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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.
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
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
(http://translatingtime.net/). 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.
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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,
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:// rbwb.org/), 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
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.
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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 (http://www.translatingtime.net/) 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
Neural Engineering Center, Wickenden 112, Department of Biomedical Engineering, Case Western
Reserve University, Cleveland, OH 44106, USA. email@example.com
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
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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.
Department of Anatomy, Pharmacology, and Forensic Medicine, University of Torino, Corso M.
D'Azeglio 52, 10126 Torino, Italy. firstname.lastname@example.org
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. email@example.com
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. firstname.lastname@example.org
A new "Molecules" module of the Brain Architecture Management System (BAMS;
http://brancusi.usc.edu/bkms) 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
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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.
11: Neuroinformatics. 2006 Summer;4(3):213-6.
The ups and downs of neuroscience shares.
12: Neuroinformatics. 2006;4(2):199-212.
A general XML schema and SPM toolbox for storage of neuro-imaging results and
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. ion.ucl.ac.uk/spm/) was created to capture the results from
activation maps using the XML schema that supports both SPM99 and SPM2 versions
(http://nbirn.net/Resources/Users/ 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.
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13: Neuroinformatics. 2006;4(2):139-62.
NeuroScholar's electronic laboratory notebook and its application to
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
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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. email@example.com
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. firstname.lastname@example.org
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
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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.
Krasnow Institute, George Mason University, Fairfax, VA, USA. email@example.com
"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.
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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,
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.
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24: Neuroinformatics. 2005;3(2):163-6.
A new era in computational neuroscience.
School of Computational Sciences, and the Krasnow Institute of Advanced Studies, Rockfish Creek
Lane, MS 2A1 George Mason University, Fairfax, VA 22030, USA. firstname.lastname@example.org
25: Neuroinformatics. 2005;3(2):115-31.
Comparison of vector space model methodologies to reconcile cross-species
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
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. email@example.com
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.
Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, P.O. Box 100131, D-
33501 Bielefeld, Germany. firstname.lastname@example.org
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
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. email@example.com
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. firstname.lastname@example.org
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
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
C & O Vogt Brain Research Institute, Heinrich Heine University Düsseldorf, Moorenstr. 5, D-40225,
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. email@example.com
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.
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-
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
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. firstname.lastname@example.org
Within this paper, we describe a neuroinformatics project (called "NeuroScholar,"
http://www.neuroscholar.org/) 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.
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
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. email@example.com
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.
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 http://www.loni.ucla.edu/MAP.
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; http://ncmir.ucsd.edu/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.