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Vike NL, Bari S, Stefanopoulos L, Lalvani S, Kim BW, Maglaveras N, Block M, Breiter HC, Katsaggelos AK. Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study. JMIR Public Health Surveill 2024; 10:e47979. [PMID: 38315620 PMCID: PMC10953811 DOI: 10.2196/47979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 08/08/2023] [Accepted: 01/10/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Despite COVID-19 vaccine mandates, many chose to forgo vaccination, raising questions about the psychology underlying how judgment affects these choices. Research shows that reward and aversion judgments are important for vaccination choice; however, no studies have integrated such cognitive science with machine learning to predict COVID-19 vaccine uptake. OBJECTIVE This study aims to determine the predictive power of a small but interpretable set of judgment variables using 3 machine learning algorithms to predict COVID-19 vaccine uptake and interpret what profile of judgment variables was important for prediction. METHODS We surveyed 3476 adults across the United States in December 2021. Participants answered demographic, COVID-19 vaccine uptake (ie, whether participants were fully vaccinated), and COVID-19 precaution questions. Participants also completed a picture-rating task using images from the International Affective Picture System. Images were rated on a Likert-type scale to calibrate the degree of liking and disliking. Ratings were computationally modeled using relative preference theory to produce a set of graphs for each participant (minimum R2>0.8). In total, 15 judgment features were extracted from these graphs, 2 being analogous to risk and loss aversion from behavioral economics. These judgment variables, along with demographics, were compared between those who were fully vaccinated and those who were not. In total, 3 machine learning approaches (random forest, balanced random forest [BRF], and logistic regression) were used to test how well judgment, demographic, and COVID-19 precaution variables predicted vaccine uptake. Mediation and moderation were implemented to assess statistical mechanisms underlying successful prediction. RESULTS Age, income, marital status, employment status, ethnicity, educational level, and sex differed by vaccine uptake (Wilcoxon rank sum and chi-square P<.001). Most judgment variables also differed by vaccine uptake (Wilcoxon rank sum P<.05). A similar area under the receiver operating characteristic curve (AUROC) was achieved by the 3 machine learning frameworks, although random forest and logistic regression produced specificities between 30% and 38% (vs 74.2% for BRF), indicating a lower performance in predicting unvaccinated participants. BRF achieved high precision (87.8%) and AUROC (79%) with moderate to high accuracy (70.8%) and balanced recall (69.6%) and specificity (74.2%). It should be noted that, for BRF, the negative predictive value was <50% despite good specificity. For BRF and random forest, 63% to 75% of the feature importance came from the 15 judgment variables. Furthermore, age, income, and educational level mediated relationships between judgment variables and vaccine uptake. CONCLUSIONS The findings demonstrate the underlying importance of judgment variables for vaccine choice and uptake, suggesting that vaccine education and messaging might target varying judgment profiles to improve uptake. These methods could also be used to aid vaccine rollouts and health care preparedness by providing location-specific details (eg, identifying areas that may experience low vaccination and high hospitalization).
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Affiliation(s)
- Nicole L Vike
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, United States
| | - Sumra Bari
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, United States
| | - Leandros Stefanopoulos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Shamal Lalvani
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Byoung Woo Kim
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, United States
| | - Nicos Maglaveras
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Martin Block
- Integrated Marketing Communications, Medill School, Northwestern University, Evanston, IL, United States
| | - Hans C Breiter
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, United States
- Department of Psychiatry, Massachusetts General Hospital, Harvard School of Medicine, Boston, MA, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
- Department of Computer Science, Northwestern University, Evanston, IL, United States
- Department of Radiology, Northwestern University, Evanston, IL, United States
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Vike NL, Bari S, Kim BW, Katsaggelos AK, Blood AJ, Breiter HC. Characterizing major depressive disorder and substance use disorder using heatmaps and variable interactions: The utility of operant behavior and brain structure relationships. PLoS One 2024; 19:e0299528. [PMID: 38466739 PMCID: PMC10927130 DOI: 10.1371/journal.pone.0299528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 02/13/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Rates of depression and addiction have risen drastically over the past decade, but the lack of integrative techniques remains a barrier to accurate diagnoses of these mental illnesses. Changes in reward/aversion behavior and corresponding brain structures have been identified in those with major depressive disorder (MDD) and cocaine-dependence polysubstance abuse disorder (CD). Assessment of statistical interactions between computational behavior and brain structure may quantitatively segregate MDD and CD. METHODS Here, 111 participants [40 controls (CTRL), 25 MDD, 46 CD] underwent structural brain MRI and completed an operant keypress task to produce computational judgment metrics. Three analyses were performed: (1) linear regression to evaluate groupwise (CTRL v. MDD v. CD) differences in structure-behavior associations, (2) qualitative and quantitative heatmap assessment of structure-behavior association patterns, and (3) the k-nearest neighbor machine learning approach using brain structure and keypress variable inputs to discriminate groups. RESULTS This study yielded three primary findings. First, CTRL, MDD, and CD participants had distinct structure-behavior linear relationships, with only 7.8% of associations overlapping between any two groups. Second, the three groups had statistically distinct slopes and qualitatively distinct association patterns. Third, a machine learning approach could discriminate between CTRL and CD, but not MDD participants. CONCLUSIONS These findings demonstrate that variable interactions between computational behavior and brain structure, and the patterns of these interactions, segregate MDD and CD. This work raises the hypothesis that analysis of interactions between operant tasks and structural neuroimaging might aide in the objective classification of MDD, CD and other mental health conditions.
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Affiliation(s)
- Nicole L. Vike
- Department of Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
| | - Sumra Bari
- Department of Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
| | - Byoung Woo Kim
- Department of Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
| | - Aggelos K. Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, Illinois, United States of America
- Department of Computer Science, Northwestern University, Evanston, Illinois, United States of America
- Department of Radiology, Northwestern University, Chicago, Illinois, United States of America
| | - Anne J. Blood
- Department of Psychiatry, Mood and Motor Control Laboratory (MAML), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Psychiatry, Laboratory of Neuroimaging and Genetics, Massachusetts General Hospital and Harvard School of Medicine, Boston, Massachusetts, United States of America
| | - Hans C. Breiter
- Department of Computer Science, University of Cincinnati, Cincinnati, Ohio, United States of America
- Department of Psychiatry, Mood and Motor Control Laboratory (MAML), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Psychiatry, Laboratory of Neuroimaging and Genetics, Massachusetts General Hospital and Harvard School of Medicine, Boston, Massachusetts, United States of America
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, Ohio, United States of America
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Bari S, Vike NL, Stetsiv K, Woodward S, Lalvani S, Stefanopoulos L, Kim BW, Maglaveras N, Breiter HC, Katsaggelos AK. The Prevalence of Psychotic Symptoms, Violent Ideation, and Disruptive Behavior in a Population With SARS-CoV-2 Infection: Preliminary Study. JMIR Form Res 2022; 6:e36444. [PMID: 35763758 PMCID: PMC9384857 DOI: 10.2196/36444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/06/2022] [Accepted: 05/14/2022] [Indexed: 11/26/2022] Open
Abstract
Background The COVID-19 disease results from infection by the SARS-CoV-2 virus to produce a range of mild to severe physical, neurological, and mental health symptoms. The COVID-19 pandemic has indirectly caused significant emotional distress, triggering the emergence of mental health symptoms in individuals who were not previously affected or exacerbating symptoms in those with existing mental health conditions. Emotional distress and certain mental health conditions can lead to violent ideation and disruptive behavior, including aggression, threatening acts, deliberate harm toward other people or animals, and inattention to or noncompliance with education or workplace rules. Of the many mental health conditions that can be associated with violent ideation and disruptive behavior, psychosis can evidence greater vulnerability to unpredictable changes and being at a greater risk for them. Individuals with psychosis can also be more susceptible to contracting COVID-19 disease. Objective This study aimed to investigate whether violent ideation, disruptive behavior, or psychotic symptoms were more prevalent in a population with COVID-19 and did not precede the pandemic. Methods In this preliminary study, we analyzed questionnaire responses from a population sample (N=366), received between the end of February 2021 and the start of March 2021 (1 year into the COVID-19 pandemic), regarding COVID-19 illness, violent ideation, disruptive behavior, and psychotic symptoms. Using the Wilcoxon rank sum test followed by multiple comparisons correction, we compared the self-reported frequency of these variables for 3 time windows related to the past 1 month, past 1 month to 1 year, and >1 year ago among the distributions of people who answered whether they tested positive or were diagnosed with COVID-19 by a clinician. We also used multivariable logistic regression with iterative resampling to investigate the relationship between these variables occurring >1 year ago (ie, before the pandemic) and the likelihood of contracting COVID-19. Results We observed a significantly higher frequency of self-reported violent ideation, disruptive behavior, and psychotic symptoms, for all 3 time windows of people who tested positive or were diagnosed with COVID-19 by a clinician. Using multivariable logistic regression, we observed 72% to 94% model accuracy for an increased incidence of COVID-19 in participants who reported violent ideation, disruptive behavior, or psychotic symptoms >1 year ago. Conclusions This preliminary study found that people who reported a test or clinician diagnosis of COVID-19 also reported higher frequencies of violent ideation, disruptive behavior, or psychotic symptoms across multiple time windows, indicating that they were not likely to be the result of COVID-19. In parallel, participants who reported these behaviors >1 year ago (ie, before the pandemic) were more likely to be diagnosed with COVID-19, suggesting that violent ideation, disruptive behavior, in addition to psychotic symptoms, were associated with COVID-19 with an approximately 70% to 90% likelihood.
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Affiliation(s)
- Sumra Bari
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, United States
| | - Nicole L Vike
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, United States
| | - Khrystyna Stetsiv
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, United States
| | - Sean Woodward
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, United States
| | - Shamal Lalvani
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Leandros Stefanopoulos
- Laboratory of Medical Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Byoung Woo Kim
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, United States
| | - Nicos Maglaveras
- Laboratory of Medical Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Hans C Breiter
- Laboratory of Neuroimaging and Genetics, Division of Psychiatric Neuroscience, Massachusetts General Hospital, Charlestown, MA, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
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The paired A-Not A design within signal detection theory: Description, differentiation, power analysis and application. Behav Res Methods 2022; 54:2334-2350. [PMID: 35132585 PMCID: PMC9579092 DOI: 10.3758/s13428-021-01728-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2021] [Indexed: 11/08/2022]
Abstract
Signal detection theory gives a framework for determining how well participants can discriminate between two types of stimuli. This article first examines similarities and differences of forced-choice and A-Not A designs (also known as the yes-no or one-interval). Then it focuses on the latter, in which participants have to classify stimuli, presented to them one at a time, as belonging to one of two possible response categories. The A-Not A task can be, on a first level, replicated or non-replicated, and the sub-design for each can be, on a second level, either a monadic, a mixed, or a paired design. These combinations are explained, and the present article then focuses on the both the non-replicated and replicated paired A-Not A task. Data structure, descriptive statistics, inference statistics, and effect sizes are explained in general and based on example data (Düvel et al., 2020). Documents for the data analysis are given in an extensive online supplement. Furthermore, the important question of statistical power and required sample size is addressed, and several means for the calculation are explained. The authors suggest a standardized procedure for planning, conducting, and evaluating a study employing an A-Not A design.
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Woodward SF, Bari S, Vike N, Lalvani S, Stetsiv K, Kim BW, Stefanopoulos L, Maglaveras N, Breiter H, Katsaggelos AK. Anxiety, Post-COVID-Syndrome-related depression, and suicidal thinking and behavior in COVID-19 survivors (Preprint). JMIR Form Res 2022; 6:e36656. [PMID: 35763757 PMCID: PMC9604174 DOI: 10.2196/36656] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/30/2022] [Accepted: 05/01/2022] [Indexed: 11/21/2022] Open
Abstract
Background Although the mental health impacts of COVID-19 on the general population have been well studied, studies of the long-term impacts of COVID-19 on infected individuals are relatively new. To date, depression, anxiety, and neurological symptoms associated with post–COVID-19 syndrome (PCS) have been observed in the months following COVID-19 recovery. Suicidal thoughts and behavior (STB) have also been preliminarily proposed as sequelae of COVID-19. Objective We asked 3 questions. First, do participants reporting a history of COVID-19 diagnosis or a close relative having severe COVID-19 symptoms score higher on depression (Patient Health Questionnaire-9 [PHQ-9]) or state anxiety (State Trait Anxiety Index) screens than those who do not? Second, do participants reporting a COVID-19 diagnosis score higher on PCS-related PHQ-9 items? Third, do participants reporting a COVID-19 diagnosis or a close relative having severe COVID-19 symptoms score higher in STB before, during, or after the first year of the pandemic? Methods This preliminary study analyzed responses to a COVID-19 and mental health questionnaire obtained from a US population sample, whose data were collected between February 2021 and March 2021. We used the Mann-Whitney U test to detect differences in the medians of the total PHQ-9 scores, PHQ-9 component scores, and several STB scores between participants claiming a past clinician diagnosis of COVID-19 and those denying one, as well as between participants claiming severe COVID-19 symptoms in a close relative and those denying them. Where significant differences existed, we created linear regression models to predict the scores based on COVID-19 response as well as demographics to identify potential confounding factors in the Mann-Whitney relationships. Moreover, for STB scores, which corresponded to 5 questions asking about 3 different time intervals (i.e., past 1 year or more, past 1 month to 1 year, and past 1 month), we developed repeated-measures ANOVAs to determine whether scores tended to vary over time. Results We found greater total depression (PHQ-9) and state anxiety (State Trait Anxiety Index) scores in those with COVID-19 history than those without (Bonferroni P=.001 and Bonferroni P=.004) despite a similar history of diagnosed depression and anxiety. Greater scores were noted for a subset of depression symptoms (PHQ-9 items) that overlapped with the symptoms of PCS (all Bonferroni Ps<.05). Moreover, we found greater overall STB scores in those with COVID-19 history, equally in time windows preceding, during, and proceeding infection (all Bonferroni Ps<.05). Conclusions We confirm previous studies linking depression and anxiety diagnoses to COVID-19 recovery. Moreover, our findings suggest that depression diagnoses associated with COVID-19 history relate to PCS symptoms, and that STB associated with COVID-19 in some cases precede infection.
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Affiliation(s)
- Sean F Woodward
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, United States
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Sumra Bari
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, United States
| | - Nicole Vike
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, United States
| | - Shamal Lalvani
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Khrystyna Stetsiv
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, United States
| | - Byoung Woo Kim
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, United States
| | - Leandros Stefanopoulos
- Laboratory of Medical Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nicos Maglaveras
- Laboratory of Medical Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Hans Breiter
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, United States
- Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard University, Boston, MA, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
- Department of Computer Science, Northwestern University, Evanston, IL, United States
- Department of Radiology, Northwestern University, Chicago, IL, United States
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6
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Livengood SL, Sheppard JP, Kim BW, Malthouse EC, Bourne JE, Barlow AE, Lee MJ, Marin V, O'Connor KP, Csernansky JG, Block MP, Blood AJ, Breiter HC. Keypress-Based Musical Preference Is Both Individual and Lawful. Front Neurosci 2017; 11:136. [PMID: 28512395 PMCID: PMC5412065 DOI: 10.3389/fnins.2017.00136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 03/06/2017] [Indexed: 11/13/2022] Open
Abstract
Musical preference is highly individualized and is an area of active study to develop methods for its quantification. Recently, preference-based behavior, associated with activity in brain reward circuitry, has been shown to follow lawful, quantifiable patterns, despite broad variation across individuals. These patterns, observed using a keypress paradigm with visual stimuli, form the basis for relative preference theory (RPT). Here, we sought to determine if such patterns extend to non-visual domains (i.e., audition) and dynamic stimuli, potentially providing a method to supplement psychometric, physiological, and neuroimaging approaches to preference quantification. For this study, we adapted our keypress paradigm to two sets of stimuli consisting of seventeenth to twenty-first century western art music (Classical) and twentieth to twenty-first century jazz and popular music (Popular). We studied a pilot sample and then a separate primary experimental sample with this paradigm, and used iterative mathematical modeling to determine if RPT relationships were observed with high R2 fits. We further assessed the extent of heterogeneity in the rank ordering of keypress-based responses across subjects. As expected, individual rank orderings of preferences were quite heterogeneous, yet we observed mathematical patterns fitting these data similar to those observed previously with visual stimuli. These patterns in music preference were recurrent across two cohorts and two stimulus sets, and scaled between individual and group data, adhering to the requirements for lawfulness. Our findings suggest a general neuroscience framework that predicts human approach/avoidance behavior, while also allowing for individual differences and the broad diversity of human choices; the resulting framework may offer novel approaches to advancing music neuroscience, or its applications to medicine and recommendation systems.
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Affiliation(s)
- Sherri L Livengood
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA.,Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA
| | - John P Sheppard
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA.,Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA.,David Geffen School of Medicine, University of California, Los AngelesLos Angeles, CA, USA
| | - Byoung W Kim
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA.,Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA.,Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersBoston, MA, USA
| | - Edward C Malthouse
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA.,Medill Integrated Marketing Communications, Northwestern UniversityEvanston, IL, USA
| | - Janet E Bourne
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA.,Music Department, Bates CollegeLewiston, ME, USA
| | - Anne E Barlow
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA.,KV 265, The Communication of Science through ArtWillow Springs, IL, USA
| | - Myung J Lee
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA.,Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA.,Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersBoston, MA, USA
| | - Veronica Marin
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA
| | - Kailyn P O'Connor
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA
| | - John G Csernansky
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA
| | - Martin P Block
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA.,Medill Integrated Marketing Communications, Northwestern UniversityEvanston, IL, USA
| | - Anne J Blood
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA.,Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersBoston, MA, USA.,Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General HospitalBoston, MA, USA.,Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General HospitalBoston, MA, USA.,Department of Neurology, Massachusetts General HospitalBoston, MA, USA
| | - Hans C Breiter
- Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern UniversityChicago, IL, USA.,Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA.,Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersBoston, MA, USA.,Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General HospitalBoston, MA, USA.,Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General HospitalBoston, MA, USA
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