Explanipedia

Structured Code Review in Mental Health Research Open

Inês Pereira, Herman Galioulline, Réka Enz, Florian M. Schönleitner, Stephanie Mellor , et al. · 2025

Errors in scientific code pose a significant risk to the accuracy of research. Yet, formal code review remains uncommon in academia. This is a particular challenge for interdisciplinary fields, such as mental health research, which increas…

Machine Learning Model for Response to Internet-Delivered CBT vs Antidepressant Medication Open

Chi Tak Lee, Derek Richards, Jakob Heinzle, Anna K Hanlon, Kevin Lynch , et al. · 2025

Importance Many treatments exist for depression, yet none are universally effective. Multivariable predictive models support personalized treatment selection. Objective To develop a model predicting response to internet-delivered cognitive…

Differences in layer-specific activation of the insula during interoceptive vs. exteroceptive attention Open

Matthias Müller-Schrader, Frederike H. Petzschner, Jakob Heinzle, Lars Kasper, Katharina V. Wellstein , et al. · 2025

Attention modulates the relative weighting of information and plays a critical role in modern theories of perception. While its role in exteroception - the perception of the external environment - has been extensively studied, attentional …

Structured Code Review in Translational Neuromodeling and Computational Psychiatry Open

Inês A. C. Pereira, Herman Galioulline, Réka Enz, Alexander J. Hess, Matthias Müller-Schrader , et al. · 2025

Errors in scientific code pose a significant risk to the accuracy of research. Yet, formal code review remains uncommon in academia. Drawing on our experience implementing code review in Translational Neuromodeling and Computational Psychi…

Predictive Modelling of Depression Treatment Response using Individual Symptoms and Latent Factors Open

Susan Buehler, Jakob Heinzle, Klaas Ε. Stephan, Chi Tak Lee, Mahmoud Ali Eladawi , et al. · 2025

Machine learning models have increasingly been used to identify predictors of treatment response in depression, and it is hoped that they may eventually help with clinical decision making. However, the performance of these models has gener…

Predictive modelling of clinically significant depressive symptoms after coronary artery bypass graft surgery: protocol for a multicentre observational study in two Swiss hospitals (the PsyCor study) Open

Asimina Lazaridou, Sinthujan Sivakumar, Hector Rodriguez Cetina Biefer, Sonja Weilenmann, Mary Princip , et al. · 2025

Introduction Coronary artery bypass grafting (CABG) remains one of the most commonly performed cardiac surgeries worldwide. Despite surgical advancements, a significant proportion of patients experience psychological distress following sur…

Machine Learning Prediction of Treatment Response to Internet-Delivered Cognitive Behavioural Therapy vs. Antidepressant Medication Open

Chi Tak Lee, Derek Richards, Anna Hanlon, Kevin G. Lynch, Siobhán Harty , et al. · 2025

Objective. Many treatments exist for depression, yet none are universally effective. Multivariable predictive models may be used for personalised treatment selection. We developed a model of treatment response for patients receiving intern…

Bayesian Workflow for Generative Modeling in Computational Psychiatry Open

Alexander J. Hess, Sandra Iglesias, Laura Köchli, Stephanie Marino, Matthias Müller-Schrader , et al. · 2025

Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been …

Refining the Allostatic Self-Efficacy Theory of Fatigue and Depression Using Causal Inference Open

Alexander J. Hess, Dina von Werder, Olivia K. Harrison, Jakob Heinzle, Klaas Ε. Stephan · 2024

Allostatic self-efficacy (ASE) represents a computational theory of fatigue and depression. In brief, it postulates that (i) fatigue is a feeling state triggered by a metacognitive diagnosis of loss of control over bodily states (persisten…

Test-retest reliability of auditory MMN measured with OPM-MEG Open

László Demkó, Sandra Iglesias, Stephanie Mellor, Katja Brand, Alexandra Kalberer , et al. · 2024

In this paper, we report results from an investigation of auditory mismatch responses as measured by magnetoencephalography (MEG) based on optically pumped magnetometers (OPM). Specifically, as part of a quality control study, we examined …

Thermoceptive predictions and prediction errors in the anterior insula Open

Birte Toussaint, Jakob Heinzle, Nicole Friedli, Nicole Zahnd, Elena Federici , et al. · 2024

Contemporary theories of interoception propose that the brain constructs a model of the body for predicting the states and allostatic needs of all organs, including the skin, and updates this model using prediction error signals. However, …

Refining the Allostatic Self-Efficacy Theory of Fatigue and Depression Using Causal Inference Open

Alexander J. Hess, Dina von Werder, Olivia K. Harrison, Jakob Heinzle, Klaas Ε. Stephan · 2024

Allostatic self-efficacy (ASE) represents a computational theory of fatigue and depression. In brief, it postulates that (i) fatigue is a feeling state triggered by a metacognitive diagnosis of loss of control over bodily states (persisten…

A computationally informed distinction of interoception and exteroception Open

Birte Toussaint, Jakob Heinzle, Klaas Ε. Stephan · 2024

Bayesian Workflow for Generative Modeling in Computational Psychiatry Open

Alexander J. Hess, Sandra Iglesias, Laura Köchli, Stephanie Marino, Matthias Müller-Schrader , et al. · 2024

Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been …

Predicting future depressive episodes from resting-state fMRI with generative embedding Open

Herman Galioulline, Stefan Frässle, Samuel J. Harrison, Inês Pereira, Jakob Heinzle , et al. · 2023

After a first episode of major depressive disorder (MDD), there is substantial risk for a long-term remitting-relapsing course. Prevention and early interventions are thus critically important. Various studies have examined the feasibility…

A tightly controlled fMRI dataset for receptive field mapping in human visual cortex Open

Joram Soch, Kai Görgen, Jakob Heinzle, John–Dylan Haynes · 2023

Combining Different Response Data Modalities for Robust Inference in the Hierarchical Gaussian Filter Open

Alexander J. Hess, Sandra Iglesias, Stefan Frässle, Laura Köchli, Stephanie Marino , et al. · 2023

Predicting Future Depressive Episodes from Resting-State fMRI with Generative Embedding Open

Herman Galioulline, Stefan Frässle, Sam Harrison, Inês Pereira, Jakob Heinzle , et al. · 2022

After a first episode of major depressive disorder (MDD), there is substantial risk for a long-term remitting-relapsing course. Prevention and early interventions are thus critically important. Various studies have examined the feasibility…

Archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil Open

Steve Kwatcho Kengdo, Bernhard Ahrens, Ye Tian, Jakob Heinzle, Wolfgang Wanek , et al. · 2022

This is the archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil; submitted to the Journal Science of the Total Environment.

Archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil Open

Steve Kwatcho Kengdo, Bernhard Ahrens, Ye Tian, Jakob Heinzle, Wolfgang Wanek , et al. · 2022

This is the archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil; submitted to the Journal Science of the Total Environment.

Advances in spiral fMRI: A high-resolution dataset Open

Lars Kasper, Maria Engel, Jakob Heinzle, Matthias Müller-Schrader, Nadine N. Graedel , et al. · 2022

GABAergic modulation of conflict adaptation and response inhibition Open

Eduardo A. Aponte, Kaja Faßbender, Jakob Heinzle, Ulrich Ettinger · 2022

Adaptive behavior is only possible by stopping stereotypical actions to generate new plans according to internal goals. It is response inhibition —the ability to stop actions automatically triggered by exogenous cues— that allows for the f…

Increase in Carbon Input by Enhanced Fine Root Turnover in a Long-Term Warmed Forest Soil Open

Steve Kwatcho Kengdo, Bernhard Ahrens, Ye Tian, Jakob Heinzle, Wolfgang Wanek , et al. · 2022

Advances in spiral fMRI: A high-resolution study with single-shot acquisition Open

Lars Kasper, Maria Engel, Jakob Heinzle, Matthias Müller-Schrader, Nadine N. Graedel , et al. · 2021

Spiral fMRI has been put forward as a viable alternative to rectilinear echo-planar imaging, in particular due to its enhanced average k-space speed and thus high acquisition efficiency. This renders spirals attractive for contemporary fMR…

Conductance-based dynamic causal modeling: A mathematical review of its application to cross-power spectral densities Open

Inês Pereira, Stefan Frässle, Jakob Heinzle, Dario Schöbi, Cao Tri , et al. · 2021

Technical note: A fast and robust integrator of delay differential equations in DCM for electrophysiological data Open

Dario Schöbi, Cao-Tri Do, Stefan Frässle, Marc Tittgemeyer, Jakob Heinzle , et al. · 2021

Dynamic causal models (DCMs) of electrophysiological data allow, in principle, for inference on hidden, bulk synaptic function in neural circuits. The directed influences between the neuronal elements of modeled circuits are subject to del…

An introduction to thermodynamic integration and application to dynamic causal models Open

Eduardo A. Aponte, Yu Yao, Sudhir Raman, Stefan Frässle, Jakob Heinzle , et al. · 2021

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry Open

Stefan Frässle, Eduardo A. Aponte, Saskia Bollmann, Kay H. Brodersen, Ting Cao , et al. · 2021

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two close…

Model-based prediction of muscarinic receptor function from auditory mismatch negativity responses Open

Dario Schöbi, Fabienne Homberg, Stefan Frässle, Heike Endepols, Rosalyn Moran , et al. · 2021

Conductance-based Dynamic Causal Modeling: A mathematical review of its\n application to cross-power spectral densities Open

Inês A. C. Pereira, Stefan Frässle, Jakob Heinzle, Dario Schöbi, Cao Tri Do , et al. · 2021

Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden\n(latent) neuronal states, based on measurements of brain activity. Since its\nintroduction in 2003 for functional magnetic resonance imaging data, DCM has\nbeen…

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