View topic-related background literature as well as scientific publications of the DynaMORE consortium itself. Once new publications are available, we list them here. All DynaMORE publications will be made openly accessible, either immediately (GOLD open access) or after a maximum embargo period of 6 months following publication (GREEN open access).

DynaMORE Publications (count: 22)

2022

• Weermeijer J et al. (2022). Applying multiverse analysis to experience sampling data: Investigating whether preprocessing choices affect robustness of conclusions. Behav Res.
• Riepenhausen A et al. (2022). Coping with COVID: risk and resilience factors for mental health in a German representative panel study. Psychological Medicine, 1-11. PDF
• Waller L et al. (2022). ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data. Human Brain Mapping. 43 (9), pp. 2727–2742. PDF
• Köber G et al. (2022). Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods. Preprint. PDF
• Wackerhagen C et al. (2022). Study protocol description: Dynamic Modelling of Resilience – Observational Study (DynaM-OBS). Preprint.

2021

• Ahrens KF et al. (2021). Impact of COVID-19 lockdown on mental health in Germany: longitudinal observation of different mental health trajectories and protective factors. Translational Psychiatry. 11, Article number: 392. PDF
• Kalisch R et al. (2021). The Frequent Stressor and Mental Health Monitoring-Paradigm: A Proposal for the Operationalization and Measurement of Resilience and the Identification of Resilience Processes in Longitudinal Observational Studies. Front Psychol. 12, Article number: 710493. PDF
• Lindert J and Tüscher O (2021). Editorial: Resilience: Life Events, Trajectories and the Brain. Front Psychiatry. 12, Article number: 645687. PDF
• van den Berg YHM et al. (2021). Emerging Adults’ Mental Health During the COVID-19 Pandemic: A Prospective Longitudinal Study on the Importance of Social Support. Emerg Adulthood. Online First. PDF
• Veer IM et al. (2021). Psycho-social factors associated with mental resilience in the Corona lockdown. Transl Psychiatry. 11, Article number: 67. PDF

2020

• Chmitorz A et al. (2020). Assessment of Microstressors in Adults: Questionnaire Development and Ecological Validation of the Mainz Inventory of Microstressors. J Med Internet Res – Mental Health. 7(2), Article number: e14566. PDF (please also view this correction)
• Chmitorz A et al. (2020). Longitudinal determination of resilience in humans to identify mechanisms of resilience to modern-life stressors: the longitudinal resilience assessment (LORA) study. Eur Arch Psychiatry Clin Neuroscience. PDF
• Kalisch R et al. (2020). A generic solution for the operationalization and measurement of resilience and resilience processes in longitudinal observations: rationale and basic design of the MARP and LORA studies. PsyArXiv Preprints. PDF
• Köber G et al. (2020). Individualizing deep dynamic models for psychological resilience data. medRxiv Preprints. PDF
• Marciniak MA et al. (2020). Standalone Smartphone Cognitive Behavioral Therapy–Based Ecological Momentary Interventions to Increase Mental Health: Narrative Review. JMIR Mhealth Uhealth. 8(11): e19836. PDF
• Mey LK et al. (2020). Increases of negative affect following daily hassles are not moderated by neuroticism: An ecological momentary assessment study. Stress & Health. PDF

2019

• Kalisch R et al. (2019). Deconstructing and reconstructing resilience: a dynamic network approach. Perspect Psychol Sci. 14(5): pp. 765–777. PDF
• Kampa M et al. (2019). Replication of fMRI group activations in the neuroimaging battery for the Mainz Resilience Project (MARP). NeuroImage. 204, Article number: 116223. PDF
• Kasanova Z et al. (2019). Temporal associations between sleep quality and paranoia across the paranoia continuum: An experience sampling study. J Abnorm Psychol. 129(1): pp. 122–130.
• Wang H et al. (2019). Toward Understanding Developmental Disruption of Default Mode Network Connectivity Due to Early Life Stress. Biol Psychiatry Cogn Neurosci Neuroimaging. 4(1): pp. 5-7.

2018

• Kampa M et al. (2018). A Combined Behavioral and Neuroimaging Battery to Test Positive Appraisal Style Theory of Resilience in Longitudinal Studies. bioRxiv Preprints, Article number: 470435. PDF
• Kunzler AM et al. (2018). Aktuelle Konzepte der Resilienzforschung. Nervenarzt. 89: pp. 747–753. PDF

Scientific Background Literature

• Binder H et al. (2011). An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models. Biom J. 53(2): pp. 170-189.
• Chmitorz A et al. (2018). Intervention studies to foster resilience – A systematic review and proposal for a resilience framework in future intervention studies. Clin Psychol Rev. 59: pp. 78-100.
• Hermans EJ and Fernández G (2015). Heterogeneity of cognitive-neurobiological determinants of resilience. Behav Brain Sci. 38, Article number: e103.
• Kalisch R et al. (2017). The resilience framework as a strategy to combat stress-related disorders. Nature Human Behaviour. 1: pp. 784-790.
• Kalisch R, Müller MB, and Tüscher O (2015). A conceptual framework for the neurobiological study of resilience. Behav Brain Sci. 38, Article number: e92.
• Kasanova Z et al. (2016). Early-Life Stress Affects Stress-Related Prefrontal Dopamine Activity in Healthy Adults, but Not in Individuals with Psychotic Disorder. PLoS One. 11(3), Article number: e0150746.
• Kleim B and Galatzer-Levy IR (2015). Appreciating methodological complexity and integrating neurobiological perspectives to advance the science of resilience. Behav Brain Sci. 38, Article number: e108.
• Kobylińska D and Karwowska D (2015). How automatic activation of emotion regulation influences experiencing negative emotions. Front Psychol. 6, Article number: 1628.
• Lin T et al. (2015). A neurobehavioral account for individual differences in resilience to chronic military stress. Psychol Med. 45(5): pp. 1011-1023.
• Maiwald T at al. (2016). Driving the model to its limit: Profile likelihood-based model reduction. PLoS One. 11(9), Article number: e0162366.
• Schiavone G, Lamichhane B, and Van Hoof C (2017). The Double Layer Methodology and the Validation of Eigenbehavior Techniques Applied to Lifestyle Modeling. BioMed Res Int. Article number: 4593956.
• Walter H, Erk S, and Veer IM (2015). The temporal dynamics of resilience: Neural recovery as a biomarker. Behav Brain Sci. 38, Article number: e126.
• Wieringa FP et al. (2017). Wearable sensors: Can they benefit patients with chronic kidney disease? Expert Rev Med Devices. 14(7): pp. 505-519.