Bouncingback fromadversity: Towardsa dynamical...
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Bouncing back from adversity: Towards a dynamicalconceptualization of psychological resilience
Gaby Lunansky
Conference on Complex Systems 2018, Thessaloniki27-09-2018
PhD supervisors: Denny Borsboom & Claudia van Borkulo
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Bouncing back from adversity: Towards a dynamical conceptualization of psychological resilience
Gaby Lunansky | [email protected] | University of Amsterdam
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Why do we want to estimate psychologicalnetworks?
Gaby Lunansky | [email protected] | University of Amsterdam
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Why do we want to estimate psychologicalnetworks?
Gaby Lunansky | [email protected] | University of Amsterdam
Can we evaluate the state of thenetwork by looking at the structureof the network?
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Psychological resilience• Why do some people collapse after a disruptive event, while others seem totake the punch and go back to their normal state quite rapidly?
• Some ability to cope with stressful life events
• What is this ability and how should we study it?
• New paradigm: Studying health, not disease• Focus on preventing illness
Gaby Lunansky | [email protected] | University of Amsterdam
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Resilience as a trait vs. process• Debate in the clinical field regarding the nature of resilience drives the field into two different directions(Jacelon, 1997)• Some researchers look for static, protective trait-like factors
E.g.: humor (Wagnild & Young, 1990),having a positive outlook (Cowen & Work,1988), having warm & cohesive families (Mulholland et al., 1991) or autonomy & high self-esteem (Masten & Garmezy, 1985)
Gaby Lunansky | [email protected] | University of Amsterdam
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Resilience as a trait vs. process
• Other researchers conceptualize resilience as a dynamical process
E.g. searching for generalunderlying, causal mechanismsof resilience (Kalisch, Muller & Tuscher, 2015)or studying the ‘steeling effect’ (Rutter, 2012)
Gaby Lunansky | [email protected] | University of Amsterdam
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Resilience research in different fields
Hosseini et al., 2016
Gaby Lunansky | [email protected] | University of Amsterdam
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How to study resilience?
Two possible operationalizations:• Time: how rapidly does the system recover?• Intensity: how big can the pertubation be until the state of the system shifts?
Gaby Lunansky | [email protected] | University of Amsterdam
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Resilience indicators in ecology
Engineering resilience vs
Ecological resilience
(Dakos, Carpenter, van Nes & Scheffer, 2015)
Gaby Lunansky | [email protected] | University of Amsterdam
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Resilience indicators in ecology
Engineering resilience vs
Ecological resilience
(Dakos, Carpenter, van Nes & Scheffer, 2015)
Gaby Lunansky | [email protected] | University of Amsterdam
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Resilience indicators in ecology
Engineering resilience vs
Ecological resilience
(Dakos, Carpenter, van Nes & Scheffer, 2015)
Gaby Lunansky | [email protected] | University of Amsterdam
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Resilience indicators in ecology
Engineering resilience vs
Ecological resilience
(Dakos, Carpenter, van Nes & Scheffer, 2015)
Gaby Lunansky | [email protected] | University of Amsterdam
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Resilience indicators in ecology
Engineering resilience vs
Ecological resilience
(Dakos, Carpenter, van Nes & Scheffer, 2015)
Gaby Lunansky | [email protected] | University of Amsterdam
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Early warnings
• We can apply this theory to psychopathology• Is a person close to a ’critical transition’?
• Not possible to know the exact trajectory of a complex system like a psychiatric disorder…
… but we can look at indicators!
• Critical slowing downVan de Leemput et al., 2013
Gaby Lunansky | [email protected] | University of Amsterdam
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RECAP
Can we evaluate the state of thenetwork by looking at the structure of
the network?Gaby Lunansky | [email protected] | University of Amsterdam
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Resilience and Psychological Networks
• Highly relevant for the clinical practice, however, no quantitative indicators forresilience models in psychology
• First indicator: Connectivity strength of the edges(Cramer et al., 2016)
Gaby Lunansky | [email protected] | University of Amsterdam
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LOWConnectivity
http://ccl.northwestern.edu/netlogo/models/community/Symptom_Spread_Model
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HIGHConnectivity
http://ccl.northwestern.edu/netlogo/models/community/Symptom_Spread_Model
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A simulation study: setup
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X7
X1
X4
X6
X5
X3
X2X9
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A
VATSPUD data
A simulation study: setup
• VATSPUD data: about 8900 male
and female twins, interview,
prevalence MD about 17%
• Parameter estimation with IsingFit (van Borkulo et al., 2014)
THRESHOLD PARAMETERS
WEIGHT PARAMETERS
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A simulation study: setup
∣THRESHOLDS∣
WEIGHTS
VATSPUD data
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A simulation study: setup
∣THRESHOLDS∣
WEIGHTS
VATSPUD data
WEAK
X7
X1
X4
X6
X5
X3
X2
X9
X8
MEDIUM
X7
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X6
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STRONG
X7
X1
X4
X6
X5
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X2
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X8
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A simulation study: setup
∣THRESHOLDS∣
WEIGHTS
VATSPUD data
WEAK
X7
X1
X4
X6
X5
X3
X2
X9
X8
MEDIUM
X7
X1
X4
X6
X5
X3
X2
X9
X8
STRONG
X7
X1
X4
X6
X5
X3
X2
X9
X8
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A simulation study: results
Spontaneous recovery
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Project: Resilience Indicators for PsychologicalNetworks• We know the connectivity of the edge
weights influences symptom activationspread (Cramer, van Borkulo, Giltay, van der Maas, Kendler, Scheffer & Borsboom, 2016)
• How does the architecture of a networkinfluence its resilience?
Cramer et al., 2016, Fig 1: An analogy between vulnerability in a network and
spacing of domino tiles.
Gaby Lunansky | [email protected] | University of Amsterdam
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Dynamical network model of resilience
Positive and negative nodes
Ising modelVan Borkulo et al., 2014
Gaby Lunansky | [email protected] | University of Amsterdam
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Dynamical network model of resilience
Glauber dynamics
Dalege et al. (2018)
Transformation to Y = {0,1}
!" = $% &" + 1 with inverse relation &" = 2!" − 1
Haslbeck, Kruis & Marsman (in preparation)
Gaby Lunansky | [email protected] | University of Amsterdam
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Project: Resilience Indicators for PsychologicalNetworksHow does the architecture of a network influence its resilience?
Shiny Happy HarrySystem Architecture
High thresholds of positive nodesHigh connectivity between positive cluster
Low connectivity in negative clusterLow thresholds of negative nodes
Gaby Lunansky | [email protected] | University of Amsterdam
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Project: Resilience Indicators for PsychologicalNetworksHow does the architecture of a network influence its resilience?
Miserable Mark System Architecture
High thresholds of negative nodesHigh connectivity in negative cluster
Low thresholds of positive nodesLow connectivity in positive cluster
Gaby Lunansky | [email protected] | University of Amsterdam
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Shiny simulation in R
https://gabylunansky.shinyapps.io/shinyappresilience/
Lunansky, van Borkulo, Bringmann, Gijzel, Hartmann, Wichers & Cramer (in preparation)
Gaby Lunansky | [email protected] | University of Amsterdam
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Conclusions
We are taking the first steps into forming a theoretical network model of resilience
Resilience indicators could help taking the next step in network psychometrics, helping to evaluate and predict the future state of the network by looking at the structure of the network
Gaby Lunansky | [email protected] | University of Amsterdam
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ReferencesCramer, A. O., van Borkulo, C. D., Giltay, E. J., van der Maas, H. L., Kendler, K. S., Scheffer, M., & Borsboom, D. (2016). Major depression as a complex dynamic system. PLoS One, 11(12),
e0167490
Cowen, E. L., & Work, W. C. (1988). Resilient children, psychological wellness, and primary prevention. American Journal of Community Psychology, 16(4), 591-607.
Dakos, V., Carpenter, S. R., van Nes, E. H., & Scheffer, M. (2015). Resilience indicators: prospects and limitations for early warnings of regime shifts. Philosophical Transactions of the RoyalSociety of London B: Biological Sciences, 370(1659), 20130263.
Dalege, J., Borsboom, D., van Harreveld, F., & van der Maas, H. L. J. (accepted). The Attitudinal Entropy (AE) Framework as a General Theory of Individual Attitudes. Psychological Inquiry.
Haslbeck, Kruis & Marsman (in preparation)
Hosseini, S., Barker, K., & Ramirez-Marquez, J. E. (2016). A review of definitions and measures of system resilience. Reliability Engineering & System Safety, 145, 47-61.
Kalisch, R., Müller, M. B., & Tüscher, O. (2015). A conceptual framework for the neurobiological study of resilience. Behavioral and Brain Sciences, 38.
Masten, A. S., & Garmezy, N. (1985). Risk, vulnerability, and protective factors in developmental psychopathology. In Advances in clinical child psychology (pp. 1-52). Springer, Boston, MA
Rutter, M. (2012). Resilience as a dynamic concept. Development and psychopathology, 24(2), 335-344.
Van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientificreports, 4, 5918.
van de Leemput, I. A., Wichers, M., Cramer, A. O., Borsboom, D., Tuerlinckx, F., Kuppens, P., ... & Derom, C. (2014). Critical slowing down as early warning for the onset and termination ofdepression. Proceedings of the National Academy of Sciences, 111(1), 87-92.
Wagnild, G., & Young, H. M. (1990). Resilience among older women. Image: The Journal of Nursing Scholarship, 22(4), 252-255.
Gaby Lunansky | [email protected] | University of Amsterdam