Prodekanin für Forschung

Prof. Dr. Andrea Hildebrandt

+49 (0)441 798-4629

Referentin für Forschung

Dr. Beena Punnamoottil

+49 (0)441 798-2142

Wann und Wo

Freitag, 28. Juni 2024 ab 12.00 Uhr

Carl von Ossietzky Universität Oldenburg
Uhlhornsweg 86
26129 Oldenburg

A14 – Hörsaalzentrum des Campus Haarentor


#41 Imaging patients with aMCI and naMCI while performing Emotion Recognition Tasks

Hauptautor*innen: Rachana Mahadevan

Co-Autor*innen: Dr. Stephanie Rosemann, Prof. Dr. Andrea Hildebrandt, Prof. Dr. Mandy Roheger


Mild cognitive impairment (MCI) is defined as a syndrome in which individuals have a greater cognitive decline as compared to other individuals who belong to the same age category and educational level. Individuals with MCI display cognitive decline in various domains such as attention, language, memory, executive functioning, and visuospatial construction. Thus, the identification of MCI patients can play an important role in early intervention, prevention, and proper treatment. Emotion recognition impairments are well documented in Alzheimer's disease and other dementias, but it is less understood whether they are also present in mild cognitive impairment (MCI) and hence the purpose of this study will be to investigate emotion recognition and processing in patients with mild cognitive impairment (MCI). Although research in this area is at its infancy, there is some evidence that Emotion recognition in MCI is compromised when compared to normal aging and there is at least some evidence suggesting that negative emotions are more compromised. To summarize, in our planned study, we aim to investigate the emotion recognition abilities of patients with aMCI and naMCI using a dynamic emotion recognition paradigm applied during an MRI and assess the structural and functional outcomes.

#42 Socio-economic equity in the impact of population-based interventions to reduce alcohol consumption: A systematic review

Hauptautor*innen: Jennifer Eidswick

Co-Autor*innen: Mandy Roheger, Diana Gürtler, Jennis Freyer-Adam, & Sophie Baumann


Even minimal alcohol consumption is linked to poorer mental and physical health outcomes, underscoring the need for interventions that motivate the general public to reduce alcohol intake. However, achieving a measurable reduction in alcohol consumption—and consequently, alcohol-related harm, disease, disability, and mortality—across all sub-populations is challenging. Various strategies exist, including indirect interventions such as pricing, taxation, availability policies, mass media campaigns, and regulations on marketing, as well as direct initiatives targeting individuals. Few studies have systematically summarized the literature on social equity in the impact of population-based alcohol interventions, and none have provided a comprehensive view of equity impacts beyond effectiveness or efficacy. This systematic review aims to identify interventions that reduce alcohol consumption across entire populations while preserving equity across different socio-economic positions (SEPs). Following search strategies outlined by the Cochrane Handbook for Systematic Reviews of Interventions, two authors (JE, DG) will independently identify eligible articles, extract relevant data, and assess risk of bias. Pairwise meta-analyses and meta-regression analyses will be conducted to address our research question. Understanding when and where SEP inequalities occur in these interventions is essential for developing fair and equitable policies to reduce alcohol-related morbidity and mortality in the population.

#43 Will you score? Motion capture of basketball shooting combined with mobile electroencephalography - on your smartphone

Hauptautor*innen: Miguel Angel Contreras-Altamirano

Co-Autor*innen: Melanie Klapprott , Paul Maanen, Stefan Debener


We developed a highly portable setup consisting of two tripods and two off-the-shelf Android smartphones. One smartphone was used to wirelessly collect 32-channel mobile EEG data along with video data. The second smartphone was used to capture behavioral patterns in real time by running an artificial intelligence motion capture application and streaming the derived body movement data along with wireless motion sensor signals attached to the dominant hand into a local area network. All data streams were time-synchronized using Lab Streaming Layer and stored on a smartphone. N=26 basketball players performed 120 free throws each. First, we investigated whether our setup allows to capture the Readiness Potential (RP) that precedes voluntary actions. Furthermore, we predicted that the RP would differ in morphology for successful (hits) versus unsuccessful (misses) shots. The results show that a low-budget, small and lightweight acquisition setup consisting of only two smartphones and a mobile EEG is sufficient to reliably capture brain-body relations in natural settings. Here, we document for the first time the identification of RP in a complex whole-body action such as basketball free-throw shooting in a real-world environment. At the group level, there were no significant differences in the RP between hits and misses.

#44 Improving the diagnostic quality and robustness of neurometric markers of individual differences through multiverse analysis

Hauptautor*innen: Cassie Ann Short

Co-Autor*innen: Yusuf Cosku Inceler, Rosina Zollner, Andrea Hildebrandt


Neurometric biomarkers of individual differences must be reliable and robust to serve diagnostic purposes. Reliability enables precise assessment and personalized treatment decisions based on stable, dependable biomarkers, while robustness ensures that these biomarkers can accurately reflect true physiological changes rather than variations due to measurement error. However, the reliability and robustness of neurometric biomarkers across heterogeneity in data preprocessing and estimation approaches remains largely unknown. We aim to improve the reliability and robustness of neurometric biomarkers through methodological improvements and individualized approaches, focusing on the P300, an event-related potential that serves as a clinically important tool for assessing cognitive dysfunction. In a sample of 160 adults, we applied multiverse analysis to evaluate the reliability and robustness of P300 latency estimates across a variety of single-trial EEG algorithms (filtering and peak picking, single electrode template matching, maximum likelihood estimation, and residue iteration decomposition), four defensible reference schemes, and four defensible electrode clusters. We present distributions of reliability and theoretical plausibility of P300 latency estimates across the resulting 64-pipeline multiverse, demonstrating how this meta-scientific approach can be applied to enhance the transparency and rigor of individualized neurometric biomarker estimation. This methodological rigor can inform method development for improved individualized diagnostic accuracy.

(Stand: 05.06.2024)  | 
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