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  1. 1. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 1 of 15 NEUROINFORMATICS – REVIEW ARTICLES 1: Cell Mol Biol (Noisy-le-grand). 2007 Jan 20;52(6):16-23. The increasing influence of medical image processing in clinical neuroimaging. Barillot C. CNRS VisAGeS U746 Unit/Project, IRISA UMR 6074 Campus de Beaulieu, Rennes France. This paper review the evolution of clinical neuroinformatics domain in the passed and gives an outlook how this research field will evolve in clinical neurology (e.g. Epilepsy, Multiple Sclerosis, Dementia) and neurosurgery (e.g. image guided surgery, intra-operative imaging, the definition of the Operation Room of the future). These different issues, as addressed by the VisAGeS research team, are discussed in more details and the benefits of a close collaboration between clinical scientists (radiologist, neurologist and neurosurgeon) and computer scientists are shown to give adequate answers to the series of problems which needs to be solved for a more effective use of medical images in clinical neurosciences. 2: Neuroinformatics. 2007 Winter; 5(1):79-94. Web-based method for translating neurodevelopment from laboratory species to humans. Clancy B, Kersh B, Hyde J, Darlington RB, Anand KJ, Finlay BL. Department of Biology, University of Central Arkansas, Conway, Arkansas; Dept of Pediatrics, Neurobiology & Developmental Sciences, University of Arkansas for Medical Sciences, Arkansas Children's Hospital Research Institute, Little Rock, Arkansas. Biomedical researchers and medical professionals are regularly required to compare a vast quantity of neurodevelopmental literature obtained from an assortment of mammals whose brains grow at diverse rates, including fast developing experimental rodent species and slower developing humans. In this article, we introduce a database-driven website, which was created to address this problem using statistical-based algorithms to integrate hundreds of empirically derived developing neural events in 10 mammalian species ( The site, based on a statistical model that has evolved over the past decade, currently incorporates 102 different neurodevelopmental events obtained from 10 species: hamsters, mice, rats, rabbits, spiny mice, guinea pigs, ferrets, cats, rhesus monkeys, and humans. Data are arranged in a Structured Query Language database, which allows comparative brain development measured in postconception days to be converted and accessed in real time, using Hypertext Preprocessor language. Algorithms applied to the database also allow predictions for dates of specific neurodevelopmental events where empirical data are not available, including for the human embryo and fetus. By designing a web-based portal, we seek to make these comparative data readily available to all those who need to efficiently estimate the timing of neurodevelopmental events in the human fetus, laboratory species, or across several different species. In an effort to further refine and expand the applicability of this database, we include a mechanism to submit additional data.
  2. 2. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 2 of 15 3: Neuroinformatics. 2007 Winter;5(1):35-58. Toward a workbench for rodent brain image data: systems architecture and design. Moene IA, Subramaniam S, Darin D, Leergaard TB, Bjaalie JG. Neural Systems and Graphics Computing Laboratory, Centre for Molecular Biology and Neuroscience & Institute of Basic Medical Sciences, University of Oslo, P.O. Box 1105 Blindern, N-0317 Oslo, Norway. We present a novel system for storing and manipulating microscopic images from sections through the brain and higher-level data extracted from such images. The system is designed and built on a three-tier paradigm and provides the research community with a web-based interface for facile use in neuroscience research. The Oracle relational database management system provides the ability to store a variety of objects relevant to the images and provides the framework for complex querying of data stored in the system. Further, the suite of applications intimately tied into the infrastructure in the application layer provide the user the ability not only to query and visualize the data, but also to perform analysis operations based on the tools embedded into the system. The presentation layer uses extant protocols of the modern web browser and this provides ease of use of the system. The present release, named Functional Anatomy of the Cerebro-Cerebellar System (FACCS), available through The Rodent Brain Workbench (http://, is targeted at the functional anatomy of the cerebro-cerebellar system in rats, and holds axonal tracing data from these projections. The system is extensible to other circuits and projections and to other categories of image data and provides a unique environment for analysis of rodent brain maps in the context of anatomical data. The FACCS application assumes standard animal brain atlas models and can be extended to future models. The system is available both for interactive use from a remote web-browser client as well as for download to a local server machine. 4: Neuroinformatics. 2007 Winter; 5(1):1-2. A second look back. De Schutter E. Theoretical Neurobiology, University of Antwerp, Belgium. This issue opens the fifth volume of Neuroinformatics, which is a good time to look at how the journal is doing, as it has evolved quite a bit as I wrote a similar editorial for the second volume (De Schutter, 2004). What has not changed is that we are very proud about our editorial work. Our impact factor is excellent for a journal with a strong emphasis on informatics and methods, we started at 3.0 for 2004 and are now at 3.9. This puts us heads and shoulders above all computational neuroscience, machine learning, and neuroscience methods' journals. We rank in the top-half of neuroscience journals, better than many classic neuroscience titles, and do even better in informatics in which we are ranked fourth in interdisciplinary computer science. This high impact factor is supported by two trends, a positive and a negative one. Rather negative is that we publish relatively few articles, in fact, the third and fourth volumes contained a quarter less articles than the first two. This helps of course with the impact factor but also reflects a rather low article submission rate. We expect that the good impact factor will help to solve this problem but will also make sure that a higher influx of manuscripts will not lead to a lowering of the quality of the journal. Nevertheless, this volume will still include only four issues; the increase to six volumes has been postponed till we get a permanent increase in article submission.
  3. 3. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 3 of 15 5: Neurotoxicology. 2007 Feb 15; [Epub ahead of print] Extrapolating brain development from experimental species to humans. Clancy B, Finlay BL, Darlington RB, Anand KJ. University of Central Arkansas, AR, United States; University of Arkansas for Medical Sciences Little Rock, AR, United States. To better understand the neurotoxic effects of diverse hazards on the developing human nervous system, researchers and clinicians rely on data collected from a number of model species that develop and mature at varying rates. We review the methods commonly used to extrapolate the timing of brain development from experimental mammalian species to humans, including morphological comparisons, "rules of thumb" and "event-based" analyses. Most are unavoidably limited in range or detail, many are necessarily restricted to rat/human comparisons, and few can identify brain regions that develop at different rates. We suggest this issue is best addressed using "neuroinformatics", an analysis that combines neuroscience, evolutionary science, statistical modeling and computer science. A current use of this approach relates numeric values assigned to 10 mammalian species and hundreds of empirically derived developing neural events, including specific evolutionary advances in primates. The result is an accessible, online resource ( that can be used to equate dates in the neurodevelopmental literature across laboratory species to humans, predict neurodevelopmental events for which data are lacking in humans, and help to develop clinically relevant experimental models. 6: Methods Inf Med. 2007;46(2):142-6. Neural engineering--a new discipline for analyzing and interacting with the nervous system. Durand DM. Neural Engineering Center, Wickenden 112, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA. OBJECTIVES: The field of neural engineering focuses on an area of research at the interface between neuroscience and engineering. The area of neural engineering was first associated with the brain machine interface but is much broader and encompasses experimental, computational, and theoretical aspects of neural interfacing, neuroelectronics, neuromechanical systems, neuroinformatics, neuroimaging, neural prostheses, artificial and biological neural circuits, neural control, neural tissue regeneration, neural signal processing, neural modelling and neuro-computation. One of the goals of neural engineering is to develop a selective interface for the peripheral nervous system. METHODS: Nerve cuffs electrodes have been developed to either reshape or maintain the nerve into an elongated shape in order to increase the circumference to cross sectional ratio. It is then possible to place many electrodes around the nerve to achieve selectivity. This new cuff (flat interface nerve electrode: FINE) was applied to the hypoglossal nerve and the sciatic nerve in dogs and cats to estimate the selectivity of the interface. RESULTS: By placing many contacts close to the axons, three different types of selectivity were achieved: 1) The FINE could generate a high degree of stimulation selectivity as estimated by the individual fascicle recording. 2) Similarly, recording selectivity was also demonstrated and blind source algorithms were applied to recover the signals. 3) Finally, by placing arrays of electrodes along the nerve, small fiber diameters could be excited before large fibers thereby reversing the recruitment order. CONCLUSION: Taking advantage of the fact that nerves are not round
  4. 4. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 4 of 15 but oblong or flat allows a novel design for selective nerve interface with the peripheral nervous system. This new design has found applications in many disorders of the nervous system such as bladder incontinence, obstructive sleep apnea and stroke. 7: Neuroinformatics. 2006 Winter;4(4):319-20. Brain maps and connectivity representation. Vercelli A. Department of Anatomy, Pharmacology, and Forensic Medicine, University of Torino, Corso M. D'Azeglio 52, 10126 Torino, Italy. 8: Neuroinformatics. 2006 Winter;4(4):299-317. Neuroanatomical affiliation visualization-interface system. Palombi O, Shin JW, Watson C, Paxinos G. POWMRI, The University of New South Wales, Randwick NSW, Australia. A number of knowledge management systems have been developed to allow users to have access to large quantity of neuroanatomical data. The advent of three-dimensional (3D) visualization techniques allows users to interact with complex 3D object. In order to better understand the structural and functional organization of the brain, we present Neuroanatomical Affiliations Visualization-Interface System (NAVIS) as the original software to see brain structures and neuroanatomical affiliations in 3D. This version of NAVIS has made use of the fifth edition of "The Rat Brain in Stereotaxic coordinates" (Paxinos and Watson, 2005). The NAVIS development environment was based on the scripting language name Python, using visualization toolkit (VTK) as 3D-library and wxPython for the graphic user interface. The following manuscript is focused on the nucleus of the solitary tract (Sol) and the set of affiliated structures in the brain to illustrate the functionality of NAVIS. The nucleus of the Sol is the primary relay center of visceral and taste information, and consists of 14 distinct subnuclei that differ in cytoarchitecture, chemoarchitecture, connections, and function. In the present study, neuroanatomical projection data of the rat Sol were collected from selected literature in PubMed since 1975. Forty-nine identified projection data of Sol were inserted in NAVIS. The standard XML format used as an input for affiliation data allows NAVIS to update data online and/or allows users to manually change or update affiliation data. NAVIS can be extended to nuclei other than Sol. 9: Neuroinformatics. 2006 Winter;4(4):275-98. A new module for on-line manipulation and display of molecular information in the brain architecture management system. Bota M, Swanson LW. The Neuroscience Research Institute, University of Southern California, Los Angeles, California 90089-2520, USA. A new "Molecules" module of the Brain Architecture Management System (BAMS; is described. With this module, BAMS becomes the first online knowledge management system to handle central nervous system (CNS) region and celltype chemoarchitectonic data in the context of axonal connections between regions and cell types, in multiple species. The "Molecules" module implements a general knowledge representation schema for data and metadata collated from published and unpublished material, and allows insertion of complex reports about the presence of molecules collated
  5. 5. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 5 of 15 from the literature. For different CNS neural regions and cell types, the module's database structure includes representation of molecule expression revealed by various techniques including in situ hybridization and immunohistochemistry, molecule coexpression and time- dependent level changes, and physiological state of subjects. The metadata representation allows online comparison and evaluation of inserted experiments, and "Molecules"structure allows rapid development of data transfer protocols enabling neuroinformatics visualization tools to display gene expression patterns residing in BAMS, in terms of levels of expressed molecules and in situ hybridization data. The module's web interface allows users to construct lists of CNS regions containing a molecule (depending on physiological state), retrieve further details about inserted records, compare time-dependent data within and across experiments, reconstruct gene expression patterns, and construct complex reports from individual experiments. 10: Neuroinformatics. 2006 Winter;4(4):271-3. Where's the beef? Missing data in the information age. Kennedy D. 11: Neuroinformatics. 2006 Summer;4(3):213-6. The ups and downs of neuroscience shares. Ascoli GA. 12: Neuroinformatics. 2006;4(2):199-212. A general XML schema and SPM toolbox for storage of neuro-imaging results and anatomical labels. Keator DB, Gadde S, Grethe JS, Taylor DV, Potkin SG; FIRST BIRN. University of California, Irvine, CA. With the increased frequency of multisite, large-scale collaborative neuro-imaging studies, the need for a general, self-documenting framework for the storage and retrieval of activation maps and anatomical labels becomes evident. To address this need, we have developed and extensible markup language (XML) schema and associated tools for the storage of neuro-imaging activation maps and anatomical labels. This schema, as part of the XML- based Clinical Experiment Data Exchange (XCEDE) schema, provides storage capabilities for analysis annotations, activation threshold parameters, and cluster and voxel-level statistics. Activation parameters contain information describing the threshold, degrees of freedom, FWHM smoothness, search volumes, voxel sizes, expected voxels per cluster, and expected number of clusters in the statistical map. Cluster and voxel statistics can be stored along with the coordinates, threshold, and anatomical label information. Multiple threshold types can be documented for a given cluster or voxel along with the uncorrected and corrected probability values. Multiple atlases can be used to generate anatomical labels and stored for each significant voxel or cluter. Additionally, a toolbox for Statistical Parametric Mapping software (http://www. fil. was created to capture the results from activation maps using the XML schema that supports both SPM99 and SPM2 versions ( Applications/xcede/SPM_XMLTools.htm). Support for anatomical labeling is available via the Talairach Daemon (http://ric.uthscsa. edu/projects/talairachdaemon.html) and Automated Anatomical Labeling (http://www.
  6. 6. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 6 of 15 13: Neuroinformatics. 2006;4(2):139-62. NeuroScholar's electronic laboratory notebook and its application to neuroendocrinology. Khan AM, Hahn JD, Cheng WC, Watts AG, Burns GA. Neuroscience Research Institute, Department of Biological Sciences, 3641 Watt Way, Hedco Neurosciences Building, University of Southern California, Los Angeles, CA 90089-2520, USA. Scientists continually relate information from the published literature to their current research. The challenge of this essential and time-consuming activity increases as the body of scientific literature continues to grow. In an attempt to lessen the challenge, we have developed an Electronic Laboratory Notebook (ELN) application. Our ELN functions as a component of another application we have developed, an open-source knowledge management system for the neuroscientific literature called NeuroScholar (http://www. neuroscholar. org/). Scanned notebook pages, images, and data files are entered into the ELN, where they can be annotated, organized, and linked to similarly annotated excerpts from the published literature within Neuroscholar. Associations between these knowledge constructs are created within a dynamic node-and-edge user interface. To produce an interactive, adaptable knowledge base. We demonstrate the ELN's utility by using it to organize data and literature related to our studies of the neuroendocrine hypothalamic paraventricular nucleus (PVH). We also discuss how the ELN could be applied to model other neuroendocrine systems; as an example we look at the role of PVH stressor- responsive neurons in the context of their involvement in the suppression of reproductive function. We present this application to the community as open-source software and invite contributions to its development. 14: Neuroinformatics. 2006;4(2):131-8. A view of the digital landscape for neuroscience at NIH. Huerta MF, Liu Y, Glanzman DL. 15: Neuroinformatics. 2006;4(2):129-30. On the future of the human brain project. De Schutter E, Ascoli GA, Kennedy DN. 16: Proc Natl Acad Sci U S A. 2006 Jul 11;103(28):10775-80. Epub 2006 Jun 30. Arithmetic processing in the brain shaped by cultures. Tang Y, Zhang W, Chen K, Feng S, Ji Y, Shen J, Reiman EM, Liu Y. Institute of Neuroinformatics and Laboratory for Brain and Mind, Dalian University of Technology, Dalian 116023, China. The universal use of Arabic numbers in mathematics raises a question whether these digits are processed the same way in people speaking various languages, such as Chinese and English, which reflect differences in Eastern and Western cultures. Using functional MRI, we demonstrated a differential cortical representation of numbers between native Chinese and English speakers. Contrasting to native English speakers, who largely employ a
  7. 7. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 7 of 15 language process that relies on the left perisylvian cortices for mental calculation such as a simple addition task, native Chinese speakers, instead, engage a visuo-premotor association network for the same task. Whereas in both groups the inferior parietal cortex was activated by a task for numerical quantity comparison, functional MRI connectivity analyses revealed a functional distinction between Chinese and English groups among the brain networks involved in the task. Our results further indicate that the different biological encoding of numbers may be shaped by visual reading experience during language acquisition and other cultural factors such as mathematics learning strategies and education systems, which cannot be explained completely by the differences in languages per se. 17: Neuroinformatics. 2006 Winter;4(1):51-64. Imaging genomics applied to anxiety, stress response, and resiliency. Xu K, Ernst M, Goldman D. Laboratory of Neurogenetics, National Institute on Alcohol Abuse and Alcohollism, National Institute of Health, Rockville, MD 20852, USA. Anxiety and stress response/resiliency are heritable traits central to the etiology of multiple psychiatric diseases, but efforts to identify genetic variation influencing this broad domain of neurobiological function are hampered by the coarseness of the phenotypic measures and the effects of environmental factors. Neuroimaging offers a powerful approach for assessing functional neuronal activity. Neurophysiological measures can serve as intermediate phenotypes more directly linked to small gene effects, compared with behavioral end points of neural dysfunction. Imaging genomics is a relatively new research area that is concerned with linking functional gene variants and brain information processing. Here, we will focus on processes affected by anxiety and stress. Neuroimaging has been combined with genetic analysis to reveal genetic effects of functional variants of the serotonin transporter (5-HTT) and catechol-O-methyltransferase (COMT) genes on brain response to stressful stimuli. The low-expressing allele of the 5-HTT promoter polymorphism (HTTLPR) is associated with anxiety and with greater amygdala and other regional responses to emotional. The COMT Met158 allele leads to lower COMT activity and has also been associated with anxiety, and the effect of this gene is apparently additive with HTTLPR. Individuals with Met158 genotypes are more sensitive to pain stress and, as shown by C11 Carfentanil imaging, have diminished ability to upregulate opioid release after pain/stress. These results suggest that functional variants of 5-HTT and COMT impact brain functions involved in stress and anxiety. 18: J Affect Disord. 2006 May;92(1):133-8. Epub 2006 Feb 20. Neuroinformatics: a new tool for studying the brain. Bloom FE, Morrison JH, Young WG. Neurome Inc., 11149 North Torrey Pines Road, La Jolla, CA 92037, USA. BACKGROUND: Central nervous system diseases constitute a major target for drug development. Genes expressed by the nervous system may represent half or more of the mammalian genome, with literally tens of thousands of gene products. METHODS: Better methods are therefore required to accelerate the pace of mapping gene expression patterns in the mouse brain and to evaluate the progressive phenotypic changes in genetic models of human brain diseases. CONCLUSIONS: Recent studies of mouse models of Amyotrophic Lateral Sclerosis and Alzheimer's disease illustrate how such data could be used for drug development. Since these two diseases-- especially Alzheimer's Disease-- entail disordered
  8. 8. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 8 of 15 behavior, cognition and emotions, the framework and the methodology described in this article might in the future find applications in research on affective disorders. 19: Science. 2006 Jan 13;311(5758):176. Continuing progress in neuroinformatics. Gazzaniga MS, Van Horn JD, Bloom F, Shepherd GM, Raichle M, Jones E. 20: Neuroinformatics. 2005;3(4):315-8. Dangerous phase. Schiff SJ. Krasnow Institute, George Mason University, Fairfax, VA, USA. "Use a quiet reference." How many times have we heard this mantra during training or practice, interpreting electroencephalogram (EEG) tracings, or implanting intracranial electrodes? How many of us have used common reference EEG for synchrony studies in recent years? Far too many. Perhaps one source of this problem is the number 104. This is the relatively small number of citations to the reference Fein et al. (1988), which should have put to rest any further use of referential EEG for coherence measurements. And in retrospect, a more careful reading by us of Nunez's (1981) text would have instructed us not to do this. How such warnings have managed to escape integration into common knowledge and practice is troublesome. Electrical potentials are all measured with respect to other potentials. Technically, a potential difference is calculated by integrating the electrical field over a given path from one place to another in EEG terms, we mea sure a potential with respect to another potential, measured at one or more electrodes. All EEG potential measurements reflect the paths used to measure those potentials, and do not directly reflect localized regions of the brain beneath one electrode. Worse, in scalp EEG, the layers of cerebrospinal fluid, dura, skull, and scalp serve to smooth, filter, spread out, and redirect currents generated within the brain so that the measured scalp potentials bear a rather tenuous relationship to the underlying (presumably dipole) current sources. In calculating coherence, it is easy to show that if the potential differences are all made with respect to a common reference, then the amplitude of the reference can dominate the coherence estimate (Fein et al., 1988). In recent years, phase synchronization has been increasingly applied to analyze the dynamics of nonlinear systems (Pikovsky et al., 2000). In Guevara et al. (in this issue), we see the extension of Fein's results for phase coherency. The geometry of Fig. 1 in Guevara et al. should be imprinted on all of us the amplitude of a common reference can dominate the calculated phase synchronization. There is far too much literature within the past decade that calculated phase synchronization from common referenced EEG.The good news is that the fix to remove common reference artifacts is simple. The bad news is that the interpretation of reference- free synchronization results from brain signals requires considerable caution. 21: Neuroinformatics. 2005;3(4):287-92. The impact of neuroinformatics. Kennedy DN.
  9. 9. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 9 of 15 22: Neurosurg Focus. 2005 Oct 15;19(4):E4. Bioinformatics and functional magnetic resonance imaging in clinical populations: practical aspects of data collection, analysis, interpretation, and management. Vincent DJ, Hurd MW. Department of Radiology, Medical University of South Carolina, Charleston, South Carolina 29425, USA. In this paper the authors review the issues associated with bioinformatics and functional magnetic resonance (fMR) imaging in the context of neurosurgery. They discuss the practical aspects of data collection, analysis, interpretation, and the management of large data sets, and they consider the challenges involved in the adoption of fMR imaging into clinical neurosurgical practice. Their goal is to provide neurosurgeons and other clinicians with a better understanding of some of the current issues associated with bioinformatics or neuroinformatics and fMR imaging. Thousands to tens of thousands of images are typically acquired during an fMR imaging session. It is essential to follow an activation task paradigm exactly to obtain an accurate representation of cortical activation. These images are then interactively postprocessed offline to produce an activation map, or in some cases a series of maps. The maps may then be viewed and interpreted in consultation with a neurosurgeon and/or other clinicians. After this consultation, long-term archiving of the processed fMR activation maps along with the standard structural MR images is a complex but necessary final step in this process. The fMR modality represents a valuable tool in the neurosurgical planning process that is still in the developmental stages for routine clinical use, but holds exceptional promise for patient care. 23: Neuroinformatics. 2005;3(3):243-62. Methods for quantifying the informational structure of sensory and motor data. Lungarella M, Pegors T, Bulwinkle D, Sporns O. Department of Mechano-Informatics, School of Information Science and Technology, University of Tokyo, 113-8656 Tokyo, Japan. Embodied agents (organisms and robots) are situated in specific environments sampled by their sensors and within which they carry out motor activity. Their control architectures or nervous systems attend to and process streams of sensory stimulation, and ultimately generate sequences of motor actions, which in turn affect the selection of information. Thus, sensory input and motor activity are continuously and dynamically coupled with the surrounding environment. In this article, we propose that the ability of embodied agents to actively structure their sensory input and to generate statistical regularities represents a major functional rationale for the dynamic coupling between sensory and motor systems. Statistical regularities in the multimodal sensory data relayed to the brain are critical for enabling appropriate developmental processes, perceptual categorization, adaptation, and learning. To characterize the informational structure of sensory and motor data, we introduce and illustrate a set of univariate and multivariate statistical measures (available in an accompanying Matlab toolbox). We show how such measures can be used to quantify the information structure in sensory and motor channels of a robot capable of saliency-based attentional behavior, and discuss their potential importance for understanding sensorimotor coordination in organisms and for robot design.
  10. 10. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 10 of 15 24: Neuroinformatics. 2005;3(2):163-6. A new era in computational neuroscience. Blackwell KT. School of Computational Sciences, and the Krasnow Institute of Advanced Studies, Rockfish Creek Lane, MS 2A1 George Mason University, Fairfax, VA 22030, USA. 25: Neuroinformatics. 2005;3(2):115-31. Comparison of vector space model methodologies to reconcile cross-species neuroanatomical concepts. Srinivas PR, Wei SH, Cristianini N, Jones EG, Gorin FA. Center for Neuroscience, UC Davis, Davis, CA, USA. Generating informational thesauri that classify, cross-reference, and retrieve diverse and highly detailed neuroscientific information requires identifying related neuroanatomical terms and acronyms within and between species (Gorin et al., 2001) Manual construction of such informational thesauri is laborious, and we describe implementing and evaluating a neuroanatomical term and acronym reconciliation (NTAR) system to assist domain experts with this task. NTAR is composed of two modules. The neuroanatomical term extraction (NTE) module employs a hidden Markov model (HMM) in conjunction with lexical rules to extract neuroanatomical terms (NT) and acronyms (NA) from textual material. The output of the NTE is formatted into collections of term- or acronym-indexed documents composed of sentences and word phrases extracted from textual material. The second information retrieval (IR) module utilizes a vector space model (VSM) and includes a novel, automated relevance feedback algorithm. The IR module retrieves statistically related neuroanatomical terms and acronyms in response to queried neuroanatomical terms and acronyms. Neuroanatomical terms and acronyms retrieval obtained from term-based inquiries were compared with (1) term retrieval obtained by including automated relevance feedback and with (2) term retrieval using "document-to-document" comparisons (context-based VSM). The retrieval of synonymous and similar primate and macaque thalamic terms and acronyms in response to a query list of human thalamic terminology by these three IR approaches was compared against a previously published, manually constructed concordance table of homologous cross-species terms and acronyms. Term-based VSM with automated relevance feedback retrieved 70% and 80% of these primate and macaque terms and acronyms, respectively, listed in the concordance table. Automated feedback algorithm correctly identified 87% of the macaque terms and acronyms that were independently selected by a domain expert as being appropriate for manual relevance feedback. Context- based VSM correctly retrieved 97% and 98% of the primate and macaque terms and acronyms listed in the term homology table. These results indicate that the NTAR system could assist neuroscientists with thesauri creation for closely related, highly detailed neuroanatomical domains. 26: Genet Mol Res. 2004 Dec 30;3(4):564-74. Bioinformatics: perspectives for the future. Costa Lda F. Cybernetic Vision Research Group, Institute of Physics at São Carlos, University of São Paulo, Caixa Postal 369, 13560-970 São Carlos, SP, Brazil.
  11. 11. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 11 of 15 I give here a very personal perspective of Bioinformatics and its future, starting by discussing the origin of the term (and area) of bioinformatics and proceeding by trying to foresee the development of related issues, including pattern recognition/data mining, the need to reintegrate biology, the potential of complex networks as a powerful and flexible framework for bioinformatics and the interplay between bio- and neuroinformatics. Human resource formation and market perspective are also addressed. Given the complexity and vastness of these issues and concepts, as well as the limited size of a scientific article and finite patience of the reader, these perspectives are surely incomplete and biased. However, it is expected that some of the questions and trends that are identified will motivate discussions during the IcoBiCoBi round table (with the same name as this article) and perhaps provide a more ample perspective among the participants of that conference and the readers of this text. 27: J Biomed Inform. 2004 Oct;37(5):380-91. Multivariate image analysis in biomedicine. Nattkemper TW. Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, P.O. Box 100131, D- 33501 Bielefeld, Germany. In recent years, multivariate imaging techniques are developed and applied in biomedical research in an increasing degree. In research projects and in clinical studies as well m- dimensional multivariate images (MVI) are recorded and stored to databases for a subsequent analysis. The complexity of the m-dimensional data and the growing number of high throughput applications call for new strategies for the application of image processing and data mining to support the direct interactive analysis by human experts. This article provides an overview of proposed approaches for MVI analysis in biomedicine. After summarizing the biomedical MVI techniques the two level framework for MVI analysis is illustrated. Following this framework, the state-of-the-art solutions from the fields of image processing and data mining are reviewed and discussed. Motivations for MVI data mining in biology and medicine are characterized, followed by an overview of graphical and auditory approaches for interactive data exploration. The paper concludes with summarizing open problems in MVI analysis and remarks upon the future development of biomedical MVI analysis. 28: Neuroinformatics. 2004;2(3):271-4. A gateway to the future of neuroinformatics. Gardner D, Shepherd GM. Laboratory of Neuroinformatics, Department of Physiology and Biophysics, Weill Medical College of Cornell University, NY, USA. 29: Neuroinformatics. 2004;2(2):145-62. The small world of the cerebral cortex. Sporns O, Zwi JD. Department of Psychology, Indiana University, Bloomington 47405, USA. While much information is available on the structural connectivity of the cerebral cortex, especially in the primate, the main organizational principles of the connection patterns linking brain areas, columns and individual cells have remained elusive. We attempt to characterize
  12. 12. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 12 of 15 a wide variety of cortical connectivity data sets using a specific set of graph theory methods. We measure global aspects of cortical graphs including the abundance of small structural motifs such as cycles, the degree of local clustering of connections and the average path length. We examine large-scale cortical connection matrices obtained from neuroanatomical data bases, as well as probabilistic connection matrices at the level of small cortical neuronal populations linked by intra-areal and inter-areal connections. All cortical connection matrices examined in this study exhibit "small-world" attributes, characterized by the presence of abundant clustering of connections combined with short average distances between neuronal elements. We discuss the significance of these universal organizational features of cortex in light of functional brain anatomy. Supplementary materials are at 30: Neuroinformatics. 2004;2(2):127-44. Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database. Kötter R. C & O Vogt Brain Research Institute, Heinrich Heine University Düsseldorf, Moorenstr. 5, D-40225, Germany. Connectivity is the key to understanding distributed and cooperative brain functions. Detailed and comprehensive data on large-scale connectivity between primate brain areas have been collated systematically from published reports of experimental tracing studies. Although the majority of the data have been made easily available for online retrieval, the multiplicity of brain maps and the precise requirements of anatomical naming limit the intuitive access to the data. The quality of data retrieval can be improved by observing a small set of conventions in data representation. Standardized interfaces open up further opportunities for automated search and retrieval, for flexible visualization of data, and for interoperability with other databases. This article provides a discussion and examples in text and image of the capabilities of the online interface to the CoCoMac database of primate connectivity. These serve to point out sources of potential confusion and failure, and to demonstrate the automated interfacing with other neuroinformatics resources that facilitate selection and processing of connectivity data, for example, for computational modelling and interpretation of functional imaging studies. 31: Annu Rev Neurosci. 2004;27:419-51. Neuronal circuits of the neocortex. Douglas RJ, Martin KA. Institute of Neuroinformatics, University/ETH Zurich, Zurich 8057, Switzerland. We explore the extent to which neocortical circuits generalize, i.e., to what extent can neocortical neurons and the circuits they form be considered as canonical? We find that, as has long been suspected by cortical neuroanatomists, the same basic laminar and tangential organization of the excitatory neurons of the neocortex is evident wherever it has been sought. Similarly, the inhibitory neurons show characteristic morphology and patterns of connections throughout the neocortex. We offer a simple model of cortical processing that is consistent with the major features of cortical circuits: The superficial layer neurons within local patches of cortex, and within areas, cooperate to explore all possible interpretations of different cortical input and cooperatively select an interpretation consistent with their various cortical and subcortical inputs.
  13. 13. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 13 of 15 32: Nat Neurosci. 2004 May;7(5):467-72. E-neuroscience: challenges and triumphs in integrating distributed data from molecules to brains. Martone ME, Gupta A, Ellisman MH. Department of Neurosciences, National Center for Microscopy and Imaging Research and The Center for Research in Biological Systems, The University of California San Diego, La Jolla, California 92093- 0608, USA. Imaging, from magnetic resonance imaging (MRI) to localization of specific macromolecules by microscopies, has been one of the driving forces behind neuroinformatics efforts of the past decade. Many web-accessible resources have been created, ranging from simple data collections to highly structured databases. Although many challenges remain in adapting neuroscience to the new electronic forum envisioned by neuroinformatics proponents, these efforts have succeeded in formalizing the requirements for effective data sharing and data integration across multiple sources. In this perspective, we discuss the importance of spatial systems and ontologies for proper modeling of neuroscience data and their use in a large-scale data integration effort, the Biomedical Informatics Research Network (BIRN). 33: Neuroinformatics. 2003;1(1):81-109. Tools and approaches for the construction of knowledge models from the neuroscientific literature. Burns GA, Khan AM, Ghandeharizadeh S, O'Neill MA, Chen YS. K-Mechanics Research Group, 3641 Watt Way, Hedco Neuroscience Building, University of Southern California, Los Angeles, CA 90089-2520, USA. Within this paper, we describe a neuroinformatics project (called "NeuroScholar," that enables researchers to examine, manage, manipulate, and use the information contained within the published neuroscientific literature. The project is built within a multi-level, multi-component framework constructed with the use of software engineering methods that themselves provide code-building functionality for neuroinformaticians. We describe the different software layers of the system. First, we present a hypothetical usage scenario illustrating how NeuroScholar permits users to address large-scale questions in a way that would otherwise be impossible. We do this by applying NeuroScholar to a "real-world" neuroscience question: How is stress-related information processed in the brain? We then explain how the overall design of NeuroScholar enables the system to work and illustrate different components of the user interface. We then describe the knowledge management strategy we use to store interpretations. Finally, we describe the software engineering framework we have devised (called the "View-Primitive- Data Model framework," [VPDMf]) to provide an open-source, accelerated software development environment for the project. We believe that NeuroScholar will be useful to experimental neuroscientists by helping them interact with the primary neuroscientific literature in a meaningful way, and to neuroinformaticians by providing them with useful, affordable software engineering tools.
  14. 14. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 14 of 15 34: Neuroinformatics. 2003;1(1):65-80. A percolation approach to neural morphometry and connectivity. Costa Lda F, Manoel ET. Cybernetic Vision Research Group, IFSC-USP, Caixa Postal 369, 13560-970, São Carlos, SP, Brazil. This article addresses the issues of neural shape characterization and analysis from the perspective of one of the main roles played by neural shapes, namely, connectivity. This study is oriented toward the geometry at the individual cell level and involves the use of the percolation concept from statistical mechanics, which is reviewed in an accessible fashion. The characterization of the neural cell geometry with respect to connectivity is performed in terms of critical percolation probability obtained experimentally while considering several types of geometrical interactions between cells, therefore directly expressing the potential for connections defined by each situation. Two basic situations are considered: dendrite- dendrite and dendrite-axon interactions. The obtained results corroborate the potential of the critical percolation probability as a valuable resource for characterizing, classifying, and analyzing the morphology of neural cells. 35: Neuroinformatics. 2003;1(1):1-2. An information science infrastructure for neuroscience. Ascoli GA, De Schutter E, Kennedy DN. 36: Neuroinformatics. 2003;1(2):149-65. Neuroscience data and tool sharing: a legal and policy framework for neuroinformatics. Eckersley P, Egan GF, Amari S, Beltrame F, Bennett R, Bjaalie JG, Dalkara T, De Schutter E, Gonzalez C, Grillner S, Herz A, Hoffmann KP, Jaaskelainen IP, Koslow SH, Lee SY, Matthiessen L, Miller PL, da Silva FM, Novak M, Ravindranath V, Ritz R, Ruotsalainen U, Subramaniam S, Toga AW, Usui S, van Pelt J, Verschure P, Willshaw D, Wrobel A, Tang Y; OECD Working Group on Neuroinformatics. Department of Computer Science & Software Engineering, Intellectual Property Research Institute of Australia, The University of Melbourne. The requirements for neuroinformatics to make a significant impact on neuroscience are not simply technical--the hardware, software, and protocols for collaborative research--they also include the legal and policy frameworks within which projects operate. This is not least because the creation of large collaborative scientific databases amplifies the complicated interactions between proprietary, for-profit R&D and public "open science." In this paper, we draw on experiences from the field of genomics to examine some of the likely consequences of these interactions in neuroscience. Facilitating the widespread sharing of data and tools for neuroscientific research will accelerate the development of neuroinformatics. We propose approaches to overcome the cultural and legal barriers that have slowed these developments to date. We also draw on legal strategies employed by the Free Software community, in suggesting frameworks neuroinformatics might adopt to reinforce the role of public-science databases, and propose a mechanism for identifying and allowing "open science" uses for data whilst still permitting flexible licensing for secondary commercial research. 37: Neuroinformatics. 2003;1(2):145-7. From data to knowledge. Ascoli GA.
  15. 15. neuroinformaticsref1docdocdoc4105.doc 1/29/15 Page 15 of 15 38: Neuroinformatics. 2003;1(4):397-410. The informatics of a C57BL/6J mouse brain atlas. MacKenzie-Graham A, Jones ES, Shattuck DW, Dinov ID, Bota M, Toga AW. Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles, CA. The Mouse Atlas Project (MAP) aims to produce a framework for organizing and analyzing the large volumes of neuroscientific data produced by the proliferation of genetically modified animals. Atlases provide an invaluable aid in understanding the impact of genetic manipulations by providing a standard for comparison. We use a digital atlas as the hub of an informatics network, correlating imaging data, such as structural imaging and histology, with text-based data, such as nomenclature, connections, and references. We generated brain volumes using magnetic resonance microscopy (MRM), classical histology, and immunohistochemistry, and registered them into a common and defined coordinate system. Specially designed viewers were developed in order to visualize multiple datasets simultaneously and to coordinate between textual and image data. Researchers can navigate through the brain interchangeably, in either a text-based or image-based representation that automatically updates information as they move. The atlas also allows the independent entry of other types of data, the facile retrieval of information, and the straight-forward display of images. In conjunction with centralized servers, image and text data can be kept current and can decrease the burden on individual researchers' computers. A comprehensive framework that encompasses many forms of information in the context of anatomic imaging holds tremendous promise for producing new insights. The atlas and associated tools can be found at 39: Neuroinformatics. 2003;1(4):379-95. The cell-centered database: a database for multiscale structural and protein localization data from light and electron microscopy. Martone ME, Zhang S, Gupta A, Qian X, He H, Price DL, Wong M, Santini S, Ellisman MH. Department of Neurosciences, University of California at San Diego, San Diego, CA, USA. The creation of structured shared data repositories for molecular data in the form of web- accessible databases like GenBank has been a driving force behind the genomic revolution. These resources serve not only to organize and manage molecular data being created by researchers around the globe, but also provide the starting point for data mining operations to uncover interesting information present in the large amount of sequence and structural data. To realize the full impact of the genomic and proteomic efforts of the last decade, similar resources are needed for structural and biochemical complexity in biological systems beyond the molecular level, where proteins and macromolecular complexes are situated within their cellular and tissue environments. In this review, we discuss our efforts in the development of neuroinformatics resources for managing and mining cell level imaging data derived from light and electron microscopy. We describe the main features of our web- accessible database, the Cell Centered Database (CCDB;, designed for structural and protein localization information at scales ranging from large expanses of tissue to cellular microdomains with their associated macromolecular constituents. The CCDB was created to make 3D microscopic imaging data available to the scientific community and to serve as a resource for investigating structural and macromolecular complexity of cells and tissues, particularly in the rodent nervous system.