CCNP Meetings

Michael W. Cole
Associate Professor
Center for Molecular and Behavioral Neuroscience (CMBN)
Rutgers University – Newark
http://www.colelab.org

Understanding neurocognitive computations – such as those disrupted in mental disorders – will require not just localizing cognitive information distributed throughout the brain but also determining how that information got there. Brain connectivity clearly has something to do with it, and decades of “connectionist” (and recent “deep learning”) theory suggests connectivity patterns specify distributed neural computations. I will share my laboratory’s efforts to map the human brain’s functional network organization and to determine how that organization shapes distributed cognitive processes and mental health. This has involved identification of “flexible hubs” – cortical regions that adaptively shift their connectivity to implement instructed task procedures. We have recently linked flexible hubs to the regulation of mental health. Also central to these efforts is the estimation of activity flow – the movement of evoked activity through brain network connections. Estimating activity flow quantifies the likely contribution of a network organization (such as one estimated using resting-state functional connectivity) to function-specific activity patterns (such as fMRI responses to cognitive events). I will cover application of activity flow mapping to predict whole-brain activation patterns across a variety of tasks, such as tasks involving working memory and rapid instructed task learning, as well as in the context of pre-clinical Alzheimer’s disease. These developments promise to better integrate theoretical/computational neuroscience and empirical neuroscience with mutual benefits across both fields.

Joseph Kable
Department of Psychology
University of Pennsylvania

Depression is characterized by obvious changes in decision-making that cause distress and impairment. Here I will discuss some of our recent work aimed at understanding the specificity and dimensionality of decisional impairments in depression. We tested a large group of participants diagnosed with Major Depressive Disorder (MDD, n = 64) and healthy controls (n=64) on a comprehensive battery of nine value-based decision-making tasks which yield ten distinct measures. MDD participants were worse at learning from rewards and punishments, had more pessimistic predictions about the future, and were less persistent; but showed no differences in time or risk preferences or social bargaining behavior. Performance on learning, expectation, and persistence tasks each loaded on unique dimensions in a factor analysis and accounted for unique variance in predicting depressed status. Computational modeling of reward and punishment learning further revealed evidence for both hyposensitivity to outcomes and reduced learning rates in MDD relative to healthy controls. Our results highlight several learning and decision making processes that are the impacted in MDD whose more detailed study could shed light on distinct dimensions of this heterogeneous disorder.

Jay Singh
Department of Psychiatry
University of Pennsylvania

The relationship between mental health and violence remains a controversial one. However, much attention has been paid to this relationship in recent years, with the number of secure inpatient beds in psychiatric hospitals on the rise and surveys of the criminal justice system establishing prisons and jails as the largest providers of mental healthcare in the United States. Hence, establishing valid and reliable methods of identifying patients who will commit violent acts has become an important health and safety issue. In this seminar, we will discuss current evidence-based methods of assessing violence risk in psychiatric populations, including both biological (neurological correlates and genetic markers) and psychosocial (actuarial risk assessment tools and structured professional judgment instruments) methods.

*Please note that this meeting will take place at the Princeton Neuroscience Institute this week.*

Evan M. Kleiman
Department of Psychology
Rutgers University

In other areas of science (biology, chemistry, etc.), we understand phenomena of interest by directly observing and studying them as they occur. Historically, however, we have not done this in the study of suicide because, until recently, the tools to do so have not been available. Indeed, this lack of information regarding the real-time occurrence of suicidal thoughts and behaviors may be a reason why despite all of the knowledge about suicidal thoughts and behaviors that has accumulated over the past 100 years, the suicide death rate in the United States is the same now as it was 100 years ago. The goal of my presentation will be to discuss how two new technologies—smartphone-based real-time monitoring (also called Ecological Momentary Assessment or Experience Sampling) and wearable physiological monitoring—offer to help us better understand suicidal thoughts and behaviors.

First, I will begin the presentation with an overview of findings from two smartphone-based real-time monitoring studies that describe how suicidal thoughts fluctuate throughout the day and how we can use these fluctuations to identify meaningful subtypes of individuals at risk for suicidal behaviors. Second, I will discuss findings from several other real-time monitoring studies on factors that predict suicidal thinking over just a few hours. Third, I will discuss new findings that use wearable physiological monitors to detect distress associated with suicidal thinking. Finally, I will conclude the presentation by discussing how integrating these new technologies offers great promise to go beyond improving our understanding of suicidal thoughts and behaviors to creating interventions to prevent suicidal thoughts and behaviors.

Leor Hackel
Department of Psychology
Rutgers University

People’s social and personal well-being hinges on the ability to form social bonds; in turn, this requires interacting with others and learning whether to spend time with them again in the future. How do we learn about others during such interactions? On one hand, people often learn through positive and negative feedback—a type of learning rooted in reward-based reinforcement. Yet, in social interactions, people often look beyond the immediate reinforcing value of an interaction to encode higher-level social impressions, and these may also impact future choices. Here, I will present a program of research investigating how we learn about people by making choices and experiencing feedback. This work demonstrates that people gravitate not only toward partners who provide rewarding outcomes (e.g., a valued gift), but also to those who display valued social traits (e.g., generosity). Both types of learning involve ventral striatum, while trait-based learning further recruits neural regions associated with social impression updating. Moreover, people use trait knowledge to select social partners in a flexible way across contexts. Finally, I will consider how this model can inform social deficits in disorders such as Borderline Personality Disorder.

David T. Hsu
Departments of Psychiatry and Psychology
Stony Brook University

Humans depend on others for survival and emotional well-being. Social rejection – when one is not wanted or liked – is a direct threat to this need, leading to sadness, anxiety, anger, and impulsivity. Several psychiatric disorders stem from abnormal responses to rejection, including major depressive, social anxiety, borderline personality, and substance/alcohol use disorders, yet the neural regulation of rejection is poorly understood. In this talk, I will present laboratory models of social rejection and how they are used in combination with neuroimaging techniques to examine neural responses to social rejection. I will present our work using positron emission tomography (PET) to examine the endogenous opioid response to rejection and show that this response is reduced in depressed individuals, suggesting an inability to regulate social “pain.” I will also present our work using functional magnetic resonance imaging (fMRI) to show abnormal neural responses to rejection in depressed women. Lastly, I will present work suggesting that the experience of rejection can be manipulated with neuromodulation, and discuss novel methods for identifying and treating those who are particularly sensitive to rejection.

Akram Bakkour
Department of Psychology
Zuckerman Mind Brain Behavior Institute
Columbia University

Memory is central to adaptive behavior, allowing past experience to guide decisions and actions. Indeed, decisions are often informed by memories. However, the neurobiological mechanisms by which episodic memory guides decisions and the consequences for behavior remain poorly understood. We use a computational framework developed for the study of perceptual decisions and adapt it to better understand the cognitive and neural mechanisms of value-based decision making. In this work, we use computational models to generate predictions that we test using fMRI and behavior in patient populations. This framework was adopted to better understand the basic mechanisms by which memory enters the decision process, whether value is constructed, and how preferences may be manipulated. Several psychiatric disorders are characterized by maladaptive decisions that lead to adverse outcomes. I demonstrate the utility of our framework for psychiatry by characterizing choices about food in patients with anorexia nervosa, an eating disorder typified by persistent and stereotypical choices of low-fat, low-calorie foods to the point of starvation. Using computational models to generate predictions that are testable using the tools of cognitive neuroscience offers promise in the search for novel interventions in the treatment of psychiatric disorders.

Xiaosi Gu
Departments of Psychiatry & Neuroscience
Icahn School of Medicine at Mount Sinai

To maintain the normal functioning of a society, individuals must be able to learn to adapt to norms. More importantly, norms are not static construls but instead, can be changed and updated; in other words, individuals have the ability to influence social others. In this talk, I will present our recent work using computational models of decision-making, in conjunction with fMRI and lesion approaches, that investigates how humans learn to adapt to norms and how they influence others. Our findings suggest that different neural regions (e.g. vmPFC, insula) compute dissociable parameters that drive norm learning. Furthermore, humans are able to engage model-based planning to influence others, a process involving both mesolimbic and lateral frontal control regions. Taken together, these findings reveal the non-static nature of human interactions and the proactive feature of social exchange behaviors.

Guillermo Horga
Department of Psychiatry
Columbia University

Predictive coding and related models of perceptual inference provide a concrete framework to understand how expectations are integrated into subjective perceptual experiences and how sensory information affects the formation and updating of beliefs about hidden states. Recent work has begun to link specific computations underlying these processes to psychotic phenomena such as hallucinations and delusions. However, it remains unclear how the computations underlying perceptual inference relate to the well-established increase in nigro-striatal dopamine function in psychosis, what is the specific nature of abnormalities in these computations, and whether they can explain the concurrence of hallucinations and delusions that characterizes the psychotic syndrome of schizophrenia. During my presentation, I will discuss recent work from our group that capitalizes on computational models of inference and which suggests that deficient belief updating is a core mechanism underlying both hallucinations and delusions in schizophrenia, a mechanism that depends on nigro-striatal dopamine. Our findings further suggest the existence of symptom-specific pathways consistent with a hierarchical belief-updating model whereby lower-level deficits relate to hallucinations and higher-level deficits relate to delusions. I will also discuss the implications of this work for generating novel downstream targets that are more proximal to specific symptoms of psychosis relative to upstream abnormalities in dopaminergic nigro-striatal pathways.

Sarah W. Yip
Department of Psychiatry
Yale School of Medicine

Despite advances, the effectiveness of most substance-use interventions remains highly variable across individuals and multiple quit-attempts are standard. This talk will present recent findings demonstrating the ability of connectome-based predictive modeling (CPM)¬—a data-driven method of identifying neural networks subserving specific behaviors—to predict abstinence during substance-use treatment. Evidence for largely dissociable neural substrates of cocaine versus opioid use in poly-addicted individuals will also be presented and clinical implications will be discussed.