NIH WORKSHOP: INFORMATICS FOR DATA AND RESOURCE DISCOVERY IN ADDICTION RESEARCH July 8, 2010 Case Study 5 (NEMO): Informatics tools to support theoretical and practical integration of human neuroscience data Gwen Frishkoff, Ph.D. Psychology & Neuroscience, Georgia State University NeuroInformatics Center, University of Oregon http://nemo.nic.uoregon.edu
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NIH WORKSHOP: INFORMATICS FOR DATA AND RESOURCE DISCOVERY IN ADDICTION RESEARCH July 8, 2010
NIH WORKSHOP: INFORMATICS FOR DATA AND RESOURCE DISCOVERY IN ADDICTION RESEARCH July 8, 2010. Case Study 5 (NEMO) : Informatics tools to support theoretical and practical integration of human neuroscience data Gwen Frishkoff, Ph.D. Psychology & Neuroscience, Georgia State University - PowerPoint PPT Presentation
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NIH WORKSHOP: INFORMATICS FOR DATA AND RESOURCE DISCOVERY IN ADDICTION RESEARCH
July 8, 2010
Case Study 5 (NEMO):Informatics tools to support
theoretical and practical integration of human neuroscience data
Gwen Frishkoff, Ph.D.Psychology & Neuroscience, Georgia State University
NeuroInformatics Center, University of Oregon
http://nemo.nic.uoregon.edu
Neuro–Informatics: Crossing the language divide
What the computer scientist says…
Should wewrite out the data to XML or
RDF triples? And do you plan to use ontology rules
to do complex reasoning or just use SQL to query the
data?
What the neuroscientist hears…
Blah blah blah blah blah…data… blah blah
blah? And blah blah blah…the data?
GOALS FOR THIS TUTORIAL
• What is an ontology & what’s it for?– Why bother? (Case Study: Classification of EEG/ERP data)
– What are some “best practices” in ontology design & implementation?
• What is RDF & what’s it for?– How does RDF represent information?– How is it used to link data to ontologies?– How can ontology-based annotation be used to
support classification of data?
Case Study 5 (NEMO): Neural ElectroMagnetic Ontologies
The problem (pattern classification)
The methods & tools ontologies RDF database
Proof of concept (a worked example)
The challenge (pattern classification)
The methods & tools ontologies RDF database
Proof of concept (a worked example)
Case Study 5 (NEMO): Neural ElectroMagnetic Ontologies
2-min Primer on EEG/ERP Methods
EEGs (“brainwaves” or flunctuations in brain electrical potentials) are recorded by placing two or more electrodes on the scalp surface.
256-channel Geodesic Sensor Net ~5,000 ms
Event-related potentials (ERP)
ERPs (“event-related potentials”) are the result of averaging across multiple segments of EEG, time-locking to an event of interest.
ERP Patterns (“Components”)
1. TIME — peak latency, duration (WHEN in time)2. SPACE— scalp “topography” (WHERE on scalp)3. FUNCTION — sensitivity to experiment factors
Donchin & Duncan-Johnson, 1977
ERP Patterns are characterized by 3 dimensions:
120 ms
• Tried and true method for noninvasive brain functional mapping
• Millisecond temporal resolution• Direct measure neuronal activity• Portable and inexpensive• Recent innovations give new windows
into rich, multi-dimensional patterns– More spatial info (high-density EEG)– More temporal & spectral info (JTF, etc.)– Multimodal integration & joint recordings
of EEG and fMRI– Specificity of different patterns
beyond “reduction in P300” amplitude…
1 sec
Brain Electrophysiology (EEG/ERP): The promise (Biomarkers of addiction?)
Brain Electrophysiology (EEG/ERP): The challenge
• An embarrassment of riches– A wealth of data
– A plethora of methods
• A lack of integration– How to compare patterns across studies, labs?
– How to do valid meta-analyses in ERP research?
• A need for robust pattern classification– Bottom-up (data-driven) methods– Top-down (knowledge-driven) methods
An embarrassment of riches
410 ms
450 ms
330 ms
Peak latency 410 ms
A lack of standardization
Will the “real” N400 please step forward?
Hypothetical Database Query: Show me all the N400 patterns in data set X.
Putative “N400”-labeled patterns
Parietal N400
≠≠
Frontal N400
Parietal P600
A Need for Integration
Neural ElectroMagnetic Ontologies(NEMO)
The driving goal is to develop methods and tools to support cross-lab, cross-experiment integration of EEG and MEG data
We bring a set of methods & tools to bear to address this:
A set of formal (OWL) ontologies for representation of EEG/MEG and ERP/ERF data
A suite of tools for ontology-based annotation and analysis of EEG and ERP data
An RDF database that stores annotated data from our NEMO ERP consortium and supports ERP pattern classification via SPARQL queries
The challenge (EEG pattern classification)
The methods & tools ontologies RDF database
Proof of concept (a worked example)
Case Study 5 (NEMO): Neural ElectroMagnetic Ontologies
What’s an ontology & what’s it for?“Highly semantically
structured”
What does this mean & what
does it buy us?
Ontologies for high-level, explicit
representation of domain knowledge
theoretical integration*
*NOTE: We can record pattern definitions from
literature in ontology without committing to
the truth of these records now and forever
Science evolves… So do ontologies!!
Maryann: “Avoid ontology
wars…”
Ontology design principles(based on OBO Foundry
recommendations)1. Factor the domain to generate modular
(“orthogonal”) ontologies that can be reused, integrated for other projects
Recall: The goal is to formulate pattern definitions, use them to classify data, and ultimately to revise them based on
meta-analysis results
Observed Pattern = “N400” iff
Event type is onset of meaningful stimulus (e.g., word) AND
Peak latency is between 300 and 500 ms AND
Scalp region of interest (ROI) is centroparietal AND
Polarity over ROI is negative(>0)
The rule (just the temporal criterion)as it appears in Protégé
Protégé rendering
OWL/RDF rendering
Typical tabular representation of summary ERP data
Peak latency measurement
ERP observation (pattern extracted from “raw” ERP data)
The “RDF Triple”In RDF form: <001> <type> <NEMO_0000093>
Subject – Predicate –Object In natural language:
The data represented in row A is an instance of (“is a”) some ERP pattern.
That is, measurements (cells) are “about” ERP patterns (rows).
In graph form:
RDF Triple #2
In natural language =
The data represented in cell Z (row A, column 1) is an instance of (“is a”) a peak latency temporal measurement (i.e., the time at which the pattern is of maximal amplitude)
In RDF form: <002> <type> <NEMO_0745000>
Subject – Predicate –Object
RDF Triple #3
This graph represents an assertion, expressed in RDF =<001> <is_peak_latency_measurement_of> <002>
The data represented in cell Z is a temporal property of the ERP pattern represented in row A
Recall: Pattern definition is encoded in the ontology (not in RDF data rep!)
This is the inference that we want to make
Pattern classification is the goal
The challenge (EEG pattern classification)
The methods & tools ontologies RDF database
Proof of concept (a worked example)
Case Study 5 (NEMO): Neural ElectroMagnetic Ontologies
Formulating pattern rules in the ontology
First, we write the rule in semi-natural language:
IF (1) 001 type ERP_spatiotemporal pattern• and (2) 002 type peak_latency_measurement_datum• and (3) 002 is_peak_latency_measurement_of 001,• and (4) 002 has_numeric_value X,• and (5) 500 >= X >= 300 (X has datatype decimal)
(in reality, there are spatial, temporal, & functional criteria…)
THEN (6) 001 type N400_pattern
Translating the rule into OWL/RDF
Next, we convert the rule to a SPARQL query by replacing natural language terms with corresponding URI (tags) from NEMO ontology
Finally, we load Virtuoso’s SPARQL interface http://nemo.nic.uoregon.edu:8890/sparql
& then cut and paste the query into the Query textbox and click Run Query.
…. And Virtuoso returns the following results (for ex):
As a result, we can deduce that ERP observations 0002, 0003, 0004, 0006, and 0140 are
N400 pattern instances… QED
Cycles of Knowledge discovery & Knowledge Engineering (i.e., Onto Dev’t)
✓
Take-home message from CARMEN project:
“Raw data remains static; metadata evolves.”
(note this implies that the ontology also evolves!)
“Data integrity is preserved; the science has room to develop”
NEMO Database
Design
Linking Shared Data & Resources(http://linkeddata.org/)
NIF
NEMO
CARMENHeadIT
NOW YOU SHOULD KNOW…
• What is an ontology & what’s it for?– Why bother?– What are some “best practices” in ontology
design & implementation?
• What is RDF & what’s it for?– How does RDF represent information?– How is it used to link data to ontologies?– How can ontology-based annotation be used to
support classification of data?
Funding from the National Institutes of Health (NIBIB), R01-MH084812 (Dou, Frishkoff, Malony)
NEMO Ontology Task ForceRobert M. Frank (NIC)Dejing Dou (CIS)Paea LePendu (CIS)Haishan Liu (CIS)Allen Malony (NIC, CIS)Snezana Nikolic (PSY, GSU)