Applying a Dynamical Systems Model and Network Theory to Major Depressive Disorder

Mental disorders like major depressive disorder can be seen as complex dynamical systems. In this study we investigate the dynamic behaviour of individuals to see whether or not we can expect a transition to another mood state. We introduce a mean field model to a binomial process, where we reduce a dynamic multidimensional system (stochastic cellular automaton) to a one-dimensional system to analyse the dynamics. Using maximum likelihood estimation, we can estimate the parameter of interest which, in combination with a bifurcation diagram, reflects the expectancy that someone has to transition to another mood state. After validating the proposed method with simulated data, we apply this method to two empirical examples, where we show its use in a clinical sample consisting of patients diagnosed with major depressive disorder, and a general population sample. Results showed that the majority of the clinical sample was categorized as having an expectancy for a transition, while the majority of the general population sample did not have this expectancy. We conclude that the mean field model has great potential in assessing the expectancy for a transition between mood states. With some extensions it could, in the future, aid clinical therapists in the treatment of depressed patients.Comment: arXiv admin note: text overlap with arXiv:1610.0504

Comparing network structures on three aspects:A permutation test

Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of (a) network structure, (b) edge (connection) strength, and (c) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: The Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).</p

Comparing network structures on three aspects:A permutation test

Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of (a) network structure, (b) edge (connection) strength, and (c) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: The Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).</p

Pitching Emotions: The Interpersonal Effects of Emotions in Professional Baseball

Sports games are inherently emotional situations, but surprisingly little is known about the social consequences of these emotions. We examined the interpersonal effects of emotional expressions in professional baseball. Specifically, we investigated whether pitchers' facial displays influence how pitches are assessed and responded to. Using footage from MLB World Series finals, we isolated incidents where the pitcher's face was visible before a pitch. A pre-study indicated that participants consistently perceived anger, happiness, and worry in pitchers' facial displays. An independent sample then predicted pitch characteristics and batter responses based on the same perceived emotional displays. Participants expected pitchers perceived as happy to throw more accurate balls, pitchers perceived as angry to throw faster and more difficult balls, and pitchers perceived as worried to throw slower and less accurate balls. Batters were expected to approach (swing) when faced with a pitcher perceived as happy and to avoid (no swing) when faced with a pitcher perceived as worried. Whereas previous research focused on using emotional expressions as information regarding past and current situations, our work suggests that people also use perceived emotional expressions to predict future behavior. Our results attest to the impact perceived emotional expressions can have on professional sports

Comparing network structures on three aspects: A permutation test

The network approach, in which psychological constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure, to a more comparative stance, in which the goal is to compare network structures across groups. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT). NCT is a statistical test that compares two network structures on three types of characteristics. Performance of NCT is evaluated by means of a simulation study. Simulated data shows that NCT performs well in various circumstances for all three tests: when the groups are simulated to be similar, the error rate (i.e., NCT indicating that they are different, while the simulated networks are similar) is adequately low, and when the groups are simulated to be different, the ability to detect a difference is sufficiently high when the difference between simulated networks and the sample size are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed.Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of (a) network structure, (b) edge (connection) strength, and (c) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: The Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed

Centrality analysis of the 20 CES-D symptoms, self-efficacy (GSE) and intervention (RX).

<p>Centrality analysis of the 20 CES-D symptoms, self-efficacy (GSE) and intervention (RX).</p

Results of the network comparison test based on global strength and network invariance.

<p>Results of the network comparison test based on global strength and network invariance.</p

Demographic and clinical characteristics of the sample.

<p>Demographic and clinical characteristics of the sample.</p

Overall mean and standard deviations (SD) of the 20 CES-D symptoms included in the network analysis¹.

<p>Overall mean and standard deviations (SD) of the 20 CES-D symptoms included in the network analysis<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0191675#t002fn001" target="_blank">¹</a>.</p

Network structure of the 20 CES-D symptoms, self-efficacy (GSE) and intervention (RX).

<p>Network structure of the 20 CES-D symptoms, self-efficacy (GSE) and intervention (RX).</p