Complex affect dynamics add minimal value in predicting psychological outcomes and brain structure
Poster No:
596
Submission Type:
Abstract Submission
Authors:
Yinuo Shu1, Priscila Levi2, Toby Constable2, James Pang3, Jeggan Tiego2, Bree Hartshorn1, Jessica Kwee1, Kate Fortune1, Mark Bellgrove4, Alex Fornito3
Institutions:
1Monash University, Clayton, VIC, 2Monash University, Melbourne, VIC, 3Monash University, Clayton, Victoria, 4Monash University, Melbourne, Australia
First Author:
Co-Author(s):
Introduction:
Emotions are inherently dynamic, changing over time in response to internal and external influences. Research indicates that these patterns of emotional fluctuations, called affect dynamics (AD), are strongly linked to psychological well-being (Sperry et al., 2020). Rising interest in AD and its relation to well-being has led to increasingly complex AD measures (Hamaker et al., 2015). However, a pivotal study by Dejonckheere et al. (2019) aggregated data from 1777 different individuals across 15 different studies to show that these metrics generally fail to capture additional variance in outcome measures of psychological well-being than the simplest metrics, such as mean and standard deviation (s.d.) of positive affect (PA) and negative affect (NA), challenging the necessity and utility of complex AD measures.
Here, we extend Dejonckheere et al.'s (2019) work by performing a similar analysis in a single sample, avoiding the limitations that arise from aggregating multi-site data. We evaluate a much broader range of AD measures and psychological outcomes, and also consider correlations with measures of regional grey matter morphometry in the brain.
Here, we extend Dejonckheere et al.'s (2019) work by performing a similar analysis in a single sample, avoiding the limitations that arise from aggregating multi-site data. We evaluate a much broader range of AD measures and psychological outcomes, and also consider correlations with measures of regional grey matter morphometry in the brain.
Methods:
This study utilized data from 314 participants (97 male; age: 18-45 years) recruited from the general community with a diverse range of mental health histories. Participants completed a 28-day ecological momentary assessment (EMA) survey via daily questionnaires administered through their smartphones, which included daily measurements of positive and negative affect using the Positive and Negative Affect Schedule (PANAS-10), alongside assessments of stress, sleep quality, and substance use.
AD measures were calculated using existing metrics from Dejonckheere et al. (2019), including Mean PA/NA (M), Variance (s.d.), Relative variance (s.d.*), Mean Square of Successive Differences (MSSD), Auto-regression (AR), Emotion-network density (D), Intraclass Correlation (ICC), PA–NA correlation (ρ), and the Gini coefficient (G). Six novel measures were introduced to capture time-varying structures within emotion networks, derived using a sliding time window approach and multilayer network analysis (Mucha et al., 2010; Sizemore & Bassett, 2018). These include network, PA, and NA promiscuity and flexibility, calculated by averaging node diversity and switching rates across network, PA, and NA nodes, respectively.
Predictive relationships between these metrics and 120 psychological indicators-including subscales from five psychometric questionnaires, behavioral dynamics (e.g., alcohol use, stress level variability), and brain structures (e.g., grey matter volume (GMV), cortical thickness, surface area)-were assessed using 10-fold cross-validated linear regression.
AD measures were calculated using existing metrics from Dejonckheere et al. (2019), including Mean PA/NA (M), Variance (s.d.), Relative variance (s.d.*), Mean Square of Successive Differences (MSSD), Auto-regression (AR), Emotion-network density (D), Intraclass Correlation (ICC), PA–NA correlation (ρ), and the Gini coefficient (G). Six novel measures were introduced to capture time-varying structures within emotion networks, derived using a sliding time window approach and multilayer network analysis (Mucha et al., 2010; Sizemore & Bassett, 2018). These include network, PA, and NA promiscuity and flexibility, calculated by averaging node diversity and switching rates across network, PA, and NA nodes, respectively.
Predictive relationships between these metrics and 120 psychological indicators-including subscales from five psychometric questionnaires, behavioral dynamics (e.g., alcohol use, stress level variability), and brain structures (e.g., grey matter volume (GMV), cortical thickness, surface area)-were assessed using 10-fold cross-validated linear regression.
Results:
Complex AD measures added no more than 5.2% explained variance beyond mean and standard deviation metrics in predicting 120 psychological, behavioral, and brain structure outcomes. See Fig. 1 for three examples of the added explanatory power of all affect dynamic measures in the linear prediction of general depression, productiveness, and whole brain GMV beyond M and s.d. in PA and NA. Blue, yellow, and red bars reflect the predicted R² (a negative value indicates overfitting) for each measure alone, when controlling for M in PA and NA, and when controlling for M and s.d. in PA and NA, respectively.
·Fig.1 Three examples of the added explanatory power of affect dynamic measures in the linear prediction
Conclusions:
Complex AD metrics based on EMA add minimal value beyond basic measures in predicting psychological well-being, behavioral dynamics, and brain structures.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other 1
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
ADULTS
Affective Disorders
Cortex
Data analysis
Emotions
MRI
Psychiatric
Statistical Methods
1|2Indicates the priority used for review
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Provide references using APA citation style.
Dejonckheere, E., Mestdagh, M., Houben, M., Rutten, I., Sels, L., Kuppens, P., & Tuerlinckx, F. (2019). Complex affect dynamics add limited information to the prediction of psychological well-being. Nature human behaviour, 3(5), 478-491.
Hamaker, E. L., Ceulemans, E., Grasman, R. P., & Tuerlinckx, F. (2015). Modeling AD: State of the art and future challenges. Emotion Review, 7(4), 316-322.
Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E., & Barrett, L. F. (2012). The brain basis of emotion: a meta-analytic review. Behavioral and brain sciences, 35(3), 121-143.
Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J. P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. science, 328(5980), 876-878.
Sizemore, A. E., & Bassett, D. S. (2018). Dynamic graph metrics: Tutorial, toolbox, and tale. NeuroImage, 180, 417-427.
Sperry, S. H., Walsh, M. A., & Kwapil, T. R. (2020). Emotion dynamics concurrently and prospectively predict mood psychopathology. Journal of affective disorders, 261, 67-75.
Welborn, B. L., Papademetris, X., Reis, D. L., Rajeevan, N., Bloise, S. M., & Gray, J. R. (2009). Variation in orbitofrontal cortex volume: relation to sex, emotion regulation and affect. Social cognitive and affective neuroscience, 4(4), 328-339.