The Human Brain Project: Neuroinformatics Tools for Integrating, Searching and Modeling Multidisciplinary

P ERSPECTIVES I. Segev and W. Rall – Models of excitable dendrites and spines

Modeling: From Ions to Networks (Koch, C. and Segev, I., eds), 68 Mainen, Z.F. and Sejnowski, T.J. (1996) Nature 382, 363–366 pp. 251–291, MIT Press 69 Borst, A. and Egelhaaf, M. (1994) Trends Neurosci. 17, 257–263 43 Jack, J.J.B., Noble, D. and Tsien, R.W. (1975) Electric Current 70 Mel, W.B. (1993) J. Neurophysiol. 70, 1086–1101 Flow in Excitable Cells, Clarendon Press 71 Segev, I. (1995) in The Handbook of Brain Theory and Neuronal 44 Koch, C. Biophysics of Computation: Information Processing in Networks (Arbib, M.A., ed.), pp. 282–289, MIT Press Single Neurons, Oxford University Press (in press) 72 Softky, W. (1994) Neuroscience 58, 13–41 45 Koch, C. and Segev, I., eds (1998) Methods in Neuronal Modeling: 73 Wilson, C.J. (1995) J. Comput. Neurosci. 2, 91–116 From Ions to Networks, MIT Press 74 Miller, J.P., Rinzel, J. and Rall, W. (1985) Brain Res. 325, 46 McKenna, T., Davis, J. and Zornetzer, S.F., eds (1992) Single 325–330 Neuron Computation, Academic Press 75 Perkel, D.H. and Perkel, D.J. (1985) Brain Res. 325, 331–335 47 Rall, W. et al. (1992) Physiol. Rev. 72, S159–S186 76 Baer, S.M. and Rinzel, J. (1991) J. Neurophysiol. 65, 874–890 48 Rall, W. and Shepherd, G.M. (1968) J. Neurophysiol. 31, 884–915 77 Rall, W. and Segev, I. (1987) in Synaptic Function (Edelman, G.M., 49 Rall, W. (1967) J. Neurophysiol. 30, 1138–1168 Gall, E.E. and Cowan, W.M., eds), pp. 605–636, Wiley and Sons 50 Jack, J.J.B. et al. (1971) J. Physiol. 215, 353–380 78 Shepherd, G.M. et al. (1985) Proc. Natl. Acad. Sci. U. S. A. 82, 51 Mendell, L.M. and Henneman, E. (1971) J. Neurophysiol. 34, 2192–2195 171–187 79 De Schutter, E. and Bower, J. (1994) Proc. Natl. Acad. Sci. U. S. A. 52 Rall, W. et al. (1967) J. Neurophysiol. 30, 1169–1193 91, 4736–4740 53 Redman, S. and Walmsley, B. (1983) J. Physiol. 343, 117–133 80 Segev, I. and Rall, W. (1991) Soc. Neurosci. Abstr. 17, 1515 54 Agmon-Snir, H. and Segev, I. (1993) J. Neurophysiol. 70, 81 Rapp, M. et al. (1996) Proc. Natl. Acad. Sci. U. S. A. 93, 2066–2085 11985–11990 55 Rall, W. and Rinzel, J. (1973) Biophys. J. 13, 648–688 82 Segev, I. and Rall, W. (1988) J. Neurophysiol. 60, 499–523 56 Rinzel, J. and Rall, W. (1974) Biophys. J. 14, 759–790 83 Colbert, C.M. and Johnston, D. (1996) J. Neurosci. 16, 57 Zador, A.M., Agmon-Snir, H. and Segev, I. (1995) J. Neurosci. 6676–6686 15, 1669–1682 84 Amitai, Y. et al. (1993) Cereb. Cortex 3, 26–38 Acknowledegments 58 Rall, W. et al. (1966) Exp. Neurol. 14, 44–56 85 Schwindt, P.C. and Crill, W.E. (1998) J. Neurophysiol. 79, This work was 59 Dodge, F.A. and Cooley, J.W. (1973) IBM J. Res. Devel. 17, 2432–2446 219–229 86 Turner, R.W. et al. (1991) J. Neurosci. 11, 2270–2280 supported by grants 60 Bunow, B., Fleshman, J. and Segev, I. (1985) Biol. Cybern. 53, 87 Segal, M. (1995) J. Neurophysiol. 74, 484–488 from the ONR and 41–56 88 Mainen, Z.F. et al. (1995) Neuron 15, 1427–1439 the Israeli Academy 61 Moore, J.W. et al. (1983) J. Physiol. 336, 301–311 89 Murphy, T.H. et al. (1994) Nature 263, 529–532 62 Goldstein, S.S. and Rall, W. (1974) Biophys. J. 14, 731–757 90 Buzsaki, G. et al. (1996) Proc. Natl. Acad. Sci. U. S. A. 93, of Science. We 63 Khodorov, B.I. and Timin, Ye N. (1975) Prog. Biophys. Mol. Biol. 9921–9925 thank M. Rapp, 30, 145–184 91 Kim, H.G., Beierlein, M. and Connors, B.W. (1995) S. Redman and 64 Manor, Y., Koch, C. and Segev, I. (1991) Biophys. J. 60, J. Neurophysiol. 74, 1810–1815 1424–1437 92 Bernander, O. et al. (1991) Proc. Natl. Acad. Sci. U. S. A. 88, A. Thomson for 65 Parnas, I. and Segev, I. (1979) J. Physiol. 295, 323–343 11569–11573 their critical reviews 66 Traub, R. et al. (1991) J. Neurophysiol. 66, 635–649 93 Shepherd, G.M. and Brayton, R.K. (1987) Neuroscience 21, of the article. 67 Pinsky, P. and Rinzel, J. (1994) J. Comput. Neurosci. 1, 39–60 151–165

The Human Brain Project: neuroinformatics tools for integrating, searching and modeling multidisciplinary

Gordon M. neuroscience data Shepherd, Jason S. Mirsky and Gordon M. Shepherd, Jason S. Mirsky, Matthew D. Healy, Michael S. Singer, Michael S. Singer are in the Section Emmanouil Skoufos, Michael S. Hines, Prakash M. Nadkarni and Perry L. Miller of Neurobiology, and Matthew D. Healy, Emmanouil What is neuroinformatics? What is the Human Brain Project? Why should you care? Supported Skoufos, Prakash by a consortium of US funding agencies, the Human Brain Project aims to bring to the analysis M. Nadkarni and of brain function the same advantages of Internet-accessible databases and database tools that Perry L. Miller are at the Center for have been crucial to the development of molecular biology and the Human Genome Project. Medical The much greater complexity of neural data, however, makes this a far more challenging task. Informatics and Dept of As a pilot project in this new initiative, we review some of the progress that has been made and Anesthesiology, indicate some of the problems, challenges and opportunities that lie ahead. Yale University School of Medicine, Trends Neurosci. (1998) 21, 460–468 333 Cedar Street, New Haven, CT 06510, USA. HE HUMAN BRAIN PROJECT originated during change, was proceeding at a pace that outstripped the Michael S. Hines Tthe 1980s in discussions between neuroscientists abilities of most neuroscientists to use these technol- is at the Dept of and forward-looking program directors at the National ogies. From the viewpoint of the funding agencies this Computer Science, Institutes of Health and the National Science was cause for concern, because these were the kinds of Yale University, Foundation1. They realized that the development of new ‘enabling technologies’ that would allow neuroscien- New Haven, technologies for creating databases and database search tists to make much more efficient use of their data CT 06520, USA. tools, and of electronic means for information ex- (and the agencies to get ‘more bang for the buck’).

460 TINS Vol. 21, No. 11, 1998 0166 - 2236/98/$ – see front matter. © 1998 Elsevier Science. All rights reserved. PII: S0166-2236(98)01300-9 G.M. Shepherd et al. – The Human Brain Project P ERSPECTIVES

Box 1. Organizations sponsoring the Human Brain Project

National Institute of Mental Health Office of Naval Research National Institute on Drug Abuse National Aeronautics and Space Administration National Science Foundation Fogarty International Center National Institute on Aging Department of Energy National Institute on Child Health and Human National Institute on Alcohol Abuse and Alcoholism Development National Heart, Lung, and Blood Institute National Institute on Deafness and Other Communication National Institute of Dental Research Disorders National Institute of Neurological Disorders and Stroke National Library of Medicine National Cancer Institute

In the traditional mode of experimental science, Finally, it acquired the name Human Brain Project to each laboratory generates its data to solve a problem, indicate a vision complementary to the Human Genome publishes a fraction of it, and moves on. The pub- Project, of providing informatics support to enable lished data remain in the hard copy volume or reprint, neuroscientists to carry out a complete mapping of the inaccessible by electronic means; the unpublished molecules, cells, circuits and systems in the human data are lost forever. In the electronic age, this is out- brain, in health and disease. Fortuitously, the emergence dated; in an age of limited national resources, it is an of the World Wide Web at about the same time pro- inefficient use of the taxpayers’ dollars. vided the ideal means for linking the pilot projects and This classical mold was broken during the 1980s at making their databases and tools accessible to each the gene and protein level by the gene-sequencing other and to neuroscientists everywhere. community. Because of the simple linear nature of the It should be emphasized that the Human Brain data, genetic and protein sequences could easily be Project, like the Human Genome Project, embraces all submitted, in parallel with journal publication, to cen- species. This recognizes the fundamental principle tral databases where they could be archived and made underlying modern biomedical research, that basic available for further analysis. This analysis is carried research on all species is needed to contribute to an out by ‘informatics tools’, which are the programs that understanding of the human and to improvements in enable a database to be queried to generate new in- human health. formation, from simple questions such as identifying The first five years similarities, which might represent homologies, with any arbitrary sequence, to computationally intensive From the start it was recognized that neuroscience searches such as correlated mutation analysis2. These data are much more complex than sequence data; as tools have become an integral part of carrying out noted by Peter Pearson, ‘genome project informatics is research on genes and proteins, as essential to molecu- trivial by comparison’4. The informatics problems in lar biology as are its experimental methods. The suc- neuroscience are therefore far more daunting than cess of the Human Genome Project in sequencing the those currently being addressed in molecular genetics genome for the human and for other species has or most other fields. Some indication of this complexity therefore depended on both efficient experimental is indicated in Table 1, which shows that neuroscience methods and efficient informatics tools. The necessary data come at a number of organizational levels. conditions that make possible the efficient electronic In addition, at each level, the data come from mul- use of the sequence data include: simple linear data; tiple disciplines, in two and three spatial dimensions, quality control by refereed publication; requirement for and can vary in a fourth dimension, time. Thus, submitting published sequences in central databases; whereas in the Human Genome Project mapping the automated data submission; automated data search gene is the primary goal, with the informatics aspects tools. being of secondary importance, in the Human Brain In order to consider how to develop informatics Project the main problem is not only organizing the tools that would be as effective for neuroscience data neuroscience data but also developing new informat- as those for gene and protein data, a committee, rep- ics tools that can deal with the much more complex resenting a broad range of neuroscientists and infor- forms of data and the relationships between them. matics specialists, was convened by the Institute of The goal of the first five-year period of funding of Medicine around 1990. After nearly two years of delib- pilot studies was therefore modest: to explore the fea- eration, it recommended3 that a pilot program be set sibility of different approaches to these difficult prob- up to fund laboratories and laboratory groups that would lems. Some of the initial projects will be summarized combine experimental approaches with the develop- briefly; Web addresses are provided in Box 2. ment of electronic ‘enabling technologies’. This envis- Human brain imaging aged a new research specialty of ‘neuroinformatics’. One large effort has been directed to brain images. Out of this came a funding program in 1993 that was Many large centers are actively pursuing positron unusual in several respects. First, a project would emission tomography (PET), magnetic resonance require not only experimental neuroscientists but also imaging (MRI), functional MRI (fMRI) and magneto- informatics scientists as integral members of the encephalography (MEG), in addition to traditional research team. Second, its funds would be derived electroencephalographic (EEG) mapping of brain from a wide range of federal agencies that support activity. These studies generate large numbers of very neuroscience research and wished to extend that sup- large datasets (many gigabytes and terabytes of data) port with the new methods (see Box 1). of image data. As expressed by Vincent Cerf, one of

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TABLE 1. Levels of function Studies such as this one are addressing how to charac- terize quantitatively the relatedness of 3D images ob- Levels of brain function Types of experimental neuroscience data tained in different subjects by different laboratories. These and other advances can make brain imaging much Behavior Performance quantitation, video monitoring, drug more accurate and make much better use of the data, testing so that there is reduced duplication of effort, increased Distributed systems 2D and 3D axon-tracing between regions, sharing of data, and more effective collaborations be- electrophysiological recordings (spike timing), tween laboratories. The better use of resources is a sig- brain imaging and 3D brain maps nificant gain when funding multiple imaging centers. Specific regions 2D and 3D cytoarchitectonics of layers and Brain maps and atlases functional columns, transmitter-receptor These types of studies logically require the develop- localization, anatomical, physiological and metabolic ment of brain atlases of normal brains for comparison maps with brains in different disease states. There have been Nerve cells 3D cell morphology, 3D functional imaging, several initiatives taken, inside and outside the electrophysiological recordings of action-potential Human Brain Project, in this direction. One of the firing patterns and membrane currents earliest of these was Brain Browser7 for the rat brain. Neuronal compartments 3D imaging of axon terminals, growth cones, Digital atlases are being developed for several species, dendrites, dendritic spines, 3D localization of including human8,9. These have utility for research, organelles and synaptic microcircuits and also have become integral parts of the teaching of Microcircuits 3D fine structure and imaging of synaptic patterns, neuroanatomy to medical students and graduate stu- synaptic pharmacology, action-potential firing dents. Although the main aim of the Human Brain patterns and synaptic currents and potentials Project is to provide tools for neuroscience research, Organelles 2D and 3D fine structure and molecular composition the added benefits for science education, including of synapses, mitochondria, microtubules, etc.; school-age children and the general public, will be of recordings of synaptic currents and potentials considerable value. Molecules 3D molecular models of receptors, channels, In addition to the need for brain atlases, the analy- enzymes and structural proteins, molecular sis of brain function requires the construction of maps physiology and pharmacology of transmitters, of organization within regions. Leading the way are modulators, hormones, guidance molecules, growth maps of the visual areas, which have become ex- factors and gene-transcription factors tremely complex, with dozens of sub-areas and their 10 Genes DNA and protein sequences interconnections . Mapping these areas requires con- verting the 3D surfaces of the visual cortical areas into 2D representations. Sophisticated software for doing the pioneers in the development of the Internet: ‘We the necessary conversion and warping has been devel- are in a very infantile state…regarding the indexing, oped (CARET) and is being made available on the Web cataloging and searching of nontextual information… . (v1.wustl.edu/caret) so that it can be applied not only It’s one thing to find text words in documents that are to visual cortex mapping but also to other areas. alike; it’s something else to find similar images that Neuronal properties relate to each other’5. An important commitment from the start of the The first five years of the Human Brain Project have Human Brain Project was to support informatics tools seen a concerted effort by a number of laboratories for data at all levels of brain organization. Thus, as towards solving the informatics problems involved in shown in Table 1, between sequence data at the most comparing human brain images. These problems basic level and brain images at the highest level are a include: how to construct reference brain images; how number of levels in which neuronal membrane prop- to relate brain images from different patients and from erties are crucial for the neural basis of brain function. different laboratories, a process known as ‘warping’; Several pilot projects are dealing with data at these dif- how to exchange these large datasets efficiently ferent levels, as indicated in Box 2. Thus, databases for between laboratories; how to make such archives of the fine structure of synapses, neuronal membrane brain images available for further ‘data mining’. An properties, neuronal morphology and physiological data example of progress towards these goals is provided are being constructed. Links are being built to other by the International Consortium for Brain Imaging parallel efforts, such as the G Protein Coupled Receptor (ICBM) (http://www.loni.ucla.edu/icbm), consisting consortium (GPCRDB: www.swift.embl-heidelberg. of laboratories at UCLA, University of Texas San de/7TM/), a database of 3D reconstructions of vestibu- Antonio, Montreal Neurological Institute, Stanford lar hair cells and their nerve terminals (biocomp.arc. University, Albert Einstein College of Medicine in nasa.gov/reconstructions) and the Ion Channel Net- New York and Heinrich Heine University in Germany. work (a comprehensive database of membrane proper- A typical study6 has involved comparing a representa- ties; www.le.ac.ak.csn), to name just a few examples. tive normal brain (Fig. 1A) with the brain of a patient Links are also being constructed to public sequence with Alzheimer’s disease (Fig. 1B). The authors de- databases such as GenBank and SwissProt, as well as to scribe an algorithm that calculates a high-dimensional focused sequence databases such as for Caenorhabditis volumetric warp that brings both 3D images into elegans (eatworms.swmed.edu). register with each other (Fig. 1C). This warp involves Integrating data into models 384 ϫ384 ϫ256 ϫ3 (~0.1 billion) degrees of freedom. In order to understand the significance of a particu- In the example shown, the cerebellum of the lar property in a particular neuron, it is necessary to Alzheimer’s patient is contracted, and more subtle assess that property in relation to other properties at variations in the posterior frontal and cingulate that site. Integrating data in this way leads naturally regions are seen (Fig. 1D). to the construction of computational models, which

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Box 2. Pilot projects in neuroinformatics funded by the Human Brain Project

Neuronal properties Brain imaging Neuronal vulnerability and informatics in human disease Extended thin-plate splines for brain variation in three F. Bloom, Scripps Research Institute dimensions www-hbp-np.scripps.edu/home; www-hbp.scripps.edu F. Bookstein, University of Michigan neurozoom.scripps.edu/ServerData/HTMLs/Software/ ftp://brainmap.med.umich.edu/pub/edgewarp NZ/Server/default.html Structural information framework for brain mapping www-cajal.ucsd.edu; www.med.uci.edu/~hbp/index.html J. Brinkley, University of Washington Development of a three-dimensional cell-centered neural www1.biostr.washington.edu/brain project database Advanced methods for neuroimaging data analysis M. Ellisman, University of California, San Diego J. Cohen, University of Pittsburgh Somatosensory cortical neuron physiology: a Web database Mapping brain function by combined MRI, MEG and D. Gardner, Cornell University Medical College fMRI cortex.med.cornell.edu J. George, Los Alamos National Laboratory Three-dimensional structure and function of synapses in Goal-directed magnetic resonance brain micro-imaging the brain R. Jacobs, California Institute of Technology K. Harris, Children’s Hospital, Harvard Medical School www.gg.caltech.edu/brain Three-dimensional reconstruction of synapses Anatomical morphological analysis of MR brain images P. Luther, University of Maryland D. Kennedy, Massachusetts General Hospital Integration of multidisciplinary sensory data neuro-www.mgh.harvard.edu/cma/ibsr G. Shepherd, Yale Medical School Spatially oriented database for digital brain images senselab.med.yale.edu/ordb; senselab.med.yale.edu/ S. Letovsky, Johns Hopkins University neurondb; braid.rad.jhu.edu/braid senselab.med.yale.edu/modeldb Functional magnetic resonance imaging methods for three-dimensional mapping of brain after early injury Neuronal and network models D. Levin, University of Chicago Neural plasticity: data and computational structures mri.uchicago.edu M. Arbib, University of Southern California Enhancement of functional and neurochemical brain www-hbp.usc.edu/HBP/ patterns A simulator-based neuronal database A. Levy, Brookhaven National Laboratory J. Bower, California Institute of Technology www.ccd.bnl.gov/visualization/examples.html www.bbb.caltech.edu/hbp/ A probabilistic reference for the human brain Parallel simulation of large-scale neuronal models J. Mazziotta, University of California, Los Angeles N. Goddard, Carnegie Mellon University brainmapping.loni.ucla.edu Brain maps and atlases Imaging software and methods for mapping brain devel- High-resolution brain atlas – data acquisition and user access opment R. Sidman, Harvard University A. Reiss, Stanford University Reconstructions and representations of cerebral cortex Spatial and temporal patterns in functional neuroimaging D. van Essen, Washington University D. Rottenberg, VA Medical Center, Minneapolis v1.wustl.edu pet.med.va.gov:8080/hbp provide for a quantitative assessment of the signifi- pursued parallel theoretical studies using compu- cance of particular properties rather than a subjective tational models12 to interpret the physiological data and impression. For this reason, integration of data into point to testable hypotheses for further experiments. models has been designated as one of the important The neuroinformatics component of SenseLab has goals of the Human Brain Project. Computational focused on several problems that required solving in models of neurons, running in GENESIS and in order to construct informatics tools to aid our experi- NEURON, are an integral part of several of the pilot mental analysis of olfactory neuron properties and of projects (see Box 2). Computational simulations of odor activity maps. We have constructed these tools neural processing in specific brain regions, involving with a view to their more general use in assisting neuro- both small- and large-scale neuronal networks, are scientists in analysing and interpreting data at the also being developed (see Box 2). level of molecular properties, dendrites, neurons and Our project, termed SenseLab, uses the olfactory neuronal circuits. The problems that have been ad- system as a model for addressing the problem of inte- dressed include: data mining; the storage and analysis grating multidisciplinary neuronal data. We describe of unpublished data; integrating multidisciplinary here in more detail some of the progress that has been data for given neurons and parts of neurons; develop- made, as an example of how an experimental labora- ing search tools to enable membrane property tory has become involved in the development of homologies to be identified across different neurons; neuroinformatics tools that are needed for our automating the inputting of data into neuronal research and that also may be of broader utility. models; and ensuring data quality control. We will describe our initial progress in each of these areas. Goals of SenseLab Using unpublished data Our experimental research uses a range of physiological, anatomical and molecular approaches to Only a small part of neuroscience data is published; a understanding the organization of synaptic circuits significant problem, therefore, is how to make unpub- and the neural basis of sensory processing in the olfac- lished data accessible and useful. An example is sequence tory system11. In addition, we have for many years data for olfactory receptors, which could comprise up to

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1000 genes, making them the largest family in the genome13. This presents an enormous challenge to laboratories attempting to clone and sequence these genes. In response to requests from several laboratories, we constructed a focused database of gene and protein sequences for these receptors, called Olfactory Receptor Database (ORDB) (senselab.med. yale.edu/ordb)14. A special feature is the provision of a private section in which laboratories can deposit unpublished fragments and longer sequences. A BLAST sequence comparison tool15 enables anonymous searches for sequence similarities against a submitted sequence. If a similar, potentially homologous, sequence is found, the submitter is notified of the laboratory that deposited it, so that the laboratory can be contacted and they can dis- Fig. 1. Comparison of the magnetic resonance images of a brain of a normal subject (A) with an Alzheimer’s cuss whether to share information patient (B). A warping algorithm is used to bring the brain images into register (C). The results show contraction of and decide whether to develop a full the cerebellar region and smaller variations in the posterior frontal and cingulate region (D). Abbreviations: CALCa, cal- sequence for publication. This makes cerine fissure (anterior area); CALCp, calcerine fissure (posterior area); CALL, corpus callosum; CING, cingulate sulcus; use of unpublished data, and also PAOC, deep internal surface of the pareito-occipital sulcus; SYLV, medial surface equidistant between the banks of the creates a user group that enhances sylvian fissure; VTIi, ventricular surface, inferior horn (inferior surface); VTIs, ventricular surface, inferior horn (superior communication between laborato- surface); VTSi, ventricular surface, superior horn (inferior surface); VTSs, ventricular surface, superior horn (superior ries and fosters a cooperative spirit surface). Adapted from Ref. 6 (see also loni.ucla.edu/~thompson/GORDON/1_warp_LARGE.gif). in the field. Currently there are over 40 laboratories in the user group, and over 200 sequences in the database. Eventually one would like to A Extracellular 306 457 have a user group that shares data freely without need N 316 505 for protecting unpublished data with anonymity. However, the field of olfactory receptor gene sequenc- 430 ing is extremely competitive and very scattered world- 509 633 wide, so the private section of the database serves as a 427 426 622 means to begin to build a community in which more 521 open sharing can take place. Data mining I II III IV V VI VII In most neuroscience articles, published data remain C 141 534 locked in the hard copy journal. In contrast, sequence Intracellular data, by being deposited in electronic form, are accessible to anyone for further analysis. This has given rise to a new form of research called ‘data mining’, in B 306 457 316 which datasets generated for one purpose can be 505 examined for other purposes. An example is the similarity searches that led to the identification of a new 509 430 form of K+ channel16. 427 633 An advantage of the ORDB is that it facilitates the use 426 of published as well as unpublished data for further data 622 mining. An example is a series of computational studies 521 carried out on published sequences to analyse olfactory- receptor sequences for significant amino acid residue sites that might be involved in interactions with the deter- 141 534 minants of odor molecules. One study involved construction of receptor models and automated docking of Fig. 2. Molecular models from data mining by the method of correlated mutation analysis odor ligands17. A second18 involved the method of cor- (CMA). (A) Schematic representation of the seven transmembrane helices and interhelical loops related mutation analysis, in which pairwise analysis of of a G-protein-coupled receptor (GPCR) belonging to the olfactory-receptor gene family. Four classes of residue positions showing correlated mutations are indicated: squares indicate residues residue differences through a set of sequences enables the that are identical to ligand-binding residues of a molecular model of olfactory receptor 5 (Ref. 17); identification of residue sites that are likely to be func- filled symbols indicate residues that have been shown to bind ligands in other GPCRs; indicate tionally significant (see Fig. 2). A third method involved residues showing positive selection; open circles indicate residues that are unique to this CMA computational analysis of positive selection moments19. study. (B) Diagram of the pairwise correlations for the residue positions shown in (A) (Ref. 18). These methods have all pointed to a consensus binding

464 TINS Vol. 21, No. 11, 1998 G.M. Shepherd et al. – The Human Brain Project P ERSPECTIVES pocket20 which might be involved in transducing odor molecule determinants. These studies can help to guide point mutations of residues when expression systems for olfactory receptors are developed. These studies illustrate, not only data mining, but the integrating of sequence data into molecular models in a way that can give insight into the mechanisms underlying neural processing, in this case the percep- tion and discrimination of odors. Molecular models are a type of computational model that is new for most neuroscientists. This type of modeling will become more important as more is learned about the molecular basis of neuronal function. If the models are any good, they will suggest experiments on specific residues, which will lead back to refinements of the model, and Fig. 3. Integration of neuronal properties in the mitral cell of the olfactory bulb. The mitral so on. These molecular models will also eventually cell is the first relay neuron in the pathway for the sense of smell. A wide range of properties need to be incorporated into compartmental neuronal mediates synaptic responses and action potential generation in the different parts of the cell. models (see below). The figure shows a Golgi-stained mitral cell, with the distribution of neurotransmitter receptors indicated on the left and the distribution of voltage-gated membrane channels on the right. Integrating multidisciplinary data On the far right is shown a summary of the neurons in which a similarity search revealed + The analysis of brain function generates multidisci- the presence of Na, T (transient Na channel) in the corresponding distal dendrites (see Fig. 4). The integration and similarity search were performed by informatics tools in NeuronDB plinary data ranging from protein sequences through 2+ + anatomy, physiology, pharmacology, etc., to behavior. (senselab.med.yale.edu/neurondb). Abbreviations: K,Ca, Ca -activated K conductance; K,leak, resting K+ conductance. Understanding brain function requires integrating these diverse data into models for each main type of neuron; this can give insight into the mechanisms underlying computer modeling studies12, we have used the strategy the functional operations for that neuron. How to do of reducing the morphological complexity of dendritic this integration accurately and efficiently presents one of trees to ‘equivalent dendrites’, and the complexity of the greatest challenges to contemporary neuroscience. a given neuron with its equivalent dendrites to a sim- The practical problem we have pursued is analysing plified ‘canonical’ form. In the case of the mitral cell, a the mitral cell of the olfactory bulb, one of the most minimum of compartments represents the dendritic tree: complex cells in the nervous system. The mitral cell three compartments for the primary dendrite, and three receives olfactory input only in its most distal den- compartments for the combined secondary dendrites. dritic tuft. It has been postulated that the EPSP response This is sufficient to enable the critical properties of the spreads through the primary dendrite to activate an distal tuft, the primary dendrite and the secondary action potential in the cell body–axon hillock; the dendrites to be represented in their true distribution. action potential propagates forward into the axon as To test for the generality of this approach we have well as backward into the primary and secondary den- applied the canonical concept to other neurons, and drites, where it activates recurrent and lateral inhibition through den- drodendritic output synapses11,12. The postulate that action potential initiation always occurs in the cell body–axon hillock is based on studies in other neurons21,22; does this hold in the mitral cell under all conditions of synaptic input? Recently we have found that the action potential can in fact be initiated in either the soma–axon hillock or the distal primary dendrite, depending on the strength of EPSPs in the distal tuft or the strength of IPSPs in the secondary dendrites23. These synaptic actions are in turn modulated by a variety of mechanisms, including NMDA and metabotropic glutamate receptors and centrifugal brainstem systems. Some of these properties are illus- trated by the diagram in Fig. 3. Integrating all of these data is obvi- Fig. 4. Comparisons of neuronal properties across canonical neurons. The diagram illustrates the strategy in NeuronDB ously a formidable task. In order to (senselab.med.yale.edu/neurondb) of constructing reduced canonical representations of neurons with different types of move beyond this simple summary dendritic trees, which enables comparisons to be made between the properties of equivalent dendritic compartments. diagram towards a quantitative This provides the basis for making similarity searches for neuronal properties (receptors, channels, transmitters) across model, we have built on the com- different types of neurons (see Fig. 3). Using this tool, the cellular functions of different gene products can be assessed and partmental approach. Based on early compared in similar parts of neurons in relation to the complex matrix of properties in those parts. From Ref. 22.

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have incorporated into these canonical representations Having made the database searchable by the canoni- a database of neuron properties to form NeuronDB cal approach, we next developed informatics tools for (senselab.med.yale.edu/neurondb). searching for properties across neurons24. The essence For the properties of a given neuron, NeuronDB can of these tools is that, from any compartment of any be used in different practical ways (see Ref. 24). You can neuron in the database, the user can select a particular query if a given neuron has a particular type of recep- property (ion channel, neurotransmitter receptor or tor, channel or transmitter. You can query if a given neurotransmitter) and query the database for the pres- compartment has a particular property. You can query ence of that property in the corresponding compart- for all of the receptors (or channels, or transmitters) of ment of any other neuron, or in any compartment of a given neuron, or of a given compartment, or query any other neuron. For example, in our research on all properties of a given neuron or compartment. The action potential initiation in mitral cell dendrites (see responses come in two forms. The ‘terse’ form simply above), a query on voltage-gated Na+ channels in the provides the listing of the properties queried; this is distal primary dendrite brings up several other neurons most efficient for an initial query, particularly if the list with these channels (see Fig. 3). Variations in channel of properties is long. The ‘reference’ form provides three density are duly noted; links to citation databases enable features: first, an annotation box, in which a concise the user to assess immediately the experimental data, narrative commentary of the studies supporting a par- and links to ModelDB (see below) will enable the user ticular property in that compartment of that neuron is to assess immediately the functional consequences of entered; second, citations within the annotations for differing channel densities and other properties. each study with hyperlinks to the abstract of that pub- Similar queries on neurotransmitter receptors enable lication (in MedLine, PubMed); and, third, a combined the user to assess, for example, GABAergic inputs to cell reference list at the bottom of that entry for all of the somas compared with different levels of dendritic trees citations, again with their hyperlinks to the abstracts. in different neurons, with citation back-up and access to Thus, the ‘terse’ mode provides the overview for inte- model simulations. These search tools will increasingly grating the different properties of a compartment or become indispensable as neuroscience data continue neuron, whereas the ‘reference’ mode provides the growing beyond the abilities of single laboratories to documentation for closer analysis and integration. keep up with work that might be crucially relevant. The key point is that NeuronDB is not just an Construction of neuronal models and mining of archive of neuronal properties; it is a tool for enabling model databases the user to understand the significance of a molecular property within the context of other properties con- The properties of a given neuron in its different tributing to the functions at a particular site within a compartments are the basis for construction of com- particular neuron. This is a goal, not only for neuro- partmentalized computational models for that neuron. scientists, but also for molecular biologists studying We are presently constructing an interface between gene function in the emerging fields of functional NeuronDB and a modeling environment called Model genomics and pharmacogenomics. Database (senselab.med.yale.edu/modeldb). ModelDB runs models in the simulation program NEURON (Ref. Search tools for comparing multidisciplinary data 25), and can be adapted for other programs as well. In addition to integrating multidisciplinary data for a We are currently building a model for the mitral cell given neuron, we need to compare data across different in order to simulate the initiation of action potentials neurons in order to determine similarities or differences and their control by excitatory and inhibitory synaptic in properties underlying the functions of different responses as in the experimental study of Chen et al.23 neurons. For example, in analysing the significance of An exciting prospect is that, by the use of dual patch distal dendritic action potential initiation, it is impor- recordings, the parameters of the model can be much tant to compare the properties of the mitral cell distal more tightly constrained than has been possible in the dendrites with those of distal dendrites of other cells past with most neurons using only single-electrode in order to assess the functional significance of this type recordings. of activity. This is analogous to searching for similarities The importance of ModelDB is that each neuron in sequence databases, except that the problem is much model constitutes a computational tool for analysing more difficult because neuroscience data are not one- the functional properties in parallel with experimental dimensional, like sequences, but multidimensional. analysis of that neuron; critical hypotheses suggested Can neuroinformatics solve this problem, and provide by the experiments are tested in the models, and vice tools for searching neuroscience data that will be as versa. This greatly enhances the insights gained from indispensable for carrying out neuroscience research experiments, and puts the functional interpretations as the tools for searching sequence data are for carrying on a firm theoretical foundation (see Refs 26,27). The out research in molecular biology? interface between NeuronDB and ModelDB will allow The biggest obstacle to beginning to solve this prob- new or revised data in NeuronDB to be inputed auto- lem is the morphological complexity of most neurons, matically into ModelDB, making the process of con- which makes it seem impossible to compare properties structing models much more efficient and user friendly. across equivalent parts of different neurons. As already Since the models are easily accessible on the Web, it indicated in discussing the mitral cell, in NeuronDB will also make possible ‘model mining’ – that is, the free the complexity of dendritic trees is dealt with by con- use of the models for checking published results and structing an ‘equivalent dendrite’12 for the basic types exploring new functional properties of a given model. of trees: apical, basal, stellate, etc. (see Fig. 4). Within Data quality control each compartment, the basic properties of membrane channels, neurotransmitter receptors and neurotrans- In archiving neuronal properties and making them mitter substances are identified. Web-accessible to integrating and search tools, a

466 TINS Vol. 21, No. 11, 1998 G.M. Shepherd et al. – The Human Brain Project P ERSPECTIVES fundamental problem is how to control for the quality of the data. Many feel that this is the main problem to be solved before any widely accessible databases should be built. Sequence databases have opted for a simple solution, in which publication in a refereed journal is the assurance of data quality. In a similar manner, it can be argued that publication should ensure the quality of neuroscience data. However, neuroscience data are often controversial. Our solution to this problem is the annotation box described above; data are accepted if they have been published, but annotations enable the significance of the data to be highlighted and any controversial aspects noted (Fig. 5). Thus, rather than being a problem to be avoided, controversies about the data are incorporated into the database so that they can be addressed by experimenters and explored by modelers. Using this strategy, unpublished data can also be included, provided it is appropriately annotated. Fig. 5. Data quality control by use of annotation. This excerpt from NeuronDB (senselab. med.yale.edu/neurondb) shows, for the distal dendritic compartment of the spinal motor Automating data submission neuron, the entry for the glutamatergic NMDA receptor. In this ‘reference mode’, the ‘Notes’ A bottleneck to future progress is the laborious provide for annotation that gives the citation background for the development of the evidence for glutamate receptors at this level of the dendritic tree of the motor neuron. It also notes process of inputting data to neuroscience databases. studies providing evidence that Ia afferent EPSPs are mediated largely by AMPA receptors, and This process will continue to be very slow as long as contrasts this with evidence for an NMDA component, noting that the latter evidence came neuroscience data are available only in published jour- from the neonatal rat. nal articles. Sequence data present a much more attractive model, in that journals routinely require that publication of a sequence be accompanied by Authors Data Journals submission to a sequence database, so that it is available for immediate checking and further study. This is Integrated models rapidly becoming a necessity for neuroscience data. Other authors Published databases We envision that publication of research results in neuroscience journals will eventually be accompanied by a similar requirement for deposition of the essen- Unpublished databases tial findings in Web-accessible databases (Fig. 6). Thus, a study of the morphology of a given type of neuron, or of antibody staining for the distribution of a given channel or receptor within a neuron, or of recordings Fig. 6. The current (solid lines) and proposed (dashed lines) flows of neuroscience data. In of synaptic responses or channel activity, will be the current mode, authors generate experimental data that are published in journals. Journals are read by these and other authors, and the cycle repeats itself. In the case of cloning accompanied by deposit at sites such as the Ion studies, submission of raw sequences to Internet databases occurs in parallel with journal pub- Channel Network and NeuronDB. Automating the lication, so that similarity searches and data mining can occur. In the proposed mode for submission process to such databases presents a chal- neuroscience data, submission of data (neuronal morphology, physiology, cell images, etc.) to lenge, but the reward will be databases and tools that Internet databases will occur in parallel with journal publication, so that similarity searches and are available to all users for the kinds of automated data mining can also occur with these data. In addition, when the neuroscience data are integration, search comparisons and construction of integrated into neuronal and network models, it is envisioned that journal publication will also models that will greatly enhance the ability of neuro- be accompanied by deposition of the models in appropriate databases so that the models can scientists to understand the neuronal basis of brain be checked and further used. Finally, parallel submission of unpublished data to appropriate function. sequence, neuroscience and model databases will make much better use of grant-supported research results, leading to enhanced communication and collaboration between authors, as Summary and future directions indicated. Neuroscience research is generating increasing amounts of data that go far beyond the traditional Human Brain Project is committed to these goals by Acknowledgements means to analyse and understand. Compared with supporting pilot projects dealing with data across the Our work on the sequence data, many types of neuroscience data, such spectrum of neuroscience research. The vision of the Human Brain as physiological recordings, cell imaging and brain Human Brain Project is an enhancement of neuro- Project has been imaging, generate huge datasets. Much of these data are science research, closer cooperation between labora- supported by the lost after publication to further analysis and exchange tories around the world, and more effective move- National Institute of between laboratories. The Human Brain Project is dedi- ment from research results to understanding of brain Mental Health and cated to developing a new generation of electronic function and improvements in human health. the National methods that can make more efficient and widespread Institute for use of these kinds of data. The methods take the form Selected references Deafness and other 1 Huerta, M.F., Koslow, S.H. and Leshner, A.I. (1993) Trends of neuroinformatics tools: databases and software that Neurosci. 16, 436–438 Communicative can archive neuroscience data and enable these to be 2 Gobel, U. et al. (1994) Proteins 18, 309–317 Disorders (National searched in effective ways. Once developed and 3 Pechura, C. and Martin, J. (1991) Mapping the Brain and its Institutes of Functions. Integrating Enabling Technologies into Neuroscience proven effective, these tools will become as essential Health), and the Research, National Academy Press in conducting neuroscience research as are the data- 4 Roberts, L. (1993) Science 252, 1794 National Aeronautics bases and tools for gene and protein sequences. The 5 Cotton, P. (1993) J. Am. Med. Assoc. 269, 1357 and Space Agency.

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6 Thompson, P.M. and Toga, A.W. (1996) IEEE Trans. Med. 17 Singer, M.S. and Shepherd, G.M. (1994) NeuroReport 5, 1297–300 Imaging 15, 402–417 18 Singer, M.S. et al. (1995) Recept. Channels 3, 89–95 7 Bloom, F.E., Young, W.G. and Kim, Y.K. (1989) Brain Browser, 19 Singer, M.S. and Shepherd, G.M. (1996) Recept. Channels 4, Academic Press 141–147 8 Swanson, L.W. (1995) Trends Neurosci. 18, 471–474 20 Shepherd, G.M., Singer, M.S. and Greer, C.A. (1996) 9 Brinkley, J.F. and Rosse, C. (1997) J. Am. Med. Informatics Assoc. Neuroscientist 2, 262–271 4, 165–183 21 Eccles, J.C., ed. (1958) The Physiology of Nerve Cells, Johns 10 Van Essen, D.C. and Drury, H.A. (1997) J. Neurosci. 17, Hopkins Press 7079–7102 22 Stuart, G. et al. (1997) Trends Neurosci. 20, 125–131 11 Shepherd, G.M. and Greer, C.A. (1998) in The Synaptic 23 Chen, W.R., Midtgaard, J. and Shepherd, G.M. (1997) Science Organization of the Brain (Shepherd, G.M., ed.), pp. 159–203, 278, 463–467 Oxford University Press 24 Mirsky, J.S. et al. (1998) J. Neurosci. Methods 82, 105–121 12 Rall, W. and Shepherd, G.M. (1968) J. Neurophysiol. 31, 25 Hines, M.L. and Carnevale, N.T. (1997) Neural Comput. 9, 884–915 1179–1209 13 Buck, L.D. and Axel, R. (1991) Cell 65, 175–187 26 Segev, I., Rinzel, J. and Shepherd, G.M., eds (1995) The 14 Healy, M.D. et al. (1997) Chem. Senses 22, 321–326 Theoretical Foundation of Dendritic Function, MIT Press 15 Altschul, S.F. et al. (1990) J. Mol. Biol. 215, 403–410 27 Koch, C. and Segev, I., eds (1998) Methods in Neuronal Modeling: 16 Ketchum, K.A. et al. (1995) Nature 376, 690–695 From Synapses to Networks, MIT Press

L ETTERS TO THE EDITOR

the synthetic peptide do not directly block The pathophysiological the channel pore but probably interact with other sites (CNQX with residues mechanism underlying located in the agonist-binding pocket and the synthetic peptide with the antibody Rasmussen’s encephalitis: a debate paratope). (5) Channel-activating sera of rabbits Rasmussen’s encephalitis (RE) is a pediatric (1) Following the immunization of and RE patients labelled iGluR3-trans- syndrome characterized by epileptic seiz- rabbits with a large portion of the extra- fected kidney cells but not cells trans- ures associated with unilateral inflam- cellular N-terminal region of iGluR3 (fused fected with other iGluR subunits4. matory process in the brain1,2. It is, accord- to bacterial trpE protein), the filtered sera It is puzzling that He et al.9 could not ing to several recent lines of evidence, an but also IgG fractions of RE-like symp- replicate these electrophysiological re- autoimmune disease initiated by circulating tomatic rabbits elicited rapid, reversible, cordings, despite the application of plasma autoantibodies that, under certain circum- and voltage-independent opening of cationic ‘filtrates’ or IgG fractions (of RE-like symp- stances, gain access to the brain where channels in kainate (KA)-responsive cul- tomatic rabbits) that contained high titres they interact with the ionotropic glutamate tured neurons4. of anti-iGluR3 antibodies raised against a receptor 3 (iGluR3) subunit3–5. However, (2) Similar currents were also elicited similar antigen that consisted of a fusion the crucial events triggered by these by filtered sera and IgG fractions of RE protein of glutathione-S-transferase–iGluR3 autoantibodies are currently under debate. patients4. (residues 246–455)9 versus TrpE–iGluR3 In a recent article in this journal6,7, I briefly (3) The final passage filtrate of these (residues 246–458)3,4. These experiments described the findings of Twyman et al.4, sera as well as control sera depleted of were performed in KA-responsive cul- who proposed that the pathophysiological low molecular weight substances by re- tured neurons that contained the iGluR3 mechanism underlying RE involves ex- petitive filtration (as carried out for the subunit. No currents were detected by He cessive activation of iGluR3-containing ion sera of symptomatic rabbits and RE et al.9 even in the presence of cyclothia- channels by these autoantibodies and sub- patients) did not elicit currents in KA- zide, a compound that considerably attenu- sequent excitotoxic neuronal cell death. responsive cultured neurons4. These con- ates desensitization of AMPA receptors13. This type of cell death has been implicated trols included sera of pre-immune rabbits, The major observations that support in several brain disorders and chronic sera containing antibodies raised against a the idea that RE is mediated by comple- neurodegenerative diseases8. By contrast, similar portion of iGluR5 and antibodies ment activation are: He et al.9 have recently suggested that against the ␤2 subunit of the neuronal (1) Plasma or serum ‘filtrates’ of RE-like neuronal cell death in RE patients might nicotinic acetylcholine receptor, and sera symptomatic rabbits induce death of cul- result from the activation of the comple- sampled from healthy individuals and tured cortical neurons in a concentration- ment system10,11 by the anti-iGluR3 patients with other neurological diseases. dependent manner9. This cell death was autoantibodies. In any event, both types of Hence, the electrophysiological responses neither inhibited by CNQX nor by a non- mechanism could potentially induce recur- could not be attributed to traces of gluta- competitive AMPA receptor antagonist rent seizures due to uncontrolled acti- mate or other putative low molecular (GYKI 52466), whereas in control experi- vation of ion channels (primarily, by the weight agonists in the sera containing the ments these antagonists did inhibit AMPA- autoantibodies or by glutamate leaking anti-iGluR3 antibodies. induced cell death9. from damaged cells or both), as well as (4) The currents elicited by sera or IgG (2) Neither IgG fractions alone nor IgG- recruitment of various components fractions were inhibited by CNQX, a com- depleted plasma of symptomatic rabbits involved in inflammation to the site of petitive antagonist of AMPA and KA alone induce cell death, but when mixed injured neurons, thus giving rise to the receptors, as well as by a synthetic peptide together they reconstituted the cytotoxic symptoms, clinical features and histo- corresponding to the suspected antigenic capability9. However, IgG-depleted plasma pathology typical of RE (Refs 1–3,12). epitope (residues 372–395 of iGluR3). which was preincubated at 56°C for 30 min The hypothesis of excitotoxicity-medi- These inhibitory effects were specific in order to heat-inactivate the complement ated neuronal cell death in RE is mainly (see Ref. 4 for controls) and voltage- system failed to reconstitute the cytotoxic based on the following observations: independent, indicating that CNQX and capability9.

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