ontologies for mental health and disease

Representing Mental Functioning:Ontologies for mental health and disease

Janna Hastings1,2

Werner Ceusters3

Mark Jensen3

Kevin Mulligan2

Barry Smith3

1 Cheminformatics and Metabolism, European Bioinformatics Institute, UK2 Swiss Center for Affective Sciences, University of Geneva, Switzerland

3 National Center for Ontological Research, University at Buffalo, USA

ICBO MFO Workshop, 22 July 2012

Wednesday, April 12, 2023 2

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 linked to sociopathy, psychopathy, narcissism and general manipulativeness.

I want…

I think…

Why mental functioning?

How does mental functioning actually work?

Biology

Neuroscience

Chemistry

Psychology

Psychiatry

Cognitive ScienceHuman

Mouse

fMRI

Gene expression

analysis

Self-reportsMetabolic analysis

Genetic profiling

EEG

Questionnaires

PET

Theories of mental functioning have testable implications for researchinto mental disease

Abducted! Replaced!

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delusion: a disorder in which a person holds a delusion that a friend, spouse, parent, or other close family member has been replaced by an identical-looking impostor.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

Existing vocabulariesdon’t include

computable definitions

Mental Functioning Ontology (MF)



Modules under development: Mental diseases and emotions

BFO

MFOGMS

MD

MFO-EM

Domain-neutral ontological upper level

Mental FunctioningOntology

Ontology for GeneralMedical Science

Emotion Ontology

Mental Disease Ontology(Current focus on affective disorders and addiction)


Motivation and Goals


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


Modern scientific research relies on computational support

Data

Data

Data

Synthesis

Analysis

Reporting

Publication

Patient histories, EHR

Questionnairesand self-reports

Genomic and metabolomic

profiles

Caregiver, pscyhiatric reports

Brain scans …

For each question:


Ontology for standardisation

MD:0000901substance abuse

MD:0000902marijuana abuse

S:09090909marijuana

---------------------------Synonym: cannabis

Synonym: THCSynonym: dronabinol

is abuse of substance

Semantics-free unique identifiers that are stable and maintainedCODE (MD) indicates WHICH ONTOLOGY A numeric identifier is unique per term

is a

Unambiguous preferred label together with a textual definition guide the annotationof this ontology term to associated data

Synonyms and other metadata are collectedto facilitate searching, disambiguation and text processing

Synonyms may be in several languagesor reflect differing naming practices in differentdisciplines


Ontology annotations are generic across multiple databases

ID Patient Finding type Detail

1111 Smith, John MF:0000902 (marijuana abuse)

Occasional

1111 Smith, John MF:0000903 (alcohol abuse)

Occasional

1111 Smith, John MF:0000904 (nicotine abuse)

Frequent

Sample ID Sample type Conditions Genotype

1111 Illumina Golden Gate MF:0000903; MF:0000902 …

Same IDs


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?


Ontology for hierarchical organisationMD:0000046

addiction

MD:0000053process addiction

MD:0000053substance addiction

MD:0000054gambling addiction

MD:0000055sex addiction

MD:0000064internet addiction

MD:0000066benzodiazepine addiction

MD:0000065opiate addiction

MD:0000067diazepam addiction

MD:0000059heroin addiction

MD:0000068morphine 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.


… for each database out of hundreds


A shared community ontology for annotation allows unified searching across databases (e.g. GOA)

BrainMap

Brede

fMRI Data Center

RIKEN Neuroimaging

Platform

NeuroSynthOpenfMRI

Nifti


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 – addicted patients

Patient data – impaired rational control of actions

or planning

Patient data – preoccupation or other compulsive

thinking? ?


Ontologies capture explicit computable relationships between entities



disease course

realized in

MD:0001001non-canonical (impaired)

planning process

MD:0001011failed attempts to

stop substance use

has part


thinking process

MD:0001012preoccupation with

substance use

Relationshipsare namedand have definitions

They are usedfor automated reasoning andquestion answering


Related entities are themselves used in annotations

Patient data on symptom

assessment (Addiction) MD:0001001

non-canonical (impaired) planning process


thinking process Patient data on symptom

assessment (Dysexecutive

syndrome)

… which allows patient data from disparate diseases (and research into

normal functioning) to be compared


Different domains operate at different levels of granularity and focus

PATHWAYS, biological processes

METABOLIC DATA (e.g. NMR)

GENE EXPRESSION

DATA


NMR data for metabolites of cocaineis found in

metabolomics databases -- indexed

by small molecules

Urine samples of addicted patients reveal metabolites


Ontology relationships can explicitly bridge across different ontologies at different levels

MD:0000071cocaine addiction MD:0010071

cocaine addictiondisease course

realized in

MD:0020071use of cocaine

has part

S:00100100portion of cocaine

has input

CHEBI:27958cocaine

has granular part

Chemical and metabolic data

(Part of) the biochemical basis of emotion is in ChEBI

Emotions are effected in part by neurotransmitters such as dopamine, tryptophan


dopamine(CHEBI:25375)

molecular entity (CHEBI:25375)

biological role (CHEBI:24432)

neurotransmitter(CHEBI:25512)

has role

neurotransmitter receptor activity

(GO:0030594)

Molecular function (GO:0003674)

realized in

happiness(MFOEM:42)

part of

emotion(MFOEM:1)

subtype


Biological processes in affective disorders

Some mental diseases involve altered emotional functioning. (E.g. depression, bipolar disorder)

emotion

non-canonical sadness

ProcessDisposition

depression

mental disease

realized in

down-regulation of dopaminergic

system (GO:0032227)

has part

biological processMechanism of

action: complex

disturbances in underlying

systems

Addiction in MDO

Applications

• Standardisation and intelligent search / database functionality

• Behavioural and cognitive testing• Population research: clinical questionnaires• Translational research

Open questions

• Relating descriptions at the level of the brain to descriptions of mental functioning: which relationship?

• Relating different levels of description of brain functioning?

• Defining mental disease?


Availability, Contacts

Mental Functioning Ontology available at:http://mental-functioning-ontology.googlecode.com/svn/trunk/

ontology/MF.owl

Discussion mailing list:[email protected]


Acknowledgements Thanks!

Emotion Researchers in GenevaKevin Mulligan, David Sander, Julien Deonna

Chemistry, Biology, NeuroscienceChristoph Steinbeck, Nicolas le Novère, Colin Batchelor,

David Osumi-Sutherland, Jane Lomax, Gwen Frishkoff, Jessica Turner, Angela

Laird

ontologies for mental health and disease

Technology

entities substance addiction

code md

hierarchical organisation

ontology term

theories of mental functioning

ontology annotations

mental diseases

mental health