The NeuroHomology DatabaseThe NeuroHomology Database

Computational models

Database systems

Neuroanatomical data

Neurophysiological recordings

Behavioral experiments

Predictions for new experiments

Inference of new relationshipsPredictions

Neurobiological plausibility

Explanation and formalization

Construction

Organization ofinformation

Database Systems in NeurobiologyDatabase Systems in Neurobiology

Design of neurobiologically realistic models of brain systems that replicate different behavioral and physiological experiments

Motivation of WorkMotivation of Work

Creation of a theoretical framework for objective evaluation of the neurobiological information as reflected from the literature

Design of fully accessible database systems that bridge the gaps between computational models and neurobiological information

are search tools of brain structures, connections, and similarities between brain structures in different species

can lead to the discovery of new relationships between brain structures from previously unrelated data.

The NHDB as a Summary Database

URL: URL: http://bsl9.usc.edu/database/homologies-main.htmlhttp://bsl9.usc.edu/database/homologies-main.htmlThe users can

search for information related to

Brain StructuresBrain Structures

Neuroanatomical ConnectionsNeuroanatomical Connections

Homologies 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

The Problem of Many Cortical Maps for a Single Species

Adapted from Arbib, 1995 Adapted from Luppino & Rizzolatti, 2001

Multiple Cortical Structures can be Homologous in Different Species

Adapted from Rizzolatti & Arbib, 1998

The NHDB as a Summary DatabaseThe search of Brain StructuresBrain Structures can be performed in multiple ways

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Relating Cortical Structures from Different Atlases

The translation inference enginetranslation inference engine exploits a qualitative spatial inference algorithm developed in the GIS paradigm

evaluates evaluates the neuroanatomical connections of cortical structures in different parcellation schemes

can be used for reconstruction of the patterns of connectivityreconstruction of the patterns of connectivity of cortical structures in a given brain atlas, from the connectivity informationobtained in different atlases

Relating Cortical Structures from Different Atlases

disjoint (d) meet (m) identical (i)

overlap (o) contains (co)

inside (isCo)

covered by (isCv)covers (cv)

Topological and directional relations between cortical structures as adapted from GIS relations of Egenhofer & Franzosa (1991) and Sharma

(1996)

N NE

E

SESSW

W

NW R RM

M

CMCCL

L

RL

GIS Cortex

Relating Cortical Structures from Different Atlases

mmmm

ii

m: meeti: identical

Relating F4/F5 (parcellation Matelli) to FBA/FCBm( parcellation von Bonin/Bailey)

mm

Relating Cortical Structures from Different Atlases

Retrieving the topological relations between cortical structures

Relating Cortical Structures from Different Atlases

Handling Connectivity Information as Found in the Literature

II RR

CC

Any neuroanatomical connection can be described in terms of

an injection site II,

a terminal field RR

and a connection strength CC

The Search of ConnectionsConnections can be Performed in Multiple Ways

Evaluating Connections Confidence LevelsEvaluating Connections Confidence Levels

For a given connection YY that appears in nn citations

we evaluatethe connection confidence levelconnection confidence level CC as interpreted from

each citation

the technique confidence leveltechnique confidence level T,T, depending on the relative advantages and limitations

thethe combined confidence levelcombined confidence level CCCC, as CCCC = CC * TT

the overall confidence levelthe overall confidence level, OCOC as OCOC = ( = ( CCCC)/n)/n

and compute

Evaluating Connections Confidence LevelsEvaluating Connections Confidence Levels Interpreting data from the neuroanatomical literatureInterpreting data from the neuroanatomical literature

Customizing the inference engine for connectionsCustomizing the inference engine for connections Evaluating Connections Confidence LevelsEvaluating Connections Confidence Levels

Creating Connectivity ReportsCreating Connectivity Reports

Creating Connectivity ReportsCreating Connectivity Reports

V3a

V3

V2d

MT

V4

MST

FST

PO

DP

Visual

AIP

7a7a

7b

MIP

VIP

LIP

PIP

7m

Somatosensory

5 SII 3a

45

46

46v

FEF

SMA

6Ds

12m

6Vam

Frontal/Prefrontal

Strong connectionMedium/strong connectionWeak/medium connection

23

23c

24a

24d

Cingulate

Pres Para EntCA1 PrhTemporal

Example:The reconstructed pattern of connectivity of area The reconstructed pattern of connectivity of area 7a7a

7a

Reconstructing patterns of connectivity from the information existent in the database

The Concept of HomologyThe 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.

Hierarchies of structures within the nervous system:

cellular aggregates, composed of major brain regions, brain nuclei and nuclear subdivisions

cell types, including major types and subtypes

molecules grouped into families and superfamilies

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

The Concept of Degree of HomologyThe Concept of Degree of Homology

We propose the concept of degree of homologydegree of homology DGDG 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 positionrelative position

afferent connectionsafferent connections

efferent connectionsefferent connections

chemoarchitecturechemoarchitecture

cell morphologycell morphology

gross appearancegross appearance

myeloarchitecturemyeloarchitecture

functionalityfunctionality

For each of the considered criteria, an For each of the considered criteria, an index of similarity index of similarity ISIS is is associatedassociated

relative positionrelative positionhodologyhodologychemoarchitecturechemoarchitecturecytoarchitecturecytoarchitecture

are calculated as:

while those for:

gross appearancegross appearancemyeloarchitecturemyeloarchitecturefunctionalityfunctionality

take a fixed valuefixed value if there is information related to those, otherwise zero.

The indexes of similarityindexes of similarity of two brain structures from different species for:

),( mc NNsigmoidIS

mN

cN depends on the amount and the reliability of information existent in the databasecan be changed by the user

Rules Used to Evaluate the Degree of HomologyDegree of Homology

Rules Used to Evaluate the Degree of HomologyDegree of Homology

the degree of homologydegree 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 functionsmooth function of the indexes of similarity

takes values between 0 and 1

1

1

2

1

)*exp(n

iiIS

DG

Users can browse the existent homologies homologies in the database

Online Evaluation of the Degree of HomologyDegree of Homology

Customization of the Inference Engine for Evaluation of Homologies

Evaluation of the Customized Degree of Homology

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

Comparing the degrees of homology of different brain structures

Conclusions and Future Work

We designed a set of database systems that can be accessed online both in terms of searching for information and to insert new data

The structures of the designed database systems allow the members of the neuroscientific community to use them both as summary databases for neurobiological information and also as expert systems for inference of new relationships from previously unrelated data

The expert systems can lead to predictions of new functional parcellations of cortical structures and may be used for construction of hierarchies of homologous structures.

Refinement of the existent expert systems

Inclusion of more homology criteria

Automated creation of the connectivity matrices of brain structures of interest.