Browsing by Author "Acion, Laura"
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Item Associations between Suicidal Thoughts and Behaviors and Genetic Liability for Cognitive Performance, Depression, and Risk-Taking in a High-Risk Sample(Karger, 2021) Johnson, Emma C.; Aliev, Fazil; Meyers, Jacquelyn L.; Salvatore, Jessica E.; Tillman, Rebecca; Chang, Yoonhoo; Docherty, Anna R.; Bogdan, Ryan; Acion, Laura; Chan, Grace; Chorlian, David B.; Kamarajan, Chella; Kuperman, Samuel; Pandey, Ashwini; Plawecki, Martin H.; Schuckit, Marc; Tischfield, Jay; Edenberg, Howard J.; Bucholz, Kathleen K.; Nurnberger, John I.; Porjesz, Bernice; Hesselbrock, Victor; Dick, Danielle M.; Kramer, John R.; Agrawal, Arpana; Psychiatry, School of MedicineBackground: Suicidal thoughts and behaviors (STBs) and nonsuicidal self-injury (NSSI) behaviors are moderately heritable and may reflect an underlying predisposition to depression, impulsivity, and cognitive vulnerabilities to varying degrees. Objectives: We aimed to estimate the degrees of association between genetic liability to depression, impulsivity, and cognitive performance and STBs and NSSI in a high-risk sample. Methods: We used data on 7,482 individuals of European ancestry and 3,359 individuals of African ancestry from the Collaborative Study on the Genetics of Alcoholism to examine the links between polygenic scores (PGSs) for depression, impulsivity/risk-taking, and cognitive performance with 3 self-reported indices of STBs (suicidal ideation, persistent suicidal ideation defined as ideation occurring on at least 7 consecutive days, and suicide attempt) and with NSSI. Results: The PGS for depression was significantly associated with all 4 primary self-harm measures, explaining 0.6-2.5% of the variance. The PGS for risk-taking behaviors was also associated with all 4 self-harm behaviors in baseline models, but was no longer associated after controlling for a lifetime measure of DSM-IV alcohol dependence and abuse symptom counts. Polygenic predisposition for cognitive performance was negatively associated with suicide attempts (q = 3.8e-4) but was not significantly associated with suicidal ideation nor NSSI. We did not find any significant associations in the African ancestry subset, likely due to smaller sample sizes. Conclusions: Our results encourage the study of STB as transdiagnostic outcomes that show genetic overlap with a range of risk factors.Item Clinical, genomic, and neurophysiological correlates of lifetime suicide attempts among individuals with alcohol dependence(medRxiv, 2023-04-29) Barr, Peter B.; Neale, Zoe; Schulman, Jessica; Mullins, Niamh; Zhang, Jian; Chorlian, David B.; Kamarajan, Chella; Kinreich, Sivan; Pandey, Ashwini K.; Pandey, Gayathri; Saenz de Viteri, Stacey; Acion, Laura; Bauer, Lance; Bucholz, Kathleen K.; Chan, Grace; Chao, Michael; Dick, Danielle M.; Edenberg, Howard J.; Foroud, Tatiana; Goate, Alison; Hesselbrock, Victor; Johnson, Emma C.; Kramer, John; Lai, Dongbing; Plawecki, Martin H.; Salvatore, Jessica E.; Wetherill, Leah; Agrawal, Arpana; Porjesz, Bernice; Meyers, Jacquelyn L.; Medical and Molecular Genetics, School of MedicineResearch has identified clinical, genomic, and neurophysiological markers associated with suicide attempts (SA) among individuals with psychiatric illness. However, there is limited research among those with an alcohol use disorder, despite their disproportionately higher rates of SA. We examined lifetime SA in 4,068 individuals with DSM-IV alcohol dependence from the Collaborative Study on the Genetics of Alcoholism (23% lifetime suicide attempt; 53% female; 17% Admixed African American ancestries; mean age: 38). We 1) explored clinical risk factors associated with SA, 2) conducted a genome-wide association study of SA, 3) examined whether individuals with a SA had elevated polygenic scores for comorbid psychiatric conditions (e.g., alcohol use disorders, lifetime suicide attempt, and depression), and 4) explored differences in electroencephalogram neural functional connectivity between those with and without a SA. One gene-based finding emerged, RFX3 (Regulatory Factor X, located on 9p24.2) which had supporting evidence in prior research of SA among individuals with major depression. Only the polygenic score for suicide attempts was associated with reporting a suicide attempt (OR = 1.20, 95% CI = 1.06, 1.37). Lastly, we observed decreased right hemispheric frontal-parietal theta and decreased interhemispheric temporal-parietal alpha electroencephalogram resting-state coherences among those participants who reported a SA relative to those who did not, but differences were small. Overall, individuals with alcohol dependence who report SA appear to experience a variety of severe comorbidities and elevated polygenic risk for SA. Our results demonstrate the need to further investigate suicide attempts in the presence of substance use disorders.Item Density and Dichotomous Family History Measures of Alcohol Use Disorder as Predictors of Behavioral and Neural Phenotypes: A Comparative Study Across Gender and Race/Ethnicity(Wiley, 2020-03) Pandey, Gayathri; Seay, Michael J.; Meyers, Jacquelyn L.; Chorlian, David B.; Pandey, Ashwini K.; Kamarajan, Chella; Ehrenberg, Morton; Pitti, Daniel; Kinreich, Sivan; de Viteri, Stacey Subbie-Saenz; Acion, Laura; Anokhin, Andrey; Bauer, Lance; Chan, Grace; Edenberg, Howard; Hesselbrock, Victor; Kuperman, Samuel; McCutcheon, Vivia V.; Bucholz, Kathleen K.; Schuckit, Marc; Porjesz, Bernice; Biochemistry and Molecular Biology, School of MedicineBackground: Family history (FH) is an important risk factor for the development of alcohol use disorder (AUD). A variety of dichotomous and density measures of FH have been used to predict alcohol outcomes; yet, a systematic comparison of these FH measures is lacking. We compared 4 density and 4 commonly used dichotomous FH measures and examined variations by gender and race/ethnicity in their associations with age of onset of regular drinking, parietal P3 amplitude to visual target, and likelihood of developing AUD. Methods: Data from the Collaborative Study on the Genetics of Alcoholism (COGA) were utilized to compute the density and dichotomous measures. Only subjects and their family members with DSM-5 AUD diagnostic information obtained through direct interviews using the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) were included in the study. Area under receiver operating characteristic curves were used to compare the diagnostic accuracy of FH measures at classifying DSM-5 AUD diagnosis. Logistic and linear regression models were used to examine associations of FH measures with alcohol outcomes. Results: Density measures had greater diagnostic accuracy at classifying AUD diagnosis, whereas dichotomous measures presented diagnostic accuracy closer to random chance. Both dichotomous and density measures were significantly associated with likelihood of AUD, early onset of regular drinking, and low parietal P3 amplitude, but density measures presented consistently more robust associations. Further, variations in these associations were observed such that among males (vs. females) and Whites (vs. Blacks), associations of alcohol outcomes with density (vs. dichotomous) measures were greater in magnitude. Conclusions: Density (vs. dichotomous) measures seem to present more robust associations with alcohol outcomes. However, associations of dichotomous and density FH measures with different alcohol outcomes (behavioral vs. neural) varied across gender and race/ethnicity. These findings have great applicability for alcohol research examining FH of AUD.Item Development of Alcohol Use Disorder as a Function of Age, Severity, and Comorbidity with Externalizing and Internalizing Disorders in a Young Adult Cohort(Hapres Limited, 2019) Nurnberger Jr., John I.; Yang, Ziyi; Zang, Yong; Acion, Laura; Bierut, Laura; Bucholz, Kathleen; Chan, Grace; Dick, Danielle M.; Edenberg, Howard J.; Kramer, John; Kuperman, Samuel; Rice, John P.; Schuckit, Marc; Psychiatry, School of MedicineBackground: As part of the ongoing Collaborative Study of the Genetics of Alcoholism, we performed a longitudinal study of a high risk cohort of adolescents/young adults from families with a proband with an alcohol use disorder, along with a comparison group of age-matched controls. The intent was to compare the development of alcohol problems in subjects at risk with and without comorbid externalizing and internalizing psychiatric disorders. Methods: Subjects (N = 3286) were assessed with a structured psychiatric interview at 2 year intervals over 10 years (2004–2017). The age range at baseline was 12–21. Results: Subjects with externalizing disorders (with or without accompanying internalizing disorders) were at increased risk for the onset of an alcohol use disorder during the observation period. Subjects with internalizing disorders were at greater risk than those without comorbid disorders for onset of a moderate or severe alcohol use disorder. The statistical effect of comorbid disorders was greater in subjects with more severe alcohol use disorders. The developmental trajectory of drinking milestones and alcohol use disorders was also accelerated in those with more severe disorders. Conclusions: These results may be useful for counseling of subjects at risk who present for clinical care, especially those subjects manifesting externalizing and internalizing disorders in the context of a positive family history of an alcohol use disorder. We confirm and extend findings that drinking problems in subjects at greatest risk will begin in early adolescence.Item A GABRA2 Polymorphism Improves a Model for Prediction of Drinking Initiation(Elsevier, 2017-09) Kuperman, Samuel; Chan, Grace; Kramer, John; Wetherill, Leah; Acion, Laura; Edenberg, Howard J.; Foroud, Tatiana M.; Nurnberger, John, Jr.; Agrawal, Arpana; Anokhin, Andrey; Brooks, Andrew; Hesselbrock, Victor; Hesselbrock, Michie; Schuckit, Marc; Tischfield, Jay; Liu, Xiangtao; Department of Biochemistry & Molecular Biology, IU School of MedicineBackground Survival analysis was used to explore the addition of a single nucleotide polymorphism (SNP) and covariates (sex, interview age, and ancestry) on a previously published model's ability to predict onset of drinking. A SNP variant of rs279871, in the chromosome 4 gene encoding gamma-aminobutyric acid receptor (GABRA2), was selected due to its associations with alcoholism in young adults and with behaviors that increased risk for early drinking. Methods A subsample of 674 adolescents (ages 14–17) participating in the Collaborative Study on the Genetics of Alcoholism (COGA) was examined using a previously derived Cox proportional hazards model containing: 1) number of non-drinking related conduct disorder (CD) symptoms, 2) membership in a high-risk alcohol-dependent (AD) family, 3) most best friends drank (MBFD), 4) Achenbach Youth Self Report (YSR) externalizing score, and 5) YSR social problems score. The above covariates along with the SNP variant of GABRA2, rs279871, were added to this model. Five new prototype models were examined. The most parsimonious model was chosen based on likelihood ratio tests and model fit statistics. Results The final model contained four of the five original predictors (YSR social problems score was no longer significant and hence dropped from subsequent models), the three covariates, and a recessive GABRA2 rs279871 TT genotype (two copies of the high-risk allele containing thymine). The model indicated that adolescents with the high-risk TT genotype were more likely to begin drinking than those without this genotype. Conclusions The joint effect of the gene (rs279871 TT genotype) and environment (MBFD) on adolescent alcohol initiation is additive, but not interactive, after controlling for behavior problems (CD and YSR externalizing score). This suggests that the impact of the high-risk TT genotype on the onset of drinking is affected by controlling for peer drinking and does not include genotype-by-environment interactions.Item Metrics reloaded: recommendations for image analysis validation(Springer Nature, 2024) Maier-Hein, Lena; Reinke, Annika; Godau, Patrick; Tizabi, Minu D.; Buettner, Florian; Christodoulou, Evangelia; Glocker, Ben; Isensee, Fabian; Kleesiek, Jens; Kozubek, Michal; Reyes, Mauricio; Riegler, Michael A.; Wiesenfarth, Manuel; Kavur, A. Emre; Sudre, Carole H.; Baumgartner, Michael; Eisenmann, Matthias; Heckmann-Nötzel, Doreen; Rädsch, Tim; Acion, Laura; Antonelli, Michela; Arbel, Tal; Bakas, Spyridon; Benis, Arriel; Blaschko, Matthew B.; Cardoso, M. Jorge; Cheplygina, Veronika; Cimini, Beth A.; Collins, Gary S.; Farahani, Keyvan; Ferrer, Luciana; Galdran, Adrian; van Ginneken, Bram; Haase, Robert; Hashimoto, Daniel A.; Hoffman, Michael M.; Huisman, Merel; Jannin, Pierre; Kahn, Charles E.; Kainmueller, Dagmar; Kainz, Bernhard; Karargyris, Alexandros; Karthikesalingam, Alan; Kofler, Florian; Kopp-Schneider, Annette; Kreshuk, Anna; Kurc, Tahsin; Landman, Bennett A.; Litjens, Geert; Madani, Amin; Maier-Hein, Klaus; Martel, Anne L.; Mattson, Peter; Meijering, Erik; Menze, Bjoern; Moons, Karel G. M.; Müller, Henning; Nichyporuk, Brennan; Nickel, Felix; Petersen, Jens; Rajpoot, Nasir; Rieke, Nicola; Saez-Rodriguez, Julio; Sánchez, Clara I.; Shetty, Shravya; van Smeden, Maarten; Summers, Ronald M.; Taha, Abdel A.; Tiulpin, Aleksei; Tsaftaris, Sotirios A.; Van Calster, Ben; Varoquaux, Gaël; Jäger, Paul F.; Pathology and Laboratory Medicine, School of MedicineIncreasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.Item Polygenic contributions to alcohol use and alcohol use disorders across population-based and clinically ascertained samples(Cambridge University Press, 2021) Johnson, Emma C.; Sanchez-Roige, Sandra; Acion, Laura; Adams, Mark J.; Bucholz, Kathleen K.; Chan, Grace; Chao, Michael J.; Chorlian, David B.; Dick, Danielle M.; Edenberg, Howard J.; Foroud, Tatiana; Hayward, Caroline; Heron, Jon; Hesselbrock, Victor; Hickman, Matthew; Kendler, Kenneth S.; Kinreich, Sivan; Kramer, John; Kuo, Sally I-Chun; Kuperman, Samuel; Lai, Dongbing; McIntosh, Andrew M.; Meyers, Jacquelyn L.; Plawecki, Martin H.; Porjesz, Bernice; Porteous, David; Schuckit, Marc A.; Su, Jinni; Zang, Yong; Palmer, Abraham A.; Agrawal, Arpana; Clarke, Toni-Kim; Edwards, Alexis C.; Biochemistry and Molecular Biology, School of MedicineBackground: Studies suggest that alcohol consumption and alcohol use disorders have distinct genetic backgrounds. Methods: We examined whether polygenic risk scores (PRS) for consumption and problem subscales of the Alcohol Use Disorders Identification Test (AUDIT-C, AUDIT-P) in the UK Biobank (UKB; N = 121 630) correlate with alcohol outcomes in four independent samples: an ascertained cohort, the Collaborative Study on the Genetics of Alcoholism (COGA; N = 6850), and population-based cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC; N = 5911), Generation Scotland (GS; N = 17 461), and an independent subset of UKB (N = 245 947). Regression models and survival analyses tested whether the PRS were associated with the alcohol-related outcomes. Results: In COGA, AUDIT-P PRS was associated with alcohol dependence, AUD symptom count, maximum drinks (R2 = 0.47-0.68%, p = 2.0 × 10-8-1.0 × 10-10), and increased likelihood of onset of alcohol dependence (hazard ratio = 1.15, p = 4.7 × 10-8); AUDIT-C PRS was not an independent predictor of any phenotype. In ALSPAC, the AUDIT-C PRS was associated with alcohol dependence (R2 = 0.96%, p = 4.8 × 10-6). In GS, AUDIT-C PRS was a better predictor of weekly alcohol use (R2 = 0.27%, p = 5.5 × 10-11), while AUDIT-P PRS was more associated with problem drinking (R2 = 0.40%, p = 9.0 × 10-7). Lastly, AUDIT-P PRS was associated with ICD-based alcohol-related disorders in the UKB subset (R2 = 0.18%, p < 2.0 × 10-16). Conclusions: AUDIT-P PRS was associated with a range of alcohol-related phenotypes across population-based and ascertained cohorts, while AUDIT-C PRS showed less utility in the ascertained cohort. We show that AUDIT-P is genetically correlated with both use and misuse and demonstrate the influence of ascertainment schemes on PRS analyses.Item Understanding metric-related pitfalls in image analysis validation(ArXiv, 2023-09-25) Reinke, Annika; Tizabi, Minu D.; Baumgartner, Michael; Eisenmann, Matthias; Heckmann-Nötzel, Doreen; Kavur, A. Emre; Rädsch, Tim; Sudre, Carole H.; Acion, Laura; Antonelli, Michela; Arbel, Tal; Bakas, Spyridon; Benis, Arriel; Blaschko, Matthew B.; Buettner, Florian; Cardoso, M. Jorge; Cheplygina, Veronika; Chen, Jianxu; Christodoulou, Evangelia; Cimini, Beth A.; Collins, Gary S.; Farahani, Keyvan; Ferrer, Luciana; Galdran, Adrian; Van Ginneken, Bram; Glocker, Ben; Godau, Patrick; Haase, Robert; Hashimoto, Daniel A.; Hoffman, Michael M.; Huisman, Merel; Isensee, Fabian; Jannin, Pierre; Kahn, Charles E.; Kainmueller, Dagmar; Kainz, Bernhard; Karargyris, Alexandros; Karthikesalingam, Alan; Kenngott, Hannes; Kleesiek, Jens; Kofler, Florian; Kooi, Thijs; Kopp-Schneider, Annette; Kozubek, Michal; Kreshuk, Anna; Kurc, Tahsin; Landman, Bennett A.; Litjens, Geert; Madani, Amin; Maier-Hein, Klaus; Martel, Anne L.; Mattson, Peter; Meijering, Erik; Menze, Bjoern; Moons, Karel G. M.; Müller, Henning; Nichyporuk, Brennan; Nickel, Felix; Petersen, Jens; Rafelski, Susanne M.; Rajpoot, Nasir; Reyes, Mauricio; Riegler, Michael A.; Rieke, Nicola; Saez-Rodriguez, Julio; Sánchez, Clara I.; Shetty, Shravya; Summers, Ronald M.; Taha, Abdel A.; Tiulpin, Aleksei; Tsaftaris, Sotirios A.; Van Calster, Ben; Varoquaux, Gaël; Yaniv, Ziv R.; Jäger, Paul F.; Maier-Hein, Lena; Pathology and Laboratory Medicine, School of MedicineValidation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.