Klaas Enno Stephan

The paper tests a computational model of perceived control over breathing by analyzing data from a novel behavioral task, finding that perceived control depends on interoceptive outcomes and prediction errors.

This study shows that fasting-induced energy deficits and stored energy reserves jointly regulate inhibitory control and incentive motivation in humans, with distinct metabolic principles governing each.

The paper investigates layer-specific activation in the insula during interoceptive and exteroceptive attention tasks using fMRI and finds increased activity in upper cortical layers during interoceptive attention.

The paper applies Bayesian workflow to improve parameter and model identifiability in hierarchical Bayesian models using multivariate behavioural data from a reward learning task.

This study evaluates the test-retest reliability and validity of auditory MMN measured with OPM-MEG, finding good agreement with EEG/MEG results and reliable amplitude measures.

This study uses fMRI and a Bayesian model to show that the anterior insula is involved in thermoceptive predictions and prediction errors in humans.

This study extends an interoceptive learning task to measure prediction certainty continuously and applies a learning model to analyze how learning rates relate to stimuli valence and mental health factors.

The paper shows that reduced frontal alpha asymmetry during sad mood induction predicts antidepressant relapse after medication discontinuation.

This study investigates how muscarinic acetylcholine and dopamine receptor function affect auditory mismatch negativity (MMN) in schizophrenia, finding differential sensitivity to these neuromodulators.

This paper assesses the test-retest reliability of regression dynamic causal modeling (rDCM) for connectivity estimates, showing good reliability for strong connections and highlighting its potential for clinical applications.

This paper demonstrates high-resolution single-shot spiral fMRI at 7T with sub-millimeter resolution, achieving excellent image quality and BOLD sensitivity with minimal blurring artifacts.

This study investigates how anxiety relates to interoceptive processing of breathing using fMRI and computational models, finding that anxiety traits affect interoceptive prediction certainty and errors in the anterior insula.

This paper introduces a new integration scheme for delay differential equations in DCMs, offering both speed and accuracy for electrophysiological data analysis.

The paper introduces a task and analysis procedure to quantify breathing-related interoception dimensions using simulated and empirical data.

This paper introduces thermodynamic integration for Bayesian model selection and averaging in dynamic causal models, demonstrating its feasibility with efficient implementation and parallel processing.

TAPAS provides open-source tools for computational assays in psychiatry to support differential diagnosis and treatment prediction through end-to-end pipelines.

This paper uses dynamic causal models to infer muscarinic receptor function from EEG data in rats, showing graded effects and predictive validity across pharmacological conditions.

This study uses optogenetics and fMRI to show that D1 and D2 MSNs in the striatum differentially activate brain regions beyond the basal ganglia, revealing complex functional interactions.

This paper introduces regression dynamic causal modeling (rDCM) for resting-state fMRI, providing directional connectivity estimates and scalability to whole-brain networks.

This paper introduces a Hilbert-based method to estimate respiratory volume per unit time from bellows recordings, improving fMRI noise correction by better capturing atypical breathing events.

The paper investigates how changes in resting-state functional connectivity predict depression relapse after antidepressant discontinuation, finding that increased connectivity between the dorsolateral prefrontal cortex and parietal cortex is associated with relapse risk.

The paper introduces a generative model to infer directed connectivity in whole-brain networks using fMRI data, showing improved accuracy in identifying functional roles of motor areas compared to undirected methods.

This paper presents a dynamic causal model of a two-layered cortical microcircuit using magnetoencephalography to study non-invasive layered computations.

This study shows ketamine reduces high-level prediction errors in a roving MMN paradigm, supporting the role of NMDAR in hierarchical Bayesian inference.

The paper evaluates predictors of relapse after antidepressant discontinuation using clinical data and finds that standard clinical variables have low predictive power.

The paper examines abnormal processing of uncertainty in at-risk mental state individuals using computational models and fMRI, finding increased volatility estimates and altered cortical activation patterns.

The paper introduces a remote, automated, and MRI-compatible system for delivering inspiratory resistive loads to study interoception in neuroimaging studies.

This study finds that schizophrenia patients show increased choice switching in reward-based tasks, linked to heightened beliefs about environmental volatility and greater prefrontal cortex activity.

This study examines how subthalamic deep brain stimulation (STN-DBS) affects impulsivity and subjective uncertainty estimates in Parkinson’s disease patients through gambling behavior analysis.

The paper tests predictive coding theory by showing that different visual features elicit distinct prediction errors in specific brain regions during a visual mismatch task.