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1. Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Author

McWhinney, Sean, Hlinka, Jaroslav, Bakstein, Eduard, Dietze, Lorielle, Corkum, Emily, Abé, Christoph, Alda, Martin, Alexander, Nina, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Bonnekoh, Linda, Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick, Cannon, Dara, Dannlowski, Udo, Demro, Caroline, Diaz-Zuluaga, Ana, Elvsåshagen, Torbjørn, Eyler, Lisa, Fortea, Lydia, Fullerton, Janice, Goltermann, Janik, Gotlib, Ian, Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur, Jamalabadi, Hamidreza, Jansen, Andreas, Kircher, Tilo, Klahn, Anna, Kuplicki, Rayus, Lahud, Elijah, Landén, Mikael, Leehr, Elisabeth, Lopez-Jaramillo, Carlos, Mackey, Scott, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Melloni, Elisa, Mitchell, Philip, Nabulsi, Leila, Nenadić, Igor, Nitsch, Robert, Opel, Nils, Ophoff, Roel, Ortuño, Maria, Overs, Bronwyn, Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Radua, Joaquim, Repple, Jonathan, Roberts, Gloria, Rodriguez-Cano, Elena, Sacchet, Matthew, Salvador, Raymond, Savitz, Jonathan, Scheffler, Freda, Schofield, Peter, Schürmeyer, Navid, Shen, Chen, Sim, Kang, Sponheim, Scott, Stein, Dan, Stein, Frederike, Straube, Benjamin, Suo, Chao, Temmingh, Henk, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia, Urosevic, Snezana, Usemann, Paula, van Haren, Neeltje, Vargas, Cristian, Vieta, Eduard, Vilajosana, Enric, Vreeker, Annabel, Winter, Nils, Yatham, Lakshmi, Thompson, Paul, Andreassen, Ole, Ching, Christopher, and Hajek, Tomas

Subjects
MRI, bipolar disorder, body mass index, obesity, principal component analysis, psychiatry, Humans, Bipolar Disorder, Principal Component Analysis, Adult, Female, Male, Magnetic Resonance Imaging, Middle Aged, Obesity, Schizophrenia, Cerebral Cortex, Cluster Analysis, Young Adult, Brain
Abstract

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Published
2024

2. White matter microstructure in obesity and bipolar disorders: an ENIGMA bipolar disorder working group study in 2186 individuals

Author

Dietze, Lorielle M. F., McWhinney, Sean R., Favre, Pauline, Abé, Christoph, Alexander, Nina, Barkhau, Carlotta, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Carruthers, Sean P., Corkum, Emily L. V., Dannlowski, Udo, Díaz-Zuluaga, Ana M., Dohm, Katharina, Elvsåshagen, Torbjørn, Flinkenflügel, Kira, Fortea, Lydia, Furlong, Lisa S., Goldstein, Benjamin I., Grotegerd, Dominik, Gruber, Marius, Haarman, Bartholomeus C. M., Howells, Fleur M., Jahanshad, Neda, Jamalabadi, Hamidreza, Jansen, Andreas, Karantonis, James A., Kennedy, Kody G., Kircher, Tilo T. J., Klahn, Anna Luisa, Kochunov, Peter, Kraus, Anna, Landén, Mikael, López-Jaramillo, Carlos, MacIntosh, Bradley J., Mazza, Elena, McDonald, Colm, McIntosh, Andrew M., Meinert, Hannah, Meinert, Susanne, Melloni, Elisa M. T., Mitchell, Philip B., Nenadić, Igor, Opel, Nils, Phillips, Mary, Piguet, Camille, Polosan, Mircea, Pomarol-Clotet, Edith, Pouchon, Arnaud, Radua, Joaquim, Roberts, Gloria, Ross, Alex J., Rossell, Susan L., Salvador, Raymond, Sim, Kang, Soares, Jair C., Zunta-Soares, Giovana B., Stein, Frederike, Straube, Benjamin, Suo, Chao, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia I., Usemann, Paula, Van Rheenen, Tamsyn E., Versace, Amelia, Vieta, Eduard, Vilajosana, Enric, Mwangi, Benson, Wen, Wei, Whalley, Heather C., Wu, Mon-Ju, Andreassen, Ole A., Ching, Christopher R. K., Thompson, Paul M., Houenou, Josselin, and Hajek, Tomas

Published
2024
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3. DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Zeng, Ling-Li, Ching, Christopher R. K., Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Brosch, Katharina, Bülow, Robin, Colle, Romain, Connolly, Colm G., Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Cullen, Kathryn, Dannlowski, Udo, Davey, Christopher G., Dols, Annemiek, Ernsting, Jan, Evans, Jennifer W., Fisch, Lukas, Fuentes-Claramonte, Paola, Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke A., Grotegerd, Dominik, Hahn, Tim, Hamilton, J. Paul, Han, Laura K. M., Harrison, Ben J, Ho, Tiffany C., Jahanshad, Neda, Jamieson, Alec J., Karuk, Andriana, Kircher, Tilo, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Leenings, Ramona, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Meinert, Susanne, Melloni, Elisa, Mueller, Bryon A., Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Okamoto, Yasumasa, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Radua, Joaquim, Rodríguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Schrantee, Anouk, Sim, Kang, Soares, Jair C., Solanes, Aleix, Stein, Dan J., Stein, Frederike, Stolicyn, Aleks, Thomopoulos, Sophia I., Toenders, Yara J., Uyar-Demir, Aslihan, Vieta, Eduard, Vives-Gilabert, Yolanda, Völzke, Henry, Walter, Martin, Whalley, Heather C., Whittle, Sarah, Winter, Nils, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible.

Published
2023

4. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Published
2024
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5. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Gonul, Ali Saffet, Amod, Alyssa R., Ojha, Amar, Aleman, Andre, Dols, Annemiek, Scharntee, Anouk, Uyar-Demir, Aslihan, Harrison, Ben J, Irungu, Benson M., Besteher, Bianca, Klimes-Dougan, Bonnie, Penninx, Brenda W. J. H., Mueller, Bryon A., Zarate, Carlos, Davey, Christopher G., Ching, Christopher R. K., Connolly, Colm G., Fu, Cynthia H. Y., Stein, Dan J., Dima, Danai, Linden, David E. J., Mehler, David M. A., Pomarol-Clotet, Edith, Pozzi, Elena, Melloni, Elisa, Benedetti, Francesco, MacMaster, Frank P., Grabe, Hans J., Völzke, Henry, Gotlib, Ian H., Soares, Jair C., Evans, Jennifer W., Sim, Kang, Wittfeld, Katharina, Cullen, Kathryn, Reneman, Liesbeth, Oudega, Mardien L., Wright, Margaret J., Portella, Maria J., Sacchet, Matthew D., Li, Meng, Aghajani, Moji, Wu, Mon-Ju, Jaworska, Natalia, Jahanshad, Neda, van der Wee, Nic J. A., Groenewold, Nynke, Hamilton, Paul J., Saemann, Philipp, Bülow, Robin, Poletti, Sara, Whittle, Sarah, Thomopoulos, Sophia I., van, Steven J. A., Werff, der, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Ho, Tiffany C., Yang, Tony T., Basgoze, Zeynep, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (n=5,356) to provide a generalizable ML classification benchmark of major depressive disorder (MDD). Using brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD vs healthy controls (HC) with around 62% balanced accuracy, but when harmonizing the data using ComBat balanced accuracy dropped to approximately 52%. Similar results were observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may achieve more encouraging prospects., Comment: main document 37 pages; supplementary material 24 pages

Published
2022

6. Mega-analysis of association between obesity and cortical morphology in bipolar disorders: ENIGMA study in 2832 participants.

Author

Fullerton, Janice, Goikolea, Jose, Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur, Ingvar, Martin, Jahanshad, Neda, Kircher, Tilo, Krug, Axel, Kuplicki, Rayus, Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa, Mitchell, Philip, Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Ophoff, Roel, Overs, Bronwyn, Pfarr, Julia-Katharina, Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai, Roberts, Gloria, Ross, Alex, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter, Sim, Kang, Stein, Dan, Stein, Frederike, Temmingh, Henk, Thiel, Katharina, Thomopoulos, Sophia, van Haren, Neeltje, Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi, Ching, Christopher, Andreassen, Ole, Thompson, Paul, Hajek, Tomas, McWhinney, Sean, Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, Del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick, Cannon, Dara, Dannlowski, Udo, Diaz-Zuluaga, Ana, Dietze, Lorielle, Elvsåshagen, Torbjørn, and Eyler, Lisa

Subjects
Body mass index, antipsychotics, bipolar disorders, cortical thickness, heterogeneity, lithium, obesity, surface area
Abstract

BACKGROUND: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact. METHODS: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations. RESULTS: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI. CONCLUSIONS: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

Published
2023

7. Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals.

Author

McWhinney, Sean, Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, Del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick, Cannon, Dara, Dannlowski, Udo, Diaz-Zuluaga, Ana, Dietze, Lorielle, Elvsåshagen, Torbjørn, Fullerton, Janice, Goikolea, Jose, Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur, Ingvar, Martin, Kircher, Tilo, Krug, Axel, Kuplicki, Rayus, Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa, Mitchell, Philip, Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Overs, Bronwyn, Pfarr, Julia-Katharina, Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai, Roberts, Gloria, Ross, Alex, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter, Sim, Kang, Stein, Dan, Stein, Frederike, Temmingh, Henk, Thiel, Katharina, Thomopoulos, Sophia, van Haren, Neeltje, Van Gestel, Holly, Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi, Ching, Christopher, Andreassen, Ole, Thompson, Paul, Hajek, Tomas, Ophoff, Roel, and Eyler, Lisa

Subjects
bipolar disorders, body mass index, cortical thickness, heterogeneity, obesity, surface area, Bipolar Disorder, Body Mass Index, Cluster Analysis, Humans, Magnetic Resonance Imaging, Obesity, Temporal Lobe
Abstract

AIMS: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. METHODS: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. RESULTS: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. CONCLUSIONS: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD.

Published
2022

8. 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

9. In vivo hippocampal subfield volumes in bipolar disorder—A mega‐analysis from The Enhancing Neuro Imaging Genetics through Meta‐Analysis Bipolar Disorder Working Group

Author

Haukvik, Unn K, Gurholt, Tiril P, Nerland, Stener, Elvsåshagen, Torbjørn, Akudjedu, Theophilus N, Alda, Martin, Alnæs, Dag, Alonso‐Lana, Silvia, Bauer, Jochen, Baune, Bernhard T, Benedetti, Francesco, Berk, Michael, Bettella, Francesco, Bøen, Erlend, Bonnín, Caterina M, Brambilla, Paolo, Canales‐Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Dandash, Orwa, Dannlowski, Udo, Delvecchio, Giuseppe, Díaz‐Zuluaga, Ana M, Erp, Theo GM, Fatjó‐Vilas, Mar, Foley, Sonya F, Förster, Katharina, Fullerton, Janice M, Goikolea, José M, Grotegerd, Dominik, Gruber, Oliver, Haarman, Bartholomeus CM, Haatveit, Beathe, Hajek, Tomas, Hallahan, Brian, Harris, Mathew, Hawkins, Emma L, Howells, Fleur M, Hülsmann, Carina, Jahanshad, Neda, Jørgensen, Kjetil N, Kircher, Tilo, Krämer, Bernd, Krug, Axel, Kuplicki, Rayus, Lagerberg, Trine V, Lancaster, Thomas M, Lenroot, Rhoshel K, Lonning, Vera, López‐Jaramillo, Carlos, Malt, Ulrik F, McDonald, Colm, McIntosh, Andrew M, McPhilemy, Genevieve, Meer, Dennis, Melle, Ingrid, Melloni, Elisa MT, Mitchell, Philip B, Nabulsi, Leila, Nenadić, Igor, Oertel, Viola, Oldani, Lucio, Opel, Nils, Otaduy, Maria CG, Overs, Bronwyn J, Pineda‐Zapata, Julian A, Pomarol‐Clotet, Edith, Radua, Joaquim, Rauer, Lisa, Redlich, Ronny, Repple, Jonathan, Rive, Maria M, Roberts, Gloria, Ruhe, Henricus G, Salminen, Lauren E, Salvador, Raymond, Sarró, Salvador, Savitz, Jonathan, Schene, Aart H, Sim, Kang, Soeiro‐de‐Souza, Marcio G, Stäblein, Michael, Stein, Dan J, Stein, Frederike, Tamnes, Christian K, Temmingh, Henk S, Thomopoulos, Sophia I, Veltman, Dick J, Vieta, Eduard, Waltemate, Lena, Westlye, Lars T, Whalley, Heather C, Sämann, Philipp G, Thompson, Paul M, Ching, Christopher RK, Andreassen, Ole A, Agartz, Ingrid, and Group, ENIGMA Bipolar Disorder Working

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Mental Health, Serious Mental Illness, Neurosciences, Biomedical Imaging, Bipolar Disorder, Mental health, Genetics, Hippocampus, Humans, Magnetic Resonance Imaging, Neuroimaging, ENIGMA Bipolar Disorder Working Group, bipolar disorder subtype, hippocampus, large-scale, lithium, psychosis, structural brain MRI, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

Published
2022

10. What we learn about bipolar disorder from large‐scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group

Author

Ching, Christopher RK, Hibar, Derrek P, Gurholt, Tiril P, Nunes, Abraham, Thomopoulos, Sophia I, Abé, Christoph, Agartz, Ingrid, Brouwer, Rachel M, Cannon, Dara M, Zwarte, Sonja MC, Eyler, Lisa T, Favre, Pauline, Hajek, Tomas, Haukvik, Unn K, Houenou, Josselin, Landén, Mikael, Lett, Tristram A, McDonald, Colm, Nabulsi, Leila, Patel, Yash, Pauling, Melissa E, Paus, Tomas, Radua, Joaquim, Soeiro‐de‐Souza, Marcio G, Tronchin, Giulia, Haren, Neeltje EM, Vieta, Eduard, Walter, Henrik, Zeng, Ling‐Li, Alda, Martin, Almeida, Jorge, Alnæs, Dag, Alonso‐Lana, Silvia, Altimus, Cara, Bauer, Michael, Baune, Bernhard T, Bearden, Carrie E, Bellani, Marcella, Benedetti, Francesco, Berk, Michael, Bilderbeck, Amy C, Blumberg, Hilary P, Bøen, Erlend, Bollettini, Irene, Bonnin, Caterina Mar, Brambilla, Paolo, Canales‐Rodríguez, Erick J, Caseras, Xavier, Dandash, Orwa, Dannlowski, Udo, Delvecchio, Giuseppe, Díaz‐Zuluaga, Ana M, Dima, Danai, Duchesnay, Édouard, Elvsåshagen, Torbjørn, Fears, Scott C, Frangou, Sophia, Fullerton, Janice M, Glahn, David C, Goikolea, Jose M, Green, Melissa J, Grotegerd, Dominik, Gruber, Oliver, Haarman, Bartholomeus CM, Henry, Chantal, Howells, Fleur M, Ives‐Deliperi, Victoria, Jansen, Andreas, Kircher, Tilo TJ, Knöchel, Christian, Kramer, Bernd, Lafer, Beny, López‐Jaramillo, Carlos, Machado‐Vieira, Rodrigo, MacIntosh, Bradley J, Melloni, Elisa MT, Mitchell, Philip B, Nenadic, Igor, Nery, Fabiano, Nugent, Allison C, Oertel, Viola, Ophoff, Roel A, Ota, Miho, Overs, Bronwyn J, Pham, Daniel L, Phillips, Mary L, Pineda‐Zapata, Julian A, Poletti, Sara, Polosan, Mircea, Pomarol‐Clotet, Edith, Pouchon, Arnaud, Quidé, Yann, Rive, Maria M, Roberts, Gloria, Ruhe, Henricus G, Salvador, Raymond, Sarró, Salvador, Satterthwaite, Theodore D, Schene, Aart H, and Sim, Kang

Subjects
Serious Mental Illness, Brain Disorders, Biomedical Imaging, Bipolar Disorder, Clinical Research, Behavioral and Social Science, Mental Health, Neurosciences, Mental health, Neurological, Good Health and Well Being, Cerebral Cortex, Humans, Magnetic Resonance Imaging, Meta-Analysis as Topic, Multicenter Studies as Topic, Neuroimaging, bipolar disorder, cortical surface area, cortical thickness, ENIGMA, mega-analysis, meta-analysis, MRI, neuroimaging, psychiatry, volume, ENIGMA Bipolar Disorder Working Group, Cognitive Sciences, Experimental Psychology
Abstract

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

Published
2022

11. Association between body mass index and subcortical brain volumes in bipolar disorders–ENIGMA study in 2735 individuals

Author

McWhinney, Sean R, Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J, Cannon, Dara M, Dannlowski, Udo, Díaz-Zuluaga, Ana M, Elvsåshagen, Torbjørn, Eyler, Lisa T, Fullerton, Janice M, Goikolea, Jose M, Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus CM, Hahn, Tim, Howells, Fleur M, Ingvar, Martin, Kircher, Tilo TJ, Krug, Axel, Kuplicki, Rayus T, Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik F, Martyn, Fiona M, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa MT, Mitchell, Philip B, Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Ophoff, Roel A, Overs, Bronwyn J, Pfarr, Julia-Katharina, Pineda-Zapata, Julian A, Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai G, Roberts, Gloria, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter R, Sim, Kang, Stein, Dan J, Stein, Frederike, Temmingh, Henk S, Thiel, Katharina, van Haren, Neeltje EM, Gestel, Holly Van, Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi N, Ching, Christopher RK, Andreassen, Ole, Thompson, Paul M, and Hajek, Tomas

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Mental Health, Biomedical Imaging, Nutrition, Brain Disorders, Obesity, Neurosciences, Clinical Research, Prevention, Mental health, Amygdala, Bipolar Disorder, Body Mass Index, Brain, Humans, Magnetic Resonance Imaging, ENIGMA Bipolar Disorders Working Group, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles  and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.

Published
2021

14. Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals

Author

Favre, Pauline, Pauling, Melissa, Stout, Jacques, Hozer, Franz, Sarrazin, Samuel, Abé, Christoph, Alda, Martin, Alloza, Clara, Alonso-Lana, Silvia, Andreassen, Ole A, Baune, Bernhard T, Benedetti, Francesco, Busatto, Geraldo F, Canales-Rodríguez, Erick J, Caseras, Xavier, Chaim-Avancini, Tiffany Moukbel, Ching, Christopher RK, Dannlowski, Udo, Deppe, Michael, Eyler, Lisa T, Fatjo-Vilas, Mar, Foley, Sonya F, Grotegerd, Dominik, Hajek, Tomas, Haukvik, Unn K, Howells, Fleur M, Jahanshad, Neda, Kugel, Harald, Lagerberg, Trine V, Lawrie, Stephen M, Linke, Julia O, McIntosh, Andrew, Melloni, Elisa MT, Mitchell, Philip B, Polosan, Mircea, Pomarol-Clotet, Edith, Repple, Jonathan, Roberts, Gloria, Roos, Annerine, Rosa, Pedro GP, Salvador, Raymond, Sarró, Salvador, Schofield, Peter R, Serpa, Mauricio H, Sim, Kang, Stein, Dan J, Sussmann, Jess E, Temmingh, Henk S, Thompson, Paul M, Verdolini, Norma, Vieta, Eduard, Wessa, Michele, Whalley, Heather C, Zanetti, Marcus V, Leboyer, Marion, Mangin, Jean-François, Henry, Chantal, Duchesnay, Edouard, and Houenou, Josselin

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Serious Mental Illness, Neurosciences, Bipolar Disorder, Biomedical Imaging, Mental Health, Clinical Research, Mental health, Adult, Brain, Corpus Callosum, Diffusion Tensor Imaging, Female, Humans, Male, Neural Pathways, White Matter, ENIGMA Bipolar Disorder Working Group, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological psychology
Abstract

Fronto-limbic white matter (WM) abnormalities are assumed to lie at the heart of the pathophysiology of bipolar disorder (BD); however, diffusion tensor imaging (DTI) studies have reported heterogeneous results and it is not clear how the clinical heterogeneity is related to the observed differences. This study aimed to identify WM abnormalities that differentiate patients with BD from healthy controls (HC) in the largest DTI dataset of patients with BD to date, collected via the ENIGMA network. We gathered individual tensor-derived regional metrics from 26 cohorts leading to a sample size of N = 3033 (1482 BD and 1551 HC). Mean fractional anisotropy (FA) from 43 regions of interest (ROI) and average whole-brain FA were entered into univariate mega- and meta-analyses to differentiate patients with BD from HC. Mega-analysis revealed significantly lower FA in patients with BD compared with HC in 29 regions, with the highest effect sizes observed within the corpus callosum (R2 = 0.041, Pcorr

Published
2019

15. Correction: Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals

Author

Favre, Pauline, Pauling, Melissa, Stout, Jacques, Hozer, Franz, Sarrazin, Samuel, Abé, Christoph, Alda, Martin, Alloza, Clara, Alonso-Lana, Silvia, Andreassen, Ole A, Baune, Bernhard T, Benedetti, Francesco, Busatto, Geraldo F, Canales-Rodríguez, Erick J, Caseras, Xavier, Chaim-Avancini, Tiffany Moukbel, Ching, Christopher RK, Dannlowski, Udo, Deppe, Michael, Eyler, Lisa T, Fatjo-Vilas, Mar, Foley, Sonya F, Grotegerd, Dominik, Hajek, Tomas, Haukvik, Unn K, Howells, Fleur M, Jahanshad, Neda, Kugel, Harald, Lagerberg, Trine V, Lawrie, Stephen M, Linke, Julia O, McIntosh, Andrew, Melloni, Elisa MT, Mitchell, Philip B, Polosan, Mircea, Pomarol-Clotet, Edith, Repple, Jonathan, Roberts, Gloria, Roos, Annerine, Rosa, Pedro GP, Salvador, Raymond, Sarró, Salvador, Schofield, Peter R, Serpa, Mauricio H, Sim, Kang, Stein, Dan J, Sussmann, Jess E, Temmingh, Henk S, Thompson, Paul M, Verdolini, Norma, Vieta, Eduard, Wessa, Michele, Whalley, Heather C, Zanetti, Marcus V, Leboyer, Marion, Mangin, Jean-François, Henry, Chantal, Duchesnay, Edouard, and Houenou, Josselin

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Neurosciences, Psychology, ENIGMA Bipolar Disorder Working Group, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological psychology
Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2019

19. One-year mental health outcomes in a cohort of COVID-19 survivors

Author

Vai, Benedetta, Bollettini, Irene, Melloni, Elisa Maria Teresa, Mazza, Elena Beatrice, Aggio, Veronica, Calesella, Federico, Paolini, Marco, Caselani, Elisa, Colombo, Federica, D’orsi, Greta, Di Pasquasio, Camilla, Fiore, Paola, Calvisi, Stefania, Canti, Valentina, Castellani, Jacopo, Cilla, Marta, Cinel, Elena, Damanti, Sarah, Ferrante, Marica, Martinenghi, Sabina, Santini, Chiara, Vitali, Giordano, Mazza, Mario Gennaro, Palladini, Mariagrazia, De Lorenzo, Rebecca, Bravi, Beatrice, Poletti, Sara, Furlan, Roberto, Ciceri, Fabio, Rovere-Querini, Patrizia, and Benedetti, Francesco

Published
2022
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20. Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Author

Onderzoeksgroep 1, McWhinney, Sean R., Hlinka, Jaroslav, Bakstein, Eduard, Dietze, Lorielle M.F., Corkum, Emily L.V., Abé, Christoph, Alda, Martin, Alexander, Nina, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Bonnekoh, Linda M., Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Demro, Caroline, Diaz-Zuluaga, Ana, Elvsåshagen, Torbjørn, Eyler, Lisa T., Fortea, Lydia, Fullerton, Janice M., Goltermann, Janik, Gotlib, Ian H., Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur M., Jamalabadi, Hamidreza, Jansen, Andreas, Kircher, Tilo, Klahn, Anna Luisa, Kuplicki, Rayus, Lahud, Elijah, Landén, Mikael, Leehr, Elisabeth J., Lopez-Jaramillo, Carlos, Mackey, Scott, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Melloni, Elisa, Mitchell, Philip B., Nabulsi, Leila, Nenadić, Igor, Nitsch, Robert, Opel, Nils, Ophoff, Roel A., Ortuño, Maria, Overs, Bronwyn J., Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Radua, Joaquim, Repple, Jonathan, Roberts, Gloria, Rodriguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Savitz, Jonathan, Scheffler, Freda, Schofield, Peter R., Schürmeyer, Navid, Shen, Chen, Sim, Kang, Sponheim, Scott R., Stein, Dan J., Stein, Frederike, Straube, Benjamin, Suo, Chao, Temmingh, Henk, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia I., Urosevic, Snezana, Usemann, Paula, van Haren, Neeltje E.M., Vargas, Cristian, Vieta, Eduard, Vilajosana, Enric, Vreeker, Annabel, Winter, Nils R., Yatham, Lakshmi N., Thompson, Paul M., Andreassen, Ole A., Ching, Christopher R.K., Hajek, Tomas, Onderzoeksgroep 1, McWhinney, Sean R., Hlinka, Jaroslav, Bakstein, Eduard, Dietze, Lorielle M.F., Corkum, Emily L.V., Abé, Christoph, Alda, Martin, Alexander, Nina, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Bonnekoh, Linda M., Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Demro, Caroline, Diaz-Zuluaga, Ana, Elvsåshagen, Torbjørn, Eyler, Lisa T., Fortea, Lydia, Fullerton, Janice M., Goltermann, Janik, Gotlib, Ian H., Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur M., Jamalabadi, Hamidreza, Jansen, Andreas, Kircher, Tilo, Klahn, Anna Luisa, Kuplicki, Rayus, Lahud, Elijah, Landén, Mikael, Leehr, Elisabeth J., Lopez-Jaramillo, Carlos, Mackey, Scott, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Melloni, Elisa, Mitchell, Philip B., Nabulsi, Leila, Nenadić, Igor, Nitsch, Robert, Opel, Nils, Ophoff, Roel A., Ortuño, Maria, Overs, Bronwyn J., Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Radua, Joaquim, Repple, Jonathan, Roberts, Gloria, Rodriguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Savitz, Jonathan, Scheffler, Freda, Schofield, Peter R., Schürmeyer, Navid, Shen, Chen, Sim, Kang, Sponheim, Scott R., Stein, Dan J., Stein, Frederike, Straube, Benjamin, Suo, Chao, Temmingh, Henk, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia I., Urosevic, Snezana, Usemann, Paula, van Haren, Neeltje E.M., Vargas, Cristian, Vieta, Eduard, Vilajosana, Enric, Vreeker, Annabel, Winter, Nils R., Yatham, Lakshmi N., Thompson, Paul M., Andreassen, Ole A., Ching, Christopher R.K., and Hajek, Tomas

Published
2024

21. Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Author

McWhinney, Sean R., Hlinka, Jaroslav, Bakstein, Eduard, Dietze, Lorielle M.F., Corkum, Emily L.V., Abé, Christoph, Alda, Martin, Alexander, Nina, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Bonnekoh, Linda M., Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Demro, Caroline, Diaz-Zuluaga, Ana, Elvsåshagen, Torbjørn, Eyler, Lisa T., Fortea, Lydia, Fullerton, Janice M., Goltermann, Janik, Gotlib, Ian H., Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur M., Jamalabadi, Hamidreza, Jansen, Andreas, Kircher, Tilo, Klahn, Anna Luisa, Kuplicki, Rayus, Lahud, Elijah, Landén, Mikael, Leehr, Elisabeth J., Lopez-Jaramillo, Carlos, Mackey, Scott, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Melloni, Elisa, Mitchell, Philip B., Nabulsi, Leila, Nenadić, Igor, Nitsch, Robert, Opel, Nils, Ophoff, Roel A., Ortuño, Maria, Overs, Bronwyn J., Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Radua, Joaquim, Repple, Jonathan, Roberts, Gloria, Rodriguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Savitz, Jonathan, Scheffler, Freda, Schofield, Peter R., Schürmeyer, Navid, Shen, Chen, Sim, Kang, Sponheim, Scott R., Stein, Dan J., Stein, Frederike, Straube, Benjamin, Suo, Chao, Temmingh, Henk, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia I., Urosevic, Snezana, Usemann, Paula, van Haren, Neeltje E.M., Vargas, Cristian, Vieta, Eduard, Vilajosana, Enric, Vreeker, Annabel, Winter, Nils R., Yatham, Lakshmi N., Thompson, Paul M., Andreassen, Ole A., Ching, Christopher R.K., Hajek, Tomas, McWhinney, Sean R., Hlinka, Jaroslav, Bakstein, Eduard, Dietze, Lorielle M.F., Corkum, Emily L.V., Abé, Christoph, Alda, Martin, Alexander, Nina, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Bonnekoh, Linda M., Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Demro, Caroline, Diaz-Zuluaga, Ana, Elvsåshagen, Torbjørn, Eyler, Lisa T., Fortea, Lydia, Fullerton, Janice M., Goltermann, Janik, Gotlib, Ian H., Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur M., Jamalabadi, Hamidreza, Jansen, Andreas, Kircher, Tilo, Klahn, Anna Luisa, Kuplicki, Rayus, Lahud, Elijah, Landén, Mikael, Leehr, Elisabeth J., Lopez-Jaramillo, Carlos, Mackey, Scott, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Melloni, Elisa, Mitchell, Philip B., Nabulsi, Leila, Nenadić, Igor, Nitsch, Robert, Opel, Nils, Ophoff, Roel A., Ortuño, Maria, Overs, Bronwyn J., Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Radua, Joaquim, Repple, Jonathan, Roberts, Gloria, Rodriguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Savitz, Jonathan, Scheffler, Freda, Schofield, Peter R., Schürmeyer, Navid, Shen, Chen, Sim, Kang, Sponheim, Scott R., Stein, Dan J., Stein, Frederike, Straube, Benjamin, Suo, Chao, Temmingh, Henk, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia I., Urosevic, Snezana, Usemann, Paula, van Haren, Neeltje E.M., Vargas, Cristian, Vieta, Eduard, Vilajosana, Enric, Vreeker, Annabel, Winter, Nils R., Yatham, Lakshmi N., Thompson, Paul M., Andreassen, Ole A., Ching, Christopher R.K., and Hajek, Tomas

Abstract

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associ

Published
2024

29. A Homer 1 gene variant influences brain structure and function, lithium effects on white matter, and antidepressant response in bipolar disorder: A multimodal genetic imaging study

Author

Benedetti, Francesco, Poletti, Sara, Locatelli, Clara, Mazza, Elena, Lorenzi, Cristina, Vitali, Alice, Riberto, Martina, Brioschi, Silvia, Vai, Benedetta, Bollettini, Irene, Melloni, Elisa, Aggio, Veronica, Falini, Andrea, De Bartolomeis, Andrea, and Colombo, Cristina

Published
2018
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31. Concurrent validity and reliability of suicide risk assessment instruments: A meta-analysis of 20 instruments across 27 international cohorts.

Author

Campos, Adrian I., primary, Van Velzen, Laura S., additional, Veltman, Dick J., additional, Pozzi, Elena, additional, Ambrogi, Sonia, additional, Ballard, Elizabeth D., additional, Banaj, Nerisa, additional, Başgöze, Zeynep, additional, Bellow, Sophie, additional, Benedetti, Francesco, additional, Bollettini, Irene, additional, Brosch, Katharina, additional, Canales-Rodríguez, Erick J., additional, Clarke-Rubright, Emily K., additional, Colic, Lejla, additional, Connolly, Colm G., additional, Courtet, Philippe, additional, Cullen, Kathryn R., additional, Dannlowski, Udo, additional, Dauvermann, Maria R., additional, Davey, Christopher G., additional, Deverdun, Jeremy, additional, Dohm, Katharina, additional, Erwin-Grabner, Tracy, additional, Goya-Maldonado, Roberto, additional, Fani, Negar, additional, Fortea, Lydia, additional, Fuentes-Claramonte, Paola, additional, Gonul, Ali Saffet, additional, Gotlib, Ian H., additional, Grotegerd, Dominik, additional, Harris, Mathew A., additional, Harrison, Ben J., additional, Haswell, Courtney C., additional, Hawkins, Emma L., additional, Hill, Dawson, additional, Hirano, Yoshiyuki, additional, Ho, Tiffany C., additional, Jollant, Fabrice, additional, Jovanovic, Tanja, additional, Kircher, Tilo, additional, Klimes-Dougan, Bonnie, additional, le Bars, Emmanuelle, additional, Lochner, Christine, additional, McIntosh, Andrew M., additional, Meinert, Susanne, additional, Mekawi, Yara, additional, Melloni, Elisa, additional, Mitchell, Philip, additional, Morey, Rajendra A., additional, Nakagawa, Akiko, additional, Nenadić, Igor, additional, Olié, Emilie, additional, Pereira, Fabricio, additional, Phillips, Rachel D., additional, Piras, Fabrizio, additional, Poletti, Sara, additional, Pomarol-Clotet, Edith, additional, Radua, Joaquim, additional, Ressler, Kerry J., additional, Roberts, Gloria, additional, Rodriguez-Cano, Elena, additional, Sacchet, Matthew D., additional, Salvador, Raymond, additional, Sandu, Anca-Larisa, additional, Shimizu, Eiji, additional, Singh, Aditya, additional, Spalletta, Gianfranco, additional, Steele, J. Douglas, additional, Stein, Dan J., additional, Stein, Frederike, additional, Stevens, Jennifer S., additional, Teresi, Giana I., additional, Uyar-Demir, Aslihan, additional, van der Wee, Nic J., additional, van der Werff, Steven J., additional, van Rooij, Sanne J. H., additional, Vecchio, Daniela, additional, Verdolini, Norma, additional, Vieta, Eduard, additional, Waiter, Gordon D., additional, Whalley, Heather, additional, Whittle, Sarah L., additional, Yang, Tony T., additional, Zarate, Carlos A., additional, Thompson, Paul M., additional, Jahanshad, Neda, additional, van Harmelen, Anne-Laura, additional, Blumberg, Hilary P., additional, Schmaal, Lianne, additional, and Rentería, Miguel E., additional

Published
2023
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32. Mega-analysis of association between obesity and cortical morphology in bipolar disorders:ENIGMA study in 2832 participants

Author

Mcwhinney, Sean R., Abé, Christoph, Alda, Martin, Benedetti, Francesco, Boen, Erlend, Del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Diaz-Zuluaga, Ana M., Dietze, Lorielle M.F., Elvsåshagen, Torbjorn, Eyler, Lisa T., Fullerton, Janice M., Goikolea, Jose M., Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus C.M., Hahn, Tim, Howells, Fleur M., Ingvar, Martin, Jahanshad, Neda, Kircher, Tilo T.J., Krug, Axel, Kuplicki, Rayus T., Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik F., Martyn, Fiona M., Mazza, Elena, Mcdonald, Colm, Mcphilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa M.T., Mitchell, Philip B., Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Ophoff, Roel A., Overs, Bronwyn J., Pfarr, Julia Katharina, Pineda-Zapata, Julian A., Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai G., Roberts, Gloria, Ross, Alex, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter R., Sim, Kang, Stein, Dan J., Stein, Frederike, Temmingh, Henk S., Thiel, Katharina, Thomopoulos, Sophia I., Van Haren, Neeltje E.M., Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi N., Ching, Christopher R.K., Andreassen, Ole A., Thompson, Paul M., Hajek, Tomas, Mcwhinney, Sean R., Abé, Christoph, Alda, Martin, Benedetti, Francesco, Boen, Erlend, Del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Diaz-Zuluaga, Ana M., Dietze, Lorielle M.F., Elvsåshagen, Torbjorn, Eyler, Lisa T., Fullerton, Janice M., Goikolea, Jose M., Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus C.M., Hahn, Tim, Howells, Fleur M., Ingvar, Martin, Jahanshad, Neda, Kircher, Tilo T.J., Krug, Axel, Kuplicki, Rayus T., Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik F., Martyn, Fiona M., Mazza, Elena, Mcdonald, Colm, Mcphilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa M.T., Mitchell, Philip B., Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Ophoff, Roel A., Overs, Bronwyn J., Pfarr, Julia Katharina, Pineda-Zapata, Julian A., Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai G., Roberts, Gloria, Ross, Alex, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter R., Sim, Kang, Stein, Dan J., Stein, Frederike, Temmingh, Henk S., Thiel, Katharina, Thomopoulos, Sophia I., Van Haren, Neeltje E.M., Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi N., Ching, Christopher R.K., Andreassen, Ole A., Thompson, Paul M., and Hajek, Tomas

Abstract

Background: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact. Methods: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations. Results: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI. Conclusions: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

Published
2023

33. Long-term effect of childhood trauma: Role of inflammation and white matter in mood disorders

Author

Poletti, Sara, primary, Paolini, Marco, additional, Ernst, Julia, additional, Bollettini, Irene, additional, Melloni, Elisa, additional, Vai, Benedetta, additional, Harrington, Yasmin, additional, Bravi, Beatrice, additional, Calesella, Federico, additional, Lorenzi, Cristina, additional, Zanardi, Raffaella, additional, and Benedetti, Francesco, additional

Published
2022
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34. The parental psychological distress caused by separation from their critically ill child during the COVID-19 pandemic: A tale of two cities

Author

Camporesi, Anna, Abecasis, Francisco, Torres, Erica M., Zoia, Elena, Izzo, Francesca, Ferrario, Stefania, and Melloni, Elisa Maria Teresa

Subjects
Pediatrics, Perinatology and Child Health
Abstract

IntroductionA child’s critical illness is a stressful event for the entire family, causing significant emotional distress among parents and changes to family functioning. The Severe Acute Respiratory Syndrome-Related Coronavirus 2 (SARS-CoV-2) pandemic has abruptly caused modifications in visitation policies of Pediatric Intensive Care Units (PICUs) in many countries. We hypothesized that caregivers with no or severely restricted access to PICUs would demonstrate increased psychological distress as compared to those who had limitless access (LA) to PICUs.MethodsSociodemographic variables, levels of psychological distress, ratings of family functioning, and ability to cope with stressful events were collected with an online survey in a group of caregivers after their child’s hospitalization. Ratings of psychological distress were compared between caregivers with no/severely restricted (NA) and with LA to PICUs.ResultsMeasures of depression, anxiety, and global severity index (GSI) of psychological distress were significantly higher in NA caregivers as compared to LA. Among demographic characteristics of the sample, only gender influenced the severity of psychological symptoms: women showed an increased score on levels of somatization, depression, anxiety, and GSI. Avoidant coping style positively correlated with measures of depression. Univariate General Linear Model (GLM) analyses of the effects of sex, age, visitation policies of PICUs, and score of avoidant coping strategies on measures of psychological distress confirmed a significant univariate effect of no access to PICUs on parents’ psychopathological scores.ConclusionRestrictions imposed on visitation policies in PICU during the pandemic negatively impacted families’ psychological wellbeing. A balance between the safety of patients, families, and health care professionals and meeting the needs of families is of utmost importance.

Published
2022
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35. The role of educational attainment and brain morphology in major depressive disorder: Findings from the ENIGMA major depressive disorder consortium.

Author

Whittle, Sarah, primary, Rakesh, Divyangana, additional, Schmaal, Lianne, additional, Veltman, Dick J., additional, Thompson, Paul M., additional, Singh, Aditya, additional, Gonul, Ali Saffet, additional, Aleman, Andre, additional, Uyar Demir, Aslıhan, additional, Krug, Axel, additional, Mwangi, Benson, additional, Krämer, Bernd, additional, Baune, Bernhard T., additional, Stein, Dan J., additional, Grotegerd, Dominik, additional, Pomarol-Clotet, Edith, additional, Rodríguez-Cano, Elena, additional, Melloni, Elisa, additional, Benedetti, Francesco, additional, Stein, Frederike, additional, Grabe, Hans J., additional, Völzke, Henry, additional, Gotlib, Ian H., additional, Nenadić, Igor, additional, Soares, Jair C., additional, Repple, Jonathan, additional, Sim, Kang, additional, Brosch, Katharina, additional, Wittfeld, Katharina, additional, Berger, Klaus, additional, Hermesdorf, Marco, additional, Portella, Maria J., additional, Sacchet, Matthew D., additional, Wu, Mon-Ju, additional, Opel, Nils, additional, Groenewold, Nynke A., additional, Gruber, Oliver, additional, Fuentes-Claramonte, Paola, additional, Salvador, Raymond, additional, Goya-Maldonado, Roberto, additional, Sarró, Salvador, additional, Poletti, Sara, additional, Meinert, Susanne L., additional, Kircher, Tilo, additional, Dannlowski, Udo, additional, and Pozzi, Elena, additional

Published
2022
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37. Longitudinal Structural Brain Changes in Bipolar Disorder: A Multicenter Neuroimaging Study of 1232 Individuals by the ENIGMA Bipolar Disorder Working Group

Author

Abé, Christoph, primary, Ching, Christopher R.K., additional, Liberg, Benny, additional, Lebedev, Alexander V., additional, Agartz, Ingrid, additional, Akudjedu, Theophilus N., additional, Alda, Martin, additional, Alnæs, Dag, additional, Alonso-Lana, Silvia, additional, Benedetti, Francesco, additional, Berk, Michael, additional, Bøen, Erlend, additional, Bonnin, Caterina del Mar, additional, Breuer, Fabian, additional, Brosch, Katharina, additional, Brouwer, Rachel M., additional, Canales-Rodríguez, Erick J., additional, Cannon, Dara M., additional, Chye, Yann, additional, Dahl, Andreas, additional, Dandash, Orwa, additional, Dannlowski, Udo, additional, Dohm, Katharina, additional, Elvsåshagen, Torbjørn, additional, Fisch, Lukas, additional, Fullerton, Janice M., additional, Goikolea, Jose M., additional, Grotegerd, Dominik, additional, Haatveit, Beathe, additional, Hahn, Tim, additional, Hajek, Tomas, additional, Heindel, Walter, additional, Ingvar, Martin, additional, Sim, Kang, additional, Kircher, Tilo T.J., additional, Lenroot, Rhoshel K., additional, Malt, Ulrik F., additional, McDonald, Colm, additional, McWhinney, Sean R., additional, Melle, Ingrid, additional, Meller, Tina, additional, Melloni, Elisa M.T., additional, Mitchell, Philip B., additional, Nabulsi, Leila, additional, Nenadić, Igor, additional, Opel, Nils, additional, Overs, Bronwyn J., additional, Panicalli, Francesco, additional, Pfarr, Julia-Katharina, additional, Poletti, Sara, additional, Pomarol-Clotet, Edith, additional, Radua, Joaquim, additional, Repple, Jonathan, additional, Ringwald, Kai G., additional, Roberts, Gloria, additional, Rodriguez-Cano, Elena, additional, Salvador, Raymond, additional, Sarink, Kelvin, additional, Sarró, Salvador, additional, Schmitt, Simon, additional, Stein, Frederike, additional, Suo, Chao, additional, Thomopoulos, Sophia I., additional, Tronchin, Giulia, additional, Vieta, Eduard, additional, Westlye, Lars T., additional, White, Adam G., additional, Yatham, Lakshmi N., additional, Zak, Nathalia, additional, Thompson, Paul M., additional, Andreassen, Ole A., additional, and Landén, Mikael, additional

Published
2022
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38. One-year mental health outcomes in a cohort of COVID-19 survivors

Author

Mazza, Mario Gennaro, primary, Palladini, Mariagrazia, additional, De Lorenzo, Rebecca, additional, Bravi, Beatrice, additional, Poletti, Sara, additional, Furlan, Roberto, additional, Ciceri, Fabio, additional, Rovere-Querini, Patrizia, additional, Benedetti, Francesco, additional, Vai, Benedetta, additional, Bollettini, Irene, additional, Melloni, Elisa Maria Teresa, additional, Mazza, Elena Beatrice, additional, Aggio, Veronica, additional, Calesella, Federico, additional, Paolini, Marco, additional, Caselani, Elisa, additional, Colombo, Federica, additional, D’orsi, Greta, additional, Di Pasquasio, Camilla, additional, Fiore, Paola, additional, Calvisi, Stefania, additional, Canti, Valentina, additional, Castellani, Jacopo, additional, Cilla, Marta, additional, Cinel, Elena, additional, Damanti, Sarah, additional, Ferrante, Marica, additional, Martinenghi, Sabina, additional, Santini, Chiara, additional, and Vitali, Giordano, additional

Published
2022
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39. A Delphi-method-based consensus guideline for definition of treatment-resistant depression for clinical trials

Author

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

Published
2021
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40. Brain correlates of depression, post-traumatic distress, and inflammatory biomarkers in COVID-19 survivors: A multimodal magnetic resonance imaging study

Author

Benedetti, Francesco, primary, Palladini, Mariagrazia, additional, Paolini, Marco, additional, Melloni, Elisa, additional, Vai, Benedetta, additional, De Lorenzo, Rebecca, additional, Furlan, Roberto, additional, Rovere-Querini, Patrizia, additional, Falini, Andrea, additional, and Mazza, Mario Gennaro, additional

Published
2021
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41. Concurrent validity and reliability of suicide risk assessment instruments: A meta analysis of 20 instruments across 27 international cohorts

Author

Campos, Adrian I., primary, Van Velzen, Laura S., additional, Veltman, Dick J., additional, Pozzi, Elena, additional, Ambrogi, Sonia, additional, Ballard, Elizabeth D., additional, Banaj, Nerisa, additional, Başgöze, Zeynep, additional, Bellow, Sophie, additional, Benedetti, Francesco, additional, Bollettini, Irene, additional, Brosch, Katharina, additional, Canales-Rodríguez, Erick J., additional, Clarke-Rubright, Emily K., additional, Colic, Lejla, additional, Connolly, Colm G., additional, Courtet, Philippe, additional, Cullen, Kathryn R., additional, Dannlowski, Udo, additional, Dauvermann, Maria R., additional, Davey, Christopher G., additional, Deverdun, Jeremy, additional, Dohm, Katharina, additional, Erwin-Grabner, Tracy, additional, Fani, Negar, additional, Fortea, Lydia, additional, Fuentes-Claramonte, Paola, additional, Gonul, Ali Saffet, additional, Gotlib, Ian H., additional, Grotegerd, Dominik, additional, Harris, Mathew A., additional, Harrison, Ben J., additional, Haswell, Courtney C., additional, Hawkins, Emma L., additional, Hill, Dawson, additional, Hirano, Yoshiyuki, additional, Ho, Tiffany C., additional, Jollant, Fabrice, additional, Jovanovic, Tanja, additional, Kircher, Tilo, additional, Klimes-Dougan, Bonnie, additional, le Bars, Emmanuelle, additional, Lochner, Christine, additional, McIntosh, Andrew M., additional, Meinert, Susanne, additional, Mekawi, Yara, additional, Melloni, Elisa, additional, Mitchell, Philip, additional, Morey, Rajendra A., additional, Nakagawa, Akiko, additional, Nenadić, Igor, additional, Olié, Emilie, additional, Pereira, Fabricio, additional, Phillips, Rachel D., additional, Piras, Fabrizio, additional, Poletti, Sara, additional, Pomarol-Clotet, Edith, additional, Radua, Joaquim, additional, Ressler, Kerry J., additional, Roberts, Gloria, additional, Rodriguez-Cano, Elena, additional, Sacchet, Matthew D., additional, Salvador, Raymond, additional, Sandu, Anca-Larisa, additional, Shimizu, Eiji, additional, Singh, Aditya, additional, Spalletta, Gianfranco, additional, Steele, J. Douglas, additional, Stein, Dan J., additional, Stein, Frederike, additional, Stevens, Jennifer S., additional, Teresi, Giana I., additional, Uyar-Demir, Aslihan, additional, van der Wee, Nic J., additional, van der Werff, Steven J., additional, van Rooij, Sanne J.H., additional, Vecchio, Daniela, additional, Verdolini, Norma, additional, Vieta, Eduard, additional, Waiter, Gordon D., additional, Whalley, Heather, additional, Whittle, Sarah L., additional, Yang, Tony T., additional, Zarate, Carlos A., additional, Thompson, Paul M., additional, Jahanshad, Neda, additional, Harmelen, Anne-Laura van, additional, Blumberg, Hilary P., additional, Schmaal, Lianne, additional, and Rentería, Miguel E., additional

Published
2021
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43. Association between body mass index and subcortical brain volumes in bipolar disorders–ENIGMA study in 2735 individuals

Author

McWhinney, Sean R., Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Díaz-Zuluaga, Ana M., Elvsåshagen, Torbjørn, Eyler, Lisa T., Fullerton, Janice M., Goikolea, Jose M., Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus C.M., Hahn, Tim, Howells, Fleur M., Ingvar, Martin, Kircher, Tilo T.J., Krug, Axel, Kuplicki, Rayus T., Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik F., Martyn, Fiona M., Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa M.T., Mitchell, Philip B., Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Ophoff, Roel A., Overs, Bronwyn J., Pfarr, Julia Katharina, Pineda-Zapata, Julian A., Pomarol-Clotet, Edith, Raduà, Joaquim, van Haren, Neeltje E.M., Vreeker, Annabel, Thompson, Paul M, Hajek, Tomas, McWhinney, Sean R., Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Dannlowski, Udo, Díaz-Zuluaga, Ana M., Elvsåshagen, Torbjørn, Eyler, Lisa T., Fullerton, Janice M., Goikolea, Jose M., Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus C.M., Hahn, Tim, Howells, Fleur M., Ingvar, Martin, Kircher, Tilo T.J., Krug, Axel, Kuplicki, Rayus T., Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik F., Martyn, Fiona M., Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa M.T., Mitchell, Philip B., Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Ophoff, Roel A., Overs, Bronwyn J., Pfarr, Julia Katharina, Pineda-Zapata, Julian A., Pomarol-Clotet, Edith, Raduà, Joaquim, van Haren, Neeltje E.M., Vreeker, Annabel, Thompson, Paul M, and Hajek, Tomas

Abstract

Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.

Published
2021

44. What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from theENIGMABipolar Disorder Working Group

Author

Ching, Christopher R. K., Hibar, Derrek P., Gurholt, Tiril P., Nunes, Abraham, Thomopoulos, Sophia I., Abe, Christoph, Agartz, Ingrid, Brouwer, Rachel M., Cannon, Dara M., de Zwarte, Sonja M. C., Eyler, Lisa T., Favre, Pauline, Hajek, Tomas, Haukvik, Unn K., Houenou, Josselin, Landen, Mikael, Lett, Tristram A., McDonald, Colm, Nabulsi, Leila, Patel, Yash, Pauling, Melissa E., Paus, Tomas, Radua, Joaquim, Soeiro-de-Souza, Marcio G., Tronchin, Giulia, van Haren, Neeltje E. M., Vieta, Eduard, Walter, Henrik, Zeng, Ling-Li, Alda, Martin, Almeida, Jorge, Alnaes, Dag, Alonso-Lana, Silvia, Altimus, Cara, Bauer, Michael, Baune, Bernhard T., Bearden, Carrie E., Bellani, Marcella, Benedetti, Francesco, Berk, Michael, Bilderbeck, Amy C., Blumberg, Hilary P., Boen, Erlend, Bollettini, Irene, del Mar Bonnin, Caterina, Brambilla, Paolo, Canales-Rodriguez, Erick J., Caseras, Xavier, Dandash, Orwa, Dannlowski, Udo, Delvecchio, Giuseppe, Diaz-Zuluaga, Ana M., Dima, Danai, Duchesnay, Edouard, Elvsashagen, Torbjorn, Fears, Scott C., Frangou, Sophia, Fullerton, Janice M., Glahn, David C., Goikolea, Jose M., Green, Melissa J., Grotegerd, Dominik, Gruber, Oliver, Haarman, Bartholomeus C. M., Henry, Chantal, Howells, Fleur M., Ives-Deliperi, Victoria, Jansen, Andreas, Kircher, Tilo T. J., Knoechel, Christian, Kramer, Bernd, Lafer, Beny, Lopez-Jaramillo, Carlos, Machado-Vieira, Rodrigo, MacIntosh, Bradley J., Melloni, Elisa M. T., Mitchell, Philip B., Nenadic, Igor, Nery, Fabiano, Nugent, Allison C., Oertel, Viola, Ophoff, Roel A., Ota, Miho, Overs, Bronwyn J., Pham, Daniel L., Phillips, Mary L., Pineda-Zapata, Julian A., Poletti, Sara, Polosan, Mircea, Pomarol-Clotet, Edith, Pouchon, Arnaud, Quide, Yann, Rive, Maria M., Roberts, Gloria, Ruhe, Henricus G., Salvador, Raymond, Sarro, Salvador, Satterthwaite, Theodore D., Schene, Aart H., Sim, Kang, Soares, Jair C., Staeblein, Michael, Stein, Dan J., Tamnes, Christian K., Thomaidis, Georgios V., Upegui, Cristian Vargas, Veltman, Dick J., Wessa, Michele, Westlye, Lars T., Whalley, Heather C., Wolf, Daniel H., Wu, Mon-Ju, Yatham, Lakshmi N., Zarate, Carlos A., Thompson, Paul M., Andreassen, Ole A., Ching, Christopher R. K., Hibar, Derrek P., Gurholt, Tiril P., Nunes, Abraham, Thomopoulos, Sophia I., Abe, Christoph, Agartz, Ingrid, Brouwer, Rachel M., Cannon, Dara M., de Zwarte, Sonja M. C., Eyler, Lisa T., Favre, Pauline, Hajek, Tomas, Haukvik, Unn K., Houenou, Josselin, Landen, Mikael, Lett, Tristram A., McDonald, Colm, Nabulsi, Leila, Patel, Yash, Pauling, Melissa E., Paus, Tomas, Radua, Joaquim, Soeiro-de-Souza, Marcio G., Tronchin, Giulia, van Haren, Neeltje E. M., Vieta, Eduard, Walter, Henrik, Zeng, Ling-Li, Alda, Martin, Almeida, Jorge, Alnaes, Dag, Alonso-Lana, Silvia, Altimus, Cara, Bauer, Michael, Baune, Bernhard T., Bearden, Carrie E., Bellani, Marcella, Benedetti, Francesco, Berk, Michael, Bilderbeck, Amy C., Blumberg, Hilary P., Boen, Erlend, Bollettini, Irene, del Mar Bonnin, Caterina, Brambilla, Paolo, Canales-Rodriguez, Erick J., Caseras, Xavier, Dandash, Orwa, Dannlowski, Udo, Delvecchio, Giuseppe, Diaz-Zuluaga, Ana M., Dima, Danai, Duchesnay, Edouard, Elvsashagen, Torbjorn, Fears, Scott C., Frangou, Sophia, Fullerton, Janice M., Glahn, David C., Goikolea, Jose M., Green, Melissa J., Grotegerd, Dominik, Gruber, Oliver, Haarman, Bartholomeus C. M., Henry, Chantal, Howells, Fleur M., Ives-Deliperi, Victoria, Jansen, Andreas, Kircher, Tilo T. J., Knoechel, Christian, Kramer, Bernd, Lafer, Beny, Lopez-Jaramillo, Carlos, Machado-Vieira, Rodrigo, MacIntosh, Bradley J., Melloni, Elisa M. T., Mitchell, Philip B., Nenadic, Igor, Nery, Fabiano, Nugent, Allison C., Oertel, Viola, Ophoff, Roel A., Ota, Miho, Overs, Bronwyn J., Pham, Daniel L., Phillips, Mary L., Pineda-Zapata, Julian A., Poletti, Sara, Polosan, Mircea, Pomarol-Clotet, Edith, Pouchon, Arnaud, Quide, Yann, Rive, Maria M., Roberts, Gloria, Ruhe, Henricus G., Salvador, Raymond, Sarro, Salvador, Satterthwaite, Theodore D., Schene, Aart H., Sim, Kang, Soares, Jair C., Staeblein, Michael, Stein, Dan J., Tamnes, Christian K., Thomaidis, Georgios V., Upegui, Cristian Vargas, Veltman, Dick J., Wessa, Michele, Westlye, Lars T., Whalley, Heather C., Wolf, Daniel H., Wu, Mon-Ju, Yatham, Lakshmi N., Zarate, Carlos A., Thompson, Paul M., and Andreassen, Ole A.

Abstract

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

Published
2020
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48. Gender-specific differences in white matter microstructure in healthy adults exposed to mild stress.

Author

Poletti, Sara, Melloni, Elisa, Mazza, Elena, Vai, Benedetta, and Benedetti, Francesco

Subjects
*DIFFUSION tensor imaging, *DIFFUSION magnetic resonance imaging, *ENDOCRINE system, *MATTER
Abstract

Stress is a powerful moderator of brain plasticity and may affect several physiological functions such as the endocrine and the immune system. The impact of stress can be protective or detrimental according to several factors such as level of the stressor and age of occurrence. Also, the impact may differ in males and females. We aim to analyze the effect of mild levels of early and recent stress on white matter microstructure in healthy volunteers. MRI acquisition of diffusion tensor images with a 3.0 T scanner was performed on 130 healthy subjects (71 males and 59 females). Severity of early and recent stress was rated, respectively, on the Risky Families Questionnaire and on the Schedule of Recent Experiences; subjects were divided into low stress and mild stress groups. Mild early stress associated with lower fractional anisotropy (FA) in the cingulate gyrus compared to low early stress. Females reported reduced FA compared to males in the low-stress group in the internal capsule, posterior corona radiata, posterior thalamic radiation, superior longitudinal fasciculus, and sagittal stratum whereas no difference was observed in the mild stress group. An additive effect of early and recent stress was observed in posterior corona radiata, retrolenticular part of the internal capsule, and superior longitudinal fasciculus. The impact of early stress on WM microstructure in healthy subjects is different in males and females. While males seem to be more sensitive to early stress, an additive effect of early and recent stress manifests itself in females. Mild levels of early stress associate with lower white matter integrity measured by fractional anisotropy. Females and males show differences in white matter integrity when exposed to low levels of early stress with females showing lower white matter integrity compared to males. No difference in white matter integrity was observed for males and females exposed to mild levels of stress. Mild stress in females is associated with higher white matter integrity. Males seem to be more sensitive to early stress while females are more affected when early stress is followed by stress in adult life. [ABSTRACT FROM AUTHOR]

Published
2020
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50. Corticolimbic Connectivity Mediates the Relationship between Adverse Childhood Experiences and Symptom Severity in Borderline Personality Disorder

Author

Vai, Benedetta, primary, Sforzini, Laura, additional, Visintini, Raffaele, additional, Riberto, Martina, additional, Bulgarelli, Chiara, additional, Ghiglino, Davide, additional, Melloni, Elisa, additional, Bollettini, Irene, additional, Poletti, Sara, additional, Maffei, Cesare, additional, and Benedetti, Francesco, additional

Published
2017
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