Lara Puhlmann, Dr. rer. nat.
Leibniz-Institut für Resilienzforschung, Mainz
from February to July 2024
Born in 1991 in Berlin
Studied Psychology and Cognitive Neuroscience at the University College London and Biological Psychology at the Max Planck Institute for Human Cognitive and Brain Sciences
College for Life Sciences
Digital Biomarkers for Resilience ResearchSince treatment-oriented approaches alone have not been successful in alleviating the globally increasing burden of mental disorders, researchers and policymakers are increasingly emphasizing the importance of early detection and prevention. Exposure to adverse events or stressors constitutes a major risk factor for the development of mental health conditions, particularly the highly prevalent depressive and anxiety-associated disorders. Therefore, one promising research avenue towards prevention is the science of resilience, which describes the maintenance or quick recovery of mental wellbeing during or after periods of adversity.
The study of individual resilience processes relies crucially on the accurate monitoring of symptoms before the onset of a disorder. However, traditional mental health assessments, such as self-report, can be insensitive to subtle and often heterogeneous subclinical symptoms. With my project, I want to examine the use of digital biomarkers (DBMs) as novel, more objective and sensitive indicators of disorder symptoms in healthy but stress-exposed adults. Clinical work suggests that patients’ facial expressivity and voice pitch derived from video-recorded clinical interviews can serve as DBMs, aiding disorder diagnosis and prognosis. To extend this work to subclinical populations, I recently developed a novel interview paradigm that captures transdiagnostic symptom clusters including anxiety, depression, and somatisation. Facial movements, vocal acoustics, and other behavioural features displayed during the interview are extracted from video recordings using machine-learning algorithms. As a first proof-of-concept, my aim is to identify the behaviours that best predict self-reported mental health problems. These may ultimately serve in future resilience research as more objective indicators of underlying psychological dysfunction, such as flattened affect.
At the Wissenschaftskolleg, I intend to evaluate data from my interview paradigm and the current literature to address the following questions: Is it possible to identify DBMs that reliably indicate subclinical transdiagnostic symptoms? If so, which type of behaviours are most informative? Lastly, I will explore the use of DBMs to assess psychological, social, and structural resources, as well as ethical questions related to digital health monitoring.
Puhlmann, Lara M. C., Sofie L. Valk, Veronika Engert, Boris C. Bernhardt, Jue Lin, Elissa S. Epel, Pascal Vrtička, and Tania Singer (2019). “Association of Short-term Change in Leukocyte Telomere Length with Cortical Thickness and Outcomes of Mental Training among Healthy Adults: A Randomized Clinical Trial.” JAMA Network Open 2 (9): e199687. https://doi.org/10.1001/jamanetworkopen.2019.9687.
Puhlmann, Lara M. C., Pascal Vrtička, Roman Linz, Tobias Stalder, Clemens Kirschbaum, Veronika Engert, and Tania Singer (2021). “Contemplative Mental Training Reduced Hair Glucocorticoid Levels in a Randomized Clinical Trial.” Psychosomatic Medicine 38 (8): 894–905. https://doi.org/10.1097/PSY.0000000000000970.
Veer, Ilya M., Antje Riepenhausen, Matthias Zerban, Carolin Wackerhagen, Lara M. C. Puhlmann, Haakon Engen, Göran Köber, et al. (2021). “Psycho-Social Factors Associated with Mental Resilience in the Corona Lockdown.” Translational Psychiatry 11: 67. https://doi.org/10.1038/s41398-020-01150-4.