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1. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm.

Author

Jiang, Yuchao, Luo, Cheng, Wang, Jijun, Palaniyappan, Lena, Chang, Xiao, Xiang, Shitong, Zhang, Jie, Duan, Mingjun, Huang, Huan, Gaser, Christian, Nemoto, Kiyotaka, Miura, Kenichiro, Hashimoto, Ryota, Westlye, Lars, Richard, Genevieve, Fernandez-Cabello, Sara, Parker, Nadine, Andreassen, Ole, Kircher, Tilo, Nenadić, Igor, Stein, Frederike, Thomas-Odenthal, Florian, Teutenberg, Lea, Usemann, Paula, Dannlowski, Udo, Hahn, Tim, Grotegerd, Dominik, Meinert, Susanne, Lencer, Rebekka, Tang, Yingying, Zhang, Tianhong, Li, Chunbo, Yue, Weihua, Zhang, Yuyanan, Yu, Xin, Zhou, Enpeng, Lin, Ching-Po, Tsai, Shih-Jen, Rodrigue, Amanda, Glahn, David, Pearlson, Godfrey, Blangero, John, Karuk, Andriana, Pomarol-Clotet, Edith, Salvador, Raymond, Fuentes-Claramonte, Paola, Garcia-León, María, Spalletta, Gianfranco, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Cheng, Jingliang, Liu, Zhening, Yang, Jie, Gonul, Ali, Uslu, Ozgul, Burhanoglu, Birce, Uyar Demir, Aslihan, Rootes-Murdy, Kelly, Calhoun, Vince, Sim, Kang, Green, Melissa, Quidé, Yann, Chung, Young, Kim, Woo-Sung, Sponheim, Scott, Demro, Caroline, Ramsay, Ian, Iasevoli, Felice, de Bartolomeis, Andrea, Barone, Annarita, Ciccarelli, Mariateresa, Brunetti, Arturo, Cocozza, Sirio, Pontillo, Giuseppe, Tranfa, Mario, Park, Min, Kirschner, Matthias, Georgiadis, Foivos, Kaiser, Stefan, Van Rheenen, Tamsyn, Rossell, Susan, Hughes, Matthew, Woods, William, Carruthers, Sean, Sumner, Philip, Ringin, Elysha, Spaniel, Filip, Skoch, Antonin, Tomecek, David, Homan, Philipp, Homan, Stephanie, Omlor, Wolfgang, Cecere, Giacomo, Nguyen, Dana, Preda, Adrian, Thomopoulos, Sophia, Jahanshad, Neda, Cui, Long-Biao, and Yao, Dezhong

Subjects
Humans, Schizophrenia, Male, Female, Adult, Algorithms, Magnetic Resonance Imaging, Gray Matter, Machine Learning, Middle Aged, Brain, Cross-Sectional Studies, Europe, Neuroimaging, Reproducibility of Results, North America, Hippocampus
Abstract

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Published
2024

2. Genome-wide analyses reveal a potential role for the MAPT, MOBP, and APOE loci in sporadic frontotemporal dementia.

Author

Manzoni, Claudia, Kia, Demis, Ferrari, Raffaele, Leonenko, Ganna, Costa, Beatrice, Saba, Valentina, Jabbari, Edwin, Tan, Manuela, Albani, Diego, Alvarez, Victoria, Alvarez, Ignacio, Andreassen, Ole, Angiolillo, Antonella, Arighi, Andrea, Baker, Matt, Benussi, Luisa, Bessi, Valentina, Binetti, Giuliano, Blackburn, Daniel, Boada, Merce, Boeve, Bradley, Borrego-Ecija, Sergi, Borroni, Barbara, Bråthen, Geir, Brooks, William, Bruni, Amalia, Caroppo, Paola, Bandres-Ciga, Sara, Clarimon, Jordi, Colao, Rosanna, Cruchaga, Carlos, Danek, Adrian, de Boer, Sterre, de Rojas, Itziar, di Costanzo, Alfonso, Dickson, Dennis, Diehl-Schmid, Janine, Dobson-Stone, Carol, Dols-Icardo, Oriol, Donizetti, Aldo, Dopper, Elise, Durante, Elisabetta, Ferrari, Camilla, Forloni, Gianluigi, Frangipane, Francesca, Fratiglioni, Laura, Kramberger, Milica, Galimberti, Daniela, Gallucci, Maurizio, García-González, Pablo, Ghidoni, Roberta, Giaccone, Giorgio, Graff, Caroline, Graff-Radford, Neill, Grafman, Jordan, Halliday, Glenda, Hernandez, Dena, Hjermind, Lena, Hodges, John, Holloway, Guy, Huey, Edward, Illán-Gala, Ignacio, Josephs, Keith, Knopman, David, Kristiansen, Mark, Kwok, John, Leber, Isabelle, Leonard, Hampton, Libri, Ilenia, Lleo, Alberto, Mackenzie, Ian, Madhan, Gaganjit, Maletta, Raffaele, Marquié, Marta, Maver, Ales, Menendez-Gonzalez, Manuel, Milan, Graziella, Miller, Bruce, Morris, Christopher, Morris, Huw, Nacmias, Benedetta, Newton, Judith, Nielsen, Jørgen, Nilsson, Christer, Novelli, Valeria, Padovani, Alessandro, Pal, Suvankar, Pasquier, Florence, Pastor, Pau, Perneczky, Robert, Peterlin, Borut, Petersen, Ronald, Piguet, Olivier, Pijnenburg, Yolande, Puca, Annibale, Rademakers, Rosa, Rainero, Innocenzo, Reus, Lianne, Richardson, Anna, and Riemenschneider, Matthias

Subjects
Humans, Frontotemporal Dementia, tau Proteins, Genome-Wide Association Study, Apolipoproteins E, Male, Female, Genetic Predisposition to Disease, Aged, Polymorphism, Single Nucleotide, Genetic Loci, Middle Aged, Case-Control Studies, Myelin Proteins
Abstract

Frontotemporal dementia (FTD) is the second most common cause of early-onset dementia after Alzheimer disease (AD). Efforts in the field mainly focus on familial forms of disease (fFTDs), while studies of the genetic etiology of sporadic FTD (sFTD) have been less common. In the current work, we analyzed 4,685 sFTD cases and 15,308 controls looking for common genetic determinants for sFTD. We found a cluster of variants at the MAPT (rs199443; p = 2.5 × 10-12, OR = 1.27) and APOE (rs6857; p = 1.31 × 10-12, OR = 1.27) loci and a candidate locus on chromosome 3 (rs1009966; p = 2.41 × 10-8, OR = 1.16) in the intergenic region between RPSA and MOBP, contributing to increased risk for sFTD through effects on expression and/or splicing in brain cortex of functionally relevant in-cis genes at the MAPT and RPSA-MOBP loci. The association with the MAPT (H1c clade) and RPSA-MOBP loci may suggest common genetic pleiotropy across FTD and progressive supranuclear palsy (PSP) (MAPT and RPSA-MOBP loci) and across FTD, AD, Parkinson disease (PD), and cortico-basal degeneration (CBD) (MAPT locus). Our data also suggest population specificity of the risk signals, with MAPT and APOE loci associations mainly driven by Central/Nordic and Mediterranean Europeans, respectively. This study lays the foundations for future work aimed at further characterizing population-specific features of potential FTD-discriminant APOE haplotype(s) and the functional involvement and contribution of the MAPT H1c haplotype and RPSA-MOBP loci to pathogenesis of sporadic forms of FTD in brain cortex.

Published
2024

3. 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

4. Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Author

Zhu, Yinghan, Maikusa, Norihide, Radua, Joaquim, Sämann, Philipp, Fusar-Poli, Paolo, Agartz, Ingrid, Andreassen, Ole, Bachman, Peter, Baeza, Inmaculada, Chen, Xiaogang, Choi, Sunah, Corcoran, Cheryl, Ebdrup, Bjørn, Fortea, Adriana, Garani, Ranjini, Glenthøj, Birte, Glenthøj, Louise, Haas, Shalaila, Hamilton, Holly, Hayes, Rebecca, He, Ying, Heekeren, Karsten, Kasai, Kiyoto, Katagiri, Naoyuki, Kim, Minah, Kristensen, Tina, Kwon, Jun, Lawrie, Stephen, Lebedeva, Irina, Lee, Jimmy, Loewy, Rachel, Mathalon, Daniel, McGuire, Philip, Mizrahi, Romina, Mizuno, Masafumi, Møller, Paul, Nemoto, Takahiro, Nordholm, Dorte, Omelchenko, Maria, Raghava, Jayachandra, Røssberg, Jan, Rössler, Wulf, Salisbury, Dean, Sasabayashi, Daiki, Smigielski, Lukasz, Sugranyes, Gisela, Takahashi, Tsutomu, Tamnes, Christian, Tang, Jinsong, Theodoridou, Anastasia, Tomyshev, Alexander, Uhlhaas, Peter, Værnes, Tor, van Amelsvoort, Therese, Waltz, James, Westlye, Lars, Zhou, Juan, Thompson, Paul, Hernaus, Dennis, Jalbrzikowski, Maria, and Koike, Shinsuke

Subjects
Humans, Psychotic Disorders, Magnetic Resonance Imaging, Male, Female, Brain, Neuroimaging, Adult, Young Adult, Machine Learning, Adolescent, Prodromal Symptoms
Abstract

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.

Published
2024

5. Using rare genetic mutations to revisit structural brain asymmetry.

Author

Kopal, Jakub, Kumar, Kuldeep, Shafighi, Kimia, Saltoun, Karin, Modenato, Claudia, Moreau, Clara, Huguet, Guillaume, Jean-Louis, Martineau, Martin, Charles-Olivier, Saci, Zohra, Younis, Nadine, Douard, Elise, Jizi, Khadije, Beauchamp-Chatel, Alexis, Kushan, Leila, Silva, Ana, van den Bree, Marianne, Linden, David, Owen, Michael, Hall, Jeremy, Lippé, Sarah, Draganski, Bogdan, Sønderby, Ida, Andreassen, Ole, Glahn, David, Thompson, Paul, Zatorre, Robert, Jacquemont, Sébastien, Bzdok, Danilo, and Bearden, Carrie

Subjects
Humans, DNA Copy Number Variations, Genome-Wide Association Study, Functional Laterality, Brain Mapping, Brain, Magnetic Resonance Imaging
Abstract

Asymmetry between the left and right hemisphere is a key feature of brain organization. Hemispheric functional specialization underlies some of the most advanced human-defining cognitive operations, such as articulated language, perspective taking, or rapid detection of facial cues. Yet, genetic investigations into brain asymmetry have mostly relied on common variants, which typically exert small effects on brain-related phenotypes. Here, we leverage rare genomic deletions and duplications to study how genetic alterations reverberate in human brain and behavior. We designed a pattern-learning approach to dissect the impact of eight high-effect-size copy number variations (CNVs) on brain asymmetry in a multi-site cohort of 552 CNV carriers and 290 non-carriers. Isolated multivariate brain asymmetry patterns spotlighted regions typically thought to subserve lateralized functions, including language, hearing, as well as visual, face and word recognition. Planum temporale asymmetry emerged as especially susceptible to deletions and duplications of specific gene sets. Targeted analysis of common variants through genome-wide association study (GWAS) consolidated partly diverging genetic influences on the right versus left planum temporale structure. In conclusion, our gene-brain-behavior data fusion highlights the consequences of genetically controlled brain lateralization on uniquely human cognitive capacities.

Published
2024

6. Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping

Author

Yang, Hao-Chun, Andreassen, Ole, Westlye, Lars Tjelta, Marquand, Andre F., Beckmann, Christian F., and Wolfers, Thomas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

The detection of heterogeneous mental disorders based on brain readouts remains challenging due to the complexity of symptoms and the absence of reliable biomarkers. This paper introduces CAM (Cortical Anomaly Detection through Masked Image Modeling), a novel self-supervised framework designed for the unsupervised detection of complex brain disorders using cortical surface features. We employ this framework for the detection of individuals on the psychotic spectrum and demonstrate its capabilities compared to state-of-the-art methods, achieving an AUC of 0.696 for Schizoaffective and 0.769 for Schizophreniform, without the need for any labels. Furthermore, the analysis of atypical cortical regions, including Pars Triangularis and several frontal areas often implicated in schizophrenia, provides further confidence in our approach. Altogether, we demonstrate a scalable approach for anomaly detection of complex brain disorders based on cortical abnormalities. The code will be made available at https://github.com/chadHGY/CAM., Comment: 5 pages, LaTeX; typos corrected, introduction refined

Published
2023

7. Genome-wide association meta-analysis identifies five loci associated with postpartum hemorrhage

Author

Westergaard, David, Steinthorsdottir, Valgerdur, Stefansdottir, Lilja, Rohde, Palle Duun, Wu, Xiaoping, Geller, Frank, Tyrmi, Jaakko, Havulinna, Aki S., Solé-Navais, Pol, Flatley, Christopher, Ostrowski, Sisse Rye, Pedersen, Ole Birger, Erikstrup, Christian, Sørensen, Erik, Mikkelsen, Christina, Bruun, Mie Topholm, Aagaard Jensen, Bitten, Brodersen, Thorsten, Ullum, Henrik, Magnus, Per, Andreassen, Ole A., Njolstad, Pål R., Kolte, Astrid Marie, Krebs, Lone, Nyegaard, Mette, Hansen, Thomas Folkmann, Feenstra, Bjarke, Daly, Mark, Lindgren, Cecilia M., Thorleifsson, Gudmar, Stefansson, Olafur A., Sveinbjornsson, Gardar, Gudbjartsson, Daniel F., Thorsteinsdottir, Unnur, Banasik, Karina, Jacobsson, Bo, Laisk, Triin, Laivuori, Hannele, Stefansson, Kari, Brunak, Søren, and Nielsen, Henriette Svarre

Published
2024
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9. Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease

Author

Bergstedt, Jacob, Pasman, Joëlle A., Ma, Ziyan, Harder, Arvid, Yao, Shuyang, Parker, Nadine, Treur, Jorien L., Smit, Dirk J. A., Frei, Oleksandr, Shadrin, Alexey A., Meijsen, Joeri J., Shen, Qing, Hägg, Sara, Tornvall, Per, Buil, Alfonso, Werge, Thomas, Hjerling-Leffler, Jens, Als, Thomas D., Børglum, Anders D., Lewis, Cathryn M., McIntosh, Andrew M., Valdimarsdóttir, Unnur A., Andreassen, Ole A., Sullivan, Patrick F., Lu, Yi, and Fang, Fang

Published
2024
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11. Polygenic liability for antipsychotic dosage and polypharmacy - a real-world registry and biobank study

Author

Koch, Elise, Kämpe, Anders, Alver, Maris, Sigurðarson, Sindri, Einarsson, Guðmundur, Partanen, Juulia, Smith, Robert L., Jaholkowski, Piotr, Taipale, Heidi, Lähteenvuo, Markku, Steen, Nils Eiel, Smeland, Olav B., Djurovic, Srdjan, Molden, Espen, Sigurdsson, Engilbert, Stefánsson, Hreinn, Stefánsson, Kári, Palotie, Aarno, Milani, Lili, O’Connell, Kevin S., and Andreassen, Ole A.

Published
2024
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13. FEMA: Fast and efficient mixed‐effects algorithm for large sample whole‐brain imaging data

Author

Parekh, Pravesh, Fan, Chun Chieh, Frei, Oleksandr, Palmer, Clare E, Smith, Diana M, Makowski, Carolina, Iversen, John R, Pecheva, Diliana, Holland, Dominic, Loughnan, Robert, Nedelec, Pierre, Thompson, Wesley K, Hagler, Donald J, Andreassen, Ole A, Jernigan, Terry L, Nichols, Thomas E, and Dale, Anders M

Subjects
Biological Psychology, Psychology, Pediatric, Bioengineering, Neurosciences, Biomedical Imaging, Underpinning research, 1.1 Normal biological development and functioning, Mental health, Neurological, Adolescent, Humans, Magnetic Resonance Imaging, Cross-Sectional Studies, Brain, Neuroimaging, Connectome, Algorithms, ABCD, longitudinal analysis, mixed models, vertex-wise, voxel-wise, whole brain, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

Published
2024

14. Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder

Author

Nievergelt, Caroline M., Maihofer, Adam X., Atkinson, Elizabeth G., Chen, Chia-Yen, Choi, Karmel W., Coleman, Jonathan R. I., Daskalakis, Nikolaos P., Duncan, Laramie E., Polimanti, Renato, Aaronson, Cindy, Amstadter, Ananda B., Andersen, Soren B., Andreassen, Ole A., Arbisi, Paul A., Ashley-Koch, Allison E., Austin, S. Bryn, Avdibegoviç, Esmina, Babić, Dragan, Bacanu, Silviu-Alin, Baker, Dewleen G., Batzler, Anthony, Beckham, Jean C., Belangero, Sintia, Benjet, Corina, Bergner, Carisa, Bierer, Linda M., Biernacka, Joanna M., Bierut, Laura J., Bisson, Jonathan I., Boks, Marco P., Bolger, Elizabeth A., Brandolino, Amber, Breen, Gerome, Bressan, Rodrigo Affonseca, Bryant, Richard A., Bustamante, Angela C., Bybjerg-Grauholm, Jonas, Bækvad-Hansen, Marie, Børglum, Anders D., Børte, Sigrid, Cahn, Leah, Calabrese, Joseph R., Caldas-de-Almeida, Jose Miguel, Chatzinakos, Chris, Cheema, Sheraz, Clouston, Sean A. P., Colodro-Conde, Lucía, Coombes, Brandon J., Cruz-Fuentes, Carlos S., Dale, Anders M., Dalvie, Shareefa, Davis, Lea K., Deckert, Jürgen, Delahanty, Douglas L., Dennis, Michelle F., Desarnaud, Frank, DiPietro, Christopher P., Disner, Seth G., Docherty, Anna R., Domschke, Katharina, Dyb, Grete, Kulenović, Alma Džubur, Edenberg, Howard J., Evans, Alexandra, Fabbri, Chiara, Fani, Negar, Farrer, Lindsay A., Feder, Adriana, Feeny, Norah C., Flory, Janine D., Forbes, David, Franz, Carol E., Galea, Sandro, Garrett, Melanie E., Gelaye, Bizu, Gelernter, Joel, Geuze, Elbert, Gillespie, Charles F., Goleva, Slavina B., Gordon, Scott D., Goçi, Aferdita, Grasser, Lana Ruvolo, Guindalini, Camila, Haas, Magali, Hagenaars, Saskia, Hauser, Michael A., Heath, Andrew C., Hemmings, Sian M. J., Hesselbrock, Victor, Hickie, Ian B., Hogan, Kelleigh, Hougaard, David Michael, Huang, Hailiang, Huckins, Laura M., Hveem, Kristian, Jakovljević, Miro, Javanbakht, Arash, Jenkins, Gregory D., Johnson, Jessica, Jones, Ian, Jovanovic, Tanja, Karstoft, Karen-Inge, Kaufman, Milissa L., Kennedy, James L., Kessler, Ronald C., Khan, Alaptagin, Kimbrel, Nathan A., King, Anthony P., Koen, Nastassja, Kotov, Roman, Kranzler, Henry R., Krebs, Kristi, Kremen, William S., Kuan, Pei-Fen, Lawford, Bruce R., Lebois, Lauren A. M., Lehto, Kelli, Levey, Daniel F., Lewis, Catrin, Liberzon, Israel, Linnstaedt, Sarah D., Logue, Mark W., Lori, Adriana, Lu, Yi, Luft, Benjamin J., Lupton, Michelle K., Luykx, Jurjen J., Makotkine, Iouri, Maples-Keller, Jessica L., Marchese, Shelby, Marmar, Charles, Martin, Nicholas G., Martínez-Levy, Gabriela A., McAloney, Kerrie, McFarlane, Alexander, McLaughlin, Katie A., McLean, Samuel A., Medland, Sarah E., Mehta, Divya, Meyers, Jacquelyn, Michopoulos, Vasiliki, Mikita, Elizabeth A., Milani, Lili, Milberg, William, Miller, Mark W., Morey, Rajendra A., Morris, Charles Phillip, Mors, Ole, Mortensen, Preben Bo, Mufford, Mary S., Nelson, Elliot C., Nordentoft, Merete, Norman, Sonya B., Nugent, Nicole R., O’Donnell, Meaghan, Orcutt, Holly K., Pan, Pedro M., Panizzon, Matthew S., Pathak, Gita A., Peters, Edward S., Peterson, Alan L., Peverill, Matthew, Pietrzak, Robert H., Polusny, Melissa A., Porjesz, Bernice, Powers, Abigail, Qin, Xue-Jun, Ratanatharathorn, Andrew, Risbrough, Victoria B., Roberts, Andrea L., Rothbaum, Alex O., Rothbaum, Barbara O., Roy-Byrne, Peter, Ruggiero, Kenneth J., Rung, Ariane, Runz, Heiko, Rutten, Bart P. F., de Viteri, Stacey Saenz, Salum, Giovanni Abrahão, Sampson, Laura, Sanchez, Sixto E., Santoro, Marcos, Seah, Carina, Seedat, Soraya, Seng, Julia S., Shabalin, Andrey, Sheerin, Christina M., Silove, Derrick, Smith, Alicia K., Smoller, Jordan W., Sponheim, Scott R., Stein, Dan J., Stensland, Synne, Stevens, Jennifer S., Sumner, Jennifer A., Teicher, Martin H., Thompson, Wesley K., Tiwari, Arun K., Trapido, Edward, Uddin, Monica, Ursano, Robert J., Valdimarsdóttir, Unnur, Van Hooff, Miranda, Vermetten, Eric, Vinkers, Christiaan H., Voisey, Joanne, Wang, Yunpeng, Wang, Zhewu, Waszczuk, Monika, Weber, Heike, Wendt, Frank R., Werge, Thomas, Williams, Michelle A., Williamson, Douglas E., Winsvold, Bendik S., Winternitz, Sherry, Wolf, Christiane, Wolf, Erika J., Xia, Yan, Xiong, Ying, Yehuda, Rachel, Young, Keith A., Young, Ross McD, Zai, Clement C., Zai, Gwyneth C., Zervas, Mark, Zhao, Hongyu, Zoellner, Lori A., Zwart, John-Anker, deRoon-Cassini, Terri, van Rooij, Sanne J. H., van den Heuvel, Leigh L., Stein, Murray B., Ressler, Kerry J., and Koenen, Karestan C.

Published
2024
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18. GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.

Author

Docherty, Anna, Mullins, Niamh, Ashley-Koch, Allison, Qin, Xuejun, Coleman, Jonathan, Shabalin, Andrey, Kang, JooEun, Murnyak, Balasz, Wendt, Frank, Adams, Mark, Campos, Adrian, DiBlasi, Emily, Fullerton, Janice, Kranzler, Henry, Bakian, Amanda, Monson, Eric, Rentería, Miguel, Walss-Bass, Consuelo, Andreassen, Ole, Behera, Chittaranjan, Bulik, Cynthia, Edenberg, Howard, Kessler, Ronald, Mann, J, Nurnberger, John, Pistis, Giorgio, Streit, Fabian, Ursano, Robert, Polimanti, Renato, Dennis, Michelle, Garrett, Melanie, Hair, Lauren, Harvey, Philip, Hauser, Elizabeth, Hauser, Michael, Huffman, Jennifer, Jacobson, Daniel, Madduri, Ravi, McMahon, Benjamin, Oslin, David, Trafton, Jodie, Awasthi, Swapnil, Berrettini, Wade, Bohus, Martin, Chang, Xiao, Chen, Hsi-Chung, Chen, Wei, Christensen, Erik, Crow, Scott, Duriez, Philibert, Edwards, Alexis, Fernández-Aranda, Fernando, Galfalvy, Hanga, Gandal, Michael, Gorwood, Philip, Guo, Yiran, Hafferty, Jonathan, Hakonarson, Hakon, Halmi, Katherine, Hishimoto, Akitoyo, Jain, Sonia, Jamain, Stéphane, Jiménez-Murcia, Susana, Johnson, Craig, Kaplan, Allan, Kaye, Walter, Keel, Pamela, Kennedy, James, Kim, Minsoo, Klump, Kelly, Levey, Daniel, Li, Dong, Liao, Shih-Cheng, Lieb, Klaus, Lilenfeld, Lisa, Marshall, Christian, Mitchell, James, Okazaki, Satoshi, Otsuka, Ikuo, Pinto, Dalila, Powers, Abigail, Ramoz, Nicolas, Ripke, Stephan, Roepke, Stefan, Rozanov, Vsevolod, Scherer, Stephen, Schmahl, Christian, Sokolowski, Marcus, Starnawska, Anna, Strober, Michael, Su, Mei-Hsin, Thornton, Laura, Treasure, Janet, Ware, Erin, Watson, Hunna, Witt, Stephanie, Woodside, D, Yilmaz, Zeynep, Zillich, Lea, and Adolfsson, Rolf

Subjects
Biological Markers, Depressive Disorders, Genetics, Schizophrenia Spectrum and Other Psychotic Disorders, Self-Harm, Suicide, Humans, Genome-Wide Association Study, Suicide, Attempted, Depressive Disorder, Major, Risk Factors, Suicidal Ideation, Polymorphism, Single Nucleotide, Genetic Predisposition to Disease, Genetic Loci
Abstract

OBJECTIVE: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. METHODS: This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. RESULTS: Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values

Published
2023

19. Sex as a Moderator between Parent Ratings of Executive Dysfunction and Social Difficulties in Children and Adolescents with Autism Spectrum Disorder

Author

Torske, Tonje, Naerland, Terje, Quintana, Daniel S., Hypher, Ruth Elizabeth, Kaale, Anett, Høyland, Anne Lise, Hope, Sigrun, Johannessen, Jarle, Øie, Merete G., and Andreassen, Ole A.

Abstract

Girls and boys might differ in autistic symptoms and associated cognitive difficulties such as executive function (EF). We investigated sex differences in the relationship between parent rated EF and autistic symptoms in 116 children and adolescents (25 girls) aged 5-19 years with an intelligence quotient above 70 and an autism spectrum disorder (ASD) diagnosis. They were rated with the behavior rating inventory of executive function (BRIEF) and the autism diagnostic interview revised (ADI-R). We found a positive association between EF and the ADI-R domains of reciprocal social interaction (p < 0.001) and communication (p = 0.001) in girls, while these relationships were small and non-significant in boys. Our results provide a greater understanding of the sex-specific characteristics of children and adolescents with ASD.

Published
2023
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20. Rare CNVs and phenome-wide profiling highlight brain structural divergence and phenotypical convergence.

Author

Kopal, Jakub, Kumar, Kuldeep, Saltoun, Karin, Modenato, Claudia, Moreau, Clara, Martin-Brevet, Sandra, Huguet, Guillaume, Jean-Louis, Martineau, Martin, Charles-Olivier, Saci, Zohra, Younis, Nadine, Tamer, Petra, Douard, Elise, Maillard, Anne, Rodriguez-Herreros, Borja, Pain, Aurèlie, Richetin, Sonia, Kushan, Leila, Silva, Ana, van den Bree, Marianne, Linden, David, Owen, Michael, Hall, Jeremy, Lippé, Sarah, Draganski, Bogdan, Sønderby, Ida, Andreassen, Ole, Glahn, David, Thompson, Paul, Jacquemont, Sébastien, Bzdok, Danilo, and Bearden, Carrie

Subjects
Humans, DNA Copy Number Variations, Brain
Abstract

Copy number variations (CNVs) are rare genomic deletions and duplications that can affect brain and behaviour. Previous reports of CNV pleiotropy imply that they converge on shared mechanisms at some level of pathway cascades, from genes to large-scale neural circuits to the phenome. However, existing studies have primarily examined single CNV loci in small clinical cohorts. It remains unknown, for example, how distinct CNVs escalate vulnerability for the same developmental and psychiatric disorders. Here we quantitatively dissect the associations between brain organization and behavioural differentiation across 8 key CNVs. In 534 CNV carriers, we explored CNV-specific brain morphology patterns. CNVs were characteristic of disparate morphological changes involving multiple large-scale networks. We extensively annotated these CNV-associated patterns with ~1,000 lifestyle indicators through the UK Biobank resource. The resulting phenotypic profiles largely overlap and have body-wide implications, including the cardiovascular, endocrine, skeletal and nervous systems. Our population-level investigation established brain structural divergences and phenotypical convergences of CNVs, with direct relevance to major brain disorders.

Published
2023

23. COGEDAP: A COmprehensive GEnomic Data Analysis Platform

Author

Akdeniz, Bayram Cevdet, Frei, Oleksandr, Hagen, Espen, Filiz, Tahir Tekin, Karthikeyan, Sandeep, Pasman, Joelle, Jangmo, Andreas, Bergsted, Jacob, Shorter, John R., Zetterberg, Richard, Meijsen, Joeri, Sonderby, Ida Elken, Buil, Alfonso, Tesli, Martin, Lu, Yi, Sullivan, Patrick, Andreassen, Ole, and Hovig, Eivind

Subjects
Quantitative Biology - Genomics
Abstract

Non-sharable sensitive data collection and analysis in large-scale consortia for genomic research is complicated. Time consuming issues in installing software arise due to different operating systems, software dependencies and running the software. Therefore, easier, more standardized, automated protocols and platforms can be a solution to overcome these issues. We have developed one such solution for genomic data analysis using software container technologies. The platform, COGEDAP, consists of different software tools placed into Singularity containers with corresponding pipelines and instructions on how to perform genome-wide association studies (GWAS) and other genomic data analysis via corresponding tools. Using a provided helper script written in Python, users can obtain auto-generated scripts to conduct the desired analysis both on high-performance computing (HPC) systems and on personal computers. The analyses can be done by running these auto-generated scripts with the software containers. The helper script also performs minor re-formatting of the input/output data, so that the end user can work with a unified file format regardless of which genetic software is used for the analysis. COGEDAP is actively being used by users from different countries/projects to conduct their genomic data analyses. Thanks to this platform, users can easily run GWAS and other genomic analyses without spending much effort on software installation, data formats, and other technical requirements.

Published
2022

24. Heritability Estimation of Cognitive Phenotypes in the ABCD Study® Using Mixed Models

Author

Smith, Diana M, Loughnan, Robert, Friedman, Naomi P, Parekh, Pravesh, Frei, Oleksandr, Thompson, Wesley K, Andreassen, Ole A, Neale, Michael, Jernigan, Terry L, and Dale, Anders M

Subjects
Biological Psychology, Psychology, Pediatric, Genetics, Mental Health, Brain Disorders, Acquired Cognitive Impairment, Mental health, Phenotype, Cognition, Brain, Research Design, Polymorphism, Single Nucleotide, Models, Genetic, Heritability, Twin studies, Mixed models, Height, Random effects, Zoology, Neurosciences, Genetics & Heredity, Biomedical and clinical sciences, Health sciences
Abstract

Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study® sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study® sample.

Published
2023

25. Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium.

Author

Schijven, Dick, Postema, Merel C, Fukunaga, Masaki, Matsumoto, Junya, Miura, Kenichiro, de Zwarte, Sonja MC, van Haren, Neeltje EM, Cahn, Wiepke, Hulshoff Pol, Hilleke E, Kahn, René S, Ayesa-Arriola, Rosa, Ortiz-García de la Foz, Víctor, Tordesillas-Gutierrez, Diana, Vázquez-Bourgon, Javier, Crespo-Facorro, Benedicto, Alnæs, Dag, Dahl, Andreas, Westlye, Lars T, Agartz, Ingrid, Andreassen, Ole A, Jönsson, Erik G, Kochunov, Peter, Bruggemann, Jason M, Catts, Stanley V, Michie, Patricia T, Mowry, Bryan J, Quidé, Yann, Rasser, Paul E, Schall, Ulrich, Scott, Rodney J, Carr, Vaughan J, Green, Melissa J, Henskens, Frans A, Loughland, Carmel M, Pantelis, Christos, Weickert, Cynthia Shannon, Weickert, Thomas W, de Haan, Lieuwe, Brosch, Katharina, Pfarr, Julia-Katharina, Ringwald, Kai G, Stein, Frederike, Jansen, Andreas, Kircher, Tilo TJ, Nenadić, Igor, Krämer, Bernd, Gruber, Oliver, Satterthwaite, Theodore D, Bustillo, Juan, Mathalon, Daniel H, Preda, Adrian, Calhoun, Vince D, Ford, Judith M, Potkin, Steven G, Chen, Jingxu, Tan, Yunlong, Wang, Zhiren, Xiang, Hong, Fan, Fengmei, Bernardoni, Fabio, Ehrlich, Stefan, Fuentes-Claramonte, Paola, Garcia-Leon, Maria Angeles, Guerrero-Pedraza, Amalia, Salvador, Raymond, Sarró, Salvador, Pomarol-Clotet, Edith, Ciullo, Valentina, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Spalletta, Gianfranco, Michielse, Stijn, van Amelsvoort, Therese, Dickie, Erin W, Voineskos, Aristotle N, Sim, Kang, Ciufolini, Simone, Dazzan, Paola, Murray, Robin M, Kim, Woo-Sung, Chung, Young-Chul, Andreou, Christina, Schmidt, André, Borgwardt, Stefan, McIntosh, Andrew M, Whalley, Heather C, Lawrie, Stephen M, du Plessis, Stefan, Luckhoff, Hilmar K, Scheffler, Freda, Emsley, Robin, Grotegerd, Dominik, Lencer, Rebekka, Dannlowski, Udo, Edmond, Jesse T, Rootes-Murdy, Kelly, Stephen, Julia M, Mayer, Andrew R, and Antonucci, Linda A

Subjects
Brain, Cerebral Cortex, Humans, Magnetic Resonance Imaging, Case-Control Studies, Schizophrenia, Female, Male, Functional Laterality, asymmetry, brain imaging, cortical, subcortical, Clinical Research, Mental Health, Neurosciences, Brain Disorders, Mental health
Abstract

Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

Published
2023

26. In vivo white matter microstructure in adolescents with early-onset psychosis: a multi-site mega-analysis

Author

Barth, Claudia, Kelly, Sinead, Nerland, Stener, Jahanshad, Neda, Alloza, Clara, Ambrogi, Sonia, Andreassen, Ole A, Andreou, Dimitrios, Arango, Celso, Baeza, Inmaculada, Banaj, Nerisa, Bearden, Carrie E, Berk, Michael, Bohman, Hannes, Castro-Fornieles, Josefina, Chye, Yann, Crespo-Facorro, Benedicto, de la Serna, Elena, Díaz-Caneja, Covadonga M, Gurholt, Tiril P, Hegarty, Catherine E, James, Anthony, Janssen, Joost, Johannessen, Cecilie, Jönsson, Erik G, Karlsgodt, Katherine H, Kochunov, Peter, Lois, Noemi G, Lundberg, Mathias, Myhre, Anne M, Pascual-Diaz, Saül, Piras, Fabrizio, Smelror, Runar E, Spalletta, Gianfranco, Stokkan, Therese S, Sugranyes, Gisela, Suo, Chao, Thomopoulos, Sophia I, Tordesillas-Gutiérrez, Diana, Vecchio, Daniela, Wedervang-Resell, Kirsten, Wortinger, Laura A, Thompson, Paul M, and Agartz, Ingrid

Subjects
Brain Disorders, Serious Mental Illness, Pediatric, Biomedical Imaging, Clinical Research, Neurosciences, Mental Health, Schizophrenia, Mental health, Good Health and Well Being, Female, Humans, Male, Adolescent, White Matter, Diffusion Tensor Imaging, Brain, Psychotic Disorders, Anisotropy, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Emerging evidence suggests brain white matter alterations in adolescents with early-onset psychosis (EOP; age of onset

Published
2023

27. 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

28. TMEM161B modulates radial glial scaffolding in neocortical development

Author

Wang, Lu, Heffner, Caleb, Vong, Keng loi, Barrows, Chelsea, Ha, Yoo-Jin, Lee, Sangmoon, Lara-Gonzalez, Pablo, Jhamb, Ishani, Van Der Meer, Dennis, Loughnan, Robert, Parker, Nadine, Sievert, David, Mittal, Swapnil, Issa, Mahmoud Y, Andreassen, Ole A, Dale, Anders, Dobyns, William B, Zaki, Maha S, Murray, Stephen A, and Gleeson, Joseph G

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Genetics, Clinical Research, Intellectual and Developmental Disabilities (IDD), Brain Disorders, Neurological, Animals, Humans, Mice, Ependymoglial Cells, Mice, Knockout, Neocortex, TMEM161B, gyrification, CDC42, knock-in crispant mice, patient-derived brain organoids
Abstract

TMEM161B encodes an evolutionarily conserved widely expressed novel 8-pass transmembrane protein of unknown function in human. Here we identify TMEM161B homozygous hypomorphic missense variants in our recessive polymicrogyria (PMG) cohort. Patients carrying TMEM161B mutations exhibit striking neocortical PMG and intellectual disability. Tmem161b knockout mice fail to develop midline hemispheric cleavage, whereas knock-in of patient mutations and patient-derived brain organoids show defects in apical cell polarity and radial glial scaffolding. We found that TMEM161B modulates actin filopodia, functioning upstream of the Rho-GTPase CDC42. Our data link TMEM161B with human PMG, likely regulating radial glia apical polarity during neocortical development.

Published
2023

29. Psychological trauma and the genetic overlap between posttraumatic stress disorder and major depressive disorder

Author

Mundy, Jessica, Hübel, Christopher, Gelernter, Joel, Levey, Daniel, Murray, Robin M, Skelton, Megan, Stein, Murray B, Maihofer, Adam X, Nievergelt, Caroline M, Baker, Dewlen G, Risborough, Victoria B, Calabrese, Joseph R, Galea, Sandro, Stein, Dan J, Koen, Nastassja, Dalvie, Shareefa, Aiello, Allison E, Roberts, Andrea L, Koenen, KC, Solovieff, Nadia, Kranzler, Henry R, Zhao, Hongyu, Farrer, Lindsay A, Johnson, Eric Otto, Rice, John P, Bierut, Laura J, Saccone, Nancy L, McFarlane, Alexander, Forbes, David, Silove, Derrick, O'Donnell, Meaghan, Bryant, Richard A, van Hooff, Miranda, Sponheim, Scott R, Disner, Seth G, Pietrzak, Robert H, Chen, Chia-Yen, Smoller, Jordan W, Ursano, Robert J, Kessler, Ronald C, Junglen, Angela G, Delahanty, Douglas L, Amstadter, Ananda B, Sheerin, Christina M, Ruggiero, Ken, McLaughlin, Katie A, Peverill, Matthew, Caldas-de-Almeida, JM, Austin, S Bryn, Gelaye, Bizu, Williams, Michelle A, Sanchez, Sixto E, Franz, Carol E, Panizzon, Matthew S, Lyons, Michael J, Kremen, William S, Andreassen, Ole A, Dale, Anders M, Rutten, Bart PF, Vinkers, Christiaan, Schijven, Dick, Geuze, Elbert, Vermetten, Eric, Luykx, Jurjen J, Boks, Marco P, Ashley-Koch, Allison E, Beckham, Jean C, Garrett, Melanie E, Hauser, Michael A, Dennis, Michelle F, Kimbrel, Nathan A, Qin, Xue-Jun, Karstoft, Karen-Inge, Andersen, Soren B, Borglum, Anders D, Hougaard, David Michael, Bybjerg-Grauholm, Jonas, Duncan, Laramie E, Bµkvad-Hansen, Marie, Nordentoft, Merete, Mors, Ole, Mortensen, PB, Werge, Thomas, Thompson, Wesley K, Wang, Yunpeng, Heath, Andrew C, Nelson, Elliot C, Martin, Nicholas G, Gordon, Scott D, Wolf, Erika J, Logue, Mark W, Miller, Mark W, McGlinchey, Regina E, Milberg, William, Erbes, Christopher R, Polusny, Melissa A, Arbisi, Paul A, and Peterson, Alan L

Subjects
Prevention, Clinical Research, Genetics, Serious Mental Illness, Depression, Post-Traumatic Stress Disorder (PTSD), Mental Health, Brain Disorders, Anxiety Disorders, Major Depressive Disorder, Human Genome, 2.3 Psychological, social and economic factors, Aetiology, Mental health, Good Health and Well Being, Posttraumatic stress disorder, major depressive disorder, psychological trauma, genetics, genetic correlations, polygenic risk scores, Million Veteran Program, Post Traumatic Stress Disorder Working Group of the Psychiatric Genomics Consortium, Neurosciences, Public Health and Health Services, Psychology, Psychiatry
Abstract

BackgroundPosttraumatic stress disorder (PTSD) and major depressive disorder (MDD) are commonly reported co-occurring mental health consequences of psychological trauma exposure. The disorders have high genetic overlap. Trauma is a complex phenotype but research suggests that trauma sensitivity has a heritable basis. We investigated whether sensitivity to trauma in those with MDD reflects a similar genetic component in those with PTSD.MethodsGenetic correlations between PTSD and MDD in individuals reporting trauma and MDD in individuals not reporting trauma were estimated, as well as with recurrent MDD and single-episode MDD, using genome-wide association study (GWAS) summary statistics. Genetic correlations were replicated using PTSD data from the Psychiatric Genomics Consortium and the Million Veteran Program. Polygenic risk scores were generated in UK Biobank participants who met the criteria for lifetime MDD (N = 29 471). We investigated whether genetic loading for PTSD was associated with reporting trauma in these individuals.ResultsGenetic loading for PTSD was significantly associated with reporting trauma in individuals with MDD [OR 1.04 (95% CI 1.01-1.07), Empirical-p = 0.02]. PTSD was significantly more genetically correlated with recurrent MDD than with MDD in individuals not reporting trauma (rg differences = ~0.2, p < 0.008). Participants who had experienced recurrent MDD reported significantly higher rates of trauma than participants who had experienced single-episode MDD (χ2 > 166, p < 0.001).ConclusionsOur findings point towards the existence of genetic variants associated with trauma sensitivity that might be shared between PTSD and MDD, although replication with better powered GWAS is needed. Our findings corroborate previous research highlighting trauma exposure as a key risk factor for recurrent MDD.

Published
2022

30. Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score

Author

Huynh-Le, Minh-Phuong, Karunamuni, Roshan, Fan, Chun Chieh, Asona, Lui, Thompson, Wesley K, Martinez, Maria Elena, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth R, Lophatananon, Artitaya, Schleutker, Johanna, Pashayan, Nora, Batra, Jyotsna, Grönberg, Henrik, Neal, David E, Nordestgaard, Børge G, Tangen, Catherine M, MacInnis, Robert J, Wolk, Alicja, Albanes, Demetrius, Haiman, Christopher A, Travis, Ruth C, Blot, William J, Stanford, Janet L, Mucci, Lorelei A, West, Catharine ML, Nielsen, Sune F, Kibel, Adam S, Cussenot, Olivier, Berndt, Sonja I, Koutros, Stella, Sørensen, Karina Dalsgaard, Cybulski, Cezary, Grindedal, Eli Marie, Menegaux, Florence, Park, Jong Y, Ingles, Sue A, Maier, Christiane, Hamilton, Robert J, Rosenstein, Barry S, Lu, Yong-Jie, Watya, Stephen, Vega, Ana, Kogevinas, Manolis, Wiklund, Fredrik, Penney, Kathryn L, Huff, Chad D, Teixeira, Manuel R, Multigner, Luc, Leach, Robin J, Brenner, Hermann, John, Esther M, Kaneva, Radka, Logothetis, Christopher J, Neuhausen, Susan L, De Ruyck, Kim, Ost, Piet, Razack, Azad, Newcomb, Lisa F, Fowke, Jay H, Gamulin, Marija, Abraham, Aswin, Claessens, Frank, Castelao, Jose Esteban, Townsend, Paul A, Crawford, Dana C, Petrovics, Gyorgy, van Schaik, Ron HN, Parent, Marie-Élise, Hu, Jennifer J, Zheng, Wei, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and Seibert, Tyler M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Aging, Prostate Cancer, Cancer, Urologic Diseases, Prevention, Genetics, Good Health and Well Being, Male, Humans, Prostate-Specific Antigen, Prostatic Neoplasms, Early Detection of Cancer, Polymorphism, Single Nucleotide, Risk Factors, Risk Assessment, Genetic Predisposition to Disease, UKGPCS collaborators, APCB, NC-LA PCaP Investigators, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, PRACTICAL Consortium, Urology & Nephrology, Clinical sciences, Oncology and carcinogenesis
Abstract

BackgroundProstate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets.MethodsIn total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.ResultsThe final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.ConclusionsWe demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

Published
2022

32. Rare variants with large effects provide functional insights into the pathology of migraine subtypes, with and without aura

Author

Bjornsdottir, Gyda, Chalmer, Mona A., Stefansdottir, Lilja, Skuladottir, Astros Th., Einarsson, Gudmundur, Andresdottir, Margret, Beyter, Doruk, Ferkingstad, Egil, Gretarsdottir, Solveig, Halldorsson, Bjarni V., Halldorsson, Gisli H., Helgadottir, Anna, Helgason, Hannes, Hjorleifsson Eldjarn, Grimur, Jonasdottir, Adalbjorg, Jonasdottir, Aslaug, Jonsdottir, Ingileif, Knowlton, Kirk U., Nadauld, Lincoln D., Lund, Sigrun H., Magnusson, Olafur Th., Melsted, Pall, Moore, Kristjan H. S., Oddsson, Asmundur, Olason, Pall I., Sigurdsson, Asgeir, Stefansson, Olafur A., Saemundsdottir, Jona, Sveinbjornsson, Gardar, Tragante, Vinicius, Unnsteinsdottir, Unnur, Walters, G. Bragi, Zink, Florian, Rødevand, Linn, Andreassen, Ole A., Igland, Jannicke, Lie, Rolv T., Haavik, Jan, Banasik, Karina, Brunak, Søren, Didriksen, Maria, T. Bruun, Mie, Erikstrup, Christian, Kogelman, Lisette J. A., Nielsen, Kaspar R., Sørensen, Erik, Pedersen, Ole B., Ullum, Henrik, Masson, Gisli, Thorsteinsdottir, Unnur, Olesen, Jes, Ludvigsson, Petur, Thorarensen, Olafur, Bjornsdottir, Anna, Sigurdardottir, Gudrun R., Sveinsson, Olafur A., Ostrowski, Sisse R., Holm, Hilma, Gudbjartsson, Daniel F., Thorleifsson, Gudmar, Sulem, Patrick, Stefansson, Hreinn, Thorgeirsson, Thorgeir E., Hansen, Thomas F., and Stefansson, Kari

Published
2023
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33. Genome-wide association study of placental weight identifies distinct and shared genetic influences between placental and fetal growth

Author

Beaumont, Robin N., Flatley, Christopher, Vaudel, Marc, Wu, Xiaoping, Chen, Jing, Moen, Gunn-Helen, Skotte, Line, Helgeland, Øyvind, Solé-Navais, Pol, Banasik, Karina, Albiñana, Clara, Ronkainen, Justiina, Fadista, João, Stinson, Sara Elizabeth, Trajanoska, Katerina, Wang, Carol A., Westergaard, David, Srinivasan, Sundararajan, Sánchez-Soriano, Carlos, Bilbao, Jose Ramon, Allard, Catherine, Groleau, Marika, Kuulasmaa, Teemu, Leirer, Daniel J., White, Frédérique, Jacques, Pierre-Étienne, Cheng, Haoxiang, Hao, Ke, Andreassen, Ole A., Åsvold, Bjørn Olav, Atalay, Mustafa, Bhatta, Laxmi, Bouchard, Luigi, Brumpton, Ben Michael, Brunak, Søren, Bybjerg-Grauholm, Jonas, Ebbing, Cathrine, Elliott, Paul, Engelbrechtsen, Line, Erikstrup, Christian, Estarlich, Marisa, Franks, Stephen, Gaillard, Romy, Geller, Frank, Grove, Jakob, Hougaard, David M., Kajantie, Eero, Morgen, Camilla S., Nohr, Ellen A., Nyegaard, Mette, Palmer, Colin N. A., Pedersen, Ole Birger, Rivadeneira, Fernando, Sebert, Sylvain, Shields, Beverley M., Stoltenberg, Camilla, Surakka, Ida, Thørner, Lise Wegner, Ullum, Henrik, Vaarasmaki, Marja, Vilhjalmsson, Bjarni J., Willer, Cristen J., Lakka, Timo A., Gybel-Brask, Dorte, Bustamante, Mariona, Hansen, Torben, Pearson, Ewan R., Reynolds, Rebecca M., Ostrowski, Sisse R., Pennell, Craig E., Jaddoe, Vincent W. V., Felix, Janine F., Hattersley, Andrew T., Melbye, Mads, Lawlor, Deborah A., Hveem, Kristian, Werge, Thomas, Nielsen, Henriette Svarre, Magnus, Per, Evans, David M., Jacobsson, Bo, Järvelin, Marjo-Riitta, Zhang, Ge, Hivert, Marie-France, Johansson, Stefan, Freathy, Rachel M., Feenstra, Bjarke, and Njølstad, Pål R.

Published
2023
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36. Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy

Author

Hindley, Guy, Shadrin, Alexey A., van der Meer, Dennis, Parker, Nadine, Cheng, Weiqiu, O’Connell, Kevin S., Bahrami, Shahram, Lin, Aihua, Karadag, Naz, Holen, Børge, Bjella, Thomas, Deary, Ian J., Davies, Gail, Hill, W. David, Bressler, Jan, Seshadri, Sudha, Fan, Chun Chieh, Ueland, Torill, Djurovic, Srdjan, Smeland, Olav B., Frei, Oleksandr, Dale, Anders M., and Andreassen, Ole A.

Published
2023
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37. Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders

Author

Segal, Ashlea, Parkes, Linden, Aquino, Kevin, Kia, Seyed Mostafa, Wolfers, Thomas, Franke, Barbara, Hoogman, Martine, Beckmann, Christian F., Westlye, Lars T., Andreassen, Ole A., Zalesky, Andrew, Harrison, Ben J., Davey, Christopher G., Soriano-Mas, Carles, Cardoner, Narcís, Tiego, Jeggan, Yücel, Murat, Braganza, Leah, Suo, Chao, Berk, Michael, Cotton, Sue, Bellgrove, Mark A., Marquand, Andre F., and Fornito, Alex

Published
2023
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38. Genome-Transcriptome-Functional Connectivity-Cognition Link Differentiates Schizophrenia From Bipolar Disorder.

Author

Chen, Jiayu, Fu, Zening, Bustillo, Juan, Perrone-Bizzozero, Nora, Lin, Dongdong, Canive, Jose, Pearlson, Godfrey, Stephen, Julia, Mayer, Andrew, Potkin, Steven, Kochunov, Peter, Elliot Hong, L, Adhikari, Bhim, Andreassen, Ole, Agartz, Ingrid, Westlye, Lars, Sui, Jing, Du, Yuhui, Hanlon, Faith, Jung, Rex, Turner, Jessica, Liu, Jingyu, Calhoun, Vince, Van Erp, Theodorus, and Macciardi, Fabio

Subjects
SNP, functional connectivity, gene expression, polygenic risk score, working memory, Humans, Bipolar Disorder, Schizophrenia, Transcriptome, Cognition Disorders, Cognition
Abstract

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) and bipolar disorder (BD) share genetic risk factors, yet patients display differential levels of cognitive impairment. We hypothesized a genome-transcriptome-functional connectivity (frontoparietal)-cognition pathway linked to SZ-versus-BD differences, and conducted a multiscale study to delineate this pathway. STUDY DESIGNS: Large genome-wide studies provided single nucleotide polymorphisms (SNPs) conferring more risk for SZ than BD, and we identified their regulated genes, namely SZ-biased SNPs and genes. We then (a) computed the polygenic risk score for SZ (PRSSZ) of SZ-biased SNPs and examined its associations with imaging-based frontoparietal functional connectivity (FC) and cognitive performances; (b) examined the spatial correlation between ex vivo postmortem expressions of SZ-biased genes and in vivo, SZ-related FC disruptions across frontoparietal regions; (c) investigated SZ-versus-BD differences in frontoparietal FC; and (d) assessed the associations of frontoparietal FC with cognitive performances. STUDY RESULTS: PRSSZ of SZ-biased SNPs was significantly associated with frontoparietal FC and working memory test scores. SZ-biased genes expressions significantly correlated with SZ-versus-BD differences in FC across frontoparietal regions. SZ patients showed more reductions in frontoparietal FC than BD patients compared to controls. Frontoparietal FC was significantly associated with test scores of multiple cognitive domains including working memory, and with the composite scores of all cognitive domains. CONCLUSIONS: Collectively, these multiscale findings support the hypothesis that SZ-biased genetic risk, through transcriptome regulation, is linked to frontoparietal dysconnectivity, which in turn contributes to differential cognitive deficits in SZ-versus BD, suggesting that potential biomarkers for more precise patient stratification and treatment.

Published
2022

39. Generalization of cortical MOSTest genome-wide associations within and across samples

Author

Loughnan, Robert J, Shadrin, Alexey A, Frei, Oleksandr, van der Meer, Dennis, Zhao, Weiqi, Palmer, Clare E, Thompson, Wesley K, Makowski, Carolina, Jernigan, Terry L, Andreassen, Ole A, Fan, Chun Chieh, and Dale, Anders M

Subjects
Epidemiology, Health Sciences, Genetics, Biomedical Imaging, Human Genome, Neurosciences, 2.1 Biological and endogenous factors, Neurological, Humans, Genome-Wide Association Study, Phenotype, Cognition, Brain, Polymorphism, Single Nucleotide, Genetic Predisposition to Disease, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242-496, replication rate: 96-97%) in independent data when compared with the established min-P approach (# replicated loci: 26-55, replication rate: 91-93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.

Published
2022

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

Author

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

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

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

Published
2022

41. Common Genetic Variation and Age of Onset of Anorexia Nervosa.

Author

Watson, Hunna J, Thornton, Laura M, Yilmaz, Zeynep, Baker, Jessica H, Coleman, Jonathan RI, Adan, Roger AH, Alfredsson, Lars, Andreassen, Ole A, Ask, Helga, Berrettini, Wade H, Boehnke, Michael, Boehm, Ilka, Boni, Claudette, Buehren, Katharina, Bulant, Josef, Burghardt, Roland, Chang, Xiao, Cichon, Sven, Cone, Roger D, Courtet, Philippe, Crow, Scott, Crowley, James J, Danner, Unna N, de Zwaan, Martina, Dedoussis, George, DeSocio, Janiece E, Dick, Danielle M, Dikeos, Dimitris, Dina, Christian, Djurovic, Srdjan, Dmitrzak-Weglarz, Monika, Docampo-Martinez, Elisa, Duriez, Philibert, Egberts, Karin, Ehrlich, Stefan, Eriksson, Johan G, Escaramís, Geòrgia, Esko, Tõnu, Estivill, Xavier, Farmer, Anne, Fernández-Aranda, Fernando, Fichter, Manfred M, Föcker, Manuel, Foretova, Lenka, Forstner, Andreas J, Frei, Oleksandr, Gallinger, Steven, Giegling, Ina, Giuranna, Johanna, Gonidakis, Fragiskos, Gorwood, Philip, Gratacòs, Mònica, Guillaume, Sébastien, Guo, Yiran, Hakonarson, Hakon, Hauser, Joanna, Havdahl, Alexandra, Hebebrand, Johannes, Helder, Sietske G, Herms, Stefan, Herpertz-Dahlmann, Beate, Herzog, Wolfgang, Hinney, Anke, Hübel, Christopher, Hudson, James I, Imgart, Hartmut, Jamain, Stephanie, Janout, Vladimir, Jiménez-Murcia, Susana, Jones, Ian R, Julià, Antonio, Kalsi, Gursharan, Kaminská, Deborah, Kaprio, Jaakko, Karhunen, Leila, Kas, Martien JH, Keel, Pamela K, Kennedy, James L, Keski-Rahkonen, Anna, Kiezebrink, Kirsty, Klareskog, Lars, Klump, Kelly L, Knudsen, Gun Peggy S, La Via, Maria C, Le Hellard, Stephanie, Leboyer, Marion, Li, Dong, Lilenfeld, Lisa, Lin, Bochao, Lissowska, Jolanta, Luykx, Jurjen, Magistretti, Pierre, Maj, Mario, Marsal, Sara, Marshall, Christian R, Mattingsdal, Morten, Meulenbelt, Ingrid, Micali, Nadia, Mitchell, Karen S, and Monteleone, Alessio Maria

Subjects
Age of onset, Anorexia nervosa, Early-onset, GWAS, Genetic risk score, Genetics, Menarche, Mendelian randomization, Puberty, Mental Health, Human Genome, Eating Disorders, Anorexia, Brain Disorders, Pediatric, Prevention, Aetiology, 2.1 Biological and endogenous factors
Abstract

BackgroundGenetics and biology may influence the age of onset of anorexia nervosa (AN). The aims of this study were to determine whether common genetic variation contributes to age of onset of AN and to investigate the genetic associations between age of onset of AN and age at menarche.MethodsA secondary analysis of the Psychiatric Genomics Consortium genome-wide association study (GWAS) of AN was performed, which included 9335 cases and 31,981 screened controls, all from European ancestries. We conducted GWASs of age of onset, early-onset AN (

Published
2022

42. Associations between depression and cardiometabolic health: A 27-year longitudinal study

Author

Ditmars, Hillary L, Logue, Mark W, Toomey, Rosemary, McKenzie, Ruth E, Franz, Carol E, Panizzon, Matthew S, Reynolds, Chandra A, Cuthbert, Kristy N, Vandiver, Richard, Gustavson, Daniel E, Eglit, Graham ML, Elman, Jeremy A, Sanderson-Cimino, Mark, Williams, McKenna E, Andreassen, Ole A, Dale, Anders M, Eyler, Lisa T, Fennema-Notestine, Christine, Gillespie, Nathan A, Hauger, Richard L, Jak, Amy J, Neale, Michael C, Tu, Xin M, Whitsel, Nathan, Xian, Hong, Kremen, William S, and Lyons, Michael J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Sleep Research, Aging, Depression, Mental Health, Clinical Research, Behavioral and Social Science, Prevention, Brain Disorders, Cardiovascular, Mental health, Good Health and Well Being, Humans, Male, Adult, Aged, Longitudinal Studies, Erectile Dysfunction, Hypercholesterolemia, Risk Factors, Hypertension, Sleep Apnea Syndromes, Cardiometabolic health, Polygenic risk scores, Neurosciences, Public Health and Health Services, Psychology, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

BackgroundClarifying the relationship between depression symptoms and cardiometabolic and related health could clarify risk factors and treatment targets. The objective of this study was to assess whether depression symptoms in midlife are associated with the subsequent onset of cardiometabolic health problems.MethodsThe study sample comprised 787 male twin veterans with polygenic risk score data who participated in the Harvard Twin Study of Substance Abuse ('baseline') and the longitudinal Vietnam Era Twin Study of Aging ('follow-up'). Depression symptoms were assessed at baseline [mean age 41.42 years (s.d. = 2.34)] using the Diagnostic Interview Schedule, Version III, Revised. The onset of eight cardiometabolic conditions (atrial fibrillation, diabetes, erectile dysfunction, hypercholesterolemia, hypertension, myocardial infarction, sleep apnea, and stroke) was assessed via self-reported doctor diagnosis at follow-up [mean age 67.59 years (s.d. = 2.41)].ResultsTotal depression symptoms were longitudinally associated with incident diabetes (OR 1.29, 95% CI 1.07-1.57), erectile dysfunction (OR 1.32, 95% CI 1.10-1.59), hypercholesterolemia (OR 1.26, 95% CI 1.04-1.53), and sleep apnea (OR 1.40, 95% CI 1.13-1.74) over 27 years after controlling for age, alcohol consumption, smoking, body mass index, C-reactive protein, and polygenic risk for specific health conditions. In sensitivity analyses that excluded somatic depression symptoms, only the association with sleep apnea remained significant (OR 1.32, 95% CI 1.09-1.60).ConclusionsA history of depression symptoms by early midlife is associated with an elevated risk for subsequent development of several self-reported health conditions. When isolated, non-somatic depression symptoms are associated with incident self-reported sleep apnea. Depression symptom history may be a predictor or marker of cardiometabolic risk over decades.

Published
2022

43. Association of Genetic Variant Linked to Hemochromatosis With Brain Magnetic Resonance Imaging Measures of Iron and Movement Disorders

Author

Loughnan, Robert, Ahern, Jonathan, Tompkins, Cherisse, Palmer, Clare E, Iversen, John, Thompson, Wesley K, Andreassen, Ole, Jernigan, Terry, Sugrue, Leo, Dale, Anders, Boyle, Mary ET, and Fan, Chun Chieh

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Genetics, Clinical Research, Digestive Diseases, Liver Disease, Rare Diseases, Prevention, Brain Disorders, Mental health, Neurological, Aged, Aged, 80 and over, Brain, Cross-Sectional Studies, Hemochromatosis, Hemochromatosis Protein, Histocompatibility Antigens Class I, Homozygote, Humans, Iron, Iron Overload, Magnetic Resonance Imaging, Male, Membrane Proteins, Middle Aged, Movement Disorders, Mutation, Retrospective Studies
Abstract

ImportanceHereditary hemochromatosis (HH) is an autosomal recessive genetic disorder that leads to iron overload. Conflicting results from previous research has led some to believe the brain is spared the toxic effects of iron in HH.ObjectiveTo test the association of the strongest genetic risk variant for HH on brainwide measures sensitive to iron deposition and the rates of movement disorders in a substantially larger sample than previous studies of its kind.Design, setting, and participantsThis cross-sectional retrospective study included participants from the UK Biobank, a population-based sample. Genotype, health record, and neuroimaging data were collected from January 2006 to May 2021. Data analysis was conducted from January 2021 to April 2022. Disorders tested included movement disorders (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10], codes G20-G26), abnormalities of gait and mobility (ICD-10 codes R26), and other disorders of the nervous system (ICD-10 codes G90-G99).ExposuresHomozygosity for p.C282Y, the largest known genetic risk factor for HH.Main outcomes and measuresT2-weighted and T2* signal intensity from brain magnetic resonance imaging scans, measures sensitive to iron deposition, and clinical diagnosis of neurological disorders.ResultsThe total cohort consisted of 488 288 individuals (264 719 female; ages 49-87 years, largely northern European ancestry), 2889 of whom were p.C282Y homozygotes. The neuroimaging analysis consisted of 836 individuals: 165 p.C282Y homozygotes (99 female) and 671 matched controls (399 female). A total of 206 individuals were excluded from analysis due to withdrawal of consent. Neuroimaging analysis showed that p.C282Y homozygosity was associated with decreased T2-weighted and T2* signal intensity in subcortical motor structures (basal ganglia, thalamus, red nucleus, and cerebellum; Cohen d >1) consistent with substantial iron deposition. Across the whole UK Biobank (2889 p.C282Y homozygotes, 485 399 controls), we found a significantly increased prevalence for movement disorders in male homozygotes (OR, 1.80; 95% CI, 1.28-2.55; P = .001) but not female individuals (OR, 1.09; 95% CI, 0.70-1.73; P = .69). Among the 31 p.C282Y male homozygotes with a movement disorder, only 10 had a concurrent HH diagnosis.Conclusions and relevanceThese findings indicate increased iron deposition in subcortical motor circuits in p.C282Y homozygotes and confirm an increased association with movement disorders in male homozygotes. Early treatment in HH effectively prevents the negative consequences of iron overload in the liver and heart. Our work suggests that screening for p.C282Y homozygosity in high-risk individuals also has the potential to reduce brain iron accumulation and to reduce the risk of movement disorders among male individuals who are homozygous for this mutation.

Published
2022

44. Multiscale neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology

Author

Park, Bo-yong, Kebets, Valeria, Larivière, Sara, Hettwer, Meike D, Paquola, Casey, van Rooij, Daan, Buitelaar, Jan, Franke, Barbara, Hoogman, Martine, Schmaal, Lianne, Veltman, Dick J, van den Heuvel, Odile A, Stein, Dan J, Andreassen, Ole A, Ching, Christopher RK, Turner, Jessica A, van Erp, Theo GM, Evans, Alan C, Dagher, Alain, Thomopoulos, Sophia I, Thompson, Paul M, Valk, Sofie L, Kirschner, Matthias, and Bernhardt, Boris C

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Depression, Neurosciences, Behavioral and Social Science, Serious Mental Illness, Mental Health, Brain Disorders, Schizophrenia, 2.1 Biological and endogenous factors, Aetiology, Neurological, Mental health, Good Health and Well Being, Autism Spectrum Disorder, Connectome, Dopamine, Humans, Neural Pathways, Serotonin, Biological sciences, Biomedical and clinical sciences
Abstract

It is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we carried out a multiscale neural contextualization of shared alterations of cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression disorder, obsessive-compulsive disorder, bipolar disorder, and schizophrenia). Our framework cross-referenced shared morphological anomalies with respect to cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we identified a cortex-wide dimension of morphological changes that described a sensory-fugal pattern, with paralimbic regions showing the most consistent alterations across conditions. The shared disease dimension was closely related to cortical gradients of microstructure as well as neurotransmitter axes, specifically cortex-wide variations in serotonin and dopamine. Multiple sensitivity analyses confirmed robustness with respect to slight variations in analytical choices. Our findings embed shared effects of common psychiatric conditions on brain structure in multiple scales of brain organization, and may provide insights into neural mechanisms of transdiagnostic vulnerability.

Published
2022

47. European and multi-ancestry genome-wide association meta-analysis of atopic dermatitis highlights importance of systemic immune regulation

Author

Budu-Aggrey, Ashley, Kilanowski, Anna, Sobczyk, Maria K., Shringarpure, Suyash S., Mitchell, Ruth, Reis, Kadri, Reigo, Anu, Mägi, Reedik, Nelis, Mari, Tanaka, Nao, Brumpton, Ben M., Thomas, Laurent F., Sole-Navais, Pol, Flatley, Christopher, Espuela-Ortiz, Antonio, Herrera-Luis, Esther, Lominchar, Jesus V. T., Bork-Jensen, Jette, Marenholz, Ingo, Arnau-Soler, Aleix, Jeong, Ayoung, Fawcett, Katherine A., Baurecht, Hansjorg, Rodriguez, Elke, Alves, Alexessander Couto, Kumar, Ashish, Sleiman, Patrick M., Chang, Xiao, Medina-Gomez, Carolina, Hu, Chen, Xu, Cheng-jian, Qi, Cancan, El-Heis, Sarah, Titcombe, Philip, Antoun, Elie, Fadista, João, Wang, Carol A., Thiering, Elisabeth, Wu, Baojun, Kress, Sara, Kothalawala, Dilini M., Kadalayil, Latha, Duan, Jiasong, Zhang, Hongmei, Hadebe, Sabelo, Hoffmann, Thomas, Jorgenson, Eric, Choquet, Hélène, Risch, Neil, Njølstad, Pål, Andreassen, Ole A., Johansson, Stefan, Almqvist, Catarina, Gong, Tong, Ullemar, Vilhelmina, Karlsson, Robert, Magnusson, Patrik K. E., Szwajda, Agnieszka, Burchard, Esteban G., Thyssen, Jacob P., Hansen, Torben, Kårhus, Line L., Dantoft, Thomas M., Jeanrenaud, Alexander C.S.N., Ghauri, Ahla, Arnold, Andreas, Homuth, Georg, Lau, Susanne, Nöthen, Markus M., Hübner, Norbert, Imboden, Medea, Visconti, Alessia, Falchi, Mario, Bataille, Veronique, Hysi, Pirro, Ballardini, Natalia, Boomsma, Dorret I., Hottenga, Jouke J., Müller-Nurasyid, Martina, Ahluwalia, Tarunveer S., Stokholm, Jakob, Chawes, Bo, Schoos, Ann-Marie M., Esplugues, Ana, Bustamante, Mariona, Raby, Benjamin, Arshad, Syed, German, Chris, Esko, Tõnu, Milani, Lili A., Metspalu, Andres, Terao, Chikashi, Abuabara, Katrina, Løset, Mari, Hveem, Kristian, Jacobsson, Bo, Pino-Yanes, Maria, Strachan, David P., Grarup, Niels, Linneberg, Allan, Lee, Young-Ae, Probst-Hensch, Nicole, Weidinger, Stephan, Jarvelin, Marjo-Riitta, Melén, Erik, Hakonarson, Hakon, Irvine, Alan D., Jarvis, Deborah, Nijsten, Tamar, Duijts, Liesbeth, Vonk, Judith M., Koppelmann, Gerard H., Godfrey, Keith M., Barton, Sheila J., Feenstra, Bjarke, Pennell, Craig E., Sly, Peter D., Holt, Patrick G., Williams, L. Keoki, Bisgaard, Hans, Bønnelykke, Klaus, Curtin, John, Simpson, Angela, Murray, Clare, Schikowski, Tamara, Bunyavanich, Supinda, Weiss, Scott T., Holloway, John W., Min, Josine L., Brown, Sara J., Standl, Marie, and Paternoster, Lavinia

Published
2023
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49. Impulsivity across severe mental disorders: a cross-sectional study of immune markers and psychopharmacotherapy

Author

Hjell, Gabriela, Rokicki, Jaroslav, Szabo, Attila, Holst, René, Tesli, Natalia, Bell, Christina, Fischer-Vieler, Thomas, Werner, Maren Caroline Frogner, Lunding, Synve Hoffart, Ormerod, Monica Bettina Elkjær Greenwood, Johansen, Ingrid Torp, Djurovic, Srdjan, Ueland, Thor, Andreassen, Ole Andreas, Melle, Ingrid, Lagerberg, Trine Vik, Mørch-Johnsen, Lynn, Steen, Nils Eiel, and Haukvik, Unn Kristin

Published
2023
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50. Genome-wide analysis of anorexia nervosa and major psychiatric disorders and related traits reveals genetic overlap and identifies novel risk loci for anorexia nervosa

Author

Bang, Lasse, Bahrami, Shahram, Hindley, Guy, Smeland, Olav B., Rødevand, Linn, Jaholkowski, Piotr P., Shadrin, Alexey, Connell, Kevin S. O’, Frei, Oleksandr, Lin, Aihua, Rahman, Zillur, Cheng, Weiqiu, Parker, Nadine, Fan, Chun C., Dale, Anders M., Djurovic, Srdjan, Bulik, Cynthia M., and Andreassen, Ole A.

Published
2023
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