Cargando…

Testing frameworks for personalizing bipolar disorder

The hallmark of bipolar disorder is a clinical course of recurrent manic and depressive symptoms of varying severity and duration. Mathematical modeling of bipolar disorder holds the promise of an ability to personalize diagnoses, to predict future mood episodes, to directly compare diverse datasets...

Descripción completa

Detalles Bibliográficos
Autores principales: Cochran, Amy L., Schultz, André, McInnis, Melvin G., Forger, Daniel B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5804032/
https://www.ncbi.nlm.nih.gov/pubmed/29391394
http://dx.doi.org/10.1038/s41398-017-0084-4
_version_ 1783298756125392896
author Cochran, Amy L.
Schultz, André
McInnis, Melvin G.
Forger, Daniel B.
author_facet Cochran, Amy L.
Schultz, André
McInnis, Melvin G.
Forger, Daniel B.
author_sort Cochran, Amy L.
collection PubMed
description The hallmark of bipolar disorder is a clinical course of recurrent manic and depressive symptoms of varying severity and duration. Mathematical modeling of bipolar disorder holds the promise of an ability to personalize diagnoses, to predict future mood episodes, to directly compare diverse datasets, and to link basic mechanisms to behavioral data. Several modeling frameworks have been proposed for bipolar disorder, which represent competing hypothesis about the basic framework of the disorder. Here, we test these hypotheses with self-report assessments of mania and depression symptoms from 178 bipolar patients followed prospectively for 4 or more years. Statistical analysis of the data did not support the hypotheses that mood arises from a rhythmic process or multiple stable states (e.g., mania or depression) or that manic and depressive symptoms are highly anti-correlated. Alternatively, it is shown that bipolar disorder could arise from an inability for mood to quickly return to normal when perturbed. This latter concept is embodied by an affective instability model that can be personalized to the clinical course of any individual with chronic disorders that have an affective component.
format Online
Article
Text
id pubmed-5804032
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-58040322018-02-09 Testing frameworks for personalizing bipolar disorder Cochran, Amy L. Schultz, André McInnis, Melvin G. Forger, Daniel B. Transl Psychiatry Article The hallmark of bipolar disorder is a clinical course of recurrent manic and depressive symptoms of varying severity and duration. Mathematical modeling of bipolar disorder holds the promise of an ability to personalize diagnoses, to predict future mood episodes, to directly compare diverse datasets, and to link basic mechanisms to behavioral data. Several modeling frameworks have been proposed for bipolar disorder, which represent competing hypothesis about the basic framework of the disorder. Here, we test these hypotheses with self-report assessments of mania and depression symptoms from 178 bipolar patients followed prospectively for 4 or more years. Statistical analysis of the data did not support the hypotheses that mood arises from a rhythmic process or multiple stable states (e.g., mania or depression) or that manic and depressive symptoms are highly anti-correlated. Alternatively, it is shown that bipolar disorder could arise from an inability for mood to quickly return to normal when perturbed. This latter concept is embodied by an affective instability model that can be personalized to the clinical course of any individual with chronic disorders that have an affective component. Nature Publishing Group UK 2018-02-02 /pmc/articles/PMC5804032/ /pubmed/29391394 http://dx.doi.org/10.1038/s41398-017-0084-4 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Cochran, Amy L.
Schultz, André
McInnis, Melvin G.
Forger, Daniel B.
Testing frameworks for personalizing bipolar disorder
title Testing frameworks for personalizing bipolar disorder
title_full Testing frameworks for personalizing bipolar disorder
title_fullStr Testing frameworks for personalizing bipolar disorder
title_full_unstemmed Testing frameworks for personalizing bipolar disorder
title_short Testing frameworks for personalizing bipolar disorder
title_sort testing frameworks for personalizing bipolar disorder
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5804032/
https://www.ncbi.nlm.nih.gov/pubmed/29391394
http://dx.doi.org/10.1038/s41398-017-0084-4
work_keys_str_mv AT cochranamyl testingframeworksforpersonalizingbipolardisorder
AT schultzandre testingframeworksforpersonalizingbipolardisorder
AT mcinnismelving testingframeworksforpersonalizingbipolardisorder
AT forgerdanielb testingframeworksforpersonalizingbipolardisorder