David Steyrl at Universität Wien - Research Interests, Publications & Academic Profile

David Steyrl

Universität Wien

Austria

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Research Interests

Cognitive Neuroscience

40%

Clinical Psychology

20%

Affective Neuroscience

40%

Trauma Therapy

30%

Neuromodulation

30%

Psychophysiology

20%

Neonatology

20%

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Recent Grants

Grant: Close

Support Grant of the Faculty of Psychology for the EU application “RECOLOUR: Neurofeedback for depression: Clinical protocols and a mobile future”

Open Date: 2022-01-01

Close Date: 2022-01-01

Grant: Close

Support Grant of the Faculty of Psychology for the FFG application “CogniOpt – Enhancing Interactive Allocation Optimization through Data-driven Human Factor Modeling”

Open Date: 2022-01-01

Close Date: 2022-01-01

Grant: Close

JOptim – Jointly Optimized Allocation of Functionality in Systems of Systems

Open Date: 2021-01-01

Close Date: 2024-01-01

David Steyrl

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Research Interests

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Recent Grants

Articles16

Neural Hyperresponsivity During the Anticipation of Tangible Social and Nonsocial Rewards in Autism Spectrum Disorder

Cross-Cultural Comparison of Beauty Judgments in Visual Art Using Machine Learning Analysis of Art Attribute Predictors Among Japanese and German Speakers

Introduction of Solid Foods in Preterm Infants and Its Impact on Growth in the First Year of Life—A Prospective Observational Study

The "SpiDa" dataset

Posterior cingulate cortex targeted real-time fMRI neurofeedback recalibrates functional connectivity with the amygdala, posterior insula, and default-mode network in PTSD

Pre- and post-task resting-state differs in clinical populations

Machine learning revealed symbolism, emotionality, and imaginativeness as primary predictors of creativity evaluations of western art paintings

Machine learning models predict PTSD severity and functional impairment: A personalized medicine approach for uncovering complex associations among heterogeneous symptom profiles

A tale of two targets: examining the differential effects of posterior cingulate cortex- and amygdala-targeted fMRI-neurofeedback in a PTSD pilot study

Relationship between psychological characteristics, personality traits, and training on performance in a neonatal resuscitation scenario: A machine learning based analysis

Differential mechanisms of posterior cingulate cortex downregulation and symptom decreases in posttraumatic stress disorder and healthy individuals using real‐time fMRI neurofeedback

Audio Feature Analysis for Acoustic Pain Detection in Term Newborns

A secondary outcome analysis of a randomized trial using a mixed lipid emulsion containing fish oil in infants with extremely low birth weight: Cognitive and behavioral outcome at preschool age

Neurodevelopmental Outcome of Extremely Low Birth Weight Infants with Cholestasis at 12 and 24 Months

Editorial: Long Term User Training and Preparation to Succeed in a Closed-Loop BCI Competition

Machine Learning Mega-Analysis Applied to the Response Time Concealed Information Test: No Evidence for Advantage of Model-Based Predictors Over Baseline

Collaborators13

Mengfan Zhang

Nadja Haiden

Angelika Berger

giorgia silani

Frank Scharnowski

Jan Mikuni

Eik Vettorazzi

Ronald Sladky

Christoph Reuter

Jean Theberge

Christoph Binder

Alexander Karner

Peter Gröpel

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