11 May 2022: Talk title: Multi-scale convergence of functional imaging and genomic signatures of psychiatric illness risk

Avram Holmes
Department of Neuroscience
Yale University

Research in my laboratory focuses on discovering the fundamental organization of large-scale human brain networks. A core motivation that drives this work is the search for specific network-level signatures or “fingerprints” that co-vary with heritable behavioral variation in the general population and mark vulnerability for psychiatric illness onset. To date, research on the biological origins of psychopathology has largely focused on discrete illness categories. Although patient groups within this diagnostic system are treated as distinct entities, there are often murky boundaries between health and disease and across the disorders themselves. To establish the etiology of these complex syndromes, we must account for diagnostic heterogeneity, both relatively selective and disorder-spanning symptoms, and the dimensional nature of genetic risk.

In this talk, I will present two converging lines of research from my laboratory that aim to identify neurobiological markers of psychiatric illness. First, I will discuss a recent effort to link individual variability across the collective set of functional brain connections with the nature and severity of symptom profiles across unipolar depression, bipolar depression, and schizophrenia. Second, I will present ongoing research that uses measures of post-mortem gene expression to examine the relative influence of inhibitory interneuron subtypes on brain function, cortical specialization, and human behavior. In doing so, I will highlight how this information can be leveraged to understand individual variability in the diverse processing capabilities of the human brain and associated vulnerability for psychiatric illness onset.

View a recording of this session here.

4 May 2022: Latent states for adaptive learning: using structure for dynamics

Matt Nassar
Department of Neuroscience
Brown University

People flexibly adjust their use of information according to context. The same piece of information, for example the unexpected outcome of an action, might be highly influential on future behavior in one situation — but utterly ignored in another one. Bayesian models have provided insight into why people display this sort of behavior, and even identified potential neural mechanisms that link to behavior in specific tasks and environments, but to date have fallen short of providing broader mechanistic insights that generalize across tasks or statistical environments. Here I’ll examine the possibility that such broader insights might be gained through careful consideration of task structure. I’ll show that we can think about a large number of sequential tasks as requiring the same inference problem — that is to infer the latent states of the world and the parameters of those latent states — with the primary distinctions within the class defined by transition structure. Then I’ll talk about how a neural network that updates latent states according to a known transition structure and learns “parameters” of the world for each latent state can explain adaptive learning behavior across environments and provide the first insights into neural correlates of adaptive learning across environments. This model generates internal signals that identify the need for latent state updating, which maps onto previous observations made in pupil dilations and P300 responses across different task environments. I will also discuss an experiment that we are currently setting up to test the idea that these signals might reflect a latent state update signal, with a focus on relationships to learning and perception. Finally, I will briefly mention some theoretical work examining how latent states could be used to shape noise correlations in neural populations in order to speed learning.

View a recording of this session here.

Links to relevant work:
https://pubmed.ncbi.nlm.nih.gov/34144114/
https://pubmed.ncbi.nlm.nih.gov/35105677/
https://pubmed.ncbi.nlm.nih.gov/34193556/

20 April 2022: Rapid and reliable digital phenotyping using computational modeling, machine learning, and mobile technology

Woo-Young Ahn
Department of Psychology
Seoul National University

Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning, and adaptive design optimization (ADO) is a promising machine-learning method that might lead to rapid, precise, and reliable markers of individual differences. In this talk, I will first discuss the importance of reliability of (bio)markers. Then, I will present a series of studies that utilized ADO in the area of decision-making and for the development of ADO-based digital phenotypes for addiction and related behaviors. Lastly, I will discuss other promising approaches that might allow us to develop (bio)markers with clinical utility.

View a recording of this session here.

6 April 2022: Trial by trial model fitting workshop, part II: Model Comparison

Yael Niv
Princeton Neuroscience Institute
Princeton University

This is a continuation of the model fitting workshop from February 23rd. In this second part of the workshop, we will discuss how to compare models and use data to determine which of several alternatives is best.

View slides from this session here.

View a recording of this session here.

30 March 2022: Leveraging technology to understand and modify sleep and social media in adolescent suicide risk

Jessica Hamilton
Department of Psychology
Rutgers University

Rates of depression and suicide among adolescents have increased in the past 10 years, and suicide is now the second leading cause of death among individuals aged 10-24. To address this major public health problem, Dr. Jessica Hamilton’s program of research focuses on identifying and modifying developmentally-informed risk and protective factors for youth suicide, particularly aimed at reducing disparities in suicide. Specifically, Dr. Hamilton will describe her research examining sleep and social media as both potential risk factors and opportunities for prevention, and how her research harnesses advancing technology to better understand and prevent adolescent suicide risk.

View a recording of this session here.

16 March 2022: Decisions under the influence of TMS and augmented reality: Opportunities for computational modeling

Travis Baker
Center for Molecular and Behavioral Neuroscience
Rutgers University

In this talk, I will present two recent studies using TMS to manipulate electrophysiological and computational correlates of decision-making, and augmented reality (AR) to manipulate real-world environments in real-time during goal-directed navigation. Because TMS offers a powerful tool for investigating causal brain-behavior relations, and AR can alter one’s ongoing perception of the real-world, such experimental applications, when paired with computational modeling, may help reconcile or dispute theories and models of decision-making, help increase the ecological validity of human decision-making studies, and advance our understanding and treatment of mental disorders, with a special focus on addictions.

View a recording of this session here.

2 March 2022: The effects of stressor exposure on goal-directed control and decision-making

Candace Raio
Department of Psychiatry
NYU Grossman School of Medicine
New York University

The ability to effectively deploy goal-directed control during learning and decision-making is essential for adaptive behavior but is often compromised under stress. Using experimental paradigms that draw upon learning models and behavioral economics, I will present research that examines how stress changes the use of goal-directed control strategies and suggest that these changes may stem from the increased cognitive cost of exercising control. Further, I will show that the subjective cost of control can be measured using a novel economic decision-making approach and that these costs are highly sensitive to changes in affective state. Finally, I will argue that stress can also confer adaptive benefits for survival and demonstrate how this may emerge in decision contexts marked by uncertainty.

View a recording of this session here.

23 February 2022: Trial by trial model fitting workshop, part I: finding optimal parameters to describe data

Yael Niv
Princeton Neuroscience Institute
Princeton University

In this workshop I will discuss the basics of fitting reinforcement learning models to human behavior. We will start by discussing probability theory and Bayes rule. Then we will work out a reinforcement learning example step by step — no advance knowledge of reinforcement learning necessary. Note that if you participated in this workshop (taught by me) in another forum, including at CCNP in 2016 (wow, time flies!), this will be the same workshop. In part two of the workshop (April 6), we will discuss model comparison.

***Prior to this CCNP workshop, it is highly recommended attendees watch this 11-min video: https://www.youtube.com/watch?v=BrK7X_XlGB8. ***

View a recording of this session here.

View slides from this session here.

2 February 2022: Understanding and Treating Learning Dysfunctions in Anxiety

Vanessa Brown
Department of Psychiatry
University of Pittsburgh

Depression and anxiety disorders are characterized by difficulties navigating rewarding and punishing experiences. Although experimental evidence for disrupted reward and punishment processing in these disorders is substantial and serve as targets for interventions like cognitive behavioral therapy, it’s still unclear what dysfunctions exist, how they relate to specific presentations and symptoms in these disorders, and how treatment affects these dysfunctions. I will present data using reinforcement learning and symptom clusters to show what learning dysfunctions are present and how they relate to symptoms. Then, I will present results and theorized relationships between current & potential treatments and effects on learning in these disorders.

View this recorded session here.

15 December 2021: Motivation for cognitive and physical effort in depression

Laura Bustamante
Princeton Neuroscience Institute
Princeton University

Background: Cognitive control-related symptoms of Major Depressive Disorder (MDD) (e.g., difficulty concentrating) contribute substantially to disability. Often cognitive symptoms and poorer performance on cognitive control tasks are interpreted as a reduced ability to exert control. We propose instead that cognitive symptoms may be due, in part, to reduced motivation to exert control because of increased cognitive effort “costs”.
Methods: We developed a novel experiential learning task, using patch-foraging, to derive a quantitative estimate of people’s cognitive effort costs. This measure allows us to, 1) dissociate motivation from ability, 2) compare cognitive effort costs in MDD relative to controls, 3) assess specificity of impairment with respect to differences from physical effort costs, self-efficacy, and reward responsiveness, and 4) map these axes of motivation onto MDD symptom heterogeneity.
Results: Preliminary results (N=30, 20 MDD) showed participants avoided exerting cognitive and physical effort, as reflected by lower patch-leaving thresholds in high- relative to low-effort environments of each condition (physical: beta=-0.37, df=29.15, t=-2.58, p<0.015; cognitive: beta=-0.46, df=29.29, t=-2.41, p<0.023). More depressed participants assigned disproportionately higher cost to physical effort (beta=13.07, df=27, t=3.01, p0.719), cognitive ability (beta=-3.16, df=26, t=-0.54, p>0.59), or in physical ability (beta=-0.23, , df=27, t=-0.60, p>0.55).
Conclusions: Resolving the underlying mechanisms of cognitive impairments in MDD has implications for developing treatments. By our account, if MDD patients are found to assign higher cognitive effort costs, interventions should target willingness to engage control (e.g., by providing motivational input) rather than cognitive control performance (e.g., cognitive training).

View this recorded session here.