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dc.contributor.authorMetzner, Christoph
dc.contributor.authorMaeki-Marttunen, Tuomo
dc.contributor.authorZurowski, Bartosz
dc.contributor.authorSteuber, Volker
dc.date.accessioned2018-10-17T15:04:32Z
dc.date.available2018-10-17T15:04:32Z
dc.date.issued2018-07-23
dc.identifier.citationMetzner , C , Maeki-Marttunen , T , Zurowski , B & Steuber , V 2018 , ' Modules for Automated Validation and Comparison of Models of Neurophysiological and Neurocognitive Biomarkers of Psychiatric Disorders: ASSRUnit - A Case Study Computational Psychiatry ' , Computational Psychiatry , vol. 2 , pp. 74-91 . https://doi.org/10.1162/cpsy_a_00015
dc.identifier.issn2379-6227
dc.identifier.otherPURE: 13463261
dc.identifier.otherPURE UUID: 6d2bc7e5-5f68-4f3d-b53e-208903cb2aaa
dc.identifier.urihttp://hdl.handle.net/2299/20718
dc.description© 2018 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
dc.description.abstractThe characterisation of biomarkers and endophenotypic measures has been a central goal of research in psychiatry over the last years. While most of this research has focused on the identification of biomarkers and endophenotypes, using various experimental approaches, it has been recognised that their instantiations, through computational models, have a great potential to help us understand and interpret these experimental results. However, the enormous increase in available neurophysiological and neurocognitive as well as computational data also poses new challenges. How can a researcher stay on top of the experimental literature? How can computational modelling data be efficiently compared to experimental data? How can computational modelling most effectively inform experimentalists? Recently, a general scientific framework for the generation of executable tests that automatically compare model results to experimental observations, SciUnit, has been proposed. Here we exploit this framework for research in psychiatry to address the challenges mentioned above. We extend the SciUnit framework by adding an experimental database, which contains a comprehensive collection of relevant experimental observations, and a prediction database, which contains a collection of predictions generated by computational models. Together with appropriately designed SciUnit tests and methods to mine and visualise the databases, model data and test results, this extended framework has the potential to greatly facilitate the use of computational models in psychiatry. As an initial example we present ASSRUnit, a module for auditory steady-state response deficits in psychiatric disorders.en
dc.language.isoeng
dc.relation.ispartofComputational Psychiatry
dc.subjectBiomarkers
dc.subjectEndophenotypes
dc.subjectComputational Models
dc.subjectAuditory Steady-State Responses
dc.subjectPsychiatric Disorders
dc.subjectSchizophrenia
dc.titleModules for Automated Validation and Comparison of Models of Neurophysiological and Neurocognitive Biomarkers of Psychiatric Disorders: ASSRUnit - A Case Study Computational Psychiatryen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionCentre of Data Innovation Research
dc.description.statusPeer reviewed
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1162/cpsy_a_00015
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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