TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry
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Abstract
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 closely intertwined fields: (i) Translational Neuromodeling (TN), which develops “computational assays” for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the T ranslational A lgorithms for P sychiatry- A dvancing S cience (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.
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The standard HGF as published in the TAPAS toolbox version 6.0.1 was used to model belief state trajectories about emotions and colours separately, similar to Stefanics et al. (2019).
“We used the standard HGF as published in the TAPAS toolbox version 6.0.1 (Frässle et al., 2021) to model belief state trajectories about emotions and colours separately, similar to Stefanics et al. (2019).”
From Are prediction error modifications domain specific in autism but domain general in ADHD? (2025) · cites this paper as Frässle et al., 2021
The ‘Inference > Generative Models of Behavioral Data’ section describes the HGF and identifies it as a model implemented in the TAPAS toolbox. Our copy does not identify version 6.0.1, so it supports the identification of the software but not that specific version. The separate modeling of emotions and colours is presented as the citing authors’ own use, not a result attributed to Frässle et al. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThe tools are freely available as part of the TAPAS software collection.
“These tools are freely available as part of the TAPAS software collection (Frässle et al., 2021, https://github.com/ComputationalPsychiatry).”
From The generalized Hierarchical Gaussian Filter (2026) · cites this paper as Frässle et al., 2021
The authors provide implementations in two open-source projects available as part of the TAPAS software collection.
“We provide such implementations in two open-source projects (Legrand et al., 2024; Thestrup Waade et al., in prep) which are available as part of the TAPAS software collection (https://github.com/ComputationalPsychiatry Frässle et al., 2021).”
From The generalized Hierarchical Gaussian Filter (2026) · cites this paper as Frässle et al., 2021
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Cites
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) · doi:10.1201/b16018 · Cited by 27,276 · see citing works
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630 · see citing works
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728 · see citing works
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.015 · Cited by 11,314 · see citing works
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913 · see citing works
Pattern Recognition and Machine Learning
(2006) · doi:10.1007/978-0-387-45528-0 · Cited by 9,824 · see citing works
Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526 · see citing works
Scikit-learn: Machine Learning in Python
(2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2078195 · Cited by 8,539 · see citing works
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429 · see citing works
Kernel Methods for Pattern Analysis
Shawe-Taylor, J. S., & Cristianini, N. (2004) · Cambridge University Press eBooks · doi:10.1017/cbo9780511809682 · Cited by 6,610 · see citing works
The WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651 · see citing works
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · cited 2× · see citing works
“Specifically, prominent “Bayesian brain” theories like predictive coding (170, 171) postulate that supragranular and infragranular cortical layers convey different signals via their efferent cortico-cortical connections.”
BackgroundThe minimal preprocessing pipelines for the Human Connectome Project
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.127 · Cited by 5,253 · see citing works
Friston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583 · see citing works
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578 · see citing works
Classical conditioning II: Current research and theory.
Black, A. H., & Prokasy, W. F. (1972) · Appleton-Century-Crofts eBooks · Cited by 4,449 · see citing works
Large-scale brain networks and psychopathology: a unifying triple network model
Menon, V. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.08.003 · Cited by 4,168 · see citing works
fMRIPrep: A robust preprocessing pipeline for functional MRI
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., et al. (2019) · Nature Methods · doi:10.1038/s41592-018-0235-4 · Cited by 3,905 · see citing works
Movement‐Related effects in fMRI time‐series
Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S. J., & Turner, R. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910350312 · Cited by 3,774 · see citing works
Methods to detect, characterize, and remove motion artifact in resting state fMRI
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.048 · Cited by 3,748 · see citing works
Statistical Mechanics of Fluid Mixtures
Kirkwood, J. G. (1935) · The Journal of Chemical Physics · doi:10.1063/1.1749657 · Cited by 3,454 · see citing works
Functional and Effective Connectivity: A Review
Friston, K. J. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0008 · Cited by 3,415 · see citing works
Statistical Parametric Mapping: The Analysis of Functional Brain Images
Penny, W. D., Friston, K. J., Ashburner, J., Kiebel, S. J., & Thomas, N. (2007) · UCL Discovery (University College London) · Cited by 3,339 · see citing works
Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.78 · Cited by 2,780 · see citing works
Statistical Inference for Probabilistic Functions of Finite State Markov Chains
Baum, L. E., & Petrie, T. (1966) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177699147 · Cited by 2,775 · see citing works
Dalmau, J., Tüzün, E., Wu, H., Masjuan, J., Rossi, J. E., Voloschin, A., Baehring, J. M., Shimazaki, H., Koide, R., King, D., et al. (2007) · Annals of Neurology · doi:10.1002/ana.21050 · Cited by 2,492 · see citing works
Machine learning for neuroimaging with scikit-learn
Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., & Varoquaux, G. P. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00014 · Cited by 2,435 · see citing works
Alsop, D. C., Detre, J. A., Golay, X., Günther, M., Hendrikse, J., Hernández-García, L., Lu, H., MacIntosh, B. J., Parkes, L. M., Smits, M., et al. (2014) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25197 · Cited by 2,218 · see citing works
Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H., Reiss, A. L., & Schatzberg, A. F. (2007) · Biological Psychiatry · doi:10.1016/j.biopsych.2006.09.020 · Cited by 2,214 · see citing works
The free-energy principle: a rough guide to the brain?
Friston, K. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.04.005 · Cited by 1,831 · see citing works
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.44 · Cited by 1,775 · see citing works
Uncertainty, Neuromodulation, and Attention
Yu, A. J., & Dayan, P. (2005) · Neuron · doi:10.1016/j.neuron.2005.04.026 · Cited by 1,774 · see citing works
Dynamics of blood flow and oxygenation changes during brain activation: the Balloon model
Buxton, R. B., Wong, E. C., & Frank, L. R. (1998) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910390602 · Cited by 1,665 · see citing works
Limbic-cortical dysregulation: a proposed model of depression
(1997) · The Journal of Neuropsychiatry and Clinical Neurosciences · doi:10.1176/jnp.9.3.471 · Cited by 1,585 · see citing works
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank
Alfaro‐Almagro, F., Jenkinson, M., Bangerter, N. K., Andersson, J., Griffanti, L., Douaud, G., Sotiropoulos, S. N., Jbabdi, S., Hernández-Fernández, M., Vallée, E., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.034 · Cited by 1,563 · see citing works
Interoception and Mental Health: A Roadmap.
Khalsa, S. S., Adolphs, R., Cameron, O. G., Critchley, H. D., Davenport, P. W., Feinstein, J. S., Feusner, J. D., Garfinkel, S. N., Lane, R. D., Mehling, W. E., et al. (2018) · Biological psychiatry. Cognitive neuroscience and neuroimaging · doi:10.1016/j.bpsc.2017.12.004 · Cited by 1,453 · see citing works
Self-citationCorrection for geometric distortion in echo planar images from B0 field variations
Jezzard, P., & Balaban, R. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340111 · Cited by 1,428 · see citing works
Bayesian model selection for group studies
Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.025 · Cited by 1,423 · see citing works
Self-citationBirn, R. M., Diamond, J. B., Smith, M. A., & Bandettini, P. A. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.048 · Cited by 1,342 · see citing works
(1995) · PubMed · Cited by 1,320 · see citing works
Primary and secondary saccades to goals defined by instructions
Hallett, P. E. (1978) · Vision Research · doi:10.1016/0042-6989(78)90218-3 · Cited by 1,262 · see citing works
When the world becomes ‘too real’: a Bayesian explanation of autistic perception
Pellicano, E., & Burr, D. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.08.009 · Cited by 1,156 · see citing works
Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring
Stephan, K. E., Friston, K. J., & Frith, C. D. (2009) · Schizophrenia Bulletin · doi:10.1093/schbul/sbn176 · Cited by 1,134 · see citing works
Self-citationNonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics
Friston, K. J., Mechelli, A., Turner, R., & Price, C. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0630 · Cited by 1,131 · see citing works
Respiratory sinus arrhythmia in humans: how breathing pattern modulates heart rate
Hirsch, J. A., & Bishop, B. (1981) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.1981.241.4.h620 · Cited by 1,105 · see citing works
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python
Wiecki, T. V., Sofer, I., & Frank, M. J. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00014 · Cited by 1,047 · see citing works
Computational psychiatry as a bridge from neuroscience to clinical applications
Huys, Q. J. M., Maia, T. V., & Frank, M. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4238 · Cited by 1,002 · see citing works
Stein's Estimation Rule and its Competitors—An Empirical Bayes Approach
Efron, B., & Morris, C. (1973) · Journal of the American Statistical Association · doi:10.1080/01621459.1973.10481350 · Cited by 981 · see citing works
Kapur, S., Phillips, A. G., & Insel, T. R. (2012) · Molecular Psychiatry · doi:10.1038/mp.2012.105 · Cited by 980 · see citing works
Machine Learning Approaches for Clinical Psychology and Psychiatry
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966 · see citing works
MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0184661 · Cited by 920 · see citing works
Cross-validation failure: Small sample sizes lead to large error bars
Varoquaux, G. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.061 · Cited by 897 · see citing works
Variational free energy and the Laplace approximation
Friston, K., Mattout, J., Trujillo-Barreto, N., Ashburner, J., & Penny, W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.08.035 · Cited by 880 · see citing works
Mapping early brain development in autism
Courchesne, E., Pierce, K., Schumann, C. M., Redcay, E., Buckwalter, J. A., Kennedy, D. P., & Morgan, J. (2007) · Neuron · doi:10.1016/j.neuron.2007.10.016 · Cited by 860 · see citing works
Comparing dynamic causal models.
Penny, W. D., Stephan, K. E., Mechelli, A., & Friston, K. J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.03.026 · Cited by 848 · see citing works
Self-citationSynaptic plasticity and dysconnection in schizophrenia
Stephan, K. E., Baldeweg, T., & Friston, K. J. (2006) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.10.005 · Cited by 841 · see citing works
Self-citationMontague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.11.018 · Cited by 809 · see citing works
Ten simple rules for dynamic causal modeling
Stephan, K. E., Penny, W. D., Moran, R. J., den Ouden, H. E. M., Daunizeau, J., & Friston, K. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.015 · Cited by 803 · see citing works
Self-citationAssessing and tuning brain decoders: Cross-validation, caveats, and guidelines
Varoquaux, G. P., Raamana, P. R., Engemann, D. A., Hoyos-Idrobo, A., Schwartz, Y., & Thirion, B. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.038 · Cited by 801 · see citing works
Ten simple rules for the computational modeling of behavioral data
Wilson, R. C., & Collins, A. G. E. (2019) · eLife · doi:10.7554/elife.49547 · Cited by 728 · see citing works
From reinforcement learning models to psychiatric and neurological disorders
Maia, T. V., & Frank, M. J. (2011) · Nature Neuroscience · doi:10.1038/nn.2723 · Cited by 715 · see citing works
Analysing connectivity with Granger causality and dynamic causal modelling
Friston, K., Moran, R., & Seth, A. K. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.11.010 · Cited by 684 · see citing works
Pavlovian conditioning-induced hallucinations result from overweighting of perceptual priors
Powers, A. R., Mathys, C., & Corlett, P. R. (2017) · Science · doi:10.1126/science.aan3458 · Cited by 684 · see citing works
Self-citationComputing Bayes factors using thermodynamic integration.
Lartillot, N., & Philippe, H. (2006) · Systematic Biology · doi:10.1080/10635150500433722 · Cited by 668 · see citing works
Resting-state fMRI confounds and cleanup
Murphy, K., Birn, R. M., & Bandettini, P. A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.001 · Cited by 666 · see citing works
Maxwell, S. E. (2004) · Psychological Methods · doi:10.1037/1082-989x.9.2.147 · Cited by 655 · see citing works
A Bayesian foundation for individual learning under uncertainty
Mathys, C. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00039 · Cited by 655 · see citing works
Self-citationInfluence of heart rate on the BOLD signal: The cardiac response function
Chang, C. E., Cunningham, J. P., & Glover, G. H. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.029 · Cited by 652 · see citing works
Birn, R. M., Smith, M. A., Jones, T. B., & Bandettini, P. A. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.11.059 · Cited by 645 · see citing works
Physiological noise in oxygenation‐sensitive magnetic resonance imaging
Krüger, G., & Glover, G. H. (2001) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1240 · Cited by 628 · see citing works
Dynamic causal modeling of evoked responses in EEG and MEG
David, O., Kiebel, S. J., Harrison, L. M., Mattout, J., Kilner, J. M., & Friston, K. J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.10.045 · Cited by 620 · see citing works
Bayesian model reduction and empirical Bayes for group (DCM) studies
Friston, K. J., Vladimir Litvak, A., Oswal, A., Razi, A., Stephan, K. E., van Wijk, B. C. M., Ziegler, G., & Zeidman, P. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.015 · Cited by 605 · see citing works
Self-citationThe dysconnection hypothesis (2016)
Friston, K., Brown, H. R., Siemerkus, J., & Stephan, K. E. (2016) · Schizophrenia Research · doi:10.1016/j.schres.2016.07.014 · Cited by 561 · see citing works
Self-citationFailing to deactivate: Resting functional abnormalities in autism
Kennedy, D. P., Redcay, E., & Courchesne, E. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600674103 · Cited by 543 · see citing works
Sources and implications of whole-brain fMRI signals in humans
Power, J. D., Plitt, M. H., Laumann, T. O., & Martin, A. D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.038 · Cited by 534 · see citing works
Computational psychiatry: the brain as a phantastic organ
Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014) · The Lancet Psychiatry · doi:10.1016/s2215-0366(14)70275-5 · Cited by 531 · see citing works
Self-citationThe antisaccade task as a research tool in psychopathology: A critical review
Hutton, S. B., & Ettinger, U. (2006) · Psychophysiology · doi:10.1111/j.1469-8986.2006.00403.x · Cited by 516 · see citing works
Spurious group differences due to head motion in a diffusion MRI study
Yendiki, A., Koldewyn, K., Kakunoori, S., Kanwisher, N., & Fischl, B. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.027 · Cited by 508 · see citing works
Chang, C. E., & Glover, G. H. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.012 · Cited by 486 · see citing works
Kass, R. E., & Steffey, D. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478825 · Cited by 474 · cited 1× · see citing works
“Notably, in the context of hierarchical Bayesian models, there is a principled way of estimating priors by exploiting measurements from multiple subjects: empirical Bayes [EB; (155–157)].”
The secret lives of experiments: Methods reporting in the fMRI literature
Carp, J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.07.004 · Cited by 473 · see citing works
Adults with autism overestimate the volatility of the sensory environment
Lawson, R. P., Mathys, C., & Rees, G. (2017) · Nature Neuroscience · doi:10.1038/nn.4615 · Cited by 470 · cited 2× · see citing works
“Focusing on patients with autism spectrum disorder (ASD), Lawson et al. (18) utilized the HGF to provide evidence that ASD patients tend to overestimate volatility in the face of environmental changes.”
BackgroundMethodSelf-citationUncertainty in perception and the Hierarchical Gaussian Filter
Mathys, C. D., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., & Stephan, K. E. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00825 · Cited by 465 · see citing works
Self-citationHead motion during MRI acquisition reduces gray matter volume and thickness estimates
Reuter, M., Tisdall, M. D., Qureshi, A., Buckner, R. L., van der Kouwe, A. J. W., & Fischl, B. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.006 · Cited by 457 · see citing works
Comparing hemodynamic models with DCM
Stephan, K. E., Weiskopf, N., Drysdale, P. M., Robinson, P. A., & Friston, K. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.040 · Cited by 441 · see citing works
Self-citationPoser, B. A., Versluis, M. J., Hoogduin, J. M., & Norris, D. G. (2006) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20900 · Cited by 440 · see citing works
The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread
Jirsa, V., Proix, T., Perdikis, D., Woodman, M., Wang, H., Gonzalez-Martinez, J. A., Bernard, C., Bénar, C., Guye, M., Chauvel, P., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.049 · Cited by 434 · see citing works
A phenome-wide examination of neural and cognitive function
Poldrack, R. A., Congdon, E. L., Triplett, W. T., Gorgolewski, K. J., Karlsgodt, K. H., Mumford, J. A., Sabb, F. W., Freimer, N. B., London, E. D., Cannon, T. D., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.110 · Cited by 414 · see citing works
The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data
Kasper, L., Bollmann, S., Diaconescu, A. O., Hutton, C., Heinzle, J., Iglesias, S., Hauser, T. U., Sebold, M., Manjaly, Z., Pruessmann, K. P., et al. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.10.019 · Cited by 413 · see citing works
Self-citationMaking big data open: data sharing in neuroimaging
Poldrack, R. A., & Gorgolewski, K. J. (2014) · Nature Neuroscience · doi:10.1038/nn.3818 · Cited by 412 · see citing works
Functional hypofrontality and working memory dysfunction in schizophrenia
Carter, C. S., Perlstein, W., Ganguli, R., Brar, J., Mintun, M., & Cohen, J. D. (1998) · American Journal of Psychiatry · doi:10.1176/ajp.155.9.1285 · Cited by 403 · see citing works
Deco, G., & Kringelbach, M. L. (2014) · Neuron · doi:10.1016/j.neuron.2014.08.034 · Cited by 402 · see citing works
VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data
Daunizeau, J., Adam, V., & Rigoux, L. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003441 · Cited by 385 · see citing works
Localization of Cardiac-Induced Signal Change in fMRI
Dagli, M. S., Ingeholm, J. E., & Haxby, J. V. (1999) · NeuroImage · doi:10.1006/nimg.1998.0424 · Cited by 385 · see citing works
Toward New Approaches to Psychotic Disorders: The NIMH Research Domain Criteria Project
Cuthbert, B. N., & Insel, T. R. (2010) · Schizophrenia Bulletin · doi:10.1093/schbul/sbq108 · Cited by 360 · see citing works
A unified Bayesian framework for MEG/EEG source imaging
Wipf, D., & Nagarajan, S. S. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.02.059 · Cited by 355 · see citing works
Ahn, W., Haines, N., & Zhang, L. (2017) · Computational Psychiatry · doi:10.1162/cpsy_a_00002 · Cited by 354 · see citing works
Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning
Iglesias, S., Mathys, C., Brodersen, K. H., Kasper, L., Piccirelli, M., den Ouden, H. E. M., & Stephan, K. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.009 · Cited by 342 · see citing works
Self-citationHuber, L., Handwerker, D. A., Jangraw, D. C., Chen, G., Hall, A., Stüber, C., González-Castillo, J., Ivanov, D., Marrett, S., Guidi, M., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.11.005 · Cited by 341 · see citing works
A systematic review of resting-state functional-MRI studies in major depression
Wang, L., Hermens, D. F., Hickie, I. B., & Lagopoulos, J. (2012) · Journal of Affective Disorders · doi:10.1016/j.jad.2012.04.013 · Cited by 333 · see citing works
Dynamic causal modelling: A critical review of the biophysical and statistical foundations
Daunizeau, J., David, O., & Stephan, K. E. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.062 · Cited by 331 · see citing works
Self-citationMulti-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals
Kundu, P., Voon, V., Balchandani, P., Lombardo, M. V., Poser, B. A., & Bandettini, P. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.033 · Cited by 324 · see citing works
Wang, X., & Krystal, J. H. (2014) · Neuron · doi:10.1016/j.neuron.2014.10.018 · Cited by 309 · see citing works
Almeida, J. R. C. D., Versace, A., Mechelli, A., Hassel, S., Quevedo, K., Kupfer, D. J., & Phillips, M. L. (2009) · Biological Psychiatry · doi:10.1016/j.biopsych.2009.03.024 · Cited by 305 · see citing works
Deep neural networks in psychiatry
Durstewitz, D., Koppe, G., & Meyer‐Lindenberg, A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0365-9 · Cited by 286 · see citing works
Computational psychiatry: towards a mathematically informed understanding of mental illness
Adams, R. A., Huys, Q. J. M., & Roiser, J. P. (2015) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2015-310737 · Cited by 285 · see citing works
Bullmore, E. T., Frangou, S., & Murray, R. M. (1997) · Schizophrenia Research · doi:10.1016/s0920-9964(97)00114-x · Cited by 265 · see citing works
Disturbances of voluntary control of saccadic eye movements in schizophrenic patients
Fukushima, J., Fukushima, K., Chiba, T., Tanaka, S., Yamashita, I., & Κato, M. (1988) · Biological Psychiatry · doi:10.1016/0006-3223(88)90050-9 · Cited by 264 · see citing works
Data sharing in neuroimaging research
Poline, J., Breeze, J. L., Ghosh, S., Gorgolewski, K. J., Halchenko, Y. O., Hanke, M., Haselgrove, C., Helmer, K. G., Keator, D. B., Marcus, D. S., et al. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2012.00009 · Cited by 262 · see citing works
Computational approaches to psychiatry
Stephan, K. E., & Mathys, C. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.12.007 · Cited by 260 · see citing works
Self-citationNeural masses and fields in dynamic causal modeling
Moran, R., Pinotsis, D. A., & Friston, K. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00057 · Cited by 255 · see citing works
Focus of attention modulates the heartbeat evoked potential
Petzschner, F. H., Weber, L. A., Wellstein, K. V., Paolini, G., Do, C. T., & Stephan, K. E. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.11.037 · Cited by 248 · see citing works
Self-citationPhysiological noise modelling for spinal functional magnetic resonance imaging studies
Brooks, J. C. W., Beckmann, C. F., Miller, K. L., Wise, R. G., Porro, C. A., Tracey, I., & Jenkinson, M. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.018 · Cited by 243 · see citing works
Postacquisition suppression of large‐vessel BOLD signals in high‐resolution fMRI
Menon, R. S. (2001) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10041 · Cited by 214 · see citing works
Reduced prefrontal-parietal effective connectivity and working memory deficits in schizophrenia
Deserno, L., Sterzer, P., Wüstenberg, T., Heinz, A., & Schlagenhauf, F. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.3405-11.2012 · Cited by 214 · see citing works
The impact of physiological noise correction on fMRI at 7 T
Hutton, C., Josephs, O., Stadler, J., Featherstone, E., Reid, A., Speck, O., Bernarding, J., & Weiskopf, N. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.04.018 · Cited by 211 · see citing works
A review on eye movement studies in childhood and adolescent psychiatry
Rommelse, N. N. J., der Stigchel, S. V., & Sergeant, J. A. (2008) · Brain and Cognition · doi:10.1016/j.bandc.2008.08.025 · Cited by 204 · see citing works
Dopamine neurons respond to errors in the prediction of sensory features of expected rewards
Takahashi, Y. K., Batchelor, H. M., Liu, B., Khanna, A., Morales, M., & Schoenbaum, G. (2017) · Neuron · doi:10.1016/j.neuron.2017.08.025 · Cited by 200 · see citing works
Raj, D., Anderson, A. W., & Gore, J. C. (2001) · Physics in Medicine and Biology · doi:10.1088/0031-9155/46/12/318 · Cited by 199 · see citing works
Hallucinations and perceptual inference
Friston, K. J. (2005) · Behavioral and Brain Sciences · doi:10.1017/s0140525x05290131 · Cited by 196 · cited 1× · see citing works
“This is consistent with the hypothesis that, in the context of a Bayesian brain, hallucinations may be explained by overly strong priors (209).”
ResultThe study of autism as a distributed disorder
Müller, R. (2007) · Mental Retardation and Developmental Disabilities Research Reviews · doi:10.1002/mrdd.20141 · Cited by 194 · see citing works
Dima, D., Roiser, J. P., Dietrich, D. E., Bonnemann, C., Lanfermann, H., Emrich, H. M., & Dillo, W. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.033 · Cited by 192 · see citing works
Psychiatric Disorders: Diagnosis to Therapy
Krystal, J. H., & State, M. W. (2014) · Cell · doi:10.1016/j.cell.2014.02.042 · Cited by 191 · see citing works
Curtis, C. E., Calkins, M. E., Grove, W. M., Feil, K. J., & Iacono, W. G. (2001) · American Journal of Psychiatry · doi:10.1176/appi.ajp.158.1.100 · Cited by 186 · see citing works
Petzschner, F. H., Weber, L. A. E., Gard, T., & Stephan, K. E. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.05.012 · Cited by 181 · see citing works
Self-citationCharting the landscape of priority problems in psychiatry, part 1: classification and diagnosis
Stephan, K. E., Bach, D. R., Fletcher, P. C., Flint, J., Frank, M. J., Friston, K. J., Heinz, A., Huys, Q. J. M., Owen, M. J., Binder, E. B., et al. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00361-2 · Cited by 180 · see citing works
Self-citationBellon, E., Haacke, E. H., Coleman, P., et al. (1986) · American Journal of Roentgenology · doi:10.2214/ajr.147.6.1271 · Cited by 178 · see citing works
Estimating Bayes factors via thermodynamic integration and population MCMC
Calderhead, B., & Girolami, M. (2009) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2009.07.025 · Cited by 176 · see citing works
Massively parallel MRI detector arrays
Keil, B. R., & Wald, L. L. (2013) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2013.02.001 · Cited by 175 · see citing works
Dissecting psychiatric spectrum disorders by generative embedding
Brodersen, K. H., Deserno, L., Schlagenhauf, F., Lin, Z., Penny, W. D., Buhmann, J. M., & Stephan, K. E. (2014) · NeuroImage: Clinical · doi:10.1016/j.nicl.2013.11.002 · Cited by 173 · see citing works
Self-citationAutomatic quality assessment in structural brain magnetic resonance imaging
Mortamet, B., Bernstein, M. A., Jack, C. R., Gunter, J. L., Ward, C. P., Britson, P. J., Meuli, R. A., Thiran, J., & Krüger, G. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21992 · Cited by 171 · see citing works
Translational perspectives for computational neuroimaging
Stephan, K. E., Iglesias, S., Heinzle, J., & Diaconescu, A. O. (2015) · Neuron · doi:10.1016/j.neuron.2015.07.008 · Cited by 169 · see citing works
Self-citationEvaluating fMRI preprocessing pipelines
Strother, S. C. (2006) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2006.1607667 · Cited by 169 · see citing works
Frässle, S., Lomakina, E. I., Razi, A., Friston, K. J., Buhmann, J. M., & Stephan, K. E. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.090 · Cited by 169 · see citing works
Self-citationObserving the observer (I): meta-bayesian models of learning and decision-making
Daunizeau, J., den Ouden, H. E. M., Pessiglione, M., Kiebel, S. J., Stephan, K. E., & Friston, K. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015554 · Cited by 167 · see citing works
Self-citationComputational neuroimaging strategies for single patient predictions
Stephan, K. E., Schlagenhauf, F., Huys, Q. J. M., Raman, S., Aponte, E. A., Brodersen, K. H., Rigoux, L., Moran, R., Jean Daunizeau, A., Dolan, R. J., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.038 · Cited by 166 · see citing works
Self-citationGenerative embedding for model-based classification of fMRI data
Brodersen, K. H., Schofield, T. M., Leff, A. P., Ong, C. S., Lomakina, E. I., Buhmann, J. M., & Stephan, K. E. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002079 · Cited by 165 · see citing works
Self-citationInteroception of breathing and its relationship with anxiety
Harrison, O. K., Köchli, L., Marino, S., Luechinger, R., Hennel, F., Brand, K., Hess, A. J., Frässle, S., Iglesias, S., Vinckier, F., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.045 · Cited by 163 · see citing works
Self-citationSnellenberg, J. X. V., Torres, I. J., & Thornton, A. E. (2006) · Neuropsychology · doi:10.1037/0894-4105.20.5.497 · Cited by 162 · see citing works
Pharmacological Fingerprints of Contextual Uncertainty
Marshall, L., Mathys, C., Ruge, D., de Berker, A. O., Dayan, P., Stephan, K. E., & Bestmann, S. (2016) · PLOS Biology · doi:10.1371/journal.pbio.1002575 · Cited by 158 · see citing works
Self-citationTowards the virtual brain: network modeling of the intact and the damaged brain.
Jirsa, V., Sporns, O., Breakspear, M. J., Deco, G., & McIntosh, A. R. (2010) · PubMed · Cited by 151 · see citing works
Schlösser, R. G. M., Wagner, G., Koch, K., Dahnke, R., Reichenbach, J. R., & Sauer, H. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.08.002 · Cited by 148 · see citing works
A hemodynamic model for layered BOLD signals
Heinzle, J., Koopmans, P. J., den Ouden, H. E. M., Raman, S., & Stephan, K. E. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.025 · Cited by 144 · see citing works
Self-citationHow to control for confounds in decoding analyses of neuroimaging data
Snoek, L., Miletić, S., & Scholte, H. S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.074 · Cited by 143 · see citing works
New Approaches to Psychiatric Diagnostic Classification
Owen, M. J. (2014) · Neuron · doi:10.1016/j.neuron.2014.10.028 · Cited by 141 · see citing works
Horn, J. D. V., Grethe, J. S., Kostelec, P. J., Woodward, J. B., Aslam, J. A., Rus, D. L., Rockmore, D. N., & Gazzaniga, M. S. (2001) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2001.0916 · Cited by 136 · see citing works
Denham, M. C., Maritz, J. S., & Lwin, T. (1990) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348214 · Cited by 136 · see citing works
Physiologically informed dynamic causal modeling of fMRI data
Havlíček, M., Roebroeck, A. F., Friston, K. J., Gardumi, A., Ivanov, D., & Uludaǧ, K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.078 · Cited by 130 · see citing works
Mourão-Miranda, J., Reinders, A. A. T. S., Rocha-Rego, V., Lappin, J., Rondina, J. M., Morgan, C., Morgan, K. D., Fearon, P., Jones, P. B., Doody, G. A., et al. (2011) · Psychological Medicine · doi:10.1017/s0033291711002005 · Cited by 130 · see citing works
A failure to grasp the affective meaning of actions in autism spectrum disorder subjects
Grèzes, J., Wicker, B., Berthoz, S., & de Gelder, B. (2009) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2009.02.021 · Cited by 129 · see citing works
A dynamical model of the laminar BOLD response
Havlíček, M., & Uludaǧ, K. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116209 · Cited by 127 · see citing works
Computational nosology and precision psychiatry
Friston, K. J., Redish, A. D., & Gordon, J. A. (2017) · Computational Psychiatry · doi:10.1162/cpsy_a_00001 · Cited by 126 · see citing works
Schmaal, L., Marquand, A. F., Rhebergen, D., van Tol, M., Ruhé, H. G., van der Wee, N. J. A., Veltman, D. J., & Penninx, B. W. J. H. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.11.018 · Cited by 113 · see citing works
KFAS: Exponential Family State Space Models in R
Helske, J. (2017) · Journal of Statistical Software · doi:10.18637/jss.v078.i10 · Cited by 111 · see citing works
A Roadmap for the Development of Applied Computational Psychiatry
Paulus, M., Huys, Q., & Maia, T. (2016) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.05.001 · Cited by 108 · see citing works
What big data can do for treatment in psychiatry
Gillan, C. M., & Whelan, R. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.003 · Cited by 106 · see citing works
A generative model of whole-brain effective connectivity
Frässle, S., Lomakina, E. I., Kasper, L., Manjaly, Z. M., Leff, A. P., Pruessmann, K. P., Buhmann, J. M., & Stephan, K. E. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.058 · Cited by 104 · see citing works
Self-citationLefebvre, S., Demeulemeester, M., Leroy, A., Delmaire, C., Lopes, R., Pins, D., Thomas, P., & Jardri, R. (2016) · Human Brain Mapping · doi:10.1002/hbm.23197 · Cited by 99 · see citing works
Gain Adaptation Beats Least Squares
Sutton, R. S. (2006) · Cited by 95 · see citing works
Regression dynamic causal modeling for resting‐state fMRI
Frässle, S., Harrison, S. J., Heinzle, J., Clementz, B. A., Tamminga, C. A., Sweeney, J. A., Gershon, E. S., Keshavan, M. S., Pearlson, G. D., Powers, A. R., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25357 · Cited by 89 · see citing works
Self-citationMoran, R. J., Jung, F., Kumagai, T., Endepols, H., Graf, R., Dolan, R. J., Friston, K. J., Stephan, K. E., & Tittgemeyer, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022790 · Cited by 88 · cited 1× · see citing works
“In particular, mathematical models of neuroimaging data, as obtained using functional magnetic resonance imaging (fMRI) and electro/magnetoencephalography (EEG/MEG), hold great promise as they might offer readouts of the symptom-producing physiological processes underlying brain disorders (9–14).”
Self-citationReilly, J. L., Frankovich, K., Hill, S. K., Gershon, E. S., Keefe, R. S. E., Keshavan, M. S., Pearlson, G. D., Tamminga, C. A., & Sweeney, J. A. (2013) · Schizophrenia Bulletin · doi:10.1093/schbul/sbt132 · Cited by 88 · see citing works
No-Reference Image Quality Metrics for Structural MRI
Woodard, J. P., & Carley-Spencer, M. P. (2006) · Neuroinformatics · doi:10.1385/ni:4:3:243 · Cited by 87 · see citing works
The Filter Detection Task for measurement of breathing-related interoception and metacognition
Harrison, O. K., Garfinkel, S. N., Marlow, L., Finnegan, S., Marino, S., Köchli, L., Allen, M., Finnemann, J., Keur‐Huizinga, L., Harrison, S. J., et al. (2021) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108185 · Cited by 84 · see citing works
Self-citationBiophysical network models and the human connectome
Woolrich, M. W., & Stephan, K. E. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.059 · Cited by 78 · see citing works
Self-citationIon channels in EEG: isolating channel dysfunction in NMDA receptor antibody encephalitis
Symmonds, M., Moran, C., Leite, M. I., Buckley, C. J., Irani, S. R., Stephan, K. E., Friston, K. J., & Moran, R. (2018) · Brain · doi:10.1093/brain/awy107 · Cited by 77 · see citing works
Self-citationDopamine neuron ensembles signal the content of sensory prediction errors
Stalnaker, T. A., Howard, J. D., Takahashi, Y. K., Gershman, S. J., Kahnt, T., & Schoenbaum, G. (2019) · eLife · doi:10.7554/elife.49315 · Cited by 74 · see citing works
Pizarro, R., Cheng, X., Barnett, A. S., Lemaître, H., Verchinski, B. A., Goldman, A. L., Xiao, E., Luo, Q., Berman, K. F., Callicott, J. H., et al. (2016) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2016.00052 · Cited by 73 · see citing works
Weber, L. A. E., Diaconescu, A. O., Mathys, C. D., Schmidt, A., Kometer, M., Vollenweider, F. X., & Stephan, K. E. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.3069-19.2020 · Cited by 73 · see citing works
Self-citationNoninvasive functional imaging of cerebral blood volume with vascular‐space‐occupancy (VASO) MRI
Lu, H., Hua, J., & van Zijl, P. C. M. (2013) · NMR in Biomedicine · doi:10.1002/nbm.2905 · Cited by 72 · see citing works
Atypical processing of uncertainty in individuals at risk for psychosis
Cole, D. M., Diaconescu, A. O., Pfeiffer, U. J., Brodersen, K. H., Mathys, C. D., Julkowski, D., Ruhrmann, S., Schilbach, L., Tittgemeyer, M., Vogeley, K. T., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102239 · Cited by 72 · see citing works
Self-citationCholinergic Stimulation Enhances Bayesian Belief Updating in the Deployment of Spatial Attention
Vossel, S., Bauer, M., Mathys, C. D., Adams, R. A., Dolan, R. J., Stephan, K. E., & Friston, K. J. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0091-14.2014 · Cited by 65 · see citing works
Self-citationCharting the landscape of priority problems in psychiatry, part 2: pathogenesis and aetiology
Stephan, K. E., Binder, E. B., Breakspear, M., Dayan, P., Johnstone, E. C., Meyer-Lindenberg, A., Schnyder, U., Wang, X.-J., Bach, D. R., Fletcher, P. C., et al. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00360-0 · Cited by 64 · see citing works
Self-citationBaojuan Li, A., Cui, L., Xi, Y., Friston, K. J., Guo, F., Wang, H., Zhang, L.-C., Bai, Y., Tan, Q.-R., Yin, H. J., et al. (2017) · Neuroscience Bulletin · doi:10.1007/s12264-017-0101-x · Cited by 62 · see citing works
Pulse sequences and parallel imaging for high spatiotemporal resolution MRI at ultra-high field
Poser, B. A., & Setsompop, K. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.006 · Cited by 62 · see citing works
Automated quality control of brain MR images
Gedamu, E. L., Collins, D. L., & Arnold, D. L. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21434 · Cited by 59 · see citing works
A method for the dynamic correction of B 0 -related distortions in single-echo EPI at 7 T
Dymerska, B., Poser, B. A., Barth, M., Trattnig, S., & Robinson, S. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.009 · Cited by 59 · see citing works
Gradient-based MCMC samplers for dynamic causal modelling
Sengupta, B., Friston, K. J., & Penny, W. D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.043 · Cited by 58 · see citing works
Verstynen, T., & Deshpande, V. S. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.090 · Cited by 58 · see citing works
Observing the Observer (II): Deciding When to Decide
Jean Daunizeau, A., den Ouden, H. E. M., Pessiglione, M., Kiebel, S. J., Friston, K. J., & Stephan, K. E. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015555 · Cited by 58 · see citing works
Self-citationSimulation of human respiration in fMRI with a mechanical model
Brosch, J. R., Talavage, T. M., Ulmer, J. L., & Nyenhuis, J. A. (2002) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2002.1010854 · Cited by 56 · see citing works
Parameter estimation in the magnitude-only and complex-valued fMRI data models
Rowe, D. B. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.048 · Cited by 55 · see citing works
New acquisition techniques and their prospects for the achievable resolution of fMRI
Bollmann, S., & Barth, M. (2020) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101936 · Cited by 54 · see citing works
Self-citationTest-retest reliability of dynamic causal modeling for fMRI
Frässle, S., Stephan, K. E., Friston, K. J., Steup, M., Krach, S., Paulus, F. M., & Jansen, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.040 · Cited by 52 · see citing works
Self-citationPredicting individual clinical trajectories of depression with generative embedding
Frässle, S., Marquand, A. F., Schmaal, L., Dinga, R., Veltman, D. J., van der Wee, N. J. A., van Tol, M., Schöbi, D., Penninx, B. W. J. H., & Stephan, K. E. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102213 · Cited by 51 · see citing works
Self-citationBrowning, M. D., Carter, C. S., Chatham, C. H., den Ouden, H. E. M., Gillan, C. M., Baker, J. T., Chekroud, A. M., Cools, R., Dayan, P., Gold, J. M., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.12.026 · Cited by 47 · see citing works
Self-citationGradient-free MCMC methods for dynamic causal modelling
Sengupta, B., Friston, K. J., & Penny, W. D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.008 · Cited by 46 · see citing works
Early development of arterial spin labeling to measure regional brain blood flow by MRI
Koretsky, A. P. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.005 · Cited by 46 · see citing works
Iglesias, S., Kasper, L., Harrison, S. J., Manka, R., Mathys, C. D., & Stephan, K. E. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117590 · Cited by 45 · see citing works
Self-citationKirilina, E. P., Lutti, A., Poser, B. A., Blankenburg, F., & Weiskopf, N. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.071 · Cited by 44 · see citing works
Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling
Cooray, G., Sengupta, B., Douglas, P. K., Englund, M., Wickström, R., & Friston, K. J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.064 · Cited by 43 · see citing works
Portugal, L. C. L., Schrouff, J., Stiffler, R., Bertocci, M. A., Bebko, G. M., Chase, H. W., Lockovich, J., Aslam, H. A., Graur, S., Greenberg, T., et al. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101813 · Cited by 40 · see citing works
Generative models for clinical applications in computational psychiatry
Frässle, S., Yao, Y., Schöbi, D., Aponte, E. A., Heinzle, J., & Stephan, K. E. (2018) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1460 · Cited by 37 · see citing works
Self-citationWhole-brain estimates of directed connectivity for human connectomics
Frässle, S., Manjaly, Z. M., Tri, C., Kasper, L., Pruessmann, K. P., & Stephan, K. E. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117491 · Cited by 37 · see citing works
Self-citationRadulescu, E., Minati, L., Ganeshan, B., Harrison, N. A., Gray, M. A., Beacher, F. D. C. C., Chatwin, C., Young, R. C. D., & Critchley, H. D. (2013) · NeuroImage: Clinical · doi:10.1016/j.nicl.2013.05.010 · Cited by 36 · see citing works
Schofield, T. M., Penny, W. D., Stephan, K. E., Crinion, J., Thompson, A. J., Price, C. J., & Leff, A. P. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.4670-11.2012 · Cited by 36 · see citing works
Self-citationFiliou, M. D., & Turck, C. W. (2011) · International review of neurobiology · doi:10.1016/b978-0-12-387718-5.00001-8 · Cited by 34 · see citing works
Suarez, J. A., Howard, J. D., Schoenbaum, G. M., & Kahnt, T. (2019) · eLife · doi:10.7554/elife.43962 · Cited by 34 · see citing works
GABA-ergic Dynamics in Human Frontotemporal Networks Confirmed by Pharmaco-Magnetoencephalography
Adams, N., Hughes, L. E., Phillips, H. N., Shaw, A. D., Murley, A. G., Nesbitt, D. J., Cope, T., Bevan-Jones, W. R., Passamonti, L., & Rowe, J. B. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.1689-19.2019 · Cited by 34 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [1]American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (DSM-5 R). Arlington, VA: American Psychiatric Publishing. 10.1176/appi.journals.9780890425596 · DOI
- [2]World Health Organization (1990). International Classification of Diseases. Geneva: World Health Organization Press.
- [102]Ashburner J. Preparing fMRI Data for Statistical Analysis. Fmri Techniques and Protocols, 2nd Edition. (2016) 119: 155– 81. 10.1007/978-1-4939-5611-_61 · DOI
- [155]Banerjee S Carlin BP Gelfand AE. Hierarchical Modeling and Analysis for Spatial Data, Second edition. ed. Boca Raton: CRC Press, Taylor and Francis Group (2015).
Cited by
Sorted by citations
Testing predictive coding theories of autism spectrum disorder using models of active inference
Arthur, T., Vine, S., Buckingham, G., Brosnan, M., Wilson, M., & Harris, D. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011473 · Cited by 21 · see citing works
Hauke, D. J., Charlton, C. E., Schmidt, A., Griffiths, J., Woods, S. W., Ford, J. M., Srihari, V. H., Roth, V., Diaconescu, A. O., & Mathalon, D. H. (2022) · doi:10.1101/2022.12.20.22283712 · Cited by 18 · see citing works
Bayesian Workflow for Generative Modeling in Computational Psychiatry
Hess, A. J., Iglesias, S., Köchli, L., Marino, S., Müller-Schrader, M., Rigoux, L., Mathys, C., Harrison, O. K., Heinzle, J., Frässle, S., et al. (2025) · Computational Psychiatry · doi:10.5334/cpsy.116 · Cited by 10 · see citing works
Self-citationAre prediction error modifications domain specific in autism but domain general in ADHD?
Plank, I. S., Yurova, A., Pior, A., Nowak, J., Bierlich, A. M., Papazov, B., Shi, Z., & Falter-Wagner, C. M. (2025) · Imaging neuroscience · doi:10.1162/imag.a.942 · Cited by 3 · see citing works
“We used the standard HGF as published in the TAPAS toolbox version 6.0.1 ( Fr ä ssle et al., 2021) to model belief state trajectories about emotions and colours separately, similar to Stefanics et al. (2019).”
InfluentialThe generalized Hierarchical Gaussian Filter
Weber, L. A., Waade, P. T., Legrand, N., Møller, A. H., Stephan, K. E., & Mathys, C. (2026) · eLife · doi:10.7554/elife.110174
Self-citationPredicting emotional valence in autism: a preregistered study in the Bayesian Brain framework
Plank, I. S., Pior, A., Yurova, A., Nowak, J., Shi, Z., & Falter-Wagner, C. M. (2026) · Molecular Autism · doi:10.1186/s13229-026-00730-3 · Cited by 2 · see citing works
“0.2 [40].”
InfluentialClosed-form predictive coding via hierarchical Gaussian filters
Baskakovs, A., Estebe, S., Enevoldsen, K., Nielbo, K., Mathys, C., & Legrand, N. (2026) · arXiv · doi:10.48550/arxiv.2605.20293
“…semantics, and individually fittable parameters has made the framework a reference in cognitive neuroscience [Lawson et al., 2017, Powers et al., 2017, Mikus et al., 2025], with open-source implementations such as PyHGF [Legrand et al., 2024] distributed via TAPAS [Frässle et al., 2021].”
Self-citation
- Authors
- Frässle, S., Aponte, E. A., Bollmann, S., Brodersen, K. H., Do, C. T., Harrison, O. K., Harrison, S. J., Heinzle, J., Iglesias, S., Kasper, L., Lomakina, E. I., Mathys, C., Müller-Schrader, M., Pereira, I., Petzschner, F. H., Raman, S., Schöbi, D., Toussaint, B., Weber, L. A., Yao, Y., & Stephan, K. E.
- Affiliations
- University of Zurich (CH)
- Harvard University (US)
- University Health Network (CA)
- Corresponding author
- Stephan, Klaas E.
- ORCID
- Venue
- Frontiers in Psychiatry
- Publisher
- Frontiers Media SA
- Published
- 2021-06-02
- Volume
- 12
- Pages
- 680811-680811
- ISSN
- 1664-0640 (ISSN-L)
- PMC
- PMC8206497
- OpenAlex
- W3166255770
- Cited by
- 169
- References
- 233
- Citations by year
- 2026: 26 · 2025: 39 · 2024: 39 · 2023: 37 · 2022: 18 · 2021: 10
- FWCI
- 3.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychiatry (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Scopus (Elsevier) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- National Science Foundation
- Royal Society (793580, 2017-403)
- European Commission (HORIZON2020, 793580)
- Royal Society Te Apārangi (793580, 2017-403)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (179377)
- Eidgenössische Technische Hochschule Zürich (793580, 2017-403)
- Universität Zürich (793580, 2017-403)
- René und Susanne Braginsky Stiftung (2017-403, 793580)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Undergraduate Neuroscience Education and Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- computational psychiatry, open source software, experimental design optimization, measurement strategy optimization, data quality control, artifact removal, statistical inference, clinical decision support, differential diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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