Stefan Frässle

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 paper introduces bi-cross-validation to evaluate and compare dynamic functional connectivity models in fMRI, showing it balances model fit and complexity effectively.

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 paper uses dynamic causal modelling to show that reduced self-inhibition in the sensorimotor cortex contributes to motor fatigability.

This study identifies atypical functional topography and reduced hierarchical signal flow in temporal lobe epilepsy, linked to microstructural white matter changes and cognitive impairments.

This study shows that tonic and burst firing patterns of the locus coeruleus modulate brain activity and dynamics in mice through distinct mechanisms.

This study characterizes the anatomy of the human default mode network using histology and neuroimaging, revealing its cytoarchitectural diversity and unique signal flow properties.

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 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.

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.

The paper develops a continuous surface model of the mesiotemporal lobe's iso-to-allocortex transition and links it to functional dynamics and cognitive systems.

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 introduces thermodynamic integration for dynamic causal modeling, showing it provides more robust log evidence estimates than variational Bayes in complex models.