Neuroinformatics Databases for the Structure-Function Studies of the Brain

 

 

Category

Source Name

The Needs/Challenges

Purposes/Features/Methods

Contents

Applications

URL

References

Neuroinformatics Databases about the Brain Structures The Internet Brain Volume Database (IBVD) Available data from neuroanatomic volumetric measurements;

Various data from different species;

Data with different features of gender, age, and pathology

For data integrative and access Data about various species, different diseases Meta-analysis;

Quality assurance

http://www.nitrc.org/projects/ibvd Kennedy et al., 2012
Brainpeps Peptides that can cross the blood-brain barrier (BBB) need to be collected and organized To support diagnostic and therapeutic applications of the peptides Available BBB data from the literature;

BBB transport information

Peptide selections for analyzing BBB responses;

Quantitative analyses of peptide structure-function (BBB behavior) associations;

Analyzing the BBB behaviors and responses to various compounds;

Analysis of the associations between the different BBB transport methods

 

http://brainpeps.ugent.be/ Van Dorpe, et al., 2012
The OASIS (Open Access Series of Imaging Studies) Brain Database       To test if the average of the midsagittal corpus callosum cross-sectional area (CCA) in females is larger than those in males in average http://www.oasis-brains.org Ardekani et al., 2012
Neuroinformatics Databases about the Brain Functions and Functional Analyses The Brainmap Database

 

Human functional brain mapping studies have produced a large volume of data Supports the application of the activation likelihood estimation (ALE) method

 

 

 

Published neuroimaging data;

The metadata about the experimental design;

To build a probabilistic atlas

The meta-analysis of the published neuroimaging studies;

To develop an ontological system for studying function-structure relationships

http://www.brainmap.org Laird et al., 2009
BrainKnowledge To connect fMRI image datasets to the literature Methods:

Literature extraction and mining;

Automatic extractions from fMRI literatures;

Co-occurrence models and brain association patterns;

The association between brain structures and functions

Indexed literature, Medline abstracts;

fMRI experimental results;

The comparison between experimental data with the data in the literature

To query for brain activation models from a brain function;

To search functions from brain structures;

To compare the fMRI data with those from the literature

http://brainknowledge.ee.ntu.edu.tw Hsiao et al., 2011
The Allen Brain Atlas To study the midbrain dopaminergic neurons that are associated with

the control of emotion, motivation,

and motor behavior

To study the connections between the loss of a subpopulation substantia nigra pars compacta and Parkinson’s disease The collection of in situ hybridization data A linked database of the expressed genes in the neuronal population http://www.brain-map.org

 

 

 

 

 

 

Alavian et al., 2009
MethylomeDB Genome-wide brain DNA methylation profiles are needed to analyze the epigenetic mark in the mammalian brain;

To study how aberrant DNA methylation changes are connected to many neurodevelopmental and neuropsychiatric disorders including schizophrenia and depression

For studies of brain functions and behaviors;

Brain methylome data;

Whole-genome DNA methylation profiles of human and mouse brain specimens;

Supports cross-species comparative epigenomic analyses;

Supports studies of schizophrenia and depression methylomes

Methylation profiles of samples of non-psychiatric controls, schizophrenia, and depression;

Mouse forebrain sample specimen for cross-species analyses;

Published DNA methylation data associated with brain development and function

 

 

Data visualization with at single-CpG resolution;

Wiggle and microarray formats;

Data download for specific samples

 

 

http://epigenomics.columbia.edu/methylomedb/index.html

 

 

 

 

 

 

Xin et al., 2012
The Stanley Neuropathology Consortium Integrative Database (SNCID) To study the atypical antipsychotics bind receptor, e.g., dopamine D(2) receptors (DRD2);

5-HT(2) receptors (HTR2A);

α-2 adrenergic receptors (ADRA2A);

muscarinic receptors (CHRM1/4)

To study:

Deficits in antipsychotic receptors;

Related pathways, e.g., Immune and inflammatory reactions and apoptosis networks were related to group II metabotropic glutamate receptors (GRM2);

Potential target for future antipsychotics;

 

 

 

 

Applications: The associations between the targets, e.g., Associations with the neurotrophic factor BDNF mRNA levels;

Myelination associated with DRD2 mRNA levels and ADRA2A activity in the frontal cortex

 

Potential antipsychotics may affect pathways different from current ones;

Data mining approaches may be useful for the studies of the efficacy and side-effects of the antipsychotics

http://sncid.stanleyresearch.org

 

 

 

 

 

 

Kim et al., 2012

 

References

 

Kennedy, D. N., Hodge, S. M., Gao, Y., Frazier, J. A., & Haselgrove, C. (2012). The internet brain volume database: a public resource for storage and retrieval of volumetric data. Neuroinformatics, 10(2), 129–140. doi:10.1007/s12021-011-9130-1

Van Dorpe, S., Bronselaer, A., Nielandt, J., Stalmans, S., Wynendaele, E., Audenaert, K., … De Spiegeleer, B. (2012). Brainpeps: the blood-brain barrier peptide database. Brain structure & function, 217(3), 687–718. doi:10.1007/s00429-011-0375-0

Ardekani, B. A., Figarsky, K., & Sidtis, J. J. (2012). Sexual Dimorphism in the Human Corpus Callosum: An MRI Study Using the OASIS Brain Database. Cerebral cortex (New York, N.Y.: 1991). doi:10.1093/cercor/bhs253

Laird, A. R., Eickhoff, S. B., Kurth, F., Fox, P. M., Uecker, A. M., Turner, J. A., …Fox, P. T. (2009). ALE Meta-Analysis Workflows Via the Brainmap Database: Progress Towards A Probabilistic Functional Brain Atlas. Frontiers in neuroinformatics, 3, 23. doi:10.3389/neuro.11.023.2009

Hsiao, M.-Y., Chen, C.-C., & Chen, J.-H. (2011). BrainKnowledge: a human brain function mapping knowledge-base system. Neuroinformatics, 9(1), 21–38. doi:10.1007/s12021-010-9083-9

Alavian, K. N., & Simon, H. H. (2009). Linkage of cDNA expression profiles of mesencephalic dopaminergic neurons to a genome-wide in situ hybridization database. Molecular neurodegeneration, 4, 6. doi:10.1186/1750-1326-4-6

Xin, Y., Chanrion, B., O’Donnell, A. H., Milekic, M., Costa, R., Ge, Y., & Haghighi, F. G. (2012). MethylomeDB: a database of DNA methylation profiles of the brain. Nucleic acids research, 40(Database issue), D1245–1249. doi:10.1093/nar/gkr1193

Kim, S., Zavitsanou, K., Gurguis, G., & Webster, M. J. (2012). Neuropathology markers and pathways associated with molecular targets for antipsychotic drugs in postmortem brain tissues: Exploration of drug targets through the Stanley Neuropathology Integrative Database. European neuropsychopharmacology: the journal of the European College of Neuropsychopharmacology, 22(10), 683–694. doi:10.1016/j.euroneuro.2012.01.010

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