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Test-retest reliability of regression dynamic causal modeling [article]

Stefan Frässle, Klaas Enno Stephan
2021 bioRxiv   pre-print
Frässle, Klaas E.  ...  for this preprint this version posted June 1, 2021.; https://doi.org/10.1101/2021.06.01.446526 doi: bioRxiv preprint SUPPLEMENTARY MATERIAL Test-retest reliability of regression dynamic causal modeling Stefan  ... 
doi:10.1101/2021.06.01.446526 fatcat:x7vbckd7yzbazed2awaw5fzjta

Thermodynamic integration for dynamic causal models [article]

Eduardo A. Aponte, Sudhir Raman, Stefan Frässle, Jakob Heinzle, Will D. Penny, Klaas E. Stephan
2018 bioRxiv   pre-print
A comprehensive description of the experimental design and analysis can 718 be found in Frassle et al. (2016b). 719 author/funder. All rights reserved. No reuse allowed without permission.  ...  Figure 5 : 5 Four different models used in (Frassle et al., 2016a; 2016b) representing different hypotheses of the putative mechanisms underlying hemispheric lateralization in the face perception network  ... 
doi:10.1101/471417 fatcat:j4wreyhc7rdo5bnlgikty7rvca

Regression DCM for fMRI

Stefan Frässle, Ekaterina I. Lomakina, Adeel Razi, Karl J. Friston, Joachim M. Buhmann, Klaas E. Stephan
2017 NeuroImage  
et al., 2016a (Frässle et al., , 2016b (Frässle et al., , 2016c .  ...  et al., 2016b (Frässle et al., , 2016c .  ... 
doi:10.1016/j.neuroimage.2017.02.090 pmid:28259780 fatcat:e2cvf6ta35duzmzmwjtg4eqbae

Test-retest reliability of regression dynamic causal modeling

Stefan Frässle, Klaas E. Stephan
2021 Network Neuroscience  
This complements previous methodological assessments of face and construct validity of rDCM Frässle, Lomakina, Kasper, et al., 2018; Frässle, Lomakina, Razi, et al., 2017; Frässle, Manjaly, et al., 2021  ...  Regression dynamic causal modeling (rDCM) is a generative model of fMRI data that was developed with these objectives in mind (Frässle, Lomakina, Kasper, et al., 2018; Frässle, Lomakina, Razi, et al.,  ... 
doi:10.1162/netn_a_00215 pmid:35356192 pmcid:PMC8959103 fatcat:tgkavkq3lfgl7obtsmgy47way4

Comparison of fMRI paradigms assessing visuospatial processing: Robustness and reproducibility

Verena Schuster, Peer Herholz, Kristin M. Zimmermann, Stefan Westermann, Stefan Frässle, Andreas Jansen, Suliann Ben Hamed
2017 PLoS ONE  
Author Contributions Conceptualization: Verena Schuster, Stefan Frässle, Andreas Jansen.  ... 
doi:10.1371/journal.pone.0186344 pmid:29059201 pmcid:PMC5653292 fatcat:kncpeod7xrekviz2yzfaxyfdrm

Whole-brain estimates of directed connectivity for human connectomics [article]

Stefan Frässle, Zina-Mary Manjaly, Cao Tri Do, Lars Kasper, Klaas P Pruessmann, Klaas E Stephan
2020 bioRxiv   pre-print
Inversion of a large-scale circuit model reveals a cortical hierarchy in Frässle, S. et al. A generative model of whole-brain effective connectivity.  ...  Neuroimage 179, 505-529, doi:10.1016/j.neuroimage.2018.05.058 (2018). 34 Frässle, S. et al. Regression DCM for fMRI. Neuroimage 155, 406-421, doi:10.1016/j.neuroimage.2017.02.090 (2017). Stephan, K.  ... 
doi:10.1101/2020.02.20.958124 fatcat:kqfbxtnmifbvbgrtjsarbnn7ci

An Introduction to Thermodynamic Integration and Application to Dynamic Causal Models [article]

Eduardo Aponte, Yu Yao, Sudhir Raman, Stefan Frassle, Jakob Heinzle, Will Penny, Klaas Stephan
2020 bioRxiv   pre-print
In generative modeling of neuroimaging data, such as dynamic causal modeling (DCM), one typically considers several alternative models, either to determine the most plausible explanation for observed data (Bayesian model selection) or to account for model uncertainty (Bayesian model averaging). Both procedures rest on estimates of the model evidence, a principled trade-off between model accuracy and complexity. In the context of DCM, the log evidence is usually approximated using variational
more » ... es. Although this approach is highly efficient, it makes distributional assumptions and is vulnerable to local extrema. This paper introduces the use of thermodynamic integration (TI) for Bayesian model selection and averaging in the context of DCM. TI is based on Markov chain Monte Carlo sampling which is asymptotically exact but orders of magnitude slower than variational Bayes. In this paper, we explain the theoretical foundations of TI, covering key concepts such as the free energy and its origins in statistical physics. Our aim is to convey an in-depth understanding of the method starting from its historical origin in statistical physics. In addition, we demonstrate the practical application of TI via a series of examples which serve to guide the user in applying this method. Furthermore, these examples demonstrate that, given an efficient implementation and hardware capable of parallel processing, the challenge of high computational demand can be overcome successfully. The TI implementation presented in this paper is freely available as part of the open source software TAPAS.
doi:10.1101/2020.12.21.423807 fatcat:gyiqumjkefhhflgbkpvv2lskaq

Handedness is related to neural mechanisms underlying hemispheric lateralization of face processing

Stefan Frässle, Sören Krach, Frieder Michel Paulus, Andreas Jansen
2016 Scientific Reports  
Frässle 1,2,3 , Sören Krach 4 , Frieder Michel Paulus 4 & Andreas Jansen 2,5 Figure 1 . 1 Figure1.  ...  variables. 1 Scientific 1 RepoRts | 6:27153 | DOI: 10.1038/srep27153 www.nature.com/scientificreports Handedness is related to neural mechanisms underlying hemispheric lateralization of face processing Stefan  ... 
doi:10.1038/srep27153 pmid:27250879 pmcid:PMC4890016 fatcat:di6am4cawnh7foqpjlb2m3eviy

Perceptual Rivalry: Reflexes Reveal the Gradual Nature of Visual Awareness

Marnix Naber, Stefan Frässle, Wolfgang Einhäuser, Hans P. O. p. de Beeck
2011 PLoS ONE  
Rivalry is a common tool to probe visual awareness: a constant physical stimulus evokes multiple, distinct perceptual interpretations ("percepts") that alternate over time. Percepts are typically described as mutually exclusive, suggesting that a discrete (all-or-none) process underlies changes in visual awareness. Here we follow two strategies to address whether rivalry is an all-or-none process: first, we introduce two reflexes as objective measures of rivalry, pupil dilation and optokinetic
more » ... ystagmus (OKN); second, we use a continuous input device (analog joystick) to allow observers a gradual subjective report. We find that the "reflexes" reflect the percept rather than the physical stimulus. Both reflexes show a gradual dependence on the time relative to perceptual transitions. Similarly, observers' joystick deflections, which are highly correlated with the reflex measures, indicate gradual transitions. Physically simulating wave-like transitions between percepts suggest piece-meal rivalry (i.e., different regions of space belonging to distinct percepts) as one possible explanation for the gradual transitions. Furthermore, the reflexes show that dominance durations depend on whether or not the percept is actively reported. In addition, reflexes respond to transitions with shorter latencies than the subjective report and show an abundance of short dominance durations. This failure to report fast changes in dominance may result from limited access of introspection to rivalry dynamics. In sum, reflexes reveal that rivalry is a gradual process, rivalry's dynamics is modulated by the required action (response mode), and that rapid transitions in perceptual dominance can slip away from awareness.
doi:10.1371/journal.pone.0020910 pmid:21677786 pmcid:PMC3109001 fatcat:dptb7tfb4vbbblrxntrrtan7sm

Hemodynamic modeling of aspirin effects on BOLD responses at 7T [article]

Cao-Tri Do, Zina-Mary Manjaly, Jakob Heinzle, Dario Schöbi, Lars Kasper, Klaas P. Pruessmann, Klaas Enno Stephan, Stefan Frässle
2020 medRxiv   pre-print
Aspirin is considered a potential confound for functional magnetic resonance imaging (fMRI) studies. This is because aspirin affects the synthesis of prostaglandin, a vasoactive mediator centrally involved in neurovascular coupling, a process that underlies the blood oxygenated level dependent (BOLD) response. Aspirin-induced changes in BOLD signal are a potential confound for fMRI studies of patients (e.g. with cardiovascular conditions or stroke) who receive low-dose aspirin prophylactically
more » ... nd are compared to healthy controls that do not take aspirin. To examine the severity of this potential confound, we combined high field (7 Tesla) MRI during a simple hand movement task with a biophysically informed hemodynamic model. Comparing elderly volunteers with vs. without aspirin medication, we tested for putative effects of low-dose chronic aspirin on the BOLD response. Specifically, we fitted hemodynamic models to BOLD signal time courses from 14 regions of the human motor system and examined whether model parameter estimates were significantly altered by aspirin. While our analyses indicate that hemodynamics differed across regions, consistent with the known regional variability of the BOLD response, we neither found a significant main effect of aspirin (i.e., an average effect across brain regions) nor an expected drug×region interaction. While our sample size is not sufficiently large to rule out small-to-medium global effects of aspirin, we had adequate statistical power for detecting the expected interaction. Altogether, our analysis suggests that low-dose aspirin, as used for prophylactic purposes, does not strongly affect BOLD signals and may not represent a critical confound for fMRI studies.
doi:10.1101/2020.01.30.20019729 fatcat:xyhufgwha5g7lcaug6ou5gjet4

The Unique Cytoarchitecture and Wiring of the Human Default Mode Network [article]

Casey Paquola, Margaret Garber, Stefan Frässle, Jessica Royer, Shahin Tavakol, Raúl Cruces, Elizabeth Jefferies, Jonathan Smallwood, Boris Bernhardt
2021 bioRxiv   pre-print
., 2019) and directed effective connectivity (Frässle et al., 2021b) .  ...  of the TAPAS software package (Frässle et al., 2021a) , a highly scalable generative model of effective connectivity that allows inferences on the directionality of signal flow.  ... 
doi:10.1101/2021.11.22.469533 fatcat:apkz5rhxkbcxljwuhh7xxk2n2y

Variational Bayesian inversion for hierarchical unsupervised generative embedding (HUGE)

Yu Yao, Sudhir S. Raman, Michael Schiek, Alex Leff, Stefan Frässle, Klaas E. Stephan
2018 NeuroImage  
Stefan van Waasen of the Central Institute ZEA-2-Electronic Systems at Research Center Jülich, Germany for his generous support, as well as discussion and suggestions. Appendix A.  ... 
doi:10.1016/j.neuroimage.2018.06.073 pmid:29964187 fatcat:netigxuyxvcvfi4wncpboxnixm

Whole-brain estimates of directed connectivity for human connectomics

Stefan Frässle, Zina M. Manjaly, Cao T. Do, Lars Kasper, Klaas P. Pruessmann, Klaas E. Stephan
2020 NeuroImage  
Second, rDCM is still in an early development stage and the current implementation is subject to methodological limitations ( Frässle et al., 2018a ( Frässle et al., , 2017 .  ...  This paper presents such a validation study for regression dynamic causal modeling (rDCM; Frässle et al., 2018a Frässle et al., , 2017 . rDCM is a recently introduced generative model of fMRI data that  ... 
doi:10.1016/j.neuroimage.2020.117491 pmid:33115664 fatcat:ecmo2rig3fdb7jywmal3sdxofq

A generative model of whole-brain effective connectivity

Stefan Frässle, Ekaterina I. Lomakina, Lars Kasper, Zina M. Manjaly, Alex Leff, Klaas P. Pruessmann, Joachim M. Buhmann, Klaas E. Stephan
2018 NeuroImage  
The development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free
more » ... ameter) for precise estimation of all model parameters. This paper introduces sparsity constraints to the variational Bayesian framework of rDCM as a solution to these problems in the domain of task-based fMRI. This sparse rDCM approach enables highly efficient effective connectivity analyses in whole-brain networks and does not require a priori assumptions about the network's connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. Following the derivation of the variational Bayesian update equations for sparse rDCM, we use both simulated and empirical data to assess the face validity of the model. In particular, we show that it is feasible to infer effective connection strengths from fMRI data using a network with more than 100 regions and 10,000 connections. This demonstrates the feasibility of whole-brain inference on effective connectivity from fMRI data -in single subjects and with a run-time below 1 min when using parallelized code. We anticipate that sparse rDCM may find useful application in connectomics and clinical neuromodeling -for example, for phenotyping individual patients in terms of whole-brain network structure. Klaas E (2018). A generative model of whole-brain effective connectivity. NeuroImage, 179(1):505-529. A B S T R A C T The development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free parameter) for precise estimation of all model parameters. This paper introduces sparsity constraints to the variational Bayesian framework of rDCM as a solution to these problems in the domain of task-based fMRI. This sparse rDCM approach enables highly efficient effective connectivity analyses in whole-brain networks and does not require a priori assumptions about the network's connectivity structure but prunes fully (all-to-all) connected networks as part of model inversion. Following the derivation of the variational Bayesian update equations for sparse rDCM, we use both simulated and empirical data to assess the face validity of the model. In particular, we show that it is feasible to infer effective connection strengths from fMRI data using a network with more than 100 regions and 10,000 connections. This demonstrates the feasibility of whole-brain inference on effective connectivity from fMRI datain single subjects and with a run-time below 1 min when using parallelized code. We anticipate that sparse rDCM may find useful application in connectomics and clinical neuromodelingfor example, for phenotyping individual patients in terms of wholebrain network structure.
doi:10.1016/j.neuroimage.2018.05.058 pmid:29807151 fatcat:hsczojmxvrhw5ceumn54v6xxaq

Technical Note: A fast and robust integrator of delay differential equations in DCM for electrophysiological data [article]

Dario Schöbi, Cao Tri Do, Stefan Frässle, Marc Tittgemeyer, Jakob Heinzle, Klaas Enno Stephan
2020 bioRxiv   pre-print
., 2020) ; for review, see (Frässle et al., 2018) ).  ... 
doi:10.1101/2020.12.28.424540 fatcat:prffdpkkg5bqpnascht6na2xse
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