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513 results on '"Grotegerd, Dominik"'

1. Differences in the neural correlates of schizophrenia with positive and negative formal thought disorder in patients with schizophrenia in the ENIGMA dataset.

Author: Sharkey, Rachel, Bacon, Chelsea, Peterson, Zeru, Rootes-Murdy, Kelly, Salvador, Raymond, Pomarol-Clotet, Edith, Karuk, Andriana, Homan, Philipp, Ji, Ellen, Omlor, Wolfgang, Homan, Stephanie, Georgiadis, Foivos, Kaiser, Stefan, Kirschner, Matthias, Ehrlich, Stefan, Dannlowski, Udo, Grotegerd, Dominik, Goltermann, Janik, Meinert, Susanne, Kircher, Tilo, Stein, Frederike, Brosch, Katharina, Krug, Axel, Nenadic, Igor, Sim, Kang, Spalletta, Gianfranco, Banaj, Nerisa, Sponheim, Scott, Demro, Caroline, Ramsay, Ian, King, Margaret, Quidé, Yann, Green, Melissa, Nguyen, Dana, Preda, Adrian, Calhoun, Vince, Turner, Jessica, van Erp, Theo, and Nickl-Jockschat, Thomas
Subjects: Humans, Schizophrenia, Male, Female, Adult, Brain, Middle Aged, Schizophrenic Psychology, Neuroimaging, Cohort Studies, Magnetic Resonance Imaging, Thinking
Abstract: Formal thought disorder (FTD) is a clinical key factor in schizophrenia, but the neurobiological underpinnings remain unclear. In particular, the relationship between FTD symptom dimensions and patterns of regional brain volume loss in schizophrenia remains to be established in large cohorts. Even less is known about the cellular basis of FTD. Our study addresses these major obstacles by enrolling a large multi-site cohort acquired by the ENIGMA Schizophrenia Working Group (752 schizophrenia patients and 1256 controls), to unravel the neuroanatomy of FTD in schizophrenia and using virtual histology tools on implicated brain regions to investigate the cellular basis. Based on the findings of previous clinical and neuroimaging studies, we decided to separately explore positive, negative and total formal thought disorder. We used virtual histology tools to relate brain structural changes associated with FTD to cellular distributions in cortical regions. We identified distinct neural networks positive and negative FTD. Both networks encompassed fronto-occipito-amygdalar brain regions, but positive and negative FTD demonstrated a dissociation: negative FTD showed a relative sparing of orbitofrontal cortical thickness, while positive FTD also affected lateral temporal cortices. Virtual histology identified distinct transcriptomic fingerprints associated for both symptom dimensions. Negative FTD was linked to neuronal and astrocyte fingerprints, while positive FTD also showed associations with microglial cell types. These results provide an important step towards linking FTD to brain structural changes and their cellular underpinnings, providing an avenue for a better mechanistic understanding of this syndrome.
Published: 2024

2. 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

3. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Author: Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S, New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I, Breier, Alan, Brodaty, Henry, Buckner, Randy L, Buitelaar, Jan K, Cannon, Dara M, Caseras, Xavier, Clark, Vincent P, Conrod, Patricia J, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E, Franke, Barbara, Glahn, David C, Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hatton, Sean, Hickie, Ian B, Pol, Hilleke E Hulshoff, Jamieson, Alec J, Jernigan, Terry L, Jiang, Jiyang, Kalnin, Andrew J, Kang, Sim, Kochan, Nicole A, Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S, Satterthwaite, Theodore D, Saykin, Andrew J, Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W, Soares, Jair C, Spalletta, Gianfranco, Tamnes, Christian K, Trollor, Julian N, Ent, Dennis Van't, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M, Williams, Steven CR, Wu, Mon‐Ju, Zunta‐Soares, Giovana B, Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang, and Group, ENIGMA‐Lifespan Working
Subjects: Biological Psychology, Psychology, Neurosciences, Clinical Research, Aging, Mental health, Neurological, Humans, Adolescent, Female, Aged, Adult, Child, Young Adult, Male, Brain, Aged, 80 and over, Child, Preschool, Middle Aged, Magnetic Resonance Imaging, Neuroimaging, Sample Size, benchmarking, brain aging, brainAGE, ENIGMA‐Lifespan Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract: Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.
Published: 2024

4. Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis.

Author: Kennedy, Eamonn, Liebel, Spencer, Lindsey, Hannah, Vadlamani, Shashank, Lei, Pui-Wa, Adamson, Maheen, Alda, Martin, Alonso-Lana, Silvia, Anderson, Tim, Arango, Celso, Asarnow, Robert, Avram, Mihai, Ayesa-Arriola, Rosa, Babikian, Talin, Banaj, Nerisa, Bird, Laura, Borgwardt, Stefan, Brodtmann, Amy, Brosch, Katharina, Caeyenberghs, Karen, Calhoun, Vince, Chiaravalloti, Nancy, Cifu, David, Crespo-Facorro, Benedicto, Dalrymple-Alford, John, Dams-OConnor, Kristen, Dannlowski, Udo, Darby, David, Davenport, Nicholas, DeLuca, John, Diaz-Caneja, Covadonga, Disner, Seth, Dobryakova, Ekaterina, Ehrlich, Stefan, Esopenko, Carrie, Ferrarelli, Fabio, Frank, Lea, Franz, Carol, Fuentes-Claramonte, Paola, Genova, Helen, Giza, Christopher, Goltermann, Janik, Grotegerd, Dominik, Gruber, Marius, Gutierrez-Zotes, Alfonso, Ha, Minji, Haavik, Jan, Hinkin, Charles, Hoskinson, Kristen, Hubl, Daniela, Irimia, Andrei, Jansen, Andreas, Kaess, Michael, Kang, Xiaojian, Kenney, Kimbra, Keřková, Barbora, Khlif, Mohamed, Kim, Minah, Kindler, Jochen, Kircher, Tilo, Knížková, Karolina, Kolskår, Knut, Krch, Denise, Kremen, William, Kuhn, Taylor, Kumari, Veena, Kwon, Junsoo, Langella, Roberto, Laskowitz, Sarah, Lee, Jungha, Lengenfelder, Jean, Liou-Johnson, Victoria, Lippa, Sara, Løvstad, Marianne, Lundervold, Astri, Marotta, Cassandra, Marquardt, Craig, Mattos, Paulo, Mayeli, Ahmad, McDonald, Carrie, Meinert, Susanne, Melzer, Tracy, Merchán-Naranjo, Jessica, Michel, Chantal, Morey, Rajendra, Mwangi, Benson, Myall, Daniel, Nenadić, Igor, Newsome, Mary, Nunes, Abraham, OBrien, Terence, Oertel, Viola, Ollinger, John, Olsen, Alexander, Ortiz García de la Foz, Victor, Ozmen, Mustafa, Pardoe, Heath, Parent, Marise, Piras, Fabrizio, and Piras, Federica
Subjects: Parkinson’s disease, attention-deficit/hyperactivity disorder, bipolar disorder, dementia, depression, memory, schizophrenia, stroke, traumatic brain injury, verbal learning
Abstract: Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinsons disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.
Published: 2024

5. 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

6. 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

7. The interplay between polygenic score for tumor necrosis factor-α, brain structural connectivity, and processing speed in major depression

Author: Flinkenflügel, Kira, Gruber, Marius, Meinert, Susanne, Thiel, Katharina, Winter, Alexandra, Goltermann, Janik, Usemann, Paula, Brosch, Katharina, Stein, Frederike, Thomas-Odenthal, Florian, Wroblewski, Adrian, Pfarr, Julia-Katharina, David, Friederike S., Beins, Eva C., Grotegerd, Dominik, Hahn, Tim, Leehr, Elisabeth J., Dohm, Katharina, Bauer, Jochen, Forstner, Andreas J., Nöthen, Markus M., Jamalabadi, Hamidreza, Straube, Benjamin, Alexander, Nina, Jansen, Andreas, Witt, Stephanie H., Rietschel, Marcella, Nenadić, Igor, van den Heuvel, Martijn P., Kircher, Tilo, Repple, Jonathan, and Dannlowski, Udo
Published: 2024
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8. Interaction of perceived social support and childhood maltreatment on limbic responsivity towards negative emotional stimuli in healthy individuals

Author: Borgers, Tiana, Rinck, Anne, Enneking, Verena, Klug, Melissa, Winter, Alexandra, Gruber, Marius, Kraus, Anna, Dohm, Katharina, Leehr, Elisabeth J., Grotegerd, Dominik, Förster, Katharina, Goltermann, Janik, Bauer, Jochen, Dannlowski, Udo, and Redlich, Ronny
Published: 2024
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9. Neural foundation of the diathesis-stress model: longitudinal gray matter volume changes in response to stressful life events in major depressive disorder and healthy controls

Author: Thomas-Odenthal, Florian, Ringwald, Kai, Teutenberg, Lea, Stein, Frederike, Alexander, Nina, Bonnekoh, Linda M., Brosch, Katharina, Dohm, Katharina, Flinkenflügel, Kira, Grotegerd, Dominik, Hahn, Tim, Jansen, Andreas, Leehr, Elisabeth J., Meinert, Susanne, Pfarr, Julia-Katharina, Renz, Harald, Schürmeyer, Navid, Stief, Thomas, Straube, Benjamin, Thiel, Katharina, Usemann, Paula, Winter, Alexandra, Krug, Axel, Nenadić, Igor, Dannlowski, Udo, and Kircher, Tilo
Published: 2024
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10. Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation.

Author: Ge, Ruiyang, Yu, Yuetong, Qi, Yi, Fan, Yu-Nan, Chen, Shiyu, Gao, Chuntong, Haas, Shalaila, New, Faye, Boomsma, Dorret, Brodaty, Henry, Brouwer, Rachel, Buckner, Randy, Caseras, Xavier, Crivello, Fabrice, Crone, Eveline, Erk, Susanne, Fisher, Simon, Franke, Barbara, Glahn, David, Dannlowski, Udo, Grotegerd, Dominik, Gruber, Oliver, Hulshoff Pol, Hilleke, Schumann, Gunter, Tamnes, Christian, Walter, Henrik, Wierenga, Lara, Jahanshad, Neda, Thompson, Paul, and Frangou, Sophia
Subjects: Humans, Male, Female, Longevity, Benchmarking, Brain, Models, Statistical, Algorithms
Abstract: The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.
Published: 2024

11. Structural brain abnormalities and aggressive behaviour in schizophrenia: Mega-analysis of data from 2095 patients and 2861 healthy controls via the ENIGMA consortium

Author: Lamsma, Jelle, Raine, Adrian, Kia, Seyed M, Cahn, Wiepke, Arold, Dominic, Banaj, Nerisa, Barone, Annarita, Brosch, Katharina, Brouwer, Rachel, Brunetti, Arturo, Calhoun, Vince D, Chew, Qian H, Choi, Sunah, Chung, Young-Chul, Ciccarelli, Mariateresa, Cobia, Derin, Cocozza, Sirio, Dannlowski, Udo, Dazzan, Paola, de Bartolomeis, Andrea, Di Forti, Marta, Dumais, Alexandre, Edmond, Jesse T, Ehrlich, Stefan, Evermann, Ulrika, Flinkenflügel, Kira, Georgiadis, Foivos, Glahn, David C, Goltermann, Janik, Green, Melissa J, Grotegerd, Dominik, Guerrero-Pedraza, Amalia, Ha, Minji, Hong, Elliot L, Pol, Hilleke Hulshoff, Iasevoli, Felice, Kaiser, Stefan, Kaleda, Vasily, Karuk, Andriana, Kim, Minah, Kircher, Tilo, Kirschner, Matthias, Kochunov, Peter, Kwon, Jun Soo, Lebedeva, Irina, Lencer, Rebekka, Marques, Tiago R, Meinert, Susanne, Murray, Robin, Nenadić, Igor, Nguyen, Dana, Pearlson, Godfrey, Piras, Fabrizio, Pomarol-Clotet, Edith, Pontillo, Giuseppe, Potvin, Stéphane, Preda, Adrian, Quidé, Yann, Rodrigue, Amanda, Rootes-Murdy, Kelly, Salvador, Raymond, Skoch, Antonin, Sim, Kang, Spalletta, Gianfranco, Spaniel, Filip, Stein, Frederike, Thomas-Odenthal, Florian, Tikàsz, Andràs, Tomecek, David, Tomyshev, Alexander, Tranfa, Mario, Tsogt, Uyanga, Turner, Jessica A, van Erp, Theo GM, van Haren, Neeltje EM, van Os, Jim, Vecchio, Daniela, Wang, Lei, Wroblewski, Adrian, and Nickl-Jockschat, Thomas
Subjects: Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Schizophrenia, Brain Disorders, Neurosciences, Biomedical Imaging, Serious Mental Illness, Mental Health, Mental Illness, Mental health
Abstract: BACKGROUND: Schizophrenia is associated with an increased risk of aggressive behaviour, which may partly be explained by illness-related changes in brain structure. However, previous studies have been limited by group-level analyses, small and selective samples of inpatients and long time lags between exposure and outcome. METHODS: This cross-sectional study pooled data from 20 sites participating in the international ENIGMA-Schizophrenia Working Group. Sites acquired T1-weighted and diffusion-weighted magnetic resonance imaging scans in a total of 2095 patients with schizophrenia and 2861 healthy controls. Measures of grey matter volume and white matter microstructural integrity were extracted from the scans using harmonised protocols. For each measure, normative modelling was used to calculate how much patients deviated (in z-scores) from healthy controls at the individual level. Ordinal regression models were used to estimate the associations of these deviations with concurrent aggressive behaviour (as odds ratios [ORs] with 99% confidence intervals [CIs]). Mediation analyses were performed for positive symptoms (i.e., delusions, hallucinations and disorganised thinking), impulse control and illness insight. Aggression and potential mediators were assessed with the Positive and Negative Syndrome Scale, Scale for the Assessment of Positive Symptoms or Brief Psychiatric Rating Scale. RESULTS: Aggressive behaviour was significantly associated with reductions in total cortical volume (OR [99% CI] = 0.88 [0.78, 0.98], p = .003) and global white matter integrity (OR [99% CI] = 0.72 [0.59, 0.88], p = 3.50 × 10-5) and additional reductions in dorsolateral prefrontal cortex volume (OR [99% CI] = 0.85 [0.74, 0.97], p =.002), inferior parietal lobule volume (OR [99% CI] = 0.76 [0.66, 0.87], p = 2.20 × 10-7) and internal capsule integrity (OR [99% CI] = 0.76 [0.63, 0.92], p = 2.90 × 10-4). Except for inferior parietal lobule volume, these associations were largely mediated by increased severity of positive symptoms and reduced impulse control. CONCLUSIONS: This study provides evidence that the co-occurrence of positive symptoms, poor impulse control and aggressive behaviour in schizophrenia has a neurobiological basis, which may inform the development of therapeutic interventions.
Published: 2024

12. Beyond the Global Brain Differences: Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers.

Author: Boen, Rune, Kaufmann, Tobias, van der Meer, Dennis, Frei, Oleksandr, Agartz, Ingrid, Ames, David, Andersson, Micael, Armstrong, Nicola, Artiges, Eric, Atkins, Joshua, Bauer, Jochen, Benedetti, Francesco, Boomsma, Dorret, Brodaty, Henry, Brosch, Katharina, Buckner, Randy, Cairns, Murray, Calhoun, Vince, Caspers, Svenja, Cichon, Sven, Corvin, Aiden, Crespo-Facorro, Benedicto, Dannlowski, Udo, David, Friederike, de Geus, Eco, de Zubicaray, Greig, Desrivières, Sylvane, Doherty, Joanne, Donohoe, Gary, Ehrlich, Stefan, Eising, Else, Espeseth, Thomas, Fisher, Simon, Forstner, Andreas, Fortaner-Uyà, Lidia, Frouin, Vincent, Fukunaga, Masaki, Ge, Tian, Glahn, David, Goltermann, Janik, Grabe, Hans, Green, Melissa, Groenewold, Nynke, Grotegerd, Dominik, Grøntvedt, Gøril, Hahn, Tim, Hashimoto, Ryota, Hehir-Kwa, Jayne, Henskens, Frans, Holmes, Avram, Håberg, Asta, Haavik, Jan, Jacquemont, Sebastien, Jansen, Andreas, Jockwitz, Christiane, Jönsson, Erik, Kikuchi, Masataka, Kircher, Tilo, Kumar, Kuldeep, Le Hellard, Stephanie, Leu, Costin, Linden, David, Liu, Jingyu, Loughnan, Robert, Mather, Karen, McMahon, Katie, McRae, Allan, Medland, Sarah, Meinert, Susanne, Moreau, Clara, Morris, Derek, Mowry, Bryan, Mühleisen, Thomas, Nenadić, Igor, Nöthen, Markus, Nyberg, Lars, Ophoff, Roel, Owen, Michael, Pantelis, Christos, Paolini, Marco, Paus, Tomas, Pausova, Zdenka, Persson, Karin, Quidé, Yann, Marques, Tiago, Sachdev, Perminder, Sando, Sigrid, Schall, Ulrich, Scott, Rodney, Selbæk, Geir, Shumskaya, Elena, Silva, Ana, Sisodiya, Sanjay, Stein, Frederike, Stein, Dan, Straube, Benjamin, Streit, Fabian, Strike, Lachlan, Teumer, Alexander, and Teutenberg, Lea
Subjects: 15q11.2 BP1-BP2, 1q21.1 distal, Brain structure, Copy number variants, Intraindividual variability, Magnetic resonance imaging, Humans, Chromosome Deletion, Brain, Magnetic Resonance Imaging, Abnormalities, Multiple, Chromosomes, Human, Pair 15, DNA Copy Number Variations
Abstract: BACKGROUND: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure. METHODS: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individuals regional difference and global difference, were used to test for regional differences that diverge from the global difference. RESULTS: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness. CONCLUSIONS: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment.
Published: 2024

13. Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia

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 T, Richard, Genevieve, Fernandez-Cabello, Sara, Parker, Nadine, Andreassen, Ole A, 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 L, Glahn, David, Pearlson, Godfrey, Blangero, John, Karuk, Andriana, Pomarol-Clotet, Edith, Salvador, Raymond, Fuentes-Claramonte, Paola, Garcia-León, María Ángeles, Spalletta, Gianfranco, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Cheng, Jingliang, Liu, Zhening, Yang, Jie, Gonul, Ali Saffet, Uslu, Ozgul, Burhanoglu, Birce Begum, Demir, Aslihan Uyar, Rootes-Murdy, Kelly, Calhoun, Vince D, Sim, Kang, Green, Melissa, Quidé, Yann, Chung, Young Chul, Kim, Woo-Sung, Sponheim, Scott R, Demro, Caroline, Ramsay, Ian S, Iasevoli, Felice, de Bartolomeis, Andrea, Barone, Annarita, Ciccarelli, Mariateresa, Brunetti, Arturo, Cocozza, Sirio, Pontillo, Giuseppe, Tranfa, Mario, Park, Min Tae M, Kirschner, Matthias, Georgiadis, Foivos, Kaiser, Stefan, Van Rheenen, Tamsyn E, Rossell, Susan L, Hughes, Matthew, Woods, William, Carruthers, Sean P, Sumner, Philip, Ringin, Elysha, Spaniel, Filip, Skoch, Antonin, Tomecek, David, Homan, Philipp, Homan, Stephanie, Omlor, Wolfgang, Cecere, Giacomo, Nguyen, Dana D, Preda, Adrian, Thomopoulos, Sophia, Jahanshad, Neda, Cui, Long-Biao, and Yao, Dezhong
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Psychology, Machine Learning and Artificial Intelligence, Mental Illness, Clinical Research, Brain Disorders, Serious Mental Illness, Neurosciences, Schizophrenia, Mental Health, Behavioral and Social Science, Biomedical Imaging, Mental health, Good Health and Well Being, ENIGMA, artificial intelligence, brain gray matter, schizophrenia, structural MRI, subtype
Abstract: Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.
Published: 2023

14. The German research consortium for the study of bipolar disorder (BipoLife): a quality assurance protocol for MR neuroimaging data

Author: Vogelbacher, Christoph, Sommer, Jens, Bopp, Miriam H. A., Falkenberg, Irina, Ritter, Philipp S., Bermpohl, Felix, Attar, Catherine Hindi, Einenkel, Karolin E., Gruber, Oliver, Juckel, Georg, Flasbeck, Vera, Hautzinger, Martin, Pfennig, Andrea, Matura, Silke, Reif, Andreas, Grotegerd, Dominik, Dannlowski, Udo, Kircher, Tilo, Bauer, Michael, and Jansen, Andreas
Published: 2024
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15. Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis

Author: Meyhoefer, Inga, Sprenger, Andreas, Derad, David, Grotegerd, Dominik, Leenings, Ramona, Leehr, Elisabeth J., Breuer, Fabian, Surmann, Marian, Rolfes, Karen, Arolt, Volker, Romer, Georg, Lappe, Markus, Rehder, Johanna, Koutsouleris, Nikolaos, Borgwardt, Stefan, Schultze-Lutter, Frauke, Meisenzahl, Eva, Kircher, Tilo T. J., Keedy, Sarah S., Bishop, Jeffrey R., Ivleva, Elena I., McDowell, Jennifer E., Reilly, James L., Hill, Scot Kristian, Pearlson, Godfrey D., Tamminga, Carol A., Keshavan, Matcheri S., Gershon, Elliot S., Clementz, Brett A., Sweeney, John A., Hahn, Tim, Dannlowski, Udo, and Lencer, Rebekka
Published: 2024
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16. Factor analysis of lifetime psychopathology and its brain morphometric and genetic correlates in a transdiagnostic sample

Author: Krug, Axel, Stein, Frederike, David, Friederike S., Schmitt, Simon, Brosch, Katharina, Pfarr, Julia-Katharina, Ringwald, Kai G., Meller, Tina, Thomas-Odenthal, Florian, Meinert, Susanne, Thiel, Katharina, Winter, Alexandra, Waltemate, Lena, Lemke, Hannah, Grotegerd, Dominik, Opel, Nils, Repple, Jonathan, Hahn, Tim, Streit, Fabian, Witt, Stephanie H., Rietschel, Marcella, Andlauer, Till F. M., Nöthen, Markus M., Philipsen, Alexandra, Nenadić, Igor, Dannlowski, Udo, Kircher, Tilo, and Forstner, Andreas J.
Published: 2024
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17. Association between resting-state connectivity patterns in the defensive system network and treatment response in spider phobia—a replication approach

Author: Leehr, Elisabeth J., Seeger, Fabian R., Böhnlein, Joscha, Gathmann, Bettina, Straube, Thomas, Roesmann, Kati, Junghöfer, Markus, Schwarzmeier, Hanna, Siminski, Niklas, Herrmann, Martin J., Langhammer, Till, Goltermann, Janik, Grotegerd, Dominik, Meinert, Susanne, Winter, Nils R., Dannlowski, Udo, and Lueken, Ulrike
Published: 2024
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18. Autistic and non-autistic individuals show the same amygdala activity during emotional face processing

Author: Langenbach, Benedikt P., Grotegerd, Dominik, Mulders, Peter C. R., Tendolkar, Indira, van Oort, Jasper, Duyser, Fleur, van Eijndhoven, Philip, Vrijsen, Janna N., Dannlowski, Udo, Kampmann, Zarah, and Koelkebeck, Katja
Published: 2024
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19. Persistence of amygdala hyperactivity to subliminal negative emotion processing in the long-term course of depression

Author: Klug, Melissa, Enneking, Verena, Borgers, Tiana, Jacobs, Charlotte M., Dohm, Katharina, Kraus, Anna, Grotegerd, Dominik, Opel, Nils, Repple, Jonathan, Suslow, Thomas, Meinert, Susanne, Lemke, Hannah, Leehr, Elisabeth J., Bauer, Jochen, Dannlowski, Udo, and Redlich, Ronny
Published: 2024
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20. White and gray matter alterations in bipolar I and bipolar II disorder subtypes compared with healthy controls – exploring associations with disease course and polygenic risk

Author: Thiel, Katharina, Lemke, Hannah, Winter, Alexandra, Flinkenflügel, Kira, Waltemate, Lena, Bonnekoh, Linda, Grotegerd, Dominik, Dohm, Katharina, Hahn, Tim, Förster, Katharina, Kanske, Philipp, Repple, Jonathan, Opel, Nils, Redlich, Ronny, David, Friederike, Forstner, Andreas J., Stein, Frederike, Brosch, Katharina, Thomas-Odenthal, Florian, Usemann, Paula, Teutenberg, Lea, Straube, Benjamin, Alexander, Nina, Jamalabadi, Hamidreza, Jansen, Andreas, Witt, Stephanie H., Andlauer, Till F. M., Pfennig, Andrea, Bauer, Michael, Nenadić, Igor, Kircher, Tilo, Meinert, Susanne, and Dannlowski, Udo
Published: 2024
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21. 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

22. Volume of subcortical brain regions in social anxiety disorder: mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group.

Author: Groenewold, Nynke, Bas-Hoogendam, Janna, Amod, Alyssa, Laansma, Max, Van Velzen, Laura, Aghajani, Moji, Hilbert, Kevin, Oh, Hyuntaek, Salas, Ramiro, Jackowski, Andrea, Pan, Pedro, Salum, Giovanni, Blair, James, Blair, Karina, Hirsch, Joy, Pantazatos, Spiro, Schneier, Franklin, Talati, Ardesheer, Roelofs, Karin, Volman, Inge, Blanco-Hinojo, Laura, Cardoner, Narcís, Pujol, Jesus, Beesdo-Baum, Katja, Ching, Christopher, Thomopoulos, Sophia, Jansen, Andreas, Kircher, Tilo, Krug, Axel, Nenadić, Igor, Stein, Frederike, Dannlowski, Udo, Grotegerd, Dominik, Lemke, Hannah, Meinert, Susanne, Winter, Alexandra, Erb, Michael, Kreifelts, Benjamin, Gong, Qiyong, Lui, Su, Zhu, Fei, Mwangi, Benson, Soares, Jair, Wu, Mon-Ju, Bayram, Ali, Canli, Mesut, Tükel, Raşit, Westenberg, P, Heeren, Alexandre, Cremers, Henk, Hofmann, David, Straube, Thomas, Doruyter, Alexander, Lochner, Christine, Peterburs, Jutta, Van Tol, Marie-José, Gur, Raquel, Kaczkurkin, Antonia, Larsen, Bart, Satterthwaite, Theodore, Filippi, Courtney, Gold, Andrea, Harrewijn, Anita, Zugman, André, Bülow, Robin, Grabe, Hans, Völzke, Henry, Wittfeld, Katharina, Böhnlein, Joscha, Dohm, Katharina, Kugel, Harald, Schrammen, Elisabeth, Zwanzger, Peter, Leehr, Elisabeth, Sindermann, Lisa, Ball, Tali, Fonzo, Gregory, Paulus, Martin, Stein, Murray, Klumpp, Heide, Phan, K, Furmark, Tomas, Månsson, Kristoffer, Manzouri, Amirhossein, Avery, Suzanne, Blackford, Jennifer, Clauss, Jacqueline, Feola, Brandee, Harper, Jennifer, Sylvester, Chad, Lueken, Ulrike, Veltman, Dick, Winkler, Anderson, Jahanshad, Neda, Pine, Daniel, Thompson, Paul, Stein, Dan, Van der Wee, Nic, and Simmons, Alan
Subjects: Adult, Adolescent, Humans, Phobia, Social, Magnetic Resonance Imaging, Brain, Anxiety, Neuroimaging
Abstract: There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE
Published: 2023

23. The neural signature of psychomotor disturbance in depression

Author: Wüthrich, Florian, Lefebvre, Stephanie, Mittal, Vijay A., Shankman, Stewart A., Alexander, Nina, Brosch, Katharina, Flinkenflügel, Kira, Goltermann, Janik, Grotegerd, Dominik, Hahn, Tim, Jamalabadi, Hamidreza, Jansen, Andreas, Leehr, Elisabeth J., Meinert, Susanne, Nenadić, Igor, Nitsch, Robert, Stein, Frederike, Straube, Benjamin, Teutenberg, Lea, Thiel, Katharina, Thomas-Odenthal, Florian, Usemann, Paula, Winter, Alexandra, Dannlowski, Udo, Kircher, Tilo, and Walther, Sebastian
Published: 2024
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24. Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

Author: Bruin, Willem B., Zhutovsky, Paul, van Wingen, Guido A., Bas-Hoogendam, Janna Marie, Groenewold, Nynke A., Hilbert, Kevin, Winkler, Anderson M., Zugman, Andre, Agosta, Federica, Åhs, Fredrik, Andreescu, Carmen, Antonacci, Chase, Asami, Takeshi, Assaf, Michal, Barber, Jacques P., Bauer, Jochen, Bavdekar, Shreya Y., Beesdo-Baum, Katja, Benedetti, Francesco, Bernstein, Rachel, Björkstrand, Johannes, Blair, Robert J., Blair, Karina S., Blanco-Hinojo, Laura, Böhnlein, Joscha, Brambilla, Paolo, Bressan, Rodrigo A., Breuer, Fabian, Cano, Marta, Canu, Elisa, Cardinale, Elise M., Cardoner, Narcís, Cividini, Camilla, Cremers, Henk, Dannlowski, Udo, Diefenbach, Gretchen J., Domschke, Katharina, Doruyter, Alexander G. G., Dresler, Thomas, Erhardt, Angelika, Filippi, Massimo, Fonzo, Gregory A., Freitag, Gabrielle F., Furmark, Tomas, Ge, Tian, Gerber, Andrew J., Gosnell, Savannah N., Grabe, Hans J., Grotegerd, Dominik, Gur, Ruben C., Gur, Raquel E., Hamm, Alfons O., Han, Laura K. M., Harper, Jennifer C., Harrewijn, Anita, Heeren, Alexandre, Hofmann, David, Jackowski, Andrea P., Jahanshad, Neda, Jett, Laura, Kaczkurkin, Antonia N., Khosravi, Parmis, Kingsley, Ellen N., Kircher, Tilo, Kostic, Milutin, Larsen, Bart, Lee, Sang-Hyuk, Leehr, Elisabeth J., Leibenluft, Ellen, Lochner, Christine, Lui, Su, Maggioni, Eleonora, Manfro, Gisele G., Månsson, Kristoffer N. T., Marino, Claire E., Meeten, Frances, Milrod, Barbara, Jovanovic, Ana Munjiza, Mwangi, Benson, Myers, Michael J., Neufang, Susanne, Nielsen, Jared A., Ohrmann, Patricia A., Ottaviani, Cristina, Paulus, Martin P., Perino, Michael T., Phan, K. Luan, Poletti, Sara, Porta-Casteràs, Daniel, Pujol, Jesus, Reinecke, Andrea, Ringlein, Grace V., Rjabtsenkov, Pavel, Roelofs, Karin, Salas, Ramiro, Salum, Giovanni A., Satterthwaite, Theodore D., Schrammen, Elisabeth, Sindermann, Lisa, Smoller, Jordan W., Soares, Jair C., Stark, Rudolf, Stein, Frederike, Straube, Thomas, Straube, Benjamin, Strawn, Jeffrey R., Suarez-Jimenez, Benjamin, Sylvester, Chad M., Talati, Ardesheer, Thomopoulos, Sophia I., Tükel, Raşit, van Nieuwenhuizen, Helena, Werwath, Kathryn, Wittfeld, Katharina, Wright, Barry, Wu, Mon-Ju, Yang, Yunbo, Zilverstand, Anna, Zwanzger, Peter, Blackford, Jennifer U., Avery, Suzanne N., Clauss, Jacqueline A., Lueken, Ulrike, Thompson, Paul M., Pine, Daniel S., Stein, Dan J., van der Wee, Nic J. A., Veltman, Dick J., and Aghajani, Moji
Published: 2024
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25. 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

26. Obesity and brain structure in schizophrenia – ENIGMA study in 3021 individuals

Author: McWhinney, Sean R, Brosch, Katharina, Calhoun, Vince D, Crespo-Facorro, Benedicto, Crossley, Nicolas A, Dannlowski, Udo, Dickie, Erin, Dietze, Lorielle MF, Donohoe, Gary, Du Plessis, Stefan, Ehrlich, Stefan, Emsley, Robin, Furstova, Petra, Glahn, David C, Gonzalez- Valderrama, Alfonso, Grotegerd, Dominik, Holleran, Laurena, Kircher, Tilo TJ, Knytl, Pavel, Kolenic, Marian, Lencer, Rebekka, Nenadić, Igor, Opel, Nils, Pfarr, Julia-Katharina, Rodrigue, Amanda L, Rootes-Murdy, Kelly, Ross, Alex J, Sim, Kang, Škoch, Antonín, Spaniel, Filip, Stein, Frederike, Švancer, Patrik, Tordesillas-Gutiérrez, Diana, Undurraga, Juan, Vázquez-Bourgon, Javier, Voineskos, Aristotle, Walton, Esther, Weickert, Thomas W, Weickert, Cynthia Shannon, Thompson, Paul M, van Erp, Theo GM, Turner, Jessica A, and Hajek, Tomas
Subjects: Biomedical Imaging, Obesity, Neurosciences, Serious Mental Illness, Mental Health, Schizophrenia, Nutrition, Clinical Research, Brain Disorders, 2.1 Biological and endogenous factors, Aetiology, Mental health, Humans, Depressive Disorder, Major, Brain, Magnetic Resonance Imaging, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: Schizophrenia is frequently associated with obesity, which is linked with neurostructural alterations. Yet, we do not understand how the brain correlates of obesity map onto the brain changes in schizophrenia. We obtained MRI-derived brain cortical and subcortical measures and body mass index (BMI) from 1260 individuals with schizophrenia and 1761 controls from 12 independent research sites within the ENIGMA-Schizophrenia Working Group. We jointly modeled the statistical effects of schizophrenia and BMI using mixed effects. BMI was additively associated with structure of many of the same brain regions as schizophrenia, but the cortical and subcortical alterations in schizophrenia were more widespread and pronounced. Both BMI and schizophrenia were primarily associated with changes in cortical thickness, with fewer correlates in surface area. While, BMI was negatively associated with cortical thickness, the significant associations between BMI and surface area or subcortical volumes were positive. Lastly, the brain correlates of obesity were replicated among large studies and closely resembled neurostructural changes in major depressive disorders. We confirmed widespread associations between BMI and brain structure in individuals with schizophrenia. People with both obesity and schizophrenia showed more pronounced brain alterations than people with only one of these conditions. Obesity appears to be a relevant factor which could account for heterogeneity of brain imaging findings and for differences in brain imaging outcomes among people with schizophrenia.
Published: 2022

27. Shared and distinct structural brain networks related to childhood maltreatment and social support: connectome-based predictive modeling

Author: Winter, Alexandra, Gruber, Marius, Thiel, Katharina, Flinkenflügel, Kira, Meinert, Susanne, Goltermann, Janik, Winter, Nils R., Borgers, Tiana, Stein, Frederike, Jansen, Andreas, Brosch, Katharina, Wroblewski, Adrian, Thomas-Odenthal, Florian, Usemann, Paula, Straube, Benjamin, Alexander, Nina, Jamalabadi, Hamidreza, Nenadić, Igor, Bonnekoh, Linda M., Dohm, Katharina, Leehr, Elisabeth J., Opel, Nils, Grotegerd, Dominik, Hahn, Tim, van den Heuvel, Martijn P., Kircher, Tilo, Repple, Jonathan, and Dannlowski, Udo
Published: 2023
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28. Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Author: Patel, Yash, Shin, Jean, Abé, Christoph, Agartz, Ingrid, Alloza, Clara, Alnæs, Dag, Ambrogi, Sonia, Antonucci, Linda A, Arango, Celso, Arolt, Volker, Auzias, Guillaume, Ayesa-Arriola, Rosa, Banaj, Nerisa, Banaschewski, Tobias, Bandeira, Cibele, Başgöze, Zeynep, Cupertino, Renata Basso, Bau, Claiton HD, Bauer, Jochen, Baumeister, Sarah, Bernardoni, Fabio, Bertolino, Alessandro, Bonnin, Caterina Del Mar, Brandeis, Daniel, Brem, Silvia, Bruggemann, Jason, Bülow, Robin, Bustillo, Juan R, Calderoni, Sara, Calvo, Rosa, Canales-Rodríguez, Erick J, Cannon, Dara M, Carmona, Susanna, Carr, Vaughan J, Catts, Stanley V, Chenji, Sneha, Chew, Qian Hui, Coghill, David, Connolly, Colm G, Conzelmann, Annette, Craven, Alexander R, Crespo-Facorro, Benedicto, Cullen, Kathryn, Dahl, Andreas, Dannlowski, Udo, Davey, Christopher G, Deruelle, Christine, Díaz-Caneja, Covadonga M, Dohm, Katharina, Ehrlich, Stefan, Epstein, Jeffery, Erwin-Grabner, Tracy, Eyler, Lisa T, Fedor, Jennifer, Fitzgerald, Jacqueline, Foran, William, Ford, Judith M, Fortea, Lydia, Fuentes-Claramonte, Paola, Fullerton, Janice, Furlong, Lisa, Gallagher, Louise, Gao, Bingchen, Gao, Si, Goikolea, Jose M, Gotlib, Ian, Goya-Maldonado, Roberto, Grabe, Hans J, Green, Melissa, Grevet, Eugenio H, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Haavik, Jan, Hahn, Tim, Harrison, Ben J, Heindel, Walter, Henskens, Frans, Heslenfeld, Dirk J, Hilland, Eva, Hoekstra, Pieter J, Hohmann, Sarah, Holz, Nathalie, Howells, Fleur M, Ipser, Jonathan C, Jahanshad, Neda, Jakobi, Babette, Jansen, Andreas, Janssen, Joost, Jonassen, Rune, Kaiser, Anna, Kaleda, Vasiliy, Karantonis, James, King, Joseph A, Kircher, Tilo, Kochunov, Peter, Koopowitz, Sheri-Michelle, Landén, Mikael, Landrø, Nils Inge, and Lawrie, Stephen
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Psychology, Neurosciences, Genetics, Mental Health, Serious Mental Illness, Mental Illness, Women's Health, Brain Disorders, Preterm, Low Birth Weight and Health of the Newborn, Pregnancy, Pediatric, Perinatal Period - Conditions Originating in Perinatal Period, Behavioral and Social Science, Prevention, Schizophrenia, 2.1 Biological and endogenous factors, 2.3 Psychological, social and economic factors, Neurological, Reproductive health and childbirth, Mental health, Good Health and Well Being, Attention Deficit Disorder with Hyperactivity, Autism Spectrum Disorder, Bipolar Disorder, Cerebral Cortex, Child, Depressive Disorder, Major, Female, Humans, Infant, Newborn, Magnetic Resonance Imaging, Premature Birth, Cortical growth, Cortical surface area, Mental illness, Neurodevelopment, Neurogenesis, Psychiatric disorders, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological sciences, Biomedical and clinical sciences
Abstract: BackgroundMorphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life.MethodsInterregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed.ResultsAcross the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth.ConclusionsOur findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy.
Published: 2022

29. 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

30. Concurrent inflammation-related brain reorganization in multiple sclerosis and depression

Author: Molina Galindo, Lara S., Gonzalez-Escamilla, Gabriel, Fleischer, Vinzenz, Grotegerd, Dominik, Meinert, Susanne, Ciolac, Dumitru, Person, Maren, Stein, Frederike, Brosch, Katharina, Nenadić, Igor, Alexander, Nina, Kircher, Tilo, Hahn, Tim, Winter, Yaroslav, Othman, Ahmed E., Bittner, Stefan, Zipp, Frauke, Dannlowski, Udo, and Groppa, Sergiu
Published: 2024
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31. Exploring the complex interrelation between depressive symptoms, risk, and protective factors: A comprehensive network approach

Author: Iovoli, Flavio, Hall, Mila, Nenadic, Igor, Straube, Benjamin, Alexander, Nina, Jamalabadi, Hamidreza, Jansen, Andreas, Stein, Frederike, Brosch, Katharina, Thomas-Odenthal, Florian, Usemann, Paula, Teutenberg, Lea, Wroblewski, Adrian, Pfarr, Julia, Thiel, Katharina, Flinkenflügel, Kira, Meinert, Susanne, Grotegerd, Dominik, Hahn, Tim, Goltermann, Janik, Gruber, Marius, Repple, Jonathan, Enneking, Verena, Winter, Alexandra, Dannlowski, Udo, Kircher, Tilo, and Rubel, Julian A.
Published: 2024
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32. Altered brain dynamic in major depressive disorder: state and trait features

Author: Javaheripour, Nooshin, Colic, Lejla, Opel, Nils, Li, Meng, Maleki Balajoo, Somayeh, Chand, Tara, Van der Meer, Johan, Krylova, Marina, Izyurov, Igor, Meller, Tina, Goltermann, Janik, Winter, Nils R., Meinert, Susanne, Grotegerd, Dominik, Jansen, Andreas, Alexander, Nina, Usemann, Paula, Thomas-Odenthal, Florian, Evermann, Ulrika, Wroblewski, Adrian, Brosch, Katharina, Stein, Frederike, Hahn, Tim, Straube, Benjamin, Krug, Axel, Nenadić, Igor, Kircher, Tilo, Croy, Ilona, Dannlowski, Udo, Wagner, Gerd, and Walter, Martin
Published: 2023
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33. Association of hospitalization with structural brain alterations in patients with affective disorders over nine years

Author: Förster, Katharina, Grotegerd, Dominik, Dohm, Katharina, Lemke, Hannah, Enneking, Verena, Meinert, Susanne, Redlich, Ronny, Heindel, Walter, Bauer, Jochen, Kugel, Harald, Suslow, Thomas, Ohrmann, Patricia, Carballedo, Angela, O’Keane, Veronica, Fagan, Andrew, Doolin, Kelly, McCarthy, Hazel, Kanske, Philipp, Frodl, Thomas, and Dannlowski, Udo
Published: 2023
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34. Genetic variants associated with longitudinal changes in brain structure across the lifespan

Author: Brouwer, Rachel M, Klein, Marieke, Grasby, Katrina L, Schnack, Hugo G, Jahanshad, Neda, Teeuw, Jalmar, Thomopoulos, Sophia I, Sprooten, Emma, Franz, Carol E, Gogtay, Nitin, Kremen, William S, Panizzon, Matthew S, Olde Loohuis, Loes M, Whelan, Christopher D, Aghajani, Moji, Alloza, Clara, Alnæs, Dag, Artiges, Eric, Ayesa-Arriola, Rosa, Barker, Gareth J, Bastin, Mark E, Blok, Elisabet, Bøen, Erlend, Breukelaar, Isabella A, Bright, Joanna K, Buimer, Elizabeth EL, Bülow, Robin, Cannon, Dara M, Ciufolini, Simone, Crossley, Nicolas A, Damatac, Christienne G, Dazzan, Paola, de Mol, Casper L, de Zwarte, Sonja MC, Desrivières, Sylvane, Díaz-Caneja, Covadonga M, Doan, Nhat Trung, Dohm, Katharina, Fröhner, Juliane H, Goltermann, Janik, Grigis, Antoine, Grotegerd, Dominik, Han, Laura KM, Harris, Mathew A, Hartman, Catharina A, Heany, Sarah J, Heindel, Walter, Heslenfeld, Dirk J, Hohmann, Sarah, Ittermann, Bernd, Jansen, Philip R, Janssen, Joost, Jia, Tianye, Jiang, Jiyang, Jockwitz, Christiane, Karali, Temmuz, Keeser, Daniel, Koevoets, Martijn GJC, Lenroot, Rhoshel K, Malchow, Berend, Mandl, René CW, Medel, Vicente, Meinert, Susanne, Morgan, Catherine A, Mühleisen, Thomas W, Nabulsi, Leila, Opel, Nils, de la Foz, Víctor Ortiz-García, Overs, Bronwyn J, Paillère Martinot, Marie-Laure, Redlich, Ronny, Marques, Tiago Reis, Repple, Jonathan, Roberts, Gloria, Roshchupkin, Gennady V, Setiaman, Nikita, Shumskaya, Elena, Stein, Frederike, Sudre, Gustavo, Takahashi, Shun, Thalamuthu, Anbupalam, Tordesillas-Gutiérrez, Diana, van der Lugt, Aad, van Haren, Neeltje EM, Wardlaw, Joanna M, Wen, Wei, Westeneng, Henk-Jan, Wittfeld, Katharina, Zhu, Alyssa H, Zugman, Andre, Armstrong, Nicola J, Bonfiglio, Gaia, Bralten, Janita, Dalvie, Shareefa, Davies, Gail, Di Forti, Marta, Ding, Linda, Donohoe, Gary, Forstner, Andreas J, and Gonzalez-Peñas, Javier
Subjects: Biological Psychology, Psychology, Genetics, Biomedical Imaging, Mental Health, Biotechnology, Human Genome, Prevention, Aging, Brain Disorders, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Mental health, Neurological, Brain, Genome-Wide Association Study, Humans, Longevity, Magnetic Resonance Imaging, IMAGEN Consortium, Cognitive Sciences, Neurology & Neurosurgery, Biological psychology
Abstract: Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.
Published: 2022

35. Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

Author: Gallo, Selene, El-Gazzar, Ahmed, Zhutovsky, Paul, Thomas, Rajat M., Javaheripour, Nooshin, Li, Meng, Bartova, Lucie, Bathula, Deepti, Dannlowski, Udo, Davey, Christopher, Frodl, Thomas, Gotlib, Ian, Grimm, Simone, Grotegerd, Dominik, Hahn, Tim, Hamilton, Paul J., Harrison, Ben J., Jansen, Andreas, Kircher, Tilo, Meyer, Bernhard, Nenadić, Igor, Olbrich, Sebastian, Paul, Elisabeth, Pezawas, Lukas, Sacchet, Matthew D., Sämann, Philipp, Wagner, Gerd, Walter, Henrik, Walter, Martin, and van Wingen, Guido
Published: 2023
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36. Brain Structural Network Connectivity of Formal Thought Disorder Dimensions in Affective and Psychotic Disorders

Author: Stein, Frederike, Gruber, Marius, Mauritz, Marco, Brosch, Katharina, Pfarr, Julia-Katharina, Ringwald, Kai G., Thomas-Odenthal, Florian, Wroblewski, Adrian, Evermann, Ulrika, Steinsträter, Olaf, Grumbach, Pascal, Thiel, Katharina, Winter, Alexandra, Bonnekoh, Linda M., Flinkenflügel, Kira, Goltermann, Janik, Meinert, Susanne, Grotegerd, Dominik, Bauer, Jochen, Opel, Nils, Hahn, Tim, Leehr, Elisabeth J., Jansen, Andreas, de Lange, Siemon C., van den Heuvel, Martijn P., Nenadić, Igor, Krug, Axel, Dannlowski, Udo, Repple, Jonathan, and Kircher, Tilo
Published: 2024
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37. Cortical and subcortical neuroanatomical signatures of schizotypy in 3004 individuals assessed in a worldwide ENIGMA study

Author: Kirschner, Matthias, Hodzic-Santor, Benazir, Antoniades, Mathilde, Nenadic, Igor, Kircher, Tilo, Krug, Axel, Meller, Tina, Grotegerd, Dominik, Fornito, Alex, Arnatkeviciute, Aurina, Bellgrove, Mark A, Tiego, Jeggan, Dannlowski, Udo, Koch, Katharina, Hülsmann, Carina, Kugel, Harald, Enneking, Verena, Klug, Melissa, Leehr, Elisabeth J, Böhnlein, Joscha, Gruber, Marius, Mehler, David, DeRosse, Pamela, Moyett, Ashley, Baune, Bernhard T, Green, Melissa, Quidé, Yann, Pantelis, Christos, Chan, Raymond, Wang, Yi, Ettinger, Ulrich, Debbané, Martin, Derome, Melodie, Gaser, Christian, Besteher, Bianca, Diederen, Kelly, Spencer, Tom J, Fletcher, Paul, Rössler, Wulf, Smigielski, Lukasz, Kumari, Veena, Premkumar, Preethi, Park, Haeme RP, Wiebels, Kristina, Lemmers-Jansen, Imke, Gilleen, James, Allen, Paul, Kozhuharova, Petya, Marsman, Jan-Bernard, Lebedeva, Irina, Tomyshev, Alexander, Mukhorina, Anna, Kaiser, Stefan, Fett, Anne-Kathrin, Sommer, Iris, Schuite-Koops, Sanne, Paquola, Casey, Larivière, Sara, Bernhardt, Boris, Dagher, Alain, Grant, Phillip, van Erp, Theo GM, Turner, Jessica A, Thompson, Paul M, Aleman, André, and Modinos, Gemma
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Neurosciences, Serious Mental Illness, Schizophrenia, Clinical Research, Mental Health, Mental health, Good Health and Well Being, Bipolar Disorder, Female, Humans, Magnetic Resonance Imaging, Male, Psychotic Disorders, Schizotypal Personality Disorder, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract: Neuroanatomical abnormalities have been reported along a continuum from at-risk stages, including high schizotypy, to early and chronic psychosis. However, a comprehensive neuroanatomical mapping of schizotypy remains to be established. The authors conducted the first large-scale meta-analyses of cortical and subcortical morphometric patterns of schizotypy in healthy individuals, and compared these patterns with neuroanatomical abnormalities observed in major psychiatric disorders. The sample comprised 3004 unmedicated healthy individuals (12-68 years, 46.5% male) from 29 cohorts of the worldwide ENIGMA Schizotypy working group. Cortical and subcortical effect size maps with schizotypy scores were generated using standardized methods. Pattern similarities were assessed between the schizotypy-related cortical and subcortical maps and effect size maps from comparisons of schizophrenia (SZ), bipolar disorder (BD) and major depression (MDD) patients with controls. Thicker right medial orbitofrontal/ventromedial prefrontal cortex (mOFC/vmPFC) was associated with higher schizotypy scores (r = 0.067, pFDR = 0.02). The cortical thickness profile in schizotypy was positively correlated with cortical abnormalities in SZ (r = 0.285, pspin = 0.024), but not BD (r = 0.166, pspin = 0.205) or MDD (r = -0.274, pspin = 0.073). The schizotypy-related subcortical volume pattern was negatively correlated with subcortical abnormalities in SZ (rho = -0.690, pspin = 0.006), BD (rho = -0.672, pspin = 0.009), and MDD (rho = -0.692, pspin = 0.004). Comprehensive mapping of schizotypy-related brain morphometry in the general population revealed a significant relationship between higher schizotypy and thicker mOFC/vmPFC, in the absence of confounding effects due to antipsychotic medication or disease chronicity. The cortical pattern similarity between schizotypy and schizophrenia yields new insights into a dimensional neurobiological continuity across the extended psychosis phenotype.
Published: 2022

38. 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

39. FreeSurfer‐based segmentation of hippocampal subfields: A review of methods and applications, with a novel quality control procedure for ENIGMA studies and other collaborative efforts

Author: Sämann, Philipp G, Iglesias, Juan Eugenio, Gutman, Boris, Grotegerd, Dominik, Leenings, Ramona, Flint, Claas, Dannlowski, Udo, Clarke‐Rubright, Emily K, Morey, Rajendra A, Erp, Theo GM, Whelan, Christopher D, Han, Laura KM, Velzen, Laura S, Cao, Bo, Augustinack, Jean C, Thompson, Paul M, Jahanshad, Neda, and Schmaal, Lianne
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Psychology, Mental Health, Behavioral and Social Science, Neurosciences, Aging, Brain Disorders, Biomedical Imaging, Neurodegenerative, Bioengineering, Neurological, Mental health, Hippocampus, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Multicenter Studies as Topic, Neuroimaging, Quality Control, ENIGMA, FreeSurfer, MRI, hippocampal subfields, hippocampal subregions, hippocampus, quality control, segmentation, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract: Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013-12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi-)genetics. Finally, we highlight points where FreeSurfer-based hippocampal subfield studies may be optimized.
Published: 2022

40. Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Author: Dima, Danai, Modabbernia, Amirhossein, Papachristou, Efstathios, Doucet, Gaelle E, Agartz, Ingrid, Aghajani, Moji, Akudjedu, Theophilus N, Albajes-Eizagirre, Anton, Alnaes, Dag, Alpert, Kathryn I, Andersson, Micael, Andreasen, Nancy C, Andreassen, Ole A, Asherson, Philip, Banaschewski, Tobias, Bargallo, Nuria, Baumeister, Sarah, Baur-Streubel, Ramona, Bertolino, Alessandro, Bonvino, Aurora, Boomsma, Dorret I, Borgwardt, Stefan, Bourque, Josiane, Brandeis, Daniel, Breier, Alan, Brodaty, Henry, Brouwer, Rachel M, Buitelaar, Jan K, Busatto, Geraldo F, Buckner, Randy L, Calhoun, Vincent, Canales-Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Castellanos, Francisco X, Cervenka, Simon, Chaim-Avancini, Tiffany M, Ching, Christopher RK, Chubar, Victoria, Clark, Vincent P, Conrod, Patricia, Conzelmann, Annette, Crespo-Facorro, Benedicto, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Dale, Anders M, Davey, Christopher, de Geus, Eco JC, de Haan, Lieuwe, de Zubicaray, Greig I, den Braber, Anouk, Dickie, Erin W, Di Giorgio, Annabella, Doan, Nhat Trung, Dørum, Erlend S, Ehrlich, Stefan, Erk, Susanne, Espeseth, Thomas, Fatouros-Bergman, Helena, Fisher, Simon E, Fouche, Jean-Paul, Franke, Barbara, Frodl, Thomas, Fuentes-Claramonte, Paola, Glahn, David C, Gotlib, Ian H, Grabe, Hans-Jörgen, Grimm, Oliver, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Gruner, Patricia, Gur, Rachel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hartman, Catharine A, Hatton, Sean N, Heinz, Andreas, Heslenfeld, Dirk J, Hibar, Derrek P, Hickie, Ian B, Ho, Beng-Choon, Hoekstra, Pieter J, Hohmann, Sarah, Holmes, Avram J, Hoogman, Martine, Hosten, Norbert, Howells, Fleur M, Hulshoff Pol, Hilleke E, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Jernigan, Terry L, Jiang, Jiyang, Jönsson, Erik G, Joska, John A, Kahn, Rene, and Kalnin, Andrew
Subjects: Karolinska Schizophrenia Project, Amygdala, Hippocampus, Thalamus, Corpus Striatum, Humans, Human Development, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Child, Child, Preschool, Female, Male, Young Adult, Neuroimaging, ENIGMA, brain morphometry, longitudinal trajectories, multisite, Neurosciences, Aging, Aetiology, 2.1 Biological and endogenous factors, Mental health, Cognitive Sciences, Experimental Psychology
Abstract: Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
Published: 2022

41. Reproducibility in the absence of selective reporting: An illustration from large‐scale brain asymmetry research

Author: Kong, Xiang‐Zhen, Mathias, Samuel R, Guadalupe, Tulio, Abé, Christoph, Agartz, Ingrid, Akudjedu, Theophilus N, Aleman, Andre, Alhusaini, Saud, Allen, Nicholas B, Ames, David, Andreassen, Ole A, Vasquez, Alejandro Arias, Armstrong, Nicola J, Asherson, Phil, Bergo, Felipe, Bastin, Mark E, Batalla, Albert, Bauer, Jochen, Baune, Bernhard T, Baur‐Streubel, Ramona, Biederman, Joseph, Blaine, Sara K, Boedhoe, Premika, Bøen, Erlend, Bose, Anushree, Bralten, Janita, Brandeis, Daniel, Brem, Silvia, Brodaty, Henry, Yüksel, Dilara, Brooks, Samantha J, Buitelaar, Jan, Bürger, Christian, Bülow, Robin, Calhoun, Vince, Calvo, Anna, Canales‐Rodríguez, Erick Jorge, Cannon, Dara M, Caparelli, Elisabeth C, Castellanos, Francisco X, Cendes, Fernando, Chaim‐Avancini, Tiffany Moukbel, Chantiluke, Kaylita, Chen, Qun‐lin, Chen, Xiayu, Cheng, Yuqi, Christakou, Anastasia, Clark, Vincent P, Coghill, David, Connolly, Colm G, Conzelmann, Annette, Córdova‐Palomera, Aldo, Cousijn, Janna, Crow, Tim, Cubillo, Ana, Dannlowski, Udo, de Bruttopilo, Sara Ambrosino, de Zeeuw, Patrick, Deary, Ian J, Demeter, Damion V, Di Martino, Adriana, Dickie, Erin W, Dietsche, Bruno, Doan, Nhat Trung, Doherty, Colin P, Doyle, Alysa, Durston, Sarah, Earl, Eric, Ehrlich, Stefan, Ekman, Carl Johan, Elvsåshagen, Torbjørn, Epstein, Jeffery N, Fair, Damien A, Faraone, Stephen V, Fernández, Guillén, Flint, Claas, Filho, Geraldo Busatto, Förster, Katharina, Fouche, Jean‐Paul, Foxe, John J, Frodl, Thomas, Fuentes‐Claramonte, Paola, Fullerton, Janice M, Garavan, Hugh, do Santos Garcia, Danielle, Gotlib, Ian H, Goudriaan, Anna E, Grabe, Hans Jörgen, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Gurholt, Tiril, Haavik, Jan, Hahn, Tim, Hansell, Narelle K, Harris, Mathew A, Hartman, Catharina A, del Carmen Valdés Hernández, Maria, and Heslenfeld, Dirk
Subjects: Biological Psychology, Psychology, Neurosciences, Neurological, Adolescent, Adult, Aged, Brain Cortical Thickness, Cerebral Cortex, Datasets as Topic, Humans, Magnetic Resonance Imaging, Middle Aged, Multicenter Studies as Topic, Neuroimaging, Publication Bias, Reproducibility of Results, Young Adult, ENIGMA Laterality Working Group, P-hacking, multisite collaboration, publication bias, reproducibility, team science, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract: The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p-hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left-right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta-analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an "ideal publishing environment," that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically-used sample sizes.
Published: 2022

42. Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3–90 years

Author: Frangou, Sophia, Modabbernia, Amirhossein, Williams, Steven CR, Papachristou, Efstathios, Doucet, Gaelle E, Agartz, Ingrid, Aghajani, Moji, Akudjedu, Theophilus N, Albajes‐Eizagirre, Anton, Alnæs, Dag, Alpert, Kathryn I, Andersson, Micael, Andreasen, Nancy C, Andreassen, Ole A, Asherson, Philip, Banaschewski, Tobias, Bargallo, Nuria, Baumeister, Sarah, Baur‐Streubel, Ramona, Bertolino, Alessandro, Bonvino, Aurora, Boomsma, Dorret I, Borgwardt, Stefan, Bourque, Josiane, Brandeis, Daniel, Breier, Alan, Brodaty, Henry, Brouwer, Rachel M, Buitelaar, Jan K, Busatto, Geraldo F, Buckner, Randy L, Calhoun, Vincent, Canales‐Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Castellanos, Francisco X, Cervenka, Simon, Chaim‐Avancini, Tiffany M, Ching, Christopher RK, Chubar, Victoria, Clark, Vincent P, Conrod, Patricia, Conzelmann, Annette, Crespo‐Facorro, Benedicto, Crivello, Fabrice, Crone, Eveline A, Dale, Anders M, Dannlowski, Udo, Davey, Christopher, Geus, Eco JC, Haan, Lieuwe, Zubicaray, Greig I, Braber, Anouk, Dickie, Erin W, Di Giorgio, Annabella, Doan, Nhat Trung, Dørum, Erlend S, Ehrlich, Stefan, Erk, Susanne, Espeseth, Thomas, Fatouros‐Bergman, Helena, Fisher, Simon E, Fouche, Jean‐Paul, Franke, Barbara, Frodl, Thomas, Fuentes‐Claramonte, Paola, Glahn, David C, Gotlib, Ian H, Grabe, Hans‐Jörgen, Grimm, Oliver, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Gruner, Patricia, Gur, Rachel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hartman, Catharine A, Hatton, Sean N, Heinz, Andreas, Heslenfeld, Dirk J, Hibar, Derrek P, Hickie, Ian B, Ho, Beng‐Choon, Hoekstra, Pieter J, Hohmann, Sarah, Holmes, Avram J, Hoogman, Martine, Hosten, Norbert, Howells, Fleur M, Pol, Hilleke E Hulshoff, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Jernigan, Terry L, Jiang, Jiyang, Jönsson, Erik G, Joska, John A, and Kahn, Rene
Subjects: Behavioral and Social Science, Neurosciences, Aging, Adolescent, Adult, Aged, Aged, 80 and over, Cerebral Cortex, Child, Child, Preschool, Cross-Sectional Studies, Female, Human Development, Humans, Male, Middle Aged, Neuroimaging, Young Adult, aging, cortical thickness, development, trajectories, Karolinska Schizophrenia Project, Cognitive Sciences, Experimental Psychology
Abstract: Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
Published: 2022

43. 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

44. 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

45. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Author: Han, Laura KM, Dinga, Richard, Hahn, Tim, Ching, Christopher RK, Eyler, Lisa T, Aftanas, Lyubomir, Aghajani, Moji, Aleman, André, Baune, Bernhard T, Berger, Klaus, Brak, Ivan, Filho, Geraldo Busatto, Carballedo, Angela, Connolly, Colm G, Couvy-Duchesne, Baptiste, Cullen, Kathryn R, Dannlowski, Udo, Davey, Christopher G, Dima, Danai, Duran, Fabio LS, Enneking, Verena, Filimonova, Elena, Frenzel, Stefan, Frodl, Thomas, Fu, Cynthia HY, Godlewska, Beata R, Gotlib, Ian H, Grabe, Hans J, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Hall, Geoffrey B, Harrison, Ben J, Hatton, Sean N, Hermesdorf, Marco, Hickie, Ian B, Ho, Tiffany C, Hosten, Norbert, Jansen, Andreas, Kähler, Claas, Kircher, Tilo, Klimes-Dougan, Bonnie, Krämer, Bernd, Krug, Axel, Lagopoulos, Jim, Leenings, Ramona, MacMaster, Frank P, MacQueen, Glenda, McIntosh, Andrew, McLellan, Quinn, McMahon, Katie L, Medland, Sarah E, Mueller, Bryon A, Mwangi, Benson, Osipov, Evgeny, Portella, Maria J, Pozzi, Elena, Reneman, Liesbeth, Repple, Jonathan, Rosa, Pedro GP, Sacchet, Matthew D, Sämann, Philipp G, Schnell, Knut, Schrantee, Anouk, Simulionyte, Egle, Soares, Jair C, Sommer, Jens, Stein, Dan J, Steinsträter, Olaf, Strike, Lachlan T, Thomopoulos, Sophia I, van Tol, Marie-José, Veer, Ilya M, Vermeiren, Robert RJM, Walter, Henrik, van der Wee, Nic JA, van der Werff, Steven JA, Whalley, Heather, Winter, Nils R, Wittfeld, Katharina, Wright, Margaret J, Wu, Mon-Ju, Völzke, Henry, Yang, Tony T, Zannias, Vasileios, de Zubicaray, Greig I, Zunta-Soares, Giovana B, Abé, Christoph, Alda, Martin, Andreassen, Ole A, Bøen, Erlend, Bonnin, Caterina M, Canales-Rodriguez, Erick J, Cannon, Dara, Caseras, Xavier, Chaim-Avancini, Tiffany M, Elvsåshagen, Torbjørn, Favre, Pauline, Foley, Sonya F, and Fullerton, Janice M
Subjects: Depression, Aging, Brain Disorders, Biomedical Imaging, Serious Mental Illness, Mental Health, Major Depressive Disorder, Behavioral and Social Science, Clinical Research, Neurosciences, 2.3 Psychological, social and economic factors, Aetiology, Mental health, Adolescent, Adult, Aged, Brain, Depressive Disorder, Major, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.
Published: 2021

46. Higher body weight-dependent neural activation during reward processing

Author: Richter, Maike, Widera, Sophia, Malz, Franziska, Goltermann, Janik, Steinmann, Lavinia, Kraus, Anna, Enneking, Verena, Meinert, Susanne, Repple, Jonathan, Redlich, Ronny, Leehr, Elisabeth J., Grotegerd, Dominik, Dohm, Katharina, Kugel, Harald, Bauer, Jochen, Arolt, Volker, Dannlowski, Udo, and Opel, Nils
Published: 2023
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47. Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder

Author: Hahn, Tim, Winter, Nils R., Ernsting, Jan, Gruber, Marius, Mauritz, Marco J., Fisch, Lukas, Leenings, Ramona, Sarink, Kelvin, Blanke, Julian, Holstein, Vincent, Emden, Daniel, Beisemann, Marie, Opel, Nils, Grotegerd, Dominik, Meinert, Susanne, Heindel, Walter, Witt, Stephanie, Rietschel, Marcella, Nöthen, Markus M., Forstner, Andreas J., Kircher, Tilo, Nenadic, Igor, Jansen, Andreas, Müller-Myhsok, Bertram, Andlauer, Till F. M., Walter, Martin, van den Heuvel, Martijn P., Jamalabadi, Hamidreza, Dannlowski, Udo, and Repple, Jonathan
Published: 2023
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48. Associations of gestational age with gyrification and neurocognition in healthy adults

Author: Schmitt, Simon, Ringwald, Kai G., Meller, Tina, Stein, Frederike, Brosch, Katharina, Pfarr, Julia-Katharina, Hahn, Tim, Lemke, Hannah, Meinert, Susanne, Repple, Jonathan, Thiel, Katharina, Waltemate, Lena, Winter, Alexandra, Grotegerd, Dominik, Dempfle, Astrid, Jansen, Andreas, Krug, Axel, Dannlowski, Udo, Nenadić, Igor, and Kircher, Tilo
Published: 2023
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49. Narcissistic dimensions and depressive symptoms in patients across mental disorders in cognitive behavioural therapy and in psychoanalytic interactional therapy in Germany: a prospective cohort study

Author: Richter, Maike, Mota, Simon, Hater, Leonie, Bratek, Rebecca, Goltermann, Janik, Barkhau, Carlotta, Gruber, Marius, Repple, Jonathan, Storck, Michael, Blitz, Rogério, Grotegerd, Dominik, Masuhr, Oliver, Jaeger, Ulrich, Baune, Bernhard T, Dugas, Martin, Walter, Martin, Dannlowski, Udo, Buhlmann, Ulrike, Back, Mitja, and Opel, Nils
Published: 2023
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50. Negative Stressful Life Events and Social Support Are Associated With White Matter Integrity in Depressed Patients and Healthy Control Participants: A Diffusion Tensor Imaging Study

Author: Flinkenflügel, Kira, Meinert, Susanne, Thiel, Katharina, Winter, Alexandra, Goltermann, Janik, Strathausen, Lea, Brosch, Katharina, Stein, Frederike, Thomas-Odenthal, Florian, Evermann, Ulrika, Wroblewski, Adrian, Usemann, Paula, Pfarr, Julia-Katharina, Grotegerd, Dominik, Hahn, Tim, Leehr, Elisabeth J., Dohm, Katharina, Bauer, Jochen, Jamalabadi, Hamidreza, Straube, Benjamin, Alexander, Nina, Jansen, Andreas, Nenadić, Igor, Krug, Axel, Kircher, Tilo, and Dannlowski, Udo
Published: 2023
Full Text: View/download PDF

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