Browsing by Subject "Suicidality"

Now showing 1 - 4 of 4
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    Atypical Cortical Activation during Risky Decision-making in Disruptive Behavior Disordered Youth with Histories of Suicidal Ideation
    (Elsevier, 2020) Dir, Allyson L.; Allebach, Christian L.; Hummer, Tom A.; Adams, Zachary; Aalsma, Matthew C.; Finn, Peter R.; Nurnberger, John I.; Hulvershorn, Leslie A.; Psychiatry, School of Medicine
    Background: Suicidality is a leading cause of death among adolescents. In addition to other psychiatric conditions, youths with attention-deficit/hyperactivity disorder (ADHD) and disruptive behavior disorders (DBDs) are at heightened risk for suicide. Decision-making deficits are a hallmark symptom of ADHD and DBDs and are also implicated in suicidal behavior. We examined behavioral and neural differences in decision making among youths with ADHD and DBDs with (SI+) and without (SI-) histories of suicidal ideation. Methods: The Balloon Analog Risk Task, a risky decision-making task, was completed by 57 youths with ADHD and DBDs (38% SI+) during functional magnetic resonance imaging. Mean stop wager (mean wager at which youths bank money) was the primary measure of risk taking. We conducted whole-brain and region-of-interest analyses in the anterior cingulate cortex and orbitofrontal cortex (OFC) during choice (win vs. inflate) and outcome (inflate vs. explode) contrasts using parametric modulators accounting for probability of balloon explosion. Results: There were no differences between SI+ and SI- youths in Balloon Analog Risk Task performance. SI+ youths showed decreasing activation in the right medial frontal gyrus when choosing inflate as explosion probability increased compared with SI- youths. During explosions, SI- youths showed increasing activation in the left OFC as explosions became more likely. SI+ showed increasing left medial OFC activity in response to inflations as explosion probability increased. Conclusions: SI+ youths may show heightened sensitivity to immediate reward and decreased sensitivity to potential loss as evidenced by medial frontal gyrus activity. OFC findings suggest that SI+ youths may be drawn to reward even when there is high probability of loss.
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    Defining Suicidal Thought and Behavior Phenotypes for Genetic Studies
    (medRxiv, 2024-07-29) Monson, Eric T.; Colbert, Sarah M. C.; Andreassen, Ole A.; Ayinde, Olatunde O.; Bejan, Cosmin A.; Ceja, Zuriel; Coon, Hilary; DiBlasi, Emily; Izotova, Anastasia; Kaufman, Erin A.; Koromina, Maria; Myung, Woojae; Nurnberger, John I., Jr.; Serretti, Alessandro; Smoller, Jordan W.; Stein, Murray B.; Zai, Clement C.; Suicide Working Group of the Psychiatric Genomics Consortium; Aslan, Mihaela; Barr, Peter B.; Bigdeli, Tim B.; Harvey, Philip D.; Kimbrel, Nathan A.; Patel, Pujan R.; Cooperative Studies Program (CSP) #572; Ruderfer, Douglas; Docherty, Anna R.; Mullins, Niamh; Mann, J. John; Psychiatry, School of Medicine
    Background: Standardized definitions of suicidality phenotypes, including suicidal ideation (SI), attempt (SA), and death (SD) are a critical step towards improving understanding and comparison of results in suicide research. The complexity of suicidality contributes to heterogeneity in phenotype definitions, impeding evaluation of clinical and genetic risk factors across studies and efforts to combine samples within consortia. Here, we present expert and data-supported recommendations for defining suicidality and control phenotypes to facilitate merging current/legacy samples with definition variability and aid future sample creation. Methods: A subgroup of clinician researchers and experts from the Suicide Workgroup of the Psychiatric Genomics Consortium (PGC) reviewed existing PGC definitions for SI, SA, SD, and control groups and generated preliminary consensus guidelines for instrument-derived and international classification of disease (ICD) data. ICD lists were validated in two independent datasets (N = 9,151 and 12,394). Results: Recommendations are provided for evaluated instruments for SA and SI, emphasizing selection of lifetime measures phenotype-specific wording. Recommendations are also provided for defining SI and SD from ICD data. As the SA ICD definition is complex, SA code list recommendations were validated against instrument results with sensitivity (range = 15.4% to 80.6%), specificity (range = 67.6% to 97.4%), and positive predictive values (range = 0.59-0.93) reported. Conclusions: Best-practice guidelines are presented for the use of existing information to define SI/SA/SD in consortia research. These proposed definitions are expected to facilitate more homogeneous data aggregation for genetic and multisite studies. Future research should involve refinement, improved generalizability, and validation in diverse populations.
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    Polyphenic risk score shows robust predictive ability for long-term future suicidality
    (Springer, 2022) Cheng, M.; Roseberry, K.; Choi, Y.; Quast, L.; Gaines, M.; Sandusky, G.; Kline, J.A.; Bogdan, P.; Niculescu, A.B.; Psychiatry, School of Medicine
    Suicides are preventable tragedies, if risk factors are tracked and mitigated. We had previously developed a new quantitative suicidality risk assessment instrument (Convergent Functional Information for Suicidality, CFI-S), which is in essence a simple polyphenic risk score, and deployed it in a busy urban hospital Emergency Department, in a naturalistic cohort of consecutive patients. We report a four years follow-up of that population (n = 482). Overall, the single administration of the CFI-S was significantly predictive of suicidality over the ensuing 4 years (occurrence- ROC AUC 80%, severity- Pearson correlation 0.44, imminence-Cox regression Hazard Ratio 1.33). The best predictive single phenes (phenotypic items) were feeling useless (not needed), a past history of suicidality, and social isolation. We next used machine learning approaches to enhance the predictive ability of CFI-S. We divided the population into a discovery cohort (n = 255) and testing cohort (n = 227), and developed a deep neural network algorithm that showed increased accuracy for predicting risk of future suicidality (increasing the ROC AUC from 80 to 90%), as well as a similarity network classifier for visualizing patient’s risk. We propose that the widespread use of CFI-S for screening purposes, with or without machine learning enhancements, can boost suicidality prevention efforts. This study also identified as top risk factors for suicidality addressable social determinants.
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    Polyphenic risk score shows robust predictive ability for long-term future suicidality
    (Discover Mental Health, 2022-06-13) Cheng, M; Roseberry, Kyle; Choi, Y; Quast, L; Gaines, Madelynn; Sandusky, George; Kline, JA; Bogdan, Paul; Niculescu, Alexander
    Suicides are preventable tragedies, if risk factors are tracked and mitigated. We had previously developed a new quantitative suicidality risk assessment instrument (Convergent Functional Information for Suicidality, CFI-S), which is in essence a simple polyphenic risk score, and deployed it in a busy urban hospital Emergency Department, in a naturalistic cohort of consecutive patients. We report a four years follow-up of that population (n = 482). Overall, the single administration of the CFI-S was significantly predictive of suicidality over the ensuing 4 years (occurrence- ROC AUC 80%, severity- Pearson correlation 0.44, imminence-Cox regression Hazard Ratio 1.33). The best predictive single phenes (phenotypic items) were feeling useless (not needed), a past history of suicidality, and social isolation. We next used machine learning approaches to enhance the predictive ability of CFI-S. We divided the population into a discovery cohort (n = 255) and testing cohort (n = 227), and developed a deep neural network algorithm that showed increased accuracy for predicting risk of future suicidality (increasing the ROC AUC from 80 to 90%), as well as a similarity network classifier for visualizing patient's risk. We propose that the widespread use of CFI-S for screening purposes, with or without machine learning enhancements, can boost suicidality prevention efforts. This study also identified as top risk factors for suicidality addressable social determinants.