Last week in The New Yorker, neuroscientist Gary Marcus (author of Kluge: The Haphazard Evolution of the Human Mind [2009] and co-editor of The Future of The Brain [2014]), wrote a brief review/overview of the fifty-eight-page report on the National Institutes of Health (NIH) vision for the future of neuroscience—the first substantive step in developing President Obama’s BRAIN Initiative.
The report identifies six "themes" that the authors of this preliminary report feel will still be relevant when the working group begin the broader scientific plan detailing a larger vision, timelines, and milestones.
1. Use appropriate experimental system and models.From these six themes, they have identified nine research areas that the National Institutes of Mental Health (NIMH) wish to focus on:
2. Cross boundaries in interdisciplinary collaborations.
3. Integrate spatial and temporal scales.
4. Establish platforms for sharing data.
5. Validate and disseminate technology.
6. Consider ethical implications of neuroscience research.
1. Generate a Census of Cell Types.The model proposed allows no real space for psychology and psychotherapy, which seems to be the point and which serves as a continuation of move to make the brain and neuroscience the singular focus of mental health care.
2. Create Structural Maps of the Brain.
3. Develop New Large-Scale Network Recording Capabilities.
4. Develop A Suite of Tools for Circuit Manipulation.
5. Link Neuronal Activity to Behavior.
6. Integrate Theory, Modeling, Statistics, and Computation with Experimentation.
7. Delineate Mechanisms Underlying Human Imaging Technologies.
8. Create Mechanisms to Enable Collection of Human Data.
9. Disseminate Knowledge and Training.
As Marcus points out, however, "Though some dream of eliminating psychology from the discussion altogether, no neuroscientist has ever shown that we can understand the mind without psychology and cognitive science."
It's an interesting review - and I have included two sections from the report, which is freely available at the links below (as a PDF document).
A Map for the Future of Neuroscience
Posted by Gary Marcus
On Monday, the National Institutes of Health released a fifty-eight-page report on the future of neuroscience—the first substantive step in developing President Obama’s BRAIN Initiative, which seeks to “revolutionize our understanding of the human mind and uncover new ways to treat, prevent, and cure brain disorders like Alzheimer’s, schizophrenia, autism, epilepsy, and traumatic brain injury.” Assembled by an advisory panel of fifteen scientists led by Cori Bargmann, of Rockefeller University, and William Newsome, of Stanford, the report assesses the state of neuroscience and offers a vision for the field’s future.
The core challenge, as the report puts it, is simply that “brains—even small ones—are dauntingly complex”:
Information flows in parallel through many different circuits at once; different components of a single functional circuit may be distributed across many brain structures and be spatially intermixed with the components of other circuits; feedback signals from higher levels constantly modulate the activity within any given circuit; and neuromodulatory chemicals can rapidly alter the effective wiring of any circuit.To tackle the brain’s immense complexity, the report outlines nine goals for the initiative. No effort to study the brain is likely to succeed without devoting serious attention to all nine, which range from creating structural maps of its static, physical connections to developing new ways of recording continuous, dynamic activity as it perceives the world and directs action. A less flashy, equally critical goal is to create a “census” of the brain’s basic cell types, which neuroscientists haven’t yet established. (The committee also devotes attention to ethical questions that could arise, such as what should happen if neural enhancement—the use of engineering to alter the brain—becomes a realistic possibility.)
The most important goal, in my view, is buried in the middle of the list at No. 5, which seeks to link human behavior with the activity of neurons. This is more daunting than it seems: scientists have yet to even figure out how the relatively simple, three-hundred-and-two-neuron circuitry of the C. Elegans worm works, in part because there are so many possible interactions that can take place between sets of neurons. A human brain, by contrast, contains approximately eighty-six billion neurons.
To progress, we need to learn how to combine the insights of molecular biochemistry, which has come to dominate the lowest reaches of neuroscience, with the study of computation and cognition, which have moved to the forefront of fields such as cognitive psychology. (Though some dream of eliminating psychology from the discussion altogether, no neuroscientist has ever shown that we can understand the mind without psychology and cognitive science.) The key, emphasized in the report, is interdisciplinary work shared as openly as possible: “The most exciting approaches will bridge fields, linking experiment to theory, biology to engineering, tool development to experimental application, human neuroscience to non-human models, and more.”
Perhaps the least compelling aspect of the report is one of its justifications for why we should invest in neuroscience in the first place: “The BRAIN Initiative is likely to have practical economic benefits in the areas of artificial intelligence and ‘smart’ machines.” This seems unrealistic in the short- and perhaps even medium-term: we still know too little about the brain’s logical processes to mine them for intelligent machines. At least for now, advances in artificial intelligence tend to come from computer science (driven by its longstanding interest in practical tools for efficient information processing), and occasionally from psychology and linguistics (for their insights into the dynamics of thought and language). Only rarely do advances come from neuroscience. That may change someday, but it could take decades.
It would have been useful for the report to include more discussion of the Allen Institute for Brain Science, which has its own half-billion-dollar budget for neuroscience, provided by its founder, Paul Allen. Whereas the BRAIN Initiative is still only a proposal, the A.I.B.S. has, for the past decade, been building brain maps and sharing them freely. Because its recent proposal for a series of “brain observatories,” described last year in Nature, presaged Obama’s BRAIN Initiative in many ways, it arguably deserves more comment and analysis. (Full disclosure: I’m speaking at the Institute next week.)
But these are quibbles. There are plenty of reasons to invest in basic neuroscience, even if it takes decades for the field to produce significant advances in artificial intelligence. If the projects outlined in the new report deliver half of what they intend, they will revolutionize both science and medicine by giving us the first clear understanding of the circuits that underlie brain function. With those discoveries, we may see the first major advances in decades in the treatment of mental illnesses and brain injuries. More than that, we stand an excellent chance of gaining a significantly richer understanding of ourselves.
Gary Marcus is a professor of psychology at N.Y.U., the author of “Guitar Zero,” and a co-editor of the forthcoming book “The Future of The Brain: Essays by the World’s Leading Neuroscientists.”
Illustration by Nishant Choksi.
The nine research areas identified below, in the Executive Summary of the new Interim Report, are the only types of research Thomas Insel and NIMH will fund going forward. I think this is unfortunate.
Below this section, I am including one other section that should be interesting to those working in this field, the vision and philosophy of the BRAIN Initiative.
Advisory Committee to the NIH Director - Interim Report
The BRAIN Initiative: Brain Research through Advancing Innovative Neurotechnologies
September 16, 2013
INTERIM REPORT – EXECUTIVE SUMMARY
On April 2, 2013, President Obama launched the BRAIN Initiative to “accelerate the development and application of new technologies that will enable researchers to produce dynamic pictures of the brain that show how individual brain cells and complex neural circuits interact at the speed of thought.” In response to this Grand Challenge, NIH convened a working group of the Advisory Committee to the Director, NIH, to develop a rigorous plan for achieving this scientific vision. To ensure a swift start, the NIH Director asked the group to deliver an interim report identifying high priority research areas that should be considered for the BRAIN Initiative NIH funding in Fiscal Year 2014. These areas of priority are reflected in this report and, ultimately, will be incorporated into the working group’s broader scientific plan detailing a larger vision, timelines and milestones.
The goals voiced in the charge from the President and from the NIH Director are bold and ambitious. The working group agreed that in its initial stages, the best way to enable these goals is to accelerate technology development, as reflected in the name of the BRAIN Initiative: “Brain Research through Advancing Innovative Neurotechnologies.” The focus is not on technology per se, but on the development and use of tools for acquiring fundamental insight about how the nervous system functions in health and disease. In addition, since this initiative is only one part of the NIH’s substantial investment in basic and translational neuroscience, these technologies were evaluated for their potential to accelerate and advance other areas of neuroscience as well.
In analyzing these goals and the current state of neuroscience, the working group identified the analysis of circuits of interacting neurons as being particularly rich in opportunity, with potential for revolutionary advances. Truly understanding a circuit requires identifying and characterizing the component cells, defining their synaptic connections with one another, observing their dynamic patterns of activity in vivo during behavior, and perturbing these patterns to test their significance. It also requires an understanding of the algorithms that govern information processing within a circuit, and between interacting circuits in the brain as a whole. With these considerations in mind, the working group consulted extensively with the scientific community to evaluate challenges and opportunities in the field. Over the past four months, the working group met seven times and held workshops with invited experts to discuss technologies in chemistry and molecular biology; electrophysiology and optics; structural neurobiology; computation, theory, and data analysis; and human neuroscience (a full list of speakers and topics can be found in Appendix A). Workshop discussions addressed the value of appropriate experimental systems, animal and human models, and behavioral analysis. Each workshop included opportunity for public comments, which were valuable for considering the perspectives of patient advocacy groups, physicians, and members of the lay public.
Although we emphasize that this is an interim report, which will develop with much additional advice before June 2014, certain themes have already emerged that should become core principles for the NIH BRAIN Initiative.
The following research areas are identified as high-priority research areas in FY 2014.
- Use appropriate experimental system and models. The goal is to understand the human brain, but many methods and ideas will be developed first in animal models. Experiments should take advantage of the unique strengths of diverse animal systems.
- Cross boundaries in interdisciplinary collaborations. No single researcher or discovery will crack the brain’s code. The most exciting approaches will bridge fields, linking experiment to theory, biology to engineering, tool development to experimental application, human neuroscience to non-human models, and more, in innovative ways.
- Integrate spatial and temporal scales. A unified view of the brain will cross spatial and temporal levels, recognizing that the nervous system consists of interacting molecules, cells, and circuits across the entire body, and important functions can occur in milliseconds, minutes, or take a lifetime.
- Establish platforms for sharing data. Public, integrated repositories for datasets and data analysis tools, with an emphasis on user accessibility and central maintenance, would have immense value.
- Validate and disseminate technology. New methods should be critically tested through iterative interaction between tool-makers and experimentalists. After validation, mechanisms must be developed to make new tools available to all.
- Consider ethical implications of neuroscience research. BRAIN Initiative research may raise important issues about neural enhancement, data privacy, and appropriate use of brain data in law, education and business. Involvement of the President’s Bioethics Commission and neuroethics scholars will be invaluable in promoting serious and sustained consideration of these important issues. BRAIN Initiative research should hew to the highest ethical and legal standards for research with human subjects and with non-human animals under applicable federal and local laws.
#1. Generate a Census of Cell Types. It is within reach to characterize all cell types in the nervous system, and to develop tools to record, mark, and manipulate these precisely defined neurons in vivo. We envision an integrated, systematic census of neuronal and glial cell types, and new genetic and non-genetic tools to deliver genes, proteins, and chemicals to cells of interest. Priority should be given to methods that can be applied to many animal species and even to humans.
#2. Create Structural Maps of the Brain. It is increasingly possible to map connected neurons in local circuits and distributed brain systems, enabling an understanding of the relationship between neuronal structure and function. We envision improved technologies—faster, less expensive, scalable—for anatomic reconstruction of neural circuits at all scales, such as molecular markers for synapses, trans-synaptic tracers for identifying circuit inputs and outputs, and electron microscopy for detailed reconstruction. The effort would begin in animal models, but some mapping techniques may be applied to the human brain, providing for the first time cellular-level information complementary to the Human Connectome Project.
#3. Develop New Large-Scale Network Recording Capabilities. We should seize the challenge of recording dynamic neuronal activity from complete neural networks, over long periods, in all areas of the brain. There are promising opportunities both for improving existing technologies and for developing entirely new technologies for neuronal recording, including methods based on electrodes, optics, molecular genetics, and nanoscience, and encompassing different facets of brain activity, in animals and in some cases in humans.
#4. Develop A Suite of Tools for Circuit Manipulation. By directly activating and inhibiting populations of neurons, neuroscience is progressing from observation to causation, and much more is possible. To enable the immense potential of circuit manipulation, a new generation of tools for optogenetics, pharmacogenetics, and biochemical and electromagnetic modulation should be developed for use in animals and eventually in human patients. Emphasis should be placed on achieving modulation of circuits in patterns that mimic natural activity.
#5. Link Neuronal Activity to Behavior. The clever use of virtual reality, machine learning, and miniaturized recording devices has the potential to dramatically increase our understanding of how neuronal activity underlies cognition and behavior. This path can be enabled by developing technologies to quantify and interpret animal behavior, at high temporal and spatial resolution, reliably, objectively, over long periods of time, under a broad set of conditions, and in combination with concurrent measurement and manipulation of neuronal activity.
#6. Integrate Theory, Modeling, Statistics, and Computation with Experimentation. Rigorous theory, modeling and statistics are advancing our understanding of complex, nonlinear brain functions where human intuition fails. New kinds of data are accruing at increasing rates, mandating new methods of data analysis and interpretation. To enable progress in theory and data analysis, we must foster collaborations between experimentalists and scientists from statistics, physics, mathematics, engineering and computer science.
#7. Delineate Mechanisms Underlying Human Imaging Technologies. We must improve spatial resolution and/or temporal sampling of human brain imaging techniques, and develop a better understanding of cellular mechanisms underlying commonly measured human brain signals (fMRI, Diffusion Weighted Imaging (DWI), EEG, MEG, PET)—for example, by linking fMRI signals to cellular-resolution population activity of neurons and glia contained within the imaged voxel, or by linking DWI connectivity information to axonal anatomy. Understanding these links will permit more effective use of clinical tools for manipulating circuit activity, such as deep brain stimulation and transcranial magnetic stimulation.
#8. Create Mechanisms to Enable Collection of Human Data. Humans who are undergoing diagnostic brain monitoring or receiving neurotechnology for clinical applications provide an extraordinary opportunity for scientific research. This setting enables research on human brain function, the mechanisms of human brain disorders, the effect of therapy, and the value of diagnostics. Meeting this opportunity requires closely integrated research teams including clinicians, engineers, and scientists, all performing according to the highest ethical standards of clinical care and research. New mechanisms are needed to maximize the collection of this priceless information and ensure that it benefits people with brain disorders.
#9. Disseminate Knowledge and Training. Progress would be dramatically accelerated by the rapid dissemination of skills across the community. To enable the broadest possible impact of newly developed methods, and their rigorous application, support should be provided for training—for example, summer courses and course modules in computational neuroscience, statistics, imaging, electrophysiology, and optogenetics—and for educating non-neuroscientists in neuroscience.
Although these FY 2014 research priorities are presented as nine individual recommendations, the overarching vision is to combine these approaches into a single, integrated science of cells, circuits, brain and behavior. For example, there is immense added value if recordings from neuronal populations are conducted in identified cell types whose anatomical connections are established in the same study. Such an experiment is currently an exceptional tour de force; with new technology, it could become routine. In another example, neuronal populations recorded during complex behavior might be immediately retested with circuit manipulation techniques to determine their causal role in generating the behavior. Theory and modeling could be woven into successive stages of ongoing experiments, enabling effective bridges to be built from single cells to connectivity maps, population dynamics, and behavior. Facilitating this vision of integrated, seamless inquiry across levels is the initial goal of the BRAIN Initiative, to be explored and refined before the final report in June 2014.
* * * * *
SECTION I. THE BRAIN INITIATIVE: VISION AND PHILOSOPHY
On April 2, 2013, the White House proposed a major national project to unlock the mysteries of the brain—the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative. The President called on scientists to “get a dynamic picture of the brain in action and better understand how we think and how we learn and how we remember.” In response to the President’s call to action, the Director of the National Institutes of Health created this Working Group to “catalyze an interdisciplinary effort of unprecedented scope” to discover the patterns of neural activity and underlying circuit mechanisms that mediate mental and behavioral processes, including perception, memory, learning, planning, emotion, and complex thought:
“By exploring these patterns of activity, both spatially and temporally, and utilizing simpler systems to learn how circuits function, we can generate a more comprehensive understanding of how the brain produces complex thoughts and behaviors. This knowledge will be an essential guide to progress in diagnosing, treating, and potentially curing the neurological diseases and disorders that devastate so many lives.”This ambitious “American Project”, articulated eloquently by President Obama in a White House announcement, can only be achieved through innovative, multidisciplinary investigation at all levels of nervous system function—behavioral, electrophysiological, anatomical, cellular and molecular. In parallel, advances in theory, computation, and analytics will be essential to understand and manage the large quantities of new data that will soon flow from neuroscience laboratories.
— Charge to the NIH BRAIN Working Group, April 2013
Over the past five months, we have reviewed the state of the field and identified key research opportunities. In this initial report we recommend specific goals to guide the BRAIN Initiative in Fiscal Year 2014. Our final report, to follow in June 2014 will define the vision more sharply, make longer term recommendations, and suggest benchmarks for evaluating progress toward the goals.
The Goal of the BRAIN Initiative
Our charge is to understand the circuits and patterns of neural activity that give rise to mental experience and behavior. To achieve this goal for any circuit requires an integrated view of its component cell types, their local and long-range synaptic connections, their electrical and chemical activity over time, and the functional consequences of that activity at the levels of circuits, the brain, and behavior. Combining these elements is at present immensely difficult even for one circuit, yet we must also weave together the many interlocking circuits in a single brain. As the President said in his White House press conference, this is indeed a “grand challenge for the 21st century.”
As for any field and any era, progress toward these scientific goals is limited, to a large extent, by the experiments that are technically possible. But we are now within a period of rapid—perhaps revolutionary—technological innovation that could vastly accelerate progress toward an integrated understanding of neural circuits and activity. Thus for this interim report, our planning effort embraces a substantial technology emphasis, as reflected in the name of the working group: “Brain Research through Advancing Innovative Neurotechnologies.” Our focus is not on technology per se, but on the development and use of tools for acquiring fundamental insight about how the nervous system functions in health and disease. We have considered how mature technologies can be applied to neuroscience in novel ways, how new technologies of obvious relevance can be rapidly developed and integrated into regular neuroscience practice, and what longer term investments should be made in ‘blue sky’ technologies with higher risk but potentially high payoff. As the BRAIN Initiative progresses, these technologies should increasingly be used to shed light on the healthy brain and on tragic human brain disorders.
Developing these novel technologies will require intense, iterative collaboration between neuroscientists and colleagues in the biological, physical, engineering, mathematical, and statistical and behavioral sciences. Essential partners should come from the private sector as well: corporate expertise in microelectronics, optics, wireless communication, and organization and mining of ‘big data’ sets can radically accelerate the BRAIN Initiative. Finally, clinicians will be essential partners to translate new tools and knowledge into diagnostics and therapies. The new technical and conceptual approaches to be created as part of the BRAIN Initiative will exert maximal impact if accompanied by specific plans for implementation, validation and dissemination to a larger community. Catalyzing the necessary collaborations and delivering reliable tools and resources to neuroscience laboratories should be major, overarching themes of the BRAIN Initiative.
Foundational Concepts: Neural Coding, Neural Circuit Dynamics and Neuromodulation
Neural coding and neural circuit dynamics are conceptual foundations upon which to base a mechanistic understanding of the brain. At the microscopic scale, the brain consists of vast networks of neurons that are wired together with synaptic connections to form neural circuits. In an active brain, each neuron can have electrical and chemical activity that is different from that of its neighbors; thus some neurons can play specialized roles in different tasks. Yet the activity of each neuron also depends on that of the others in the circuit, through the synaptic connections that define the circuit’s architecture. Synaptic connections can change strength as a result of recent activity in the circuit, meaning that circuit architecture is constantly modified by experience. A thinking brain can therefore be viewed as an immensely complex pattern of activity distributed across multiple, ever-changing circuits.
Neural coding refers to how information about the environment, the individual’s needs, motivational states, and previous experience are represented in the electrical and chemical activity of the neurons in the circuit. In a familiar example, the neural code for color vision begins with just three basic detectors in the eye—the cone photoreceptors. Circuits in our brains combine patterns of cone activation with other inputs to discriminate over a million different colors. More sophisticated, and poorly understood, neural codes enable us to recognize instantly the voice of a friend or the dramatic light of a Rembrandt painting. Elucidating the nature of complex neural codes and the logic that underlies them is one goal of the BRAIN Initiative.
As different neurons become silent or active in a thinking brain, the pattern of activity shifts in space and time across different circuits and brain regions. These shifting patterns define what is known as neural circuit dynamics. A key to understanding how the brain works is to determine how the neural dynamics across these vast networks process information relevant to behavior. For example, what is the form of neural dynamics in a circuit that makes a decision? What are the dynamically changing patterns of activity for speaking a sentence or imagining a future action? To probe the mechanics of the brain more deeply, we must learn how the biophysical properties of neurons and the architecture of circuits shape dynamic patterns of neural activity and how these patterns interact with incoming sensory information, memory, and outgoing motor commands. In the same way that the basic electrophysiological properties of single neurons are common across brain areas and species, it is likely that many fundamental forms of neural dynamics will generalize as well. One goal of the BRAIN Initiative is the identification and characterization of universal forms of neural circuit dynamics, likely represented by dynamical motifs such as attractors, sequence generation, oscillation, persistent activity, synchrony-based computation, and others yet to be discovered.
Accompanying this rapid flow of information that drives cognition, perception, and action are slower modulatory influences associated with arousal, emotion, motivation, physiological needs, and circadian states. In some cases, these slower influences are associated with specialized neuromodulatory chemicals like serotonin and neuropeptides, often produced deep in the brain or even in peripheral tissues, that can act locally or globally to change the flow of information across other brain circuits. In effect, neuromodulatory modifications of synaptic efficacy can ‘rewire’ a circuit to produce different dynamic patterns of activity at different points in time. The BRAIN Initiative should strive for a deeper understanding of these powerful but elusive regulators of mood and behavior.
Why Now?
This is a propitious moment for a sustained national effort to unlock the secrets of the brain. The reason lies in the technological and conceptual revolution that is underway in modern neuroscience. New molecular, genetic and cellular tools are generating exquisite insights into the remarkably diverse neuronal cell types that exist within our brains, the basic ‘parts list’ of our neural circuits. Novel anatomical techniques are providing remarkable new opportunities for tracing the interconnections between brain regions and individual neurons, revealing basic brain circuit maps in unprecedented detail. Innovative electrical and optical recording tools are allowing us to measure the intricate patterns of electrical activity that exist within those circuits across a broad array of behaviors ranging from decision-making to memory to sleep. Only a short time ago, we were restricted to studying the brain’s electrical activity one nerve cell at a time; now we can record from hundreds of nerve cells, allowing us to analyze the cooperative activity of nerve cells as they operate in intact circuits; we look toward a future in which we can measure even richer patterns of brain activity, involving millions of nerve cells at any instant. Furthermore, newly invented genetic and chemically based techniques are giving us the power to modify activity in those circuits with great precision, creating extraordinary opportunities for deciphering the information-carrying codes in patterned electrical activity, and in the longer term, creating a foundation for novel therapeutic treatments for disease.
With these increasingly powerful techniques come new data sets of massive size and complexity. Reconstructing neural circuits and their dynamic activity in fine detail will require image analysis at a formidable scale as well as simultaneous activity measurements from thousands of neurons. The age of ‘big data’ for the brain is upon us. Thus, neuroscientists are seeking increasingly close collaborations with experts in computation, statistics and theory in order to mine and understand the secrets embedded in their data. These startling new technologies, many of which did not exist 10 years ago, force us to reconceive what it means to be an experimental neuroscientist today.
The challenge that now faces neuroscience lies in integrating these diverse experimental approaches and scaling them up to the level of circuits and systems. Previously, we could study the brain at very high resolution by examining individual genes, molecules, synapses, and neurons, or we could study large brain areas at low resolution with whole-brain imaging. Continued progress at both of these levels is essential, but our unique new opportunity is to study the critical intermediate level as well—the thousands and millions of neurons that make up a functional circuit. Remarkable new discoveries are possible at this intermediate level, for here we expect to observe the circuits, codes, dynamics, and information processing strategies that enable a collection of nerve cells to generate a complex, organized behavior.
The Brain and Behavior
The purpose of the brain is to generate adaptive behavior—predicting, interpreting, and responding to a complex world. As foreshadowed in the preceding section, some of the most riveting questions in neuroscience revolve around the relationship between neural circuit structure, neural dynamics, and complex behavior. Objectively measureable behavior is an indispensable anchor for the field of neuroscience—it defines the set of phenomena that we ultimately seek to explain. We benefit in this respect from the rich traditions of experimental psychology, psychophysics and neuroethology, but new innovation is needed in the analysis of behavior. Dobzhansky once said that “Nothing in biology makes sense except in the light of evolution,” and it is no exaggeration to say that nothing in neuroscience makes sense except in the light of behavior. Thus a primary theme of the BRAIN Initiative should be to illuminate how the tens of billions of neurons in the central nervous system interact to produce behavior.
In advanced organisms our concept of ‘behavior’ must be extended to include sophisticated internal cognitive processes, in addition to externally observable actions. This point is dramatized by the story of Jean-Dominique Bauby, a French magazine editor who was left in a ‘locked-in’ condition by a brainstem stroke. Bauby was robbed of all voluntary movement except the ability to blink his left eye. Using the one behavior left to him, he wrote The Diving Bell and the Butterfly, an astounding memoir of the rich internal mental life that he continued to experience after his stroke:
“My diving bell becomes less oppressive, and my mind takes flight like a butterfly. There is so much to do. You can wander off into space or in time, set out for Tierra del Fuego or for King Midas’s court. You can visit the woman you love, slide down beside her and stroke her still-sleeping face. You can build castles in Spain, steal the Golden Fleece, discover Atlantis, realize your childhood dreams and adult ambitions.”Mental life can flourish within the nervous system, even if the behavioral link to the observable world is tenuous. Thus the BRAIN Initiative should focus on internal cognitive processes and mental states in addition to overt behavior. Accordingly, a preferred experimental emphasis should be on whole animals (typically behaving animals) with a secondary emphasis on reduced circuits that maintain important connections and integrative properties.
Measuring internal cognitive processes in animals is challenging, but rigorous methods have been developed to assess perception, memory, attention, decision-making, reward prediction, and many other examples. Although we must be constantly on guard against facile anthropomorphism, the continuity of brain structure and organization across species provides confidence that some cognitive processes analogous to ours are likely to exist in the brains of animals other than humans. Improving the behavioral analysis of these cognitive processes, both in experimental animals and in humans, should be a central goal of the BRAIN Initiative.
Strategies and Experimental Systems
Ultimately, the goal of the Brain Initiative is to understand how the human brain produces cognition and behavior, and specific recommendations of this report involve human neuroscience. However, human brains are complicated and difficult to access experimentally both for ethical and practical reasons. To reach our goal of understanding the human brain, it is therefore vital that we also investigate simpler animal brains as model systems—some with behaviors as comparable as possible to humans, but some with nervous systems that are more experimentally tractable. We cannot satisfy all requirements with a single animal model; a range of experimental systems, from simple to complex, will be needed to make progress. Fortunately, many basic principles of neural organization and function are conserved across animal species, so that progress in understanding simple systems can accelerate understanding of more complex systems.
In both animals and humans, we should enumerate and describe the brain’s component parts—the different types of neurons and glia—and we should map their precise anatomical connections to obtain an accurate circuit diagram. We should measure the dynamic activity of the cells in a circuit under a variety of conditions and across a range of behaviors, and we should manipulate this activity to test causal hypotheses about how circuit activity influences behavior. Finally, we will require computationally powerful ways to analyze and understand the mechanisms by which dynamic patterns of activity in neural circuits give rise to behavior. These individual elements are clear; the challenge is how to accelerate, facilitate and combine them for maximum impact.
Studying the Typical Brain Should Accelerate Understanding of Brain Disorders
While the primary goal of the BRAIN Initiative is an understanding of normal brain function, we expect this work to provide an essential foundation for understanding neurological and psychiatric disorders. The burden of brain disorders is enormous. All of us are touched, directly or indirectly, by the ravages of degenerative diseases like Alzheimer’s and Parkinson’s, thought disorders like schizophrenia, mood and anxiety disorders like depression and post-traumatic stress disorder, and developmental disorders like autism spectrum disorders. Brain disorders limit personal independence and place enormous demands on family and society. The knowledge gained in the BRAIN initiative offers the possibility of reducing this burden.
There is reason to think that many disorders result in part from circuit dysfunction. Epilepsy is best understood as a circuit disease, where instability in neuronal communication leads to uncontrolled excitation and seizures. We currently stabilize patients by treating them chronically with potent drugs, but pinpointing abnormal circuits with new technologies could aid in the prediction of seizures and the development of more precise, localized treatments to stabilize activity. Like epilepsy, mood disorders and thought disorders are episodic, with an unstable waxing and waning of symptoms over days or years. The ability of many affected individuals to function normally at some times, and the absence of massive loss of brain cells, suggests that there may be no immovable obstacle to recovery of stable cognitive or emotional processing -- the circuitry for information flow exists, but it is not always regulated correctly. Unfortunately, there has not been a fundamentally new class of drugs for psychiatric disorders since the 1970s, largely because we understand neither how the circuits work nor how the drugs act on them. There are some clues, however. For example, the drugs we have for depression—most of which affect the neurotransmitters dopamine, serotonin, and norepinephrine—appear to act in part by modulating the flow of information between subcortical and cortical brain areas. A greater understanding of these circuits and their regulation could advance our ability to diagnose and treat thought and mood disorders.
Similarly, a better understanding of brain circuits has the potential to provide new paths to the treatment of neurological disorders. The neurodegenerative disorder Parkinson’s disease is caused by the loss of dopaminergic neurons; like many neurodegenerative disorders, it manifests itself at the level of single cells. Nonetheless, those dopaminergic neurons are circuit elements. Changing the flow of information through the damaged circuits of Parkinson’s disease patients with deep brain stimulation can dramatically improve their motor symptoms, even after most dopaminergic neurons are lost. We anticipate that refined knowledge of motor circuits will make deep brain stimulation more efficacious, and enable its use for other motor disorders. For other neurodegenerative disorders like Alzheimer’s disease and motor neuron diseases, it may also be possible to deliver a therapeutic benefit by mimicking the circuit effect of a permanently lost population of cells. But first those circuit effects must be discovered, and interventional tools for delivering the appropriate circuit-level effect must be designed and built.
We also envision new ways to repair physical damage to the brain. Stroke, traumatic brain injury and spinal cord injury result in the loss of sight or memory, paralysis, or the inability to communicate. By tapping into existing brain circuits with new stimulators and sensors, it may be possible to re-establish damaged brain pathways, or allow control of prosthetic limbs with brain signals. Such implantable devices may sound like science fiction, but they are already in development in a few patients. Their success is limited by our fragmentary understanding of the brain’s codes and instructions; there is great potential for human benefit from knowing more about the brain.
The Deliverables of the BRAIN Initiative
The BRAIN Initiative will deliver transformative scientific tools and methods that should accelerate all of basic neuroscience, translational neuroscience, and direct disease studies, as well as biology beyond neuroscience. It will deliver a foundation of knowledge about the function of the normal brain, its cellular components, the wiring of its circuits, its patterns of electrical activity at local and global scales, the causes and effects of those activity patterns, and the expression of brain activity in behavior. Through the interaction of experiment and theory, the BRAIN Initiative should elucidate the computational logic as well as the specific mechanisms of brain function at different spatial and temporal scales, defining the connections between molecules, neurons, circuits, activity, and behavior.
This new knowledge of the normal brain should form a foundation for more advanced translational research into brain disease mechanisms, diagnoses, and therapies. It should serve our colleagues in medicine, biotechnology, engineering, and the pharmaceutical and medical device industries, providing fundamental knowledge needed to ameliorate the vast human burden of brain disorders.
In addition to accelerating biomedical knowledge and treatment of disease, the BRAIN Initiative is likely to have practical economic benefits in the areas of artificial intelligence and ‘smart’ machines. Our brains can rapidly solve problems in vision, speech and motor coordination that the most powerful supercomputers cannot approach. As we learn more about the principles employed by the brain to solve these problems, new computing devices may be devised based on the cognitive architectures found in brains. Information companies are already investing in brain-inspired algorithms to enhance speech recognition, text search and language translation; the economic value of neurotech industries could someday rival that of biotech.
Finally, we hope through the BRAIN Initiative to also create a culture of neuroscience research that emphasizes worldwide collaboration, open sharing of results and tools, mutual education across disciplines, and the added value that comes from having many minds address the same questions from different angles.
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