The Depressed Decision Maker: The Application of Decision Science to Psychopathology

Is decision making impaired in mental illness populations? Can behavioral economics provide insight into clinical psychology? The present project addresses these broad questions through three studies. In the first study, two meta-analyses were conducted of experiments that used the Iowa Gambling Task (IGT) to assess value based decision making in populations with mental illness. In the first meta-analysis (63 studies, combined N = 4,978), we compared IGT performance in healthy populations and populations with mental illness. In the second meta-analysis (40 studies, combined N = 1,813), we examined raw IGT performance scores as a function of type of mental illness. The first meta-analysis demonstrated that individuals with mental illness performed significantly worse than did healthy control individuals. The second meta-analysis demonstrated no performance differences based on type of mental illness. Impairment on the IGT, however, could indicate effects from several different decision processes. Accordingly, in the second study, using multiple decision tasks we explored different aspects of decision making in a single group that exhibited reliable effects in the meta-analysis, major depressive disorder. The second study answers three questions. First, how does decision making differ in clinically depressed individuals across a range of decision tasks? Second, where are the largest differences between clinically depressed and non-depressed individuals? And finally, how well can decision task performance discriminate depressed individuals from healthy controls? Depressed individuals\u27 decision-making was significantly different across a range of decision tasks, but impaired learning and pessimism bias showed the strongest behavioral signature of depression. Decision tasks significantly predict depression, but are far outperformed by self-report measures as diagnostic tools. Overall, results suggest decision tasks are better suited to identify specific impaired processes rather than for diagnostic prediction. This study suggested depression is associated with impaired reward and punishment processing, but what are the underlying causes behind these deficits? In the third study, we performed a detailed analysis of reward and punishment learning in clinically depressed individuals, quantifying choice behavior by fitting reinforcement learning models. The results suggest that depression is characterized by hyposensitivity to reward. The reinforcement learning models show that depressed individuals engage habit-oriented model-free learning strategies in contrast to the goal-oriented model-based strategies engaged by healthy controls. Overall the three studies demonstrate how interdisciplinary research combining decision science and clinical psychology can help to better understand mental illness

Fintech for Psychological and Financial Resilience: Determinants of Financial Data Sharing Behavior for Individuals with Bipolar Disorder

Financial stability is a key challenge for individuals with bipolar disorder, a serious mental illness requiring life-long management. Symptomatic periods often lead to poor financial decision-making, including compulsive spending and risky behaviors. Widespread consumer adoption of financial technologies ("fintech") has accelerated in recent years, with numerous consumer-centric applications providing insight into personal financial behavior in exchange for access to financial data. We believe these technologies can be applied to meaningfully support individual resilience in this population and, potentially, the resilience of families and surrounding networks of care. However, little is known about this population's unique perspectives, expectations, or privacy preferences related to financial data sharing for these purposes. To this end, we deployed an online survey (N=480) to assess the privacy expectations of individuals with bipolar disorder surrounding the use of financial data as an early-warning indicator of symptoms. A factorial vignette design allowed us to vary vignette dimensions across the granularity of financial data types, context of potential data use, and recipient of data insights. This exploratory analysis demonstrates that individuals are most comfortable sharing financial data when they were the only party to receive algorithmically-generated insights, while factors such as context of use and granularity of data types were less significant. Individuals who were most willing to engage creditors or other financial technologies for assistance were significantly more willing to share with family members and clinicians.Comment: 4 pages, 1 figure, conference workshop paper (DIS 2023 - Designing for and Reflecting upon Resilience in Health and Wellbeing

Systemic neutralization of IL-17A significantly reduces breast cancer associated metastasis in arthritic mice by reducing CXCL12/SDF-1 expression in the metastatic niches

BACKGROUND: IL-17A is a pro-inflammatory cytokine that is normally associated with autoimmune arthritis and other pro-inflammatory conditions. Recently, IL-17A has emerged as a critical factor in enhancing breast cancer (BC)-associated metastases. We generated immune competent arthritic mouse models that develop spontaneous BC-associated bone and lung metastasis. Using these models, we have previously shown that neutralization of IL-17A resulted in significant reduction in metastasis. However, the underlying mechanism/s remains unknown. METHODS: We have utilized two previously published mouse models for this study: 1) the pro-arthritic mouse model (designated SKG) injected with metastatic BC cell line (4T1) in the mammary fat pad, and 2) the PyV MT mice that develop spontaneous mammary gland tumors injected with type II collagen to induce autoimmune arthritis. Mice were treated with anti-IL-17A neutralizing antibody and monitored for metastasis and assessed for pro-inflammatory cytokines and chemokines associated with BC-associated metastasis. RESULTS: We first corroborate our previous finding that in vivo neutralization of IL-17A significantly reduced metastasis to the bones and lungs in both models. Next, we report that treatment with anti-IL17A antibody significantly reduced the expression of a key chemokine, CXCL12 (also known as stromal derived factor-1 (SDF - 1)) in the bones and lungs of treated mice. CXCL12 is a ligand for CXCR4 (expressed on BC cells) and their interaction is known to be critical for metastasis. Interestingly, levels of CXCR4 in the tumor remained unchanged with treatment. Consequently, protein lysates derived from the bones and lungs of treated mice were significantly less chemotactic for the BC cells than lysates from untreated mice; and addition of exogenous SDF-1 to the lysates from treated mice completely restored BC cell migration. In addition, cytokines such as IL-6 and M-CSF were significantly reduced in the lung and bone lysates following treatment. The data presented suggests that systemic neutralization of IL-17A can block the CXCR4/SDF-1 signaling pathway by reducing the expression of SDF-1 in the metastatic niches and significantly reducing metastasis in both mouse models. CONCLUSION: In our model, neutralization of IL-17A regulates SDF-1 expression in the metastatic niches either directly or indirectly via reducing levels of IL-6 and M-CSF

The Relation Between Cognitive Function and Cerebral Vasodilatory Reactivity in Young Adults with Major Depressive Disorder

Major depressive disorder (MDD) has been associated with an elevated risk of developing neurocognitive diseases (e.g., dementia). Although the precise neurobiological mechanisms remain incompletely understood, cerebrovascular dysfunction is thought to directly contribute, at least in part, to impairments in cognitive function. Cerebral vasodilatory reactivity to a hypercapnic stimulus is blunted in older adults with MDD compared to age-matched non-depressed adults. Further, impaired cerebral vasodilation has been linked to reduced cognitive activity in older adults with depression. However, to date, limited studies have examined the relation between cognitive function and cerebrovascular function in otherwise healthy young adults with MDD. PURPOSE: We tested the hypothesis that greater hypercapnia-induced cerebral vasodilation would be related to greater fluid cognitive ability (i.e., the capacity to process and integrate new information) in young adults with MDD. METHODS: Ten adults with MDD (non-medicated; age: 22±2 yrs: body mass index: 22.8±4.5 kg/m2; education level: all enrolled in a four-year university) participated. Cognitive function was assessed via the NIH Toolbox Cognitive Function Battery (iPad). A composite fluid cognitive ability score was derived from the specific tests within the battery that measure fluid ability [e.g., Flanker, Dimensional Change Cart Sort (DCCS)]; an age-correct standard T-score of 100 indicates ability that is average compared with national data. Beat-to-beat mean arterial pressure (MAP; finger photoplethysmography), middle cerebral artery blood velocity (MCAv; transcranial Doppler ultrasound), and end-tidal carbon dioxide concentration (PETCO2; capnograph) were continuously measured during normocapnic baseline and during rebreathing-induced hypercapnia. The hypercapnia-induced (∆PETCO2=9 mmHg) increase in cerebral vascular conductance index (∆CVCi=MCAv/MAP) was used as an index of cerebral vasodilatory reactivity. RESULTS: Hypercapnia elicited an increase in CVCi in all subjects (mean: 30±12%; range: 18-60%). The age-corrected composite fluid cognitive ability standard score was 100±15 (range: 79-119). The increase in CVCi was not related to fluid cognitive ability (slope=-0.12±0.3; r2=0.02, p=0.67). In addition, the increase in CVCi was not related to either the age-corrected standard score for the Flanker task (slope=-0.38±0.4; r2=0.12, p=0.32) or for the DCCS task (slope=0.09±0.3; r2=0.02, p=0.72), both of which specifically measure executive function. CONCLUSION: These preliminary data suggest that cerebral vasodilatory reactivity to a hypercapnic stimulus is not related to fluid cognitive function in otherwise healthy college-aged adults with MDD

The Depressed Decision-Maker: How Value Based Decision Making Differs in Major Depressive Disorder

Background: Depression is clinically characterized by obvious changes in decision-making that cause distress and impairment. Though several studies suggest impairments in depressed individuals in single tasks, there has been no systematic investigation of decision-making in depression across tasks. Methods: We compare participants diagnosed with MDD (n = 64) to healthy controls (n=64) using a comprehensive battery of nine value-based decision-making tasks which yield ten distinct measures. Results: MDD participants performed worse on punishment (d = -.54) and reward learning tasks (d = .38), expressed more pessimistic predictions regarding winning money in the study (d = -.47) and were less willing to wait in a persistence task (d = -.39). Performance on learning, expectation, and persistence tasks each loaded on unique dimensions in a factor analysis and punishment learning and future expectations each accounted for unique variance in predicting depressed status. Decision-making performance alone could predict depressed status out-of-sample with 72% accuracy. Limitation: The findings are limited to MDD patients ranging between moderate to severe depression and the effects of medication could not be accounted for due to the cross sectional nature of the study design. Conclusion: These results confirm hints from single task studies that depression has the strongest effects on reinforcement learning and expectations about the future. Our results highlight decision processes that are the impacted in major depression, and whose further study could lead to a more detailed computational understanding of distinct facets of this heterogeneous disorder

Reward and punishment reversal learning in major depressive disorder

Depression has been associated with impaired reward and punishment processing, but the specific nature of these deficits is less understood and still widely debated. We analyzed reinforcement-based decision-making in individuals diagnosed with major depressive disorder (MDD) to identify the specific decision mechanisms contributing to poorer performance. Individuals with MDD (n = 64) and matched healthy controls (n = 64) performed a probabilistic reversal learning task in which they used feedback to identify which of two stimuli had the highest probability of reward (reward condition) or lowest probability of punishment (punishment condition). Learning differences were characterized using a hierarchical Bayesian reinforcement learning model. While both groups showed reinforcement learning-like behavior, depressed individuals made fewer optimal choices and adjusted more slowly to reversals in both the reward and punishment conditions. Our computational modeling analysis found that depressed individuals showed lower learning rates and, to a lesser extent, lower value sensitivity in both the reward and punishment conditions. Learning rates also predicted depression more accurately than simple performance metrics. These results demonstrate that depression is characterized by a hyposensitivity to positive outcomes, which influences the rate at which depressed individuals learn from feedback, but not a hypersensitivity to negative outcomes as has previously been suggested. Additionally, we demonstrate that computational modeling provides a more precise characterization of the dynamics contributing to these learning deficits, and offers stronger insights into the mechanistic processes affected by depression

Use of ecological momentary assessment to detect variability in mood, sleep and stress in bipolar disorder

Abstract Objective Our aim was to study within-person variability in mood, cognition, energy, and impulsivity measured in an Ecological Momentary Assessment paradigm in bipolar disorder by using modern statistical techniques. Exploratory analyses tested the relationship between bipolar disorder symptoms and hours of sleep, and levels of pain, social and task-based stress. We report an analysis of data from a two-arm, parallel group study (bipolar disorder group N = 10 and healthy control group N = 10, with 70% completion rate of 14-day surveys). Surveys of bipolar disorder symptoms, social stressors and sleep hours were completed on a smartphone at unexpected times in an Ecological Momentary Assessment paradigm twice a day. Multi-level models adjusted for potential subject heterogeneity were adopted to test the difference between the bipolar disorder and health control groups. Results Within-person variability of mood, energy, speed of thoughts, impulsivity, pain and perception of skill of tasks was significantly higher in the bipolar disorder group compared to health controls. Elevated bipolar disorder symptom domains in the evening were associated with reduced sleep time that night. Stressors were associated with worsening of bipolar disorder symptoms. Detection of symptoms when an individual is experiencing difficulty allows personalized, focused interventions.http://deepblue.lib.umich.edu/bitstream/2027.42/173794/1/13104_2019_Article_4834.pd

Total Sleep Time and Kynurenine Metabolism Associated with Mood Symptom Severity in Bipolar Disorder

Objective: Chronic, low‐level inflammation is associated with symptomatic bipolar disorder (BD) and with chronic insomnia. Disrupted sleep is a feature of episodes of both mania and depression. We examined the effect of neopterin, a marker of cellular immune activation, and kynurenine (KYN), an inflammatory byproduct of the serotonin pathway, on the association between total sleep time and depression severity in BD. Method: Twenty‐one symptomatic BD participants and 28 healthy controls (HC) were recruited and followed during usual clinical care. At baseline and after symptomatic recovery, total sleep time was objectively measured with actigraphy for 1 week and blood plasma was collected to measure the serotonin precursor tryptophan (TRP), KYN, the KYN/TRP ratio, and neopterin levels. Statistical analyses were conducted using chi‐square, independent t tests and hierarchical linear multiple regression models. Results: Total sleep time was correlated positively with depressive severity and negatively with manic severity. TRP was significantly reduced in BD participants compared to HC. KYN, TRP, and the KYN/TRP ratio were associated with depressive severity when total sleep time and body mass index (BMI) were included in the model. The KYN/TRP ratio trended towards a negative association with mania symptoms, controlling for BMI and total sleep time, in acutely symptomatic BD participants. Neopterin was not associated with sleep or mood severity. After usual clinical care, BD participants showed significantly decreased clinical symptoms but no significant differences in sleep phenotype or biomarkers. Conclusion: Inflammation, sleep, and mood are closely intertwined. Future research into the effect of inflammation on sleep in BD may lead to clinical markers of outcome

Total Sleep Time and Kynurenine Metabolism Associated with Mood Symptom Severity in Bipolar Disorder

Objective: Chronic, low‐level inflammation is associated with symptomatic bipolar disorder (BD) and with chronic insomnia. Disrupted sleep is a feature of episodes of both mania and depression. We examined the effect of neopterin, a marker of cellular immune activation, and kynurenine (KYN), an inflammatory byproduct of the serotonin pathway, on the association between total sleep time and depression severity in BD. Method: Twenty‐one symptomatic BD participants and 28 healthy controls (HC) were recruited and followed during usual clinical care. At baseline and after symptomatic recovery, total sleep time was objectively measured with actigraphy for 1 week and blood plasma was collected to measure the serotonin precursor tryptophan (TRP), KYN, the KYN/TRP ratio, and neopterin levels. Statistical analyses were conducted using chi‐square, independent t tests and hierarchical linear multiple regression models. Results: Total sleep time was correlated positively with depressive severity and negatively with manic severity. TRP was significantly reduced in BD participants compared to HC. KYN, TRP, and the KYN/TRP ratio were associated with depressive severity when total sleep time and body mass index (BMI) were included in the model. The KYN/TRP ratio trended towards a negative association with mania symptoms, controlling for BMI and total sleep time, in acutely symptomatic BD participants. Neopterin was not associated with sleep or mood severity. After usual clinical care, BD participants showed significantly decreased clinical symptoms but no significant differences in sleep phenotype or biomarkers. Conclusion: Inflammation, sleep, and mood are closely intertwined. Future research into the effect of inflammation on sleep in BD may lead to clinical markers of outcome

Indomethacin enhances anti-tumor efficacy of a MUC1 peptide vaccine against breast cancer in MUC1 transgenic mice.

In recent years, vaccines against tumor antigens have shown potential for combating invasive cancers, including primary tumors and metastatic lesions. This is particularly pertinent for breast cancer, which is the second-leading cause of cancer-related death in women. MUC1 is a glycoprotein that is normally expressed on glandular epithelium, but is overexpressed and under-glycosylated in most human cancers, including the majority of breast cancers. This under-glycosylation exposes the MUC1 protein core on the tumor-associated form of the protein. We have previously shown that a vaccine consisting of MUC1 core peptides stimulates a tumor-specific immune response. However, this immune response is dampened by the immunosuppressive microenvironment within breast tumors. Thus, in the present study, we investigated the effectiveness of MUC1 vaccination in combination with four different drugs that inhibit different components of the COX pathway: indomethacin (COX-1 and COX-2 inhibitor), celecoxib (COX-2 inhibitor), 1-methyl tryptophan (indoleamine 2,3 dioxygenase inhibitor), and AH6809 (prostaglandin E2 receptor antagonist). These treatment regimens were explored for the treatment of orthotopic MUC1-expressing breast tumors in mice transgenic for human MUC1. We found that the combination of vaccine and indomethacin resulted in a significant reduction in tumor burden. Indomethacin did not increase tumor-specific immune responses over vaccine alone, but rather appeared to reduce the proliferation and increase apoptosis of tumor cells, thus rendering them susceptible to immune cell killing