Using Local Linear Models to Capture Dynamic Interactions Between Cortisol
Objective: Previous studies have found both increased and decreased cortisol levels in depressed patients. These inconsistent findings may be explained by the fact that traditional group-based studies are not suitable to capture complex intra-individual dynamics between cortisol and affect, and inte...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Lund University Library
2016-12-01
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| Series: | Journal for Person-Oriented Research |
| Subjects: | |
| Online Access: | https://journals.lub.lu.se/jpor/article/view/20363 |
| _version_ | 1828359873369210880 |
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| author | Roelof B. Toonen Klaas J. Klaas J. Wardenaar Sanne H. Booij Elisabeth H. Bos Peter D. Jonge |
| author_facet | Roelof B. Toonen Klaas J. Klaas J. Wardenaar Sanne H. Booij Elisabeth H. Bos Peter D. Jonge |
| author_sort | Roelof B. Toonen |
| collection | DOAJ |
| description | Objective: Previous studies have found both increased and decreased cortisol levels in depressed patients. These inconsistent findings may be explained by the fact that traditional group-based studies are not suitable to capture complex intra-individual dynamics between cortisol and affect, and inter-individual differences therein. The current study used a time-series approach to gain deeper insight into the nature of these complex dynamics and to investigate possible underlying nonlinear dynamical features.
Method: Time-series data (90 measurements) were collected for cortisol and negative affect (NA) in depressed (n=15) and non-depressed (n=15) participants. The relationship between cortisol and NA in each individual was analyzed with SMAP, which estimates local linear vector autoregression (VAR) models with different degrees of nonlinearity in the prediction. The best-predicting model, and whether this model was linear or nonlinear, was determined by using the normalized root mean square error (NRMSE) between the models’ predicted values and the observed values. Univariate and multivariate models were compared to explore the connection between cortisol and NA.
Results: Nonlinear cortisol predictions were best in 90% of the participants, whereas nonlinear NA predictions were best in 39% of the participants. Multivariate analyses showed that in 48% of the participants, cortisol was better predicted when NA was included in models that otherwise consisted of time delayed values of cortisol alone. Vice versa, in 39% of the participants, NA was better predicted when cortisol was included in models that otherwise consisted of time delayed values of NA alone. The connection between cortisol and NA was stronger in the depressed group, although the results showed considerable inter-individual heterogeneity within the diagnostic groups.
Conclusion: In many individuals, cortisol and NA may be interacting parts of a common dynamical system and their con-nection may be stronger in depressed patients. |
| first_indexed | 2024-04-14T03:48:26Z |
| format | Article |
| id | doaj.art-fee4325cb3a148bbbbed78ca781ecd58 |
| institution | Directory Open Access Journal |
| issn | 2002-0244 2003-0177 |
| language | English |
| last_indexed | 2024-04-14T03:48:26Z |
| publishDate | 2016-12-01 |
| publisher | Lund University Library |
| record_format | Article |
| series | Journal for Person-Oriented Research |
| spelling | doaj.art-fee4325cb3a148bbbbed78ca781ecd582022-12-22T02:14:10ZengLund University LibraryJournal for Person-Oriented Research2002-02442003-01772016-12-012310.17505/jpor.2016.14Using Local Linear Models to Capture Dynamic Interactions Between CortisolRoelof B. Toonen0Klaas J. Klaas J. Wardenaar1Sanne H. Booij2Elisabeth H. Bos3Peter D. Jonge4University Medical Center Groningen and University of GroningenUniversity Medical Center Groningen and University of GroningenUniversity Medical Center Groningen and University of GroningenUniversity Medical Center Groningen and University of GroningenUniversity Medical Center Groningen and University of GroningenObjective: Previous studies have found both increased and decreased cortisol levels in depressed patients. These inconsistent findings may be explained by the fact that traditional group-based studies are not suitable to capture complex intra-individual dynamics between cortisol and affect, and inter-individual differences therein. The current study used a time-series approach to gain deeper insight into the nature of these complex dynamics and to investigate possible underlying nonlinear dynamical features. Method: Time-series data (90 measurements) were collected for cortisol and negative affect (NA) in depressed (n=15) and non-depressed (n=15) participants. The relationship between cortisol and NA in each individual was analyzed with SMAP, which estimates local linear vector autoregression (VAR) models with different degrees of nonlinearity in the prediction. The best-predicting model, and whether this model was linear or nonlinear, was determined by using the normalized root mean square error (NRMSE) between the models’ predicted values and the observed values. Univariate and multivariate models were compared to explore the connection between cortisol and NA. Results: Nonlinear cortisol predictions were best in 90% of the participants, whereas nonlinear NA predictions were best in 39% of the participants. Multivariate analyses showed that in 48% of the participants, cortisol was better predicted when NA was included in models that otherwise consisted of time delayed values of cortisol alone. Vice versa, in 39% of the participants, NA was better predicted when cortisol was included in models that otherwise consisted of time delayed values of NA alone. The connection between cortisol and NA was stronger in the depressed group, although the results showed considerable inter-individual heterogeneity within the diagnostic groups. Conclusion: In many individuals, cortisol and NA may be interacting parts of a common dynamical system and their con-nection may be stronger in depressed patients.https://journals.lub.lu.se/jpor/article/view/20363cortisolaffectdepressionnonlineardynamical systemstime series |
| spellingShingle | Roelof B. Toonen Klaas J. Klaas J. Wardenaar Sanne H. Booij Elisabeth H. Bos Peter D. Jonge Using Local Linear Models to Capture Dynamic Interactions Between Cortisol Journal for Person-Oriented Research cortisol affect depression nonlinear dynamical systems time series |
| title | Using Local Linear Models to Capture Dynamic Interactions Between Cortisol |
| title_full | Using Local Linear Models to Capture Dynamic Interactions Between Cortisol |
| title_fullStr | Using Local Linear Models to Capture Dynamic Interactions Between Cortisol |
| title_full_unstemmed | Using Local Linear Models to Capture Dynamic Interactions Between Cortisol |
| title_short | Using Local Linear Models to Capture Dynamic Interactions Between Cortisol |
| title_sort | using local linear models to capture dynamic interactions between cortisol |
| topic | cortisol affect depression nonlinear dynamical systems time series |
| url | https://journals.lub.lu.se/jpor/article/view/20363 |
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