=Paper=
{{Paper
|id=Vol-3073/paper25
|storemode=property
|title=Recent Refinements of the NeuroPsychological Testing Ontology
|pdfUrl=https://ceur-ws.org/Vol-3073/paper25.pdf
|volume=Vol-3073
|authors=Alexander P. Cox,Kiernan L. Johnson,Lauren Wishnie,Aaron S. Kemp,Jonathan P. Bona,Alexander D. Diehl
|dblpUrl=https://dblp.org/rec/conf/icbo/CoxJWKBD21
}}
==Recent Refinements of the NeuroPsychological Testing Ontology==
https://ceur-ws.org/Vol-3073/paper25.pdf
Recent Refinements of the NeuroPsychological Testing Ontology
Alexander P. Cox 1, Kiernan L. Johnson 1, Lauren M. Wishnie 1, Aaron S. Kemp 2,3, Jonathan
P. Bona 3, and Alexander D. Diehl 1
1
Department of Biomedical Informatics, University at Buffalo, 77 Goodall St., Buffalo, New York 14203, USA
2
Department of Psychiatry, University of Arkansas for Medical Sciences (UAMS), 4301 W. Markham, Little Rock,
AR 72205, USA
3
Department of Biomedical Informatics, UAMS, 4301 W. Markham, Little Rock, AR 72205, USA
Abstract
The NeuroPsychological Testing Ontology is designed to represent neuropsychological
assessments, the cognitive processes and functions they assess, and associated data. This paper
provides an overview of recent ontology development efforts as well as its current data
applications and future development goals.
Keywords 1
Neuropsychological testing, cognitive domains, ADNI, applied ontology
1. Introduction
The NeuroPsychological Testing Ontology (NPT) [1, 2, 3] is designed to represent standardized
neuropsychological assessments, such as those used by the Alzheimer's Disease Neuroimaging
Initiative (ADNI) [4]. NPT provides a set of classes for the annotation of neuropsychological testing
data and is designed to enable integration of results from a variety of neuropsychological tests by (i)
representing the tests in greater granularity, and (ii) connecting assays and sub-assays to the cognitive
function(s) they measure. In this way, data generated from multiple tests that are about a given cognitive
domain can be readily aggregated and studied. This is true regardless of whether the data are from entire
tests, test sub-sections, or individual test components. The selected cognitive domain can be as broad
or specific as desired and multiple domains can be combined. NPT’s overarching goal is to increase the
accuracy and usability of neuropsychological tests to support efforts to increase understanding of
cognitive domains and the conditions that affect them.
Many diseases are associated with, result in, or are diagnosed based on the presence or absence of
certain neuropsychological signs and symptoms. Hence, neuropsychological testing plays an important
role in the development of clinical pictures used in the diagnosis of patients with a range of neurological
diseases and disorders such as Alzheimer’s disease, multiple sclerosis, or following stroke or traumatic
brain injury. Two initial goals of this project are to leverage the results of neuropsychological
assessments to (i) test hypotheses about the diagnosis of Alzheimer’s disease and (ii) identify patient
populations that are likely to convert from mild cognitive impairment to dementia.
NPT is being developed in compliance with the OBO Foundry principles [5]. It extends the Ontology
for Biomedical Investigations (OBI) [6]. NPT is a corollary project of the Neurological Disease
Ontology (ND) [1, 7], which represents diseases, disorders, and syndromes that are associated with
neurodegeneration.
In building NPT, we have relied upon source tests, such as the Folstein Mini-Mental State Exam [8]
and Montreal Cognitive Assessment [9], as well as upon textbooks [10, 11] and articles about these
neuropsychological tests and the cognitive functions they measure. NPT is built using the schema for
representing assays that has been developed in OBI and consequently currently imports all of OBI.
International Conference on Biomedical Ontologies 2021, September 16–18, 2021, Bozen-Bolzano, Italy
EMAIL: apcox@buffalo.edu (A. 1); kj55@buffalo.edu (A. 2); lmwishni@buffalo.edu (A. 3); ASKemp@uams.edu (A. 4); JPBona@uams.edu
(A. 5); addiehl@buffalo.edu (A. 6)
ORCID: 0000-0002-6969-2784 (A. 1); 0000-0003-0278-3371 (A. 2); 0000-0002-7245-3450 (A. 3); 0000-0001-6925-5667 (A. 4); 0000-0003-
1402-9616 (A. 5); 0000-0001-9990-8331 (A. 6)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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2. New Developments
The NeuroPsychological Testing Ontology (NPT) was initially developed during 2012-2013. At the
end of this initial development stage, NPT was at version 1.7.2. NPT version 2.0 is pending release and
contains approximately 550 NPT terms. This number is expected to grow quickly as representations of
additional neuropsychological tests get added.
While the previous effort provided a good start on these efforts, there are multiple reasons for
continuing the NPT effort. First, there are many more neuropsychological tests in use. These tests
provide supplementary perspectives and data for identical, overlapping, or additional cognitive
functions and domains. Second, there are often many versions of the “same” test and it is important to
capture which test version is used as well as the differences between versions so their results can be
combined as accurately as possible. This is particularly important to ensure greater fidelity when
conducting meta-analyses. Third, many NPT terms previously either lacked definitions or benefited
from improved or more informative definitions or other annotations. Fourth, while NPT already
included many logical axioms to provide built-in reasoning, some axioms have been refactored while
others have been added.
The status of neuropsychological assessments represented in NPT is shown in Table 1.
Table 1
Neuropsychological Tests Represented in NPT
Neuropsychological Test Status in NPT
Folstein Mini-Mental State Examination (MMSE) Completed, Revised
Montreal Cognitive Assessment (MoCA) Completed, Revised
Alzheimer’s Disease Assessment Scale (ADAS-COG) Under Review
Auditory Verbal Learning Test (AVLT) Completed
Trail-Making Test Completed
Boston Naming Test Completed, Revised
Clock Drawing Test Completed
Linguistic Fluency (Semantic/Phonetic) In Progress
Wechsler Adult Intelligence Scale - Fourth Edition (WAIS-IV) In Progress
Wechsler Memory Scale - Fourth Edition (WMS-IV) Planned
Hopkins Verbal Learning Test (HVLT) Planned
Brief Visuospatial Memory Test – Revised (BVMT-R) Planned
The overarching goal of the current NPT development effort is to ensure robust test representations
to support mapping, ingestion into triple stores, and the retrieval and compilation of neuropsychological
assessment data. An important component of this is to account for variation in test versions and test
administration by representing, for example, the specific word list, prompts, and scoring used for a
given word recall assay. This is accomplished, in part, by the creation of the NPT-ADNI application
ontology. While NPT is designed to represent “generic” versions of standardized neuropsychological
assessments, NPT-ADNI is designed to represent highly specific terms that may be unique to a single
version of a generic assessment. For example, NPT contains the term ‘MoCA delayed recall assay’ and
NPT-ADNI adds the term ‘MoCA delayed recall daisy assay’ to specify one of the target words for that
version of the MoCA. The increased semantic content is intended to facilitate more fine-grained
analysis of data, which is hypothesized to be useful for both clinical and test design analyses.
NPT and NPT-ADNI are currently being applied to MoCA and MMSE data from the ADNI dataset.
MoCA and MMSE ADNI data has been mapped using the Karma data integration tool [12]. These
mappings are then used to generate triples for the entire ADNI dataset, which includes more than 6,800
MoCA and 12,100 MMSE test results as of January 2021. The triples are loaded into a GraphDB
instance where they can be queried using customizable SPARQL queries to further analyze the
aggregated data. The power of these queries will increase as additional assay data are added.
3. Future Work
Future work will expand NPT’s coverage to include additional cognitive tests and enhance current
test representations. Despite the use of different assessment instruments, applying NPT to disease-
specific data repositories, such as ADNI [4] or the Parkinson’s Progression Markers Initiative [13], will
confer the ability to identify neuroimaging measures that correspond with cognitive impairment profiles
across common functional domains. Accordingly, NPT offers a basis for the semantic representation of
impaired cognitive functions across distinct neurodegenerative pathologies (e.g., Alzheimer’s disease
vs. Parkinson’s disease). This will be used to mine neuroimaging measures to identify candidate
biomarkers of cognitive impairment specific to a given etiology. In this way we expect NPT will serve
as a valuable tool to integrate neuropsychological data from multiple sources to look for commonalities
and differences that will give insights into diagnosis and treatment of neurodegenerative disease.
4. Acknowledgements
The authors thank Mark Jensen and Kinga Szigeti for their contributions to the development of the
initial iteration of NPT. ADD is supported by NCATS 5UL1TR001412-06.
5. References
[1] Cox AP, Jensen M, Duncan W, et al. Ontologies for the Study of Neurological Disease. Presented
July 22, 2012 at ICBO 2012: 3rd International Conference on Biomedical Ontology workshop
“Towards an Ontology of Mental Functioning”, Graz, Austria. URL:
https://github.com/addiehl/neurological-disease-ontology/blob/master/docs/ICBO2012_Paper.pdf
[2] Cox AP, Jensen M, Ruttenberg A, Szigeti K, Diehl AD. Measuring cognitive functions: hurdles in
the development of the neuropsychological testing ontology. Proceedings of the 4th International
Conference on Biomedical Ontology. 2013, CEUR Workshop Proceedings, Montreal, Canada,
July 7-12, 2013. URL: http://CEUR-WS.org/Vol-1060/icbo2013_submission_46.pdf
[3] The NeuroPsychological Testing Ontology, GitHub Repository, 2021. URL:
https://github.com/addiehl/neuropsychological-testing-ontology
[4] Weiner MW, Aisen PS, Jack CR Jr, et al. The Alzheimer's disease neuroimaging initiative:
progress report and future plans. Alzheimers Dement. 2010;6(3):202-11.e7.
doi:10.1016/j.jalz.2010.03.007
[5] Smith B, Ashburner M, Rosse C, et al. The OBO Foundry: coordinated evolution of ontologies to
support biomedical data integration. Nat Biotechnol. 2007;25(11):1251-1255.
doi:10.1038/nbt1346
[6] Bandrowski A, Brinkman R, Brochhausen M, et al. The Ontology for Biomedical Investigations.
PLoS One. 2016;11(4):e0154556. Published 2016 Apr 29. doi:10.1371/journal.pone.0154556
[7] Jensen M, Cox AP, Chaudhry N, et al. The Neurological Disease Ontology. Journal of Biomedical
Semantics 4, 42 (2013). URL: https://doi.org/10.1186/2041-1480-4-42
[8] Folstein MF, Folstein SE, McHugh PR. Mini-mental state: A practical method for grading the
cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189-198. doi:10.1016/0022-
3956(75)90026-6
[9] Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: a
brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695-699.
doi:10.1111/j.1532-5415.2005.53221.x
[10] Lezak MD, Howieson DB, Loring DW. (Eds.) (2004). Neuropsychological Assessment (4th ed.).
Oxford: Oxford University Press. https://doi.org/10.1007/s00415-005-0003-0
[11] Mitrushina M, Boone KB, Razani J, D’Elia L. (2005). Handbook of Normative Data for
Neuropsychological Assessment (2nd ed.). Oxford: Oxford University Press.
[12] Karma data integration tool. URL: https://usc-isi-i2.github.io/karma/
[13] Marek K, Jennings D, Lasch S, et al. The Parkinson Progression Marker Initiative (PPMI).
Progress in neurobiology. 2011 Dec 1;95(4):629-35. doi:10.1016/j.pneurobio.2011.09.005