-based multi-feature decoding of behavioral and disease states from ECoG, in order to advance the evolution of aDBS. By employing artificial intelligence strategies to innovate in the field of translational, personalized, medicine, this work will inform the design of novel strategies for biomarker-responsive brain stimulation.

CRCNS: Dynamical Constraints on Neural Population Activity YU, BYRON M (contact); BATISTA, AARON PAUL CARNEGIE-MELLON UNIVERSITY 2018 PAR-17-804 Active
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Cognitive and behavioral processes that unfold over time reflect, at least in part, dynamical constraints imposed by neural circuitry. Understanding these dynamics requires finely perturbing neural activity in varied ways. Drs. Batista and Yu will employ a brain-computer interface (BCI) paradigm to study neural dynamics. BCI enables perturbation of neural activity by harnessing an animal's volitional control to drive the activity of a population of neurons into specified configurations, allowing causal tests of dynamical constraints and their relation to behavior. By recording multi-neuronal activity in the motor cortex of macaque monkeys, the researchers will have a deeper insight into how movements are prepared and executed, which holds therapeutic implications for movement disorders (e.g., Parkinson’s), as well as the potential to improve the performance of BCIs that assist paralyzed patients and amputees.

CRCNS: Dynamical mechanisms of oscillation transitions in the olfactory system KAY, LESLIE M (contact); CLELAND, THOMAS A UNIVERSITY OF CHICAGO 2018 PAR-14-804 Active
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The olfactory system is an excellent model for studying the role of neural oscillations within experimentally accessible tissues, but there lacks a thorough, multi-level understanding of dynamical flexibility of the cortical circuits underlying olfaction. Drs. Kay and Cleland will establish a mechanistic model of oscillations and synchronization in the mammalian olfactory system, combining electrophysiology from awake/behaving rats with recordings from acute mouse slices of the olfactory bulb. Integrating these datasets into a common network model will explicate the construction and utility of these systemwide dynamics based on their underlying cellular and network mechanisms. The proposed work takes a fairly well-characterized network and, via computational modeling, combines studies across different levels of analysis to build a mechanistic model of a complex dynamical system.

CRCNS: Dynamics of Gain Recalibration in the Hippocampal-Entorhinal Path Integration SystemThe striking organization of hippocampal place cells and grid cells have provided unique insights into how the brain constructs and uses representations of the envi KNIERIM, JAMES J (contact); COWAN, NOAH JOHN; HEDRICK, KATHRYN ; ZHANG, KECHEN JOHNS HOPKINS UNIVERSITY 2018 PAR-18-804 Active
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The striking organization of hippocampal place cells and grid cells have provided unique insights into how the brain constructs and uses representations of the environment to guide behavior. These spatially selective cells are influenced by both internal signals and external stimuli. How do these two sets of information re-calibrate when positions in the environment change? Drs. Cowan, Hedrick, Knierim, and Zhang propose that visual feedback guides these updates. They will conduct a set of interactive computational and experimental studies to investigate in detail the computational mechanisms underlying this novel phenomenon. This project, combining electrophysiology, engineering, and modeling, will propel the theory forward to explain the network dynamics underlying path integration, with implications for mental health illness characterized by an inability to appropriately react to external information about the world.

CRCNS: Geometry-based Brain Connectome Analysis DUNSON, DAVID BRIAN (contact); ZHANG, ZHENGWU DUKE UNIVERSITY 2018 PAR-18-804 Active
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Increasing evidence suggests that an individual's brain connectome plays a fundamental role in cognitive functioning and the risk of developing mental disorders. However, large gaps between image acquisition and in connectome construction and data analysis have limited progress in understanding the relationships between brain connectome structure and phenotypes. Drs. Dunson and Zhang will develop transformative tools to enhance understanding of how the brain connectome varies according to individual differences. The toolbox will be applied to the Human Connectome Project and UK Biobank datasets and rigorously validated. By reducing measurement errors in connectome construction, and improving the inference of relationships between connectome structure and an individual's mental health and substance use, this project can revolutionize mechanistic understanding and clinical practice in prevention and treatment of mental health disorders.

CRCNS: Improving Bioelectronic Selectivity with Intrafascicular Stimulation JUNG, RANU (contact); ABBAS, JAMES J FLORIDA INTERNATIONAL UNIVERSITY 2018 PAR-18-804 Active
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Electrical stimulation technology for activating small groups of peripheral nerve fibers could form the foundation of bioelectronic systems to influence metabolic processes, enhance immune system function, regulate gastrointestinal activity, or treat a variety of medical conditions. Drs. Jung and Abbas propose to enhance the clinical viability of these techniques by developing stimulation strategies that can selectively activate small groups of fibers that produce the desired clinical effect without producing undesirable side effects. The longitudinal intrafascicular electrodes (LIFEs) produced in this international collaboration will have multiple points of contact on nerve fibers and stimulation pulse flexibility for targeted activation in anesthetized rabbits.

CRCNS: Joint coding of shape and texture in the primate brain PASUPATHY, ANITHA UNIVERSITY OF WASHINGTON 2018 PAR-18-804 Active
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A fundamental capacity of the primate visual system is its ability to process both the form and texture of visual stimuli. Using a combination of primate neurophysiology experiments, behavior and computational modeling, Dr. Pasupathy hopes to achieve a new level of understanding about how the non-human primate brain integrates visual information about form and surface properties. Shared stimuli and computational approaches will permit combining the groups' electrophysiological and computational investigations in primate visual cortex with data from Japanese collaborators who perform psychophysical studies in humans. These findings could bring researchers closer to devising strategies to alleviate and treat brain disorders of impaired form and texture processing resulting from dysfunctions in the occipito-temporal pathway.

CRCNS: Modeling the nanophysiology of dendritic spines YUSTE, RAFAEL COLUMBIA UNIV NEW YORK MORNINGSIDE 2018 PAR-18-804 Active
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Dendritic spines mediate essentially all excitatory connections and are thus critical elements in the brain, but their function is still poorly understood. In particular, a key question is whether or not they are electrical compartments. Dr. Rafael Yuste will explore the application of a broad theory to accurately model the constraints that the nanostructure of dendritic spines places on electrical current flow. Specifically, his team will combine modeling approaches to extract features from data, and experimental approaches to study how the geometry and composition of a dendritic spine affect the electrical and ionic fluxes and the coupling between the synapse and the dendrite. The work will help understand how synaptic voltages are shaped by dendritic spines, a phenomenon that is affected in many mental and neurological diseases.

CRCNS: Modeling the role of auditory feedback in speech motor control HOUDE, JOHN FRANCIS (contact); NAGARAJAN, SRIKANTAN S UNIVERSITY OF CALIFORNIA, SAN FRANCISCO 2018 PAR-18-804 Active
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The importance of auditory feedback in speaking is underscored by the many diseases with speech disorders whose etiology have been wholly or partially ascribed to underlying deficits in auditory feedback processing, including autism, stuttering, schizophrenia, dementia, and Parkinson's disease. Drs. Houde and Nagarajan propose to investigate a computational model of speech that assumes state-feedback control by the auditory system. This project could lead to better understanding of the role of auditory feedback, which may lead to improved diagnosis and treatment for these speech impairments.

CRCNS: Modulating Neural Population Interactions between Cortical Areas YU, BYRON M (contact); SMITH, MATTHEW A CARNEGIE-MELLON UNIVERSITY 2018 PAR-18-804 Active
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The brain networks underlying visual attention remain poorly understood, in particular how populations of neurons communicate across regions to facilitate attention. Causal interventions, such as micro-stimulation, are a critically important way to test theories of communication between brain regions as well as to develop potential therapies. The overarching goal of Drs. Smith and Yu's project is to identify and optimize patterns of micro-stimulation in one brain region that influence another brain region, and in turn behavior. Their approach combines advanced physiological methods for simultaneous recording in multiple brain areas, a rigorous quantitative approach to understanding neuronal communication, and a novel optimization approach to using micro-stimulation to modulate neuronal activity and behavior. The implications of this work have extremely broad scope and may reveal fundamental principles by which inter-area communication supports myriad perceptual and cognitive abilities.

CRCNS: MOVE!-MOdeling of fast Movement for Enhancement via neuroprosthetics SARMA, SRIDEVI V JOHNS HOPKINS UNIVERSITY 2018 PAR-18-804 Active
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Tracking fast unpredictable movements is a valuable skill, applicable in many situations (e.g., chasing prey). The sensorimotor control system (SCS) is responsible for such actions and its performance depends on neurons, communication between brains and muscles, and muscle dynamics whose contributions have not been explicitly quantified. Dr. Sridevi Sarma and a team of investigators will build upon new theory developed using feedback control principles and an appropriately simplified model of the SCS to identify how neural computing, delays, and muscles interact during the generation of fast movements. In doing so, the group will seek to restore motor performance, and more importantly restore fast and agile movements, in patients with movement disorders via neuroprosthetic devices that are designed using a validated model of the sensorimotor control system and modern control theory.

CRCNS: Multi-scale models of proprioceptive encoding for sensorimotor control TING, LENA H EMORY UNIVERSITY 2018 PAR-16-804 Active
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Proprioception, or one’s relative sense of body position and strength during movement, is guided by muscle spindle sensory afferents. While altered muscle spindle function is implicated in a wide range of sensorimotor impairments and neurological disorders, the basic mechanisms of muscle spindle sensory encoding are not well understood. To address this issue, Dr. Ting will develop a novel, mechanistic model of muscle spindle sensory encoding to that will test hypotheses about the role of molecular, cellular, and circuit level mechanisms on sensorimotor control in healthy and impaired humans and animals. The model will be a useful platform to integrate classical and new findings of muscle spindle function spanning multiple levels. Importantly, the model will improve our basic understanding of how sensory impairments alter both sensing and moving, and to drive the development of new treatments.

CRCNS: Neural Basis of Planning LEE, DAEYEOL (contact); MA, WHEE KY YALE UNIVERSITY 2018 PAR-18-804 Active
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Strategic planning is important for humans and other animals during learning and decision making. While mechanisms for reinforcement learning have been well studied, how the brain utilizes knowledge of the environment to plan sequential actions remains unexplored. To address this issue, Drs. Lee and Ma, PIs with complementary expertise will investigate how different subdivisions of the primate prefrontal cortex contribute to the evaluation and arbitration of different learning algorithms during strategic planning in primates. By taking advantage of recent advances in machine learning and decision neuroscience, the proposed studies will elucidate how multiple learning algorithms are simultaneously implemented and coordinated via specific patterns of activity in the prefrontal cortex. The results from these studies will transform the behavioral and analytical paradigms used to study high-order planning and their neural underpinnings in humans and animals.

CRCNS: Neural signals that maintain/refresh LTP and memory GRIFFITH, LESLIE C BRANDEIS UNIVERSITY 2018 PAR-16-804 Active
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Understanding the molecular basis of memory storage through long-term potentiation (LTP) has major implications for memory disorders and stroke. Neural signals maintain and refresh LTP and require low levels of calcium, but whether achievement of this level is dependent on spontaneous neural activity is not known. To address this issue, Dr. Griffith will use acute hippocampal slices, behavioral observations in Drosophila, and computational modeling to test the role of spontaneous neural signals in memory refresh and maintenance. This project has the potential to bear importantly on the fundamental question of whether refresh reactions are mediated by spontaneous activity, providing important information towards understanding and treatment of memory disorders.

CRCNS: Real-time neural decoding for calcium imaging CHEN, RONG (contact); BHATTACHARYYA, SHUVRA S UNIVERSITY OF MARYLAND BALTIMORE 2018 PAR-18-804 Active
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Real-time neural decoding predicts behavior based on neural data, provided it can do so at the same pace with which the behavior is being monitored. While miniature cellular imaging is fast becoming a powerful way to study neural circuits by recording activity with cellular spatial resolution and sub-second temporal resolution, it also generates massive amounts of high-dimensional spatiotemporal data, with which real-time neural decoding has yet to keep apace. Drs. Bhattacharyya and Chen propose to develop a software platform, called RNDC-Lab (Real-time Neural Decoding for Cellular imaging Laboratory), that will provide integrated capabilities for design of and experimentation with novel real-time neural decoding systems for miniature cellular imaging. RNDC-Lab will provide a framework and platform for cost-efficient, real-time signal processing, the success of this project carries therapeutic implications for improving precise neuromodulation systems.

CRCNS: Rhythm generation in rodent spinal cord DOUGHERTY, KIMBERLY J DREXEL UNIVERSITY 2018 PAR-15-804 Active
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Understanding the rhythm-generating mechanisms that give rise to locomotion are critical to inform therapeutic interventions following injury or motor disorders. Spinal circuitry orchestrating the rhythm and patterning of locomotion are located in the lumbar spinal cord. In a collaborative project, Dr. Dougherty will use state-of-art experimental studies of spinal neurons and neural circuits in combination with computational modeling to dissect the organization and operating mechanisms of the spinal locomotor central pattern generator. The identification of rhythm generating mechanisms and the organization of spinal flexor and extensor circuitries will provide essential insights that can be applied to treatments and recovery of function following spinal cord injury or other motor disorders involving abnormal spinal locomotor processing.

CRCNS: Sparse odor coding in the olfactory bulb RINBERG, DMITRY (contact); KOULAKOV, ALEXEI NEW YORK UNIVERSITY SCHOOL OF MEDICINE 2018 PAR-14-804 Active
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Animals learn about their environment through their sensory systems, and the mammalian olfactory system is ideal to understand the computations in brain areas that format this incoming information for easy and flexible extraction by downstream brain areas. Drs. Koulakov and Rinberg will utilize recently developed theoretical frameworks, new optical methods for stimulus control, and multi-neuron recordings, to carry out a collaborative project that tests the basic principles of sensory processing in the olfactory system. This project will help elucidate the general principles of olfactory information processing by demonstrating how sensory representations can be dynamically tuned to reflect particular tasks faced by the organism. Because about 1-2% of people in North America experience a smell disorder and loss in sense of smell can negatively affect quality of life, this work holds important implications for clinical and therapeutic interventions.

CRCNS: Theory and Experiments to Elucidate Neural Coding in the Reward Circuit WITTEN, DANIELA (contact); WITTEN, ILANA UNIVERSITY OF WASHINGTON 2018 PAR-18-804 Active
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Dopamine neurons are implicated in a wide range of normal behavioral functions, as well as a wide range of neuropsychiatric diseases, including addiction. Dr. Witten's group will perform two-photon imaging in the midbrain of mice while they learn a complex decision-making task and incorporate a suite of statistical tools to address challenges in analyzing the activity and behavioral data. The identification of sub-populations of dopamine neurons with different functional properties could provide much-needed insight into how dopamine neurons contribute to the neurobiology of addiction.

CRCNS: Theory-guided studies of cortical mechanisms of multi-input integration MILLER, KENNETH D (contact); VAN HOOSER, STEPHEN D COLUMBIA UNIVERSITY HEALTH SCIENCES 2018 PAR-18-804 Active
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Processing in cortical circuitry is critical to healthy development, underlies features of intelligence, and malfunctions during disease. Drs. Miller and Van Hooser will test the predictions of a powerful framework for understanding how the sensory cortex globally integrates multiple sources of input, bottom-up and top-down, to produce neuronal responses, and ultimately, perception. Combining a novel theory on neural responses, the stabilized supralinear network, with optical and genetic manipulations of visual cortical circuits in awake ferrets, the group will probe how the visual cortex responds to various natural stimuli. Understanding such global integration occurring in the cortex could lead to the improvement of prosthetic devices that interface with the brain to treat blindness and other disorders.

CRCNS: US-France Modeling & Predicting BCI Learning from Dynamic Networks BASSETT, DANIELLE SMITH UNIVERSITY OF PENNSYLVANIA 2018 PAR-15-804 Active
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Brain-computer interfaces (BCIs) are increasingly used for control and communication, and for treatment of neurological disorders, yet despite their societal and clinical impact, many engineering challenges remain. In particular, voluntarily modulating brain activity to control a BCI requires several weeks or months to reach high performance, affecting the user’s daily life. To characterize the neural mechanisms of BCI learning and predict future performance, Dr. Danielle Bassett and a collaborative international team will leverage experimental data and interdisciplinary theoretical techniques. They will characterize brain networks at multiple scales, developing models to predict the ability to control the BCI, as well as methods to engineer BCI frameworks for adapting to neural plasticity. This project will enable a comprehensive understanding of the neural mechanisms of BCI learning, fostering the design of viable BCI frameworks that improve usability and performance.

CRCNS: US-French Research Proposal: Neurovascular coupling-democracy or oligarchy? DREW, PATRICK JAMES PENNSYLVANIA STATE UNIVERSITY-UNIV PARK 2018 PAR-15-804 Active
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Hemodynamic signals, such as those measured by functional magnetic resonance imaging (fMRI), are used to non-invasively image brain activity, but it is not known whether changes in blood flow are governed by average neural activity, or the activity of the most active neurons. Drs. Drew and Charpak, along with an international collaborative team, will use in vivo two-photon imaging, in close coordination with computational analysis methods, to investigate how neural activity is coupled to changes in blood flow. The combination of these two approaches will yield a quantitative understanding of how blood flow changes relate to neural activity, and a determination of the mechanisms underlying neurovascular coupling. A deeper understanding of the conversion of these hemodynamic signals into neural activity will inform the interpretation of human imaging studies, with clinical and therapeutic implications.

CRCNS: US-Japan Research Proposal: The Computational Principles of a Neural Face Processing System FREIWALD, WINRICH ROCKEFELLER UNIVERSITY 2018 PAR-18-804 Active
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A fundamental gap remains in the understanding of how neural circuits represent complex objects like faces and permit facial recognition. The neural mechanisms of face processing are essential to human social life, and altered social perception is characteristic of many pervasive neurodevelopmental disorders. Dr. Freiwald plans to identify the neural mechanisms and computational principles underlying face recognition circuitry and explore how alterations to these circuits impair function. Integrating functional magnetic resonance imaging with electrophysiological recordings in the targeted brain regions of non-human primates, the group could uncover details of more general visual object recognition as well as advancing understanding of the circuit mechanisms for social perception.

CRCNS:Navigation Through A Memory Space in the Rodent Hippocampus HOWARD, MARC W BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) 2018 PAR-16-804 Active
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One primary function of memory is to remember the past in order to anticipate and make decisions about the future. Neurophysiological findings show that the rodent hippocampus stores representations of past events, and that hippocampal theta oscillations may provide a mechanism to imagine future paths through space. Dr. Marc Howard and collaborators will use a combination of empirical work, advanced data analyses and computational modeling to develop a hypothesis for how the hippocampus and frontal cortex cooperate to navigate memory space and inform future behavior. By bridging levels of description from behavior, to an abstract mathematical framework, to systems neuroscience, this work may shed new light on fundamental mechanisms underlying memory in the hippocampus, paving the way towards treatment of memory dysfunction in a myriad of neurological disorders.

Crowd coding in the brain:3D imaging and control of collective neuronal dynamics Kanold, Patrick O (contact) Losert, Wolfgang Plenz, Dietmar Univ Of Maryland, College Park 2014 RFA-NS-14-009 Complete
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Dr. Kanold and his team propose cutting edge methods to stimulate neurons at different depths in the auditory cortex, and will use new computational methods to understand complex interactions between neurons in mice while testing their ability to hear different sounds.
Data Archive for the Brain Initiative (DABI) Duncan, Dominique Pouratian, Nader Toga, Arthur W (contact) University Of Southern California 2018 RFA-MH-17-255 Active
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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

Data interface and apps for systems neurophysiology and imaging Van Hooser, Stephen D Brandeis University 2018 RFA-MH-17-256 Active
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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.

Data-driven analysis for neuronal dynamic modeling Mishne, Gal Yale University 2018 RFA-EB-17-005 Active
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The communications and interactions between neurons across the sensory-motor system require additional investigations with novel methodologies to understand dynamic activity patterns underlying behavior. Dr. Mishne will develop modular mathematical tools to automatically analyze massive amounts of high-resolution, spatiotemporal, neuronal activity data gathered from mice performing a reaching task. The proposed calcium imaging data will be processed in three modules that: develop methods for ROI (region of interest) extraction, use tensors and non-linear tools for multi-modal integration of neuronal activity with behavior, and predict future behavioral responses using a recurrent neural network approach. These methods for automated analysis, organization, and modeling of calcium imaging data gathered during behavioral tasks will be available for use by the entire community.

Development of Line-Scan Temporal Focusing for fast structural imaging of synapse assembly/disassembly in vivo Boivin, Josiah R Massachusetts Institute Of Technology 2017 RFA-MH-17-250 Active
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Dr. Boivin will contribute to the development of high-resolution, high-throughput Temporal Focusing (TF) two-photon microscopy to achieve real-time monitoring of synapse assembly/disassembly in developing neural circuits in vivo in the mouse brain.
Diagnosis of Alzheimer's Disease Using Dynamic High-Order Brain Networks Shen, Dinggang (contact) Yap, Pew-thian Univ Of North Carolina Chapel Hill 2016 RFA-EB-15-006 Active
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Despite being the most common form of dementia, Alzheimer’s disease (AD) has no known cure and current clinical diagnosis relies on subjective neuropsychological and neurobehavioral assessments. Shen and his team plan to create machine learning-based algorithms that will hone in on changes to the functional connectivity of brain networks over time—as measured by neuroimaging techniques such as diffusion MRI—as possible indicators of mild cognitive impairment (MCI), which generally occurs well before AD symptoms. The researchers will design their diagnostic tools with the flexibility to also improve the success of the early detection of other neurological disorders, including schizophrenia, autism, and multiple sclerosis.
Discovering dynamic computations from large-scale neural activity recordings Engel, Tatiana Cold Spring Harbor Laboratory 2018 RFA-EB-17-005 Active
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Dynamic neuronal activity patterns underlie behavioral and cognitive functions in healthy and disordered brains, but large-scale recordings of this activity produce massive amounts of data requiring complex computations. Dr. Engel’s project provides a novel theoretical framework for analytically modeling the process by which temporally diverse responses of single neurons contribute to population activity during decision making. The group will validate unbiased, computational methods to examine dynamic activity in primate and mouse cortices and incorporate this framework into their freely available “BrainFlow” software and visualization tools.

Dynamic network computations for foraging in an uncertain environment Angelaki, Dora (contact) Dragoi, Valentin Pitkow, Zachary Samuel Schrater, Paul R Baylor College Of Medicine 2015 RFA-NS-15-005 Complete
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The computational strategies and underlying mechanisms the brain uses to enable animals to interact flexibly with their environment are poorly understood. These researchers will use large-scale, wireless, electrical recordings from six relevant, interconnected brain regions in freely-behaving monkeys to record neuronal activity while the animals engage in foraging behavior-a natural task that involves sensory integration, spatial navigation, memory, and complex decision-making. The research team will use theoretical models of decision-making to interpret the neural activity data gathered as the animals interact with their environment, with the ambitious goal of understanding how brains create and use internal models of the world.
EFFECTIVE CONNECTIVITY IN BRAIN NETWORKS: Discovering Latent Structure, Network Complexity and Recurrence. Hanson, Stephen Jose Rutgers The State Univ Of Nj Newark 2016 RFA-EB-15-006 Active
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A longstanding goal of neuroscience has been matching specific functions to local brain structure and neural activity. Despite success in identifying brain areas associated with cognitive tasks such as memory, attention, and language, many areas engaged during cognitive tasks are often considered “secondary” and are consequently ignored. One weakness in current methods to associate brain regions with specific functions has been the reliance on direct correlation between increased neural activity and task performance. To identify and assess how secondary areas contribute to important cognitive tasks, Hanson and his colleagues plan to extend IMaGES and develop new functional brain imaging analysis software to search for brain areas with less intuitive, but still relevant, connections to certain tasks. This project will advance efforts to analyze information flow in the brain and determine how neural pathways are altered in both health and disease.
Embedded Ensemble Encoding Antic, Srdjan D Lytton, William W (contact) Suny Downstate Medical Center 2016 RFA-EB-15-006 Active
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The enormous complexity of brain interactions provides numerous challenges in understanding and treating brain diseases such as autism, schizophrenia, and Alzheimer’s disease. A large part of this complexity lies in “the neural code,” which describes how cells in the brain communicate with one another. Lytton and his colleagues propose the development of a novel embedded-ensemble encoding theory for understanding the creation of ensembles of neurons that are believed to generate thoughts, perceptions, and actions. The heart of this theory states that temporary neuronal ensembles form among groups of neurons across the brain whose activity becomes synchronized. The ultimate goal of this project is to bridge the gap between single neurons and neural networks and derive fundamental insights into cortical function that may advance the understanding of a variety of neurological diseases.
Emergent dynamics from network connectivity: a minimal model Curto, Carina Pennsylvania State University-univ Park 2016 RFA-EB-15-006 Active
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Many networks in the brain exhibit emergent dynamics: that is, they display patterns of neural activity that are shaped by the intrinsic structure of the network, rather than modified by an external input. Such dynamics are believed to underlie central pattern generators for locomotion, oscillatory activity in cortex and hippocampus, and the complex interplay between sensory-driven responses and ongoing spontaneous activity. The goal of this research by Curto and her colleague is to develop a theory of how emergent dynamics can arise solely from the structure of connectivity between neurons. Having a deeper understanding of the dynamics of neural circuits is critical for studying diseases in which those dynamics are thought to be disrupted, such as Parkinson's disease, schizophrenia, and epilepsy.
Enabling ethical participation in innovative neuroscience on mental illness and addiction: towards a new screening tool enhancing informed consent for transformative research on the human brain Roberts, Laura W Stanford University 2017 RFA-MH-17-260 Active
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The NIH BRAIN Initiative aims to accelerate the development of innovative neurotechnologies and their application to reduce the burden of brain disorders, including mental illnesses and substance use disorders. However, because the brain is central to our humanity, this kind of research raises profound neuroethics issues, including questions about personal identity, and socially acceptable limits on novel neurotechnologies. Further, research involving participants with brain disorders is complex because these disorders can affect cognition, emotion, behavior, and decision-making capacity. In this project, Dr. Roberts and colleagues will assess the neuroethics issues encountered in neuroscience research related to mental illness and addiction through interviews with neuroscientists, neuroethicists, and institutional review board members. They will also study factors that influence research decision-making by people with mental illness and addiction, as compared with healthy controls and people with diabetes. Finally, they will develop a screening tool to enhance informed consent, as an evidence-informed practice to facilitate ethically sound cutting-edge human neuroscience research.
Engineering optogenetic tools for studying neuropeptide activity French, Alexander Robert Purdue University 2017 RFA-MH-17-250 Active
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Dr. French will develop a high throughput screening platform to identify peptides that activate opioid receptors in response to light, creating high-resolution tools to study the function of specific opioid neural circuits in the brain.
Ethical Safeguards for Exit and Withdrawal from Implanted Neurotechnology Research Sankary, Lauren Cleveland Clinic Lerner Com-cwru 2017 RFA-MH-17-250 Active
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Dr. Sankary will combine an assessment of the experience of research participants exiting from research studies involving implanted neurological devices with a critical evaluation of existing research practices and regulations that protect these subjects. The goal of this research is to determine the responsiveness of these safeguards to patient concerns and lay the groundwork for development of evidence-based guidelines for the ethical conduct of this research.
Ethics of Patients and Care Partners Perspectives on Personality Change in Parkinsons disease and Deep Brain Stimulation Kubu, Cynthia M. S. Cleveland Clinic Lerner Com-cwru 2017 RFA-MH-17-260 Active
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The nature and extent of personality changes following deep brain stimulation (DBS) for the treatment of Parkinson's disease (PD) are unclear. Dr. Kubu and colleagues will analyze patients’ and caregivers’ perspectives on personality characteristics (e.g., extroversion, humility) at different stages of PD and over the course of DBS (patients within one year of diagnosis, within 5 -7 years of diagnosis, and those undergoing DBS). This study will shed light on participant's most valued personality characteristics, and whether those characteristics are captured in the existing informed consent process; the influence of PD and/or DBS on personality; and the extent of agreement between patients’ and caregivers’ perceptions of personality change. These data will facilitate an enhanced, iterative informed consent process that includes systematic assessment of patients’ perceived personality changes, values, and goals; will inform understanding of identity and autonomy in the context of DBS; and may allow clinicians to ease the fears of patients receiving DBS.
Filtered Point Process Inference Framework for Modeling Neural Data Brown, Emery N. Massachusetts General Hospital 2016 RFA-EB-15-006 Active
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Spikes are key elements of neural computation and methods to improve the extraction of spike data from calcium imaging and other similar imaging methods are much in demand. Existing techniques are either extremely slow or susceptible to noise. Brown and his colleagues plan to develop a mathematical framework for analyzing neuronal spikes, and to apply it to the analysis of calcium imaging data in behaving mice and to neuroendocrine data related to the secretion of hormones in humans. This framework will shed light on sensory encoding in the rodent brain. It will also aid our understanding of pathological neuroendocrine states and improve the efficacy of treatments of hormonal disorders, including diabetes, obesity and osteoporosis.
Genetic analyses of complete circuit formation in Caenorhabditis elegans Cook, Steven Jay Columbia Univ New York Morningside 2017 RFA-MH-17-250 Active
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Using the model system C. elegans, which has a simple, well characterized nervous system, Dr. Cook will develop new tools to create an exquisitely detailed map of a circuit in live animals and reveal the genetic factors that orchestrate assembly of a complete neural circuit.
Graph theoretical analysis of the effect of brain tumors on functional MRI networks Holodny, Andrei I Makse, Hernan (contact) City College Of New York 2016 RFA-EB-15-006 Active
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Individuals with brain tumors often recover function after the brain has adapted to the tumor. It is difficult, however, to predict which patients will recover based solely on the location of the tumor. Makse and his colleagues propose to develop a software tool to analyze a neuroimaging database of 1500 patients with glial tumors in order to discover the relationship between brain disease states and tumor location. This project will extend and test their theoretical model of how the brain adapts to recover lost functions in the presence of a brain tumor. The researchers’ new software tool will also aid in the understanding, diagnosis, and treatment of brain disorders thought to be due to disruptions of brain connectivity, including Alzheimer's disease, ADHD, stoke and traumatic brain injury.
Human Agency and Brain-Computer Interfaces: Understanding users? experiences and developing a tool for improved consent Goering, Sara (contact) Klein, Eran University Of Washington 2018 RFA-MH-18-500 Active
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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.

Human Neocortical Neurosolver Hamalainen, Matti Hines, Michael L Jones, Stephanie Ruggiano (contact) Brown University 2016 RFA-EB-15-006 Active
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Magnetoencephalography (MEG) and electroencephalography (EEG) are the leading non-invasive methods for recording human brain activity with millisecond resolution. However, it is still extremely difficult to interpret the underlying cellular and circuit-level sources of these large-scale signals without simultaneous invasive recordings. This challenge limits the use of MEG and EEG in the development of treatments for neural disorders. Jones and her colleagues propose a new software tool, called the Human Neocortical Neurosolver (HNN), that allows researchers to develop and test hypotheses about the origin of non-invasively measured human brain signals obtained with MEG and EEG. The insights obtained with the HNN tool will be helpful in understanding the underpinnings of neurological and psychiatric diseases, such as autism and schizophrenia.
Illuminating Neurodevelopment through Integrated Analysis and Vizualization of Multi-Omic Data Hertzano, Ronna (contact) White, Owen R University Of Maryland Baltimore 2018 RFA-MH-17-257 Active
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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.

Imaging adult-born neurons in action using head-mounted minimicroscopes Drew, Michael R University Of Texas, Austin 2016 RFA-MH-16-725 Complete
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Human and animal research has shown that modulation of adult hippocampal neurogenesis – the generation of neurons – can lead to changes in memory and emotion, but the underlying mechanisms are not well characterized. Dr. Michael Drew and colleagues will image adult-born neurons during learning behavior via incorporating head-mounted minimicroscopes (developed with prior BRAIN Initiative support). Dr. Drew’s laboratory will perform calcium imaging experiments in awake, behaving mice during a contextual fear conditioning paradigm and, using optogenetic techniques, they will silence adult-born hippocampal neurons in order to characterize how these neurons impact the coding of context memory. These studies will enhance the understanding of the mechanisms by which changes in adult neurogenesis influences mood and cognition.
In Vivo Imaging of Local Synaptic Neuromodulation by Dopamine Evans, Paul Robert Max Planck Florida Corporation 2018 RFA-MH-17-250 Active
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Dopamine is a powerful neurotransmitter that facilitates memory formation and underlies reward-related behaviors, but current techniques to assess dopamine signaling in vivo lack sufficient specificity and spatiotemporal resolution. Evans will develop new fluorescent sensors for dopamine receptors and apply them to investigate the molecular mechanisms that underlie learning in mice in vivo. The biosensors will be used to visualize the dynamic activity of specific dopamine receptors in vitro, before they are virally expressed in the motor cortex in behaving mice. Employed during motor learning, these sensors should generate a sub-micron scale map of how dopamine receptor subtypes modulate long-term structural plasticity of cortical dendritic spines. The results could help shed light on how dopaminergic modulation correlates with structural and functional plasticity.
Informing Choice for Neurotechnological Innovation in Pediatric Epilepsy Surgery Illes, Judy (contact) Mcdonald, Patrick University Of British Columbia 2018 RFA-MH-18-500 Active
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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.

Integrating flexible neural probes with a giant cranial window for combined electrophysiology and 2-photon calcium imaging of cortex-hippocampal interactions Golshani, Peyman University Of California Los Angeles 2016 RFA-MH-16-725 Complete
  • Monitor Neural Activity
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Learning and memory retrieval may rely upon coordinated network activation in regions of the neocortex during 150-250 Hz neuronal oscillations, or ripples, in the hippocampus. Golshani’s team will implant flexible electrode arrays developed by BRAIN-funded investigators into mouse hippocampus, and will combine this technology with their large cranial window preparation. This setup should allow for long-lasting, low-noise calcium imaging of neurons across brain regions, extending from frontal to occipital cortex, bilaterally, during a memory retrieval task. The group plans to identify neurons co-activated during ripple oscillations to explore interactions between the hippocampus and neocortex, which could improve understanding of memory dysfunctions in neurodegenerative and neuropsychiatric diseases.
Integrative Functional Mapping of Sensory-Motor Pathways Dickinson, Michael H (contact) Holmes, Philip J Mann, Richard S Wilson, Rachel California Institute Of Technology 2014 RFA-NS-14-009 Complete
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Dr. Dickinson will lead an interdisciplinary team to study how the brain uses sensory information to guide movements, by recording the activity of individual neurons from across the brain in fruit flies, as they walk on a treadmill and see and smell a variety of sights and odors.
Interdisciplinary Training in Computational Neuroscience for Researchers from Graduate and Medical Students to Junior Faculty Nair, Satish S University Of Missouri-columbia 2015 RFA-MH-15-215 Complete
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A longstanding goal of neuroscience research is to understand how activity of individual neurons and within neural circuits gives rise to outputs ranging from movement to thought. Integrative and interdisciplinary training in neuroscience is necessary to help develop scientists who can work together to address this goal by using approaches from diverse fields including biology, psychology, computer science, electrical engineering, and physics. Nair and colleagues propose to develop a new training course designed to strengthen the quantitative skills of students with biological backgrounds and increase the knowledge of neuroscience concepts for those students from quantitative backgrounds. This will fill a training gap at the pre- and post-doctoral and junior faculty levels.
Investigating the hypocretin to VTA circuit in memory consolidation during sleep Borniger, Jeremy Stanford University 2018 RFA-MH-17-250 Active
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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.

Investigating the Role of Neurotensin on Valence Assignment During Associative Learning in the Basolateral Amygdala Olson, Jacob Michael Massachusetts Institute Of Technology 2017 RFA-MH-17-250 Active
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Dr. Olson will systematically identify, manipulate, and characterize the neural projections that release the neuropeptide neurotensin to the basolateral amygdala during behavior conditioning tests in mice to identify a new circuit that regulates associative learning.
Is the Treatment Perceived to be Worse than the Disease?: Ethical Concerns and Attitudes towards Psychiatric Electroceutical Interventions Cabrera Trujillo, Laura Yenisa Michigan State University 2018 RFA-MH-18-500 Active
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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.

Lagging or Leading? Linking Substantia Nigra Activity to Spontaneous Motor Sequences Adams, Ryan Prescott Datta, Sandeep R Sabatini, Bernardo L (contact) Harvard Medical School 2015 RFA-NS-15-005 Complete
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One of the goals of the BRAIN Initiative is to understand how the brain generates behaviors. These researchers are utilizing a novel 3D machine vision technology to automate classification of spontaneous behavior when freely-moving mice are confronted with stimuli; they are then correlating that information with dense recordings of neural activity in key regions of the brain implicated in movement disorders. Researchers are then manipulating the activity of specific neurons in this brain region with light to test their role in the animal’s behavior. Dr. Sabatini and colleagues offer an innovative ‘grammatical’ structure to understanding how the brain produces complex, systematic behavior.
Large-scale monitoring of sensory transformations in the mammalian olfactory system Burton, Shawn Denver University Of Utah 2017 RFA-MH-17-250 Active
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Dr. Burton will leverage recent enhancements in calcium indicators to image pre- and post-synaptic neural activity simultaneously in the mammalian olfactory system, gaining insight into how sensory information is transformed as it moves through a neural circuit.
Large-scale Network Modeling for Brain Dynamics: Statistical Learning and Optimization Luo, Xi Brown University 2016 RFA-EB-15-006 Active
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Functional MRI (fMRI) is a useful technique for examining brain-wide networks involved in tasks that involve perceiving stimuli and behaviorally responding to those stimuli. Luo and his colleagues are applying machine learning approaches for modeling whole-brain networks using fMRI and behavioral response data captured during the performance of specific tasks. The algorithms the team develops will facilitate the analysis of a wide array of neuroimaging and behavioral data, which may lead to the discovery of pharmacological targets for treating neurological and psychiatric disorders.
Learning spatio-temporal statistics from the environment in recurrent networks Brunel, Nicolas Shouval, Harel Zeev (contact) University Of Texas Hlth Sci Ctr Houston 2016 RFA-EB-15-006 Active
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Learning new tasks and interacting with new environments leads to changes in the dynamics of brain circuits. The ability of animals to incorporate the statistical properties of the environment into decision-making brain circuits is necessary for survival. Shouval and his colleagues plan to develop a theoretical model for how brain circuits implement these statistics. This model may provide novel insights into the basis of a variety of neurophysiological processes, including learning and memory.
Manifold-valued statistical models for longitudinal morphometic analysis in preclinical Alzheimer's disease (AD) Johnson, Sterling C Singh, Vikas (contact) University Of Wisconsin-madison 2016 RFA-EB-15-006 Active
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In light of the profound public health issues Alzheimer’s disease (AD) represents, there is a tremendous need for methods to identify the onset of the disease as early as possible. Singh and his colleague propose to develop novel methods for analyzing Cauchy deformation tensors (CDTs) in brain images. These methods will enable the identification of structural changes in healthy midlife adults that are predictive of AD onset. The proposed analysis will be conducted on the largest preclinical AD cohort assembled to date and will help inform how telltale clinical biomarkers of AD emerge in asymptomatic individuals at risk for the disease. These preclinical biomarkers may be used in the design of clinical trials for new therapies.
Measuring, Modeling, and Modulating Cross-Frequency Coupling Eden, Uri Tzvi Kramer, Mark Alan (contact) Boston University (charles River Campus) 2018 RFA-EB-17-005 Active
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Cross-frequency coupling (CFC) is a phenomenon through which brain rhythms of different frequencies (fast vs. slow oscillations) coordinate to enable efficient communication between and among neural networks. Current methods measure a single type of CFC related to a given research question, but do not necessarily account for different interactions or combinations between phase and amplitude in fast and slow frequency bands. Drs. Kramer and Eden will develop a more general statistical inferential framework to estimate CFC in rats by creating a method to acquire real-time phase and amplitude data for estimation of CFC to accommodate dynamic manipulations. The team will incorporate computational modeling studies to simulate CFC between the amygdala and the frontal cortex and test via in vivo experiments. This framework will allow future users to explore the basis of network communication in the brain and evaluate the causal role of cross-frequency coupling.

Mechanisms of electrical stimulation of a canonical motor microcircuit Heckman, Charles NORTHWESTERN UNIVERSITY AT CHICAGO 2018 RFA-NS-18-018 Active
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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.

Mechanisms of neural circuit dynamics in working memory Bialek, William Brody, Carlos D (contact) Seung, Hyunjune Sebastian Tank, David W Wang, Samuel Sheng-hung Witten, Ilana Princeton University 2014 RFA-NS-14-009 Complete
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Dr. Brody and his colleagues will study the underlying neuronal circuitry that contributes to short-term "working" memory, using tools to record circuit activity across many brain areas simultaneously while rodents run on a track-ball through virtual mazes projected onto a screen.
Mental, measurement, and model complexity in neuroscience Balasubramanian, Vijay Gold, Joshua I (contact) University Of Pennsylvania 2018 RFA-EB-17-005 Active
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Three specific challenges for neuroscience data include: 1) identifying the relevant spatial, temporal, and computational scales in which the underlying information-processing dynamics are best understood, 2) identifying the best ways to design and select models to account for these dynamics, and 3) inferring what the data tells us about how the brain itself processes complex information. Drs. Gold and Balasubramanian will develop theoretical tools for understanding how the brain integrates information across large temporal and spatial scales, using definitions of complexity to facilitate the analysis and interpretation of complex neural and behavioral data sets. This formal, mathematical assessment of data complexity could be used by the community for other data-driven model building and for comparisons of existing neuroscience models.

Methods from Computational Topology and Geometry for Analysing Neuronal Tree and Graph Data Mitra, Partha Pratim (contact) Wang, Yusu Cold Spring Harbor Laboratory 2016 RFA-EB-15-006 Active
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The complex tree shapes of neurons are important for their role in neuronal circuitry, but they are mathematically challenging to characterize and analyze. Mitra and his colleagues are applying advanced methods from computational topology and geometry to classify neuronal structure and its role in the function of circuits. The resulting tools will be made available to neuroscientists studying normal and diseased brain circuitry.
Model behavior in zebrafish: characterization of the startle response Meserve, Joy Hart University Of Pennsylvania 2018 RFA-MH-17-250 Active
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The startle response is disrupted (i.e., uncoordinated or weak) in several neurological and psychiatric disorders. Meserve will investigate the startle response using live imaging of neural activity in transparent larval zebrafish. The slc5a7 gene (required for acetylcholine synthesis) modulates the startle response in zebrafish, and human slc5a7 mutations are implicated in attention deficit disorder and major depression. This project will study slc5a7a’s role in neural circuit development and/or startle response. Circuit defects in slc5a7a mutants will be investigated via calcium imaging and whole-brain activity mapping of neurons known to be required for the startle response. Integrated studies on gene function, neural circuitry, and behavior will uncover the developmental stage and anatomical region where slc5a7a is required. These experiments may determine how slc5a7a promotes normal startle response, and contribute knowledge about how acetylcholine regulates behavior.
Models and Methods for Calcium Imaging Data with Application to the Allen Brain Observatory Buice, Michael Witten, Daniela (contact) University Of Washington 2018 RFA-EB-17-005 Active
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Though calcium imaging permits single-cell observations in behaving animals, variation between trials and complexities in activity-dependent calcium dynamics and fluorescent read-out create challenging data analyses. Motivated by a large-scale, publicly-available repository of calcium imaging data obtained from mouse models at the Allen Brain Observatory, Drs. Witten and Buice will develop novel statistical models, methods, and software to improve analysis techniques comparing extracellular electrophysiology and calcium imaging recordings in the context of behavior. Creating new, open, online algorithms to interpret fluorescent traces of firing neurons and building models that account for variations in neuronal activity, could improve researchers’ ability to draw rigorous and replicable conclusions on the basis of calcium imaging data.

Multi-context software for robust and reproducible neuroscience image analysis Papademetris, Xenophon (contact) Scheinost, Dustin Yale University 2017 RFA-MH-17-257 Active
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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.

Multimodal modeling framework for fusing structural and functional connectome data Nagarajan, Srikantan S. Raj, Ashish (contact) Weill Medical Coll Of Cornell Univ 2016 RFA-EB-15-006 Active
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Recent advances in the development of imaging tools are allowing researchers to both measure brain function (i.e., EEG, fMRI, PET) and the underlying structure of brain connections (i.e., diffusion MRI). Integrating functional brain activity data across imaging platforms, each of which provide unique information, has been tricky, as has been combining that data with structural connectivity data. Raj and his team are developing sophisticated modeling programs that combine this wealth of data across multiple spatial scales. These programs will provide insight into the relationship between brain function and structure and how the relationship is altered in cases of injury and disease.
MULTISCALE ANALYSIS OF SENSORY-MOTOR CORTICAL GATING IN BEHAVING MICE Jaeger, Dieter (contact) Stanley, Garrett B. Emory University 2015 RFA-NS-15-005 Complete
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The neural circuitry underlying how animals make motor decisions, especially in response to sensory or environmental cues, is not well understood. Many motor disorders, including Parkinson’s and Huntington’s disease, are linked to faulty circuits in a region of the brain called the basal ganglia. Researchers will use a variety of advanced methods to image, record, and manipulate the activity of neurons in this area as well as in the areas of the brain involved in sensory perception and movement. By employing these methods at multiple scales – from the individual neuron to neuronal networks – and then correlating these data with the behavior of awake, behaving mice, researchers hope to reveal how sensory information is integrated with input from the basal ganglia to result in the decision to initiate or suppress movement.
Multiscale Imaging of Spontaneous Activity in Cortex: Mechanisms, Development and Function Constable, R. Todd Crair, Michael (contact) Yale University 2015 RFA-NS-15-005 Complete
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Being able to observe the activity of a single neuron while simultaneously observing the activity of entire brain regions is a critical step in bridging the gap in understanding of how a collection of nerve cells ultimately generates an organized behavior. Dr. Crair and colleagues will develop and use two different imaging techniques to measure the activity of individual neurons, regions of the brain, and the whole brain, during different behavior states, such as REM and non-REM sleep, in developing mice. Bridging their analyses and insights between and within scales will allow these researchers to examine neural circuits and networks in different brain states and determine how they are modulated through development.
Network basis of action selection Komiyama, Takaki Kreitzer, Anatol (contact) Lim, Byungkook J. David Gladstone Institutes 2015 RFA-NS-15-005 Complete
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Three separate research groups are collaborating to understand in detail how three distinct areas of the brain function and work together to enable learning and decision-making behaviors. Drs. Kreitzer, Komiyama, and Lim are leveraging an impressive set of technologies to monitor and perturb different cell types in each brain region while the mice perform learning and decision-making tasks. By applying multiple recording methods across these brain regions at both the level of a single neuron and entire subpopulations of neurons, while the animals perform the same set of tasks, researchers hope to develop a single model of how vertebrate animals make choices about what to do next.

Network Connectivity Modeling of Heterogeneous Brain Data to Examine Ensembles of Activity Across Two Levels of Dimensionality Gates, Kathleen Univ Of North Carolina Chapel Hill 2016 RFA-EB-15-006 Active
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Functional MRI (fMRI) is currently the most ubiquitous imaging technique for measuring whole brain activity in humans. The usefulness of fMRI in both research and clinical settings, however, has been limited by the availability of computational tools for analyzing the data. Most tools allow researchers to track activity in brain regions within a known network, without the ability to simultaneously examine connections between various networks. Gates and her colleagues have proposed a set of software tools that enable the simultaneous analysis of within- and between-network connectivity. The tools will also make it easier to combine fMRI data across individuals in order to learn more about how whole brain activity differs across people in both health and disease.
Neural circuits in zebrafish: form, function and plasticity Cepko, Constance L Engert, Florian (contact) Lichtman, Jeff W Sompolinsky, Haim Harvard University 2014 RFA-NS-14-009 Complete
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Dr. Engert's team will combine a wide array of cutting-edge neuroscience techniques to watch the entire brain activity of a see-through fish while it swims, and to make detailed maps of its brain circuitry.
Neural ensembles underlying natural tracking behavior Fiete, Ila R. Huk, Alexander C Priebe, Nicholas J. (contact) University Of Texas, Austin 2015 RFA-NS-15-005 Complete
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Animals move their eyes to track the movement of objects around them. These researchers will measure and manipulate the activity of populations of identified neurons in marmosets during pursuit eye movements. This work will allow a detailed understanding of how the pursuit circuit integrates information from a large number regions is a critical step in bridging the gap in understanding of how a collection of nerve cells ultimately generates an organized behavior. Dr. Crair and colleagues will develop and use two different imaging techniques to measure the activity of individual neurons, regions of the brain, and the whole brain, during different behavior states, such as REM and non-REM sleep, in developing mice. Bridging their analyses and insights between and within scales will allow these researchers to examine neural circuits and networks in different brain states and determine how they are modulated through development.
Neural mechanisms and behavioral consequences of non-Gaussian likelihoods in sensorimotor learning Nemenman, Ilya M. (contact) Sober, Samuel Emory University 2016 RFA-EB-15-006 Active
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A central goal of neuroscience is to understand how learning is implemented by the nervous system. However, despite years of studies in animals and humans, our knowledge of both the computational basis of learning and its implementation by the brain is still rudimentary. This project by Nemenman and his colleagues will spawn a unified mathematical theory explaining how the brain learns complex skills. The researchers will validate their theory in songbirds, with the goal of understanding sensorimotor learning of a single acoustic parameter, pitch, which is precisely regulated by the songbird brain. A better understanding of the mechanisms underlying sensorimotor learning could guide rehabilitative strategies that exploit the plasticity of complex behavior.
Neuroethics of aDBS Systems Targeting Neuropsychiatric and Movement Disorders Goodman, Wayne K Lazaro-munoz, Gabriel (contact) Mcguire, Amy Lynn Baylor College Of Medicine 2017 RFA-MH-17-260 Active
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A technological advance beyond traditional, open-loop DBS devices, adaptive deep brain stimulation (aDBS) devices monitor local neural activity to adjust stimulation in real time when treating certain movement and neuropsychiatric disorders. However, because aDBS devices autonomously record neural data and provide neuromodulation to affect motor function and mood, these systems raise important neuroethics issues, including changes in perception of autonomy and personal identity; risk-taking propensity; and privacy, use, and ownership of neural data. In this project, Dr. Lazaro-Munoz and colleagues will gather data from participants in existing aDBS clinical trials, their caregivers, people who declined to receive aDBS, and the aDBS researchers, to identify and assess the most pressing neuroethics issues related to aDBS research and translation. The long-term goal of this research program is to develop an empirically-informed and ethically-justified framework for the responsible development and clinical translation of aDBS systems, which will help maximize the social utility of this type of novel neurotechnology.
Neuronal and Dopaminergic Contributions to Dissimilar Evoked Hemodynamic Responses in the Striatum Walton, Lindsay Univ Of North Carolina Chapel Hill 2018 RFA-MH-17-250 Active
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Blood oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) is a non-invasive imaging technique that infers increased brain activity from observed increases in cerebral blood flow. A notable exception to this relationship occurs in the striatum. Walton will investigate the activity of dopamine neurons, medium spiny neurons, and dopamine receptors, under conditions that evoke either blood vessel dilatation or constriction in the striatum. She will utilize optogenetic stimulation, synthetically-derived receptors, and receptor antagonist drugs to reveal the mechanisms underlying striatal positive and negative fMRI responses. These studies are important for the accurate interpretation of BOLD fMRI signals from brain regions with atypical hemodynamic responses.
Neuronal population dynamics within and across cortical areas Doiron, Brent UNIVERSITY OF PITTSBURGH AT PITTSBURGH 2018 RFA-EB-17-005 Active
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Drs. Doiron, Smith, and Yu will develop a method that combines large-scale network modeling, large-scale neural recordings, and neural population analyses to understand the key network principles that drive behavior. The team proposes to validate their methods using data recorded from macaque prefrontal cortex to the visual area, V4. A toolkit called Balance BEAM (Brains, Experiments, Analysis and Models), implemented in Matlab, will include a graphical user interface for designing balanced, optimized network models. If successful, Balance BEAM will allow future users to better research how neural circuits give rise to transient activity, steady-state activity, and neural variability.

Next-Generation Calcium Imaging Analysis Methods Paninski, Liam M Columbia Univ New York Morningside 2016 RFA-EB-15-006 Active
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Tracking the flow of calcium ions into and out of neurons is a good way to monitor the simultaneous activity of many neurons with single-cell resolution. The proliferation of high-resolution, high-throughput calcium imaging equipment is generating enormous 2D and 3D datasets that are challenging to interpret. Paninski and his colleagues are working on innovative statistical and computational approaches to make sense of those imaging data and to combine them with other types of data, such as those from multielectrode arrays. The proposed analytical methods will be scalable, modular, and extensible, providing flexibility to users and developers.
Nonlinear Causal Analysis of Neural Signals Sejnowski, Terrence J University Of California, San Diego 2018 RFA-EB-17-005 Active
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Understanding how neural signals from different parts of the brain impact each other over time could help reveal differences in information flow during typical and abnormal cortical states. Dr. Sejnowski proposes to develop a new algorithm for the causal analysis of time-series data called cross-dynamical Delay Differential Analysis (CD-DDA), for modeling time- series data. The work should extend CD-DDA to identify causally- connected brain network phenomena, using simulated Hodgkin-Huxley network models and electrocorticography recordings from epilepsy patients. Though this project uses electrocorticography data to validate CD- DDA, the tool can be applied to calcium recordings from single neurons, voltage sensitive dye recordings, local field potentials, EEG, MEG, and fMRI data. These analytical methods could help future users uncover the influence of information flow across cortical areas on activity of different neuronal populations.

Novel Bayesian linear dynamical systems-based methods for discovering human brain circuit dynamics in health and disease Menon, Vinod Stanford University 2016 RFA-EB-15-006 Active
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It is currently not known how dynamic patterns of brain activity are transformed into cognition, emotion, perception and action in both health and disease in humans. Menon and his colleagues plan to develop novel algorithms for identifying dynamic functional networks in the brain and characterizing network interactions between brain regions involved in cognitive tasks, translating their studies from in vivo rodent data to humans. The researchers’ algorithms will facilitate rigorous investigations of brain dynamics that support critical cognitive functions and significantly advance the understanding of dynamic processes underlying human brain function and dysfunction. As an example, one of the major goals of the project will be using the newly created algorithms to investigate aberrant functional circuits associated with cognitive impairments in Parkinson’s disease.

NWB:N: A Data Standard and Software Ecosystem for Neurophysiology Ng, Lydia Lup-ming Ruebel, Oliver (contact) University Of Calif-lawrenc Berkeley Lab 2018 RFA-MH-17-256 Active
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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.

OpenNeuro: An open archive for analysis and sharing of BRAIN Initiative data Poldrack, Russell A Stanford University 2018 RFA-MH-17-255 Active
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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.

RAVE: A New Open Software Tool for Analysis and Visualization of Electrocorticography Data Beauchamp, Michael S Baylor College Of Medicine 2018 RFA-MH-17-257 Active
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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.

Real-time statistical algorithms for controlling neural dynamics and behavior Park, Il Memming (contact) Pillow, Jonathan William State University New York Stony Brook 2018 RFA-EB-17-005 Active
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High-throughput technologies help researchers understand how neural systems perform the computations that underlie perception, cognition, and behavior, but data obtained through simultaneous recordings from large groups of neurons and fine-grained behavioral tasks create a major bottleneck in data collection and analysis. Drs. Park and Pillow intend to develop statistical tools for tracking internal states of the brain that are not directly measurable from observation of behavior and neural signals. To uncover neuronal computations required for behavioral learning, the group will develop methods for tracking and enhancing the evolution of internal brain states during learning, generating optimal stimuli corresponding to those states to perturb or correct behavior. Subsequent evolution of this work could improve clinical tracking and intervention of neurological disorders with a behavioral component, like Parkinson’s disease. The effort will result in a real-time tool for tracking internal brain and behavioral states, applicable for both basic neuroscience research and clinical applications.

Recombinant Immunolabels for Nanoprecise Brain Mapping Across Scales Trimmer, James UNIVERSITY OF CALIFORNIA AT DAVIS 2018 RFA-NS-18-005 Active
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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.

Repetitive transcranial ultrasound stimulation for modulating brain rhythms Dmochowski, Jacek City College Of New York 2018 RFA-DA-17-022 Active
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Neural oscillations, which are temporal activity patterns in the brain, are recognized as fundamental to the brain’s information processing. Dmochowski and colleagues will evaluate the safety and efficacy of a new form of transcranial ultrasound stimulation (TUS), in which ultrasonic waves will modulate the activity of neural circuits with enhanced precision and specificity (on the order of millimeters). The team will determine whether TUS could be used to modify neural oscillations. This research may contribute foundational knowledge needed for development of new, non-surgical, ultrasonic treatments for disorders associated with abnormal brain rhythms, such as schizophrenia, Parkinson’s, and epilepsy.
Resource for Multiphoton Characterization of Genetically-Encoded Probes Drobizhev, Mikhail MONTANA STATE UNIVERSITY - BOZEMAN 2018 RFA-NS-18-005 Active
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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.

Revealing the connectivity and functionality of brain stem circuits Berg, Darwin K Deschenes, Martin Freund, Yoav Shai Goulding, Martyn D Kleinfeld, David (contact) Knutsen, Per M University Of California San Diego 2014 RFA-NS-14-009 Complete
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Dr. Kleinfeld and his colleagues will use a variety of tools and techniques to create detailed maps of circuits in the brainstem, the region that regulates many life-sustaining functions such as breathing and swallowing, and match the circuits to actions they control.
SABER: Scalable Analytics for Brain Exploration Research using X-Ray Microtomography and Electron Microscopy Gray Roncal, William R Johns Hopkins University 2017 RFA-MH-17-257 Active
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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.

Taking DISCO Live: Dual pathway Imaging of Striatal Circuit Output in vivo Calakos, Nicole Duke University 2018 RFA-DA-17-022 Active
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Physicians currently use therapies that satisfactorily target the basal ganglia to treat movement disorders, but more effective treatments elude clinical implementation due to major gaps in our understanding of functional principles for basal ganglia circuits. Calakos and colleagues will apply in vivo electrophysiological recording, optical activity imaging, and optogenetics to study plasticity of basal ganglia circuitry. The team will develop an approach to image striatal projection neuron activity in the basal ganglia in mice during the formation of a habitual behavior. They will then monitor and manipulate the relative timing-to-fire between two classes of striatal projection neurons and test the behavioral consequences. The knowledge and methodology gained from this project could help reveal new mechanisms for striatal plasticity, which may inform future therapeutic targets for movement and neuropsychiatric disorders.
Technologies for spatiotemporally precise & closed-loop control of selected neurons to prevent epileptic seizures Ahmed, Omar Jamil University Of Michigan 2016 RFA-MH-16-725 Complete
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Although focal seizures start in subsets of neurons in specific brain regions, most anti-epileptic drugs are non-specific in their actions. Further, although optogenetics can be used to modulate cells expressing a specific genetic marker, those targeted cells may still have varying behavioral or clinical roles. When combined with two-photon imaging, recent innovations in digital holography using spatial light modulators (SLM) now make it possible to target visually-selected neurons for optogenetic interventions. Dr. Ahmed’s team will employ an SLM photoactivation module, which was newly developed by Bruker, to understand single neuron dynamics during seizures. The team will also implement closed-loop control of SLM-based optogenetics in mice to terminate seizures in real time by targeting a minimal number of neurons. The team will share this new software with the wider community.
The Application of Generalized Linear Models to Calcium Imaging Data for Optimal High-Dimensional Receptive Field Estimation and Identification of Latent Network Dynamics Keeley, Stephen L Princeton University 2017 RFA-MH-17-250 Active
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Dr. Keeley plans to develop and make publically available an efficient and flexible statistical framework to guide analysis of calcium imaging data, extending researchers’ ability to track the activity of hundreds or thousands of neurons at various spatial scales.
The biophysics and potential cell-type selectivity of acoustic neuromodulation Shoham, Shy NEW YORK UNIVERSITY SCHOOL OF MEDICINE 2018 RFA-NS-18-018 Active
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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.

The Brainstorm Project: A Collaborative Approach to Facilitating the Neuroethics of Bioengineered Brain Modeling Research Hyun, Insoo Case Western Reserve University 2018 RFA-MH-18-500 Active
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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.

The role of patterned activity in neuronal codes for behavior Maunsell, John Hr University Of Chicago 2014 RFA-NS-14-009 Complete
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Dr. Maunsell's team will explore how large populations of neurons process visual information, using a newly developed light stimulation technique to induce brain cell activity in the visual cortex of mice.
Toward a Theory for Macroscopic Neural Computation Based on Laplace Transform Howard, Marc W Boston University (charles River Campus) 2016 RFA-EB-15-006 Active
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Behavioral and cognitive experiments suggest that a number of sensory processes follow Weber-Fechner scaling, which states that as the intensity of an actual stimulus increases linearly, the intensity of our perception increases only logarithmically. Yet, how the brain transforms linear changes in stimuli intensity to logarithmic changes in perception is not entirely understood. Nor is it known whether other cognitive processes like memory also follow some form of Weber-Fechner scaling. Howard and his colleagues plan to develop a theoretical means for understanding logarithmic sampling by sensory systems in the interpretation of neural data. The hope is that this understanding will provide insight into basic neurophysiological processes and how the brain both represents the past and predicts the future, which can be relevant to diseases involving memory loss and disturbances in health-related decision making.
Towards a Complete Description of the Circuitry Underlying Memory replay. Soltesz, Ivan University Of California-irvine 2014 RFA-NS-14-009 Complete
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Dr. Soltesz's team will combine computer brain modeling and large-scale recordings of hundreds of neurons to understand how the brain generates sharp-wave-ripples, a neuronal activity pattern essential for learning and memory.
Unveiling the mechanisms of ultrasound neuromodulation via spatially confined stimulation and temporally resolved recording Cheng, Ji-Xin BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) 2018 RFA-NS-18-018 Active
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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.

Vertically integrated approach to visual neuroscience: microcircuits to behavior Euler, Thomas Huberman, Andrew D Meister, Markus Seung, Hyunjune Sebastian (contact) Wong, Rachel O Princeton University 2014 RFA-NS-14-009 Complete
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Dr. Seung and colleagues Thomas Euler (U Tübingen), Andrew Huberman (UC San Diego), Markus Meister (Caltech), and Rachel Wong (UW Seattle) will use state-of-the-art genetic, electrophysiological, and imaging tools to map the connectivity of the retina, the light-sensing tissue in the eye. The goal is to delineate all the retina's neural circuits and define their specific roles in visual perception and behavior.