The NeuroHomology Database

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The NeuroHomology Database
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Database Systems in Neurobiology
Neurobiological plausibility
Neuroanatomical data Neurophysiological
recordings Behavioral experiments
Computational models Database
systems
Explanation and formalization
Predictions for new experiments
Construction
Organization of information
Inference of new relationships Predictions
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Motivation of Work
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The NHDB as a Summary Database
URL http//bsl9.usc.edu/database/homologies-main.
html
The users can search for information related
to Brain Structures Neuroanatomical
Connections Homologies between brain
structures in different species insert
comments on any retrieved information from the
database create their own profiles The
collators can insert new data in the
database update previously inserted information
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The Problem of Many Cortical Maps for a Single
Species
Adapted from Arbib, 1995 Adapted from
Luppino Rizzolatti, 2001
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Multiple Cortical Structures can be Homologous in
Different Species
Adapted from Rizzolatti Arbib, 1998
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Relating Cortical Structures from Different
Atlases
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Relating Cortical Structures from Different
Atlases
The translation inference engine exploits a
qualitative spatial inference algorithm
developed in the GIS paradigm evaluates the
neuroanatomical connections of cortical
structures in different parcellation
schemes can be used for reconstruction of the
patterns of connectivity of cortical structures
in a given brain atlas, from the connectivity
information obtained in different atlases
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Relating Cortical Structures from Different
Atlases
Topological and directional relations between
cortical structures as adapted from GIS relations
of Egenhofer Franzosa (1991) and Sharma (1996)
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Relating Cortical Structures from Different
Atlases
Relating F4/F5 (parcellation Matelli) to
FBA/FCBm( parcellation von Bonin/Bailey)
m meet i identical
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Relating Cortical Structures from Different
Atlases
Retrieving the topological relations between
cortical structures
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Handling Connectivity Information as Found in the
Literature
Any neuroanatomical connection can be described
in terms of an injection site I, a terminal
field R and a connection strength C
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The Search of Connections can be Performed in
Multiple Ways
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Evaluating Connections Confidence
Levels For a given connection Y that appears in
n citations we evaluate the connection
confidence level C as interpreted from each
citation the technique confidence level T,
depending on the relative advantages and
limitations
and compute
the combined confidence level CC, as CC C
T the overall confidence level, OC as OC (?
CC)/n
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Evaluating Connections Confidence Levels
Interpreting data from the neuroanatomical
literature
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Evaluating Connections Confidence Levels
Customizing the inference engine for connections
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Creating Connectivity Reports
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Creating Connectivity Reports
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Reconstructing patterns of connectivity from the
information existent in the database
ExampleThe reconstructed pattern of
connectivity of area 7a
7a
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The Concept of Homology
The structuralist approach there is a common
structural plan across vertebrates (Bauplan)
The phylogenetic approach characters have to
be followed across species.
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Evaluating Homologies between Brain Structures
the degree of homology of a pair of brain
structures from different species depends on how
close are the those nuclei which are related with
the compared structures. the evaluation of the
degree of homology depends also on the
reliability of information inserted in the
database users can perform customized
evaluations of the degree of homology users can
comparatively evaluate the degrees of homology of
a brain structure from a given species with a
number of different structures from another
species
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The Concept of Degree of Homology
We propose the concept of degree of homology DG
as an overall measure for how close two brain
structures from different species are. The
criteria we use for evaluating the degree of
homology relative position afferent
connections efferent connections chemoarchi
tecture cell morphology gross
appearance myeloarchitecture functionality
For each of the considered criteria, an index
of similarity IS is associated
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Rules Used to Evaluate the Degree of Homology
The indexes of similarity of two brain structures
from different species for
relative position hodology chemoarchitec
ture cytoarchitecture are calculated
as while those for gross
appearance myeloarchitecture functionality

depends on the amount and the reliability of
information existent in the database can be
changed by the user
take a fixed value if there is information
related to those, otherwise zero.
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Rules Used to Evaluate the Degree of Homology
the degree of homology of two brain structures
from different species depends on how close are
the other structures in the the compared
species is considered to be a smooth function
of the indexes of similarity takes values
between 0 and 1
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Users can browse the existent homologies in the
database
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Online Evaluation of the Degree of Homology
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Customization of the Inference Engine for
Evaluation of Homologies
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Evaluation of the Customized Degree of Homology
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Evaluation of the Customized Degree of
Homology Case study the macaque homologous
structures of the rodent PrCm
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Adapted from Conde et al., 1995
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Comparing the degrees of homology of different
brain structures
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Conclusions and Future Work