| Background
An increasingly large amount of information has been accumulating about
the anatomical and neurochemical properties of brain structures and circuits.
This information can almost only be retrieved by searching publication
databases using keywords or thesaurus terms. Such a searching strategy
is surprisingly ineffective, especially in case of poorly or extensively
studied structures, because in most of the databases the abstract, but
not the whole text of the papers can be searched. To facilitate storing,
cross-referencing and retrieving of neuroscience information the design
of neuroinformatical database models and schemas were suggested (e.g.
Dashti
et al , 1997, Gorin
et al , 2001). Several databases were constructed in the
past few year, most of them store information about the mammalian brain
(e.g. CoCoMac). However,
as birds are widely used to study learning, endocrinology and ontogeny-related
problems (e.g. Bingman
et al , 2002, Matsushima
et al , 2003, Farage-Elawar,
1991, Arnold,
1990), data base of the avian brain became inevitable. Our aim was to
develop a database which is suitable to store anatomical and histochemical
data about the structure and connectivity of the avian brain.
Methods
This database was designed to be well extendable and treatable by implementing
a relational data model using MySql platform, and a php-based website
was develepod to aid accessing the data.
Brain regions
| Information on 371 brain structures is available in
an anatomy-based hierarchy (fig.1.), by which sub- and superstructures
can be easily defined. Beside the scientific names and abbreviations
we also uploaded English names and names of mammalian homologues.
During the data base construction the avian brain nomenclature was
about to be reformed,
therefore new terminology is provided under "new name".
By now, "new names" have been published
and became accepted terminology. |
|
Figure 1. Representations
of brain regions. |
| a.) main parts of the neostriatum (N) in pigeon
brain atlas provided by Karten (L_5.75)
(Click on image to enlarge!) |
|
|
b.) schematic representation of the same areas in the
hierarchy of our database
(Click on image to enlarge!) |
| (Click here
to see neostriatum in the database!) |
Connections
| Connections are defined between two regions: the sender
area projects its axons to the receiver area, where axon terminals
are found. At the moment the database contains over 1000 non-overlapping
connections. (Fig.2.) |
|
Figure 2. Representations
of connections. |
| a.) a rostro-caudal series of schematic coronal
sections through the chick brain illustrating
the distribution of labelled fibres (black dashes) following iontophoresis
of PHA-L into AA. (from Davies
et al , 1997)
(Click on image to enlarge!) |
|
|
b.) illustration of already uploaded connections
from the database: connections of the regions mentioned in fig.2.a
(Click on image to enlarge!) |
| (Click here
to see the connections of the neostriatum in the database!) |
References
The sources of our data are peer-reviewed papers and monographs. Each
data item is linked to its source, the unique PubMed identifiers of papers
or citations of books are provided. (Fig.3.)
| Figure 3. Conceptual data model of how the table
of references connects to other tables of the database.
(Click on image to enlarge!) |
 |
Future plans
Besides connectivity, the database is suitable to store other properties,
such as morphological cell
types, receptor, and neurotransmitter types, species differences, proposed
functions, volumetric and other measurements. We hope to add more of such
data.
We encourage colleagues to upload their own results. A simple on-line
form is being developed for this.
Graphical representation of connections will be provided later.
Applications
Studying
- learn about new
terminology of brain areas
- crawl along major pathways
- crawl down from super- to subregions
- check connections of areas in atlas
Research
- check out published connections when planning tracing studies.
- use it as a starting point for literature search, however, please note,
that we preferred review papers to original first reports
- comparative studies might be carried out on the traits that differ among
species
- neuromodellists can obtain relevant information to brain or neural network
models (e.g. existing circuits belong to a specific function)
- studying of the properties of the network itself might contribute to
a better understanding of the organization and evolution of the avian
brain.
Games
- none. How could anyone have fun with hodology?
Support: This project grew out of problems we faced when analysing evolutionary
independent relations in volumetric data of avian brain regions. The computers
needed for the project were purchased by the help of OTKA foundation (T-033069).
References:
1.
Arnold AP .: The passerine bird song system as a model in neuroendocrine
research. J Exp Zool Suppl. 1990; 4: 22-30.
2.
Bingman VP, Able KP .: Maps in birds: representational mechanisms
and neural bases. Curr Opin Neurobiol. 2002 Dec; 12(6): 745-50.
3.
Dashti AE, Ghandeharizadeh S, Stone J, Swanson LW, Thompson RH .:
Database challenges and solutions in neuroscientific applications. Neuroimage.
1997 Feb; 5(2): 97-115.
4.
Davies DC, Csillag A, Szekely AD, Kabai P .: Efferent connections
of the domestic chick archistriatum: a phaseolus lectin anterograde tracing
study. J Comp Neurol. 1997 Dec 29; 389(4): 679-93.
5.
Farage-Elawar M. : Development of esterase activities in the
chicken before and after hatching. Neurotoxicol Teratol. 1991 Mar-Apr;
13(2): 147-52.
6.
Gorin F, Hogarth M, Gertz M .: The challenges and rewards of
integrating diverse neuroscience information. Neuroscientist. 2001 Feb;
7(1): 18-27.
7.
Matsushima T, Izawa E, Aoki N, Yanagihara S .: The mind through
chick eyes: memory, cognition and anticipation. Zoolog Sci. 2003 Apr;
20(4): 395-408.
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