CPC Zurich 2019
Reading List

 

PSYCHIATRY & PSYCHOSOMATICS

Ashok, A. H., Marques, T. R., Jauhar, S., Nour, M. M., Goodwin, G. M., Young, A. H., & Howes, O. D. (2017). The dopamine hypothesis of bipolar affective disorder: the state of the art and implications for treatment. Molecular psychiatry, 22(5), 666. [PDF]

Breakspear, M., Roberts, G., Green, M. J., Nguyen, V. T., Frankland, A., Levy, F., ... & Mitchell, P. B. (2015). Network dysfunction of emotional and cognitive processes in those at genetic risk of bipolar disorder. Brain, 138(11), 3427-3439. [PDF]

Browning, M., Kingslake, J., Dourish, C. T., Goodwin, G. M., Harmer, C. J., & Dawson, G. R. (2019). Predicting treatment response to antidepressant medication using early changes in emotional processing. European Neuropsychopharmacology, 29(1), 66-75. [PDF]

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

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

Flückiger, C., Del Re, A. C., Wampold, B. E., & Horvath, A. O. (2018). The alliance in adult psychotherapy: A meta-analytic synthesis. Psychotherapy. [PDF]

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

Henningsen, P., Zipfel, S., & Herzog, W. (2007). Management of functional somatic syndromes. The Lancet, 369(9565), 946-955. [PDF]

Kane, J. M., & Leucht, S. (2008). Unanswered questions in schizophrenia clinical trials. Schizophrenia bulletin, 34(2), 302-309. [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]

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]

 

COMPUTATIONAL PSYCHIATRY

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., Moutoussis, M., & Williams, J. (2011). Are computational models of any use to psychiatry?. Neural Networks, 24(6), 544-551. [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]

Huys, Q. J., Maia, T. V., & Paulus, M. P. (2016). Computational psychiatry: from mechanistic insights to the development of new treatments. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1(5), 382-385. [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., Iglesias, S., Heinzle, J., & Diaconescu, A. O. (2015). Translational perspectives for computational neuroimaging. Neuron, 87(4), 716-732. [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]

Schwartenbeck, P., & Friston, K. (2016). Computational phenotyping in psychiatry: a worked example. eneuro, 3(4). [PDF]

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

 

METHODS & MODELS

MODEL INVERSION & MODEL COMPARISON

Variational Algorithms for Approximate Bayesian Inference, PhD. Thesis, Gatsby Computational Neuroscience Unit, University College London. [Thesis]

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

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

Knill, D. C., & Richards, W. (Eds.). (1996). Perception as Bayesian inference. Cambridge University Press. [Book]

Lee, M. D. (2011). How cognitive modeling can benefit from hierarchical Bayesian models.  Journal of Mathematical Psychology

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]


REINFORCEMENT LEARNING & DECISION MAKING

Huys, Q. J., Guitart-Masip, M., Dolan, R. J., & Dayan, P. (2015). Decision-theoretic psychiatry. Clinical Psychological Science, 3(3), 400-421. [PDF]

Schwartenbeck, P., Passecker, J., Hauser, T. U., FitzGerald, T. H., Kronbichler, M., & Friston, K. J. (2019). Computational mechanisms of curiosity and goal-directed exploration. eLife, 8, e41703. [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]

 

MACHINE LEARNING

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]

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]

 

MARKOV CHAIN MONTE CARLO & MARKOV DECISION PROCESSES

A Quick Introduction to Markov Chains and Markov Chain Monte Carlo [PDF]

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]

 

DYNAMIC CAUSAL MODELING & MODELS OF ADVANCED CONNECTIVITY

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

Stephan, K. E., & Friston, K. J. (2010). Analyzing effective connectivity with functional magnetic resonance imaging. Wiley Interdisciplinary Reviews: Cognitive Science, 1(3), 446-459. [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]

 

APPLICATIONS

Adams, R. A., Stephan, K. E., Brown, H. R., Frith, C. D., & Friston, K. J. (2013). The computational anatomy of psychosis. Frontiers in psychiatry, 4, 47. [PDF]

Gillan, C. M., Kosinski, M., Whelan, R., Phelps, E. A., & Daw, N. D. (2016). Characterizing a psychiatric symptom dimension related to deficits in goal-directed control. Elife, 5, e11305. [PDF]

MacDonald, A. W., & Schulz, S. C. (2009). What we know: findings that every theory of schizophrenia should explain. Schizophrenia bulletin, 35(3), 493-508. [PDF]

Moran, R. J., Kishida, K. T., Lohrenz, T., Saez, I., Laxton, A. W., Witcher, M. R., ... & Montague, P. R. (2018). The protective action encoding of serotonin transients in the human brain. Neuropsychopharmacology, 43(6), 1425. [PDF]

Moutoussis, M., Shahar, N., Hauser, T. U., & Dolan, R. J. (2018). Computation in psychotherapy, or how computational psychiatry can aid learning-based psychological therapies. Computational Psychiatry, 2, 50-73. [PDF]

Mulder, R., Murray, G., & Rucklidge, J. (2017). Common versus specific factors in psychotherapy: opening the black box. The lancet psychiatry, 4(12), 953-962. [PDF]

Rey, Y., Marin, C. E., & Silverman, W. K. (2011). Failures in cognitive?behavior therapy for children. Journal of clinical psychology, 67(11), 1140-1150. [PDF]

Pulcu, E., & Browning, M. (2019). The Misestimation of Uncertainty in Affective Disorders. Trends in Cognitive Sciences. [PDF]