CPC Zurich 2021 Reading List



Craske, M. G., & Stein, M. B. (2016). Seminar Anxiety. [PDF]

Edwards, G., Gross, M. (1976). Alcohol dependence: provisional description of a clinical syndrome. British Medical Journal 1(6017), 1058. [PDF]

Friston, K., Brown, H. R., Siemerkus, J., & Stephan, K. E. (2016). The dysconnection hypothesis (2016). Schizophrenia research, 176(2-3), 83-94. [PDF]

Fosnocht, K. M., & Ende, J. (2021). Approach to the adult patient with fatigue. Up to Date, May 2021. [PDF]

Grande, I., Berk, M., Birmaher, B., & Vieta, E. (2016). Bipolar disorder. The Lancet, 387(10027), 1561-1572. [PDF]

Henningsen, Peter, et al. "Persistent physical symptoms as perceptual dysregulation: a neuropsychobehavioral model and its clinical implications." Psychosomatic medicine 80.5 (2018): 422-431. [PDF]

Roenneberg C, Sattel H, Schaefert R, Henningsen P, Hausteiner-Wiehle C. Functional Somatic Symptoms. Dtsch Arztebl Int. 2019 Aug 9;116(33-34):553-560. doi: 10.3238/arztebl.2019.0553. PMID: 31554544; PMCID: PMC6794707. [PDF]

Kapur S, Phillips AG, Insel TR. Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Mol Psychiatry 2012; 17: 1174–79. [PDF]

Longo, D., Volkow, N., Koob, G., McLellan, A. (2016). Neurobiologic Advances from the Brain Disease Model of Addiction. The New England Journal of Medicine 374(4), 363-371. [PDF]

Malhi, G. S., & Mann, J. J. (2018). Seminar Depression. [PDF]

Manjaly Z., Harrison N. A., Critchley, H. D., Do, C. T., STefanics, G., Wenderoth, N., Lutterotti, A., Müller, A., & STephan, K. E. (2019). Pathophysiological and cognitive mechanisms of fatigue in multiple sclerosis. Journal of Neurology, Neurosurgery and Psychiatry, 90, 642 - 651. [PDF]

Stahl, S. M., & Stahl, S. M. (2013). Stahl's essential psychopharmacology: neuroscientific basis and practical applications. Cambridge university press. [BOOK]

Stephan, K. E., Bach, D. R., Fletcher, P. C., Flint, J., Frank, M. J., Friston, K. J., ... & Dayan, P. (2016). Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis. The Lancet Psychiatry, 3(1), 77-83. [PDF]

Stephan, K. E., Manjaly, Z. M., Mathys, C. D., Weber, L. A. E., Paliwal, S., Gard, T., Tittgemeyer, M., Fleming, S. M., Haker, H., Seth, A. K., & Petzschner, F. (2016). Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression. Frontiers in Human Neuroscience, 10. [PDF]

Tandon, R., Nasrallah, H. A., & Keshavan, M. S. (2009). Schizophrenia,“just the facts” 4. Clinical features and conceptualization. Schizophrenia research, 110(1-3), 1-23. [PDF]



Adams, R. A., Huys, Q. J., & Roiser, J. P. (2016). Computational psychiatry: towards a mathematically informed understanding of mental illness. J Neurol Neurosurg Psychiatry, 87(1), 53-63. [PDF]

Ahn, W.-Y., & Busemeyer, J. R. (2016). Challenges and promises for translating computational tools into clinical practice. Current Opinion in Behavioral Sciences, 1-7. [PDF]

Huys, Q. J., Maia, T. V., & Frank, M. J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature neuroscience, 19(3), 404. [PDF]

Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014). Computational psychiatry: the brain as a phantastic organ. The Lancet Psychiatry, 1(2), 148-158. [PDF]

Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in cognitive sciences, 16(1), 72-80. [PDF]

Paulus, M. P., Huys, Q. J., & Maia, T. V. (2016). A roadmap for the development of applied computational psychiatry. Biological psychiatry: cognitive neuroscience and neuroimaging, 1(5), 386-392. [PDF]

Petzschner, F. H., Weber, L. A., Gard, T., & Stephan, K. E. (2017). Computational psychosomatics and computational psychiatry: Toward a joint framework for differential diagnosis. Biological Psychiatry, 82(6), 421-430. [PDF]

Stephan, K. E., & Mathys, C. (2014). Computational approaches to psychiatry. Current opinion in neurobiology, 25, 85-92. [PDF]

Stephan, K. E., Schlagenhauf, F., Huys, Q. J., Raman, S., Aponte, E. A., Brodersen, K. H., ... & Friston, K. J. (2017). Computational neuroimaging strategies for single patient predictions. Neuroimage, 145, 180-199. [PDF]

Wang, X. J., & Krystal, J. H. (2014). Computational psychiatry. Neuron, 84(3), 638-654. [PDF]




Beal MS (2003) Variational Algorithms for Approximate Bayesian Inference, PhD. Thesis, Gatsby Computational Neuroscience Unit, University College London. [PDF]

C. Bishop: Pattern Recognition and Machine Learning. [BOOK]

Daunizeau, J., Adam, V., & Rigoux, L. (2014). VBA: a probabilistic treatment of nonlinear models for neurobiological and behavioural data. PLoS Comput Biol, 10(1), e1003441. [PDF]

Friston KJ, Mattout J, Truyillon-Barreto N, Ashburner J, Penny WD (2007) Variational free energy and the Laplace approximation. NeuroImage 34: 220-234. [PDF]

D. MacKay: Information Theory, Inference, and Learning Algorithms [BOOK]

Penny, W. D., Stephan, K. E., Mechelli, A., & Friston, K. J. (2004). Comparing dynamic causal models. Neuroimage, 22(3), 1157-1172. [PDF]

Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009). Bayesian model selection for group studies. Neuroimage, 46(4), 1004-1017. [PDF]



Bayesian Inference & Predictive Coding

Bogacz, R. (2017). A tutorial on the free-energy framework for modelling perception and learning. Journal of mathematical psychology, 76, 198-211. [PDF]

Friston, Karl. "A theory of cortical responses." Philosophical transactions of the Royal Society B: Biological sciences 360.1456 (2005): 815-836. [PDF]

Mathys, Christoph D., et al. "Uncertainty in perception and the Hierarchical Gaussian Filter." Frontiers in human neuroscience 8 (2014): 825. [PDF]

Petzschner, F. H., Glasauer, S., & Stephan, K. E. (2015). A Bayesian perspective on magnitude estimation. Trends in cognitive sciences, 19(5), 285-293. [PDF]

Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature neuroscience, 2(1), 79. [PDF]

Sterzer, P., Adams, R. A., Fletcher, P., Frith, C., Lawrie, S. M., Muckli, L., ... & Corlett, P. R. (2018). The predictive coding account of psychosis. Biological psychiatry, 84(9), 634-643. [PDF]


Active Inference

Buckley, Christopher L., et al. "The free energy principle for action and perception: A mathematical review." Journal of Mathematical Psychology 81 (2017): 55-79. [PDF]

Friston, Karl, et al. "Active inference: a process theory." Neural computation 29.1 (2017): 1-49. [PDF]

Friston, Karl J., Thomas Parr, and Bert de Vries. "The graphical brain: belief propagation and active inference." Network Neuroscience 1.4 (2017): 381-414. [PDF]

Schwartenbeck, Philipp, and Karl Friston. "Computational phenotyping in psychiatry: a worked example." ENeuro 3.4 (2016). [PDF]

Parr, T., Markovic, D., Kiebel, S. J., & Friston, K. J. (2019). Neuronal message passing using Mean-field, Bethe, and Marginal approximations. Scientific reports, 9(1), 1-18. [PDF]

Smith, R., Friston, K., & Whyte, C. (2021). A Step-by-Step Tutorial on Active Inference and its Application to Empirical Data. [Preprint]

Reinforcement Learning

Daw, N. D., & Dayan, P. (2014). The algorithmic anatomy of model-based evaluation. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1655), 20130478. [PDF]

Dayan, P., & Daw, N. D. (2008). Decision theory, reinforcement learning, and the brain. Cognitive, Affective, & Behavioral Neuroscience, 8(4), 429-453. [PDF]

Sutton RS, Barto AG. Reinforcement learning: An introduction. MIT press, 2018. [BOOK]

Markov Decision Processes

Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998). Planning and acting in partially observable stochastic domains. Artificial intelligence, 101(1-2), 99-134. [PDF]

Chapter 3 in Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018. [PDF]

Drift Diffusion Models

Gold, J. I., & Shadlen, M. N. (2002). Banburismus and the brain: decoding the relationship between sensory stimuli, decisions, and reward. Neuron, 36(2), 299-308. [PDF]

Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion decision model: Current issues and history. Trends in cognitive sciences, 20(4), 260-281. [PDF]



C. Bishop: Pattern Recognition and Machine Learning. [BOOK]

He T, Kong R, Holmes A, Nguyen M, Sabuncu M, Eickhoff SB, Bzdok D, Feng J, Yeo BT. (2018). Do Deep Neural Networks Outperform Kernel Regression for Functional Connectivity Prediction of Behavior? BioRxiv473603. [PDF]

D. MacKay: Information Theory, Inference, and Learning Algorithms. [BOOK]

Wolfers, T., Buitelaar, J. K., Beckmann, C. F., Franke, B., & Marquand, A. F. (2015). From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neuroscience & Biobehavioral Reviews, 57, 328-349. [PDF]



Daunizeau, J., David, O., & Stephan, K. E. (2011). Dynamic causal modelling: a critical review of the biophysical and statistical foundations. Neuroimage, 58(2), 312-322. [PDF]

Frässle, S., Lomakina, E. I., Razi, A., Friston, K. J., Buhmann, J. M., & Stephan, K. E. (2017). Regression DCM for fMRI. Neuroimage, 155, 406-421. [PDF]

Frässle, S., Lomakina, E. I., Kasper, L., Manjaly, Z. M., Leff, A., Pruessmann, K. P., ... & Stephan, K. E. (2018). A generative model of whole-brain effective connectivity. Neuroimage, 179, 505-529. [PDF]

Friston, K. J. (2011). Functional and effective connectivity: a review. Brain connectivity, 1(1), 13-36. [PDF]

Kahan, J., & Foltynie, T. (2013). Understanding DCM: ten simple rules for the clinician. Neuroimage, 83, 542-549. [PDF]

Smith, S. M. (2012). The future of FMRI connectivity. Neuroimage, 62(2), 1257-1266. [PDF]

Stephan, K. E., Penny, W. D., Moran, R. J., den Ouden, H. E., Daunizeau, J., & Friston, K. J. (2010). Ten simple rules for dynamic causal modeling. Neuroimage, 49(4), 3099-3109. [PDF]

Yao, Y., Raman, S. S., Schiek, M., Leff, A., Frässle, S., & Stephan, K. E. (2018). Variational Bayesian inversion for hierarchical unsupervised generative embedding (HUGE). NeuroImage, 179, 604-619. [PDF]



Ahn W., Gu, H., Shen Y., Haines N., Hahn H. A., Teater J. E., Myiung J. I. & Pitt M. A. Rapid, Precise, and reliable measurement of delay discounting using a Bayesian learning algorithm. Scientific Reports. 2020; 10:12091. [PDF]

Aylward, J., Valton, V., Ahn, W. Y., Bond, R. L., Dayan, P., Roiser, J. P., & Robinson, O. J. (2019). Altered learning under uncertainty in unmedicated mood and anxiety disorders. Nature human behaviour, 3(10), 1116-1123. [PDF]

Breakspear, M. (2017). Dynamic models of large-scale brain activity. Nature Neuroscience, 20, 340-352. [PDF]

Fosnocht, K. M., & Ende, J. (2011). Approach to the adult patient with fatigue. UpToDate (January 2019) Available from: www. uptodate. com/contents/approach-to-the-adult-patient-with-fatigue. [Text]

Shine, J.M., Breakspear, M., Bell, P. T., Ehgoetz Martens, K. A., Shine, R., Koyejo, O., Sporns, O., & Poldrack, R. A. (2019). Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nature Neuroscience, 22, 289–296. [PDF]

Tokariev, A., Roberts, J. A., Zalesky, A., Zhao, X., Vanhatalo, S:, Breakspear, M., & Cocchi, L. (2019). Large-scale brain modes reorganize between infant sleep states and carry prognostic information for preterms. Nature Communications, 10(1):2619. [PDF]



Taylor, J.A., Larsen, K. M., & Garrido, M.I. (2020). Multi-dimensional predictions of psychotic symptoms via machine-learning. Hum. Brain. Map. [PDF]

Dzafic, I., Randeniya, R., Harris, C. D., Bammel, M., & Garrido, M. I. (2020). Statistical learning and inference is impaired in the nonclinical continuum of psychosis. J Neurosc. 40(35), 6759 – 6769.  [PDF]

Larsen, K. M., Dzafic, I., Darke, H., Pertile, H., Carter, O., Sundram, S., & Garrido, M. I. (2020). Aberrant connectivity in auditory precision encoding in schizophrenia spectrum disorder and across the continuum of psychotic –like experiences. Schizophrenic Res., (20)30341-8, 0920-9964. [PDF]

Oestreich, L. K. L., Randeniya, R., & Garrido, M. I. (2019). Auditory prediction errors and auditory white matter microstructure associated with psychotic-like experiences in healthy individuals. Brain structure and function, 224(9), 3277 – 3289. [PDF]

Randeniya, R., Oestreich, L. K. L., & Garrido, M. I. (2018). Sensory prediction errors in the continuum of psychosis. Schizophrenic Res., 191, 109 – 122. [PDF]