Prof. Dr. Denis Gerstorf

Prof. Gerstorf erforscht die psychologischen und biologischen Veränderungen im Erwachsenenalter und im Alter — insbesondere wie ältere Menschen emotional reagieren, sich an Herausforderungen anpassen und welche Faktoren ihre Gesundheit und Lebensdauer beeinflussen. Aktuell untersucht er intensive Alltagsdaten von älteren Erwachsenen (z. B. emotionale Reaktionen, körperliche Aktivität, Beziehungsdynamiken) und vergleicht diese mit biologischen Markern des Alterns (Epigenetik, Biomarker), um zu verstehen, wie psychosoziale Faktoren die biologische Alterung vorhersagen. Seine Forschung nutzt Längsschnittstudien und Machine-Learning-Methoden, um Muster zu identifizieren, die für Prävention und Gesundheitsintervention im Alter relevant sind. Die Erkenntnisse sind für Gesundheitssysteme, Altersvorsorge und präventive Medizin relevant, da sie zeigen, welche psychosozialen und verhaltensbezogenen Faktoren das biologische Altern und die Sterblichkeit beeinflussen.

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• Changes in Psychological Health Across Adulthood and Old Age

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  Zeitraum: 08/2012 - 09/2012 Projektleitung: Prof. Dr. Denis Gerstorf

• Kontextuelle Faktoren, die terminale Funktionsverluste regulieren: Das Ende des Lebens als natürliches Experiment

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  Förderer: DFG Sachbeihilfe Zeitraum: 09/2013 - 12/2017 Projektleitung: Prof. Dr. Denis Gerstorf

• project coordinator BASE-II

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  Zeitraum: 01/2016 - 12/2016 Projektleitung: Prof. Dr. Denis Gerstorf

• Regional Brain Changes in Aging Healthy Adults: General Trends, Individual Differences and Modifiers

  2005

  Cerebral Cortex · DOI

  Brain aging research relies mostly on cross-sectional studies, which infer true changes from age differences. We present longitudinal measures of five-year change in the regional brain volumes in healthy adults. Average and individual differences in volume changes and the effects of age, sex and hypertension were assessed with latent difference score modeling. The caudate, the cerebellum, the hippocampus and the association cortices shrunk substantially. There was minimal change in the entorhinal and none in the primary visual cortex. Longitudinal measures of shrinkage exceeded cross-sectional estimates. All regions except the inferior parietal lobule showed individual differences in change. Shrinkage of the cerebellum decreased from young to middle adulthood, and increased from middle adulthood to old age. Shrinkage of the hippocampus, the entorhinal cortices, the inferior temporal cortex and the prefrontal white matter increased with age. Moreover, shrinkage in the hippocampus and the cerebellum accelerated with age. In the hippocampus, both linear and quadratic trends in incremental age-related shrinkage were limited to the hypertensive participants. Individual differences in shrinkage correlated across some regions, suggesting common causes. No sex differences in age trends except for the caudate were observed. We found no evidence of neuroprotective effects of larger brain size or educational attainment.

• Time-structured and net intraindividual variability: Tools for examining the development of dynamic characteristics and processes.

  2009

  Psychology and Aging · DOI

  The study of intraindividual variability is the study of fluctuations, oscillations, adaptations, and "noise" in behavioral outcomes that manifest on microtime scales. This article provides a descriptive frame for the combined study of intraindividual variability and aging/development. At the conceptual level, we show that the study of intraindividual variability provides access to dynamic characteristics-construct-level descriptions of individuals' capacities for change (e.g., lability)--and to dynamic processes--the systematic changes that individuals exhibit in response to endogenous and exogenous influences (e.g., regulation). At the methodological level, we review how quantifications of net intraindividual variability and models of time-structured intraindividual variability are used to measure and describe dynamic characteristics and processes. At the research design level, we point to the benefits of measurement-burst study designs, wherein data are obtained across multiple time scales, for the study of development.