mental functioning ontology for interdisciplinary research into mental disease, emotions and drugs

Mental Functioning Ontologyfor 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

NIF Webinar, 24 April 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?

Wednesday, April 12, 2023

Many chemicals can affect mental functioning

3

The Chemical Ontology

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a disorder in which a person holds a delusion that a friend, spouse, parent, or other close family member has been replaced by an identical-looking impostor.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


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


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


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


Current status and ongoing work in the Emotion Ontology

BFO:Entity

BFO:Continuant BFO:Occurrent

BFO:ProcessBFO:Independent

Continuant

BFOMFO

BFO:Dependent Continuant

Cognitive Representation

Affective Representation

Mental Process

Bodily ProcessBFO:Disposition

MFO-EM

Emotion Occurrent

Organism

Emotional Action Tendencies

Appraisal

Subjective Emotional Feeling

Physiological Response to

Emotion Process

inheres_in

is_output_of

Emotional Behavioural Process

Appraisal Process

has_part

agent_of

The Emotion Ontology (MFO-EM)


Types of emotion


To define the characteristics of different emotions start with canonical emotions

Emotion types (such as fear) show enormous variance across instancesJust 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 fear

appraisal process

Appraisal of dangerousness

Has part Has output

Canonical fear results from an appraisal of dangerousness


Canonical fear

canonical fear

fear

EMOTION COMPONENT CHARACTERISTIC FOR FEARAction tendency Fight-or-flightSubjective emotional feeling Negative, tense, powerlessBehavioural response Characteristic fearful facial

expressionCharacteristic appraisal Something is dangerous to me

subtype


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


Types of appraisal


Types of subjective feelings


Types of physiological responses


Types of emotional behaviour

Annotation of data from Cognitive Neuroscience of Emotion


Study Task Annotation class in MFO/MFOEM

Recognition of gender in emotional facial expressions

Visual perception of emotional facial expressions (subClassOf perception)

Recall of personal emotional memories with instructions to try re-create feeling

Memory of emotional episodes (subClassOf memory)

Listening to emotional sounds (e.g. grunts of disgust)

Auditory perception of emotional stimuli (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

(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


Availability, ContactsMental 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:[email protected]@googlegroups.com


Acknowledgements Thanks!

Buffalo OntologistsBarry Smith, Werner Ceusters, Mark Jensen

Emotion Researchers in GenevaKevin Mulligan, David Sander, Julien Deonna

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

David Osumi-Sutherland, Jane Lomax, Jessica Turner, Angela Laird

mental functioning ontology for interdisciplinary research into mental disease, emotions and drugs

Technology

theories of mental functioning

maintained md

code md

mental diseases

ontology term

ontology annotations

mental diseasecapgras

john mf