Building Multiscale Models of the Nervous
System through the BIRN: A Practical
Experience
Maryann E. Martone, Ph. D.
Biomedical Informatics Research Network
and
National Center for Microscopy and Imaging
Research
Center for Research in Biological Systems
University of California, San Diego
BIRN
Biomedical Informatics Research Network
QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture.
*Will no longer
matter where
data, instruments
and computational
resources are
located physically”
•National Center for Research Resources
•Establish cyberinfrastructure for storing,
manipulating and sharing data and
resources
–High speed, robust connectivity via
Internet2
–Data and computational resources
–Data integration over distributed databases
•Current test beds focused on neuroimaging
–Human MRI
–Human fMRI
–Mouse models of neurological disease
•Involves 30 Research Institutions and 40
research groups
http://nbirn.net
insert your
grid project here
Goals of BIRN
Project
• 21st Century scientific data management
– 20th century biology revolutionized means used to acquire data - gave
solid footing for old theories and provided the experimental bases to
flesh out the molecular details in many areas of science.
– 20th century scientific data management is not so very different than
19th century
• Depends on one scientist absorbing as much as they can with their limited
time & knowledge, seeking correlations & cogently assembling a story
– 21st Century data management seeks to grow way beyond the single
scientist/lab ==> meta-analysis & concept-driven data mining
• Biomolecular sci-data management got there through the 1990s - BIRN is
trying to deliver this capability to other domains of biomedical science
– The network is the repository
– Data and knowledge must be machine interpretable
– We are not trying to support “business as usual ” (and therein lies the
challenge)
Courtesy of Bill Bug
BIRN Test Beds
• BIRN Coordinating Center
– Develops, deploys and maintains cyberinfrastructure
– Data integration through development of federated database tools
• Morphometry BIRN
– Methodology for pooling and analyzing data across neuroimaging
sites
• anatomical differences and specific memory dysfunctions, such as
depression, mild Alzheimer’s disease, and mild cognitive impairment
• Function BIRN:
– Methodology for multi-site fMRI data collection, sharing, and
integration with focus on schizophrenia
• Mouse BIRN:
– Building multiscale brain atlases of mouse models of human
neurological disease
– Integration of multiscale imaging data with genomic data
Combining MRI data across sites
Before
After
•Calibration standards: multisite
imaging study
•Human Imaging database
•Metadata standards for
neuroimaging data transformation
Jovicich et al., Reliability in multi-site structural MRI studies: Effects of gradient non-linearity
correction on phantom and human data NeuroImage 30: 436, 2006
Keator et al., A general XML schema and SPM toolbox for storage of neuro-imaging results and
anatomical labels. Neuroinformatics, 4: 199, 2006
Multiscale Investigation of Dopamine
Transporter KO Mouse
•Duke, UCLA, UCSD, Cal
Tech, UT Memphis, Drexel
Mouse BIRN Data Integration Framework
•different model from human test beds
•groups work independently but with an eye towards sharing
1. Create multimodal
databases
2. Create conceptual
links to a shared ontology
4. Use mediator to navigate
and query across data
sources
3. Situate the data in a
common spatial framework
Challenges of Data Integration of
Distributed Data
• Semantic concordance
– Medium spiny cell vs medium spiny neuron(e)
– C57Bl/6J vs C57BLJ6 vs C57B6J
• Different representations in different databases
– Age = 21 months
– Age = Date of imaging - date of birth
• Indirect relationships
– Basal Ganglia --> medium spiny neuron
– Alzheimer’s disease --> alpha synuclein overexpressor
• Inconsistent or partial relationships
– My amygdala vs your amygdala
• Reconciling different techniques
– Gene expression with microarray vs in situ hybridization vs
immunocytochemistry
Data Integration for BIRN
Find animal models of movement disorders where the volume of
basal ganglia structures are decreased in old animals
Integrated
View
Not in database
Integrated
View Definition
Wrapper
UCSD
Knowledge
Sources
Mediator
Wrapper
Wrapper
Wrapper
Cal Tech
Duke
UCLA
What is an Ontology?
Brain
has a
Cerebellum
has a
Purkinje Cell Layer
has a
Purkinje cell
is a
neuron
– Way to communicate a shared
understanding of a field
– representation of terminological
knowledge
– concept hierarchy (“is-a”)
– further semantic relationships
between concepts (“is part of”,
“causes” etc.)
Examples:
– GO (Gene Ontology)
– NeuroNames
– Foundational model of anatomy
– Mouse Anatomy (Edinburgh)
Multiscale Investigation of
Neurological Disease
Navigating through Multi-resolution
information
Linking animal and human imaging
data
brain
Entopeduncular
nucleus
Globus pallidus,
internal segment
Animal Model
Disease
cerebellum
cerebellar cortex
Interpreting Results
Purkinje cell
dendritic spine
Microarray
Immunolabeling
Technique
Phenotype
BIRNLex: Lexicon for multiscale investigation of
neurological disease
•
•
BIRN Ontology Task Force
Lexicon not a terminiology:
– Each concept has a human readable definition
– Each concept has a unique identifier
• All synonyms have the same ID
– Current domains
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Neuroanatomy
Mouse strains
Neuropsychological assessments
Next: workflows, methods, diseases, animal
models
– Builds on previous efforts by other groups (no
intentional reinventing!)
• Foundational ontologies, Peter Fox’s paradigm
classes; Neuronames brain structures
•
Tried to utilize “best practices” promoted by
the National Center for Biomedical Ontologies
and the OBO Foundry project
– Promotes integration with other efforts
– Promotes transition to fully structured ontology
with machine-processable semantics
BIRN Mediator
BIRN Mediator
The Smart Atlas: A Grid-based GIS tool for spatial integration of
multiscale distributed brain data
Ilya
Zaslavsky,
Joshua Tran,
Asif Memon,
Willy Wong,
Haiyun He,
Amarnath
Gupta
Mediator
wrapper
wrapper
wrapper
wrapper
UCSD
Cal Tech
Duke
UCLA
SRB
Multiscale Investigation of Dopamine
Transporter KO Mouse
•Duke, UCLA, UCSD, Cal
Tech, UT Memphis, Drexel
Space limitations (1)
Cerebellar
cortex
Purkinje
Cell
Axon
Axon
Terminal
Context is often difficult
to discern across scales
-scale and contrast
mechanisms differ
Cell body
Dendritic
Tree
Dendrite
Dendritic
Spine
Space limitations (2): Multimodal Data
No magic technique
Molecular Layer
ICC
RISH
Purkinje Cell
Layer
GSCF
NRISH
Technique will determine what is seen and how
labeling is interpreted; still requires expert knowledge
Core domain: Neuroanatomy
What is the hippocampus?
•Resolution of
technique
•Data
representation
•Structural vs
functional
concepts
•Challenge in
Human BIRN
•Complicated
subcellular
anatomy of the
nervous system
Summary of Progress and Lessons Learned
• Data integration is multifaceted
– Calibration protocols
– Spatial and terminological relationships
– Engines to achieve integration
• It’s hard
– Technology and people
– Challenge: how to let the science and other technology development proceed while
trying to develop these systems (and if it can proceed, why do we need the systems?)
• Systems are only now coming on-line
– BIRN databases are being populated; BIRNLex v 1.0 is just about ready; mediator
ready
• Scope of problem is beyond any single project
– Development with an eye towards integration with other efforts
• Creating these resources is helping to clarify our thinking about difficult
concepts
– Creating the lexicon forces us to grapple with murky scientific concepts and try to
formalize them
• Most biological concepts don’t map neatly into hierarchies
– Even knocking off the low hanging fruit is helpful
Challenge: Disease Maps
Parkinson’s Disease
C0030567
Pathological feature
symptom
neuronal degeneration
C0027746
akinesia
C027746
tremor
rigidity
Lewy Body
C0085200
Dopamine neuron
Cell inclusion
C0815003
Motor deficit
C0205708
Substantia nigra
neurons
C0027882
Abnormal filaments
glia
C0041538
C0175412
Alpha synuclein
cortex
C0007776
C0027836
C024566
Basal forebrain
C0746626
Linking the human disease state to the
animal model
a-synuclein mouse
Parkinson’s Disease
Pathological feature
nuclear
inclusion
Cellular phenotype
Lewy Body
Cytoplasmic
inclusion
Cellular inclusion
Alpha
synuclein
Filamentous inclusion
neurons
ubiquitin
glia
Many Thanks To:
•
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•
Mark Ellisman
Tom Deerinck
Bill Bug, Drexel**
Carol Bean, NIH**
Christine Fennema-Notestine**
Diana Martinez-Price
Jessica Turner, UCI**
Jeff Grethe**
•
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Masako Terada
Stephan Lamont
Daniel Rubin, NCBO**
Ying Jones
Andrea Thor
Yujun Wang
**BIRN OTF
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Guy Perkins
Gina Sosinsky*
Guido Gaietta
Steven Peltier
Abel W. Lin
Joy Sargis
Tomas Molina
Lisa Fong
Lily Chen
Amarnath Gupta**
Ben Giepmanns
Josh Tran
Willy Wong
Heather Jiles
Cem Mangir