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5. Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning

Author

Corponi, Filippo, Li, Bryan M., Anmella, Gerard, Valenzuela-Pascual, Clàudia, Mas, Ariadna, Pacchiarotti, Isabella, Valentí, Marc, Grande, Iria, Benabarre, Antonio, Garriga, Marina, Vieta, Eduard, Young, Allan H, Lawrie, Stephen M., Whalley, Heather C., Hidalgo-Mazzei, Diego, and Vergari, Antonio

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Signal Processing
Abstract

Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of worldwide disease burden. However, collecting and annotating wearable data is very resource-intensive. Studies of this kind can thus typically afford to recruit only a couple dozens of patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MDs detection. In this paper, we overcome this data bottleneck and advance the detection of MDs acute episode vs stable state from wearables data on the back of recent advances in self-supervised learning (SSL). This leverages unlabelled data to learn representations during pre-training, subsequently exploited for a supervised task. First, we collected open-access datasets recording with an Empatica E4 spanning different, unrelated to MD monitoring, personal sensing tasks -- from emotion recognition in Super Mario players to stress detection in undergraduates -- and devised a pre-processing pipeline performing on-/off-body detection, sleep-wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduce E4SelfLearning, the largest to date open access collection, and its pre-processing pipeline. Second, we show that SSL confidently outperforms fully-supervised pipelines using either our novel E4-tailored Transformer architecture (E4mer) or classical baseline XGBoost: 81.23% against 75.35% (E4mer) and 72.02% (XGBoost) correctly classified recording segments from 64 (half acute, half stable) patients. Lastly, we illustrate that SSL performance is strongly associated with the specific surrogate task employed for pre-training as well as with unlabelled data availability.

Published
2023
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6. Olanzapine for young PEople with aNorexia nervosa (OPEN): results of a feasibility study

Author

Said, Olena, Stringer, Dominic, Sengun Filiz, Ece, Mutwalli, Hiba, Bektas, Sevgi, Akkese, Melahat Nur, Kellermann, Vanessa, Ireland, Katie, Tyrrell-Bunge, Elizabeth, Beishon-Murley, Demelza, Khor, Joel W. T., Allman, Lee, Barker, Joanna, Kotze, Nicus, Carter, Ben, Simic, Mima, Sually, Dilveer Singh, Bentley, Jessica, Young, Allan H., Madden, Sloane, Byford, Sarah, Landau, Sabine, Lawrence, Vanessa, Treasure, Janet, Schmidt, Ulrike, Nicholls, Dasha, and Himmerich, Hubertus

Published
2024
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10. Opioid Mechanisms and the Treatment of Depression

Author

Jelen, Luke A., Young, Allan H., Mehta, Mitul A., Ellenbroek, Bart A., Series Editor, Barnes, Thomas R. E., Series Editor, Andersen, Susan L., Series Editor, Paulus, Martin P., Series Editor, Olivier, Jocelien, Series Editor, Browning, Michael, editor, Cowen, Philip J., editor, and Sharp, Trevor, editor

Published
2024
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11. Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Author

Fu, Cynthia H. Y., Antoniades, Mathilde, Erus, Guray, Garcia, Jose A., Fan, Yong, Arnone, Danilo, Arnott, Stephen R., Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N., Frokjaer, Vibe G., Ganz, Melanie, Godlewska, Beata R., Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M., Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D., Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C., Tosun, Duygu, Victor, Teresa A., Wei, Dongtao, Wise, Toby, Zahn, Roland, Anderson, Ian M., Craighead, W. Edward, Deakin, J. F. William, Dunlop, Boadie W., Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H., Harmer, Catherine J., Kennedy, Sidney H., Knudsen, Gitte M., Mayberg, Helen S., Paulus, Martin P., Qiu, Jiang, Trivedi, Madhukar H., Whalley, Heather C., Yan, Chao-Gan, Young, Allan H., and Davatzikos, Christos

Published
2024
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13. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

Author

Fu, Cynthia HY, Erus, Guray, Fan, Yong, Antoniades, Mathilde, Arnone, Danilo, Arnott, Stephen R, Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N, Frokjaer, Vibe G, Ganz, Melanie, Garcia, Jose, Godlewska, Beata R, Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M, Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D, Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C, Tosun, Duygu, Victor, Teresa A, Wei, Dongtao, Wise, Toby, Woodham, Rachel D, Zahn, Roland, Anderson, Ian M, Deakin, JF William, Dunlop, Boadie W, Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H, Harmer, Catherine J, Kennedy, Sidney H, Knudsen, Gitte M, Mayberg, Helen S, Paulus, Martin P, Qiu, Jiang, Trivedi, Madhukar H, Whalley, Heather C, Yan, Chao-Gan, Young, Allan H, and Davatzikos, Christos

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Biomedical Imaging, Neurosciences, Brain Disorders, Clinical Research, Depression, Bioengineering, Serious Mental Illness, Major Depressive Disorder, Mental Health, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Neurological, Mental health, Humans, Depressive Disorder, Major, Prospective Studies, Reproducibility of Results, Brain, Neuroimaging, Magnetic Resonance Imaging, Artificial Intelligence, Classification, Biomarkers, Deep learning, Harmonization, Predictors, MRI, Public Health and Health Services, Psychiatry, Clinical sciences, Epidemiology, Clinical and health psychology
Abstract

BackgroundEfforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.MethodsWe describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.ResultsWe are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.ConclusionWe describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Published
2023

14. Publisher Correction: Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Author

Fu, Cynthia H. Y., Antoniades, Mathilde, Erus, Guray, Garcia, Jose A., Fan, Yong, Arnone, Danilo, Arnott, Stephen R., Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N., Frokjaer, Vibe G., Ganz, Melanie, Godlewska, Beata R., Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M., Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D., Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C., Tosun, Duygu, Victor, Teresa A., Wei, Dongtao, Wise, Toby, Zahn, Roland, Anderson, Ian M., Craighead, W. Edward, Deakin, J. F. William, Dunlop, Boadie W., Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H., Harmer, Catherine J., Kennedy, Sidney H., Knudsen, Gitte M., Mayberg, Helen S., Paulus, Martin P., Qiu, Jiang, Trivedi, Madhukar H., Whalley, Heather C., Yan, Chao-Gan, Young, Allan H., and Davatzikos, Christos

Published
2024
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15. Predictors of functional impairment in bipolar disorder: Results from 13 cohorts from seven countries by the global bipolar cohort collaborative.

Author

Burdick, Katherine, Millett, Caitlin, Yocum, Anastasia, Altimus, Cara, Andreassen, Ole, Aubin, Valerie, Belzeaux, Raoul, Berk, Michael, Biernacka, Joanna, Blumberg, Hilary, Cleare, Anthony, Diaz-Byrd, Claudia, Dubertret, Caroline, Etain, Bruno, Lagerberg, Trine, Leboyer, Marion, Martinez-Aran, Anabel, McElroy, Susan, Mitchell, Philip, Olie, Emilie, Olorunfemi, Phebe, Passerieux, Christine, Peters, Amy, Pham, Daniel, Polosan, Mircea, Potter, Julia, Sajatovic, Martha, Samalin, Ludovic, Schwan, Raymund, Shanahan, Megan, Solé, Brisa, Strawbridge, Rebecca, Stuart, Amanda, Torres, Ivan, Ueland, Torrill, Vieta, Eduard, Williams, Lana, Wrobel, Anna, Yatham, Lakshmi, Young, Allan, Nierenberg, Andrew, McInnis, Melvin, Forester, Brent, Fullerton, Janice, Frye, Mark, Gard, Sébastien, Godin, Ophelia, Haffen, Emmanuel, Eyler, Lisa, and Klaus, Federica

Subjects
Humans, Bipolar Disorder, Prospective Studies, Longitudinal Studies, Affect, Cohort Studies
Abstract

OBJECTIVES: Persistent functional impairment is common in bipolar disorder (BD) and is influenced by a number of demographic, clinical, and cognitive features. The goal of this project was to estimate and compare the influence of key factors on community function in multiple cohorts of well-characterized samples of individuals with BD. METHODS: Thirteen cohorts from 7 countries included n = 5882 individuals with BD across multiple sites. The statistical approach consisted of a systematic uniform application of analyses across sites. Each site performed a logistic regression analysis with empirically derived higher versus lower function as the dependent variable and selected clinical and demographic variables as predictors. RESULTS: We found high rates of functional impairment, ranging from 41 to 75%. Lower community functioning was associated with depressive symptoms in 10 of 12 of the cohorts that included this variable in the analysis. Lower levels of education, a greater number of prior mood episodes, the presence of a comorbid substance use disorder, and a greater total number of psychotropic medications were also associated with low functioning. CONCLUSIONS: The bipolar clinical research community is poised to work together to characterize the multi-dimensional contributors to impairment and address the barriers that impede patients complete recovery. We must also identify the core features which enable many to thrive and live successfully with BD. A large-scale, worldwide, prospective longitudinal study focused squarely on BD and its heterogeneous presentations will serve as a platform for discovery and promote major advances toward optimizing outcomes for every individual with this illness.

Published
2022

16. The clinical characterization of the adult patient with bipolar disorder aimed at personalization of management

Author

McIntyre, Roger S, Alda, Martin, Baldessarini, Ross J, Bauer, Michael, Berk, Michael, Correll, Christoph U, Fagiolini, Andrea, Fountoulakis, Kostas, Frye, Mark A, Grunze, Heinz, Kessing, Lars V, Miklowitz, David J, Parker, Gordon, Post, Robert M, Swann, Alan C, Suppes, Trisha, Vieta, Eduard, Young, Allan, and Maj, Mario

Subjects
Health Services and Systems, Health Sciences, Clinical Research, Serious Mental Illness, Patient Safety, Prevention, Behavioral and Social Science, Suicide, Mental Health, Brain Disorders, Management of diseases and conditions, 7.3 Management and decision making, 7.1 Individual care needs, Mental health, Good Health and Well Being, Bipolar disorder, clinical characterization, phenotyping, subtypes, mixed features, cognition, rapid cycling, trauma, comorbidity, social determinants, stigma, stressors, resilience, bipolar I disorder, bipolar II disorder, mania, depression, personalization, Clinical Sciences, Psychiatry, Clinical sciences, Health services and systems
Abstract

Bipolar disorder is heterogeneous in phenomenology, illness trajectory, and response to treatment. Despite evidence for the efficacy of multimodal-ity interventions, the majority of persons affected by this disorder do not achieve and sustain full syndromal recovery. It is eagerly anticipated that combining datasets across various information sources (e.g., hierarchical "multi-omic" measures, electronic health records), analyzed using advanced computational methods (e.g., machine learning), will inform future diagnosis and treatment selection. In the interim, identifying clinically meaningful subgroups of persons with the disorder having differential response to specific treatments at point-of-care is an empirical priority. This paper endeavours to synthesize salient domains in the clinical characterization of the adult patient with bipolar disorder, with the overarching aim to improve health outcomes by informing patient management and treatment considerations. Extant data indicate that characterizing select domains in bipolar disorder provides actionable information and guides shared decision making. For example, it is robustly established that the presence of mixed features - especially during depressive episodes - and of physical and psychiatric comorbidities informs illness trajectory, response to treatment, and suicide risk. In addition, early environmental exposures (e.g., sexual and physical abuse, emotional neglect) are highly associated with more complicated illness presentations, inviting the need for developmentally-oriented and integrated treatment approaches. There have been significant advances in validating subtypes of bipolar disorder (e.g., bipolar I vs. II disorder), particularly in regard to pharmacological interventions. As with other severe mental disorders, social functioning, interpersonal/family relationships and internalized stigma are domains highly relevant to relapse risk, health outcomes, and quality of life. The elevated standardized mortality ratio for completed suicide and suicidal behaviour in bipolar disorder invites the need for characterization of this domain in all patients. The framework of this paper is to describe all the above salient domains, providing a synthesis of extant literature and recommendations for decision support tools and clinical metrics that can be implemented at point-of-care.

Published
2022

19. The role of the psychedelic experience in psilocybin treatment for treatment-resistant depression

Author

Goodwin, Guy M., Aaronson, Scott T., Alvarez, Oscar, Carhart-Harris, Robin, Chai-Rees, Jamie, Croal, Megan, DeBattista, Charles, Dunlop, Boadie W., Feifel, David, Hellerstein, David J., Husain, Muhammad I., Kelly, John R., Kirlic, Namik, Licht, Rasmus W., Marwood, Lindsey, Meyer, Thomas D., Mistry, Sunil, Nowakowska, Ania, Páleníček, Tomáš, Repantis, Dimitris, Schoevers, Robert A., Simmons, Hollie, Somers, Metten, Teoh, Emma, Tsai, Joyce, Wahba, Mourad, Williams, Sam, Young, Allan H., Young, Matthew B., Zisook, Sidney, and Malievskaia, Ekaterina

Published
2025
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30. A leaky umbrella has little value: evidence clearly indicates the serotonin system is implicated in depression

Author

Jauhar, Sameer, Arnone, Danilo, Baldwin, David S., Bloomfield, Michael, Browning, Michael, Cleare, Anthony J., Corlett, Phillip, Deakin, J. F. William, Erritzoe, David, Fu, Cynthia, Fusar-Poli, Paolo, Goodwin, Guy M., Hayes, Joseph, Howard, Robert, Howes, Oliver D., Juruena, Mario F., Lam, Raymond W., Lawrie, Stephen M., McAllister-Williams, Hamish, Marwaha, Steven, Matuskey, David, McCutcheon, Robert A., Nutt, David J., Pariante, Carmine, Pillinger, Toby, Radhakrishnan, Rajiv, Rucker, James, Selvaraj, Sudhakar, Stokes, Paul, Upthegrove, Rachel, Yalin, Nefize, Yatham, Lakshmi, Young, Allan H., Zahn, Roland, and Cowen, Philip J.

Published
2023
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32. Influence of CYP2C19 and CYP2D6 on side effects of aripiprazole and risperidone: A systematic review

Author

Budde, Monika, Heilbronner, Urs, Young, Allan H., Juruena, Mario, Vieta, Eduard, Fares-Otero, Natalia E., Kas, Martien J., Nöthen, Markus, Philipsen, Alexandra, Kilarski, Laura L., Laatsch, Jonathan, Rossner, Moritz J., Wichert, Sven P., Moldovan, Ramona, Shomron, Noam, Schulze, Thomas G., King Ng, Roger Man, Olisa, Nigel, Van der Eycken, Erik, Pelgrim, Teuntje A.D., de Brabander, Emma, Kleine Schaars, Kristian, van Amelsvoort, Therese, and van Westrhenen, Roos

Published
2024
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34. A Delphi-method-based consensus guideline for definition of treatment-resistant depression for clinical trials

Author

Sforzini, Luca, Worrell, Courtney, Kose, Melisa, Anderson, Ian M, Aouizerate, Bruno, Arolt, Volker, Bauer, Michael, Baune, Bernhard T, Blier, Pierre, Cleare, Anthony J, Cowen, Philip J, Dinan, Timothy G, Fagiolini, Andrea, Ferrier, I Nicol, Hegerl, Ulrich, Krystal, Andrew D, Leboyer, Marion, McAllister-Williams, R Hamish, McIntyre, Roger S, Meyer-Lindenberg, Andreas, Miller, Andrew H, Nemeroff, Charles B, Normann, Claus, Nutt, David, Pallanti, Stefano, Pani, Luca, Penninx, Brenda WJH, Schatzberg, Alan F, Shelton, Richard C, Yatham, Lakshmi N, Young, Allan H, Zahn, Roland, Aislaitner, Georgios, Butlen-Ducuing, Florence, Fletcher, Christine, Haberkamp, Marion, Laughren, Thomas, Mäntylä, Fanni-Laura, Schruers, Koen, Thomson, Andrew, Arteaga-Henríquez, Gara, Benedetti, Francesco, Cash-Gibson, Lucinda, Chae, Woo Ri, De Smedt, Heidi, Gold, Stefan M, Hoogendijk, Witte JG, Mondragón, Valeria Jordán, Maron, Eduard, Martynowicz, Jadwiga, Melloni, Elisa, Otte, Christian, Perez-Fuentes, Gabriela, Poletti, Sara, Schmidt, Mark E, van de Ketterij, Edwin, Woo, Katherine, Flossbach, Yanina, Ramos-Quiroga, J Antoni, Savitz, Adam J, and Pariante, Carmine M

Subjects
Clinical Trials and Supportive Activities, Depression, Clinical Research, Mental Health, Brain Disorders, Serious Mental Illness, Major Depressive Disorder, Good Health and Well Being, Depressive Disorder, Major, Depressive Disorder, Treatment-Resistant, Humans, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Criteria for treatment-resistant depression (TRD) and partially responsive depression (PRD) as subtypes of major depressive disorder (MDD) are not unequivocally defined. In the present document we used a Delphi-method-based consensus approach to define TRD and PRD and to serve as operational criteria for future clinical studies, especially if conducted for regulatory purposes. We reviewed the literature and brought together a group of international experts (including clinicians, academics, researchers, employees of pharmaceutical companies, regulatory bodies representatives, and one person with lived experience) to evaluate the state-of-the-art and main controversies regarding the current classification. We then provided recommendations on how to design clinical trials, and on how to guide research in unmet needs and knowledge gaps. This report will feed into one of the main objectives of the EUropean Patient-cEntric clinicAl tRial pLatforms, Innovative Medicines Initiative (EU-PEARL, IMI) MDD project, to design a protocol for platform trials of new medications for TRD/PRD.

Published
2022

37. Electrodermal activity in bipolar disorder: Differences between mood episodes and clinical remission using a wearable device in a real-world clinical setting

Author

Anmella, Gerard, Mas, Ariadna, Sanabra, Miriam, Valenzuela-Pascual, Clàudia, Valentí, Marc, Pacchiarotti, Isabella, Benabarre, Antoni, Grande, Iria, De Prisco, Michele, Oliva, Vincenzo, Fico, Giovanna, Giménez-Palomo, Anna, Bastidas, Anna, Agasi, Isabel, Young, Allan H., Garriga, Marina, Corponi, Filippo, Li, Bryan M., de Looff, Peter, Vieta, Eduard, and Hidalgo-Mazzei, Diego

Published
2024
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40. Cognitive Remediation in Bipolar (CRiB2): study protocol for a randomised controlled trial assessing efficacy and mechanisms of cognitive remediation therapy compared to treatment as usual

Author

Tsapekos, Dimosthenis, Strawbridge, Rebecca, Cella, Matteo, Goldsmith, Kimberley, Kalfas, Michail, Taylor, Rosie H., Swidzinski, Samuel, Marwaha, Steven, Grey, Libby, Newton, Elizabeth, Shackleton, Julie, Harrison, Paul J., Browning, Michael, Harmer, Catherine, Hartland, Hannah, Cousins, David, Barton, Stephen, Wykes, Til, and Young, Allan H.

Published
2023
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42. Exploratory study of ultraviolet B (UVB) radiation and age of onset of bipolar disorder

Author

Bauer, Michael, Glenn, Tasha, Achtyes, Eric D., Alda, Martin, Agaoglu, Esen, Altınbaş, Kürsat, Andreassen, Ole A., Angelopoulos, Elias, Ardau, Raffaella, Aydin, Memduha, Ayhan, Yavuz, Baethge, Christopher, Bauer, Rita, Baune, Bernhard T., Balaban, Ceylan, Becerra-Palars, Claudia, Behere, Aniruddh P., Behere, Prakash B., Belete, Habte, Belete, Tilahun, Belizario, Gabriel Okawa, Bellivier, Frank, Belmaker, Robert H., Benedetti, Francesco, Berk, Michael, Bersudsky, Yuly, Bicakci, Şule, Birabwa-Oketcho, Harriet, Bjella, Thomas D., Brady, Conan, Cabrera, Jorge, Cappucciati, Marco, Castro, Angela Marianne Paredes, Chen, Wei-Ling, Cheung, Eric Y. W., Chiesa, Silvia, Crowe, Marie, Cuomo, Alessandro, Dallaspezia, Sara, Del Zompo, Maria, Desai, Pratikkumar, Dodd, Seetal, Etain, Bruno, Fagiolini, Andrea, Fellendorf, Frederike T., Ferensztajn-Rochowiak, Ewa, Fiedorowicz, Jess G., Fountoulakis, Kostas N., Frye, Mark A., Geoffroy, Pierre A., Gitlin, Michael J., Gonzalez-Pinto, Ana, Gottlieb, John F., Grof, Paul, Haarman, Bartholomeus C. M., Harima, Hirohiko, Hasse-Sousa, Mathias, Henry, Chantal, Hoffding, Lone, Houenou, Josselin, Imbesi, Massimiliano, Isometsä, Erkki T., Ivkovic, Maja, Janno, Sven, Johnsen, Simon, Kapczinski, Flávio, Karakatsoulis, Gregory N., Kardell, Mathias, Kessing, Lars Vedel, Kim, Seong Jae, König, Barbara, Kot, Timur L., Koval, Michael, Kunz, Mauricio, Lafer, Beny, Landén, Mikael, Larsen, Erik R., Lenger, Melanie, Licht, Rasmus W., Lopez-Jaramillo, Carlos, MacKenzie, Alan, Madsen, Helle Østergaard, Madsen, Simone Alberte Kongstad A., Mahadevan, Jayant, Mahardika, Agustine, Manchia, Mirko, Marsh, Wendy, Martinez-Cengotitabengoa, Monica, Martini, Julia, Martiny, Klaus, Mashima, Yuki, McLoughlin, Declan M., Meesters, Ybe, Melle, Ingrid, Meza-Urzúa, Fátima, Mikolas, Pavol, Mok, Yee Ming, Monteith, Scott, Moorthy, Muthukumaran, Morken, Gunnar, Mosca, Enrica, Mozzhegorov, Anton A., Munoz, Rodrigo, Mythri, Starlin V., Nacef, Fethi, Nadella, Ravi K., Nakanotani, Takako, Nielsen, René Ernst, O’Donovan, Claire, Omrani, Adel, Osher, Yamima, Ouali, Uta, Pantovic-Stefanovic, Maja, Pariwatcharakul, Pornjira, Petite, Joanne, Petzold, Johannes, Pfennig, Andrea, Ruiz, Yolanda Pica, Pinna, Marco, Pompili, Maurizio, Porter, Richard J., Quiroz, Danilo, Rabelo-da-Ponte, Francisco Diego, Ramesar, Raj, Rasgon, Natalie, Ratta-apha, Woraphat, Ratzenhofer, Michaela, Redahan, Maria, Reddy, M. S., Reif, Andreas, Reininghaus, Eva Z., Richards, Jenny Gringer, Ritter, Philipp, Rybakowski, Janusz K., Sathyaputri, Leela, Scippa, Angela M., Simhandl, Christian, Smith, Daniel, Smith, José, Stackhouse, Jr, Paul W., Stein, Dan J., Stilwell, Kellen, Strejilevich, Sergio, Su, Kuan-Pin, Subramaniam, Mythily, Sulaiman, Ahmad Hatim, Suominen, Kirsi, Tanra, Andi J., Tatebayashi, Yoshitaka, Teh, Wen Lin, Tondo, Leonardo, Torrent, Carla, Tuinstra, Daniel, Uchida, Takahito, Vaaler, Arne E., Vieta, Eduard, Viswanath, Biju, Yoldi-Negrete, Maria, Yalcinkaya, Oguz Kaan, Young, Allan H., Zgueb, Yosra, and Whybrow, Peter C.

Published
2023
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45. A randomised controlled trial to investigate the clinical effectiveness and cost effectiveness of Mindfulness-Based Cognitive Therapy (MBCT) for depressed non-responders to Increasing Access to Psychological Therapies (IAPT) high-intensity therapies: study protocol

Author

Barnhofer, Thorsten, Dunn, Barnaby D., Strauss, Clara, Ruths, Florian, Barrett, Barbara, Ryan, Mary, Ladwa, Asha, Stafford, Frances, Fichera, Roberta, Baber, Hannah, McGuinness, Ailis, Metcalfe, Isabella, Harding, Delilah, Walker, Sarah, Ganguli, Poushali, Rhodes, Shelley, Young, Allan, and Warren, Fiona

Published
2023
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