Presented at the Neuroscience Information Framework (NIF) webinar series on 24/04/2012. An overview of the Mental Functioning Ontology aims and objectives.
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Mental functioning ontology for interdisciplinary research into mental disease, emotions and drugs
1. NIF Webinar, 24 April 2012
Mental Functioning Ontology
for interdisciplinary research
into mental disease, emotions and drugs
Janna Hastings1,2
(ChEBI, MF and the Emotion Ontology)
1 Cheminformatics and Metabolism, European Bioinformatics Institute, UK
2 Swiss Center for Affective Sciences, University of Geneva, Switzerland
2. Why mental
functioning? I want…
Oxytocin is believed to play a role in various behaviors,
including orgasm, social recognition, pair bonding, anxiety …
it is sometimes referred to as the "love hormone".
The inability to secrete oxytocin and feel empathy is I think…
linked to sociopathy, psychopathy, narcissism and
general manipulativeness.
Tuesday, April 24, 2012 2
3. Many chemicals
can affect mental functioning
The Chemical
Ontology
Tuesday, April 24, 2012 3
4. How does mental functioning
actually work?
EEG
Biology Mouse
Psychology
Human Cognitive Science
fMRI
Genetic PET
profiling Gene
Neuroscience expression
analysis Psychiatry
Metabolic Chemistry Self-reports
analysis Questionnaires
5. Theories of mental functioning have
Abducted!
testable implications for research Replaced!
into mental disease
Capgras delusion:
a disorder in which a person
holds a delusion that a friend,
spouse, parent, or other close
family member has been replaced
by an identical-looking impostor.
Faulty perception?
Normal perception, faulty reasoning?
Faulty emotional reaction to perception?
Overactive imagination?
TESTABLE IMPLICATIONS
10. Bio-ontologies facilitate
interdisciplinary scientific research
1. Standardised vocabulary with definitions and
synonyms for unified database annotations
2. Hierarchical organisation for aggregation and multi-
level comparison of results
3. Community adoption for comparison of results to
other project results worldwide
4. Explicit relationships and underlying logic for
automated reasoning to related entities
5. Explicit bridging relationships between different
ontologies for exploring underlying mechanisms
Tuesday, April 24, 2012 10
11. Modern scientific research relies on
computational support
Patient histories,
EHR
Synthesis
Caregiver, Data
pscyhiatric reports
Analysis
Genomic and Data
metabolomic
profiles Reporting
Questionnaires Data Publication
and self-reports
Brain scans
Tuesday, April 24, 2012 11
12. Ontology for standardisation
Semantics-free unique identifiers that are
stable and maintained
MD:0000901
CODE (MD) indicates WHICH ONTOLOGY
substance abuse
A numeric identifier is unique per term
is a
Unambiguous preferred label together
MD:0000902
with a textual definition guide the annotation
marijuana abuse
of this ontology term to associated data
is abuse of substance
S:09090909 Synonyms and other metadata are collected
marijuana to facilitate searching, disambiguation and
--------------------------- text processing
Synonym: cannabis
Synonym: THC Synonyms may be in several languages
Synonym: dronabinol or reflect differing naming practices in different
disciplines
Tuesday, April 24, 2012 12
13. Ontology annotations are generic
across multiple databases
ID Patient Finding type Detail
1111 Smith, John MF:0000902 Occasional
(marijuana abuse)
1111 Smith, John MF:0000903 Occasional
(alcohol abuse)
1111 Smith, John MF:0000904 Frequent
(nicotine abuse)
Same IDs
Sample ID Sample type Conditions Genotype
1111 Illumina Golden Gate MF:0000903; MF:0000902 …
Tuesday, April 24, 2012 13
14. Population-wide science depends on
aggregation of data
Are there genes significantly enriched in all people
who suffer from some addiction?
Are there differences between those people who
suffer from substance addiction compared to those
who suffer from process addictions?
Are there differences between those people who
suffer from opiate substance addictions and those
who suffer from addictions to benzodiazepines?
Tuesday, April 24, 2012 14
15. Ontology for hierarchical organisation
MD:0000046
addiction
MD:0000053 MD:0000053
process addiction substance addiction
MD:0000054 MD:0000066 MD:0000065
gambling addiction benzodiazepine addiction opiate addiction
MD:0000055 MD:0000067 MD:0000059
sex addiction diazepam addiction heroin addiction
MD:0000064 MD:0000068
internet addiction morphine addiction
Every ‘sex addiction’ is a ‘process addiction’, every ‘process addiction’ is an ‘addiction’
Every ‘heroin addiction’ is an ‘opiate addiction’, every ‘opiate addiction’ is a ‘substance
addiction’, every ‘substance addiction’ is an ‘addiction’. And so on.
Tuesday, April 24, 2012 15
16. Research involves comparison of results
to existing data arising from other
projects, stored in public databases
A researcher obtains brain scans for several addicted patients. In order
to determine how they compare to existing scans of other addicted
patients and to non-addicted patients, (s)he looks in public databases.
Relating to addiction, manual examination of the BrainMap database
searchcriteria suggests two patient diagnosis categories that may
be relevant: alcoholism and pathological
gambling
… as well as many unstructured keywords
Tuesday, April 24, 2012 16
19. To amass the correct search criteria to find the data for
each comparison requires careful manual examination
… and that’s only one database out of hundreds
Tuesday, April 24, 2012 19
20. A shared community ontology for
annotation allows unified searching
across databases (e.g. GOA)
RIKEN
BrainMap
Neuroimaging
Platform
Brede Nifti
fMRI Data
OpenfMRI NeuroSynth
Center
Tuesday, April 24, 2012 20
21. Computers can’t “see” implicit
relationships between entities
Substance addiction is characterised by symptoms such as
preoccupation with substance and repeated failed attempts
to control the use of the substance. These are non-
canonical thinking and planning activities.
But, there is no easy way to automatically compare with
data from other conditions that have similar symptoms.
Patient data – Patient data –
Patient data – impaired rational preoccupation or
addicted patients control of actions other compulsive
or planning thinking
Tuesday, April 24, 2012 21
22. Ontologies capture explicit computable
relationships between entities
MD:0001002 MD:0001001
non-canonical (impaired) non-canonical (impaired)
thinking process planning process
MD:0001012 MD:0001011 Relationships
preoccupation with failed attempts to are named
substance use stop substance use
and have
definitions
has part
MD:0001053 They are used
MD:0000053 realized in
substance addiction
substance addiction for automated
disease course reasoning and
question
Tuesday, April 24, 2012 answering22
23. Related entities are themselves used
in annotations
MD:0001002
non-canonical (impaired)
thinking process Patient data on
Patient data on
symptom symptom
assessment assessment
MD:0001001 (Dysexecutive
(Addiction)
non-canonical (impaired) syndrome)
planning process
… which allows patient data
from disparate diseases (and research into
normal functioning) to be compared
Tuesday, April 24, 2012 23
24. Different domains operate at different
levels of granularity and focus
METABOLIC
DATA (e.g. NMR)
GENE
EXPRESSION
PATHWAYS, biological DATA
Tuesday, April 24, 2012 24
processes
25. Urine samples of addicted patients reveal metabolites
NMR data for
metabolites
of cocaine
is found in
metabolomics
databases -- indexed
by small molecules
Tuesday, April 24, 2012 25
26. Ontology relationships can explicitly
bridge across different ontologies at
different levels
MD:0000071
realized in MD:0010071
cocaine addiction
cocaine addiction
disease course
has part
S:00100100 has input MD:0020071
portion of cocaine use of cocaine
has granular part
CHEBI:27958
Chemical and
cocaine
metabolic data
Tuesday, April 24, 2012 26
27. Current status and ongoing work
in the Emotion Ontology
Tuesday, April 24, 2012 27
28. The Emotion Ontology (MFO-EM)
BFO:Entity BFO
MFO
BFO:Continuant BFO:Occurrent MFO-EM
BFO:Independent BFO:Dependent
Continuant Continuant BFO:Process
Organism BFO:Disposition Bodily Process
Physiological
Response to
Emotion Process
Mental Process
Cognitive
inheres_in
Representation
Appraisal
Process
Emotional Action
Tendencies Affective is_output_of
Representation Appraisal
Emotional
Behavioural Process
Subjective
Emotional Feeling
has_part
agent_of
Emotion Occurrent
30. To define the characteristics of different
emotions start with canonical emotions
Emotion types (such as fear) show enormous variance across instances
Just as do anatomical types, e.g. human bodies
Ontology expresses what is always true… But also aims to say
something useful for representation of domain knowledge.
Solution: encode such knowledge in ‘canonical’ types
canonical Has part appraisal Has output Appraisal of
fear process dangerousness
Canonical fear results from an appraisal of dangerousness
Tuesday, April 24, 2012 30
31. Canonical fear
fear
subtype
canonical
fear
EMOTION COMPONENT CHARACTERISTIC FOR FEAR
Action tendency Fight-or-flight
Subjective emotional feeling Negative, tense, powerless
Behavioural response Characteristic fearful facial
expression
Characteristic appraisal Something is dangerous to me
Tuesday, April 24, 2012 31
32. Canonical and non-canonical fear
Canonical fear gives rise to action tendencies
that are conformant to the perceived danger
Phobia =
disposition giving rise to non-canonical fear
laridaphobia : intense fear of seagulls
Tuesday, April 24, 2012 32
37. Annotation of data from Cognitive
Neuroscience of Emotion
Study Task Annotation class in MFO/MFOEM
Recognition of gender in emotional facial Visual perception of emotional facial
expressions expressions (subClassOf perception)
Recall of personal emotional memories Memory of emotional episodes
with instructions to try re-create feeling (subClassOf memory)
Listening to emotional sounds (e.g. grunts Auditory perception of emotional stimuli
of disgust) (subClassOf perception)
Viewing emotional film extracts Visual and auditory perception of
emotional stimuli (subClassOf perception)
The link from perception of emotional fear in facial
expressions to canonical fear is subject to empirical research
Tuesday, April 24, 2012 37
38. (Part of) the biochemical basis of
emotion is in ChEBI
Emotions are effected in part by
neurotransmitters such as dopamine, tryptophan
molecular entity biological role Molecular function emotion
(CHEBI:25375) (CHEBI:24432) (GO:0003674) (MFOEM:1)
subtype
neurotransmitter
happiness
dopamine neurotransmitter receptor activity
(MFOEM:42)
(CHEBI:25375) (CHEBI:25512) (GO:0030594)
has role realized in part of
Tuesday, April 24, 2012 38
39. Biological processes in affective
disorders
Some mental diseases involve altered emotional
functioning. (E.g. depression, bipolar disorder)
Disposition Process
mental
emotion biological process
disease Mechanism of
action:
complex
down-regulation disturbances in
non-canonical of dopaminergic
depression underlying
sadness system systems
(GO:0032227)
realized in has part
Tuesday, April 24, 2012 39
40. Availability, Contacts
Mental Functioning Ontology available at:
http://mental-functioning-
ontology.googlecode.com/svn/trunk/ontology/MF.owl
Emotion Ontology available at:
http://emotion-
ontology.googlecode.com/svn/trunk/ontology/MFOEM.owl
Discussion mailing lists:
mfo-discuss@googlegroups.com
emotion-ontology@googlegroups.com
Tuesday, April 24, 2012 40
41. Acknowledgements
Thanks!
Buffalo Ontologists
Barry Smith, Werner Ceusters, Mark Jensen
Emotion Researchers in Geneva
Kevin Mulligan, David Sander, Julien Deonna
Chemistry, Biology, Neuroscience
Christoph Steinbeck, Nicolas le Novère, Colin Batchelor,
David Osumi-Sutherland, Jane Lomax,
Jessica Turner, Angela Laird
Tuesday, April 24, 2012 41
Editor's Notes
There are 134 hits for ‘has role’ some psychotropic in ChEBI in February 2012. This screenshot (inter alia) shows Lithium (a mood stabilizer); chlorpromazine (an antipsychotic); valproate (antimanic); 5-methoxy-N,N-dimethyltryptamine (hallucinogen);
(Not the million dollar question, but the many billion dollars question!)We’re drowning in data and starving for knowledge! Not only different domains BUT different methods and different subjects (model organisms etc)Huge piles of different sorts of information coming out of different research areas. DIFFERENT PERSPECTIVES: if you try to get people to agree on names, they just don’t. But give them semantics-free identifiers and their own preferred (scoped) synonyms and you can get agreement on the definitions. Nobody is an expert in everything, most scientists are stuck in their narrow area of focus and expertise (which is a good thing for progress because you HAVE to become that specialised)
Different interpretations for the same results can ensue; based on the underlying theory of mental functioning. Linking the theory directly to the paradigm (tests) and the research results allows more straightforward generation of testable hypothesis for evaluating different theories… getting away from conceptual arguments, or at least helping to resolve them(Explicit logical formulation)
SNOMED, MeSH, ICD, ICF, Cognitive Atlas, Cognitive Paradigm Ontology, We will build on these vocabulary resources as sources, but maintain links so that we don’t lose mappings which have already been annotated to these sources.Most of these sources maintain controlled vocabularies but not real ontologies. There is a shortage of explicit relationships and formal (computable) definitions, so you can’t make computational inferences.
Mental functioning related anatomical structure: an anatomical structure in which there inheres the disposition to be the agent of a mental processBehaviour inducing state: a bodily quality inhering in a mental functioning related anatomical structure which leads to behaviour of some sortAffective representation: a cognitive representation sustained by an organism about its own emotionsCognitive representation: a representation which specifically depends on an anatomical structure in the cognitive system of an organismMental process: a bodily process which brings into being, sustains or modifies a cognitive representation or a behaviour inducing state
Arrows show ‘imports’ relationships between ontologies
Software engineering for integrative question-answering is made much easier by this approach, as the IDs are well-behaved strings – uniform length, numeric identifiers for quick lookup / indexing and so on.
Obviously, these questions leave aside the complexities of co-occurrences, but for the higher-level questions that would present no problem as long as aggregation occurred with the count of instances not the count of types. For the comparative questions at the lower level, you would want to exclude co-occurrences from the analysis if you were looking for genes that comparatively differed between the different classes.
BrainMap is a database of curated fMRI coordinates from published studies -- BrainMap.org.
These screenshots illustrate the search interface application for BrainMap
Examples of keywords which may be relevant – there are many others – e.g. heroin, opiate, … other sorts of drugs. No hierarchical relationships are expressed between these keywords, so a search would have to be assembled by adding them one by one – no automatic aggregation option is available.
Each database has different organisation and search criteria – here it is patient diagnosis and keywords, or at least those were the only two fields that I could find were relevant.
This desideratum may sound like wishful thinking but in fact it is ALREADY IN PLACE for the Gene Ontology and most biological databases. Databases listed here are a small selection of those that include fMRI coordinate data. For a discussion of the various brain imaging methods and results in studies of addiction, see: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851068/ ‘Imaging the addicted human brain’
It would be useful, therefore, to compare data for addicted patients with data for patients with other preoccupations or failed goal-directed behaviours. But no computational methods facilitate this type of cross-searching at present. Again, it comes down to human effort to find or create the right sort of data. Targeted studies can be designed that do this on a one-by-one basis: see, for example, http://jnnp.bmj.com/content/68/6/731.full – which compares data on dysexecutive syndrome with patients who have alcoholism. Good study design is always a good idea, but the availability of published data on various conditions would allow re-use of that data in other contexts, if the links from symptoms to disorders were made more explicit.
Psychological standard test for ‘dysexective syndrome’ => failure of normal executive functions such as planning, organising, initiating … => http://www.dwp.gov.uk/docs/no2-sum-03-test-review-2.pdfFootnote: data should be compared only if it makes sense to do so! That’s the reason for explicitly characterising and classifying symptoms
Pathway illustration sourced from KEGG: http://www.kegg.jp/kegg-bin/highlight_pathway?scale=1.0&map=map05030&keyword=addictionNMR spectrum illustration (of a derivative of cocaine) comes from http://www.justice.gov/dea/programs/forensicsci/microgram/journal_v4_num14/pg5.html
This data on metabolites of cocaine was sourced from the Human Metabolome Database (HMDB): http://www.hmdb.ca/metabolites/HMDB06348
Canonical fear also involves an action tendency to fight-or-flight, a bad (powerless, negative, anxious) feeling, a behavioural response to the emotion that includes a characteristic fearful facial expression
Cognitive neuroscience uses research “paradigms” – experimental designs intended to allow comparison of brain activation between different conditions. The subtraction of the brain activation for the control condition from the brain activation for the test condition then gives the “net” activation, which is what is reported on in the literature, subject to statistical analysis.
This is, of course, just one tiny part of the story. The overall story would have to be built up out of many, many cross-ontology links.
Depression and bipolar disorder are paradigm affective disorders.