Mental Functioning and Semantic Search in the Neuroscience Information Framework Maryann Martone Fahim Imam Funded in part by the NIH Neuroscience Blueprint HHSN C via NIDA Neuroscience Information Framework –

The Neuroscience Information Framework: Discovery and utilization of web-based resources for neuroscience A portal for finding and using neuroscience resources  A consistent framework for describing resources  Provides simultaneous search of multiple types of information, organized by category  Supported by an expansive ontology for neuroscience  Utilizes advanced technologies to search the “hidden web” UCSD, Yale, Cal Tech, George Mason, Washington Univ Supported by NIH Blueprint Literature Database Federation Registry

NIF takes a global view of resources NIF’s goal: Discover and use resources – Data – Databases – Tools – Materials – Services Federated approach: Resources are developed and maintained by the community – >150 data sources; 350M records Agile approach: the NIF system is designed to be populated quickly and allow for incremental improvements to representation and search – Contract specifies 25 sources/year NIF’s Rules for using digital resources #1: YOU HAVE TO FIND THEM!!!!!!! #2: You have to access/open them #3: You have to understand them NIF’s Rules for using digital resources #1: YOU HAVE TO FIND THEM!!!!!!! #2: You have to access/open them #3: You have to understand them Neuroscience is inherently interdisciplinary; no one technique reveals all

What do you mean by data? Databases come in many shapes and sizes Primary data : – Data available for reanalysis, e.g., microarray data sets from GEO; brain images from XNAT; microscopic images (CCDB/CIL) Secondary data – Data features extracted through data processing and sometimes normalization, e.g, brain structure volumes (IBVD), gene expression levels (Allen Brain Atlas); brain connectivity statements (BAMS) Tertiary data – Claims and assertions about the meaning of data E.g., gene upregulation/downregulation, brain activation as a function of task Registries: – Metadata – Pointers to data sets or materials stored elsewhere Data aggregators – Aggregate data of the same type from multiple sources, e.g., Cell Image Library,SUMSdb, Brede Single source – Data acquired within a single context, e.g., Allen Brain Atlas

Set of modular ontologies – 86, distinct concepts + synonyms Expressed in OWL-DL language – Supported by common DL Reasoners – Currently supports OWL 2 Closely follows OBO community best practices Avoids duplication of efforts – Standardized to the same upper level ontologies e.g., Basic Formal Ontology (BFO), OBO Relations Ontology (OBO-RO) – Relies on existing community ontologies e.g., CHEBI, GO, PRO, DOID, OBI etc. 5 Modules cover orthogonal domain e.g., Brain Regions, Cells, Molecules, Subcellular parts, Diseases, Nervous system functions, etc. Bill Bug et al. NIFSTD Ontologies Neuroscience Information Framework –

Importing into NIFSTD NIF converts to OWL and aligns to BFO, if not already – Facilitates ingestion, but can have negative consequences for search if model adds computational complexity Data sources do not make careful distinctions but use what is customary for the domain Modularity: NIF seeks to have single coverage of a sub- domain – We are not UMLS or Bioportal NIF uses MIREOT to import individual classes or branches of classes from large ontologies – NIF retains identifier of source NIF uses ID’s for names, not text strings – Avoids collision – Allows retiring of class without retiring the string NIFSTD has evolved as the ontologies have evolved; had to make many compromises based on ontologies and tools available

NIFSTD Modules and Sources NIFSTD ModulesExternal SourceImport/ Adapt Organismal taxonomyNCBI Taxonomy, GBIF, ITIS, IMSR, Jackson Labs mouse catalog; the model organisms in common use by neuroscientists are extracted from NCBI Taxonomy and kept in a separate module with mappings Adapt Molecules, Chemicals IUPHAR ion channels and receptors, Sequence Ontology (SO); NIDA drug lists from ChEBI, and imported Protein Ontology (PRO) Adapt/Import Sub-cellular anatomySub-cellular Anatomy Ontology (SAO). Extracted cell parts and subcellular structures from SAO-CORE. Imported GO Cellular Component with mapping. Adapt/Import CellCCDB, NeuronDB, NeuroMorpho.org. Terminologies; OBO Cell Ontology was not considered as it did not contain region specific cell types Adapt Gross AnatomyNeuroNames extended by including terms from BIRNLex, SumsDB, BrainMap.org, etc; Multi-scale representation of Nervous System, Macroscopic anatomy Adapt Nervous system function BIRN, BrainMap.org, MeSH, and UMLS, GO Biological functionsAdapt Nervous system dysfunction Nervous system disease from MeSH, NINDS terminology; Imported Disease Ontology (DO) with mapping Adapt/Import Phenotypic qualitiesPhenotypic Quality Ontology (PATO); Imported as part of the OBO foundry core Import Investigation: reagentsOverlaps with molecules above from ChEBI, SO, and PROAdapt/Import Investigation: instruments, protocols, plans CogPo, BIRNLexAdapt Investigation: resource type NIF, OBI, NITRC, Biomedical Resource Ontology (BRO)Adapt Biological ProcessGene Ontology (GO) biological processImport Neuroscience Information Framework –

What are the connections of the hippocampus? Hippocampus OR “Cornu Ammonis” OR “Ammon’s horn” Query expansion: Synonyms and related concepts Boolean queries Query expansion: Synonyms and related concepts Boolean queries Data sources categorized by “data type” and level of nervous system Common views across multiple sources Tutorials for using full resource when getting there from NIF Link back to record in original source

Entity mapping BIRNLex_435 Brodmann.3 Explicit mapping of database content helps disambiguate non- unique and custom terminology

Search Google: GABAergic neuron Search NIF: GABAergic neuron – NIF automatically searches for types of GABAergic neurons – Defined by OWL axioms Types of GABAergic neurons NIF Concept-Based Search Neuroscience Information Framework –

Ontological Query expansion through OntoQuest Example Query TypeOntological Expansion A single term query for Hippocampus and its synonyms synonyms(Hippocampus); expands to Hippocampus OR "Cornu ammonis" OR "Ammon's horn" OR "hippocampus proper". A conjunctive query with 3 terms transcription AND gene AND pathway A 6-term AND/OR query with one term expanded into synonyms (gene) AND (pathway) AND (regulation OR "biological regulation") AND (transcription) AND (recombinant) A conjunctive query with 2 terms, where a user chooses to select the subclasses of the 2 nd term synonyms(zebrafish AND descendants(promoter,subclassOf))), zebrafish gets expanded by synonym search and the second term transitively expands to all subclasses of promoter as well as their synonyms. A single term query for an anatomical structure where a user chooses to select all of the anatomical parts of the term along with synonyms synonyms(descendants(Hippocampus,partOf)), expands to all parts of hippocampus and all their synonyms through the ontology. All parts are joined as an “OR” operation. A conjunctive query with 2 terms, where a user chooses to select all the equivalent terms for the 2 nd term synonyms(Hippocampus) AND equivalent(synonyms(memory)), the second term uses the ontology to find all terms that are equivalent to the term memory by ontological assertion, along with synonyms. A conjunctive query with 2 terms, where a user is interested in a specific subclasses for both of the terms synonyms(x:descendants(neuron,subclassOf) where x.neurotransmitter='GABA') AND synonyms(gene where gene. name='IGF'), x is an internal variable. A query to seek all subclasses of neuron whose soma location is in any transitive part of the hippocampus synonyms(x:descendants(neuron,subclassOf) where x.soma.location = descendants (Hippocampus, partOf)) A query to seek a conceptual term that is semantically equivalent to a collection of terms rather than a single term. 'GABAergic neuron' AND Equivalent ('GABAergic neuron'), The term is recognized as ontologically equivalent to any neuron that has GABA as a neurotransmitter and therefore expands to a list of inferred neuron types OntoQuest – NIF’s ontology management system for NIFSTD ontologies Implements various graph search algorithms for ontological graphs Automated query expansion for NIFSTD terms, including the ones with defined logical restrictions. Gupta et al., 2010

NIF information space NIF developed a tiered system Domain knowledge – What you would teach someone coming into your domain NIFSTD/Ontoquest All upper level BFO categories are suppressed Claims based on data – Bridge files across domains (constructed by NIF), Databases, triple stores, – Text Data – Relational databases – Spreadsheets Concepts Data Knowledge Base Knowledge Base Concepts, Entities + data summaries Scientists search via the terms they use, not what we would like them to use-NIF needs a broad net to find relevant resources

When searching across broad information sources, need to search for what people are looking for What genes are upregulated by drugs of abuse in the adult mouse? Gene upregulated mice illegal drug

NIF “translates” common concepts through ontology and annotation standards What genes are upregulated by drugs of abuse in the adult mouse? Morphine Increased expression Adult Mouse Arbitrary but defensible

N IF S TD AND N EURO L EX W IKI Semantic wiki platform Provides simple forms for structured knowledge People can add concepts, properties, and annotations Generate hierarchies without having to learn complicated ontology tools Community can contribute – Relax rules for NIFSTD so dedicated domain scientists can contribute their knowledge and review other contributions – Teaches structuring of knowledge via red links/blue links – Process is tracked and exposed – Implemented versioning 15 Larson et al. Readily indexed by Google; queries to NIF data via NIF navigator

NeuroLex Content Structure Stephen D. Larson et al. Neurolex is becoming a significant knowledge base

Top Down Vs. Bottom up Top-down ontology construction A select few authors have write privileges Maximizes consistency of terms with each other Making changes requires approval and re-publishing Works best when domain to be organized has: small corpus, formal categories, stable entities, restricted entities, clear edges. Works best with participants who are: expert catalogers, coordinated users, expert users, people with authoritative source of judgment Bottom-up ontology construction Multiple participants can edit the ontology instantly Semantics are limited to what is convenient for the domain Not a replacement for top-down construction; sometimes necessary to increase flexibility Necessary when domain has: large corpus, no formal categories, no clear edges Necessary when participants are: uncoordinated users, amateur users, naïve catalogers Neuroscience is a domain that is less formal and neuroscientists are more uncoordinated Larson et. al NIFSTD NEUROLEX Neuroscience Information Framework –

Engaging domain scientists Memory Mental Process Cognitive process Recall Retrieval Encoding Disposition Planned process Continuant Episodic Non- declarative Mental state ???

Mental functioning is difficult to define and dissect Very few behaviors are “pure” Operationally defined through experiments What is a mental function? Activity, state, function, process Subtypes are rarely disjoint Episodic memory Semantic memory Procedural memory Declarative memory Distinctions among paradigms, assessments, tests, rating scales, tasks are often subtle Early work done in BIRN; later terms added by students and curators

Neurolex does not adhere strictly to BFO Concepts and things happily co-exist; content gets reconciled over time

Nevertheless... We do not allow duplicates We do not allow multiple inheritance – Use “role” to shortcut many relations We do try to re-factor contributions so as to avoid collisions across our domains But...once they are in the wiki, they will move about and be added to as necessary Neuinfo.org/neurolex/wiki/COGPO_00123

Cognitive-related searches through NIF fear prefrontal arousal Attention and distraction Passive viewing stroop effect sequence learning studies done on the cognitive-behavioral model of addiction memory recall self-administration Visual oddball paradigm Sexual Orientation Face recognition neurophysiology of language Olfaction Consciousness Gustatory Scientists tend to focus on tests and general concepts rather than deep considerations of cognitive processes

Mental Functioning: What NIF needs Computable taxonomies of test (assessments, paradigms, tasks) types – Test types should be related to the function they purport to measure but will only be an approximation – Not just human!!! Computable operational definitions of cognitive concepts – Translates tests into concepts used in search – Dementia rating scale scores = Dementia – Smoking assessment scores = smoker

Concluding Remarks NIFSTD is utilized to provide a semantic index to heterogeneous data sources – BFO allows us to promote a broad semantic interoperability between biomedical ontologies. – The modularity principles allows us to limit the complexity of the base ontologies NIF defines a process to form complex semantics to neuroscience concepts through NIFSTD and NeuroLex collaborative environment. – NIF encourages the use of community ontologies Moving towards building rich knowledgebase for Neuroscience that integrates with larger life science communities Neuroscience Information Framework –

Points of Discussion CogPO/CogAT/NEMO/MHO Harmonization? What kind of interplay are we looking at? Is it about re-use of ontological vocabularies? What should be the best practice for reuse? – Re-using URI vs Creating new class and Mapping – Non-semantic reuse of classes as entities (e.g., MIREOT) Is it about building new relationships between the entities covered in all these four ontologies? – What do we achieve through doing this? Are we trying to connect all the curated/ annotated experimental data-set to a common semantic layer? All of the above? What should be NIF's role? How can we help to expose your experiments and results to a broader audience through our interface? What kind of involvement can people have in terms of re-using your ontological content or contributing to your content? We want to be the 'host' of all the NS concepts and entities, but not necessarily the 'maintainer'.

What ontology isn’t (or shouldn’t be) A rigid top-down fixed hierarchy for limiting expression in the neurosciences – Not about restricting expression but how to express meaning clearly and in a machine readable form A bottomless resource-eating pit that consumes dollars and returns nothing A cure-all for all our problems A completely solved area – Applied vs theoretical Easy to understand Mike Bergman