Rutgers-Princeton Center for Computational Cognitive Neuropsychiatry

Mor Nahum
School of Occupational Therapy, Faculty of Medicine
The Hebrew University

Cognitive control, the mental operations underlying our goal-directed behaviors, have been suggested as a potential key mechanism in mental health, contribution to mental resilience, emotion regulation and reduction of depressive symptoms. However, although cognitive control ability has been shown to fluctuate, it is often captured in a one-time lab-based measurement. Here, we use ecological momentary assessment (EMA) of inhibitory control, a component of cognitive control, and mood in real life to assess their contribution to mental health. In Study 1, conducted in a group of 156 young adults during their basic combat training in IDF, we show that for those with higher levels of resilience, inhibitory control is associated with their momentary mood, such that better inhibitory control predicts better mood. In Study 2, which included a group of 106 participant with pre-clinical depression, we show that a one-time measurement of inhibitory control does not predict depressive symptoms. Instead, variability of inhibitory control across the EMA sessions predicted depressive symptoms one week later. In addition, reduced levels of inhibition predicted worse mood for those with higher baseline depressive symptoms. Finally, in Study 3, which included a group of 132 young adults with and without ADHD, we show that emotion dysregulation at baseline is not associated with a one-time measurement of inhibitory control. Instead, those with higher levels of emotion dysregulation have reduced mean EMA of inhibitory control and increased inhibition variability. These results suggest that the stability of cognitive control over time may be more relevant for prediction of mental health related outcomes. We discuss these results in light of cognitive models related to mental health and their potential clinical implications.

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Hyojeong Kim
Institute of Cognitive Science
University of Colorado Boulder

Failing to control the flow of our thoughts can impact our success in everyday life, and it is characteristic of multiple psychiatric disorders including depression and post-traumatic stress disorder. It has been demonstrated that thoughts can be removed from working memory (WM) in three differential methods (i.e., replacing a thought with something else, suppressing a specific thought, and clearing the mind of all thoughts), which recruit distinct neural regions involved in cognitive control (Banich et al., 2015). Our research has focused on how these different removal operations impact representational changes in WM and WM capacity as a result of the changes. In our removal task, participants were presented a target picture (a face, a fruit, or a scene) and then performed one of the removal operations based on a given instruction after the item offset. Employing multi-voxel pattern analysis (MVPA) on fMRI data, we were able to demonstrate that replacing, suppressing, and clearing are distinct neural processes that act upon WM information in different ways. By tracking the information contents of WM during the removal operations, we found that replacing an item was the quickest way to diminish information, followed by clearing and then suppressing. However, the representation of the removed item was mostly altered in WM when it was suppressed, followed by clearing and then replacing. Furthermore, these operations had different impacts on the encoding of new information. While maintaining an item interfered with the encoding of the next item as predicted, only suppressing an item, which displayed the most alteration of that item during removal, led to release from this proactive interference and even facilitated subsequent encoding (Kim et al., 2020). These results suggest that removal operations modulate WM representations in different ways, resulting in distinct consequences of representational alteration and WM capacity.

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Matthew Rushworth
Department of Experimental Psychology and Wellcome Trust Centre for Integrative Neuroimaging (WIN)
University of Oxford, UK

Voluntary actions are ones that appear to be initiated by people and other animals in the absence of any external instruction or cue.  However, the likelihood that voluntary actions are initiated depends on identifiable features of both the current and the recent environment, recent behaviour, and the consequences that will ensure if the action is made (for example, will it lead to reward or not).  These factors mediate their influence on the initiation of voluntary action via a distributed neural circuit spanning cortical regions such as the anterior cingulate and insula cortex and subcortical nuclei including habenula, basal forebrain, ventral tegmental area, substantia nigra, and raphe nucleus.  I review a series of recent studies conducted in non-human and human primates using a combination of neuroimaging, temporary inactivation via ultrasound stimulation, and pharmacological manipulation that begin to dissect these different influences on voluntary behaviour and to trace the anatomical pathways through which each operates on voluntary behaviour.  The timing of action initiation depends both on reward expectations and on recent behaviour and these influences are governed by changes in activity in the basal forebrain and anterior cingulate cortex, altered by disruption of activity in either of these areas, and by cholinergic manipulations.  However, whether or not an action is initiated also depends on the richness/sparseness of opportunities in the environment in general and this is tracked by activity in the raphe nucleus and altered by serotonergic manipulation.

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Ryan Smith
Laureate Institute for Brain Research
University of Tulsa

Computational modeling is a promising approach for parsing behavioral processes and dysfunctions in individuals with substance use disorders (SUDs), but it is unclear how much these processes change during the recovery period. In this talk, I will describe a study in which we assessed baseline and 1-year follow-up data on a sample of healthy controls (HCs; N = 48) and treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 83) who completed a standard ‘three-armed bandit’ task designed to assess explore-exploit behavior. I will also describe a pre-registered replication study with a new sample of 168 individuals with SUDs and 99 HCs. Computational models – based on the notion of active learning – were fit to behavior on the task. Relative to HCs, participants with SUDs were found at baseline to show slower learning rates in response to negative outcomes and less precise action selection. We then repeated these analyses when the same individuals returned and re-performed the task 1 year later to assess the stability of these baseline differences. We also examined whether baseline modelling measures could predict symptoms at follow-up. Bayesian analyses indicated that: (a) group differences in learning rates were stable over time (posterior probability = 1); (b) relationships between model parameters at baseline and follow-up were all significant and ranged from small to moderate (.25 < ICCs < .54); and (c) learning rates and/or information-seeking values at baseline were associated with substance use severity at 1-year follow-up in stimulant and opioid users (.36 < rs < .43, .002 < ps < .02). Differences in learning rates for losses replicated in the second sample, and model parameters could jointly differentiate specific substance disorders when combining samples. These findings suggest that processing dysfunctions involving learning to arbitrate between exploration and exploitation may show some stability throughout the recovery period. At the same time, individual computational differences at baseline had some predictive value for changes in substance use severity. Taken together, these results suggest active learning models may allow measurement of trait dysfunctions that could have predictive utility for substance use severity.

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Ignacio Saez
Departments of Neuroscience, Neurology and Neurosurgery
Icahn School of Medicine at Mount Sinai

My research focuses on the study of the neural basis of human cognition, particularly decision-making, through the examination of invasive brain activity in neurosurgical patients. I combine human electrophysiological and electrochemical recordings during decision-making tasks with computational models of reward and choice. This experimental combination provides access to fast, highly detailed, high signal-to-noise, multi-areal neural activity from the human brain, which is highly suited to studying the relationship of distributed neural activity to aspects of cognition and the use of neurostimulation techniques to modulate behavior and treat cognitive aspects of disease. In this talk, I will cover the different methodologies we employ in the lab, as well as current research efforts in the study of decision-making and the combination of computational psychiatry and intracranial approaches. 

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Oriel FeldmanHall
Department of Department of Cognitive, Linguistic and Psychological Sciences
Brown University

Interacting with others is one of the most inherently uncertain acts we embark on. There are a multitude of unknowns, including how to express ourselves, who to confide in, or whether to engage in risky behavior with our peers. All this uncertainty makes successfully navigating the social world a tremendous challenge. Combining behavioral and neuroscientific methods, we explore the social and emotional factors that shape and ultimately guide how humans learn to make adaptive decisions amongst this great uncertainty. In particular, we borrow models from the animal learning literature, and methods from computational neuroscience and machine learning, to examine how humans experience, process, and resolve this uncertainty to make more adaptive decisions.  

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Yael Milgram
Department of Psychology
Harvard University

Emotion regulation deficits contribute to many mental health disorders. Most research sought to understand these deficits by investigating how people regulate emotions – specifically, which emotion regulation strategies people use and how effectively they implement them. However, emotion regulation strategies are used at the service of attaining desired emotional states. Therefore, people who struggle with psychopathology might differ not only in the strategies they use, but also in the emotional states they desire. In this talk, I will present evidence suggesting that some clinical populations differ from non-clinical populations in the degree to which they are motivated to experience painful and pleasant emotions, with an emphasis on Major Depressive Disorder. I will then present studies testing the implications of these differences for the use of emotion regulation strategies, emotional experiences, and mental health. Finally, I will offer a new perspective for understanding these findings, according to which motivation to experience painful emotions in psychopathology might reflect a form of emotional self-harm.

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Laura Bustamante
Department of Psychological and Brain Sciences
Washington University in St. Louis

In this talk I will present results from a recently completed CCNP study and seek feedback on a proposed add-on neuroimaging session.

Frederike Petzschner
Department of Psychiatry and Human Behavior
Brown University

OCD has been conceptualized as a disorder arising from dysfunctional beliefs, such as overestimating threads or pathological doubts. Yet, how these beliefs lead to compulsions and obsessions remains unclear. Here, we develop a computational model to examine the specific beliefs that trigger and sustain compulsive behavior in a simple handwashing scenario. Our results demonstrate that a single belief disturbance – a lack of trust in one’s avoidance action– can trigger and maintain compulsions and is directly linked to compulsion severity. This distrust can further explain a number of seemingly unrelated phenomena in OCD including the role of not-just-right feelings, intolerance to uncertainty, overestimation of threat or perfectionisms, and deficits in reversal and state learning. In conclusion, our findings shed new light on the underlying beliefs that drive compulsive behavior in OCD, providing a step forward in building a more comprehensive theory of this complex condition.

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Gal Shoval
Princeton Neuroscience Institute
Princeton University

Gal Shoval, the clinical supervisor of the CCNP, will present the principles of diagnostic procedures of psychopathology and discuss different challenges in making diagnosis in therapy and research. The participants are welcome to prepare ahead and share some of their own concerns and prior experience for a vigorous discussion.

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