INTRA investigates the effects of a computerized inhibition training in currently abstinent patients with alcohol use disorder. It examines effects on subjective, behavioral, experimental and neurophysiological level.
More about INTRA: INTRA on SNF database; INTRA on ResearchGate
Predicting post-treatment symptom trajectories is crucial in order to inform decisions concerning type, intensity, and duration of treatment. A large body of research shows associations between predictors and post-treatment outcomes in samples with alcohol use disorder (AUD), but these models do not provide adequate predictions for an individual patient. Recently, machine learning algorithms have been used to establish predictive models in substance use disorder research. MLAUD aims to expand this research and to investigate how machine learning algorithms can be used to improve individual, post-treatment outcome predictions for patients with AUD.
More about MLAUD: MLAUD on ResearchGate
EMOPRO traces the neurophysiological correlates of emotional processing before and after a psychotherapeutic intervention targeting the processing of interpersonal pain.
More about EMOPRO: EMOPRO on ResearchGate
NECAAD investigates neurophysiological correlates of inhibition and cue reactivity with multi-channel EEG and fMRI in patients with alcohol addiction.
More about NECAAD: NECAAD on ResearchGate
The concept of motivational incongruence, as incorporated in Grawes consistency theory, refers to the fact that the experiences we make do not always match our needs and motives. The amount of motivational inconsistency is highly linked to psychological wellbeing and psychopathological symptoms. MINK traces the neurophysiological correlates of this important transdiagnostical concept with multi-channel EEG.
