Discovery and Integrative Neuroscience

Hypothesis driven research has been shown to be an excellent model for pursuing investigations in neuroscience. The Human Genome Project demonstrated the added value of discovery research, especially in areas where large amounts of data are produced. Neuroscience has become a data rich field, and one that would be enhanced by incorporating the discovery approach. Databases, as well as analytical, modeling and simulation tools, will have to be developed, and they will need to be interoperable and federated. This paper presents an overview of the development of the field of neuroscience databases and associate tools: Neuroinformatics. The primary focus is on the impact of NIH funding of this process. The important issues of data sharing, as viewed from the perspective of the scientist and private and public funding organizations, are discussed. Neuroinformatics will provide more than just a sophisticated array of information technologies to help scientists understand and integrate nervous system data. It will make available powerful models of neural functions and facilitate discovery, hypothesis formulation and electronic collaboration.

[1]  H. Rodman,et al.  Pattern of retinal projections in the California ground squirrel (Spermophilus beecheyi): Anterograde tracing study using cholera toxin , 2003, The Journal of comparative neurology.

[2]  A W Toga,et al.  Analyzing functional brain images in a probabilistic atlas: a validation of subvolume thresholding. , 2000, Journal of computer assisted tomography.

[3]  Sushil Jajodia,et al.  Enabling the sharing of neuroimaging data through well-defined intermediate levels of visibility , 2004, NeuroImage.

[4]  Gwen A. Jacobs,et al.  Predicting Emergent Properties of Neuronal Ensembles Using a Database of Individual Neurons , 2002 .

[5]  P. Cheng,et al.  Bridging Functional MR Images and Scientific Inference: Reproducibility Maps , 2003, Journal of Cognitive Neuroscience.

[6]  G. Bowker,et al.  An International Framework to Promote Access to Data , 2004, Science.

[7]  Walter Schneider,et al.  Fiswidgets - A graphical computing environment for neuroimaging analysis , 2003, Neuroinformatics.

[8]  L. K. Hansen,et al.  Activation pattern reproducibility: Measuring the effects of group size and data analysis models , 1997, Human brain mapping.

[9]  C. Haselgrove,et al.  MRI-based morphometric of typical and atypical brain development. , 2003, Mental retardation and developmental disabilities research reviews.

[10]  E H Herskovits,et al.  Is the spatial distribution of brain lesions associated with closed-head injury predictive of subsequent development of attention-deficit/hyperactivity disorder? Analysis with brain-image database. , 1999, Radiology.

[11]  A. Gjedde,et al.  Quantitative functional brain imaging with positron emission tomography , 1998 .

[12]  Richard Baldock,et al.  3 dimensional modelling of early human brain development using optical projection tomography , 2004, BMC Neuroscience.

[13]  Tyrone D. Cannon,et al.  Cortex mapping reveals regionally specific patterns of genetic and disease-specific gray-matter deficits in twins discordant for schizophrenia , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[14]  N. C. Singh,et al.  Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli , 2001 .

[15]  H. Karten,et al.  Spatial organization of the pigeon tectorotundal pathway: An interdigitating topographic arrangement , 2003, The Journal of comparative neurology.

[16]  B. Postle,et al.  Activity in Human Frontal Cortex Associated with Spatial Working Memory and Saccadic Behavior , 2000, Journal of Cognitive Neuroscience.

[17]  Anders M. Dale,et al.  Towards effective and rewarding data sharing , 2003, Neuroinformatics.

[18]  Robert W. Williams,et al.  Informatics center for mouse genomics , 2007, Neuroinformatics.

[19]  Smadar Shiffman,et al.  Application of Information Technology: BrainImageJ: A Java-based Framework for Interoperability in Neuroscience, with Specific Application to Neuroimaging , 2001, J. Am. Medical Informatics Assoc..

[20]  Lars Kai Hansen,et al.  Measuring Activation Pattern Reproducibility Using Resampling Techniques , 1998 .

[21]  T. Carlson,et al.  Patterns of Activity in the Categorical Representations of Objects , 2003 .

[22]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[23]  Mark Ellisman,et al.  e-Neuroscience: challenges and triumphs in integrating distributed data from molecules to brains , 2004, Nature Neuroscience.

[24]  E. Bullmore,et al.  Wavelets and statistical analysis of functional magnetic resonance images of the human brain , 2003, Statistical methods in medical research.

[25]  J C Mazziotta,et al.  Mapping biochemistry to metabolism: FDG-PET and amyloid burden in Alzheimer's disease. , 1999, Neuroreport.

[26]  R. Woods,et al.  Mapping Histology to Metabolism: Coregistration of Stained Whole-Brain Sections to Premortem PET in Alzheimer's Disease , 1997, NeuroImage.

[27]  D. Gardner Neurodatabase.org: networking the microelectrode , 2004, Nature Neuroscience.

[28]  Nora D Volkow,et al.  Neuroscience Networks , 2003, PLoS biology.

[29]  Marina Chicurel,et al.  Databasing the brain , 2000, Nature.

[30]  Robert H. Lee,et al.  An FPGA-based approach to high-speed simulation of conductance-based neuron models , 2007, Neuroinformatics.

[31]  Karl J. Friston,et al.  Comparing dynamic causal models , 2004, NeuroImage.

[32]  Stephen H Koslow,et al.  Sharing primary data: a threat or asset to discovery? , 2002, Nature Reviews Neuroscience.

[33]  David C. Van Essen,et al.  Application of Information Technology: An Integrated Software Suite for Surface-based Analyses of Cerebral Cortex , 2001, J. Am. Medical Informatics Assoc..

[34]  Bertram Ludäscher,et al.  A cell-centered database for electron tomographic data. , 2002, Journal of structural biology.

[35]  Stephen H. Koslow,et al.  Celebrating a decade of neuroscience databases , 2007, Neuroinformatics.

[36]  E. Gordon,et al.  Integrative Neuroscience: The Role of a Standardized Database , 2005, Clinical EEG and neuroscience.

[37]  G. Ascoli,et al.  Statistical morphological analysis of hippocampal principal neurons indicates cell‐specific repulsion of dendrites from their own cell , 2003, Journal of neuroscience research.

[38]  Scott T. Grafton,et al.  Sharing neuroimaging studies of human cognition , 2004, Nature Neuroscience.

[39]  David C. Van Essen,et al.  Surface‐Based Atlases of Cerebellar Cortex in the Human, Macaque, and Mouse , 2002 .

[40]  Dinggang Shen,et al.  An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures , 2001, IEEE Transactions on Medical Imaging.

[41]  Constance M. Pechura,et al.  Mapping the brain and its functions: integrating enabling technologies into neuroscience research , 1991 .

[42]  M. Greicius,et al.  Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI , 2004, Proc. Natl. Acad. Sci. USA.

[43]  John C. Mazziotta,et al.  A Probabilistic Atlas and Reference System for the Human Brain , 2001 .

[44]  N. Heintz Gene Expression Nervous System Atlas (GENSAT) , 2004, Nature Neuroscience.

[45]  H. Moser,et al.  Imaging cortical association tracts in the human brain using diffusion‐tensor‐based axonal tracking , 2002, Magnetic resonance in medicine.

[46]  Perry L. Miller,et al.  The Human Brain Project: neuroinformatics tools for integrating, searching and modeling multidisciplinary neuroscience data , 1998, Trends in Neurosciences.

[47]  Kristen M. Harris,et al.  Synthesis of Research: Extending Unbiased Stereology of Brain Ultrastructure to Three-dimensional Volumes , 2001, J. Am. Medical Informatics Assoc..

[48]  Karl J. Friston,et al.  A Dynamic Causal Modeling Study on Category Effects: BottomUp or TopDown Mediation? , 2003, Journal of Cognitive Neuroscience.

[49]  C. Haselgrove,et al.  MRI‐based morphometric analysis of typical and atypical brain development , 2003 .

[50]  Daniel Gardner,et al.  Model Formulation: Common Data Model for Neuroscience Data and Data Model Exchange , 2001, J. Am. Medical Informatics Assoc..

[51]  M. F. Huerta,et al.  The human brain project: an international resource , 1993, Trends in Neurosciences.

[52]  N R Smalheiser,et al.  Using ARROWSMITH: a computer-assisted approach to formulating and assessing scientific hypotheses. , 1998, Computer methods and programs in biomedicine.

[53]  T. Insel,et al.  Limits to growth: why neuroscience needs large-scale science , 2004, Nature Neuroscience.

[54]  M. Daube-Witherspoon,et al.  Quantitative functional brain imaging with positron emission tomography , 1998 .

[55]  Sandy Pittendrigh,et al.  Neurosys - A semistructured laboratory database , 2003, Neuroinformatics.

[56]  Robert W. Thatcher,et al.  NORMATIVE EEG DATABASES AND EEG BIOFEEDBACK , 1998 .

[57]  Dan Lloyd,et al.  Functional MRI and the Study of Human Consciousness , 2002, Journal of Cognitive Neuroscience.

[58]  Risto Miikkulainen,et al.  Scaling self-organizing maps to model large cortical networks , 2001, Neuroinformatics.

[59]  S. Koslow,et al.  Databasing the brain : from data to knowledge (neuroinformatics) , 2005 .