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2,940 results on '"Jaramillo, Carlos A."'

1. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study.

Author

Georgiadis, Foivos, Larivière, Sara, Glahn, David, Hong, L, Kochunov, Peter, Mowry, Bryan, Loughland, Carmel, Pantelis, Christos, Henskens, Frans, Green, Melissa, Cairns, Murray, Michie, Patricia, Rasser, Paul, Catts, Stanley, Tooney, Paul, Scott, Rodney, Schall, Ulrich, Carr, Vaughan, Quidé, Yann, Krug, Axel, Stein, Frederike, Nenadić, Igor, Brosch, Katharina, Kircher, Tilo, Gur, Raquel, Gur, Ruben, Satterthwaite, Theodore, Karuk, Andriana, Pomarol-Clotet, Edith, Radua, Joaquim, Fuentes-Claramonte, Paola, Salvador, Raymond, Spalletta, Gianfranco, Voineskos, Aristotle, Sim, Kang, Crespo-Facorro, Benedicto, Tordesillas Gutiérrez, Diana, Ehrlich, Stefan, Crossley, Nicolas, Grotegerd, Dominik, Repple, Jonathan, Lencer, Rebekka, Dannlowski, Udo, Calhoun, Vince, Rootes-Murdy, Kelly, Demro, Caroline, Ramsay, Ian, Sponheim, Scott, Schmidt, Andre, Borgwardt, Stefan, Tomyshev, Alexander, Lebedeva, Irina, Höschl, Cyril, Spaniel, Filip, Preda, Adrian, Nguyen, Dana, Uhlmann, Anne, Stein, Dan, Howells, Fleur, Temmingh, Henk, Diaz Zuluaga, Ana, López Jaramillo, Carlos, Iasevoli, Felice, Ji, Ellen, Homan, Stephanie, Omlor, Wolfgang, Homan, Philipp, Kaiser, Stefan, Seifritz, Erich, Misic, Bratislav, Valk, Sofie, Thompson, Paul, Van Erp, Theodorus, Turner, Jessica, Bernhardt, Boris, and Kirschner, Matthias

Subjects
Humans, Schizophrenia, Connectome, Adult, Female, Male, Magnetic Resonance Imaging, Cerebral Cortex, Nerve Net, Brain, Middle Aged, Neural Pathways, Young Adult
Abstract

Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenias alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.

Published
2024

2. 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

3. Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders.

Author

Service, Susan, de la Hoz, Juan, Díaz-Zuluaga, Ana, Upegui, Cristian, Gallago, Cristian, Arias, Alejandro, Sánchez, Alexandra, Teshiba, Terri, Sabatti, Chiara, Gur, Ruben, Song, Janet, Ramírez, Mauricio, Escobar, Javier, Reus, Victor, Jaramillo, Carlos, Freimer, Nelson, Blower, Sally, Bearden, Carrie, Olde Loohuis, Loes, and Okano, Justin

Abstract

BACKGROUND: Geographical variations in mood and psychotic disorders have been found in upper-income countries. We looked for geographic variation in these disorders in Colombia, a middle-income country. We analyzed electronic health records from the Clínica San Juan de Dios Manizales (CSJDM), which provides comprehensive mental healthcare for the one million inhabitants of Caldas. METHODS: We constructed a friction surface map of Caldas and used it to calculate the travel-time to the CSJDM for 16,295 patients who had received an initial diagnosis of mood or psychotic disorder. Using a zero-inflated negative binomial regression model, we determined the relationship between travel-time and incidence, stratified by disease severity. We employed spatial scan statistics to look for patient clusters. RESULTS: We show that travel-times (for driving) to the CSJDM are less than 1 h for ~50% of the population and more than 4 h for ~10%. We find a distance-decay relationship for outpatients, but not for inpatients: for every hour increase in travel-time, the number of expected outpatient cases decreases by 20% (RR = 0.80, 95% confidence interval [0.71, 0.89], p = 5.67E-05). We find nine clusters/hotspots of inpatients. CONCLUSIONS: Our results reveal inequities in access to healthcare: many individuals requiring only outpatient treatment may live too far from the CSJDM to access healthcare. Targeting of resources to comprehensively identify severely ill individuals living in the observed hotspots could further address treatment inequities and enable investigations to determine factors generating these hotspots.

Published
2024

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

Author

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

Published
2024
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7. Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders

Author

Song, Janet, Ramírez, Mauricio Castaño, Okano, Justin T., Service, Susan K., de la Hoz, Juan, Díaz-Zuluaga, Ana M., Upegui, Cristian Vargas, Gallago, Cristian, Arias, Alejandro, Sánchez, Alexandra Valderrama, Teshiba, Terri, Sabatti, Chiara, Gur, Ruben C., Bearden, Carrie E., Escobar, Javier I., Reus, Victor I., Jaramillo, Carlos Lopez, Freimer, Nelson B., Olde Loohuis, Loes M., and Blower, Sally

Published
2024
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8. 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

9. Reviving keV sterile Neutrino Dark Matter

Author

Jaramillo, Carlos

Subjects
High Energy Physics - Phenomenology
Abstract

We propose a new production mechanism for keV sterile neutrino dark matter which relies neither on the oscillations between sterile and active neutrinos nor on the decay of additional heavier particles. The dark matter neutrinos are instead produced by thermal freeze-out, much like a typical WIMP. The challenge consists in balancing a large Yukawa coupling so that the sterile neutrinos thermalize in the early universe on the one hand, and a small enough Yukawa coupling such that they are stable on cosmological scales on the other. We solve this problem by implementing varying Yukawa couplings. We achieve this by using a three-sterile neutrino seesaw extension to the SM and embedding it in a Froggatt-Nielsen model with a single flavon. Because the vev of the flavon changes during the electroweak phase transition, the effective Yukawa couplings of the fermions have different values before and after the phase transition, thus allowing for successful dark matter genesis. Additionally, the hierarchy in the flavour structure is alleviated and the origin of the light neutrino masses are explained by the interplay of the seesaw and Froggatt-Nielsen mechanisms., Comment: 19 pages

Published
2022
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10. 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

14. Collaborative outcomes study on health and functioning during infection times (COH-FIT): Insights on modifiable and non-modifiable risk and protective factors for wellbeing and mental health during the COVID-19 pandemic from multivariable and network analyses

Author

Solmi, Marco, Thompson, Trevor, Cortese, Samuele, Estradé, Andrés, Agorastos, Agorastos, Radua, Joaquim, Dragioti, Elena, Vancampfort, Davy, Thygesen, Lau Caspar, Aschauer, Harald, Schlögelhofer, Monika, Aschauer, Elena, Schneeberger, Andres, Huber, Christian G., Hasler, Gregor, Conus, Philippe, Cuénod, Kim Q. Do, von Känel, Roland, Arrondo, Gonzalo, Fusar-Poli, Paolo, Gorwood, Philip, Llorca, Pierre-Michel, Krebs, Marie-Odile, Scanferla, Elisabetta, Kishimoto, Taishiro, Rabbani, Golam, Skonieczna-Żydecka, Karolina, Brambilla, Paolo, Favaro, Angela, Takamiya, Akihiro, Zoccante, Leonardo, Colizzi, Marco, Bourgin, Julie, Kamiński, Karol, Moghadasin, Maryam, Seedat, Soraya, Matthews, Evan, Wells, John, Vassilopoulou, Emilia, Gadelha, Ary, Su, Kuan-Pin, Kwon, Jun Soo, Kim, Minah, Lee, Tae Young, Papsuev, Oleg, Manková, Denisa, Boscutti, Andrea, Gerunda, Cristiano, Saccon, Diego, Righi, Elena, Monaco, Francesco, Croatto, Giovanni, Cereda, Guido, Demurtas, Jacopo, Brondino, Natascia, Veronese, Nicola, Enrico, Paolo, Politi, Pierluigi, Ciappolino, Valentina, Pfennig, Andrea, Bechdolf, Andreas, Meyer-Lindenberg, Andreas, Kahl, Kai G., Domschke, Katharina, Bauer, Michael, Koutsouleris, Nikolaos, Winter, Sibylle, Borgwardt, Stefan, Bitter, Istvan, Balazs, Judit, Czobor, Pál, Unoka, Zsolt, Mavridis, Dimitris, Tsamakis, Konstantinos, Bozikas, Vasilios P., Tunvirachaisakul, Chavit, Maes, Michael, Rungnirundorn, Teerayuth, Supasitthumrong, Thitiporn, Haque, Ariful, Brunoni, Andre R., Costardi, Carlos Gustavo, Schuch, Felipe Barreto, Polanczyk, Guilherme, Luiz, Jhoanne Merlyn, Fonseca, Lais, Aparicio, Luana V., Valvassori, Samira S., Nordentoft, Merete, Vendsborg, Per, Hoffmann, Sofie Have, Sehli, Jihed, Sartorius, Norman, Heuss, Sabina, Guinart, Daniel, Hamilton, Jane, Kane, John, Rubio, Jose, Sand, Michael, Koyanagi, Ai, Solanes, Aleix, Andreu-Bernabeu, Alvaro, Cáceres, Antonia San José, Arango, Celso, Díaz-Caneja, Covadonga M., Hidalgo-Mazzei, Diego, Vieta, Eduard, Gonzalez-Peñas, Javier, Fortea, Lydia, Parellada, Mara, Fullana, Miquel A., Verdolini, Norma, Andrlíková, Eva, Janků, Karolina, Millan, Mark J., Honciuc, Mihaela, Moniuszko-Malinowska, Anna, Łoniewski, Igor, Samochowiec, Jerzy, Kiszkiel, Łukasz, Marlicz, Maria, Sowa, Paweł, Marlicz, Wojciech, Spies, Georgina, Stubbs, Brendon, Firth, Joseph, Sullivan, Sarah, Darcin, Asli Enez, Aksu, Hatice, Dilbaz, Nesrin, Noyan, Onur, Kitazawa, Momoko, Kurokawa, Shunya, Tazawa, Yuki, Anselmi, Alejandro, Cracco, Cecilia, Machado, Ana Inés, Estrade, Natalia, De Leo, Diego, Curtis, Jackie, Berk, Michael, Carvalho, Andre F., Ward, Philip, Teasdale, Scott, Rosenbaum, Simon, Marx, Wolfgang, Horodnic, Adrian Vasile, Oprea, Liviu, Alexinschi, Ovidiu, Ifteni, Petru, Turliuc, Serban, Ciuhodaru, Tudor, Bolos, Alexandra, Matei, Valentin, Nieman, Dorien H., Sommer, Iris, van Os, Jim, van Amelsvoort, Therese, Sun, Ching-Fang, Guu, Ta-wei, Jiao, Can, Zhang, Jieting, Fan, Jialin, Zou, Liye, Yu, Xin, Chi, Xinli, de Timary, Philippe, van Winkel, Ruud, Ng, Bernardo, Peña de León, Edilberto, Arellano, Ramon, Roman, Raquel, Sanchez, Thelma, Movina, Larisa, Morgado, Pedro, Brissos, Sofia, Aizberg, Oleg, Mosina, Anna, Krinitski, Damir, Mugisha, James, Sadeghi-Bahmani, Dena, Sheybani, Farshad, Sadeghi, Masoud, Hadi, Samira, Brand, Serge, Errazuriz, Antonia, Crossley, Nicolas, Ristic, Dragana Ignjatovic, López-Jaramillo, Carlos, Efthymiou, Dimitris, Kuttichira, Praveenlal, Kallivayalil, Roy Abraham, Javed, Afzal, Afridi, Muhammad Iqbal, James, Bawo, Seb-Akahomen, Omonefe Joy, Fiedorowicz, Jess, Daskalakis, Jeff, Yatham, Lakshmi N., Yang, Lin, Okasha, Tarek, Dahdouh, Aïcha, Tiihonen, Jari, Shin, Jae Il, Lee, Jinhee, Mhalla, Ahmed, Gaha, Lotfi, Brahim, Takoua, Altynbekov, Kuanysh, Negay, Nikolay, Nurmagambetova, Saltanat, Jamei, Yasser Abu, Weiser, Mark, and Correll, Christoph U

Published
2025
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16. Global and risk-group stratified well-being and mental health during the COVID-19 pandemic in adults: Results from the international COH-FIT Study

Author

Solmi, Marco, Thompson, Trevor, Estradé, Andrés, Agorastos, Agorastos, Radua, Joaquim, Cortese, Samuele, Dragioti, Elena, Vancampfort, Davy, Thygesen, Lau Caspar, Aschauer, Harald, Schlögelhofer, Monika, Aschauer, Elena, Schneeberger, Andres, Huber, Christian G., Hasler, Gregor, Conus, Philippe, Cuénod, Kim Q. Do, von Känel, Roland, Arrondo, Gonzalo, Fusar-Poli, Paolo, Gorwood, Philip, Llorca, Pierre-Michel, Krebs, Marie-Odile, Scanferla, Elisabetta, Kishimoto, Taishiro, Rabbani, Golam, Skonieczna-Żydecka, Karolina, Brambilla, Paolo, Favaro, Angela, Takamiya, Akihiro, Zoccante, Leonardo, Colizzi, Marco, Bourgin, Julie, Kamiński, Karol, Moghadasin, Maryam, Seedat, Soraya, Matthews, Evan, Wells, John, Vassilopoulou, Emilia, Gadelha, Ary, Su, Kuan-Pin, Kwon, Jun Soo, Kim, Minah, Lee, Tae Young, Papsuev, Oleg, Manková, Denisa, Boscutti, Andrea, Gerunda, Cristiano, Saccon, Diego, Righi, Elena, Monaco, Francesco, Croatto, Giovanni, Cereda, Guido, Demurtas, Jacopo, Brondino, Natascia, Veronese, Nicola, Enrico, Paolo, Politi, Pierluigi, Ciappolino, Valentina, Pfennig, Andrea, Bechdolf, Andreas, Meyer-Lindenberg, Andreas, Kahl, Kai G., Domschke, Katharina, Bauer, Michael, Koutsouleris, Nikolaos, Winter, Sibylle, Borgwardt, Stefan, Bitter, Istvan, Balazs, Judit, Czobor, Pál, Unoka, Zsolt, Mavridis, Dimitris, Tsamakis, Konstantinos, Bozikas, Vasilios P., Tunvirachaisakul, Chavit, Maes, Michael, Rungnirundorn, Teerayuth, Supasitthumrong, Thitiporn, Haque, Ariful, Brunoni, Andre R., Costardi, Carlos Gustavo, Schuch, Felipe Barreto, Polanczyk, Guilherme, Luiz, Jhoanne Merlyn, Fonseca, Lais, Aparicio, Luana V., Valvassori, Samira S., Nordentoft, Merete, Vendsborg, Per, Hoffmann, Sofie Have, Sehli, Jihed, Sartorius, Norman, Heuss, Sabina, Guinart, Daniel, Hamilton, Jane, Kane, John, Rubio, Jose, Sand, Michael, Koyanagi, Ai, Solanes, Aleix, Andreu-Bernabeu, Alvaro, Cáceres, Antonia San José, Arango, Celso, Díaz-Caneja, Covadonga M., Hidalgo-Mazzei, Diego, Vieta, Eduard, Gonzalez-Peñas, Javier, Fortea, Lydia, Parellada, Mara, Fullana, Miquel A., Verdolini, Norma, Andrlíková, Eva, Janků, Karolina, Millan, Mark J., Honciuc, Mihaela, Moniuszko-Malinowska, Anna, Łoniewski, Igor, Samochowiec, Jerzy, Kiszkiel, Łukasz, Marlicz, Maria, Sowa, Paweł, Marlicz, Wojciech, Spies, Georgina, Stubbs, Brendon, Firth, Joseph, Sullivan, Sarah, Darcin, Asli Enez, Aksu, Hatice, Dilbaz, Nesrin, Noyan, Onur, Kitazawa, Momoko, Kurokawa, Shunya, Tazawa, Yuki, Anselmi, Alejandro, Cracco, Cecilia, Machado, Ana Inés, Estrade, Natalia, De Leo, Diego, Curtis, Jackie, Berk, Michael, Carvalho, Andre F., Ward, Philip, Teasdale, Scott, Rosenbaum, Simon, Marx, Wolfgang, Horodnic, Adrian Vasile, Oprea, Liviu, Alexinschi, Ovidiu, Ifteni, Petru, Turliuc, Serban, Ciuhodaru, Tudor, Bolos, Alexandra, Matei, Valentin, Nieman, Dorien H., Sommer, Iris, van Os, Jim, van Amelsvoort, Therese, Sun, Ching-Fang, Guu, Ta-wei, Jiao, Can, Zhang, Jieting, Fan, Jialin, Zou, Liye, Yu, Xin, Chi, Xinli, de Timary, Philippe, van Winkel, Ruud, Ng, Bernardo, Peña de León, Edilberto, Arellano, Ramon, Roman, Raquel, Sanchez, Thelma, Movina, Larisa, Morgado, Pedro, Brissos, Sofia, Aizberg, Oleg, Mosina, Anna, Krinitski, Damir, Mugisha, James, Sadeghi-Bahmani, Dena, Sheybani, Farshad, Sadeghi, Masoud, Hadi, Samira, Brand, Serge, Errazuriz, Antonia, Crossley, Nicolas, Ristic, Dragana Ignjatovic, López-Jaramillo, Carlos, Efthymiou, Dimitris, Kuttichira, Praveenlal, Kallivayalil, Roy Abraham, Javed, Afzal, Afridi, Muhammad Iqbal, James, Bawo, Seb-Akahomen, Omonefe Joy, Fiedorowicz, Jess, Daskalakis, Jeff, Yatham, Lakshmi N., Yang, Lin, Okasha, Tarek, Dahdouh, Aïcha, Tiihonen, Jari, Shin, Jae Il, Lee, Jinhee, Mhalla, Ahmed, Gaha, Lotfi, Brahim, Takoua, Altynbekov, Kuanysh, Negay, Nikolay, Nurmagambetova, Saltanat, Jamei, Yasser Abu, Weiser, Mark, and Correll, Christoph U.

Published
2024
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19. 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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22. 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

23. 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

24. 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

26. A Functional Model for Structure Learning and Parameter Estimation in Continuous Time Bayesian Network: An Application in Identifying Patterns of Multiple Chronic Conditions

Author

Faruqui, Syed Hasib Akhter, Alaeddini, Adel, Wang, Jing, and Jaramillo, Carlos A.

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

Bayesian networks are powerful statistical models to study the probabilistic relationships among set random variables with major applications in disease modeling and prediction. Here, we propose a continuous time Bayesian network with conditional dependencies, represented as Poisson regression, to model the impact of exogenous variables on the conditional dependencies of the network. We also propose an adaptive regularization method with an intuitive early stopping feature based on density based clustering for efficient learning of the structure and parameters of the proposed network. Using a dataset of patients with multiple chronic conditions extracted from electronic health records of the Department of Veterans Affairs we compare the performance of the proposed approach with some of the existing methods in the literature for both short-term (one-year ahead) and long-term (multi-year ahead) predictions. The proposed approach provides a sparse intuitive representation of the complex functional relationships between multiple chronic conditions. It also provides the capability of analyzing multiple disease trajectories over time given any combination of prior conditions., Comment: Submitted to IEEE Access for review

Published
2020

27. Seesaw neutrino dark matter by freeze-out

Author

Jaramillo, Carlos, Lindner, Manfred, and Rodejohann, Werner

Subjects
High Energy Physics - Phenomenology
Abstract

We investigate whether right-handed neutrinos can play the role of the dark matter of the Universe and be generated by the freeze-out production mechanism. In the standard picture, the requirement of a long lifetime of the right-handed neutrinos implies a small neutrino Yukawa coupling. As a consequence, they never reach thermal equilibrium, thus prohibiting production by freeze-out. We note that this limitation is alleviated if the neutrino Yukawa coupling is large enough in the early Universe to thermalize the sterile neutrinos, and then becomes tiny at a certain moment, which makes them drop out of equilibrium. As a concrete example realization of this framework, we consider a Froggatt-Nielsen model supplemented by an additional scalar field which obeys a global symmetry (not the flavour symmetry). Initially, the vacuum expectation value of the flavon is such, that the effective neutrino Yukawa coupling is large and unsuppressed, keeping them in thermal equilibrium. At some point the new scalar also gets a vacuum expectation value that breaks the symmetry. This may occur in such a way that the vev of the flavon is shifted to a new (smaller) value. In that case, the Yukawa coupling is reduced such that the sterile neutrinos are rendered stable on cosmological time scales. We show that this mechanism works for a wide range of sterile neutrino masses., Comment: 15 pages, 5 figures

Published
2020
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33. Guía internacional para una dosificación más segura de la clozapina en adultos mediante el uso de 6 titulaciones personalizadas de dosis basados en la etnicidad, la proteína C reactiva y los niveles de clozapina

Author

de Leon, Jose, Schoretsanitis, Georgios, Smith, Robert L., Molden, Espen, Solismaa, Anssi, Seppälä, Niko, Kopeček, Miloslav, Švancer, Patrik, Olmos, Ismael, Ricciardi, Carina, Iglesias-Garcia, Celso, Iglesias-Alonso, Ana, Spina, Edoardo, Ruan, Can-Jun, Wang, Chuan-Yue, Wang, Gang, Tang, Yi-Lang, Lin, Shih-Ku, Lane, Hsien-Yuan, Kim, Yong Sik, Kim, Se Hyun, Rajkumar, Anto P., González-Esquivel, Dinora F., Jung-Cook, Helgi, Baptista, Trino, Rohde, Christopher, Nielsen, Jimmi, Verdoux, Hélène, Quiles, Clelia, Sanz, Emilio J., De las Cuevas, Carlos, Cohen, Dan, Schulte, Peter F.J., Ertuğrul, Aygün, Anıl Yağcıoğlu, A. Elif, Chopra, Nitin, McCollum, Betsy, Shelton, Charles, Cotes, Robert O., Kaithi, Arun R., Kane, John M., Farooq, Saeed, Ng, Chee H., Bilbily, John, Hiemke, Christoph, López-Jaramillo, Carlos, McGrane, Ian, Lana, Fernando, Eap, Chin B., Arrojo-Romero, Manuel, Rădulescu, Flavian Ştefan, Seifritz, Erich, Every-Palmer, Susanna, Bousman, Chad A., Bebawi, Emmanuel, Bhattacharya, Rahul, Kelly, Deanna L., Otsuka, Yuji, Lazary, Judit, Torres, Rafael, Yecora, Agustin, Motuca, Mariano, Chan, Sherry Kit Wa, Zolezzi, Monica, Ouanes, Sami, De Berardis, Domenico, Grover, Sandeep, Procyshyn, Ric M., Adebayo, Richard A., Kirilochev, Oleg O., Soloviev, Andrey, Fountoulakis, Konstantinos N., Wilkowska, Alina, Cubała, Wiesław Jerzy, Ayub, Muhammad, Silva, Alzira, Bonelli, Raphael M., Villagrán-Moreno, José María, Crespo-Facorro, Benedicto, Temmingh, Henk, Decloedt, Eric, Pedro, Maria Rosel, Takeuchi, Hiroyoshi, Tsukahara, Masaru, Gründer, Gerhard, Sagud, Marina, Celofiga, Andreja, Ignjatovic Ristic, Dragana, Ortiz, Bruno Bertolucci, Elkis, Helio, Pacheco Palha, António José, Llerena, Adrián, Fernandez-Egea, Emilio, Siskind, Dan, Weizman, Abraham, Masmoudi, Rim, Mohd Saffian, Shamin, Leung, Jonathan G., Buckley, Peter F., Marder, Stephen R., Citrome, Leslie, Freudenreich, Oliver, Correll, Christoph U., and Müller, Daniel J.

Published
2023
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34. Amber and the Cretaceous Resinous Interval

Author

Delclòs, Xavier, Peñalver, Enrique, Barrón, Eduardo, Peris, David, Grimaldi, David A., Holz, Michael, Labandeira, Conrad C., Saupe, Erin E., Scotese, Christopher R., Solórzano-Kraemer, Mónica M., Álvarez-Parra, Sergio, Arillo, Antonio, Azar, Dany, Cadena, Edwin A., Dal Corso, Jacopo, Kvaček, Jiří, Monleón-Getino, Antonio, Nel, André, Peyrot, Daniel, Bueno-Cebollada, Carlos A., Gallardo, Alejandro, González-Fernández, Beatriz, Goula, Marta, Jaramillo, Carlos, Kania-Kłosok, Iwona, López-Del Valle, Rafael, Lozano, Rafael P., Meléndez, Nieves, Menor-Salván, César, Peña-Kairath, Constanza, Perrichot, Vincent, Rodrigo, Ana, Sánchez-García, Alba, Santer, Maxime, Sarto i Monteys, Víctor, Uhl, Dieter, Viejo, José Luis, and Pérez-de la Fuente, Ricardo

Published
2023
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35. Diversity matters: opportunities in the study of the genetics of psychotic disorders in low- and middle-income countries in Latin America

Author

Fonseca, Lais, Sena, Brena F, Crossley, Nicolas, Lopez-Jaramillo, Carlos, Koenen, Karestan, Freimer, Nelson B, Bressan, Rodrigo A, Belangero, Sintia I, Santoro, Marcos L, and Gadelha, Ary

Subjects
Clinical and Health Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Schizophrenia, Mental Health, Prevention, Human Genome, Genetics, Brain Disorders, Serious Mental Illness, Aetiology, 2.3 Psychological, social and economic factors, Mental health, Good Health and Well Being, Reduced Inequalities, Developing Countries, Ethnicity, Genome-Wide Association Study, Humans, Latin America, Psychotic Disorders, psychotic disorders, schizophrenia, genetics, environment, Psychiatry, Clinical sciences, Clinical and health psychology
Abstract

Lack of diversity regarding genetic and environmental backgrounds weakens the generalization and clinical applicability of research findings on psychotic disorders. Notably, Latin Americans have been generally neglected in genetic studies, comprising less than 2% of genome-wide association study samples. But Latin American populations represent a unique opportunity for research, given the exceptionally high ethnic admixture of this group. Increasing genetic diversity is essential to improve the fine mapping of known regions associated with psychotic disorders, discover novel genetic associations, and replicate studies. Additionally, Latin America is characterized by massive social, political, and economic inequalities, all known risk factors for mental health issues, including psychotic disorders. This article aims to 1) discuss the challenges and advantages of studying Latin America's particular genetic makeup and environmental context; 2) review previous studies conducted in the region; and 3) describe three Latin American research initiatives in progress: the Neuropsychiatric Genetics of Psychosis in Mexican Populations (NeuroMEX), the Paisa, and the Latin American Network for the Study of Early Psychosis (ANDES) studies.

Published
2021

40. Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Author

Nunes, Abraham, Schnack, Hugo G, Ching, Christopher RK, Agartz, Ingrid, Akudjedu, Theophilus N, Alda, Martin, Alnæs, Dag, Alonso-Lana, Silvia, Bauer, Jochen, Baune, Bernhard T, Bøen, Erlend, Bonnin, Caterina del Mar, Busatto, Geraldo F, Canales-Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Chaim-Avancini, Tiffany M, Dannlowski, Udo, Díaz-Zuluaga, Ana M, Dietsche, Bruno, Doan, Nhat Trung, Duchesnay, Edouard, Elvsåshagen, Torbjørn, Emden, Daniel, Eyler, Lisa T, Fatjó-Vilas, Mar, Favre, Pauline, Foley, Sonya F, Fullerton, Janice M, Glahn, David C, Goikolea, Jose M, Grotegerd, Dominik, Hahn, Tim, Henry, Chantal, Hibar, Derrek P, Houenou, Josselin, Howells, Fleur M, Jahanshad, Neda, Kaufmann, Tobias, Kenney, Joanne, Kircher, Tilo TJ, Krug, Axel, Lagerberg, Trine V, Lenroot, Rhoshel K, López-Jaramillo, Carlos, Machado-Vieira, Rodrigo, Malt, Ulrik F, McDonald, Colm, Mitchell, Philip B, Mwangi, Benson, Nabulsi, Leila, Opel, Nils, Overs, Bronwyn J, Pineda-Zapata, Julian A, Pomarol-Clotet, Edith, Redlich, Ronny, Roberts, Gloria, Rosa, Pedro G, Salvador, Raymond, Satterthwaite, Theodore D, Soares, Jair C, Stein, Dan J, Temmingh, Henk S, Trappenberg, Thomas, Uhlmann, Anne, van Haren, Neeltje EM, Vieta, Eduard, Westlye, Lars T, Wolf, Daniel H, Yüksel, Dilara, Zanetti, Marcus V, Andreassen, Ole A, Thompson, Paul M, and Hajek, Tomas

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Brain Disorders, Biomedical Imaging, Neurosciences, Mental Health, Clinical Research, Mental health, Bipolar Disorder, Brain, Humans, Machine Learning, Magnetic Resonance Imaging, Neuroimaging, 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

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

Published
2020

41. Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

Author

Radua, Joaquim, Vieta, Eduard, Shinohara, Russell, Kochunov, Peter, Quidé, Yann, Green, Melissa J, Weickert, Cynthia S, Weickert, Thomas, Bruggemann, Jason, Kircher, Tilo, Nenadić, Igor, Cairns, Murray J, Seal, Marc, Schall, Ulrich, Henskens, Frans, Fullerton, Janice M, Mowry, Bryan, Pantelis, Christos, Lenroot, Rhoshel, Cropley, Vanessa, Loughland, Carmel, Scott, Rodney, Wolf, Daniel, Satterthwaite, Theodore D, Tan, Yunlong, Sim, Kang, Piras, Fabrizio, Spalletta, Gianfranco, Banaj, Nerisa, Pomarol-Clotet, Edith, Solanes, Aleix, Albajes-Eizagirre, Anton, Canales-Rodríguez, Erick J, Sarro, Salvador, Di Giorgio, Annabella, Bertolino, Alessandro, Stäblein, Michael, Oertel, Viola, Knöchel, Christian, Borgwardt, Stefan, du Plessis, Stefan, Yun, Je-Yeon, Kwon, Jun Soo, Dannlowski, Udo, Hahn, Tim, Grotegerd, Dominik, Alloza, Clara, Arango, Celso, Janssen, Joost, Díaz-Caneja, Covadonga, Jiang, Wenhao, Calhoun, Vince, Ehrlich, Stefan, Yang, Kun, Cascella, Nicola G, Takayanagi, Yoichiro, Sawa, Akira, Tomyshev, Alexander, Lebedeva, Irina, Kaleda, Vasily, Kirschner, Matthias, Hoschl, Cyril, Tomecek, David, Skoch, Antonin, van Amelsvoort, Therese, Bakker, Geor, James, Anthony, Preda, Adrian, Weideman, Andrea, Stein, Dan J, Howells, Fleur, Uhlmann, Anne, Temmingh, Henk, López-Jaramillo, Carlos, Díaz-Zuluaga, Ana, Fortea, Lydia, Martinez-Heras, Eloy, Solana, Elisabeth, Llufriu, Sara, Jahanshad, Neda, Thompson, Paul, Turner, Jessica, van Erp, Theo, collaborators, ENIGMA Consortium, Glahn, David, Pearlson, Godfrey, Hong, Elliot, Krug, Axel, Carr, Vaughan, Tooney, Paul, Cooper, Gavin, Rasser, Paul, Michie, Patricia, Catts, Stanley, Gur, Raquel, Gur, Ruben, Yang, Fude, Fan, Fengmei, Chen, Jingxu, and Guo, Hua

Subjects
Brain Disorders, Biomedical Imaging, Mental Health, Schizophrenia, Neurosciences, Mental health, Adult, Algorithms, Cerebral Cortex, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Meta-Analysis as Topic, Middle Aged, Neuroimaging, Young Adult, Brain, Cortical thickness, Gray matter, Mega-analysis, Volume, ENIGMA Consortium collaborators, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

Published
2020

42. Distinct and shared contributions of diagnosis and symptom domains to cognitive performance in severe mental illness in the Paisa population: a case-control study

Author

Service, Susan K, Upegui, Cristian Vargas, Ramírez, Mauricio Castaño, Port, Allison M, Moore, Tyler M, Umanes, Marfred Munoz, Arango, Luis Guillermo Agudelo, Díaz-Zuluaga, Ana M, Espejo, Juanita Melo, López, María Cecilia, Palacio, Juan David, Sánchez, Sergio Ruiz, Valencia, Johanna, Teshiba, Terri M, Espinoza, Alesandra, Loohuis, Loes Olde, De la Hoz Gomez, Juan, Brodey, Benjamin B, Sabatti, Chiara, Escobar, Javier I, Reus, Victor I, Jaramillo, Carlos Lopez, Gur, Ruben C, Bearden, Carrie E, and Freimer, Nelson B

Subjects
Biological Psychology, Psychology, Applied and Developmental Psychology, Depression, Behavioral and Social Science, Bipolar Disorder, Neurosciences, Mental Health, Clinical Research, Brain Disorders, Schizophrenia, Serious Mental Illness, 2.1 Biological and endogenous factors, Aetiology, 2.3 Psychological, social and economic factors, 2.4 Surveillance and distribution, Mental health, Good Health and Well Being, Adult, Case-Control Studies, Cognition, Cognition Disorders, Colombia, Depressive Disorder, Major, Female, Humans, Linear Models, Male, Middle Aged, Schizophrenic Psychology, Young Adult, Clinical Sciences, Public Health and Health Services, Clinical sciences, Applied and developmental psychology, Clinical and health psychology
Abstract

BackgroundSevere mental illness diagnoses have overlapping symptomatology and shared genetic risk, motivating cross-diagnostic investigations of disease-relevant quantitative measures. We analysed relationships between neurocognitive performance, symptom domains, and diagnoses in a large sample of people with severe mental illness not ascertained for a specific diagnosis (cases), and people without mental illness (controls) from a single, homogeneous population.MethodsIn this case-control study, cases with severe mental illness were ascertained through electronic medical records at Clínica San Juan de Dios de Manizales (Manizales, Caldas, Colombia) and the Hospital Universitario San Vicente Fundación (Medellín, Antioquía, Colombia). Participants were assessed for speed and accuracy using the Penn Computerized Neurocognitive Battery (CNB). Cases had structured interview-based diagnoses of schizophrenia, bipolar 1, bipolar 2, or major depressive disorder. Linear mixed models, using CNB tests as repeated measures, modelled neurocognition as a function of diagnosis, sex, and all interactions. Follow-up analyses in cases included symptom factor scores obtained from exploratory factor analysis of symptom data as main effects.FindingsBetween Oct 1, 2017, and Nov 1, 2019, 2406 participants (1689 cases [schizophrenia n=160; bipolar 1 disorder n=519; bipolar 2 disorder n=204; and major depressive disorder n=806] and 717 controls; mean age 39 years (SD 14); and 1533 female) were assessed. Participants with bipolar 1 disorder and schizophrenia had similar impairments in accuracy and speed across cognitive domains. Participants with bipolar 2 disorder and major depressive disorder performed similarly to controls, with subtle deficits in executive and social cognition. A three-factor model (psychosis, mania, and depression) best represented symptom data. Controlling for diagnosis, premorbid IQ, and disease severity, high lifetime psychosis scores were associated with reduced accuracy and speed across cognitive domains, whereas high depression scores were associated with increased social cognition accuracy.InterpretationCross-diagnostic investigations showed that neurocognitive function in severe mental illness is characterised by two distinct profiles (bipolar 1 disorder and schizophrenia, and bipolar 2 disorder and major depressive disorder), and is associated with specific symptom domains. These results suggest the utility of this design for elucidating severe mental illness causes and trajectories.FundingUS National Institute of Mental Health.

Published
2020

43. Contribution of common and rare variants to bipolar disorder susceptibility in extended pedigrees from population isolates.

Author

Sul, Jae Hoon, Service, Susan K, Huang, Alden Y, Ramensky, Vasily, Hwang, Sun-Goo, Teshiba, Terri M, Park, YoungJun, Ori, Anil PS, Zhang, Zhongyang, Mullins, Niamh, Olde Loohuis, Loes M, Fears, Scott C, Araya, Carmen, Araya, Xinia, Spesny, Mitzi, Bejarano, Julio, Ramirez, Margarita, Castrillón, Gabriel, Gomez-Makhinson, Juliana, Lopez, Maria C, Montoya, Gabriel, Montoya, Claudia P, Aldana, Ileana, Escobar, Javier I, Ospina-Duque, Jorge, Kremeyer, Barbara, Bedoya, Gabriel, Ruiz-Linares, Andres, Cantor, Rita M, Molina, Julio, Coppola, Giovanni, Ophoff, Roel A, Macaya, Gabriel, Lopez-Jaramillo, Carlos, Reus, Victor, Bearden, Carrie E, Sabatti, Chiara, and Freimer, Nelson B

Subjects
Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Current evidence from case/control studies indicates that genetic risk for psychiatric disorders derives primarily from numerous common variants, each with a small phenotypic impact. The literature describing apparent segregation of bipolar disorder (BP) in numerous multigenerational pedigrees suggests that, in such families, large-effect inherited variants might play a greater role. To identify roles of rare and common variants on BP, we conducted genetic analyses in 26 Colombia and Costa Rica pedigrees ascertained for bipolar disorder 1 (BP1), the most severe and heritable form of BP. In these pedigrees, we performed microarray SNP genotyping of 838 individuals and high-coverage whole-genome sequencing of 449 individuals. We compared polygenic risk scores (PRS), estimated using the latest BP1 genome-wide association study (GWAS) summary statistics, between BP1 individuals and related controls. We also evaluated whether BP1 individuals had a higher burden of rare deleterious single-nucleotide variants (SNVs) and rare copy number variants (CNVs) in a set of genes related to BP1. We found that compared with unaffected relatives, BP1 individuals had higher PRS estimated from BP1 GWAS statistics (P = 0.001 ~ 0.007) and displayed modest increase in burdens of rare deleterious SNVs (P = 0.047) and rare CNVs (P = 0.002 ~ 0.033) in genes related to BP1. We did not observe rare variants segregating in the pedigrees. These results suggest that small-to-moderate effect rare and common variants are more likely to contribute to BP1 risk in these extended pedigrees than a few large-effect rare variants.

Published
2020

47. Implicaciones metodológicas e inconsistencias de la Tercera Comunicación Nacional sobre Cambio Climático de Colombia/Methodological implications and inconsistencies of Colombia's Third National Communication on Climate Change

Author

Arias, Paola A., Villegas, Laura D., Mesa, Oscar J., Pabón, José D., Arango, Jacobo, Arango-Aramburo, Santiago, Armenteras, Dolors, Berrouet, Lina, Barahona, Rolando, Barco, Janet, Cardona, Yuley, Carvajal-Escobar, Yesid, Ceballos-Bonilla, Lina I., Cerón, Wilmar L., Escobar, Jaime H., González, Catalina, Hoyos, Isabel C., Hoyos, Natalia, Jaramillo, Carlos, Jiménez-Sánchez, Giovanni, Martínez, J. Alejandro, Poveda, Germán, Quesada, Benjamín, Quintero-Vallejo, Estela, Rendón, ?ngela M., Ruiz-Carrascal, Daniel, Salazar, Alejandro, Salazar, Juan F., Sánchez, Adriana, Vieira, Sara C., Villegas, Juan Camilo, Villegas-Palacio, Clara, and Zuluaga, Manuel D.

Published
2022
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49. 10Kin1day: A Bottom-Up Neuroimaging Initiative

Author

van den Heuvel, Martijn P, Scholtens, Lianne H, van der Burgh, Hannelore K, Agosta, Federica, Alloza, Clara, Arango, Celso, Auyeung, Bonnie, Baron-Cohen, Simon, Basaia, Silvia, Benders, Manon JNL, Beyer, Frauke, Booij, Linda, Braun, Kees PJ, Filho, Geraldo Busatto, Cahn, Wiepke, Cannon, Dara M, Chaim-Avancini, Tiffany M, Chan, Sandra SM, Chen, Eric YH, Crespo-Facorro, Benedicto, Crone, Eveline A, Dannlowski, Udo, de Zwarte, Sonja MC, Dietsche, Bruno, Donohoe, Gary, Du Plessis, Stefan, Durston, Sarah, Díaz-Caneja, Covadonga M, Díaz-Zuluaga, Ana M, Emsley, Robin, Filippi, Massimo, Frodl, Thomas, Gorges, Martin, Graff, Beata, Grotegerd, Dominik, Gąsecki, Dariusz, Hall, Julie M, Holleran, Laurena, Holt, Rosemary, Hopman, Helene J, Jansen, Andreas, Janssen, Joost, Jodzio, Krzysztof, Jäncke, Lutz, Kaleda, Vasiliy G, Kassubek, Jan, Masouleh, Shahrzad Kharabian, Kircher, Tilo, Koevoets, Martijn GJC, Kostic, Vladimir S, Krug, Axel, Lawrie, Stephen M, Lebedeva, Irina S, Lee, Edwin HM, Lett, Tristram A, Lewis, Simon JG, Liem, Franziskus, Lombardo, Michael V, Lopez-Jaramillo, Carlos, Margulies, Daniel S, Markett, Sebastian, Marques, Paulo, Martínez-Zalacaín, Ignacio, McDonald, Colm, McIntosh, Andrew M, McPhilemy, Genevieve, Meinert, Susanne L, Menchón, José M, Montag, Christian, Moreira, Pedro S, Morgado, Pedro, Mothersill, David O, Mérillat, Susan, Müller, Hans-Peter, Nabulsi, Leila, Najt, Pablo, Narkiewicz, Krzysztof, Naumczyk, Patrycja, Oranje, Bob, de la Foz, Victor Ortiz-Garcia, Peper, Jiska S, Pineda, Julian A, Rasser, Paul E, Redlich, Ronny, Repple, Jonathan, Reuter, Martin, Rosa, Pedro GP, Ruigrok, Amber NV, Sabisz, Agnieszka, Schall, Ulrich, Seedat, Soraya, Serpa, Mauricio H, Skouras, Stavros, Soriano-Mas, Carles, Sousa, Nuno, Szurowska, Edyta, Tomyshev, Alexander S, Tordesillas-Gutierrez, Diana, Valk, Sofie L, and van den Berg, Leonard H

Subjects
Biological Psychology, Psychology, Biomedical Imaging, Brain Disorders, Neurosciences, Neurological, MRI, connectome analysis, diffusion weighted MRI, brain, network, Clinical Sciences, Clinical sciences, Biological psychology
Abstract

We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain.

Published
2019

50. Deployment, suicide, and overdose among comorbidity phenotypes following mild traumatic brain injury: A retrospective cohort study from the Chronic Effects of Neurotrauma Consortium.

Author

Pugh, Mary Jo, Swan, Alicia A, Amuan, Megan E, Eapen, Blessen C, Jaramillo, Carlos A, Delgado, Roxana, Tate, David F, Yaffe, Kristine, and Wang, Chen-Pin

Subjects
Humans, Brain Concussion, Risk Factors, Retrospective Studies, Suicide, Comorbidity, Phenotype, Adult, Veterans, United States, Female, Male, Drug Overdose, General Science & Technology
Abstract

Mild traumatic brain injury in the Veteran population is frequently comorbid with pain, post-traumatic stress disorder, and/or depression. However, not everyone exposed to mild traumatic brain injury experiences these comorbidities and it is unclear what factors contribute to this variability. The objective of this study was to identify comorbidity phenotypes among Post-9/11 deployed Veterans with no or mild traumatic brain injury and examine the association of comorbidity phenotypes with adverse outcomes. We found that Veterans with mild traumatic brain injury (n = 93,003) and no brain injury (n = 434,378) were mean age of 32.0 (SD 9.21) on entering Department of Veterans Health Administration care, were predominantly Caucasian non-Hispanic (64.69%), and served in the Army (61.31%). Latent class analysis revealed five phenotypes in each subcohort; Moderately Healthy and Mental Health phenotypes were common to both. The Healthy phenotype was found only in no brain injury. Unique phenotypes in mild traumatic brain injury included Moderately Healthy+Decline, Polytrauma, and Polytrauma+Improvement. There was substantial variation in adverse outcomes. The Polytrauma+Improvement phenotype had the lowest likelihood of adverse outcomes. There were no differences between Moderately Healthy+Decline and Polytrauma phenotypes. Phenotypes of comorbidity vary significantly by traumatic brain injury status including divergence in phenotypes (and outcomes) over time in the mild traumatic brain injury subcohort. Understanding risk factors for the divergence between Polytrauma vs. Polytrauma+Improvement and Moderately Healthy vs. Moderately Healthy+Decline, will improve our ability to proactively mitigate risk, better understand the early patterns of comorbidity that are associated with neurodegenerative sequelae following mild traumatic brain injury, and plan more patient-centered care.

Published
2019

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