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.

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.

13 April 2016: Mapping cognitive states in learning & decision-making

Nicolas Schuck
Princeton Neuroscience Institute
Princeton University

Prefrontal representations of environmental states are commonly assumed to reflect a mental model of the world that guides learning, memory and decision-making. The nature of such state representations in the human brain is not well studied, however. In particular little is know about how the current state of the environment is reflected in frontal activity and how an established mental model is changed, a process that requires internal simulation and is important for the ability to make strategy improvements. I will present two studies in which we investigated such prefrontal state representations during stable task performance or sudden strategy changes. Results from fMRI pattern classification analyses showed that such representations can be decoded from orbitofrontal cortex and signals from medial PFC reflected internally simulated strategy changes.

2 March 2016: Externalizing internal mental states to train sustained attention behavior

Megan deBettencourt
Princeton Neuroscience Institute
Princeton University

Remaining focused is important for most everyday tasks, such as being productive at work or driving safely in traffic. Despite this importance, sustaining attention is difficult and prone to frequent lapses. We hypothesized that some of these behavioral errors result from an inability to accurately monitor one’s own attentional state, and that enhancing this metacognitive awareness could lead to lasting improvements in sustained attention. I’ll first describe the development of a real-time fMRI system that involves measuring attentional state and providing closed-loop neurofeedback by altering the stimuli. Then, I’ll present work from a study that used this feedback to demonstrate a training effect: participants who received accurate neurofeedback improved on a sustained attention task. Finally, I’ll extend these results to a pilot study that applied similar techniques to a group of depressed individuals, to train away a bias towards negatively valenced information. Together, these studies suggest that real-time fMRI may enable powerful, customized, and rapid cognitive training.

17 February 2016: Towards Precision Psychiatry: Statistical Platform for the Personalized Characterization of Natural Behaviors

Elizabeth Torres
Psychology, Cognitive Science, Neuroscience, Biomedical Engineering
Computational Biomedical Imaging and Modeling
Rutgers University

There is a critical need for new analytics to personalize behavioral data analysis across different fields, including kinesiology, sports science and behavioral neuroscience. Specifically, to better translate and integrate basic research into patient care we need to radically transform the methods by which we describe and interpret movement data. We introduce a new statistical platform that enables the analyses of fluctuations in motor performance from natural movements and access various data sets to illustrate their use. These include data from newborns; data from large open-databases where we examine the effects of psychotropic medications on the volitional control of bodily rhythms and data from a large cohort involving various disorders of the nervous system. We show that hidden in the ‘noise’, smoothed out by averaging movement kinematics data, lies a wealth of information that can detect very early risk for neurodevelopmental derail/stagnation; and selectively differentiates neurological and mental disorders such as Parkinson’s disease (PD), deafferentation, Autism Spectrum Disorders (ASD), and Schizophrenia (SZ) from typically developing and typically aging controls. Further we show how to use these methods to assess medication effects on motor control. We empirically estimate the statistical parameters of the probability distributions for each individual in the various groups and report the parameter ranges for each clinical group after characterization of healthy developing and aging groups. We coin this newly proposed platform for individualized behavioral analyses ‘precision phenotyping’ to distinguish it from the type of observational-behavioral phenotyping prevalent in clinical studies or from the ‘one-size-fits-all’ model in basic movement science. We further propose the use of this platform as a unifying statistical framework to characterize brain disorders of known etiology in relation to idiopathic neurological disorders with similar phenotypic manifestations.

For background, please see: http://journal.frontiersin.org/article/10.3389/fneur.2016.00008/full

3 February 2016: Evaluation of overall environmental quality: theory, experiments and potential applications to mood disorders

Nathaniel Daw
Princeton Neuroscience Institute and Psychology Department
Princeton University

I will present a very informal overview of a line of work I’ve been involved with for some years. This concerns a particular variable — the long-run average reward per timestep — which we believe the brain tracks and uses to guide behavior in a variety of circumstances. These include choices involving energetic expenditure, vigor and motivation; aspiration levels and whether to settle or seek alternative options in situations like foraging or mate selection; patience and time discounting; and self control and automaticity vs. deliberation. Computationally, this is because at least in some circumstances, the average reward measures the opportunity cost of time spent, which gives it a key role assessing tradeoffs and prioritization among different possible options. I’ll discuss some of our attempts to measure peoples’ use of this quantity experimentally, and to relate it to underlying biological systems including dopamine and stress hormones. The main goal of this presentation, however, is to brainstorm with the CCNP community as to whether this mechanism is relevant to disorders of mood, particularly depression, and about experimental strategies we might pursue to test this.