Kavli Blog

Videocast will be available for the open sessions of the upcoming NIH BRAIN Initiative Neuroethics Working Group on Monday, August 19, 2019 and the Multi-Council Working Group on Tuesday, August 20, 2019.

On Monday, August 19, 2019, the Neuroethics Working Group (NEWG) to the National Institutes of Health (NIH) Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative® will be holding its eighth meeting. The NEWG helps ensure that neuroethics is fully integrated into the BRAIN Initiative. The August meeting will feature a presentation from Dr. John Besley (Michigan State University) on strategic science communication and engagement, as well as presentations from BRAIN investigators who are conducting research on the ethical implications of advancements in neurotechnology and neuroscience. Speakers will include Dr. Winston Chiong (UCSF; NEWG Member), Dr. Cynthia Kubu (Cleveland Clinic), Dr. Gabriel Lazaro-Munoz (Baylor College of Medicine), and Dr. Kate MacDuffie (University of Washington).

The open session will begin at approximately 10:30 AM Eastern Time and can be viewed via videocast. For more information, please view the meeting agenda; a meeting summary and archived videocast will be made available later.

The following day, on Tuesday, August 20, 2019, the NIH BRAIN Initiative Multi-Council Working Group (MCWG) will convene to discuss the current state of the BRAIN Initiative and its future. The group includes representatives from each of the 10 Institutes and Centers that contribute to the NIH BRAIN Initiative (who may also serve as a liaison to an Advisory Council), with additional at-large members appointed to supplement the working group’s expertise. In addition, the working group includes ex officio members from DARPA, FDA, IARPA, and NSF — who comprise NIH’s federal partners involved in the BRAIN Initiative. The August meeting will feature updates on BRAIN Initiative programs at DARPA from Dr. Alfred Emondi and NSF from Dr. Sridhar Raghavachari, as well as presentations from Dr. Bruce Tromberg (Director of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) at NIH), and Dr. Yael Niv (Princeton University; MCWG member). NIH program staff will also provide a portfolio presentation on cell census and cell- and circuit-specific tool efforts.

The open session will take place from approximately 9:00 AM – 1:50 PM Eastern Time and can be viewed via videocast. For more information, please view the meeting agenda; a meeting summary and archived videocast will be made available later.

Noninvasive neuroimaging technology for BCI neuroprosthetics … Genetically-encoded voltage indicator ArcLight maps synaptic activity … Exploring alienation effects of DBS treatment for depression …

Implementing noninvasive neuroimaging technology for BCI neuroprosthetic device control

Brain-computer interfaces (BCI) show promise as therapeutic clinical tools, for example, using signals acquired from intracortical implants to control robotic devices, such as neuroprosthetics. However, invasive recording implants require extensive medical expertise, including high-risk surgical techniques, and are extremely expensive, limiting their use and thus their potential societal impact. While noninvasive counterparts would be ideal in this sense, noninvasive BCI devices have been less effective in capturing and decoding neural signals to the degree needed for functional use. To take on this challenge, Dr. Bin He and his team at Carnegie Mellon University have improved both the user learning (“brain” component) and machine learning (“computer” component) of noninvasive  BCI. In this study, the team used electroencephalography (EEG) recordings, with real-time electrical source imaging (ESI)  and new training task strategies to teach individuals to control a cursor and robotic arm in 2D solely with an EEG cap. The investigators created two separate training tasks to improve use of a BCI device, asking healthy participants to use motor imagination skills to control a virtual cursor while engaging with a target. One group participated in a traditional BCI discrete trials (DT) task, where they simply had to move a cursor to a static target. The other group participated in a novel, more realistic continuous pursuit (CP) task, using the cursor to chase a continuous, randomly moving target. Analyzing their results, the researchers found that DT training improved performance by 60%. However, the CP task improved performance by 500%, due to its ability to gauge a user’s BCI proficiency and provide more efficient, personalized training. Finally, the researchers tested the practicality of these results by asking participants trained on the CP task to move a robotic arm in 2D instead of a virtual cursor. This task showed a similarly successful performance, indicating a smooth transition from control of a virtual object to a real-world device. Continued improvements in this technology could allow greater access to BCI prosthetics for a wider variety of people with neurological disorders, improving clinical outcomes and restoring once-lost bodily functions without the need for invasive intracortical BCIs.

EEG-based control of a robotic arm. (Left) Experimental set-up, showing a participant controlling the robotic arm. Decoding the participant’s EEG signals of motor imagination allowed for mental control of the robotic arm, in order to chase a randomly moving target in a continuous pursuit task. (Right) Position, error, and overall performance of the robotic arm, based on decoded EEG signals, compared to the movement of the target.

Using the genetically-encoded voltage indicator ArcLight to map excitatory and inhibitory synaptic activity

Many research experiments currently monitor neuronal activity through calcium imaging, using the concentration of calcium in a cell as an indirect marker for synaptic activity. While this technology has greatly improved scientists’ imaging capabilities, especially through the use of genetically-encoded calcium sensors that are restricted to specific cell types, challenges remain. Calcium imaging provides an indirect and relatively slow measure of synaptic transmission, so it cannot distinguish the nuances of subthreshold or inhibitory changes, ligand-based influx, or spontaneous activity. Dr. Lawrence Cohen and colleagues at the Yale School of Medicine and the Korea Institute of Science and Technology have been investigating genetically-encoded voltage indicators (GEVIs) as a more direct approach to map excitatory and inhibitory synaptic connections among populations of neurons. The researchers have expanded research on a previously-developed GEVI, called ArcLight. Applying this imaging technology in the CA1 region of the mouse hippocampus in brain slices, they sought to improve resolution and reduce noise in a region that has known excitatory and inhibitory circuits. To achieve this, the investigators created genetic modifications in mice in order to either encode ArcLight in an experimental group, or encode a calcium sensor, GCaMP6f, in a control group for comparison. Fluorescence changes in ArcLight successfully modeled the characteristic pattern of voltage change caused by action potentials, with initial depolarization followed by hyperpolarization, unlike GCaMP6f. This demonstrated that ArcLight could provide a more robust signal, as it was able to represent both excitatory and inhibitory inputs, in ways consistent with the known circuitry of the region. Following this, the researchers confirmed that these changes were due to voltage changes in the cell, rather than spontaneous synaptic activity. Contrastingly, the activity of GCaMP6f could not represent inhibitory inputs or hyperpolarization and had a much slower flux and response time. Collectively, these results demonstrate a viable approach to mapping neural circuitry, combining structural mapping, which alone does not confirm functional connections, with functional imaging with GEVIs. Future advances in this technology bring hope of creating a full connectome map of the brain, which could improve our understanding of how circuits function, and how their dysfunction impacts human health.

Comparison of activity between ArcLight- and GCaMP6f-expressing neurons. (Top) Representation of fluorescence changes in ArcLight-expressing neurons in response to electric stimulation. ArcLight-expressing neurons show a quick depolarization and hyperpolarization, characteristic of an action potential. These effects are blocked using specific receptor blockers and neurotoxins, demonstrating that the activity of ArcLight responds specifically to voltage change, not spontaneous activity. (Bottom) Representation of fluorescence changes in GCaMP6f-expressing neurons in response to electric stimulation. GCaMP6f-expressing neurons show a slower depolarization without hyperpolarization, demonstrating an inability to represent inhibitory inputs. This is further demonstrated by control experiments, which show no unique responses to blockers for inhibitory signal receptors.

Exploring the possible alienating effects of DBS treatment for depression

Aligning with the BRAIN Initiative’s vision of advancing novel neurotechnologies and their role in medicine, Deep Brain Stimulation (DBS) has proven effective for certain neurological disorders, and has shown some promise as a treatment for several other neurological and psychiatric conditions. While DBS has shown potential in treating symptoms of depression, previous research has identified possible ethical concerns. For example, some studies have reported patients feeling disconnected from many aspects of their life when considering their states before and after treatment, experiencing alienation and estrangement. As a result, Dr. Gabriel Lazaro-Munoz and his team at Baylor College of Medicine aimed to study DBS patients’ experiences of alienation following treatment. In their analysis, the researchers made the distinction between alienation from self and alienation from other objects, such as one’s work and personal relationships. They found that many patients were unable to identify with themselves or felt disconnected from targeted aspects of their identity or agency, as a form of self-alienation after DBS treatment for depression. These patients claimed that they “don’t recognize” themselves, felt “like a machine,” or believed their “body is cured” but their “mind is still sick” after DBS treatment, experiencing mental states not characteristic of themselves. Beyond this, some patients failed to identify with previously enjoyed work or relationships, with varying degrees of withdrawal. Finally, the researchers identify patients with depression as a rich source for future evaluations of the relationship between DBS treatment and alienation. Since many symptoms of depression, such as anhedonia, dysphoria, and social withdrawal, can be described as varying degrees of alienation from different objects, patients who have undergone DBS can understand the differences between their feelings of alienation before and after treatment, allowing researchers to distinguish between the specific effects of DBS and general symptoms of depression. Dr. Lazaro-Munoz and his colleagues call for continued research, since alienation can deprive an individual of important improvements in their quality of life, despite potential successful clinical outcomes. While the increased utilization and effectiveness of neurotechnologies like DBS in medicine hold exciting therapeutic potential for millions of people suffering from brain-based disorders, this group has highlighted important neuroethical considerations that can inform future medical research, policy, and practice.

The BRAIN Initiative Alliance (BIA) is hosting Tools & Tech: A BRAIN Initiative Alliance Social at the 2019 Society for Neuroscience Annual Meeting. This event will allow toolmakers to interact with the research community, as well as network with one another. The BIA is seeking neurotechnology developers and engineers to feature at the event – applications should be received online by August 26, 2019.

Interested in cutting-edge neuroscience tools & technology? Come “talk shop” with some of the leading toolmakers funded by the US BRAIN Initiative! Through the BRAIN Initiative, a broad array of these tools and resources are becoming available to the research community. To promote neuroscience tool and technology dissemination, the BRAIN Initiative Alliance (BIA) is hosting Tools & Tech: A BRAIN Initiative Alliance Social at the 2019 Society for Neuroscience Annual Meeting. This is a venue for toolmakers to interact with the research community, as well as network with one another.

The BIA is seeking neurotechnology developers and engineers to feature at this event. If you are interested in participating as a featured toolmaker, fill out this short application form by August 26, 2019. Selected toolmakers will be notified in early September.

Note: In order to be eligible, your tool must be ready for the community to use AND you must be available during the date and time of the social (Sunday, October 20, 2019 from 6:30 PM – 8:30 PM). Tools or technologies that are appropriate for sharing at this event are typically in a state ready for distribution and/or use by multiple other members of the research community (i.e., at or beyond a pre-commercialization stage or have limited commercial distribution).

This informal and interactive social will take place on Sunday, October 20, 2019 from 6:30 PM – 8:30 PM in Regency Ballroom CD of the Hyatt Regency McCormick Place. The event is open to all – you do not need to be registered for SfN to attend this event. Food and beverage will be provided.

The BRAIN Initiative Alliance (BIA) is comprised of federal and non-federal members: the Allen Institute for Brain Science, Food and Drug Administration (FDA), Intelligence Advanced Research Projects Activity (IARPA), IEEE Brain, The Kavli Foundation, National Institutes of Health (NIH), National Science Foundation (NSF) and the Simons Foundation. Its mission is to coordinate and facilitate communications from its members related to the BRAIN Initiative. The Alliance seeks to inform and engage the public and the scientific community about scientific successes emerging from the BRAIN Initiative, and opportunities for further discovery.

Two new and four re-issued funding opportunity announcements (FOAs) target development of next-generation human cell-derived assays, secondary analysis and archiving of data, next-generation human brain imaging, improved resolution for non-invasive neuromodulation, and tools for high-throughput microconnectivity analysis.

NIH announces two new and four re-issued funding opportunity announcements (FOAs) that are requests for applications (RFAs) for the BRAIN Initiative. One new RFA targets development of next-generation human cell-derived assays to facilitate analysis of higher-order functional deficits relevant to disease. A second new RFA encourages secondary analysis and archiving of BRAIN Initiative data. Four re-issued RFAs target next-generation human brain imaging, improved precision in non-invasive neuromodulation, and tools to facilitate high-throughput microconnectivity analysis.

RFA-MH-20-140 [Research to Develop and Validate Advanced Human Cell-Based Assays To Model Brain Structure and Function (R01 Clinical Trial Not Allowed)]

The purpose of this FOA is to stimulate basic research to develop next-generation human cell-derived assays with improved fidelity to complex human brain, spinal cord, and/or organ circuit physiology, which will ultimately facilitate analysis of higher order functional deficits relevant to complex nervous system diseases. This includes technologies that do not rely on the use of human fetal tissue, as described in NOT-OD-19-042. This FOA is distinct from others that focus on optimization and scalability of assays for compound screening, although projects could have utility for late stage evaluation of drug efficacy and toxicity. Supported projects will be expected to enable future studies of complex nervous system development, function and aging in healthy and disease states.

Applications for this RFA are due November 1, 2019.

RFA-MH-20-120 [Secondary Analysis and Archiving of BRAIN Initiative Data (R01 Clinical Trial Not Allowed)]

This FOA aims to significantly advance new discoveries and accelerate the pace of research of the BRAIN Initiative through harnessing big data and machine learning opportunities. The BRAIN Initiative and the neuroscience field are generating massive and diverse research data across different modalities, spatiotemporal scales, and species in efforts to advance our understanding of the brain. As such, there have been significant investments in the development of an infrastructure to make data available to the research community in a useful way. This FOA encourages secondary analysis of the large amounts of existing data related to the BRAIN Initiative. The data do not need to be held in one of the funded BRAIN Initiative data archives, but the data must be held in a data archive that is readily accessible to the research community. Support will be provided for innovative analysis of relevant existing datasets using conventional or novel analytic methods, data science techniques, and machine learning approaches. Support may also be requested to prepare and submit existing data into any of the BRAIN Initiative data archives. Analyzed data, models, and analytical tools generated under this FOA are expected to be deposited into an appropriate data archive.

The next receipt date for these applications is September 6, 2019.

RFA-EB-19-001 [re-issue of EB-17-003; Proof of Concept Development of Early Stage Next Generation Human Brain Imaging (R01 Clinical Trial Not Allowed)]

This FOA aims to support early stage development of entirely new and novel noninvasive human brain imaging technologies and methods that will lead to transformative advances in our understanding of the human brain. The FOA solicits unusually bold and potentially transformative approaches and supports small-scale, proof-of-concept development based on exceptionally innovative, original, and/or unconventional concepts. The goal is to accelerate early stage development of promising and entirely new concepts that require some initial development and testing before full-scale tool development and widespread use. Therefore, applications should focus on innovative approaches and proof-of-principle initial stage development for breakthrough, noninvasive imaging technology to measure human brain processes in ways that are currently unachievable via imaging technologies in real time.

Applications for this RFA are due September 3, 2019.

RFA-EB-19-002 [re-issue of EB-17-004; Development of Next Generation Human Brain Imaging Tools and Technologies (U01 Clinical Trial Not Allowed)]

This FOA aims to support full development of entirely new or next-generation noninvasive human brain imaging tools and methods that will lead to transformative advances in our understanding of the human brain. The goal of this FOA is to develop novel and transformative imaging technologies beyond the proof-of-concept stage for novel, noninvasive imaging of human brain processes in ways that are currently unachievable in healthy persons. The intended outcome is bold and high-impact tools and methods for human neuroscience that can be broadly disseminated for use in healthy people. This FOA seeks innovative applications that are ready for full-scale development of breakthrough technologies with the intention of delivering working tools, and represents the second stage of the tool/technology development effort that started with RFA-MH-14-217 and RFA-MH-15-200.

The next receipt date for these applications is September 3, 2019.

RFA-MH-20-310 [re-issue of MH-17-240; Non-Invasive Neuromodulation – New Tools and Techniques for Spatiotemporal Precision (R01 Clinical Trial Optional)]

Non-invasive neuromodulation devices are rapidly becoming one of the classes of tools considered for the treatment and diagnosis of brain disorders and could become an alternative or an adjunct to various existing therapies. Non-invasive devices can be defined as those that do not require surgery and do not penetrate the brain parenchyma. While promising, these methods still face obstacles of limited spatial and temporal resolution. This FOA solicits grant applications in two related but distinct areas. The first area is in the development and testing of novel tools and methods of neuromodulation that go beyond existing stimulation methods, overcoming issues in resolution. The second distinct area is the significant improvement of existing stimulation methods.

The next receipt date for these applications is September 3, 2019.

RFA-MH-20-135 [re-issue of MH-18-505; Tools to Facilitate High-Throughput Microconnectivity Analysis (R01 Clinical Trial Not Allowed)]

The purpose of this FOA is to encourage applications that will develop and validate tools and resources to facilitate the detailed analysis of brain microconnectivity. Novel and augmented techniques are sought that will be broadly accessible to the community for the interrogation of microconnectivity in healthy and diseased brains. Development of technologies that will significantly drive down the cost of connectomics would enable routine mapping of the microconnectivity through longitudinal studies, or to compare normal and pathological tissues across individuals to assess variability. Advancements in both electron microscopy (EM) and super resolution light microscopic approaches are sought, as are applications that propose to develop approaches that break through existing technical barriers to substantially improve current capabilities. Proof-of-principle demonstrations and/or reference datasets enabling future development are welcome, as are improved approaches for automated segmentation and analysis strategies of neuronal structures in EM images.

The next receipt date for these applications is September 27, 2019.

Please visit our Funding Opportunities page for more details on these and other RFAs for the BRAIN Initiative.

NIH announces two newly-issued Requests for Applications (RFAs). The first RFA seeks to expand existing marmoset colonies for neuroscience research. The second RFA aims to create a Marmoset Coordination Center. Application receipt dates are in October 2019 for both RFAs.

The common marmoset (Callithrix jacchus) has recently emerged as a promising model system to understand the primate brain. In particular, marmoset behavior is similar in many ways to human behavior and the technology for developing transgenic model systems is now possible. However, existing colonies and commercial sources are currently unable to provide enough marmosets for neuroscience research. Further, a recent National Academies workshop noted this current shortage of marmoset resources and suggested the expanded production of, access to, and coordination of marmosets. To address this demand for marmosets in neuroscience research, NIH announces two requests for applications (RFAs) for the BRAIN Initiative.

RFA-MH-20-145 [Marmoset Colonies for Neuroscience Research (U24 Clinical Trials Not Allowed)]

Current marmoset resources in the United States are relatively small, limiting the genetic diversity of the population within the country as a whole. Beyond currently available sources of marmosets, NIH estimates that the neuroscience research community currently needs access to an additional 100-400 marmosets each year. This funding opportunity announcement (FOA) solicits applications to expand existing colonies to provide healthy, well-cared for, and well-documented common marmosets for neuroscience research in the United States. NIH plans to issue one or more awards to institutions with existing colonies. Applicants will be expected to describe their existing marmoset colony and their plans to expand existing infrastructure to accommodate the expanded population of the colony.

Awardees also are expected to participate in and provide health and genetic information to an NIH-Funded Marmoset Coordination Center (see below) to help the community understand the pedigree of individuals in the relatively small captive marmoset population and improve the genetic diversity of that population across multiple colonies. Additionally, while the focus of this FOA is on the distribution of wild-type animals, many in the neuroscience research community are interested in creating and distributing genetically modified marmosets. As applicants plan to expand the access to wild-type marmosets, they should also be considering how that infrastructure might eventually be used to distribute genetically modified marmosets.

Applications for this RFA are due October 3, 2019.

RFA-MH-20-150 [Marmoset Coordination Center (U24 Clinical Trials Not Allowed)]

This FOA solicits applications to create a Marmoset Coordination Center that will support and improve the use of marmosets in neuroscience research. The awardee will be responsible for two separate but related activities.

The first major duty is to coordinate information sharing across marmoset colonies. This coordination may be achieved by developing a digital repository for genomic, pedigree, and event records (date of birth, medical, reproductive history) for captive marmosets. The facility is expected to use the deposited information to assist colonies that have marmosets to improve the health and genetic diversity of the entire US marmoset population. The focus for the effort will begin with marmoset colonies that are in the United States. However, there are significant marmoset colonies in other countries, and so applicants are encouraged (but not required) to extend coordination activities to those colonies. Including foreign marmoset colonies may enable increasing the genetic diversity among the existing colonies in the United States.

The second major duty is to provide information and support to neuroscience researchers who are interested in using marmosets in their research programs. This includes offering advice about the advantages and disadvantages to incorporating marmosets in the research efforts in a laboratory and to support linking neuroscience researchers with colonies that may be able to supply marmosets for a particular research project. The coordination center will act as the centralized resource to manage requests for marmosets from the entire nation’s neuroscience research community.

Applications for this RFA are due October 18, 2019.

Please visit our Funding Opportunities page for more details on these and other RFAs for the BRAIN Initiative.

The thirteenth meeting of the NIH BRAIN Initiative Multi-Council Working Group (MCWG) included discussion of upcoming strategic planning discussions with the NIH Director’s Advisory Committee, as well as consideration of concept clearances for funding opportunities.

On May 16th, 2019, the Multi-Council Working Group (MCWG) to the National Institutes of Health (NIH) Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative® held its thirteenth meeting. The group provides ongoing oversight of the long-term scientific vision of the BRAIN Initiative, in the context of the evolving neuroscience landscape. MCWG members include a liaison to the Advisory Council of each of the 10 Institutes and Centers that contribute to the NIH BRAIN Initiative, with additional at-large members appointed to supplement the working group’s expertise. The group also includes ex officio members from DARPA, FDA, IARPA, and NSF—four of NIH’s federal partners involved in the BRAIN Initiative.

In opening remarks, Dr. Walter Koroshetz, NINDS Director, mentioned the success of the recent BRAIN Initiative Investigators (PI) Meeting, which was held from April 11-13, 2019, with approximately 1600 registrants (a 33% increase over last year). The conference included scientific presentations, poster sessions, a three-part communications workshop, and an inaugural photo and video contest. Read more about the 2019 BRAIN PI meeting here.

Dr. John Maunsell, co-chair of the NIH ACD BRAIN Initiative Working Group (WG) 2.0, provided an update on the group’s Report, which was presented to the Advisory Committee of the NIH Director earlier today, June 14th, 2019. The group lauded the success of the first half of the Initiative, noting that it will be important to continue the momentum of technology development. Examples of areas that will require increased effort include research on artificial technology and theory, distributing the new technologies that have been developed, and public engagement around neuroscience. The WG also identified potential large-scale, transformative projects including pinpointing specific circuits implicated in diseases, mapping the entire mouse brain and determining circuits controlling specific behaviors, and learning how the brain retrieves stored information.

Dr. Jim Eberwine, a member of the BRAIN MCWG and Neuroethics Working Group and co-chair of the BRAIN Neuroethics Subgroup (BNS) of the ACD BRAIN Initiative WG 2.0, provided an overview of the BNS, which has been charged with considering the neuroethical implications that arise from BRAIN Initiative funded research and developing a Neuroethics Roadmap for the BRAIN Initiative, also presented to the ACD on June 14th. The report discusses examples of neuroethics-related issues unique to BRAIN, including unexpected consequences of precise brain circuit manipulation with tools developed via the BRAIN Initiative, and companies needing to be aware of potential neuroethical implications of widespread use of their products.

Three concept clearances were presented to the MCWG. The first concerned the development of alternatives to models of the developing human nervous system. This FOA will focus on creating three-dimensional systems that contain human cells, to improve our understanding of brain development, which is currently restricted due to technical limitations and ethical concerns. Two additional concept clearances on marmoset research were also presented, focusing on the development of genetic tools and technologies to improve our understanding of primate brains, and to develop colonies and coordinate distribution of animals for research.

For more information, please view the MCWG meeting summary and the archived videocast of the meeting.

The Advisory Committee to the NIH Director (ACD) BRAIN Initiative Working Group 2.0 and BRAIN Neuroethics Subgroup (BNS) will present reports of their major findings and analysis during the ACD meeting on June 14, 2019.

After more than a year of hard work and tireless efforts, the Advisory Committee to the NIH Director (ACD) BRAIN Initiative Working Group 2.0 and BRAIN Neuroethics Subgroup will present reports of their major findings and analysis during Day 2 of the ACD Meeting on June 14, 2019, beginning approximately 10:15 AM Eastern Time. The meeting will take place in Wilson Hall of Building 1 on the main NIH campus in Bethesda, Maryland.

Drs. Catherine Dulac (Harvard University) and John Maunsell (University of Chicago), co-chairs of the BRAIN Initiative Working Group 2.0, will present a report of the group’s major findings. The group was charged with providing scientific guidance to the ACD on how best to accomplish the ambitious vision for the BRAIN Initiative first outlined in BRAIN 2025: A Scientific Vision, considering the current state of neuroscience. Through a review of BRAIN progress, workshops and town halls, and opportunities for public input, the Working Group has drafted and will present a report to guide the NIH BRAIN Initiative during the second half of its tenure.

Drs. Jim Eberwine (University of Pennsylvania) and Jeff Kahn (Johns Hopkins University), co-chairs of the BRAIN Neuroethics Subgroup (BNS), will also present a report of their group’s major findings. The BNS was charged with developing a Neuroethics Roadmap for the NIH BRAIN Initiative, characterizing the neuroethical implications that may arise as BRAIN Initiative advancements move forward.

Videocast of the meeting will be broadcast live and archived for later viewing.

Direct speech synthesis using auditory cortex recordings… Three-photon imaging of adult mouse brains through intact skulls… Tensor component analysis for unraveling complex neural circuit dynamics…

Decoding neural activity into kinematic and acoustic features yields accurate human speech waveforms

Many individuals with neurological conditions require alternative devices to communicate with the outside world. While these devices are useful, they are limited in their capacity to transmit information because they require selecting letters one-by-one to spell out words, using residual non-verbal movements or control of a cursor via a brain computer interface. Dr. Edward Chang and colleagues at the University of California San Francisco have now devised a method to decode spoken sentences directly from recorded neural activity in human cortical brain regions at the rate of normal speech. Five participants receiving intracortical monitoring for epilepsy treatment read aloud several hundred sentences while the authors used high-density electrocorticography (ECoG) to record activity in the ventral sensorimotor cortex (vSMC), superior temporal gyrus (STG), and inferior frontal gyrus (IFG) regions of their auditory cortices. Building on their previous findings that brain activity corresponds more closely to movements of the speech articulators than to the sounds, the researchers developed a two-stage decoding schema. The first stage decoded speech articulation – a kinematic feature of speech (e.g., lip, mouth, or tongue movements) – directly from neural activity in the vSMC, STG, and IFG brain regions. In the second stage, the speech encoder-decoder was trained to construct the acoustics of speech (e.g., pitch, voicing, or sound distortions) from the previously estimated kinematic features of recorded sentences. Finally, both decoding stages were integrated to produce an accurate recapitulation of the original spoken sentence. Though the decoder was trained on a specific sentence dataset, it could still decode sentences that were not included in the training data. These results represent one of the first platforms to incorporate all aspects of speech in a language decoding program. Synthesizing speech directly from auditory cortex activity not only allows for audible speech to be decoded in real time but also maintains the natural rate and captures pitch and intonation of human speech, which could be promising for completely paralyzed individuals for whom there are no adequate communication devices. For more information, please view the NIH Press Release on this work.

Synthesizing speech directly from decoded neural activity. (Top) Model for decoding speech from electrodes that recording from vSMC, STG and IFG regions of the auditory cortex. Kinematic and acoustic features were decoded, then synthesized into an acoustic (speech) waveform. (Bottom) Spectrograms represent the similarities in visual representation of frequency output of two spoken sentences, compared to decoded, synthesized speech.

Three-photon imaging of mouse brains through intact skulls shows vast improvements over two-photon imaging

To fully appreciate murine neural networks in normal and diseased states, the natural environment of the brain must be preserved as much as possible. Non-invasive technologies, such as MRI, do not provide single cell resolution, and several routine procedures that make the mouse brain more accessible to live fluorescence imaging, including skull thinning and cranial window implantation, potentially alter the brain microenvironment and communication between cell types. In recent years, two-photon optical imaging (2PM) has been used for deep imaging of in vivo mouse brains, but bone scatters light, limiting resolution and depth. Dr. Chris Xu and colleagues from Cornell and Stanford Universities have now demonstrated that three-photon microscopy (3PM) can overcome these problems and achieve greater imaging depth of mouse brains and decreased background through intact mouse skulls. 3PM achieves greater cortical depths and decreased background over 2PM by using three photons to excite a fluorescent molecule to a higher electronic state versus two. The higher order excitation of 3PM is responsible for an improved signal-to-noise ratio resulting in clear, higher contrast images of uncompromised mouse brains as well as larger fields-of-view. The research team also found that they could reduce skull surface roughness by using a glue that matched the refractive index of the skull, which also insulated the skull from the air and preserved transparency for chronic imaging.  This technology also attains cortical depths greater than 500 μm using conventional dyes and captures calcium activity with high spatiotemporal resolution. Dr. Xu and his team’s work indicates 3PM’s superior functionality at imaging mouse brains through intact mouse skull, paving the way for a better understanding of true and biologically relevant neural states at both the level of structure and function. This work was supported by funding from BRAIN Initiative Alliance members, including the National Science Foundation (NSF’s NeuroNex) and Defense Advanced Research Projects Agency (DARPA).

Imaging of a mouse brain through an intact skull preserves cellular structure and function. A three-dimensional reconstruction of a murine cortical brain column imaged through an intact skull. 3PM was used to excite neurons that are labeled by red fluorescent protein. The scale for depth is set at 0, or right below the skull.

Tensor component analysis generates dimensional reduction of neural circuit dynamics within and across trials

Historically, principal component analysis (PCA) has been used to understand complex and large-scale neural activity data. PCA reduces information on neural dynamics within a trial, rather than across trials. Despite PCA’s utility, neural circuits operate on extremely diverse timescales ranging from milliseconds to weeks, or across trials. A subtype of PCA, known as tensor component analysis (TCA), can reduce information in an unbiased and unsupervised fashion across three separate dimensions: time, trials, and neural activity. This information can provide vast insights into cognitive states. Dr. Surya Ganguli and his team at Stanford University used TCA to forge neural descriptions of artificial learning, rodent navigation, and primate motor learning. The goal of these studies was to generate a simplified understanding of all trials, all neural activity in each trial, and the timescale in which these neural circuits operate in the prefrontal cortex (or artificial modeling thereof). Neural activity of the murine prefrontal cortex was recorded using calcium imaging while mice navigated through a maze. TCA determined that neural circuitries involved in rodent navigation could be characterized as discrete behavioral elements based on starting and ending conditions or whether mice received a reward. To determine if TCA could distinguish discrete from continuous neural data sets, studies using a brain-machine interface were conducted. Learning behavior was quantified while a rhesus macaque operated a continuous computer cursor and electrodes recorded neural activity from the motor cortex. TCA analysis revealed the neural circuitries involved in the macaque’s movement errors as well as those involved in correcting errors. These results suggest that TCA provides an understanding of both neural activity that regulates thoughts and actions (fast), and activity that regulates underlying behavior and learning processes (slow). Dr. Ganguli and colleagues offer an extensive framework for uncovering low-dimensional descriptions of recorded neural information. TCA represents an important and broadly applicable approach to analyze the dynamics of any complex biological system – particularly those that were previously studied in only one dimension.

Two-dimensional learning outputs from a primate brain-machine interface estimated using TCA. (A) Schematic of brain-machine interface. (B) Cyan data points represent the beginning of each trial while magenta points represent the end of each trial. (C) Representation of time taken for macaque to move the cursor to a target. The red line signifies when the visuomotor perturbation was introduced (trial #31). (D) Representation of TCA analysis of the top 50 recorded motor cortex activity spikes, or multiunit spike trains (component #1), time (component #2) and trials (component #3). Component #2 and #3 capture learning after visuomotor perturbations were introduced.

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