PSY 1451: Debugging the Brain: Computational Approaches to Mental Dysfunction

PSY 1451 2022 Fall / Full Term / Section: 001 / Class number: 17492

Thursday 09:45 AM - 11:45 AM

Northwest building, room B127

This course examines recent work applying computational models to mental disorders. These models formalize psychopathology in terms of breakdown in fundamental neurocognitive processes, linking normal and abnormal brain function within a common framework. Computational modeling has already begun to yield insights, and even possible treatments, for a wide range of disorders, including schizophrenia, autism, Parkinson’s, depression, obsessive-compulsive disorder, and attention-deficit hyperactivity disorder. The course will consist of weekly readings from the primary literature, with one student leading the discussion of each paper.

 

Eligibility

The course is open to graduate students in the Department of Psychology and Program in Neuroscience, as well as to undergraduates who have taken one of the following: PSY/NEURO 1401 (Computational Cognitive Neuroscience), NEURO 120 (Introductory Computational Neuroscience), PSY 18 (Psychopathology), or with permission from the instructor. When submitting a petition, please list which of these prerequisites you satisfy.

 

Course Requirements

Grading will be based on the following elements:

(1) Final paper (50%): an 8-12 page research paper is due on the first day of the exam period (December 8). Students must submit a 1-page proposal for the final paper by November 21; the instructor will provide feedback on this initial proposal. The research paper should develop a computational theory of previously studied psychiatric data, or make predictions about new experiments.

(2) Class participation (50%): students are expected to participate in each class. Additionally, one student will be assigned to lead the discussion about one reading. The student will begin by giving a short (10 minute) summary of the paper along with discussion questions, and then the class will have an open discussion.

 

Grading Rubric

94-100 A       90-93 A-        87-89 B+       83-86 B

80-82   B-     77-79 C+       73-76 C         70-72 C-

67-69 D+
       63-66 D
         60-62 D-        Below 60 E (fail)

 

Academic Honor

You are expected to submit your own, original work for the exam and the final paper. Any misconduct will be reported, as is required by the college. Discussing your ideas with others and getting feedback on your work is encouraged, but you are required to cite any and all ideas that are not your own, and ensure that any assignments you turn in are your own writing and the result of your own research.

 

Accessibility

Any student needing academic adjustments or accommodations is requested to present their letter from the Accessible Education Office (AEO) and speak with the professor by the end of the second week of the term, (specific date). Failure to do so may result in the Course Head’s inability to respond in a timely manner. All discussions will remain confidential, although AEO may be consulted to discuss appropriate implementation.

 

Class 1 (9/1): Introduction and overview

 

Huys, Q.J.M., Maia, T., & Frank, M.J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19, 404-413.

 

Class 2 (9/8): Schizophrenia, part 1

 

Maia, T.V. & Frank, M.J. (2016). An integrative perspective on the role of dopamine in schizophrenia. Biological Psychiatry, 81, 52.

 

Starc, M., Murray, J. D., Santamauro, N., Savic, A., Diehl, C., Cho, Y. T., ... & Anticevic, A. (2017). Schizophrenia is associated with a pattern of spatial working memory deficits consistent with cortical disinhibition. Schizophrenia Research, 181, 107-116.

 

Class 3 (9/15): Schizophrenia, part 2

 

Stephan, K. E., Baldeweg, T., & Friston, K. J. (2006). Synaptic plasticity and dysconnection in schizophrenia. Biological Psychiatry, 59, 929-939.

 

Jardri, R., Duverne, S., Litvinova, A. S., & Denève, S. (2017). Experimental evidence for circular inference in schizophrenia. Nature Communications, 8, 1-13.

 

Class 4 (9/22): Psychosis and hallucinations

 

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, 634-643.

 

Cassidy, C. M., Balsam, P. D., Weinstein, J. J., Rosengard, R. J., Slifstein, M., Daw, N. D., ... & Horga, G. (2018). A perceptual inference mechanism for hallucinations linked to striatal dopamine. Current Biology, 28, 503-514.

 

Class 5 (9/29): Addiction

 

Redish, A.D., Jensen, S., Johnson, A. (2008). A unified framework for addiction: vulnerabilities in the decision process. Behavioral and Brain Sciences, 31, 415–37.

 

Dayan, P. (2009). Dopamine, reinforcement learning, and addiction. Pharmacopsychiatry, 42, 56–65.

 

Class 6 (10/6): Obsessive-compulsive disorder

 

Rolls, E. T., Loh, M., & Deco, G. (2008). An attractor hypothesis of obsessive–compulsive disorder. European Journal of Neuroscience, 28(4), 782-793.

 

Fradkin, I., Adams, R. A., Parr, T., Roiser, J. P., & Huppert, J. D. (2020). Searching for an anchor in an unpredictable world: A computational model of obsessive compulsive disorder. Psychological review, 127, 672.

 

Class 7 (10/13): Autism

 

Pellicano, E., & Burr, D. (2012). When the world becomes “too real”: A Bayesian explanation of autistic perception. Trends in Cognitive Sciences, 16, 504–510.

 

Rosenberg, A., Patterson, J.S., & Angelaki, D.E. (2015). A computational perspective on autism. Proceedings of the National Academy of Sciences, 112, 9158–9165.

 

Class 8 (10/20): Depression

 

Huys, Q.M., Daw, N.D., & Dayan, P. (2015). Depression: a decision-theoretic analysis. Annual Review of Neuroscience, 38, 1-23.

 

Brown, V. M., Zhu, L., Solway, A., Wang, J. M., McCurry, K. L., King-Casas, B., & Chiu, P. H. (2021). Reinforcement learning disruptions in individuals with depression and sensitivity to symptom change following cognitive behavioral therapy. JAMA Psychiatry, 78, 1113-1122.

 

Class 9 (10/27): Anxiety

 

Browning, M., Behrens, T. E., Jocham, G., O'reilly, J. X., & Bishop, S. J. (2015). Anxious individuals have difficulty learning the causal statistics of aversive environments. Nature Neuroscience, 18(4), 590-596. 

 

Zorowitz, S., Momennejad, I., & Daw, N. D. (2020). Anxiety, avoidance, and sequential evaluation. Computational Psychiatry, 4.

 

Class 10 (11/3): Post-traumatic stress disorder

 

Kube, T., Berg, M., Kleim, B., & Herzog, P. (2020). Rethinking post-traumatic stress disorder–A predictive processing perspective. Neuroscience & Biobehavioral Reviews, 113, 448-460.

 

Norbury, A., Brinkman, H., Kowalchyk, M., Monti, E., Pietrzak, R. H., Schiller, D., & Feder, A. (2021). Latent cause inference during extinction learning in trauma-exposed individuals with and without PTSD. Psychological Medicine, 1-12. 

 

Class 11 (11/10): Transdiagnostic approaches

 

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.

 

Moutoussis, M., Garzón, B., Neufeld, S., Bach, D. R., Rigoli, F., Goodyer, I., ... & Dolan, R. J. (2021). Decision-making ability, psychopathology, and brain connectivity. Neuron, 109, 2025-2040.

  

Class 12 (11/17): Network approaches

 

Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16, 5-13.

 

McNally, R. J., Robinaugh, D. J., Wu, G. W., Wang, L., Deserno, M. K., & Borsboom, D. (2015). Mental disorders as causal systems: A network approach to posttraumatic stress disorder. Clinical Psychological Science, 3, 836-849.

 

Class 13 (12/1): Big data and machine learning

 

Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., ... & Corlett, P. R. (2016). Cross-trial prediction of treatment outcome in depression: a machine learning approach. The Lancet Psychiatry, 3, 243-250.

 

Gkotsis, G., Oellrich, A., Velupillai, S., Liakata, M., Hubbard, T. J., Dobson, R. J., & Dutta, R. (2017). Characterisation of mental health conditions in social media using Informed Deep Learning. Scientific Reports, 7, 1-11.