18 January 2017: The Cerebellum, Sensitive Periods, and Autism

Sam Wang
Princeton Neuroscience Institute
Princeton Universit

Although the cerebellum is mainly known as a sensory/motor structure, its influence and connections reach many regions known for cognitive function, emotion, and reward. I will present evidence for the idea that the cerebellum acts during sensitive periods to shape the developing brain. This hypothesis can explain a wide range of observations in autism, and may illuminate how the brain’s wiring is shaped by early-life sensory experience.

30 November 2016: Mesolimbic-hippocampal interactions support adaptive and maladaptive behavior

Deepu Murty
Department of Psychiatry
University of Pittsburgh

As we navigate through the world, we are inundated with immense amounts of information—to much information to veridically encode into long-term memory. Rather than attempt to encode all of this information, memory is adaptive. Information that is most relevant to achieving future goals is prioritized in long-term memory. In my talk, I will present a series of behavioral and neuroimaging studies that characterize (1) how the neural systems underlying motivation facilitate episodic memory and choice behavior and (2) how aberrant connectivity within this circuit results in maladaptive behavior in psychosis. Together, these studies support a model in which individuals tailor their memories of the environment to their goal states during encoding, which provides a foundation of information to support both future adaptive and maladaptive behavior.

9 November 2016: Where to put emotions in RL?

Quentin Huys
Translational Neuromodeling Unit
ETH Zurich and University of Zurich

Emotions are complex and hard to define. But emotions are extremely important to psychiatry, and hence it is important for computational psychiatry to understand to what extent the prominent theoretical frameworks are able to capture important features of emotions. This talk will be divided into three parts.

First, I will describe some work on Pavlovian influences on simple and complex choice behaviour. Second, I will briefly review decision-making work on depression, arguing that a model-based account is necessary. Third, I will introduce a speculative notion of emotions as implementing approximate metareasoning strategies and discuss how this could qualitatively account for important features of emotions and emotion regulation.

26 October 2016: Oscillations and Neuronal Dynamics in Schizophrenia: The Search for Basic Symptoms and Translational Opportunities

Peter Uhlhaas
Department of Psychology
University of Glasgow

A considerable body of work over the last 10 years combining non-invasive electrophysiology (electroencephalography/magnetoencephalography) in patient populations with preclinical research has contributed to the conceptualization of schizophrenia as a disorder associated with aberrant neural dynamics and disturbances in excitation/inhibition (E/I) balance parameters. Specifically, I will propose that recent technological and analytic advances in MEG provide novel opportunities to address these fundamental questions as well as establish important links with translational research.

We have carried out several studies which have tested the importance of neural oscillations in the pathophysiology of schizophrenia through a combination of MEG-measurements in ScZ-patients and pharmacological manipulations in healthy volunteers which target the NMDA-receptor. These results highlight a pronounced impairment in high-frequency activity in both chronic and unmedicated patients which could provide novel insights into basic circuit mechanisms underlying cognitive and perceptual dysfunctions.

Our recent work has employed MEG to understand the developmental trajectory of neural oscillations during adolescence and the possibility to develop a biomarker for early detection and diagnosis of ScZ. We found marked changes in the amplitude of high-frequency oscillations and synchrony that were particularly pronounced during the transition from adolescence to adulthood. Moreover, data from participants meeting ultra-high risk criteria for psychosis suggest that signatures of aberrant neuronal dynamics are already present prior to the onset of psychosis, highlighting the importance of advancing biomarkers for early intervention and diagnosis.

28 September 2016: The relationship of impulsivity and cortical thickness in depressed and non-depressed adolescents

Yuli Fradkin
Department of Psychiatry
Robert Wood Johnson Medical School
Rutgers University Behavioral Health Care

Major Depressive Disorder (MDD) is recognized to be heterogeneous in terms of brain structure abnormality findings across studies, which might reflect previously unstudied traits that confer variability to neuroimaging measurements. The purpose of this study was to examine the relationships between trait impulsivity and MDD diagnosis on adolescent brain structure. We predicted that adolescents with depression who were high on trait impulsivity would have more abnormal cortical structure than depressed patients or non-MDD who were low on impulsivity. We recruited 58 subjects, including 29 adolescents (ages 12-19) with a primary DSM-IV diagnosis of MDD and a history of suicide attempt and 29 demographically-matched healthy control participants. Our GLM-based analyses sought to describe differences in the linear relationships between cortical thickness and impulsivity trait levels. As hypothesized, we found significant moderation effects in rostral middle frontal gyrus and paracentral lobule cortical thickness for different subscales of the Barratt Impulsiveness Scale. However, although these brain-behavior relationships differed between diagnostic study groups, they were not simple additive effects as we had predicted. In conclusion, the findings confirm that dimensions of impulsivity have discrete neural correlates, and show that relationships between impulsivity and brain structure are expressed differently in adolescents with MDD compared to non-MDD adolescents.

15 June 2016: The role of mood in reward learning: function and dysfunction

Eran Eldar
Wellcome Trust Centre for Neuroimaging
Max Planck UCL Centre for Computational Psychiatry and Ageing Research
University College London

Unexpected rewards impact our mood, which may in turn impact our evaluation of subsequent rewards. I will show how this two-way interaction between mood and reward learning may serve an adaptive role, ‘correcting’ learning to account for widespread changes in reward availability in the environment. I will then present theoretical and experimental evidence indicating that this mechanism can also have maladaptive consequences, in particular by engendering mood instability that may contribute to psychiatric mood disorders.

Postdoc position available at the Niv Lab at Princeton University

The lab of Dr. Yael Niv at Princeton University (http://www.princeton.edu/~nivlab) is seeking a talented postdoctoral or more senior research associate to work as part of the newly formed CCNP. Research in the lab focuses on behavioral and imaging experiments and computational modeling of learning and decision making and their interaction with attention and memory processes.

The successful candidate will be exceptionally talented and motivated, with a clinical research background and a keen interest in applying methods from computational cognitive modeling to help understand and treat mental illness. The specific area of research is flexible, including anxiety and mood disorders, addiction, PTSD, OCD, schizophrenia. We are particularly looking for someone with initiative and creative ideas who will help chart a path for the lab in transitioning to research in the emerging field of computational psychiatry.

The Princeton Neuroscience Institute and the Psychology Department at Princeton are highly interdisciplinary and collaborative, and provide excellent support for career development. You will be joining a vibrant and international research group, and will have opportunities to interact with and learn from a large number of world-class researchers in cognitive and computational neuroscience. Initial appointments are for one year with the possibility of reappointment based on satisfactory performance and funding.

Essential Qualifications: PhD in psychology, psychiatry, neuroscience, cognitive science, or other closely related field; a strong track record of research; and experience with research on mental illness and psychiatric populations.

Preferred Qualification: Proficiency with computer programming (Matlab, Python, R or equivalent) and strong analytical and quantitative skills are strongly preferred. Experience with behavioral experiments (decision making/psychophysics) and model-based data analysis, fMRI (event related designs and model-based analysis techniques), and/or computational modeling (machine learning, reinforcement learning, Bayesian models) are an advantage, though not strictly required.

If you are interested please email a CV, research statement, and contact information for at least 2 references to yael@princeton.edu, and upload these materials at https://jobs.princeton.edu (Requisition #1600435).

Princeton University is an equal opportunity/affirmative action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. This position is subject to the University’s background check policy.

8 June 2016: Malfunction and bounded rationality views of psychopathology

Peter Hitchcock
Department of Psychology
Drexel University

I will contrast two views of mental health problems: the malfunction and bounded rationality views. The malfunction view states that mental health problems are due to faulty machinery which produces irrational behavior. The bounded rationality view states that people with mental health difficulties act in a rational, which is to say goal-consistent, manner, but that their goals are sometimes maladaptive due to the boundedness of rationality (where boundedness is due to the limits of knowledge and computational capacity, and to the opportunity cost inherent in action selection). According to the malfunction view, biology is the science of prime relevance to mental health; according to the bounded rationality view, the science of intelligence is most relevant. Computational researchers have a long history of adopting a stance of bounded rationality when characterizing new problems and describing human behavior. What is less well-known is that cognitive-behavioral therapy researchers adopt a strikingly similar stance during case formulation and treatment development, as I will show through examples. The aim of this talk is to brainstorm with the CCNP community about whether this similar approach to solving problems offers opportunities for generating new predictions and for making progress on problems in common.

25 May 2016: Mechanisms of visual working memory impairment in schizophrenia

Molly Erickson
Department of Psychiatry
Robert Wood Johnson Medical School
Rutgers University Behavioral Health Care

People with schizophrenia have robust and reliable deficits in working memory capacity that are associated with broader cognitive abilities and overall functional outcome. Though working memory impairment is often considered a core cognitive disturbance in this population, the mechanisms that give rise to this impairment are not yet known. In the present work, I explore behavioral explanations for this impairment, such as poor selective attention, task engagement, and attentional fatigue. I will also describe a novel biomarker of working memory consolidation that is impaired in patients and may provide a mechanistic link between physiological processes and cognitive disruption in schizophrenia.

27 April 2016: Learning, attention and cognitive aging: computational perspectives

Angela Radulescu
Princeton Neuroscience Institute and Department of Psychology
Princeton University

While much is known about reinforcement learning in simple scenarios in which a single stimulus is associated with a rewarding outcome, less is understood about how humans and animals learn in multidimensional settings in which stimulus attributes relevant for reward are not known in advance. In this talk, I will present data suggesting that reinforcement learning in complex environments relies on selective attention to uncover those aspects of the environment that are predictive of reward. I will then show how the interaction of reinforcement learning and attention changes with aging. In a multidimensional choice task, behavior of both young and older adults was explained well by a reinforcement learning model that uses selective attention to constrain learning. However, the model suggested that older adults restricted their learning to fewer features, employing more focused attention than younger adults. Furthermore, this difference in strategy predicted age-related deficits in accuracy. I will discuss these results suggesting that a narrower filter of attention may reflect an adaptation to the reduced capabilities of the reinforcement learning system.

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