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Modules for Automated Validation and Comparison of Models of Neurophysiological and Neurocognitive Biomarkers of Psychiatric Disorders: ASSRUnit - A Case Study Computational Psychiatry. / Metzner, Christoph; Maeki-Marttunen, Tuomo; Zurowski, Bartosz; Steuber, Volker.

In: Computational Psychiatry, Vol. 2, 23.07.2018, p. 74-91.

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@article{6d2bc7e55f684f3db53e208903cb2aaa,
title = "Modules for Automated Validation and Comparison of Models of Neurophysiological and Neurocognitive Biomarkers of Psychiatric Disorders: ASSRUnit - A Case Study Computational Psychiatry",
abstract = "The 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.",
keywords = "Biomarkers, Endophenotypes, Computational Models, Auditory Steady-State Responses, Psychiatric Disorders, Schizophrenia",
author = "Christoph Metzner and Tuomo Maeki-Marttunen and Bartosz Zurowski and Volker Steuber",
note = "{\circledC} 2018 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.",
year = "2018",
month = "7",
day = "23",
doi = "10.1162/cpsy_a_00015",
language = "English",
volume = "2",
pages = "74--91",
journal = "Computational Psychiatry",
issn = "2379-6227",
publisher = "The MIT Press",

}

RIS

TY - JOUR

T1 - Modules for Automated Validation and Comparison of Models of Neurophysiological and Neurocognitive Biomarkers of Psychiatric Disorders: ASSRUnit - A Case Study Computational Psychiatry

AU - Metzner, Christoph

AU - Maeki-Marttunen, Tuomo

AU - Zurowski, Bartosz

AU - Steuber, Volker

N1 - © 2018 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

PY - 2018/7/23

Y1 - 2018/7/23

N2 - The 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.

AB - The 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.

KW - Biomarkers

KW - Endophenotypes

KW - Computational Models

KW - Auditory Steady-State Responses

KW - Psychiatric Disorders

KW - Schizophrenia

U2 - 10.1162/cpsy_a_00015

DO - 10.1162/cpsy_a_00015

M3 - Article

VL - 2

SP - 74

EP - 91

JO - Computational Psychiatry

JF - Computational Psychiatry

SN - 2379-6227

ER -