Author: Johanne

Nadja R. Ging-Jehli
Department of Cognitive, Linguistic & Psychological Sciences
Brown University

In the digital age, where large-scale data collection is increasingly feasible, computational psychiatry faces a key question: can we rely solely on online studies with calibrated tasks that focus on behavior, or do we still need in-person studies that integrate physiological measures? Across two studies, I will demonstrate that combining mechanistic assessments with physiological measures provides more insights than behavioral analyses alone. In the first study, we employed an instrumental learning task to investigate learning impairments in major depressive disorder (MDD) and bipolar disorder (BP). The mechanistic task allowed us to distinguish between processes linked to reinforcement learning and working memory. Despite similar behavioral deficits across clinical groups, model-based EEG analyses revealed distinct neurocomputational profiles specific to each disorder. In the second study, we investigated how adults with attention-deficit/hyperactivity disorder (ADHD) respond to contextual changes during a social strategic interaction game, using eye-tracking and diffusion decision modeling to analyze their behavior. Although adults with ADHD engaged in riskier actions when stakes were high, model-based eye-tracking revealed that this was not due to impulsivity. Instead, they focused more on the potential benefits than the costs of collaboration. Further analysis showed that individuals with higher ADHD symptom severity were more sensitive to contextual changes, highlighting the dynamic nature of impulsivity.These findings emphasize the value of neurocomputational assessments, incorporating physiological measures, in distinguishing psychiatric disorders and capturing reactivity to contextual change as a potentially important transdiagnostic marker.

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Gabriela Rosenblau
Department of Psychological and Brain Sciences
The George Washington University

This talk will discuss our approach to examining social knowledge and how it is used for learning. In current projects, we are extending our previous line of work on social learning to investigate how various adult and adolescent cohorts flexibly represent, employ, and acquire rich social knowledge when learning about others. We are using a combination of computational modeling, and neuroimaging to understand how adults and adolescents efficiently employ and refine social knowledge structures during learning. These projects also involve individuals with clinical disorders, such as Autism Spectrum Disorder and Borderline Personality Disorder, but can be integrated into a transdiagnostic approach to specifying social deficits across a wide range of clinical disorders.

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Jonathan Rosier
Institute of Cognitive Neuroscience
Div of Psychology & Lang Sciences
University of College London

Motivational symptoms of depression are debilitating and associated with poor clinical outcome, but the mechanisms underlying them are poorly understood. This talk will present data examining how cognitive processes related to effort-based decision making for reward are associated with depressive symptoms, using a computational approach. In the first part of the talk results from two cross-sectional behavioural studies, including >250 participants (healthy volunteers, unmedicated depressed patients, first degree relatives and remitted depressed patients), will be presented. Participants completed a rewarded physical effort task using a grip squeeze device, and motivational symptoms were assessed through questionnaires. Data were analysed using a hierarchical computational approach, with model parameters estimated in a Bayesian framework using sampling. In the non-clinical study (N=90), general depressive symptoms were associated with lower reward sensitivity, while anhedonia was related to a lack of willingness to engage in high effort challenges. In the clinical study (N=180), current or past depression was associated with a lower overall propensity to accept challenges, independent of reward or effort level. Preliminary results from studies examining the effect of L-Dopa on effort-based decisions in healthy and depressed volunteers (N=80) and patients with Parkinson’s disease with (N=30) and without depression (N=30) will be presented, with clear effects on effort-based decision making observed only in the latter. Ongoing work examining the impact of physical activity on motivational symptoms and effort-based decisions will be outlined briefly. These studies illuminate the cognitive and brain mechanisms contributing to depressive symptoms related to disrupted motivational processing, providing some clues to potential avenues for intervention strategies for these debilitating symptoms.

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Julia Sheffield
Department of Psychiatry & Behavioral Sciences and Psychopharmacology
Vanderbilt University Medical Research Center

Persecutory delusions are common and distressing psychotic symptoms that represent the extreme end of a paranoia continuum. Treatments for persecutory delusions require advancement. Understanding the cognitive mechanisms underlying paranoia and persecutory delusions can help identify novel targets. In this talk, Dr. Sheffield will present background on the predictive coding model of delusions and evidentiary support for elevated volatility priors as a contributor to delusion severity. Dr. Sheffield utilizes longitudinal and clinical trials designs to test the modifiability of volatility priors, and their neurobiological correlates, in order to determine whether they represent a viable treatment target.

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Avram Holmes
Department of Psychiatry
Robert Wood Johnson Medical School (RWJMS)

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 converging lines of research from my laboratory that aim to identify neurobiological markers of psychiatric illness. First, I will discuss our recent efforts 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 discuss a pending research project where we will seek to identify cellular and genetic contributors to the functioning of large-scale brain networks and characterize their relationship to longitudinal trajectories of clinical presentation in patients with affective and psychotic illnesses.

Aaron Heller
Department of Psychology
University of Miami

The specific way emotions ebb and flow in life differentiate someone who has depression from someone who do not. Given such a central role for emotion in well-being and psychopathology, it is critical that we understand what causes emotion in everyday life and whether these emotional causes tell us anything about mental health. It is especially important to understand what causes real-world emotion because to be able change emotion we need to understand what drives it in the first place. Using a combination of mobile tracking, experience sampling, and functional neuroimaging methods, I will present studies addressing the role that deviations from expectations play in driving naturalistic emotion, that deviations from expectations drive emotion differently in people who are depressed from those who are not, and that deviations from expectations are learning signals that help explain how individuals may develop mental health difficulties. This work suggests that how we form, update, and respond to real-world expectations play a key role in promoting well-being as well as preventing psychopathology.  

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Melissa Brotman
Neuroscience and Novel Therapeutics Unit
National Institute of Health

The goal of the Neuroscience and Novel Therapeutics Unit (NNT) is to develop and test psychological treatments for anxiety and irritability, two of the most common and impairing pediatric mental health problems. Despite their prevalence and public health impact, advances in treatment have stalled. To capitalize on this opportunity, NNT deploys an experimental medicine approach. First, we identify and probe mechanistically relevant behaviors and corresponding brain-based dysfunction. Second, we develop interventions modifying putative behavioral and neural targets integrating in vivo, mobile metrics. Our work implicates two psychological processes in both anxiety and irritability: (1) aberrant threat processing, and (2) impaired inhibitory control. Exposure therapy, a behavioral technique, engages both threat (e.g., salience network) and inhibition (e.g., executive control networks). Exposure therapy is a first line treatment for anxiety disorders. However, the neural mechanisms mediating improvement remain unknown. Critically, the clinical application beyond fear-based disorders has only recently been considered. Both clinically impairing irritability and anxiety are stimulus-evoked, high arousal, negative valence states, with differential downstream behavioral manifestations.

In this presentation, I will present two studies. First, in a large, unmedicated sample of youth with anxiety disorders and healthy controls, we identified specific brain-based regions associated with clinical improvement in anxiety (e.g., fronto-parietal regions), as well as subcortical circuitry (e.g., amygdala) showing sustained dysfunction following exposure therapy. Second, because like fear, anger is an acute stimulus driven emotional state, we conceptualized, tested, and demonstrated efficacy for a novel application of exposure therapy for youth with impairing irritability and anger. I will also share some early work where we are augmenting our work on brain-based behavioral therapeutics by developing, assessing, and validating digital mobile health applications.

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Christine Constantinople
NYU Center for Neural Science
New York University

Midbrain dopamine (DA) neurons are thought to be critical for reinforcement learning as well as motor control. A major outstanding question for the field is understanding how dopamine can satisfy these dual functions within the striatum, the input structure of the basal ganglia. I will describe our ongoing efforts to reconcile these dual functions of dopamine using rats performing a value-based decision-making task that includes reward- and movement-related events at distinct time points. Fiber photometry measurement of DA release in dorsomedial striatum revealed that only some event-aligned phasic DA signals were accompanied by movement, the amplitude of which predicted the vigor of upcoming contralateral movements. Phasic DA in the absence of movement signaled reward prediction errors (RPEs) and modulated rats’ behavior on subsequent trials. Electrophysiological recordings in the dorsomedial striatum showed trial-by-trial changes in firing rates at RPE-associated events, but not at movement-associated events. These data demonstrate DA-dependent plasticity and behavioral change following RPE on a trial-by-trial basis. To explain the dual role of DA in supporting movement and learning at distinct time points within a single trial, we hypothesized that acetylcholine’s (ACh) effect on striatal cell excitability may act as a gating mechanism. Fiber photometry measurement of ACh release in DMS revealed a unique pre-burst rise in ACh at movement initiation, and dips at RPE events, and optogenetic activation of ACh cells reliably initiated movements and impaired learning. These data suggest that the neuromodulator acetylcholine dynamically gates whether dopamine in the striatum is used for learning or moving on a moment-by-moment basis.

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Nili Solomonov
Department of Psychiatry
Weil Cornell Medical College

Psychotherapy is effective in reducing depression. Yet, 40-60% of patients remain symptomatic at treatment end. Identifying brain‐based and clinical mechanisms of action that underlie symptomatic improvement can guide the development of neuroscience‐informed, scalable and effective interventions. Dr. Solomonov will present studies conducted using imaging and computational approaches to detect mechanisms of action and improve psychotherapies. Studies will focus on understudied populations, including middle‐aged and older adults, mothers with postpartum depression and healthcare workers suffering from depression and anxiety.ffort and critical dynamics in EEG data while participants perform various levels of the N-back working memory task.

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Peter Manza
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
National Institute of Health

The addictiveness of stimulant drugs such as methylphenidate (MP) depends crucially on how fast they raise dopamine in the brain. Yet the brain circuits underlying the rate dependency to drug reward have not been resolved. We used simultaneous PET-fMRI to link dynamic changes in brain dopamine signaling, functional brain activity/connectivity, and the self-reported experience of ‘high’ in 20 healthy adults receiving MP at different speeds: slow (oral 60mg) and fast (intravenous-IV 0.25mg/kg) doses in a double-blind, counterbalanced, placebo-controlled study. We estimated speed of striatal dopamine increases to oral and IV MP and then tested where brain activity/connectivity was associated with slow and fast dopamine kinetics. The two administrations produced dramatically different effects on brain functional activation and connectivity despite a comparable overall magnitude of dopamine increases. These data demonstrate how fast dopamine increases generate unique effects on brain function that have relevance for the addictive potential of drugs.

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