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5. Contributors

Author

Acampora, Gregory Alexander, primary, Ahmad, Zeba N., additional, Alpay, Menekse, additional, Alpert, Jonathan E., additional, Babadi, Baktash, additional, Baek, Ji Hyun, additional, Baig, Mizra, additional, Bains, Ashika, additional, Baker, Amanda Waters, additional, Baldi, Olivia, additional, Beach, Scott R., additional, Beck, BJ, additional, Beckwith, Noor, additional, Benedek, David M., additional, Beresin, Eugene V., additional, Biederman, Joseph, additional, Bird, Suzanne A., additional, Blais, Mark A., additional, Bosson, Rahel, additional, Brendel, Rebecca Weintraub, additional, Bui, Eric, additional, Camprodon, Joan A., additional, Capawana, Michael R., additional, Caplan, Jason P., additional, Carter, Christopher, additional, Cassano, Paolo, additional, Cather, Corinne, additional, Celano, Christopher M., additional, Chang, Trina E., additional, Charoenpong, Prangthip, additional, Chemali, Zeina N., additional, Chen, Justin, additional, Chopra, Amit, additional, Choukas, Nathaniel, additional, Chung, Sun Young, additional, Cohen, Jonah, additional, Cohen, Lee S., additional, Colvin, Mary K. (Molly), additional, Conteh, Nkechi, additional, Crain, Laura D., additional, Cremens, M. Cornelia, additional, Cusin, Cristina, additional, Dekel, Sharon, additional, Denysenko, Lex, additional, Dickerson, Bradford C., additional, Donovan, Abigail L., additional, Doorley, James, additional, Dougherty, Darin D., additional, Ducharme, Simon, additional, Eddy, Kamryn T., additional, Edersheim, Judith G., additional, Evanoff, Anastasia B., additional, Fava, Maurizio, additional, Finn, Christine T., additional, Fernandez-Robles, Carlos, additional, Fishel, Anne K., additional, Forchelli, Gina, additional, Freudenreich, Oliver, additional, Fricchione, Gregory L., additional, Friedman, Nora D.B., additional, Gatchel, Jennifer R., additional, Gelaye, Bizu, additional, Georgiopoulos, Anna M., additional, Ghaznavi, Sharmin, additional, Ginsburg, Richard, additional, Gold, Alexandra K., additional, Gordon, Christopher D., additional, Gray, Caroline A., additional, Greenberg, Donna B., additional, Greer, Joseph, additional, Hazen, Eric P., additional, Henry, Michael E., additional, Herman, John B., additional, Himes, Susan, additional, Hogan, Charlotte, additional, Holt, Daphne J., additional, Huffman, Jeffery C., additional, Huguenel, Brynn, additional, Ipek, Simay, additional, Irwin, Kelly Edwards, additional, Ivkovic, Ana, additional, Jacobs, Jamie, additional, Jagodnik, Kathleen M., additional, Jain, Felipe A., additional, Jankauskaite, Greta, additional, Januzzi, James L., additional, Jenike, Michael A., additional, Jenkins, Jonathan, additional, Johnson, Justin M., additional, Julian, John N., additional, Kamali, Masoud, additional, Kaneko, Yoshio A., additional, Katz, Tamar C., additional, Keuroghlian, Alex, additional, Keuthen, Nancy J., additional, Khoshbin, Shahram, additional, Kim, Hyun-Hee, additional, Kim, Youngjung R., additional, Koh, Katherine A., additional, Kohrman, Samuel I., additional, Kontos, Nicholas, additional, Lagomasino, Isabel T., additional, Leval, Rebecca, additional, Leveroni, Catherine, additional, Lim, Carol, additional, Luccarelli, James, additional, Madarasmi, Saira, additional, Madva, Elizabeth N., additional, McCoy, Thomas H., additional, Milosavljevic, Nada, additional, Mischoulon, David, additional, Miyares, Peyton, additional, Morelli, Leah W., additional, Rodriguez, Alejandra E. Morfin, additional, Murray, Evan D., additional, Murray, Helen Burton, additional, Nejad, Shamim H., additional, Newhouse, Amy L., additional, Nicolson, Stephen E., additional, Nierenberg, Andrew A., additional, Nisavic, Mladen, additional, Nonacs, Ruta M., additional, Öngür, Dost, additional, Onyeaka, Henry, additional, Orr, Scott P., additional, Ostacher, Michael J., additional, Pace-Schott, Edward F., additional, Papakostas, George I., additional, Paudel, Shreedhar, additional, Peay, Celeste, additional, Pederson, Aderonke Bamgbose, additional, Penava, Susan J., additional, Perez, David L., additional, Perlis, Roy H., additional, Peters, Amy T., additional, Pinsky, Elizabeth G., additional, Pollak, Lauren Norton, additional, Pollastri, Alisha R., additional, Post, Loren M., additional, Powell, Alicia D., additional, Prager, Laura M., additional, Praschan, Nathan, additional, Price, Bruce H., additional, Prince, Jefferson B., additional, Probert, Julia M., additional, Prom, Maria C., additional, Punko, Diana, additional, Rauch, Scott L., additional, Raviola, Giuseppe J., additional, Reilly-Harrington, Noreen A., additional, Ritchie, Elspeth Cameron, additional, Rivas-Vazquez, Rafael, additional, Robinson, Ellen M., additional, Roffman, Joshua L., additional, Rubin, David H., additional, Ruchensky, Jared R., additional, Salvi, Joshua D., additional, Sanders, Kathy M., additional, Sanders, Wesley M., additional, Schlozman, Steven C., additional, Schouten, Ronald, additional, Schuster, Randi, additional, Shafer, Linda C., additional, Sheets, Jennifer, additional, Sher, Yelizaveta, additional, Sherman, Janet Cohen, additional, Sinclair, Samuel Justin, additional, Smith, Felicia A., additional, Sockalingam, Sanjeev, additional, Sogg, Stephanie, additional, Sorg, Emily M., additional, Sprich, Susan E., additional, Stein, Michelle B., additional, Stern, Theodore A., additional, Stoler, Joan M., additional, Stone, Mira, additional, Surman, Craig B.H., additional, Sylvia, Louisa G., additional, Tanev, Kaloyan S., additional, Tayeb, Haythum O., additional, Taylor, John B., additional, Thom, Robyn P., additional, Thomas, Jennifer J., additional, Tillman, Emma M., additional, Traeger, Lara, additional, Trinh, Nhi-Ha, additional, Uchida, Mai, additional, Ulman, Kathleen Hubbs, additional, Valera, Eve M., additional, Van Alphen, Manjola U., additional, Vazquez, Rafael, additional, Viguera, Adele C., additional, Wang, Betty, additional, Weilburg, Jeffrey B., additional, Weinberg, Marc, additional, Weinstein, Sylvie J., additional, Weisholtz, Daniel, additional, Wilens, Timothy E., additional, Wilhelm, Sabine, additional, Winkelman, John W., additional, Wright, Christopher L., additional, Wynn, Gary H., additional, Yeung, Albert, additional, Zakhary, Lisa, additional, and Zambrano, Juliana, additional

Published
2025
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6. Bipolar Disorder

Author

Perlis, Roy H., primary, Ostacher, Michael J., additional, Gold, Alexandra K., additional, Kamali, Masoud, additional, Peters, Amy T., additional, Nierenberg, Andrew A., additional, and Sylvia, Louisa G., additional

Published
2025
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9. Lithium response in bipolar disorder is associated with focal adhesion and PI3K-Akt networks: a multi-omics replication study

Author

Ou, Anna H., Rosenthal, Sara B., Adli, Mazda, Akiyama, Kazufumi, Akula, Nirmala, Alda, Martin, Amare, Azmeraw T., Ardau, Raffaella, Arias, Bárbara, Aubry, Jean-Michel, Backlund, Lena, Bauer, Michael, Baune, Bernhard T., Bellivier, Frank, Benabarre, Antonio, Bengesser, Susanne, Bhattacharjee, Abesh Kumar, Biernacka, Joanna M., Cervantes, Pablo, Chen, Guo-Bo, Chen, Hsi-Chung, Chillotti, Caterina, Cichon, Sven, Clark, Scott R., Colom, Francesc, Cousins, David A., Cruceanu, Cristiana, Czerski, Piotr M., Dantas, Clarissa R., Dayer, Alexandre, Del Zompo, Maria, Degenhardt, Franziska, DePaulo, J. Raymond, Étain, Bruno, Falkai, Peter, Fellendorf, Frederike Tabea, Ferensztajn-Rochowiak, Ewa, Forstner, Andreas J., Frisén, Louise, Frye, Mark A., Fullerton, Janice M., Gard, Sébastien, Garnham, Julie S., Goes, Fernando S., Grigoroiu-Serbanescu, Maria, Grof, Paul, Gruber, Oliver, Hashimoto, Ryota, Hauser, Joanna, Heilbronner, Urs, Herms, Stefan, Hoffmann, Per, Hofmann, Andrea, Hou, Liping, Jamain, Stephane, Jiménez, Esther, Kahn, Jean-Pierre, Kassem, Layla, Kato, Tadafumi, Kittel-Schneider, Sarah, König, Barbara, Kuo, Po-Hsiu, Kusumi, Ichiro, Lackner, Nina, Laje, Gonzalo, Landén, Mikael, Lavebratt, Catharina, Leboyer, Marion, Leckband, Susan G., Jaramillo, Carlos A. López, MacQueen, Glenda, Maj, Mario, Manchia, Mirko, Marie-Claire, Cynthia, Martinsson, Lina, Mattheisen, Manuel, McCarthy, Michael J., McElroy, Susan L., McMahon, Francis J., Mitchell, Philip B., Mitjans, Marina, Mondimore, Francis M., Monteleone, Palmiero, Nievergelt, Caroline M., Nöthen, Markus M., Novák, Tomas, Ösby, Urban, Ozaki, Norio, Papiol, Sergi, Perlis, Roy H., Pisanu, Claudia, Potash, James B., Pfennig, Andrea, Reich-Erkelenz, Daniela, Reif, Andreas, Reininghaus, Eva Z., Rietschel, Marcella, Rouleau, Guy A., Rybakowski, Janusz K., Schalling, Martin, Schofield, Peter R., Schubert, K. Oliver, Schulze, Thomas G., Schweizer, Barbara W., Seemüller, Florian, Severino, Giovanni, Shekhtman, Tatyana, Shilling, Paul D., Shimoda, Kazutaka, Simhandl, Christian, Slaney, Claire M., Squassina, Alessio, Stamm, Thomas, Stopkova, Pavla, Tighe, Sarah K., Tortorella, Alfonso, Turecki, Gustavo, Vieta, Eduard, Volkert, Julia, Witt, Stephanie, Wray, Naomi R., Wright, Adam, Young, L. Trevor, Zandi, Peter P., and Kelsoe, John R.

Published
2024
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15. Depressive Symptoms and Conspiracy Beliefs

Author

Green, Jon, Druckman, James N., Baum, Matthew A., Lazer, David, Ognyanova, Katherine, and Perlis, Roy H.

Abstract

Conspiratorial beliefs can endanger individuals and societies by increasing the likelihood of harmful behaviors such as the flouting of public health guidelines. While scholars have identified various correlates of conspiracy beliefs, one factor that has received scant attention is depressive symptoms. We use three large surveys to explore the connection between depression and conspiracy beliefs. We find a consistent association, with the extent of the relationship depending on individual and situational factors. Interestingly, those from relatively advantaged demographic groups (i.e., White, male, high income, educated) exhibit a stronger relationship between depression and conspiracy beliefs than those not from such groups. Furthermore, situational variables that ostensibly increase stress--such as having COVID-19 or parenting during COVID-19--exacerbate the relationship while those that seem to decrease stress, such as social support, vitiate it. The results provide insight about the development of targeted interventions and accentuate the need for theorizing about the mechanisms that lead depression to correlate with conspiracy beliefs.

Published
2023
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19. Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens

Author

Jacobs, Maia, He, Jeffrey, Pradier, Melanie F., Lam, Barbara, Ahn, Andrew C., McCoy, Thomas H., Perlis, Roy H., Doshi-Velez, Finale, and Gajos, Krzysztof Z.

Subjects
Computer Science - Human-Computer Interaction
Abstract

Major depressive disorder is a debilitating disease affecting 264 million people worldwide. While many antidepressant medications are available, few clinical guidelines support choosing among them. Decision support tools (DSTs) embodying machine learning models may help improve the treatment selection process, but often fail in clinical practice due to poor system integration. We use an iterative, co-design process to investigate clinicians' perceptions of using DSTs in antidepressant treatment decisions. We identify ways in which DSTs need to engage with the healthcare sociotechnical system, including clinical processes, patient preferences, resource constraints, and domain knowledge. Our results suggest that clinical DSTs should be designed as multi-user systems that support patient-provider collaboration and offer on-demand explanations that address discrepancies between predictions and current standards of care. Through this work, we demonstrate how current trends in explainable AI may be inappropriate for clinical environments and consider paths towards designing these tools for real-world medical systems., Comment: To appear in ACM CHI Conference on Human Factors in Computing Systems (CHI '21), May 8-13, 2021, Yokohama, Japan

Published
2021
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20. Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible

Author

Pradier, Melanie F., Zazo, Javier, Parbhoo, Sonali, Perlis, Roy H., Zazzi, Maurizio, and Doshi-Velez, Finale

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

We propose Preferential MoE, a novel human-ML mixture-of-experts model that augments human expertise in decision making with a data-based classifier only when necessary for predictive performance. Our model exhibits an interpretable gating function that provides information on when human rules should be followed or avoided. The gating function is maximized for using human-based rules, and classification errors are minimized. We propose solving a coupled multi-objective problem with convex subproblems. We develop approximate algorithms and study their performance and convergence. Finally, we demonstrate the utility of Preferential MoE on two clinical applications for the treatment of Human Immunodeficiency Virus (HIV) and management of Major Depressive Disorder (MDD)., Comment: 10 pages, 5 figures, 4 tables, AMIA 2021 Virtual Informatics Summit

Published
2021

26. Prediction-Constrained Topic Models for Antidepressant Recommendation

Author

Hughes, Michael C., Hope, Gabriel, Weiner, Leah, McCoy, Thomas H., Perlis, Roy H., Sudderth, Erik B., and Doshi-Velez, Finale

Subjects
Computer Science - Learning, Statistics - Machine Learning
Abstract

Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks. We propose a framework for training supervised latent Dirichlet allocation that balances two goals: faithful generative explanations of high-dimensional data and accurate prediction of associated class labels. Existing approaches fail to balance these goals by not properly handling a fundamental asymmetry: the intended task is always predicting labels from data, not data from labels. Our new prediction-constrained objective trains models that predict labels from heldout data well while also producing good generative likelihoods and interpretable topic-word parameters. In a case study on predicting depression medications from electronic health records, we demonstrate improved recommendations compared to previous supervised topic models and high- dimensional logistic regression from words alone., Comment: Accepted poster at NIPS 2017 Workshop on Machine Learning for Health (https://ml4health.github.io/2017/)

Published
2017

27. Dysregulated protocadherin-pathway activity as an intrinsic defect in induced pluripotent stem cell–derived cortical interneurons from subjects with schizophrenia

Author

Shao, Zhicheng, Noh, Haneul, Bin Kim, Woong, Ni, Peiyan, Nguyen, Christine, Cote, Sarah E, Noyes, Elizabeth, Zhao, Joyce, Parsons, Teagan, Park, James M, Zheng, Kelvin, Park, Joshua J, Coyle, Joseph T, Weinberger, Daniel R, Straub, Richard E, Berman, Karen F, Apud, Jose, Ongur, Dost, Cohen, Bruce M, McPhie, Donna L, Rapoport, Judith L, Perlis, Roy H, Lanz, Thomas A, Xi, Hualin Simon, Yin, Changhong, Huang, Weihua, Hirayama, Teruyoshi, Fukuda, Emi, Yagi, Takeshi, Ghosh, Sulagna, Eggan, Kevin C, Kim, Hae-Young, Eisenberg, Leonard M, Moghadam, Alexander A, Stanton, Patric K, Cho, Jun-Hyeong, and Chung, Sangmi

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Neurosciences, Psychology, Pharmacology and Pharmaceutical Sciences, Stem Cell Research, Brain Disorders, Regenerative Medicine, Mental Health, Schizophrenia, 2.1 Biological and endogenous factors, Aetiology, Mental health, Neurological, Animals, Cadherins, Female, Humans, Induced Pluripotent Stem Cells, Interneurons, Male, Mice, Mice, Knockout, Prefrontal Cortex, Protocadherins, Signal Transduction, Synapses, Cognitive Sciences, Neurology & Neurosurgery, Biological psychology
Abstract

We generated cortical interneurons (cINs) from induced pluripotent stem cells derived from 14 healthy controls and 14 subjects with schizophrenia. Both healthy control cINs and schizophrenia cINs were authentic, fired spontaneously, received functional excitatory inputs from host neurons, and induced GABA-mediated inhibition in host neurons in vivo. However, schizophrenia cINs had dysregulated expression of protocadherin genes, which lie within documented schizophrenia loci. Mice lacking protocadherin-α showed defective arborization and synaptic density of prefrontal cortex cINs and behavioral abnormalities. Schizophrenia cINs similarly showed defects in synaptic density and arborization that were reversed by inhibitors of protein kinase C, a downstream kinase in the protocadherin pathway. These findings reveal an intrinsic abnormality in schizophrenia cINs in the absence of any circuit-driven pathology. They also demonstrate the utility of homogenous and functional populations of a relevant neuronal subtype for probing pathogenesis mechanisms during development.

Published
2019

28. Tissue- and cell-type-specific molecular and functional signatures of 16p11.2 reciprocal genomic disorder across mouse brain and human neuronal models

Author

Tai, Derek J.C., Razaz, Parisa, Erdin, Serkan, Gao, Dadi, Wang, Jennifer, Nuttle, Xander, de Esch, Celine E., Collins, Ryan L., Currall, Benjamin B., O’Keefe, Kathryn, Burt, Nicholas D., Yadav, Rachita, Wang, Lily, Mohajeri, Kiana, Aneichyk, Tatsiana, Ragavendran, Ashok, Stortchevoi, Alexei, Morini, Elisabetta, Ma, Weiyuan, Lucente, Diane, Hastie, Alex, Kelleher, Raymond J., Perlis, Roy H., Talkowski, Michael E., and Gusella, James F.

Published
2022
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29. Cerebrospinal fluid concentration of complement component 4A is increased in first episode schizophrenia

Author

Gracias, Jessica, Orhan, Funda, Hörbeck, Elin, Holmén-Larsson, Jessica, Khanlarkani, Neda, Malwade, Susmita, Goparaju, Sravan K., Schwieler, Lilly, Demirel, İlknur Ş., Fu, Ting, Fatourus-Bergman, Helena, Pelanis, Aurimantas, Goold, Carleton P., Goulding, Anneli, Annerbrink, Kristina, Isgren, Anniella, Sparding, Timea, Schalling, Martin, Yañez, Viviana A. Carcamo, Göpfert, Jens C., Nilsson, Johanna, Brinkmalm, Ann, Blennow, Kaj, Zetterberg, Henrik, Engberg, Göran, Piehl, Fredrik, Sheridan, Steven D., Perlis, Roy H., Cervenka, Simon, Erhardt, Sophie, Landen, Mikael, and Sellgren, Carl M.

Published
2022
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32. Identifying the Common Genetic Basis of Antidepressant Response

Author

Kasper, Siegfried, Zohar, Joseph, Souery, Daniel, Montgomery, Stuart, Albani, Diego, Forloni, Gianluigi, Ferentinos, Panagiotis, Rujescu, Dan, Mendlewicz, Julien, Wray, Naomi R., Ripke, Stephan, Mattheisen, Manuel, Trzaskowski, Maciej, Byrne, Enda M., Abdellaoui, Abdel, Adams, Mark J., Agerbo, Esben, Air, Tracy M., Andlauer, Till F.M., Bacanu, Silviu-Alin, Bækvad-Hansen, Marie, Beekman, Aartjan T.F., Bigdeli, Tim B., Binder, Elisabeth B., Bryois, Julien, Buttenschøn, Henriette N., Bybjerg-Grauholm, Jonas, Cai, Na, Castelao, Enrique, Christensen, Jane Hvarregaard, Clarke, Toni-Kim, Coleman, Jonathan R.I., Colodro-Conde, Lucía, Couvy-Duchesne, Baptiste, Craddock, Nick, Crawford, Gregory E., Davies, Gail, Deary, Ian J., Degenhardt, Franziska, Derks, Eske M., Direk, Nese, Dolan, Conor V., Dunn, Erin C., Eley, Thalia C., Escott-Price, Valentina, Hassan Kiadeh, Farnush Farhadi, Finucane, Hilary K., Foo, Jerome C., Forstner, Andreas J., Frank, Josef, Gaspar, Héléna A., Gill, Michael, Goes, Fernando S., Gordon, Scott D., Grove, Jakob, Hall, Lynsey S., Hansen, Christine Søholm, Hansen, Thomas F., Herms, Stefan, Hickie, Ian B., Hoffmann, Per, Homuth, Georg, Horn, Carsten, Hottenga, Jouke-Jan, Hougaard, David M., Howard, David M., Ising, Marcus, Jansen, Rick, Jones, Ian, Jones, Lisa A., Jorgenson, Eric, Knowles, James A., Kohane, Isaac S., Kraft, Julia, Kretzschmar, Warren W., Kutalik, Zoltán, Li, Yihan, Lind, Penelope A., MacIntyre, Donald J., MacKinnon, Dean F., Maier, Robert M., Maier, Wolfgang, Marchini, Jonathan, Mbarek, Hamdi, McGrath, Patrick, McGuffin, Peter, Medland, Sarah E., Mehta, Divya, Middeldorp, Christel M., Mihailov, Evelin, Milaneschi, Yuri, Milani, Lili, Mondimore, Francis M., Montgomery, Grant W., Mostafavi, Sara, Mullins, Niamh, Nauck, Matthias, Ng, Bernard, Nivard, Michel G., Nyholt, Dale R., O’Reilly, Paul F., Oskarsson, Hogni, Owen, Michael J., Painter, Jodie N., Pedersen, Carsten Bøcker, Pedersen, Marianne Giørtz, Peterson, Roseann E., Peyrot, Wouter J., Pistis, Giorgio, Posthuma, Danielle, Quiroz, Jorge A., Qvist, Per, Rice, John P., Riley, Brien P., Rivera, Margarita, Mirza, Saira Saeed, Schoevers, Robert, Schulte, Eva C., Shen, Ling, Shi, Jianxin, Shyn, Stanley I., Sigurdsson, Engilbert, Sinnamon, Grant C.B., Smit, Johannes H., Smith, Daniel J., Stefansson, Hreinn, Steinberg, Stacy, Streit, Fabian, Strohmaier, Jana, Tansey, Katherine E., Teismann, Henning, Teumer, Alexander, Thompson, Wesley, Thomson, Pippa A., Thorgeirsson, Thorgeir E., Traylor, Matthew, Treutlein, Jens, Trubetskoy, Vassily, Uitterlinden, André G., Umbricht, Daniel, Van der Auwera, Sandra, van Hemert, Albert M., Viktorin, Alexander, Visscher, Peter M., Wang, Yunpeng, Webb, Bradley T., Weinsheimer, Shantel Marie, Wellmann, Jürgen, Willemsen, Gonneke, Witt, Stephanie H., Wu, Yang, Xi, Hualin S., Yang, Jian, Zhang, Futao, Arolt, Volker, Baune, Bernhard T., Berger, Klaus, Boomsma, Dorret I., Cichon, Sven, Dannlowski, Udo, de Geus, E.J.C., DePaulo, J. Raymond, Domenici, Enrico, Domschke, Katharina, Esko, Tõnu, Grabe, Hans J., Hamilton, Steven P., Hayward, Caroline, Heath, Andrew C., Kendler, Kenneth S., Kloiber, Stefan, Lewis, Glyn, Li, Qingqin S., Lucae, Susanne, Madden, Pamela A.F., Magnusson, Patrik K., Martin, Nicholas G., McIntosh, Andrew M., Metspalu, Andres, Mors, Ole, Mortensen, Preben Bo, Müller-Myhsok, Bertram, Nordentoft, Merete, Nöthen, Markus M., O’Donovan, Michael C., Paciga, Sara A., Pedersen, Nancy L., Penninx, Brenda W.J.H., Perlis, Roy H., Porteous, David J., Potash, James B., Preisig, Martin, Rietschel, Marcella, Schaefer, Catherine, Schulze, Thomas G., Smoller, Jordan W., Stefansson, Kari, Tiemeier, Henning, Uher, Rudolf, Völzke, Henry, Weissman, Myrna M., Werge, Thomas, Lewis, Cathryn M., Levinson, Douglas F., Breen, Gerome, Børglum, Anders D., Sullivan, Patrick F., Pain, Oliver, Hodgson, Karen, Marshe, Victoria S., Campos, Adrian I., Carrillo-Roa, Tania, Cattaneo, Annamaria, Als, Thomas D., Dernovsek, Mojca Z., Fabbri, Chiara, Henigsberg, Neven, Hauser, Joanna, Kennedy, James L., Lenze, Eric J., Müller, Daniel J., Mulsant, Benoit H., Perroud, Nader, Rentería, Miguel E., Reynolds, Charles F., III, Wigmore, Eleanor M., Aitchison, Katherine J., Biernacka, Joanna M., Bondolfi, Guido, Kato, Masaki, Liu, Yu-Li, Serretti, Alessandro, Tsai, Shih-Jen, Turecki, Gustavo, and Weinshilboum, Richard

Published
2022
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35. Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models

Author

Hughes, Michael C., Weiner, Leah, Hope, Gabriel, McCoy Jr., Thomas H., Perlis, Roy H., Sudderth, Erik B., and Doshi-Velez, Finale

Subjects
Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Learning
Abstract

Supervisory signals have the potential to make low-dimensional data representations, like those learned by mixture and topic models, more interpretable and useful. We propose a framework for training latent variable models that explicitly balances two goals: recovery of faithful generative explanations of high-dimensional data, and accurate prediction of associated semantic labels. Existing approaches fail to achieve these goals due to an incomplete treatment of a fundamental asymmetry: the intended application is always predicting labels from data, not data from labels. Our prediction-constrained objective for training generative models coherently integrates loss-based supervisory signals while enabling effective semi-supervised learning from partially labeled data. We derive learning algorithms for semi-supervised mixture and topic models using stochastic gradient descent with automatic differentiation. We demonstrate improved prediction quality compared to several previous supervised topic models, achieving predictions competitive with high-dimensional logistic regression on text sentiment analysis and electronic health records tasks while simultaneously learning interpretable topics.

Published
2017

36. Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians.

Author

Rathnam, Sarah, Hart, Kamber L., Sharma, Abhishek, Verhaak, Pilar F., McCoy, Thomas H., Doshi-Velez, Finale, and Perlis, Roy H.

Subjects
SEROTONIN uptake inhibitors, MENTAL depression, ACADEMIC medical centers, ELECTRONIC health records, TRICYCLIC antidepressants
Abstract

Key Points: Question: To what extent do differences in clinician setting explain variability in major depression treatments and outcomes? Findings: In this cohort study derived from electronic health record data, antidepressant prescribing patterns and outcomes varied significantly between prescriber groups. Clinician clusters were significantly associated with clinical outcomes. Meaning: Studies of antidepressant prescribing in real-world settings, and efforts at risk stratification or personalization of care, should include information on treatment setting and other clinician-level factors alongside individual patient characteristics. Importance: While abundant work has examined patient-level differences in antidepressant treatment outcomes, little is known about the extent of clinician-level differences. Understanding these differences may be important in the development of risk models, precision treatment strategies, and more efficient systems of care. Objective: To characterize differences between outpatient clinicians in treatment selection and outcomes for their patients diagnosed with major depressive disorder across academic medical centers, community hospitals, and affiliated clinics. Design, Setting, and Participants: This was a longitudinal cohort study using data derived from electronic health records at 2 large academic medical centers and 6 community hospitals, and their affiliated outpatient networks, in eastern Massachusetts. Participants were deidentified clinicians who billed at least 10 International Classification of Diseases, Ninth Revision (ICD-9) or Tenth Revision (ICD-10) diagnoses of major depressive disorder per year between 2008 and 2022. Data analysis occurred between September 2023 and January 2024. Main Outcomes and Measures: Heterogeneity of prescribing, defined as the number of distinct antidepressants accounting for 75% of prescriptions by a given clinician; proportion of patients who did not return for follow-up after an index prescription; and proportion of patients receiving stable, ongoing antidepressant treatment. Results: Among 11 934 clinicians treating major depressive disorder, unsupervised learning identified 10 distinct clusters on the basis of ICD codes, corresponding to outpatient psychiatry as well as oncology, obstetrics, and primary care. Between these clusters, substantial variability was identified in the proportion of selective serotonin reuptake inhibitors, selective norepinephrine reuptake inhibitors, and tricyclic antidepressants prescribed, as well as in the number of distinct antidepressants prescribed. Variability was also detected between clinician clusters in loss to follow-up and achievement of stable treatment, with the former ranging from 27% to 69% and the latter from 22% to 42%. Clinician clusters were significantly associated with treatment outcomes. Conclusions and Relevance: Groups of clinicians treating individuals diagnosed with major depressive disorder exhibit marked differences in prescribing patterns as well as longitudinal patient outcomes defined by electronic health records. Incorporating these group identifiers yielded similar prediction to more complex models incorporating individual codes, suggesting the importance of considering treatment context in efforts at risk stratification. This cohort study investigates differences between outpatient clinicians in treatment selection and outcomes for their patients diagnosed with major depressive disorder across academic medical centers, community hospitals, and affiliated clinics. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Performance of a Maternal Risk Stratification System for Predicting Low Apgar Scores.

Author

Kishkovich, Thomas P., James, Kaitlyn E., McCoy, Thomas H., Perlis, Roy H., Kaimal, Anjali J., and Clapp, Mark A.

Subjects
DISEASE risk factors, RISK assessment, PREDICTION models, SECONDARY analysis, RECEIVER operating characteristic curves, CHILD health services, LOGISTIC regression analysis, RETROSPECTIVE studies, DESCRIPTIVE statistics, LONGITUDINAL method, APGAR score, GESTATIONAL age, PREGNANCY complications, CALIBRATION, DISCRIMINATION (Sociology), CONFIDENCE intervals, CHILDREN
Abstract

Objective Maternal risk stratification systems are increasingly employed in predicting and preventing obstetric complications. These systems focus primarily on maternal morbidity, and few tools exist to stratify neonatal risk. We sought to determine if a maternal risk stratification score was associated with neonatal morbidity. Study Design Retrospective cohort study of patients with liveborn infants born at ≥24 weeks at four hospitals in one health system between January 1, 2020, and December 31, 2020. The Expanded Obstetric Comorbidity Score (EOCS) is used as the maternal risk score. The primary neonatal outcome was 5-minute Apgar <7. Logistic regression models determined associations between EOCS and neonatal morbidity. Secondary analyses were performed, including stratifying outcomes by gestational age and limiting analysis to "low-risk" term singletons. Model discrimination assessed using the area under the receiver operating characteristic curves (AUC) and calibration via calibration plots. Results A total of 14,497 maternal–neonatal pairs were included; 236 (1.6%) had 5-minute Apgar <7; EOCS was higher in 5-minute Apgar <7 group (median 41 vs. 11, p < 0.001). AUC for EOCS in predicting Apgar <7 was 0.72 (95% Confidence Interval (CI) 0.68, 0.75), demonstrating relatively good discrimination. Calibration plot revealed that those in the highest EOCS decile had higher risk of neonatal morbidity (7.6 vs. 1.7%, p < 0.001). When stratified by gestational age, discrimination weakened with advancing gestational age: AUC 0.70 for <28 weeks, 0.63 for 28 to 31 weeks, 0.64 for 32 to 36 weeks, and 0.61 for ≥37 weeks. When limited to term low-risk singletons, EOCS had lower discrimination for predicting neonatal morbidity and was not well calibrated. Conclusion A maternal morbidity risk stratification system does not perform well in most patients giving birth, at low risk for neonatal complications. The findings suggest that the association between EOCS and 5-minute Apgar <7 likely reflects a relationship with prematurity. This study cautions against intentional or unintentional extrapolation of maternal morbidity risk for neonatal risk, especially for term deliveries. Key Points EOCS had moderate discrimination for Apgar <7. Predictive performance declined when limited to low-risk term singletons. Relationship between EOCS and Apgar <7 was likely driven by prematurity. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Risk of Incident Psychosis and Mania With Prescription Amphetamines.

Author

Moran, Lauren V., Skinner, Joseph P., Shinn, Ann K., Nielsen, Kathryn, Rao, Vinod, Taylor, S. Trevor, Cohen, Talia R., Erkol, Cemre, Merchant, Jaisal, Mujica, Christin A., Perlis, Roy H., and Ongur, Dost

Subjects
SUBSTANCE-induced disorders, BIPOLAR disorder, PSYCHOSES, ELECTRONIC health records, PEOPLE with mental illness
Abstract

Objective: Amphetamine prescribing has increased in the United States in recent years. Previous research identified an increased risk of incident psychosis with prescription amphetamines. The purpose of this study was to examine the impact of dose levels of prescription amphetamines on the risk of this rare but serious adverse outcome. Methods: A case-control study using electronic health records was conducted to compare the odds of incident psychosis or mania with past-month exposure to prescription amphetamines. Case subjects were patients ages 16–35 hospitalized at McLean Hospital for incident psychosis or mania between 2005 and 2019. Control subjects were patients with an initial psychiatric hospitalization for other reasons, most commonly depression and/or anxiety. Amphetamine doses were converted to dextroamphetamine equivalents and divided into terciles. Secondary analyses evaluated the odds of psychosis or mania with methylphenidate use. Results: Among 1,374 case subjects and 2,748 control subjects, the odds of psychosis and mania were increased for individuals with past-month prescription amphetamine use compared with no use (adjusted odds ratio=2.68, 95% CI=1.90–3.77). A dose-response relationship was observed; high doses of amphetamines (>30 mg dextroamphetamine equivalents) were associated with 5.28-fold increased odds of psychosis or mania. Past-month methylphenidate use was not associated with increased odds of psychosis or mania compared with no use (adjusted odds ratio=0.91, 95% CI=0.54–1.55). Conclusions: Although use of hospitalized control subjects excludes individuals with less severe disease, leading to selection bias, the study results suggest that caution should be exercised when prescribing high doses of amphetamines, with regular screening for symptoms of psychosis or mania. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Analysis of the Influence of microRNAs in Lithium Response in Bipolar Disorder

Author

Reinbold, Céline S, Forstner, Andreas J, Hecker, Julian, Fullerton, Janice M, Hoffmann, Per, Hou, Liping, Heilbronner, Urs, Degenhardt, Franziska, Adli, Mazda, Akiyama, Kazufumi, Akula, Nirmala, Ardau, Raffaella, Arias, Bárbara, Backlund, Lena, Benabarre, Antonio, Bengesser, Susanne, Bhattacharjee, Abesh K, Biernacka, Joanna M, Birner, Armin, Marie-Claire, Cynthia, Cervantes, Pablo, Chen, Guo-Bo, Chen, Hsi-Chung, Chillotti, Caterina, Clark, Scott R, Colom, Francesc, Cousins, David A, Cruceanu, Cristiana, Czerski, Piotr M, Dayer, Alexandre, Étain, Bruno, Falkai, Peter, Frisén, Louise, Gard, Sébastien, Garnham, Julie S, Goes, Fernando S, Grof, Paul, Gruber, Oliver, Hashimoto, Ryota, Hauser, Joanna, Herms, Stefan, Jamain, Stéphane, Jiménez, Esther, Kahn, Jean-Pierre, Kassem, Layla, Kittel-Schneider, Sarah, Kliwicki, Sebastian, König, Barbara, Kusumi, Ichiro, Lackner, Nina, Laje, Gonzalo, Landén, Mikael, Lavebratt, Catharina, Leboyer, Marion, Leckband, Susan G, Jaramillo, Carlos A López, MacQueen, Glenda, Manchia, Mirko, Martinsson, Lina, Mattheisen, Manuel, McCarthy, Michael J, McElroy, Susan L, Mitjans, Marina, Mondimore, Francis M, Monteleone, Palmiero, Nievergelt, Caroline M, Ösby, Urban, Ozaki, Norio, Perlis, Roy H, Pfennig, Andrea, Reich-Erkelenz, Daniela, Rouleau, Guy A, Schofield, Peter R, Schubert, K Oliver, Schweizer, Barbara W, Seemüller, Florian, Severino, Giovanni, Shekhtman, Tatyana, Shilling, Paul D, Shimoda, Kazutaka, Simhandl, Christian, Slaney, Claire M, Smoller, Jordan W, Squassina, Alessio, Stamm, Thomas J, Stopkova, Pavla, Tighe, Sarah K, Tortorella, Alfonso, Turecki, Gustavo, Volkert, Julia, Witt, Stephanie H, Wright, Adam J, Young, L Trevor, Zandi, Peter P, Potash, James B, DePaulo, J Raymond, Bauer, Michael, Reininghaus, Eva, Novák, Tomáš, and Aubry, Jean-Michel

Subjects
Mental Health, Brain Disorders, Clinical Research, Serious Mental Illness, Bipolar Disorder, Human Genome, Genetics, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, Mental health, bipolar disorder, lithium response, microRNA, common variants, genome-wide association study, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Bipolar disorder (BD) is a common, highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. Lithium is the best-established long-term treatment for BD, even though individual response is highly variable. Evidence suggests that some of this variability has a genetic basis. This is supported by the largest genome-wide association study (GWAS) of lithium response to date conducted by the International Consortium on Lithium Genetics (ConLiGen). Recently, we performed the first genome-wide analysis of the involvement of miRNAs in BD and identified nine BD-associated miRNAs. However, it is unknown whether these miRNAs are also associated with lithium response in BD. In the present study, we therefore tested whether common variants at these nine candidate miRNAs contribute to the variance in lithium response in BD. Furthermore, we systematically analyzed whether any other miRNA in the genome is implicated in the response to lithium. For this purpose, we performed gene-based tests for all known miRNA coding genes in the ConLiGen GWAS dataset (n = 2,563 patients) using a set-based testing approach adapted from the versatile gene-based test for GWAS (VEGAS2). In the candidate approach, miR-499a showed a nominally significant association with lithium response, providing some evidence for involvement in both development and treatment of BD. In the genome-wide miRNA analysis, 71 miRNAs showed nominally significant associations with the dichotomous phenotype and 106 with the continuous trait for treatment response. A total of 15 miRNAs revealed nominal significance in both phenotypes with miR-633 showing the strongest association with the continuous trait (p = 9.80E-04) and miR-607 with the dichotomous phenotype (p = 5.79E-04). No association between miRNAs and treatment response to lithium in BD in either of the tested conditions withstood multiple testing correction. Given the limited power of our study, the investigation of miRNAs in larger GWAS samples of BD and lithium response is warranted.

Published
2018

42. Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits

Author

Kang, JooEun, primary, Castro, Victor M., additional, Ripperger, Michael, additional, Venkatesh, Sanan, additional, Burstein, David, additional, Linnér, Richard Karlsson, additional, Rocha, Daniel B., additional, Hu, Yirui, additional, Wilimitis, Drew, additional, Morley, Theodore, additional, Han, Lide, additional, Kim, Rachel Youngjung, additional, Feng, Yen-Chen Anne, additional, Ge, Tian, additional, Heckers, Stephan, additional, Voloudakis, Georgios, additional, Chabris, Christopher, additional, Roussos, Panos, additional, McCoy, Thomas H, additional, Walsh, Colin G., additional, Perlis, Roy H., additional, and Ruderfer, Douglas M., additional

Published
2024
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44. Tissue- and cell-type-specific molecular and functional signatures of 16p11.2 reciprocal genomic disorder across mouse brain and human neuronal models

Author

Tai, Derek J.C., primary, Razaz, Parisa, additional, Erdin, Serkan, additional, Gao, Dadi, additional, Wang, Jennifer, additional, Nuttle, Xander, additional, de Esch, Celine E., additional, Collins, Ryan L., additional, Currall, Benjamin B., additional, O’Keefe, Kathryn, additional, Burt, Nicholas D., additional, Yadav, Rachita, additional, Wang, Lily, additional, Mohajeri, Kiana, additional, Aneichyk, Tatsiana, additional, Ragavendran, Ashok, additional, Stortchevoi, Alexei, additional, Morini, Elisabetta, additional, Ma, Weiyuan, additional, Lucente, Diane, additional, Hastie, Alex, additional, Kelleher, Raymond J., additional, Perlis, Roy H., additional, Talkowski, Michael E., additional, and Gusella, James F., additional

Published
2024
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47. Phenotypic signatures in clinical data enable systematic identification of patients for genetic testing

Author

Morley, Theodore J., Han, Lide, Castro, Victor M., Morra, Jonathan, Perlis, Roy H., Cox, Nancy J., and Bastarache, Lisa

Subjects
Rare diseases -- Diagnosis -- Genetic aspects, Genetic screening -- Methods, Biological sciences, Health
Abstract

Around 5% of the population is affected by a rare genetic disease, yet most endure years of uncertainty before receiving a genetic test. A common feature of genetic diseases is the presence of multiple rare phenotypes that often span organ systems. Here, we use diagnostic billing information from longitudinal clinical data in the electronic health records (EHRs) of 2,286 patients who received a chromosomal microarray test, and 9,144 matched controls, to build a model to predict who should receive a genetic test. The model achieved high prediction accuracies in a held-out test sample (area under the receiver operating characteristic curve (AUROC), 0.97; area under the precision-recall curve (AUPRC), 0.92), in an independent hospital system (AUROC, 0.95; AUPRC, 0.62), and in an independent set of 172,265 patients in which cases were broadly defined as having an interaction with a genetics provider (AUROC, 0.9; AUPRC, 0.63). Patients carrying a putative pathogenic copy number variant were also accurately identified by the model. Compared with current approaches for genetic test determination, our model could identify more patients for testing while also increasing the proportion of those tested who have a genetic disease. We demonstrate that phenotypic patterns representative of a wide range of genetic diseases can be captured from EHRs to systematize decision-making for genetic testing, with the potential to speed up diagnosis, improve care and reduce costs. Machine learning of electronic health records identifies individuals with a high suspicion of a wide range of genetic diseases and prioritizes those individuals for genetic testing., Author(s): Theodore J. Morley [sup.1] [sup.2] , Lide Han [sup.1] [sup.2] , Victor M. Castro [sup.3] , Jonathan Morra [sup.4] , Roy H. Perlis [sup.3] , Nancy J. Cox [sup.1] [...]

Published
2021
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49. The association between genetically determined ABO blood types and major depressive disorder

Author

Garvert, Linda, Baune, Bernhard T, Berger, Klaus, Boomsma, Dorret I, Breen, Gerome, Greinacher, Andreas, Hamilton, Steven P, Levinson, Douglas F, Lewis, Cathryn M, Lucae, Susanne, Magnusson, Patrik K E, Martin, Nicholas G, McIntosh, Andrew M, Mors, Ole, Müller-Myhsok, Bertram, Penninx, Brenda W J H, Perlis, Roy H, Pistis, Giorgio, Potash, James B, Preisig, Martin, Rietschel, Marcella, Shi, Jianxin, Smoller, Jordan W, Tiemeier, Henning, Uher, Rudolf, Völker, Uwe, Völzke, Henry, Weissman, Myrna M, Grabe, Hans J, and Van der Auwera, Sandra

Published
2021
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50. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology

Author

Mullins, Niamh, Forstner, Andreas J., O’Connell, Kevin S., Coombes, Brandon, Coleman, Jonathan R. I., Qiao, Zhen, Als, Thomas D., Bigdeli, Tim B., Børte, Sigrid, Bryois, Julien, Charney, Alexander W., Drange, Ole Kristian, Gandal, Michael J., Hagenaars, Saskia P., Ikeda, Masashi, Kamitaki, Nolan, Kim, Minsoo, Krebs, Kristi, Panagiotaropoulou, Georgia, Schilder, Brian M., Sloofman, Laura G., Steinberg, Stacy, Trubetskoy, Vassily, Winsvold, Bendik S., Won, Hong-Hee, Abramova, Liliya, Adorjan, Kristina, Agerbo, Esben, Al Eissa, Mariam, Albani, Diego, Alliey-Rodriguez, Ney, Anjorin, Adebayo, Antilla, Verneri, Antoniou, Anastasia, Awasthi, Swapnil, Baek, Ji Hyun, Bækvad-Hansen, Marie, Bass, Nicholas, Bauer, Michael, Beins, Eva C., Bergen, Sarah E., Birner, Armin, Bøcker Pedersen, Carsten, Bøen, Erlend, Boks, Marco P., Bosch, Rosa, Brum, Murielle, Brumpton, Ben M., Brunkhorst-Kanaan, Nathalie, Budde, Monika, Bybjerg-Grauholm, Jonas, Byerley, William, Cairns, Murray, Casas, Miquel, Cervantes, Pablo, Clarke, Toni-Kim, Cruceanu, Cristiana, Cuellar-Barboza, Alfredo, Cunningham, Julie, Curtis, David, Czerski, Piotr M., Dale, Anders M., Dalkner, Nina, David, Friederike S., Degenhardt, Franziska, Djurovic, Srdjan, Dobbyn, Amanda L., Douzenis, Athanassios, Elvsåshagen, Torbjørn, Escott-Price, Valentina, Ferrier, I. Nicol, Fiorentino, Alessia, Foroud, Tatiana M., Forty, Liz, Frank, Josef, Frei, Oleksandr, Freimer, Nelson B., Frisén, Louise, Gade, Katrin, Garnham, Julie, Gelernter, Joel, Giørtz Pedersen, Marianne, Gizer, Ian R., Gordon, Scott D., Gordon-Smith, Katherine, Greenwood, Tiffany A., Grove, Jakob, Guzman-Parra, José, Ha, Kyooseob, Haraldsson, Magnus, Hautzinger, Martin, Heilbronner, Urs, Hellgren, Dennis, Herms, Stefan, Hoffmann, Per, Holmans, Peter A., Huckins, Laura, Jamain, Stéphane, Johnson, Jessica S., Kalman, Janos L., Kamatani, Yoichiro, Kennedy, James L., Kittel-Schneider, Sarah, Knowles, James A., Kogevinas, Manolis, Koromina, Maria, Kranz, Thorsten M., Kranzler, Henry R., Kubo, Michiaki, Kupka, Ralph, Kushner, Steven A., Lavebratt, Catharina, Lawrence, Jacob, Leber, Markus, Lee, Heon-Jeong, Lee, Phil H., Levy, Shawn E., Lewis, Catrin, Liao, Calwing, Lucae, Susanne, Lundberg, Martin, MacIntyre, Donald J., Magnusson, Sigurdur H., Maier, Wolfgang, Maihofer, Adam, Malaspina, Dolores, Maratou, Eirini, Martinsson, Lina, Mattheisen, Manuel, McCarroll, Steven A., McGregor, Nathaniel W., McGuffin, Peter, McKay, James D., Medeiros, Helena, Medland, Sarah E., Millischer, Vincent, Montgomery, Grant W., Moran, Jennifer L., Morris, Derek W., Mühleisen, Thomas W., O’Brien, Niamh, O’Donovan, Claire, Olde Loohuis, Loes M., Oruc, Lilijana, Papiol, Sergi, Pardiñas, Antonio F., Perry, Amy, Pfennig, Andrea, Porichi, Evgenia, Potash, James B., Quested, Digby, Raj, Towfique, Rapaport, Mark H., DePaulo, J. Raymond, Regeer, Eline J., Rice, John P., Rivas, Fabio, Rivera, Margarita, Roth, Julian, Roussos, Panos, Ruderfer, Douglas M., Sánchez-Mora, Cristina, Schulte, Eva C., Senner, Fanny, Sharp, Sally, Shilling, Paul D., Sigurdsson, Engilbert, Sirignano, Lea, Slaney, Claire, Smeland, Olav B., Smith, Daniel J., Sobell, Janet L., Søholm Hansen, Christine, Soler Artigas, Maria, Spijker, Anne T., Stein, Dan J., Strauss, John S., Świątkowska, Beata, Terao, Chikashi, Thorgeirsson, Thorgeir E., Toma, Claudio, Tooney, Paul, Tsermpini, Evangelia-Eirini, Vawter, Marquis P., Vedder, Helmut, Walters, James T. R., Witt, Stephanie H., Xi, Simon, Xu, Wei, Yang, Jessica Mei Kay, Young, Allan H., Young, Hannah, Zandi, Peter P., Zhou, Hang, Zillich, Lea, Adolfsson, Rolf, Agartz, Ingrid, Alda, Martin, Alfredsson, Lars, Babadjanova, Gulja, Backlund, Lena, Baune, Bernhard T., Bellivier, Frank, Bengesser, Susanne, Berrettini, Wade H., Blackwood, Douglas H. R., Boehnke, Michael, Børglum, Anders D., Breen, Gerome, Carr, Vaughan J., Catts, Stanley, Corvin, Aiden, Craddock, Nicholas, Dannlowski, Udo, Dikeos, Dimitris, Esko, Tõnu, Etain, Bruno, Ferentinos, Panagiotis, Frye, Mark, Fullerton, Janice M., Gawlik, Micha, Gershon, Elliot S., Goes, Fernando S., Green, Melissa J., Grigoroiu-Serbanescu, Maria, Hauser, Joanna, Henskens, Frans, Hillert, Jan, Hong, Kyung Sue, Hougaard, David M., Hultman, Christina M., Hveem, Kristian, Iwata, Nakao, Jablensky, Assen V., Jones, Ian, Jones, Lisa A., Kahn, René S., Kelsoe, John R., Kirov, George, Landén, Mikael, Leboyer, Marion, Lewis, Cathryn M., Li, Qingqin S., Lissowska, Jolanta, Lochner, Christine, Loughland, Carmel, Martin, Nicholas G., Mathews, Carol A., Mayoral, Fermin, McElroy, Susan L., McIntosh, Andrew M., McMahon, Francis J., Melle, Ingrid, Michie, Patricia, Milani, Lili, Mitchell, Philip B., Morken, Gunnar, Mors, Ole, Mortensen, Preben Bo, Mowry, Bryan, Müller-Myhsok, Bertram, Myers, Richard M., Neale, Benjamin M., Nievergelt, Caroline M., Nordentoft, Merete, Nöthen, Markus M., O’Donovan, Michael C., Oedegaard, Ketil J., Olsson, Tomas, Owen, Michael J., Paciga, Sara A., Pantelis, Chris, Pato, Carlos, Pato, Michele T., Patrinos, George P., Perlis, Roy H., Posthuma, Danielle, Ramos-Quiroga, Josep Antoni, Reif, Andreas, Reininghaus, Eva Z., Ribasés, Marta, Rietschel, Marcella, Ripke, Stephan, Rouleau, Guy A., Saito, Takeo, Schall, Ulrich, Schalling, Martin, Schofield, Peter R., Schulze, Thomas G., Scott, Laura J., Scott, Rodney J., Serretti, Alessandro, Shannon Weickert, Cynthia, Smoller, Jordan W., Stefansson, Hreinn, Stefansson, Kari, Stordal, Eystein, Streit, Fabian, Sullivan, Patrick F., Turecki, Gustavo, Vaaler, Arne E., Vieta, Eduard, Vincent, John B., Waldman, Irwin D., Weickert, Thomas W., Werge, Thomas, Wray, Naomi R., Zwart, John-Anker, Biernacka, Joanna M., Nurnberger, John I., Cichon, Sven, Edenberg, Howard J., Stahl, Eli A., McQuillin, Andrew, Di Florio, Arianna, Ophoff, Roel A., and Andreassen, Ole A.

Published
2021
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