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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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Lo Presti S, Mattavelli G, Canessa N, Gianelli C. Risk perception and behaviour during the COVID-19 pandemic: Predicting variables of compliance with lockdown measures. PLoS One 2022; 17:e0262319. [PMID: 34986209 PMCID: PMC8730439 DOI: 10.1371/journal.pone.0262319] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 12/22/2021] [Indexed: 01/10/2023] Open
Abstract
The COVID-19 pandemic and the measures to counteract it have highlighted the role of individual differences in evaluating and reacting to emergencies, and the challenges inherent in promoting precautionary behaviours. We aimed to explore the psychological and cognitive factors modulating behaviour and intentions during the national lockdown in Italy. We administered an online questionnaire (N = 244) that included tests for assessing personality traits (Temperament and Character Inventory; Locus of Control of Behaviour) and moral judgment (Moral Foundations Questionnaire), alongside behavioural economics tasks addressing different facets of risk attitude (loss aversion, risk aversion and delay discounting). We then assessed the extent to which individual variations in these dimensions modulated participants' compliance with the lockdown norms. When assessing their joint contribution via multiple regressions, lockdown adherence was mostly predicted by internal locus of control, psycho-economic dimensions suggestive of long-sighted and loss-averse attitudes, as well as personality traits related to cautionary behaviour, such as harm avoidance, and the authority moral concern. These findings show that a multi-domain assessment of the factors underlying personal intentions, and thus driving compliance with government measures, can help predict individuals' actions during health emergencies. This evidence points to factors that should be considered when developing interventions and communication strategies to promote precautionary behaviours.
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Affiliation(s)
- Sara Lo Presti
- IUSS Cognitive Neuroscience (ICON) Center, Scuola Universitaria Superiore IUSS, Pavia, Italy
| | - Giulia Mattavelli
- IUSS Cognitive Neuroscience (ICON) Center, Scuola Universitaria Superiore IUSS, Pavia, Italy
- Istituti Clinici Scientifici Maugeri IRCCS, Cognitive Neuroscience Laboratory of Pavia Institute, Pavia, Italy
| | - Nicola Canessa
- IUSS Cognitive Neuroscience (ICON) Center, Scuola Universitaria Superiore IUSS, Pavia, Italy
- Istituti Clinici Scientifici Maugeri IRCCS, Cognitive Neuroscience Laboratory of Pavia Institute, Pavia, Italy
| | - Claudia Gianelli
- IUSS Cognitive Neuroscience (ICON) Center, Scuola Universitaria Superiore IUSS, Pavia, Italy
- Istituti Clinici Scientifici Maugeri IRCCS, Cognitive Neuroscience Laboratory of Pavia Institute, Pavia, Italy
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Warren C, Schneider S, Smith KB, Hibbing JR. Motivated viewing: Selective exposure to political images when reasoning is not involved. PERSONALITY AND INDIVIDUAL DIFFERENCES 2020. [DOI: 10.1016/j.paid.2019.109704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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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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Viswanathan V, Sheppard JP, Kim BW, Plantz CL, Ying H, Lee MJ, Raman K, Mulhern FJ, Block MP, Calder B, Lee S, Mortensen DT, Blood AJ, Breiter HC. A Quantitative Relationship between Signal Detection in Attention and Approach/Avoidance Behavior. Front Psychol 2017; 8:122. [PMID: 28270776 PMCID: PMC5318395 DOI: 10.3389/fpsyg.2017.00122] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 01/17/2017] [Indexed: 11/13/2022] Open
Abstract
This study examines how the domains of reward and attention, which are often studied as independent processes, in fact interact at a systems level. We operationalize divided attention with a continuous performance task and variables from signal detection theory (SDT), and reward/aversion with a keypress task measuring approach/avoidance in the framework of relative preference theory (RPT). Independent experiments with the same subjects showed a significant association between one SDT and two RPT variables, visualized as a three-dimensional structure. Holding one of these three variables constant, further showed a significant relationship between a loss aversion-like metric from the approach/avoidance task, and the response bias observed during the divided attention task. These results indicate that a more liberal response bias under signal detection (i.e., a higher tolerance for noise, resulting in a greater proportion of false alarms) is associated with higher "loss aversion." Furthermore, our functional model suggests a mechanism for processing constraints with divided attention and reward/aversion. Together, our results argue for a systematic relationship between divided attention and reward/aversion processing in humans.
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Affiliation(s)
- Vijay Viswanathan
- Medill Integrated Marketing Communications, Northwestern UniversityEvanston, IL, USA; Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA
| | - John P Sheppard
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of MedicineChicago, IL, USA
| | - Byoung W Kim
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of MedicineChicago, IL, USA; Laboratory of Neuroimaging and Genetics, and Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; MGH Center for Translational Research in Prescription Drug Abuse, Department of Anesthesia, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersChicago, IL, USA
| | - Christopher L Plantz
- Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign Urbana, IL, USA
| | - Hao Ying
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Department of Electrical Engineering, Wayne State UniversityDetroit, MI, USA
| | - Myung J Lee
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of MedicineChicago, IL, USA; Laboratory of Neuroimaging and Genetics, and Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; MGH Center for Translational Research in Prescription Drug Abuse, Department of Anesthesia, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersChicago, IL, USA
| | - Kalyan Raman
- Medill Integrated Marketing Communications, Northwestern UniversityEvanston, IL, USA; Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA
| | - Frank J Mulhern
- Medill Integrated Marketing Communications, Northwestern UniversityEvanston, IL, USA; Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA
| | - Martin P Block
- Medill Integrated Marketing Communications, Northwestern UniversityEvanston, IL, USA; Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA
| | - Bobby Calder
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Department of Marketing, Kellogg School of Management, Northwestern UniversityEvanston, IL, USA
| | - Sang Lee
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Laboratory of Neuroimaging and Genetics, and Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; MGH Center for Translational Research in Prescription Drug Abuse, Department of Anesthesia, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersChicago, IL, USA
| | - Dale T Mortensen
- Department of Economics, Northwestern University College of Arts and Sciences Evanston, IL, USA
| | - Anne J Blood
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Laboratory of Neuroimaging and Genetics, and Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; MGH Center for Translational Research in Prescription Drug Abuse, Department of Anesthesia, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersChicago, IL, USA
| | - Hans C Breiter
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of MedicineChicago, IL, USA; Laboratory of Neuroimaging and Genetics, and Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; MGH Center for Translational Research in Prescription Drug Abuse, Department of Anesthesia, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersChicago, IL, USA
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