- Mood as Momentum: Affective Instability and Daily Experience
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Neurocomputational Studies of Mood-Related Momentum Dynamics Linking Reward Learning, Valuation and Responsivity
PI: Alexandre Y. Dombrovski
CO-I: Eran P. Eldar, Michael N. HallquistSupported by the National Institute of Mental Health
PUBLIC HEALTH RELEVANCE:
Many people with mental disorders suffer from drastic mood swings, which can escalate to the point of despair and even attempting suicide. This work will help understand the brain and behavior processes that underlie mood swings; if successful, this study will help clinicians predict mood swings using phone apps and wearable sensors.
PROJECT SUMMARY:
The RDoC Positive Valence Systems (PVS) encompass motivational processes underlying normal reward- guided behavior and its alterations in many mental disorders. Yet, the theoretical links between the PVS constructs of Reward Responsiveness, Learning, and Valuation remain under-specified. Hence, our goal is to unify them under a new model of computational reinforcement learning with momentum dynamics wherein momentum reflects whether recent outcomes have generally exceeded or fallen short of our expectations, signaling an improving or worsening reward rate. Momentum is closely linked with mood and our model offers new insights into the interplay of mood and reward learning. Thus, we are seeking to provide a mechanistic account of transdiagnostic mood dynamics and affective instability (AI), a dimension of psychopathology seen in depression, anxiety, eating and personality disorders, and suicidal behavior. While ecological momentary assessment (EMA) studies of AI have shown how mood changes over time in mental illness, to date we have no formal model that can explain why it changes thus. On the other hand, lab-based experimental studies have used tools from cognitive neuroscience to explore potential neural mechanisms of affective instability. Though promising, lab studies are too brief to capture the temporal dynamics of AI in psychopathology, which typically unfold over hours or days. Here, we overcome the limitations of EMA and laboratory studies to date by bringing together key elements of both within a framework grounded in reinforcement learning and dynamical systems theory. To this end, we will combine mood tracking with learning experiments carried out in daily life over 4 weeks, concurrently recording neurophysiological signals via wearable heart rate and electroencephalography sensors. We have shown that this platform captures the behavioral and physiological effects of positive and negative outcomes, and that physiological learning signals predict day-to-day changes in subjects’ mood. We will use this platform to examine PVS constructs and AI in individuals sampled from the community (Sample 1, n = 300) and in a clinical sample of individuals with borderline personality (Sample 2, n = 150) recruited from two ongoing studies in Pittsburgh and State College, PA. In our earlier study, mood induction that impacted reward valuation also impacted striatal reward responsiveness. Here, we will investigate the cortico-striatal substrates of momentum dynamics in relation to real-life mood fluctuations by combining mobile longitudinal assessment with model-based fMRI. During the scan, subjects will choose between experimental stimuli they previously encountered in different moods. This will allow us to examine how mood impacts neural valuation and learning signals and how learning signals shape future mood. Our interdisciplinary team has expertise in computational modeling of mood and its integration with EMA, physiology and imaging (Eldar), computational model-augmented functional imaging and EMA in clinical populations (Dombrovski, Hallquist), and neuroimaging methods (Hallquist).
- Suicidal Behavior in Borderline Personality
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Psychobiology of Suicidal Behavior in Borderline Personality Disorder
PI: Alexandre Y. Dombrovski
Supported by the National Institute of Mental Health
PUBLIC HEALTH RELEVANCE:
Clinicians caring for patients with borderline personality disorder (BPD) are faced with a high rate of suicide attempts (70% in our sample) and non-suicidal self-injury. Against this background, it is difficult to judge which patients are at the highest risk for dying by suicide. This study seeks to describe emotional, behavioral, and brain signatures of medically serious suicidal behavior in BPD, distinguishing it from less severe forms.
Learn More: Interpersonal dysfunction in borderline personality: a decision neuroscience perspective
A Clinician's Perspective:
The longitudinal study represents a decades-long effort examining psychosocial factors in subjects with characteristics of BPD. Participants are recruited from outpatient and inpatient treatment settings, as well as from the greater Pittsburgh community. Those interested in joining the study undergo a series of diagnostic interviews and self-reports to determine eligibility, administered by a clinician. Once enrolled, participants work with a clinician to compile baseline data on demographic information such as family history, education, and work history, as well as clinical data related to suicidal behavior, impulsivity, aggression, quality of life, and other relevant areas.
Contact is maintained over the ensuing years through follow-up interviews. Participants touch base with the study at regular intervals to report updates in demographics, treatment, socioeconomic factors, and other life changes, while also sharing experiences related to suicidality, impulsivity, and further behavioral indicators. Brief diagnostic assessments are conducted at follow-up to track changes in BPD and other comorbid presentations.
The larger study is in its fourth decade and continues to actively enroll new participants, many of whom have passed follow-up milestones of 10, 20, even 30-plus years. Aside from the immediate material compensation, those who remain active say they stay motivated by contributing to a large body of work that aims to increase understanding of BPD and its outcomes. Through maintaining long-term contact, many also find some benefit in tracking and reflecting on their own changes over time.
PROJECT SUMMARY:
This is a longitudinal study of suicidal behavior in >300 people with borderline personality disorder (BPD), >60% of whom have attempted suicide. Our earlier studies focused on the pathway from interpersonal experiences to suicidal behavior, integrating three timescales: (1) naturalistic prediction of suicidal behavior over years; (2) prediction of suicidal ideation over days; and (3) experimental interrogation of decision processes over minutes. Taken together, our findings show that the emergence of suicidal ideation from interpersonal conflict is catalyzed by internalizing psychopathology, whereas the transition to suicidal behavior is facilitated by externalizing psychopathology and neurobehavioral alterations in decision-making.
Building on this work, we will (1) examine interpersonal traits and specific facets that cause decompensation in BPD and facilitate transitions in the suicidal process on a timescale of years, (2) improve individualized prediction of emotion dysregulation and suicidal thoughts on a timescale of hours to days, and (3) advance a neurocomputational account of the failed search for solutions in a crisis on a timescale of minutes. Our team has expertise in suicide research (Alex Dombrovski, PI), borderline personality and experience sampling (co-investigators Michael Hallquist [UNC] and Aidan Wright [U Michigan], and consultant Pilkonis), decision neuroscience (Dombrovski and Hallquist), and quantitative methods including machine learning (Hallquist and Wright, consultant Nick Jacobson, Dartmouth).
Innovations include a focus on clinically salient facets of interpersonal traits, integration of intensive and extended ecological momentary assessment (EMA) with passive sensing, an investigation of dynamic decision-making under high cognitive load supported by an original computational model, and a recently developed and validated multi-level approach to functional magnetic resonance imaging (fMRI) analysis. Clinically, understanding the suicidogenic effects of interpersonal trait facets and elaboration of personalized models of suicide risk will advance suicide prediction and development of just-in-time interventions. Expected results will advance the field of suicide research by unifying conceptual models of the suicidal process with hierarchical dimensional models of psychopathology, identifying general vs. person-specific suicidogenic processes, and elucidating decision-making under cognitive demands representative of the suicidal crisis.
- Exploration and Late-Life Suicidal Behavior
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Reward Learning in Late-Life Suicidal Behavior
PI: Alexandre Y. Dombrovski
Supported by the National Institute of Mental Health
US suicide rates are rising, and clinicians need to better understand why some people and not others go on from thinking about suicide to acting on their thoughts. We will test the idea that people at risk for suicide fail to make good decisions in a crisis and will investigate why this may happen with decision experiments, brain imaging and mathematical models of decision-making.
Learn More: Search for solutions, learning, simulation, and choice processes in suicidal behavior
PROJECT SUMMARY:
We have only a limited understanding of why some people, but not others, progress from contemplating to attempting suicide. In the past, we have shown that depressed older adults whose decision-making is impaired are more likely to progress from suicidal ideation to action. Specifically, using decision experiments, computational modeling, and functional magnetic resonance imaging (fMRI), we have found replicable deficits in learning and choice processes paralleled by altered ventromedial and dorsolateral prefrontal abstract learning signals.
Here, we will extend these findings by examining how people at risk for suicide make decisions under cognitive and emotional demands that are more representative of the suicidal crisis. In our computational framework these demands include (i) a high information load and (ii) constraints on information processing imposed by time pressure and impending threats. We have developed and validated new experimental and computational methods for studying information-processing bottlenecks during decision-making. Specifically, our reinforcement learning computational model applied to behavioral and neuroimaging data enables us to examine how people use their limited resources to make good decisions under high information load. Our preliminary studies show that decision-making in this context (i) relies on resource-rational strategies for managing information load, (ii) is subserved by dorsal attention and cingulo-opercular networks, (iii) is likely disrupted in attempted suicide, (iv) is paralleled by abnormal dorsal attention network responses to information load.
We will thus test the general hypothesis that people at risk for suicide are prone to information-processing bottlenecks arising from alterations in these cortical networks. We will perform decision experiments and cognitive computational models (Aim 1) in a discovery sample and a non-overlapping replication sample (n = 200 each) to ensure that findings are robust to the clinical and cognitive heterogeneity of suicidal behavior. Both samples will include individuals maximally representative of suicide victims, namely older depressed suicide attempters, about half of whom survived near-lethal attempts. Functional neuroimaging experiments manipulating information load will interrogate the neurocomputational dynamics of the dorsal attention network and cingulo-opercular network during decision-making in one sample (n = 200, Aim 2). A careful characterization of psychopathology, personality, cognition, psychotropic exposure and brain damage from suicide attempts will allow us to control for key confounds.
- Personality and Neural Correlates of Defensive Action
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Dispositional Negativity and the Pavlovian Control of Active and Passive Defensive Behavior
PI: Timothy A. Allen
Supported by the National Institute of Mental Health
PUBLIC HEALTH RELEVANCE:
Dispositional negativity is a transdiagnostic risk factor for psychopathology defined by a heightened reactivity to threat and punishment that manifests in the form of frequent and intense negative affect. The present application leverages the capabilities of modern computational neuroscience to examine the latent decision processes that contribute to maladaptive threat responding in individuals with elevated dispositional negativity. Understanding the neurocomputational basis of dispositional negativity will help to facilitate the development of behavioral and computational indices that can be targeted for modification in novel transdiagnostic interventions.
PROJECT SUMMARY:
Neuroticism, or the tendency to experience frequent and intense negative affect, is a core feature of psychopathology that is thought to reflect a hyperresponsiveness to uncertainty, threat, and punishment. While neuroticism is responsible for tremendous personal, social, and economic burden, little is currently known about its underlying neurocomputational mechanisms.
Computationally, responses to threat and punishment are guided by multiple learning systems. A Pavlovian mechanism generates rapid, fixed responses to specific stimuli, whereas an instrumental mechanism uses the outcome of previous behavioral responses to flexibly guide future decision-making. In the PANDA study, we explore the possibility that elevated neuroticism reflects a Pavlovian predominance over instrumental behavior in aversive contexts, leading to disadvantageous defensive responses.
To investigate this question, we integrate behavioral experiments, physiological measures, computational modeling, and functional magnetic resonance imaging (fMRI) methods to interrogate the processes that govern decision-making in aversive contexts. Ultimately, the goals of this work are to generate novel insights into the mechanisms that drive individual differences in neuroticism and identify clinical targets for transdiagnostic interventions aimed at reducing negative affect.
- Cortical Oscillatory Dynamics and Decision Deficits in Suicidal Behavior
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Cortical Oscillatory Dynamics and Decision Deficits in Suicidal Behavior
PI: Aliona Tsypes
PUBLIC HEALTH RELEVANCE:
When faced with a crisis, some people but not others transition from thinking about suicide to acting on these thoughts. This project will use a formal theoretical framework combined with electroencephalography to test the hypothesis that individuals at risk for suicide become cognitively overwhelmed under challenging life circumstances and make poor choices as a result. The anticipated impact of this proposal includes the identification of more precise targets for cognitive clinical interventions.
PROJECT SUMMARY:
Suicide is a complex issue and understanding why people attempt suicide is difficult. Despite many research efforts, progress in understanding and preventing suicide has been slow for several reasons. First, this may be due in part to the limitations of current theories that rely on verbal descriptions of mental processes rather than mathematical models. Frameworks grounded in learning theory and decision neuroscience, such as reinforcement learning, provide a promising approach for uncovering the cognitive and decision-making processes involved in suicide.
Second, when someone is considering how to act in a crisis, they are faced with increased demands on decision-making. These demands are imposed by the need to consider multiple decision options, high levels of uncertainty, and limited time for decision-making. Current neural models of suicide risk do not take into account the dynamic nature of decision-making in real-life situations. In contrast, electrical recordings of brain activity combined with reinforcement learning may help to better understand the brain mechanisms involved in real-time decision-making.
This project uses a reinforcement learning task with a validated computational model to examine the decision-making processes involved in suicide. It also examines the neural encoding of reinforcement history in attempted suicide by focusing on the alpha, beta, and theta oscillations involved in working memory and cognitive control. The ways in which mood, stress, and neural activity influence behavior are also being investigated. We are recruiting a total of 120 adults with varying histories of suicidal thinking and attempts from inpatient, outpatient, and community settings.
Selected Publications