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1. deepmriprep: Voxel-based Morphometry (VBM) Preprocessing via Deep Neural Networks

Author

Fisch, Lukas, Winter, Nils R., Goltermann, Janik, Barkhau, Carlotta, Emden, Daniel, Ernsting, Jan, Konowski, Maximilian, Leenings, Ramona, Borgers, Tiana, Flinkenflügel, Kira, Grotegerd, Dominik, Kraus, Anna, Leehr, Elisabeth J., Meinert, Susanne, Stein, Frederike, Teutenberg, Lea, Thomas-Odenthal, Florian, Usemann, Paula, Hermesdorf, Marco, Jamalabadi, Hamidreza, Jansen, Andreas, Nenadic, Igor, Straube, Benjamin, Kircher, Tilo, Berger, Klaus, Risse, Benjamin, Dannlowski, Udo, and Hahn, Tim

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

Voxel-based Morphometry (VBM) has emerged as a powerful approach in neuroimaging research, utilized in over 7,000 studies since the year 2000. Using Magnetic Resonance Imaging (MRI) data, VBM assesses variations in the local density of brain tissue and examines its associations with biological and psychometric variables. Here, we present deepmriprep, a neural network-based pipeline that performs all necessary preprocessing steps for VBM analysis of T1-weighted MR images using deep neural networks. Utilizing the Graphics Processing Unit (GPU), deepmriprep is 37 times faster than CAT12, the leading VBM preprocessing toolbox. The proposed method matches CAT12 in accuracy for tissue segmentation and image registration across more than 100 datasets and shows strong correlations in VBM results. Tissue segmentation maps from deepmriprep have over 95% agreement with ground truth maps, and its non-linear registration, using supervised SYMNet, predicts smooth deformation fields comparable to CAT12. The high processing speed of deepmriprep enables rapid preprocessing of extensive datasets and thereby fosters the application of VBM analysis to large-scale neuroimaging studies and opens the door to real-time applications. Finally, deepmripreps straightforward, modular design enables researchers to easily understand, reuse, and advance the underlying methods, fostering further advancements in neuroimaging research. deepmriprep can be conveniently installed as a Python package and is publicly accessible at https://github.com/wwu-mmll/deepmriprep.

Published
2024

2. 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

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

Author

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

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

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

Published
2024

4. 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

6. 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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7. 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

8. 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

9. More Alike than Different: Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder across Neuroimaging Modalities

Author

Winter, Nils R., Leenings, Ramona, Ernsting, Jan, Sarink, Kelvin, Fisch, Lukas, Emden, Daniel, Blanke, Julian, Goltermann, Janik, Opel, Nils, Barkhau, Carlotta, Meinert, Susanne, Dohm, Katharina, Repple, Jonathan, Mauritz, Marco, Gruber, Marius, Leehr, Elisabeth J., Grotegerd, Dominik, Redlich, Ronny, Jansen, Andreas, Nenadic, Igor, Nöthen, Markus, Forstner, Andreas, Rietschel, Marcella, Groß, Joachim, Bauer, Jochen, Heindel, Walter, Andlauer, Till, Eickhoff, Simon, Kircher, Tilo, Dannlowski, Udo, and Hahn, Tim

Subjects
Quantitative Biology - Neurons and Cognition, Quantitative Biology - Quantitative Methods
Abstract

Introduction: Identifying neurobiological differences between patients suffering from Major Depressive Disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta- and mega-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression. Methods: Here, we systematically investigate healthy controls and MDD patients across a comprehensive range of modalities including structural magnetic resonance imaging (MRI), diffusion tensor imaging, functional task-based and resting-state MRI under near-ideal conditions. To this end, we quantify the upper bounds of univariate effect sizes, predictive utility, and distributional dissimilarity in a fully harmonized cohort of N=1,809 participants. We compare the results to an MDD polygenic risk score (PRS) and environmental variables. Results: The upper bound of the effect sizes range from partial eta squared = .004 to .017, distributions overlap between 89% and 95%, with classification accuracies ranging between 54% and 55% across neuroimaging modalities. This pattern remains virtually unchanged when considering only acutely or chronically depressed patients. Differences are comparable to those found for PRS, but substantially smaller than for environmental variables. Discussion: We provide a large-scale, multimodal analysis of univariate biological differences between MDD patients and controls and show that even under near-ideal conditions and for maximum biological differences, deviations are extremely small and similarity dominates. We sketch an agenda for a new focus of future research in biological psychiatry facilitating quantitative, theory-driven research, an emphasis on computational psychiatry and multivariate machine learning approaches, as well as the utilization of ecologically valid phenotyping., Comment: 12 pages, 3 figures

Published
2021

10. 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

11. 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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12. 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, Heuvel, Martijn P. van den, Jamalabadi, Hamidreza, Dannlowski, Udo, and Repple, Jonathan

Subjects
Quantitative Biology - Neurons and Cognition, Electrical Engineering and Systems Science - Systems and Control
Abstract

Background: A therapeutic intervention in psychiatry can be viewed as an attempt to influence the brain's large-scale, dynamic network state transitions underlying cognition and behavior. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability - i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Methods: From Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n=692) and healthy controls (n=820). Results: First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. Conclusions: We show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health., Comment: 24 pages, 1 figure

Published
2021

13. 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

14. 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

15. Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: A cluster analysis approach

Author

Pfarr, Julia-Katharina, Meller, Tina, Brosch, Katharina, Stein, Frederike, Thomas-Odenthal, Florian, Evermann, Ulrika, Wroblewski, Adrian, Ringwald, Kai G., Hahn, Tim, Meinert, Susanne, Winter, Alexandra, Thiel, Katharina, Flinkenflügel, Kira, Jansen, Andreas, Krug, Axel, Dannlowski, Udo, Kircher, Tilo, Gaser, Christian, and Nenadić, Igor

Published
2023
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16. 10Kin1day: A Bottom-Up Neuroimaging Initiative

Author

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

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

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

Published
2019

18. Brain structural correlates of recurrence following the first episode in patients with major depressive disorder

Author

Lemke, Hannah, Klute, Hannah, Skupski, Jennifer, Thiel, Katharina, Waltemate, Lena, Winter, Alexandra, Breuer, Fabian, Meinert, Susanne, Klug, Melissa, Enneking, Verena, Winter, Nils R., Grotegerd, Dominik, Leehr, Elisabeth J., Repple, Jonathan, Dohm, Katharina, Opel, Nils, Stein, Frederike, Meller, Tina, Brosch, Katharina, Ringwald, Kai G., Pfarr, Julia-Katharina, Thomas-Odenthal, Florian, Hahn, Tim, Krug, Axel, Jansen, Andreas, Heindel, Walter, Nenadić, Igor, Kircher, Tilo, and Dannlowski, Udo

Published
2022
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20. 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 J., Artiges, Eric, Atkins, Joshua R., Bauer, Jochen, Benedetti, Francesco, Boomsma, Dorret I., Brodaty, Henry, Brosch, Katharina, Buckner, Randy L., Cairns, Murray J., Calhoun, Vince, Caspers, Svenja, Cichon, Sven, Corvin, Aiden P., Crespo-Facorro, Benedicto, Dannlowski, Udo, David, Friederike S., de Geus, Eco J.C., de Zubicaray, Greig I., Desrivières, Sylvane, Doherty, Joanne L., Donohoe, Gary, Ehrlich, Stefan, Eising, Else, Espeseth, Thomas, Fisher, Simon E., Forstner, Andreas J., Fortaner-Uyà, Lidia, Frouin, Vincent, Fukunaga, Masaki, Ge, Tian, Glahn, David C., Goltermann, Janik, Grabe, Hans J., Green, Melissa J., Groenewold, Nynke A., Grotegerd, Dominik, Grøntvedt, Gøril Rolfseng, Hahn, Tim, Hashimoto, Ryota, Hehir-Kwa, Jayne Y., Henskens, Frans A., Holmes, Avram J., Håberg, Asta K., Haavik, Jan, Jacquemont, Sebastien, Jansen, Andreas, Jockwitz, Christiane, Jönsson, Erik G., Kikuchi, Masataka, Kircher, Tilo, Kumar, Kuldeep, Le Hellard, Stephanie, Leu, Costin, Linden, David E., Liu, Jingyu, Loughnan, Robert, Mather, Karen A., McMahon, Katie L., McRae, Allan F., Medland, Sarah E., Meinert, Susanne, Moreau, Clara A., Morris, Derek W., Mowry, Bryan J., Mühleisen, Thomas W., Nenadić, Igor, Nöthen, Markus M., Nyberg, Lars, Ophoff, Roel A., Owen, Michael J., Pantelis, Christos, Paolini, Marco, Paus, Tomas, Pausova, Zdenka, Persson, Karin, Quidé, Yann, Marques, Tiago Reis, Sachdev, Perminder S., Sando, Sigrid B., Schall, Ulrich, Scott, Rodney J., Selbæk, Geir, Shumskaya, Elena, Silva, Ana I., Sisodiya, Sanjay M., Stein, Frederike, Stein, Dan J., Straube, Benjamin, Streit, Fabian, Strike, Lachlan T., Teumer, Alexander, Teutenberg, Lea, Thalamuthu, Anbupalam, Tooney, Paul A., Tordesillas-Gutierrez, Diana, Trollor, Julian N., van ’t Ent, Dennis, van den Bree, Marianne B.M., van Haren, Neeltje E.M., Vázquez-Bourgon, Javier, Völzke, Henry, Wen, Wei, Wittfeld, Katharina, Ching, Christopher R.K., Westlye, Lars T., Thompson, Paul M., Bearden, Carrie E., Selmer, Kaja K., Alnæs, Dag, Andreassen, Ole A., Sønderby, Ida E., Boen, Rune, Kaufmann, Tobias, van der Meer, Dennis, Frei, Oleksandr, Agartz, Ingrid, Ames, David, Andersson, Micael, Armstrong, Nicola J., Artiges, Eric, Atkins, Joshua R., Bauer, Jochen, Benedetti, Francesco, Boomsma, Dorret I., Brodaty, Henry, Brosch, Katharina, Buckner, Randy L., Cairns, Murray J., Calhoun, Vince, Caspers, Svenja, Cichon, Sven, Corvin, Aiden P., Crespo-Facorro, Benedicto, Dannlowski, Udo, David, Friederike S., de Geus, Eco J.C., de Zubicaray, Greig I., Desrivières, Sylvane, Doherty, Joanne L., Donohoe, Gary, Ehrlich, Stefan, Eising, Else, Espeseth, Thomas, Fisher, Simon E., Forstner, Andreas J., Fortaner-Uyà, Lidia, Frouin, Vincent, Fukunaga, Masaki, Ge, Tian, Glahn, David C., Goltermann, Janik, Grabe, Hans J., Green, Melissa J., Groenewold, Nynke A., Grotegerd, Dominik, Grøntvedt, Gøril Rolfseng, Hahn, Tim, Hashimoto, Ryota, Hehir-Kwa, Jayne Y., Henskens, Frans A., Holmes, Avram J., Håberg, Asta K., Haavik, Jan, Jacquemont, Sebastien, Jansen, Andreas, Jockwitz, Christiane, Jönsson, Erik G., Kikuchi, Masataka, Kircher, Tilo, Kumar, Kuldeep, Le Hellard, Stephanie, Leu, Costin, Linden, David E., Liu, Jingyu, Loughnan, Robert, Mather, Karen A., McMahon, Katie L., McRae, Allan F., Medland, Sarah E., Meinert, Susanne, Moreau, Clara A., Morris, Derek W., Mowry, Bryan J., Mühleisen, Thomas W., Nenadić, Igor, Nöthen, Markus M., Nyberg, Lars, Ophoff, Roel A., Owen, Michael J., Pantelis, Christos, Paolini, Marco, Paus, Tomas, Pausova, Zdenka, Persson, Karin, Quidé, Yann, Marques, Tiago Reis, Sachdev, Perminder S., Sando, Sigrid B., Schall, Ulrich, Scott, Rodney J., Selbæk, Geir, Shumskaya, Elena, Silva, Ana I., Sisodiya, Sanjay M., Stein, Frederike, Stein, Dan J., Straube, Benjamin, Streit, Fabian, Strike, Lachlan T., Teumer, Alexander, Teutenberg, Lea, Thalamuthu, Anbupalam, Tooney, Paul A., Tordesillas-Gutierrez, Diana, Trollor, Julian N., van ’t Ent, Dennis, van den Bree, Marianne B.M., van Haren, Neeltje E.M., Vázquez-Bourgon, Javier, Völzke, Henry, Wen, Wei, Wittfeld, Katharina, Ching, Christopher R.K., Westlye, Lars T., Thompson, Paul M., Bearden, Carrie E., Selmer, Kaja K., Alnæs, Dag, Andreassen, Ole A., and Sønderby, Ida E.

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 individual’s 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

21. 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, Nenadić, Igor, Krug, Axel, Dannlowski, Udo, Repple, Jonathan, Kircher, Tilo, 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, Nenadić, Igor, Krug, Axel, Dannlowski, Udo, Repple, Jonathan, and Kircher, Tilo

Abstract

BACKGROUND: The psychopathological syndrome of formal thought disorder (FTD) is present in schizophrenia (SZ) but is also highly prevalent in major depression (MDD) and bipolar disorder (BD). It remains unknown how alterations in the structural white matter connectome of the brain correlate with psychopathological FTD dimensions across affective and psychotic disorders.METHODS: Using FTD items of the SAPS and SANS, we performed exploratory and confirmatory factor analyses in N=864 patients with MDD (n=689), BD (n=108) or SZ (n=67) to identify psychopathological FTD dimensions. We used T1 and diffusion-weighted magnetic resonance imaging to reconstruct the structural connectome of the brain. To investigate the association of FTD sub-dimensions and global structural connectome measures, we employed linear regression models. We used network-based statistic (NBS) to identify subnetworks of white matter fiber tracts associated with FTD symptomatology.RESULTS: Three psychopathological FTD dimensions were delineated, i.e. disorganization, emptiness, and incoherence. "Disorganization" and "incoherence" were associated with global dysconnectivity. NBS identified subnetworks associated with FTD dimensions "disorganization" and "emptiness" but not with "incoherence". Post-hoc analyses on subnetworks did not reveal diagnosis x FTD dimension interaction effects. Results remained stable after correcting for medication and disease severity. Confirmatory analyses showed a substantial overlap of nodes from both subnetworks with cortical brain regions previously associated with FTD in SZ.CONCLUSIONS: We demonstrated white matter subnetwork dysconnectivity in MDD, BD and SZ associated with FTD dimensions that comprise predominantly brain regions implicated in speech. Results open an avenue for transdiagnostic, psychopathology informed, dimensional studies in pathogenetic research.

Published
2024

22. Social support and hippocampal volume are negatively associated in adults with previous experience of childhood maltreatment

Author

Forster, Katharina, Danzer, Lorenz, Redlich, Ronny, Opel, Nils, Grotegerd, Dominik, Leehr, Elisabeth J., Dohm, Katharina, Enneking, Verena, Meinert, Susanne, Goltermann, Janik, Lemke, Hannah, Waltemate, Lena, Thiel, Katharina, Behnert, Katja, Brosch, Katharina, Stein, Frederike, Meller, Tina, Ringwald, Kai, Schmitt, Simon, Steinstrater, Olaf, Jansen, Andreas, Krug, Axel, Nenadic, Igor, Kircher, Tilo, Hahn, Tim, Kugel, Harald, Heindel, Walter, Repple, Jonathan, and Dannlowski, Udo

Subjects
Hippocampus (Brain) -- Physiological aspects, Child abuse -- Physiological aspects -- Psychological aspects, Social networks -- Psychological aspects -- Physiological aspects, Health, Psychology and mental health
Abstract

Background: Childhood maltreatment has been associated with reduced hippocampal volume in healthy individuals, whereas social support, a protective factor, has been positively associated with hippocampal volumes. In this study, we investigated how social support is associated with hippocampal volume in healthy people with previous experience of childhood maltreatment. Methods: We separated a sample of 446 healthy participants into 2 groups using the Childhood Trauma Questionnaire: 265 people without maltreatment and 181 people with maltreatment. We measured perceived social support using a short version of the Social Support Questionnaire. We examined hippocampal volume using automated segmentation (Freesurfer). We conducted a social support x group analysis of covariance on hippocampal volumes controlling for age, sex, total intracranial volume, site and verbal intelligence. Results: Our analysis revealed significantly lower left hippocampal volume in people with maltreatment (left [F.sub.1,432] = 5.686, p = 0.018; right [F.sub.1,433] = 3.371, p = 0.07), but no main effect of social support emerged. However, we did find a significant social support x group interaction for left hippocampal volume (left [F.sub.1,432] = 5.712, p = 0.017; right [F.sub.1,433] = 3.480, p = 0.06). In people without maltreatment, we observed a trend toward a positive association between social support and hippocampal volume. In contrast, social support was negatively associated with hippocampal volume in people with maltreatment. Limitations: Because of the correlative nature of our study, we could not infer causal relationships between social support, maltreatment and hippocampal volume. Conclusion: Our results point to a complex dynamic between environmental risk, protective factors and brain structure--in line with previous evidence--suggesting a detrimental effect of maltreatment on hippocampal development., Introduction Childhood maltreatment is an adverse social experience and is a risk factor for many mental disorders. (1) It is also associated with neuroendocrine dysregulation (e.g., hypercortisolism) and alterations in [...]

Published
2021
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23. Childhood maltreatment and cognitive functioning: the role of depression, parental education, and polygenic predisposition

Author

Goltermann, Janik, Redlich, Ronny, Grotegerd, Dominik, Dohm, Katharina, Leehr, Elisabeth J., Böhnlein, Joscha, Förster, Katharina, Meinert, Susanne, Enneking, Verena, Richter, Maike, Repple, Jonathan, DeVillers, Immanuel, Kloecker, Marine, Jansen, Andreas, Krug, Axel, Nenadić, Igor, Brosch, Katharina, Meller, Tina, Stein, Frederike, Schmitt, Simon, Rietschel, Marcella, Streit, Fabian, Witt, Stephanie H., Forstner, Andreas J., Nöthen, Markus M., Baune, Bernhard T., Andlauer, Till F. M., Kircher, Tilo, Opel, Nils, and Dannlowski, Udo

Published
2021
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24. Elevated accuracy in recognition of subliminal happy facial expressions in patients with panic disorder after psychotherapy.

Author

Zirong Qian, Yunbo Yang, Domschke, Katharina, Gerlach, Alexander L., Hamm, Alfons, Richter, Jan, Herrmann, Martin J., Deckert, Jürgen, Arolt, Volker, Zwanzger, Peter, Lotze, Martin, Pfleiderer, Bettina, Wittchen, Hans-Ulrich, Lang, Thomas, Ströhle, Andreas, Konrad, Carsten, Rief, Winfried, Suslow, Thomas, Jansen, Andreas, and Kircher, Tilo

Subjects
COGNITIVE therapy, PSYCHOTHERAPY, PANIC disorders, FACIAL expression, FACIAL expression & emotions (Psychology), RECOGNITION (Psychology), AGORAPHOBIA
Abstract

Background: Individuals with anxiety disorders (ADs) often display hypervigilance to threat information, although this response may be less pronounced following psychotherapy. This study aims to investigate the unconscious recognition performance of facial expressions in patients with panic disorder (PD) posttreatment, shedding light on alterations in their emotional processing biases. Methods: Patients with PD (n=34) after (exposure-based) cognitive behavior therapy and healthy controls (n=43) performed a subliminal affective recognition task. Emotional facial expressions (fearful, happy, or mirrored) were displayed for 33 ms and backwardly masked by a neutral face. Participants completed a forced choice task to discriminate the briefly presented facial stimulus and an uncovered condition where only the neutral mask was shown. We conducted a secondary analysis to compare groups based on their four possible response types under the four stimulus conditions and examined the correlation of the false alarm rate for fear responses to non-fearful (happy, mirrored, and uncovered) stimuli with clinical anxiety symptoms. Results: The patient group showed a unique selection pattern in response to happy expressions, with significantly more correct "happy" responses compared to controls. Additionally, lower severity of anxiety symptoms after psychotherapy was associated with a decreased false fear response rate with nonthreat presentations. Conclusion: These data suggest that patients with PD exhibited a "happy-face recognition advantage" after psychotherapy. Less symptoms after treatment were related to a reduced fear bias. Thus, a differential facial emotion detection task could be a suitable tool to monitor response patterns and biases in individuals with ADs in the context of psychotherapy. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Status of the COMET experiment

Author

Jansen Andreas

Subjects
Physics, QC1-999
Abstract

The COMET (COherent Muon to Electron Transition) experiment, currently being built in Tokai, Japan, will search for the coherent neutrinoless transition of muons to electrons in the coulomb field of atomic nuclei. The process is highly suppressed in the Standard Model and therefore provides a promising channel to probe new physics. The experiment will be carried out in a staged approach. Phase-I of COMET aims to search for the process with a single event sensitivity of 𝒪(10−15). Additionally, precise measurements of muon beam dynamics and detector prototyping for Phase-II will be conducted. Utilizing a much higher intensity proton beam, a more complex and longer muon/electron transport system, and gained experience from Phase-I, an improvement of at least four orders of magnitude over the current best branching ratio limit up to 𝒪(10−17) is envisioned for Phase-II. This article will give a status report for Phase-I of COMET.

Published
2023
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26. The German research consortium for the study of bipolar disorder (BipoLife): a magnetic resonance imaging study protocol

Author

Vogelbacher, Christoph, Sommer, Jens, Schuster, Verena, Bopp, Miriam H. A., Falkenberg, Irina, Ritter, Philipp S., Bermpohl, Felix, Hindi Attar, Catherine, Rauer, Lisa, Einenkel, Karolin E., Treutlein, Jens, Gruber, Oliver, Juckel, Georg, Flasbeck, Vera, Mulert, Christoph, Hautzinger, Martin, Pfennig, Andrea, Matura, Silke, Reif, Andreas, Grotegerd, Dominik, Dannlowski, Udo, Kircher, Tilo, Bauer, Michael, and Jansen, Andreas

Published
2021
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27. Individuals at increased risk for development of bipolar disorder display structural alterations similar to people with manifest disease

Author

Mikolas, Pavol, Bröckel, Kyra, Vogelbacher, Christoph, Müller, Dirk K., Marxen, Michael, Berndt, Christina, Sauer, Cathrin, Jung, Stine, Fröhner, Juliane Hilde, Fallgatter, Andreas J., Ethofer, Thomas, Rau, Anne, Kircher, Tilo, Falkenberg, Irina, Lambert, Martin, Kraft, Vivien, Leopold, Karolina, Bechdolf, Andreas, Reif, Andreas, Matura, Silke, Stamm, Thomas, Bermpohl, Felix, Fiebig, Jana, Juckel, Georg, Flasbeck, Vera, Correll, Christoph U., Ritter, Philipp, Bauer, Michael, Jansen, Andreas, and Pfennig, Andrea

Published
2021
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29. Altered resting-state functional connectome in major depressive disorder: a mega-analysis from the PsyMRI consortium

Author

Javaheripour, Nooshin, Li, Meng, Chand, Tara, Krug, Axel, Kircher, Tilo, Dannlowski, Udo, Nenadić, Igor, Hamilton, J. Paul, Sacchet, Matthew D., Gotlib, Ian H., Walter, Henrik, Frodl, Thomas, Grimm, Simone, Harrison, Ben J., Wolf, Christian Robert, Olbrich, Sebastian, van Wingen, Guido, Pezawas, Lukas, Parker, Gordon, Hyett, Matthew P., Sämann, Philipp G., Hahn, Tim, Steinsträter, Olaf, Jansen, Andreas, Yuksel, Dilara, Kämpe, Robin, Davey, Christopher G., Meyer, Bernhard, Bartova, Lucie, Croy, Ilona, Walter, Martin, and Wagner, Gerd

Published
2021
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30. A genome-wide association study of the longitudinal course of executive functions

Author

Wendel, Bernadette, Papiol, Sergi, Andlauer, Till F. M., Zimmermann, Jörg, Wiltfang, Jens, Spitzer, Carsten, Senner, Fanny, Schulte, Eva C., Schmauß, Max, Schaupp, Sabrina K., Repple, Jonathan, Reininghaus, Eva, Reimer, Jens, Reich-Erkelenz, Daniela, Opel, Nils, Nenadić, Igor, Meinert, Susanne, Konrad, Carsten, Klöhn-Saghatolislam, Farahnaz, Kircher, Tilo, Kalman, Janos L., Juckel, Georg, Jansen, Andreas, Jäger, Markus, Heilbronner, Maria, von Hagen, Martin, Gade, Katrin, Figge, Christian, Fallgatter, Andreas J., Dietrich, Detlef E., Dannlowski, Udo, Comes, Ashley L., Budde, Monika, Baune, Bernhard T., Arolt, Volker, Anghelescu, Ion-George, Anderson-Schmidt, Heike, Adorjan, Kristina, Falkai, Peter, Schulze, Thomas G., Bickeböller, Heike, and Heilbronner, Urs

Published
2021
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32. Reduced fractional anisotropy in depressed patients due to childhood maltreatment rather than diagnosis

Author

Meinert, Susanne, Repple, Jonathan, Nenadic, Igor, Krug, Axel, Jansen, Andreas, Grotegerd, Dominik, Förster, Katharina, Enneking, Verena, Dohm, Katharina, Schmitt, Simon, Stein, Frederike, Brosch, Katharina, Meller, Tina, Redlich, Ronny, Böhnlein, Joscha, Sindermann, Lisa, Goltermann, Janik, Leehr, Elisabeth J., Opel, Nils, Aldermann, Leni, Reuter, Andreas, Schubotz, Ricarda I., Hahn, Tim, Kircher, Tilo, and Dannlowski, Udo

Published
2019
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33. Seroepidemiological and Molecular Survey for the Detection of SARS-CoV-2 Infection among Children in Iran, September 2020 to June 2021: 1-Year Cross-Sectional Study

Author

Mansour Ghanaie, Roxana, primary, Boone, Idesbald, additional, Shamshiri, Ahmad Reza, additional, Karimi, Abdollah, additional, Amirali, Arezu, additional, Marhamati, Noushin, additional, Rostami, Mohammad Hossein, additional, Pashaei, Niloofar, additional, Janbazi, Shahriar, additional, Azimi, Leila, additional, Khodaei, Hannan, additional, Fallah, Fatemeh, additional, Eckmanns, Tim, additional, Jansen, Andreas, additional, Baradaran, Hamid Reza, additional, Momeny Ourimi, Maryam, additional, Maham, Saeed, additional, Elikaei, Ameneh, additional, and Alebouyeh, Masoud, additional

Published
2023
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34. Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes

Author

Huth, Fabian, primary, Tozzi, Leonardo, additional, Marxen, Michael, additional, Riedel, Philipp, additional, Bröckel, Kyra, additional, Martini, Julia, additional, Berndt, Christina, additional, Sauer, Cathrin, additional, Vogelbacher, Christoph, additional, Jansen, Andreas, additional, Kircher, Tilo, additional, Falkenberg, Irina, additional, Thomas-Odenthal, Florian, additional, Lambert, Martin, additional, Kraft, Vivien, additional, Leicht, Gregor, additional, Mulert, Christoph, additional, Fallgatter, Andreas J., additional, Ethofer, Thomas, additional, Rau, Anne, additional, Leopold, Karolina, additional, Bechdolf, Andreas, additional, Reif, Andreas, additional, Matura, Silke, additional, Biere, Silvia, additional, Bermpohl, Felix, additional, Fiebig, Jana, additional, Stamm, Thomas, additional, Correll, Christoph U., additional, Juckel, Georg, additional, Flasbeck, Vera, additional, Ritter, Philipp, additional, Bauer, Michael, additional, Pfennig, Andrea, additional, and Mikolas, Pavol, additional

Published
2023
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35. Volume of subcortical brain regions in social anxiety disorder: mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group

Author

UCL - SSH/IPSY - Psychological Sciences Research Institute, UCL - SSS/IONS/NEUR - Clinical Neuroscience, Groenewold, Nynke A., Bas-Hoogendam, Janna Marie, Amod, Alyssa R., Laansma, Max A., Van Velzen, Laura S., Aghajani, Moji, Hilbert, Kevin, Oh, Hyuntaek, Salas, Ramiro, Jackowski, Andrea P., Pan, Pedro M., Salum, Giovanni A., Blair, James R., Blair, Karina S., Hirsch, Joy, Pantazatos, Spiro P., Schneier, Franklin R., Talati, Ardesheer, Roelofs, Karin, Volman, Inge, Blanco-Hinojo, Laura, Cardoner, Narcís, Pujol, Jesus, Beesdo-Baum, Katja, Ching, Christopher R. K., Thomopoulos, Sophia I., 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 C., Wu, Mon-Ju, Bayram, Ali, Canli, Mesut, Tükel, Raşit, Westenberg, P. Michiel, Heeren, Alexandre, Cremers, Henk R., Hofmann, David, Straube, Thomas, Doruyter, Alexander G. G., Lochner, Christine, Peterburs, Jutta, Van Tol, Marie-José, Gur, Raquel E., Kaczkurkin, Antonia N., Larsen, Bart, Satterthwaite, Theodore D., Filippi, Courtney A., Gold, Andrea L., Harrewijn, Anita, Zugman, André, Bülow, Robin, Grabe, Hans J., Völzke, Henry, Wittfeld, Katharina, Böhnlein, Joscha, Dohm, Katharina, Kugel, Harald, Schrammen, Elisabeth, Zwanzger, Peter, Leehr, Elisabeth J., Sindermann, Lisa, Ball, Tali M., Fonzo, Gregory A., Paulus, Martin P., Simmons, Alan, Stein, Murray B., Klumpp, Heide, Phan, K. Luan, Furmark, Tomas, Månsson, Kristoffer N. T., Manzouri, Amirhossein, Avery, Suzanne N., Blackford, Jennifer Urbano, Clauss, Jacqueline A., Feola, Brandee, Harper, Jennifer C., Sylvester, Chad M., Lueken, Ulrike, Veltman, Dick J., Winkler, Anderson M., Jahanshad, Neda, Pine, Daniel S., Thompson, Paul M., Stein, Dan J., Van der Wee, Nic J. A., UCL - SSH/IPSY - Psychological Sciences Research Institute, UCL - SSS/IONS/NEUR - Clinical Neuroscience, Groenewold, Nynke A., Bas-Hoogendam, Janna Marie, Amod, Alyssa R., Laansma, Max A., Van Velzen, Laura S., Aghajani, Moji, Hilbert, Kevin, Oh, Hyuntaek, Salas, Ramiro, Jackowski, Andrea P., Pan, Pedro M., Salum, Giovanni A., Blair, James R., Blair, Karina S., Hirsch, Joy, Pantazatos, Spiro P., Schneier, Franklin R., Talati, Ardesheer, Roelofs, Karin, Volman, Inge, Blanco-Hinojo, Laura, Cardoner, Narcís, Pujol, Jesus, Beesdo-Baum, Katja, Ching, Christopher R. K., Thomopoulos, Sophia I., 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 C., Wu, Mon-Ju, Bayram, Ali, Canli, Mesut, Tükel, Raşit, Westenberg, P. Michiel, Heeren, Alexandre, Cremers, Henk R., Hofmann, David, Straube, Thomas, Doruyter, Alexander G. G., Lochner, Christine, Peterburs, Jutta, Van Tol, Marie-José, Gur, Raquel E., Kaczkurkin, Antonia N., Larsen, Bart, Satterthwaite, Theodore D., Filippi, Courtney A., Gold, Andrea L., Harrewijn, Anita, Zugman, André, Bülow, Robin, Grabe, Hans J., Völzke, Henry, Wittfeld, Katharina, Böhnlein, Joscha, Dohm, Katharina, Kugel, Harald, Schrammen, Elisabeth, Zwanzger, Peter, Leehr, Elisabeth J., Sindermann, Lisa, Ball, Tali M., Fonzo, Gregory A., Paulus, Martin P., Simmons, Alan, Stein, Murray B., Klumpp, Heide, Phan, K. Luan, Furmark, Tomas, Månsson, Kristoffer N. T., Manzouri, Amirhossein, Avery, Suzanne N., Blackford, Jennifer Urbano, Clauss, Jacqueline A., Feola, Brandee, Harper, Jennifer C., Sylvester, Chad M., Lueken, Ulrike, Veltman, Dick J., Winkler, Anderson M., Jahanshad, Neda, Pine, Daniel S., Thompson, Paul M., Stein, Dan J., and Van der Wee, Nic J. A.

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 < 0.001; right: d = −0.158, pFWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, pFWE = 0.006; right: d = 0.099, pFWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood.

Published
2023

36. 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, Harrison, Ben J., Jansen, Andreas, Kircher, Tilo, Meyer, Bernhard, Nenadic, Igor, Olbrich, Sebastian, Paul, Elisabeth, Pezawas, Lukas, Sacchet, Matthew D., Saemann, Philipp, Wagner, Gerd, Walter, Henrik, Walter, Martin, PsyMRI, Guido, van Wingen, Guido, 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, Harrison, Ben J., Jansen, Andreas, Kircher, Tilo, Meyer, Bernhard, Nenadic, Igor, Olbrich, Sebastian, Paul, Elisabeth, Pezawas, Lukas, Sacchet, Matthew D., Saemann, Philipp, Wagner, Gerd, Walter, Henrik, Walter, Martin, PsyMRI, Guido, and van Wingen, Guido

Abstract

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies., Funding Agencies|Netherlands Organization for Scientific Research (NWO) [628.011.023]; Philips Research; ZonMW (Vidi) [016.156.318]; Region Ostergoetland; Phyllis and Jerome Lyle Rappaport Foundation; BIAL Foundation; Brain and Behavior Research Foundation; Center for Depression, Anxiety, and Stress Research at McLean Hospital; Ad Astra Chandaria Foundation [DA1151/5-2, SFB-TRR58, Dan3/012/17]; German Research Foundation (DFG); Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [1024570]; European Commission [KLI 597-827]; Australian National Health and Medical Research Council of Australia (NHMRC) [F3514-B1, GR 4510/2-1]; Austrian Science Fund (FWF) [WA1539/4-1]; Science Foundation Ireland (SFI); [FOR2107 DA1151/5-1]; [H2020-634541]; [1064643]; [KLI-148-B00]

Published
2023
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37. Volume of subcortical brain regions in social anxiety disorder : mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group

Author

Groenewold, Nynke A., Bas-Hoogendam, Janna Marie, Amod, Alyssa R., Laansma, Max A., Van Velzen, Laura S., Aghajani, Moji, Hilbert, Kevin, Oh, Hyuntaek, Salas, Ramiro, Jackowski, Andrea P., Pan, Pedro M., Salum, Giovanni A., Blair, James R., Blair, Karina S., Hirsch, Joy, Pantazatos, Spiro P., Schneier, Franklin R., Talati, Ardesheer, Roelofs, Karin, Volman, Inge, Blanco-Hinojo, Laura, Cardoner, Narcís, Pujol, Jesus, Beesdo-Baum, Katja, Ching, Christopher R. K., Thomopoulos, Sophia I., 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 C., Wu, Mon-Ju, Bayram, Ali, Canli, Mesut, Tükel, Raşit, Westenberg, P. Michiel, Heeren, Alexandre, Cremers, Henk R., Hofmann, David, Straube, Thomas, Doruyter, Alexander G. G., Lochner, Christine, Peterburs, Jutta, Van Tol, Marie-José, Gur, Raquel E., Kaczkurkin, Antonia N., Larsen, Bart, Satterthwaite, Theodore D., Filippi, Courtney A., Gold, Andrea L., Harrewijn, Anita, Zugman, André, Bülow, Robin, Grabe, Hans J., Völzke, Henry, Wittfeld, Katharina, Böhnlein, Joscha, Dohm, Katharina, Kugel, Harald, Schrammen, Elisabeth, Zwanzger, Peter, Leehr, Elisabeth J., Sindermann, Lisa, Ball, Tali M., Fonzo, Gregory A., Paulus, Martin P., Simmons, Alan, Stein, Murray B., Klumpp, Heide, Phan, K. Luan, Furmark, Tomas, Månsson, Kristoffer N. T., Manzouri, Amirhossein, Avery, Suzanne N., Blackford, Jennifer Urbano, Clauss, Jacqueline A., Feola, Brandee, Harper, Jennifer C., Sylvester, Chad M., Lueken, Ulrike, Veltman, Dick J., Winkler, Anderson M., Jahanshad, Neda, Pine, Daniel S., Thompson, Paul M., Stein, Dan J., Van der Wee, Nic J. A., Groenewold, Nynke A., Bas-Hoogendam, Janna Marie, Amod, Alyssa R., Laansma, Max A., Van Velzen, Laura S., Aghajani, Moji, Hilbert, Kevin, Oh, Hyuntaek, Salas, Ramiro, Jackowski, Andrea P., Pan, Pedro M., Salum, Giovanni A., Blair, James R., Blair, Karina S., Hirsch, Joy, Pantazatos, Spiro P., Schneier, Franklin R., Talati, Ardesheer, Roelofs, Karin, Volman, Inge, Blanco-Hinojo, Laura, Cardoner, Narcís, Pujol, Jesus, Beesdo-Baum, Katja, Ching, Christopher R. K., Thomopoulos, Sophia I., 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 C., Wu, Mon-Ju, Bayram, Ali, Canli, Mesut, Tükel, Raşit, Westenberg, P. Michiel, Heeren, Alexandre, Cremers, Henk R., Hofmann, David, Straube, Thomas, Doruyter, Alexander G. G., Lochner, Christine, Peterburs, Jutta, Van Tol, Marie-José, Gur, Raquel E., Kaczkurkin, Antonia N., Larsen, Bart, Satterthwaite, Theodore D., Filippi, Courtney A., Gold, Andrea L., Harrewijn, Anita, Zugman, André, Bülow, Robin, Grabe, Hans J., Völzke, Henry, Wittfeld, Katharina, Böhnlein, Joscha, Dohm, Katharina, Kugel, Harald, Schrammen, Elisabeth, Zwanzger, Peter, Leehr, Elisabeth J., Sindermann, Lisa, Ball, Tali M., Fonzo, Gregory A., Paulus, Martin P., Simmons, Alan, Stein, Murray B., Klumpp, Heide, Phan, K. Luan, Furmark, Tomas, Månsson, Kristoffer N. T., Manzouri, Amirhossein, Avery, Suzanne N., Blackford, Jennifer Urbano, Clauss, Jacqueline A., Feola, Brandee, Harper, Jennifer C., Sylvester, Chad M., Lueken, Ulrike, Veltman, Dick J., Winkler, Anderson M., Jahanshad, Neda, Pine, Daniel S., Thompson, Paul M., Stein, Dan J., and Van der Wee, Nic J. A.

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 < 0.001; right: d = −0.158, pFWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, pFWE = 0.006; right: d = 0.099, pFWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adul

Published
2023
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38. Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

Author

Repple, Jonathan, Gruber, Marius, Mauritz, Marco, de Lange, Siemon C., Winter, Nils Ralf, Opel, Nils, Goltermann, Janik, Meinert, Susanne, Grotegerd, Dominik, Leehr, Elisabeth J., Enneking, Verena, Borgers, Tiana, Klug, Melissa, Lemke, Hannah, Waltemate, Lena, Thiel, Katharina, Winter, Alexandra, Breuer, Fabian, Grumbach, Pascal, Hofmann, Hannes, Stein, Frederike, Brosch, Katharina, Ringwald, Kai G., Pfarr, Julia, Thomas-Odenthal, Florian, Meller, Tina, Jansen, Andreas, Nenadic, Igor, Redlich, Ronny, Bauer, Jochen, Kircher, Tilo, Hahn, Tim, van den Heuvel, Martijn, Dannlowski, Udo, Repple, Jonathan, Gruber, Marius, Mauritz, Marco, de Lange, Siemon C., Winter, Nils Ralf, Opel, Nils, Goltermann, Janik, Meinert, Susanne, Grotegerd, Dominik, Leehr, Elisabeth J., Enneking, Verena, Borgers, Tiana, Klug, Melissa, Lemke, Hannah, Waltemate, Lena, Thiel, Katharina, Winter, Alexandra, Breuer, Fabian, Grumbach, Pascal, Hofmann, Hannes, Stein, Frederike, Brosch, Katharina, Ringwald, Kai G., Pfarr, Julia, Thomas-Odenthal, Florian, Meller, Tina, Jansen, Andreas, Nenadic, Igor, Redlich, Ronny, Bauer, Jochen, Kircher, Tilo, Hahn, Tim, van den Heuvel, Martijn, and Dannlowski, Udo

Abstract

BACKGROUND: Altered brain structural connectivity has been implicated in the pathophysiology of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). However, it is unknown which part of these connectivity abnormalities are disorder specific and which are shared across the spectrum of psychotic and affective disorders. We investigated common and distinct brain connectivity alterations in a large sample (N = 1743) of patients with SZ, BD, or MDD and healthy control (HC) subjects.METHODS: This study examined diffusion-weighted imaging-based structural connectome topology in 720 patients with MDD, 112 patients with BD, 69 patients with SZ, and 842 HC subjects (mean age of all subjects: 35.7 years). Graph theory-based network analysis was used to investigate connectome organization. Machine learning algorithms were trained to classify groups based on their structural connectivity matrices.RESULTS: Groups differed significantly in the network metrics global efficiency, clustering, present edges, and global connectivity strength with a converging pattern of alterations between diagnoses (e.g., efficiency: HC > MDD > BD > SZ, false discovery rate-corrected p = .028). Subnetwork analysis revealed a common core of edges that were affected across all 3 disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders but could discriminate each diagnosis from HC. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis.CONCLUSIONS: We found shared and specific signatures of structural white matter dysconnectivity in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results showed a compromised brain communication across a spectrum of major psychiatric disorders.

Published
2023
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39. Bridging big data: procedures for combining non-equivalent cognitive measures from the ENIGMA Consortium

Author

ENIGMA Consortium, Kennedy, Eamonn, Vadlamani, Shashank, Lindsey, Hannah M., Lei, Pui-Wa, Jo-Pugh, Mary, Adamson, Maheen, Alda, Martin, Alonso-Lana, Silvia, Ambrogi, Sonia, Anderson, Tim J., Arango, Celso, Asarnow, Robert F., Avram, Mihai, Ayesa-Arriola, Rosa, Babikian, Talin, Banaj, Nerisa, Bird, Laura J., Borgwardt, Stefan, Brodtmann, Amy, Brosch, Katharina, Caeyenberghs, Karen, Calhoun, Vince D., Chiaravalloti, Nancy D., Cifu, David X., Crespo-Facorro, Benedicto, Dalrymple-Alford, John C., Dams-O’Connor, Kristen, Dannlowski, Udo, Darby, David, Davenport, Nicholas, DeLuca, John, Díaz-Caneja, Covadonga M., Disner, Seth G., Dobryakova, Ekaterina, Ehrlich, Stefan, Esopenko, Carrie, Ferrarelli, Fabio, Frank, Lea E., Franz, Carol, Fuentes-Claramonte, Paola, Genova, Helen, Giza, Christopher C., Goltermann, Janik, Grotegerd, Dominik, Gruber, Marius, Gutiérrez-Zotes, Alfonso, Ha, Minji, Haavik, Jan, Hinkin, Charles, Hoskinson, Kristen R., Hubl, Daniela, Irimia, Andrei, Jansen, Andreas, Kaess, Michael, Kang, Xiaojian, Kenney, Kimbra, Keřková, Barbora, Khlif, Mohamed Salah, Kim, Minah, Kindler, Jochen, Kircher, Tilo, Knı́žková, Karolina, Kolskår, Knut K., Krch, Denise, Kremen, William S., Kuhn, Taylor, Kumari, Veena, Kwon, Jun Soo, Langella, Roberto, Laskowitz, Sarah, Lee, Jungha, Lengenfelder, Jean, Liebel, Spencer W., Liou-Johnson, Victoria, Lippa, Sara M., Løvstad, Marianne, Lundervold, Astri, Marotta, Cassandra, Marquardt, Craig A., Mattos, Paulo, Mayeli, Ahmad, McDonald, Carrie R., Meinert, Susanne, Melzer, Tracy R., Merchán Naranjo, Jessica, Michel, Chantal, Morey, Rajendra A., Mwangi, Benson, Myall, Daniel J., Nenadić, Igor, Newsome, Mary R., Nunes, Abraham, O’Brien, Terence J., Oertel, Viola, Ollinger, John, Olsen, Alexander, Ortiz Garcı́a de la Foz, Victor, Ozmen, Mustafa, Pardoe, Heath, Parent, Marise, Piras, Fabrizio, Piras, Federica, Pomarol-Clotet, Edith, Repple, Jonathan, Richard, Geneviève, Rodriguez, Jonathan, Rodriguez, Mabel, Rootes-Murdy, Kelly, Rowland, Jared, Ryan, Nicholas P., Salvador, Raymond, Sanders, Anne-Marthe, Schmidt, André, Soares, Jair C., Spalleta, Gianfranco, Španiel, Filip, Stasenko, Alena, Stein, Frederike, Straube, Benjamin, Thames, April, Thomas-Odenthal, Florian, Thomopoulos, Sophia I., Tone, Erin, Torres, Ivan, Troyanskaya, Maya, Turner, Jessica A., Ulrichsen, Kristine M., Umpierrez, Guillermo, Vilella, Elisabet, Vivash, Lucy, Walker, William C., Werden, Emilio, Westlye, Lars T., Wild, Krista, Wroblewski, Adrian, Wu, Mon-Ju, Wylie, Glenn R., Yatham, Lakshmi N., Zunta-Soares, Giovana B., Thompson, Paul M., Tate, David F., Hillary, Frank G., Dennis, Emily L., Wilde, Elisabeth A., ENIGMA Consortium, Kennedy, Eamonn, Vadlamani, Shashank, Lindsey, Hannah M., Lei, Pui-Wa, Jo-Pugh, Mary, Adamson, Maheen, Alda, Martin, Alonso-Lana, Silvia, Ambrogi, Sonia, Anderson, Tim J., Arango, Celso, Asarnow, Robert F., Avram, Mihai, Ayesa-Arriola, Rosa, Babikian, Talin, Banaj, Nerisa, Bird, Laura J., Borgwardt, Stefan, Brodtmann, Amy, Brosch, Katharina, Caeyenberghs, Karen, Calhoun, Vince D., Chiaravalloti, Nancy D., Cifu, David X., Crespo-Facorro, Benedicto, Dalrymple-Alford, John C., Dams-O’Connor, Kristen, Dannlowski, Udo, Darby, David, Davenport, Nicholas, DeLuca, John, Díaz-Caneja, Covadonga M., Disner, Seth G., Dobryakova, Ekaterina, Ehrlich, Stefan, Esopenko, Carrie, Ferrarelli, Fabio, Frank, Lea E., Franz, Carol, Fuentes-Claramonte, Paola, Genova, Helen, Giza, Christopher C., Goltermann, Janik, Grotegerd, Dominik, Gruber, Marius, Gutiérrez-Zotes, Alfonso, Ha, Minji, Haavik, Jan, Hinkin, Charles, Hoskinson, Kristen R., Hubl, Daniela, Irimia, Andrei, Jansen, Andreas, Kaess, Michael, Kang, Xiaojian, Kenney, Kimbra, Keřková, Barbora, Khlif, Mohamed Salah, Kim, Minah, Kindler, Jochen, Kircher, Tilo, Knı́žková, Karolina, Kolskår, Knut K., Krch, Denise, Kremen, William S., Kuhn, Taylor, Kumari, Veena, Kwon, Jun Soo, Langella, Roberto, Laskowitz, Sarah, Lee, Jungha, Lengenfelder, Jean, Liebel, Spencer W., Liou-Johnson, Victoria, Lippa, Sara M., Løvstad, Marianne, Lundervold, Astri, Marotta, Cassandra, Marquardt, Craig A., Mattos, Paulo, Mayeli, Ahmad, McDonald, Carrie R., Meinert, Susanne, Melzer, Tracy R., Merchán Naranjo, Jessica, Michel, Chantal, Morey, Rajendra A., Mwangi, Benson, Myall, Daniel J., Nenadić, Igor, Newsome, Mary R., Nunes, Abraham, O’Brien, Terence J., Oertel, Viola, Ollinger, John, Olsen, Alexander, Ortiz Garcı́a de la Foz, Victor, Ozmen, Mustafa, Pardoe, Heath, Parent, Marise, Piras, Fabrizio, Piras, Federica, Pomarol-Clotet, Edith, Repple, Jonathan, Richard, Geneviève, Rodriguez, Jonathan, Rodriguez, Mabel, Rootes-Murdy, Kelly, Rowland, Jared, Ryan, Nicholas P., Salvador, Raymond, Sanders, Anne-Marthe, Schmidt, André, Soares, Jair C., Spalleta, Gianfranco, Španiel, Filip, Stasenko, Alena, Stein, Frederike, Straube, Benjamin, Thames, April, Thomas-Odenthal, Florian, Thomopoulos, Sophia I., Tone, Erin, Torres, Ivan, Troyanskaya, Maya, Turner, Jessica A., Ulrichsen, Kristine M., Umpierrez, Guillermo, Vilella, Elisabet, Vivash, Lucy, Walker, William C., Werden, Emilio, Westlye, Lars T., Wild, Krista, Wroblewski, Adrian, Wu, Mon-Ju, Wylie, Glenn R., Yatham, Lakshmi N., Zunta-Soares, Giovana B., Thompson, Paul M., Tate, David F., Hillary, Frank G., Dennis, Emily L., and Wilde, Elisabeth A.

Abstract

Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.

Published
2023

40. A systematic evaluation of machine learning-based biomarkers for major depressive disorder across modalities

Author

Winter, Nils R., Blanke, Julian, Leenings, Ramona, Ernsting, Jan, Fisch, Lukas, Sarink, Kelvin, Barkhau, Carlotta, Thiel, Katharina, Flinkenflügel, Kira, Winter, Alexandra, Goltermann, Janik, Meinert, Susanne, Dohm, Katharina, Repple, Jonathan, Gruber, Marius, Leehr, Elisabeth Johanna, Opel, Nils, Grotegerd, Dominik, Redlich, Ronny, Nitsch, Robert, Bauer, Jochen, Heindel, Walter, Groß, Joachim, Andlauer, Till, Forstner, Andreas Josef, Nöthen, Markus Maria, Rietschel, Marcella, Hofmann, Stefan G., Pfarr, Julia-Katharina, Teutenberg, Lea, Usemann, Paula, Thomas-Odenthal, Florian, Wroblewski, Adrian, Brosch, Katharina, Stein, Frederike, Jansen, Andreas, Jamalabadi, Hamidreza, Alexander, Nina, Straube, Benjamin, Nenadić, Igor, Kircher, Tilo, Dannlowski, Udo, Hahn, Tim, Winter, Nils R., Blanke, Julian, Leenings, Ramona, Ernsting, Jan, Fisch, Lukas, Sarink, Kelvin, Barkhau, Carlotta, Thiel, Katharina, Flinkenflügel, Kira, Winter, Alexandra, Goltermann, Janik, Meinert, Susanne, Dohm, Katharina, Repple, Jonathan, Gruber, Marius, Leehr, Elisabeth Johanna, Opel, Nils, Grotegerd, Dominik, Redlich, Ronny, Nitsch, Robert, Bauer, Jochen, Heindel, Walter, Groß, Joachim, Andlauer, Till, Forstner, Andreas Josef, Nöthen, Markus Maria, Rietschel, Marcella, Hofmann, Stefan G., Pfarr, Julia-Katharina, Teutenberg, Lea, Usemann, Paula, Thomas-Odenthal, Florian, Wroblewski, Adrian, Brosch, Katharina, Stein, Frederike, Jansen, Andreas, Jamalabadi, Hamidreza, Alexander, Nina, Straube, Benjamin, Nenadić, Igor, Kircher, Tilo, Dannlowski, Udo, and Hahn, Tim

Abstract

Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy. Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression. Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification. Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch

Published
2023

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

Author

Schijven, Dick; https://orcid.org/0000-0001-5190-7241, Postema, Merel C; https://orcid.org/0000-0002-1536-7062, Fukunaga, Masaki; https://orcid.org/0000-0003-1010-2644, Matsumoto, Junya; https://orcid.org/0000-0003-4228-3208, Miura, Kenichiro, de Zwarte, Sonja M C, van Haren, Neeltje E M, Cahn, Wiepke, Hulshoff Pol, Hilleke E, Kahn, René S, Ayesa-Arriola, Rosa; https://orcid.org/0000-0003-0570-5352, de la Foz, Víctor Ortiz-García; https://orcid.org/0000-0002-0627-1827, Tordesillas-Gutierrez, Diana; https://orcid.org/0000-0003-1458-3932, Vázquez-Bourgon, Javier; https://orcid.org/0000-0002-5478-3376, Crespo-Facorro, Benedicto, Alnæs, Dag; https://orcid.org/0000-0001-7361-5418, Dahl, Andreas; https://orcid.org/0000-0002-2668-8371, Westlye, Lars T; https://orcid.org/0000-0001-8644-956X, Agartz, Ingrid, Andreassen, Ole A; https://orcid.org/0000-0002-4461-3568, Jönsson, Erik G, Kochunov, Peter, Bruggemann, Jason M, Catts, Stanley V, Michie, Patricia T, Mowry, Bryan J, Quidé, Yann, Rasser, Paul E; https://orcid.org/0000-0003-3482-721X, Schall, Ulrich; https://orcid.org/0000-0001-9041-4562, Scott, Rodney J; https://orcid.org/0000-0001-7724-3404, Carr, Vaughan J; https://orcid.org/0000-0002-8907-5804, Green, Melissa J; https://orcid.org/0000-0002-9361-4874, Henskens, Frans A; https://orcid.org/0000-0003-2358-5630, Loughland, Carmel M, Pantelis, Christos; https://orcid.org/0000-0002-9565-0238, Weickert, Cynthia Shannon, Weickert, Thomas W, de Haan, Lieuwe, Brosch, Katharina; https://orcid.org/0000-0002-0526-8095, Pfarr, Julia-Katharina, Ringwald, Kai G, Stein, Frederike, Jansen, Andreas, Kircher, Tilo T J, Nenadić, Igor, Krämer, Bernd; https://orcid.org/0000-0002-1145-9103, Gruber, Oliver; https://orcid.org/0000-0003-1841-7413, Satterthwaite, Theodore D; https://orcid.org/0000-0001-7072-9399, Bustillo, Juan; https://orcid.org/0000-0001-8730-8152, Mathalon, Daniel H, Preda, Adrian, Calhoun, Vince D; https://orcid.org/0000-0001-9058-0747, Ford, Judith M; https://orcid.org/0000-0002-6500-6548, Potkin, Steven G, Chen, Jingxu, Tan, Yunlong; https://orcid.org/0000-0002-3522-3912, Wang, Zhiren, Xiang, Hong, Fan, Fengmei, Bernardoni, Fabio, Ehrlich, Stefan; https://orcid.org/0000-0003-2132-4445, Fuentes-Claramonte, Paola; https://orcid.org/0000-0002-1428-7976, Garcia-Leon, Maria Angeles, Guerrero-Pedraza, Amalia, Salvador, Raymond; https://orcid.org/0000-0001-5557-1562, Sarró, Salvador; https://orcid.org/0000-0003-1835-2189, Pomarol-Clotet, Edith, Ciullo, Valentina; https://orcid.org/0000-0002-6870-6259, Piras, Fabrizio; https://orcid.org/0000-0003-3566-5494, Vecchio, Daniela, Banaj, Nerisa, Spalletta, Gianfranco; https://orcid.org/0000-0002-7432-4249, Michielse, Stijn, van Amelsvoort, Therese, Dickie, Erin W, Voineskos, Aristotle N; https://orcid.org/0000-0003-0156-0395, Sim, Kang; https://orcid.org/0000-0003-3209-9626, Ciufolini, Simone, Dazzan, Paola, Murray, Robin M; https://orcid.org/0000-0003-0829-0519, Kim, Woo-Sung, Chung, Young-Chul, Andreou, Christina, Schmidt, André; https://orcid.org/0000-0001-6055-8397, Borgwardt, Stefan; https://orcid.org/0000-0002-5792-3987, McIntosh, Andrew M, Whalley, Heather C, Lawrie, Stephen M; https://orcid.org/0000-0002-2444-5675, du Plessis, Stefan, Luckhoff, Hilmar K, Scheffler, Freda; https://orcid.org/0000-0002-8898-8599, Emsley, Robin, Grotegerd, Dominik, Lencer, Rebekka, Dannlowski, Udo; https://orcid.org/0000-0002-0623-3759, Edmond, Jesse T, Rootes-Murdy, Kelly, Stephen, Julia M, Mayer, Andrew R, Antonucci, Linda A, Fazio, Leonardo; https://orcid.org/0000-0003-4000-974X, Pergola, Giulio; https://orcid.org/0000-0002-9193-1841, Bertolino, Alessandro, Díaz-Caneja, Covadonga M; https://orcid.org/0000-0001-8538-3175, Janssen, Joost, Lois, Noemi G; https://orcid.org/0000-0002-6600-072X, Arango, Celso; https://orcid.org/0000-0003-3382-4754, Tomyshev, Alexander S, Lebedeva, Irina, Cervenka, Simon, Sellgren, Carl M, Georgiadis, Foivos, Kirschner, Matthias; https://orcid.org/0000-0002-9486-1439, Kaiser, Stefan, Hajek, Tomas, Skoch, Antonin; https://orcid.org/0000-0002-1739-3256, Spaniel, Filip, Kim, Minah; https://orcid.org/0000-0001-8668-0817, Kwak, Yoo Bin, Oh, Sanghoon; https://orcid.org/0000-0002-7394-5211, Kwon, Jun Soo; https://orcid.org/0000-0002-1060-1462, James, Anthony; https://orcid.org/0000-0002-2742-8328, Bakker, Geor, Knöchel, Christian, Stäblein, Michael, Oertel, Viola, Uhlmann, Anne; https://orcid.org/0000-0002-1753-7811, Howells, Fleur M, Stein, Dan J; https://orcid.org/0000-0001-7218-7810, Temmingh, Henk S; https://orcid.org/0000-0002-7688-0759, Diaz-Zuluaga, Ana M, Pineda-Zapata, Julian A, López-Jaramillo, Carlos, Homan, Stephanie; https://orcid.org/0000-0003-1449-7508, Ji, Ellen; https://orcid.org/0000-0003-1527-8868, Surbeck, Werner; https://orcid.org/0000-0002-4733-795X, Homan, Philipp; https://orcid.org/0000-0001-9034-148X, Fisher, Simon E; https://orcid.org/0000-0002-3132-1996, Franke, Barbara; https://orcid.org/0000-0003-4375-6572, Glahn, David C; https://orcid.org/0000-0002-4749-6977, Gur, Ruben C; https://orcid.org/0000-0002-9657-1996, Hashimoto, Ryota, Jahanshad, Neda, Luders, Eileen; https://orcid.org/0000-0001-5659-992X, Medland, Sarah E, Thompson, Paul M; https://orcid.org/0000-0002-4720-8867, Turner, Jessica A, van Erp, Theo G M, Francks, Clyde; https://orcid.org/0000-0002-9098-890X, Schijven, Dick; https://orcid.org/0000-0001-5190-7241, Postema, Merel C; https://orcid.org/0000-0002-1536-7062, Fukunaga, Masaki; https://orcid.org/0000-0003-1010-2644, Matsumoto, Junya; https://orcid.org/0000-0003-4228-3208, Miura, Kenichiro, de Zwarte, Sonja M C, van Haren, Neeltje E M, Cahn, Wiepke, Hulshoff Pol, Hilleke E, Kahn, René S, Ayesa-Arriola, Rosa; https://orcid.org/0000-0003-0570-5352, de la Foz, Víctor Ortiz-García; https://orcid.org/0000-0002-0627-1827, Tordesillas-Gutierrez, Diana; https://orcid.org/0000-0003-1458-3932, Vázquez-Bourgon, Javier; https://orcid.org/0000-0002-5478-3376, Crespo-Facorro, Benedicto, Alnæs, Dag; https://orcid.org/0000-0001-7361-5418, Dahl, Andreas; https://orcid.org/0000-0002-2668-8371, Westlye, Lars T; https://orcid.org/0000-0001-8644-956X, Agartz, Ingrid, Andreassen, Ole A; https://orcid.org/0000-0002-4461-3568, Jönsson, Erik G, Kochunov, Peter, Bruggemann, Jason M, Catts, Stanley V, Michie, Patricia T, Mowry, Bryan J, Quidé, Yann, Rasser, Paul E; https://orcid.org/0000-0003-3482-721X, Schall, Ulrich; https://orcid.org/0000-0001-9041-4562, Scott, Rodney J; https://orcid.org/0000-0001-7724-3404, Carr, Vaughan J; https://orcid.org/0000-0002-8907-5804, Green, Melissa J; https://orcid.org/0000-0002-9361-4874, Henskens, Frans A; https://orcid.org/0000-0003-2358-5630, Loughland, Carmel M, Pantelis, Christos; https://orcid.org/0000-0002-9565-0238, Weickert, Cynthia Shannon, Weickert, Thomas W, de Haan, Lieuwe, Brosch, Katharina; https://orcid.org/0000-0002-0526-8095, Pfarr, Julia-Katharina, Ringwald, Kai G, Stein, Frederike, Jansen, Andreas, Kircher, Tilo T J, Nenadić, Igor, Krämer, Bernd; https://orcid.org/0000-0002-1145-9103, Gruber, Oliver; https://orcid.org/0000-0003-1841-7413, Satterthwaite, Theodore D; https://orcid.org/0000-0001-7072-9399, Bustillo, Juan; https://orcid.org/0000-0001-8730-8152, Mathalon, Daniel H, Preda, Adrian, Calhoun, Vince D; https://orcid.org/0000-0001-9058-0747, Ford, Judith M; https://orcid.org/0000-0002-6500-6548, Potkin, Steven G, Chen, Jingxu, Tan, Yunlong; https://orcid.org/0000-0002-3522-3912, Wang, Zhiren, Xiang, Hong, Fan, Fengmei, Bernardoni, Fabio, Ehrlich, Stefan; https://orcid.org/0000-0003-2132-4445, Fuentes-Claramonte, Paola; https://orcid.org/0000-0002-1428-7976, Garcia-Leon, Maria Angeles, Guerrero-Pedraza, Amalia, Salvador, Raymond; https://orcid.org/0000-0001-5557-1562, Sarró, Salvador; https://orcid.org/0000-0003-1835-2189, Pomarol-Clotet, Edith, Ciullo, Valentina; https://orcid.org/0000-0002-6870-6259, Piras, Fabrizio; https://orcid.org/0000-0003-3566-5494, Vecchio, Daniela, Banaj, Nerisa, Spalletta, Gianfranco; https://orcid.org/0000-0002-7432-4249, Michielse, Stijn, van Amelsvoort, Therese, Dickie, Erin W, Voineskos, Aristotle N; https://orcid.org/0000-0003-0156-0395, Sim, Kang; https://orcid.org/0000-0003-3209-9626, Ciufolini, Simone, Dazzan, Paola, Murray, Robin M; https://orcid.org/0000-0003-0829-0519, Kim, Woo-Sung, Chung, Young-Chul, Andreou, Christina, Schmidt, André; https://orcid.org/0000-0001-6055-8397, Borgwardt, Stefan; https://orcid.org/0000-0002-5792-3987, McIntosh, Andrew M, Whalley, Heather C, Lawrie, Stephen M; https://orcid.org/0000-0002-2444-5675, du Plessis, Stefan, Luckhoff, Hilmar K, Scheffler, Freda; https://orcid.org/0000-0002-8898-8599, Emsley, Robin, Grotegerd, Dominik, Lencer, Rebekka, Dannlowski, Udo; https://orcid.org/0000-0002-0623-3759, Edmond, Jesse T, Rootes-Murdy, Kelly, Stephen, Julia M, Mayer, Andrew R, Antonucci, Linda A, Fazio, Leonardo; https://orcid.org/0000-0003-4000-974X, Pergola, Giulio; https://orcid.org/0000-0002-9193-1841, Bertolino, Alessandro, Díaz-Caneja, Covadonga M; https://orcid.org/0000-0001-8538-3175, Janssen, Joost, Lois, Noemi G; https://orcid.org/0000-0002-6600-072X, Arango, Celso; https://orcid.org/0000-0003-3382-4754, Tomyshev, Alexander S, Lebedeva, Irina, Cervenka, Simon, Sellgren, Carl M, Georgiadis, Foivos, Kirschner, Matthias; https://orcid.org/0000-0002-9486-1439, Kaiser, Stefan, Hajek, Tomas, Skoch, Antonin; https://orcid.org/0000-0002-1739-3256, Spaniel, Filip, Kim, Minah; https://orcid.org/0000-0001-8668-0817, Kwak, Yoo Bin, Oh, Sanghoon; https://orcid.org/0000-0002-7394-5211, Kwon, Jun Soo; https://orcid.org/0000-0002-1060-1462, James, Anthony; https://orcid.org/0000-0002-2742-8328, Bakker, Geor, Knöchel, Christian, Stäblein, Michael, Oertel, Viola, Uhlmann, Anne; https://orcid.org/0000-0002-1753-7811, Howells, Fleur M, Stein, Dan J; https://orcid.org/0000-0001-7218-7810, Temmingh, Henk S; https://orcid.org/0000-0002-7688-0759, Diaz-Zuluaga, Ana M, Pineda-Zapata, Julian A, López-Jaramillo, Carlos, Homan, Stephanie; https://orcid.org/0000-0003-1449-7508, Ji, Ellen; https://orcid.org/0000-0003-1527-8868, Surbeck, Werner; https://orcid.org/0000-0002-4733-795X, Homan, Philipp; https://orcid.org/0000-0001-9034-148X, Fisher, Simon E; https://orcid.org/0000-0002-3132-1996, Franke, Barbara; https://orcid.org/0000-0003-4375-6572, Glahn, David C; https://orcid.org/0000-0002-4749-6977, Gur, Ruben C; https://orcid.org/0000-0002-9657-1996, Hashimoto, Ryota, Jahanshad, Neda, Luders, Eileen; https://orcid.org/0000-0001-5659-992X, Medland, Sarah E, Thompson, Paul M; https://orcid.org/0000-0002-4720-8867, Turner, Jessica A, van Erp, Theo G M, and Francks, Clyde; https://orcid.org/0000-0002-9098-890X

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, using MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets in the ENIGMA consortium, 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 macro-structural 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

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

Author

Groenewold, Nynke A, Bas-Hoogendam, Janna Marie, Amod, Alyssa R, Laansma, Max A, Van Velzen, Laura S, Aghajani, Moji, Hilbert, Kevin, Oh, Hyuntaek, Salas, Ramiro, Jackowski, Andrea P, Pan, Pedro M, Salum, Giovanni A, Blair, James R, Blair, Karina S, Hirsch, Joy, Pantazatos, Spiro P, Schneier, Franklin R, Talati, Ardesheer, Roelofs, Karin, Volman, Inge, Blanco-Hinojo, Laura, Cardoner, Narcís, Pujol, Jesus, Beesdo-Baum, Katja, Ching, Christopher R K, Thomopoulos, Sophia I, 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 C, Wu, Mon-Ju, Bayram, Ali, Canli, Mesut, Tükel, Raşit, Westenberg, P Michiel, Heeren, Alexandre, Cremers, Henk R, Hofmann, David, Straube, Thomas, Doruyter, Alexander G G, Lochner, Christine, Peterburs, Jutta, Van Tol, Marie-José, Gur, Raquel E, Kaczkurkin, Antonia N, Larsen, Bart, Satterthwaite, Theodore D, Filippi, Courtney A, Gold, Andrea L, Harrewijn, Anita, Zugman, André, Bülow, Robin, Grabe, Hans J, Völzke, Henry, Wittfeld, Katharina, Böhnlein, Joscha, Dohm, Katharina, Kugel, Harald, Schrammen, Elisabeth, Zwanzger, Peter, Leehr, Elisabeth J, Sindermann, Lisa, Ball, Tali M, Fonzo, Gregory A, Paulus, Martin P, Simmons, Alan, Stein, Murray B, Klumpp, Heide, Phan, K Luan, Furmark, Tomas, Månsson, Kristoffer N T, Manzouri, Amirhossein, Avery, Suzanne N, Blackford, Jennifer Urbano, Clauss, Jacqueline A, Feola, Brandee, Harper, Jennifer C, Sylvester, Chad M, Lueken, Ulrike, Veltman, Dick J, Winkler, Anderson M, Jahanshad, Neda, Pine, Daniel S, Thompson, Paul M, Stein, Dan J, Van der Wee, Nic J A, Groenewold, Nynke A, Bas-Hoogendam, Janna Marie, Amod, Alyssa R, Laansma, Max A, Van Velzen, Laura S, Aghajani, Moji, Hilbert, Kevin, Oh, Hyuntaek, Salas, Ramiro, Jackowski, Andrea P, Pan, Pedro M, Salum, Giovanni A, Blair, James R, Blair, Karina S, Hirsch, Joy, Pantazatos, Spiro P, Schneier, Franklin R, Talati, Ardesheer, Roelofs, Karin, Volman, Inge, Blanco-Hinojo, Laura, Cardoner, Narcís, Pujol, Jesus, Beesdo-Baum, Katja, Ching, Christopher R K, Thomopoulos, Sophia I, 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 C, Wu, Mon-Ju, Bayram, Ali, Canli, Mesut, Tükel, Raşit, Westenberg, P Michiel, Heeren, Alexandre, Cremers, Henk R, Hofmann, David, Straube, Thomas, Doruyter, Alexander G G, Lochner, Christine, Peterburs, Jutta, Van Tol, Marie-José, Gur, Raquel E, Kaczkurkin, Antonia N, Larsen, Bart, Satterthwaite, Theodore D, Filippi, Courtney A, Gold, Andrea L, Harrewijn, Anita, Zugman, André, Bülow, Robin, Grabe, Hans J, Völzke, Henry, Wittfeld, Katharina, Böhnlein, Joscha, Dohm, Katharina, Kugel, Harald, Schrammen, Elisabeth, Zwanzger, Peter, Leehr, Elisabeth J, Sindermann, Lisa, Ball, Tali M, Fonzo, Gregory A, Paulus, Martin P, Simmons, Alan, Stein, Murray B, Klumpp, Heide, Phan, K Luan, Furmark, Tomas, Månsson, Kristoffer N T, Manzouri, Amirhossein, Avery, Suzanne N, Blackford, Jennifer Urbano, Clauss, Jacqueline A, Feola, Brandee, Harper, Jennifer C, Sylvester, Chad M, Lueken, Ulrike, Veltman, Dick J, Winkler, Anderson M, Jahanshad, Neda, Pine, Daniel S, Thompson, Paul M, Stein, Dan J, and Van der Wee, Nic J A

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, p FWE = 0.037; right: d = -0.104, p FWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, p FWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, p FWE < 0.001; right: d = -0.158, p FWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, p FWE = 0.006; right: d = 0.099, p FWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adol

Published
2023

43. Genetic variants associated with longitudinal changes in brain structure across the lifespan

Author

Brouwer, Rachel M, Klein, Marieke, Kremen, William S, Guimaraes, Joao P O F T, Homuth, Georg, Hottenga, Jouke-Jan, Knol, Maria J, Kwok, John B J, Le Hellard, Stephanie, Mather, Karen A, Milaneschi, Yuri, Morris, Derek W, Nöthen, Markus M., Panizzon, Matthew S, Papiol, Sergi, Rietschel, Marcella, Santoro, Marcos L, Steen, Vidar M, Stein, Jason L, Streit, Fabian, Tankard, Rick M, Teumer, Alexander, van 't Ent, Dennis, van der Meer, Dennis, Olde Loohuis, Loes M, van Eijk, Kristel R, Vassos, Evangelos, Vázquez-Bourgon, Javier, Witt, Stephanie H, Consortium, IMAGEN, Adams, Hieab H H, Agartz, Ingrid, Ames, David, Amunts, Katrin, Andreassen, Ole A, Whelan, Christopher D, Arango, Celso, Banaschewski, Tobias, Baune, Bernhard T, Belangero, Sintia I, Bokde, Arun L W, Boomsma, Dorret I, Bressan, Rodrigo A, Brodaty, Henry, Buitelaar, Jan K, Cahn, Wiepke, Aghajani, Moji, Caspers, Svenja, Cichon, Sven, Crespo-Facorro, Benedicto, Cox, Simon R, Dannlowski, Udo, Elvsåshagen, Torbjørn, Espeseth, Thomas, Falkai, Peter, Fisher, Simon E, Flor, Herta, Alloza, Clara, Fullerton, Janice M, Garavan, Hugh, Gowland, Penny A, Grabe, Hans, Hahn, Tim, Heinz, Andreas, Hillegers, Manon, Hoare, Jacqueline, Hoekstra, Pieter J, Ikram, Mohammad A, Alnæs, Dag, Jackowski, Andrea P, Jansen, Andreas, Jönsson, Erik G, Kahn, Rene S, Kircher, Tilo, Korgaonkar, Mayuresh S, Krug, Axel, Lemaitre, Herve, Malt, Ulrik F, Martinot, Jean-Luc, Artiges, Eric, McDonald, Colm, Mitchell, Philip B, Muetzel, Ryan L, Murray, Robin M, Nees, Frauke, Nenadić, Igor, Oosterlaan, Jaap, Ophoff, Roel A, Pan, Pedro M, Penninx, Brenda W J H, Ayesa-Arriola, Rosa, Poustka, Luise, Sachdev, Perminder S, Salum, Giovanni A, Schofield, Peter R, Schumann, Gunter, Shaw, Philip, Sim, Kang, Smolka, Michael N, Stein, Dan J, Trollor, Julian N, Barker, Gareth J, van den Berg, Leonard H, Veldink, Jan H, Walter, Henrik, Westlye, Lars T, Whelan, Robert, White, Tonya, Wright, Margaret J, Medland, Sarah E, Franke, Barbara, Thompson, Paul M, Grasby, Katrina L, Bastin, Mark E, Hulshoff Pol, Hilleke E, Brühl, Rüdiger, Papadopoulos Orfanos, Dimitri, Paus, Tomáš, Millenet, Sabina, Blok, Elisabet, Bøen, Erlend, Breukelaar, Isabella A, Bright, Joanna K, Buimer, Elizabeth E L, Bülow, Robin, Cannon, Dara M, Ciufolini, Simone, Crossley, Nicolas A, Schnack, Hugo G, Damatac, Christienne G, Dazzan, Paola, de Mol, Casper L, de Zwarte, Sonja M C, Desrivières, Sylvane, Díaz-Caneja, Covadonga M, Doan, Nhat Trung, Dohm, Katharina, Fröhner, Juliane H, Goltermann, Janik, Jahanshad, Neda, Grigis, Antoine, Grotegerd, Dominik, Han, Laura K M, Harris, Mathew, Hartman, Catharina A, Heany, Sarah J, Heindel, Walter, Heslenfeld, Dirk J., Hohmann, Sarah, Ittermann, Bernd, Teeuw, Jalmar, Jansen, Philip R, Janssen, Joost, Jia, Tianye, Jiang, Jiyang, Jockwitz, Christiane, Karali, Temmuz, Keeser, Daniel, Koevoets, Martijn G J C, Lenroot, Rhoshel K, Malchow, Berend, Thomopoulos, Sophia I, Mandl, René C W, 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, Sprooten, Emma, Redlich, Ronny, Marques, Tiago Reis, Repple, Jonathan, Roberts, Gloria, Roshchupkin, Gennady V, Setiaman, Nikita, Shumskaya, Elena, Stein, Frederike, Sudre, Gustavo, Takahashi, Shun, Franz, Carol E, Thalamuthu, Anbupalam, Tordesillas-Gutiérrez, Diana, van der Lugt, Aad, van Haren, Neeltje E M, Wardlaw, Joanna M, Wen, Wei, Westeneng, Henk-Jan, Wittfeld, Katharina, Zhu, Alyssa H, Zugman, Andre, Gogtay, Nitin, Armstrong, Nicola J, Bonfiglio, Gaia, Bralten, Janita, Dalvie, Shareefa, Davies, Gail, Di Forti, Marta, Ding, Linda, Donohoe, Gary, Forstner, Andreas J, Gonzalez-Peñas, Javier, Child and Adolescent Psychiatry / Psychology, Erasmus MC other, Anesthesiology, Epidemiology, Radiology & Nuclear Medicine, Clinical Genetics, Psychiatry, the IMAGEN Consortium, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Human genetics, Amsterdam Neuroscience - Complex Trait Genetics, Pediatric surgery, APH - Aging & Later Life, APH - Mental Health, APH - Digital Health, European Commission, European Research Council, Comunidad de Madrid, Instituto de Salud Carlos III, Fundación Mutua Madrileña, Fundación Alicia Koplowitz, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Clinical Cognitive Neuropsychiatry Research Program (CCNP), Psychiatry 2, RS: MHeNs - R2 - Mental Health, Psychiatrie & Neuropsychologie, Complex Trait Genetics, Cognitive Psychology, Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, APH - Methodology, Clinical Neuropsychology, IBBA, General Paediatrics, ARD - Amsterdam Reproduction and Development, and Paediatrics

Subjects
Neuroinformatics, EXPRESSION, Aging, SUSCEPTIBILITY LOCI, Longevity, SURFACE-AREA, INDIVIDUAL-DIFFERENCES, All institutes and research themes of the Radboud University Medical Center, SDG 3 - Good Health and Well-being, Aging/genetics, Humans, ddc:610, GENOME-WIDE ASSOCIATION, METAANALYSIS, RISK, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], General Neuroscience, 220 Statistical Imaging Neuroscience, Longevity/genetics, Brain, Magnetic Resonance Imaging, genetics [Aging], VOLUME, RELIABILITY, sense organs, CORTICAL THICKNESS, genetics [Longevity], Genome-Wide Association Study
Abstract

et al., 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., The ENIGMA-Plasticity Working Group is part of the ENIGMA World Aging Center, funded by NIA grants R56 AG058854 and R01 AG058854. The ENIGMA Consortium core funding was supported by NIH Consortium Grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centers of Excellence. 1000BRAINS: 1000BRAINS is a population-based cohort based on the Heinz-Nixdorf Recall Study and is supported, in part, by the German National Cohort. We thank the Heinz Nixdorf Foundation (Germany) for their generous support in terms of the Heinz Nixdorf Study. The authors are supported by the Initiative and Networking Fund of the Helmholtz Association (Svenja Caspers) and the European Union’s Horizon 2020 Research and Innovation Programme under grant agreements 785907 (Human Brain Project SGA2; Svenja Caspers, Sven Cichon and Katrin Amunts). This work was further supported by the German Federal Ministry of Education and Research (BMBF) through the Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders) under the auspices of the e:Med Program (grant 01ZX1314A; Sven Cichon) and by the Swiss National Science Foundation (SNSF, grant 156791; Sven Cichon). The authors acknowledge grants supporting their work from the European Union’s Horizon 2020 Research and Innovation Programme (H2020/2014–2020) under grant agreements 667302 (CoCA), 728018 (Eat2beNICE), 785907 (HBP SGA2) and 772376 (EScORIAL) and the Netherlands ALS Foundation.Additional support is received from the European Community’s Seventh Framework Programme (FP7/2007—2013) under grant agreements n° 602805 (Aggressotype), n° 603016 (MATRICS), n° 602450 (IMAGEMEND) and n° 278948 (TACTICS) and from the European Community’s Horizon 2020 Programme (H2020/2014—2020) under grant agreements n° 643051 (MiND) and n° 667302 (CoCA). FP7 Ideas: the European Research Council (ERC-230374 to D.B.). This work was supported by Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012 and PI19/024), co-financed by ERDF Funds from the European Commission, ‘A way of making Europe’, CIBERSAM; Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds; European Union Seventh Framework Program under grant agreements FP7- HEALTH-2013-2.2.1-2-603196 (Project PSYSCAN) and European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement 115916, Project PRISM and grant agreement 777394, Project AIMS-2-TRIALS), Fundación Familia Alonso, Fundación Alicia Koplowitz and Fundación Mutua Madrileña. The Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313). The FP7 project MATRICS (603016). The research leading to these results also received support from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement 278948 (TACTICS), 602805 (Aggressotype), 603016 (MATRICS) and 602450 (Imagemend) and the Innovation Medicine Initiative grants 115300 (EU-AIMS) and 777394 (AIMS-2-TRIALS).PAFIP: PAFIP was supported by the Instituto de Salud Carlos III (PI14/00639, PI14/00918 and PI17/01056) and Fundación Instituto de Investigación Marqués de Valdecilla (NCT0235832 and NCT025KG Jebsen Stiftelsen and H2020 CoMorMent (847776).34363). European Union Marie Curie Research Training Network (MRTN-CT-2006-035987).

Published
2022

44. Subcortical shape alterations in major depressive disorder

Author

Ho, Tiffany C, Gutman, Boris, Grotegerd, Dominik, Redlich, Ronny, Jansen, Andreas, Kircher, Tilo, Krug, Axel, Meinert, Susanne, Nenadic, Igor, Opel, Nils, Dinga, Richard, Veltman, Dick J, Pozzi, Elena, Schnell, Knut, Veer, Ilya, Psychiatry, Brain Research Department of, Walter, Henrik, Gotlib, Ian H, Sacchet, Matthew D, Aleman, André, Groenewold, Nynke A, Stein, Dan J, Li, Meng, Grabe, Hans J, Walter, Martin, Ching, Christopher R K, Jahanshad, Neda, Ragothaman, Anjanibhargavi, Isaev, Dmitry, Zavaliangos-Petropulu, Artemis, Thompson, Paul M, Sämann, Philipp G, Schmaal, Lianne, Hosten, Norbert, Wittfeld, Katharina, Völzke, Henry, Baune, Bernhard, Dannlowski, Udo, Förster, Katharina, Academic Medical Center, Ontwikkelingspsychologie (Psychologie, FMG), Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Neuroscience - Brain Imaging, Clinical Cognitive Neuropsychiatry Research Program (CCNP), and Clinical Neuropsychology

Subjects
pathology [Corpus Striatum], CORTEX, diagnostic imaging [Corpus Striatum], STRESS, hippocampus, nucleus accumbens, MODELS, shape analysis, Neuroimaging, diagnostic imaging [Depressive Disorder, Major], behavioral disciplines and activities, pathology [Thalamus], Thalamus, mental disorders, Humans, Multicenter Studies as Topic, HIPPOCAMPAL SUBFIELDS, Radiology, Nuclear Medicine and imaging, ddc:610, diagnostic imaging [Amygdala], BRAIN, NEURONS, Research Articles, METAANALYSIS, diagnostic imaging [Hippocampus], pathology [Depressive Disorder, Major], diagnostic imaging [Thalamus], Depressive Disorder, Major, Radiological and Ultrasound Technology, major depressive disorder (MDD), ABNORMALITIES, ENIGMA, BIPOLAR DISORDER, amygdala, Corpus Striatum, AMYGDALA VOLUME, pathology [Hippocampus], Neurology, nervous system, pathology [Amygdala], Neurology (clinical), Anatomy, Research Article
Abstract

Alterations in regional subcortical brain volumes have been investigated as part of the efforts of an international consortium, ENIGMA, to identify reliable neural correlates of major depressive disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work by precisely mapping localized MDD-related differences in subcortical regions using shape analysis. In this meta-analysis of subcortical shape from the ENIGMA-MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta-analysis. Relative to CTL, patients with adolescent-onset MDD (≤ 21 years) had lower thickness and surface area of the subiculum, cornu ammonis (CA) 1 of the hippocampus and basolateral amygdala (Cohen's d = −0.164 to −0.180). Relative to first-episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala (Cohen's d = −0.173 to −0.184). Our results suggest that previously reported MDD-associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.

Published
2022

45. Fachkräfteengpässe im verarbeitenden Gewerbe in NRW – in manchen Berufsfeldern wird es nicht erst perspektivisch knapp

Author

Jansen, Andreas

Subjects
ddc:300, Fakultät für Gesellschaftswissenschaften » Institut Arbeit und Qualifikation (IAQ)
Abstract

Noch zeigt sich in der Mehrheit der untersuchten Berufsbereiche in NRW zumindest kein flächendeckender Fachkräfteengpass. Flächendeckende Engpässe lassen sich vor allem für Handwerksberufe feststellen. Besonders dramatisch ist die Situation im Berufsbereich Sanitär-, Heizungs- und Klimatechnik sowie in der Zimmerei. Strukturstarke städtische Agglomerationsräume sind in geringerem Maße von Fachkräfteengpässen betroffen als große, stark ländlich geprägte Flächenkreise. Neben der Zuwanderung sollte die Ausschöpfung des heimischen Fachkräftepotenzials stärker in den Blick genommen werden., IAQ-Report, vol. 2023, no. 04

Published
2023
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47. Structural brain alterations associated with suicidal thoughts and behaviors in young people: results from 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortium

Author

van Velzen, Laura, Dauvermann, Maria, Colic, Lejla, Villa, Luca, Savage, Hannah, Toenders, Yara, Zhu, Alyssa, Bright, Joanna, Campos, Adrián, Salminen, Lauren, Ambrogi, Sonia, Ayesa-Arriola, Rosa, Banaj, Nerisa, Başgöze, Zeynep, Bauer, Jochen, Blair, Karina, Blair, Robert James, Brosch, Katharina, Cheng, Yuqi, Colle, Romain, Connolly, Colm, Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Crespo-Facorro, Benedicto, Cullen, Kathryn, Dannlowski, Udo, Davey, Christopher, Dohm, Katharina, Fullerton, Janice, Gonul, Ali Saffet, Gotlib, Ian, Grotegerd, Dominik, Hahn, Tim, Harrison, Ben, He, Mengxin, Hickie, Ian, Ho, Tiffany, Iorfino, Frank, Jansen, Andreas, Jollant, Fabrice, Kircher, Tilo, Klimes-Dougan, Bonnie, Klug, Melissa, Leehr, Elisabeth, Lippard, Elizabeth, Mclaughlin, Katie, Meinert, Susanne, Miller, Adam Bryant, Mitchell, Philip, Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Overs, Bronwyn, Pfarr, Julia-Katharina, Piras, Fabrizio, Ringwald, Kai, Roberts, Gloria, Romer, Georg, Sanches, Marsal, Sheridan, Margaret, Soares, Jair, Spalletta, Gianfranco, Stein, Frederike, Teresi, Giana, Tordesillas-Gutiérrez, Diana, Uyar-Demir, Aslihan, van der Wee, Nic, van der Werff, Steven, Vermeiren, Robert, Winter, Alexandra, Wu, Mon-Ju, Yang, Tony, Thompson, Paul, Rentería, Miguel, Jahanshad, Neda, Blumberg, Hilary, van Harmelen, Anne-Laura, Schmaal, Lianne, Instituto de Investigación Marqués de Valdecilla, German Research Foundation, National Institutes of Health (US), European Commission, van Velzen, Laura S [0000-0002-6003-5227], Dauvermann, Maria R [0000-0002-2873-8512], Villa, Luca M [0000-0001-5091-6410], Toenders, Yara J [0000-0002-4117-1143], Campos, Adrián I [0000-0003-3468-8619], Ambrogi, Sonia [0000-0002-2897-358X], Ayesa-Arriola, Rosa [0000-0003-0570-5352], Başgöze, Zeynep [0000-0002-0306-4849], Bauer, Jochen [0000-0001-9345-2126], Brosch, Katharina [0000-0002-0526-8095], Connolly, Colm G [0000-0002-4051-3682], Cullen, Kathryn R [0000-0001-9631-3770], Dannlowski, Udo [0000-0001-7117-2908], Davey, Christopher G [0000-0003-1431-3852], Fullerton, Janice M [0000-0003-4014-4490], Gonul, Ali Saffet [0000-0003-3522-1359], Hahn, Tim [0000-0001-6541-3795], He, Mengxin [0000-0003-2714-3565], Hickie, Ian B [0000-0001-8832-9895], Ho, Tiffany C [0000-0002-4500-6364], Ojha, Amar [0000-0002-1038-0225], Overs, Bronwyn J [0000-0002-7138-5556], Piras, Fabrizio [0000-0003-3566-5494], Ringwald, Kai G [0000-0001-5886-6745], Sanches, Marsal [0000-0002-6993-6822], Soares, Jair C [0000-0002-5466-5628], Spalletta, Gianfranco [0000-0002-7432-4249], Tordesillas-Gutiérrez, Diana [0000-0003-1458-3932], Uyar-Demir, Aslihan [0000-0002-0856-4260], Winter, Alexandra [0000-0003-3759-1425], Wu, Mon-Ju [0000-0002-0540-8027], Rentería, Miguel E [0000-0003-4626-7248], Blumberg, Hilary P [0000-0002-6363-4698], Apollo - University of Cambridge Repository, Neurology, Developmental Neuroscience in Society, University of Melbourne, University of Cambridge [UK] (CAM), Yale School of Medicine [New Haven, Connecticut] (YSM), University of Southern California (USC), University of Queensland [Brisbane], Fondazione Santa Lucia [IRCCS], Clinical and Behavioral Neurology [IRCCS Santa Lucia], Universidad de Cantabria [Santander], University of Minnesota Medical School, University of Minnesota System, Westfälische Wilhelms-Universität Münster = University of Münster (WWU), Boys Town National Research Hospital [Omaha], Philipps Universität Marburg = Philipps University of Marburg, Kunming Medical University (KMU), Université Paris-Saclay, Florida State University [Tallahassee] (FSU), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institute for Molecular Bioscience, Universidad de Sevilla / University of Sevilla, University of New South Wales [Kensington], Ege University [Izmir], Stanford University, The University of Sydney, University of Texas at Austin [Austin], Harvard University, University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), University of Texas Health Science Center, The University of Texas Health Science Center at Houston (UTHealth), University of Pittsburgh Medical Center [Pittsburgh, PA, États-Unis] (UPMC), Neuroscience Research Australia (NeuRA), Hospital Universitario Marqués de Valdecilla [Santander], Leiden University Medical Center (LUMC), Universiteit Leiden, University of California [San Francisco] (UC San Francisco), University of California (UC), and Keck School of Medicine [Los Angeles]

Subjects
Cortical Thickness, History, Adolescent, Volume, Mood Disorders, [SCCO.NEUR]Cognitive science/Neuroscience, Brain, Suicide, Attempted, Neuroimaging, Adolescents, Reliability, Suicidal Ideation, Sample, Regions, Cellular and Molecular Neuroscience, Psychiatry and Mental health, SDG 3 - Good Health and Well-being, Bipolar, Adults, Humans, Molecular Biology, Gray
Abstract

Funder: MQ Brighter Futures Award MQBFC/2 and the U.S. National Institute of Mental Health under Award Number R01MH117601. National Suicide Prevention Research Fund, managed by Suicide Prevention Australia, Funder: MQ Brighter Futures Award MQBFC/2. Interdisziplinäres Zentrum für Klinische Forschung, UKJ, Funder: Italian Ministry of Health grant RC17-18-19-20-21/A, Funder: Instituto de Salud Carlos III through the projects PI14/00639, PI14/00918 and PI17/01056 (Co-funded by European Regional Development Fund/European Social Fund "Investing in your future") and Fundación Instituto de Investigación Marqués de Valdecilla (NCT0235832 and NCT02534363), Funder: National Institute of Mental Health (K23MH090421), the National Alliance for Research on Schizophrenia and Depression, the University of Minnesota Graduate School, the Minnesota Medical Foundation, and the Biotechnology Research Center (P41 RR008079 to the Center for Magnetic Resonance Research), University of Minnesota, and the Deborah E. Powell Center for Women’s Health Seed Grant, University of Minnesota, Funder: Medical Leader Foundation of Yunnan Province (L2019011) and Famous Doctors Project of Yunnan Province Plan (YNWR-MY-2018-041), Funder: CJ Martin Fellowship (NHMRC app 1161356). “Investissements d’avenir” ANR-10-IAIHU-06, Funder: German Research Foundation (DFG, grant FOR2107-DA1151/5-1 and DA1151/5-2 to UD, and DFG grants HA7070/2-2, HA7070/3, HA7070/4 to TH), Funder: Australian National Health and Medical Research Council of Australia (NHMRC) Project Grants 1024570 NHMRC Career Development Fellowships (1061757), Funder: Medical Faculty Münster, Innovative Medizinische Forschung (Grant IMF KO 1218 06), Funder: Australian National Medical and Health Research Council (Program Grant 1037196 and Investigator Grant 1177991 to PBM, Project Grant 1066177 to JMF), the Lansdowne Foundation, Good Talk and the Keith Pettigrew Family Bequest (PM) Janette Mary O’Neil Research Fellowship. IHG is supported in part by R37MH101495, Funder: Australian National Health and Medical Research Council of Australia (NHMRC) Project Grants 1064643 (principal investigator, BJH) NHMRC Career Development Fellowships (1124472), Funder: National Institute of Mental Health (K01MH106805). Klingenstein Third Generation Foundation, the National Institute of Mental Health (K01MH117442), the Stanford Maternal Child Health Research Institute, and the Stanford Center for Cognitive and Neurobiological Imaging. TCH receives partial support from the Ray and Dagmar Dolby Family Fund, Funder: German Research Foundation (DFG, grant FOR2107-JA 1890/7-1 and JA 1890/7-2 to AJ, and DFG, grant FOR2107-KI588/14-1 and FOR2107-KI588/14-2 to TK), Funder: NIAAA (K01AA027573, R21AA027884) and the American Foundation for Suicide Prevention, Funder: National Institute of Mental Health (R01-MH103291), Funder: National Center for Complementary and Integrative Health (NCCIH) R21AT009173 and R61AT009864 National Center for Advancing Translational Sciences (CTSI), National Institutes of Health, through UCSF-CTSI UL1TR001872 American Foundation for Suicide Prevention (AFSP) SRG-1-141-18 UCSF Research Evaluation and Allocation Committee (REAC) and J. Jacobson Fund to TTY; by the National Institute of Mental Health (NIMH) R01MH085734 and the Brain and Behavior Research Foundation (formerly NARSAD), Funder: MQ Brighter Futures Award MQBFC/2 R61MH111929RC1MH088366, R01MH070902, R01MH069747, American Foundation for Suicide Prevention, International Bipolar Foundation, Brain and Behavior Research Foundation, For the Love of Travis Foundation and Women’s Health Research at Yale, Funder: MQ Brighter Futures Award MQBFC/2 Social Safety and Resilience programme of Leiden University, Funder: MQ Brighter Futures Award MQBFC/2 National Institute of Mental Health under Award Number R01MH117601 NHMRC Career Development Fellowship (1140764), Identifying brain alterations associated with suicidal thoughts and behaviors (STBs) in young people is critical to understanding their development and improving early intervention and prevention. The ENIGMA Suicidal Thoughts and Behaviours (ENIGMA-STB) consortium analyzed neuroimaging data harmonized across sites to examine brain morphology associated with STBs in youth. We performed analyses in three separate stages, in samples ranging from most to least homogeneous in terms of suicide assessment instrument and mental disorder. First, in a sample of 577 young people with mood disorders, in which STBs were assessed with the Columbia Suicide Severity Rating Scale (C-SSRS). Second, in a sample of young people with mood disorders, in which STB were assessed using different instruments, MRI metrics were compared among healthy controls without STBs (HC; N = 519), clinical controls with a mood disorder but without STBs (CC; N = 246) and young people with current suicidal ideation (N = 223). In separate analyses, MRI metrics were compared among HCs (N = 253), CCs (N = 217), and suicide attempters (N = 64). Third, in a larger transdiagnostic sample with various assessment instruments (HC = 606; CC = 419; Ideation = 289; HC = 253; CC = 432; Attempt=91). In the homogeneous C-SSRS sample, surface area of the frontal pole was lower in young people with mood disorders and a history of actual suicide attempts (N = 163) than those without a lifetime suicide attempt (N = 323; FDR-p = 0.035, Cohen's d = 0.34). No associations with suicidal ideation were found. When examining more heterogeneous samples, we did not observe significant associations. Lower frontal pole surface area may represent a vulnerability for a (non-interrupted and non-aborted) suicide attempt; however, more research is needed to understand the nature of its relationship to suicide risk.

Published
2022

48. Diffusion magnetic resonance imaging connectome features are predictive of functional lateralization of semantic processing in the anterior temporal lobes

Author

Zahnert, Felix, primary, Kräling, Gunter, additional, Melms, Leander, additional, Belke, Marcus, additional, Kleinholdermann, Urs, additional, Timmermann, Lars, additional, Hirsch, Martin, additional, Jansen, Andreas, additional, Mross, Peter, additional, Menzler, Katja, additional, Habermehl, Lena, additional, and Knake, Susanne, additional

Published
2022
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49. Association between genetic risk for type 2 diabetes and structural brain connectivity in major depressive disorder

Author

Repple, Jonathan, König, Amelie, de Lange, Siemon C, Opel, Nils, Redlich, Ronny, Meinert, Susanne, Grotegerd, Dominik, Mauritz, Marco, Hahn, Tim, Borgers, Tiana, Leehr, Elisabeth J, Winter, Nils, Goltermann, Janik, Enneking, Verena, Fingas, Stella M, Lemke, Hannah, Waltemate, Lena, Dohm, Katharina, Richter, Maike, Mehler, David M A, Holstein, Vincent, Gruber, Marius, Nenadic, Igor, Krug, Axel, Brosch, Katharina, Schmitt, Simon, Stein, Frederike, Meller, Tina, Jansen, Andreas, Steinsträter, Olaf, Amare, Azmeraw T, Kircher, Tilo, Baune, Bernhard T, van den Heuvel, Martijn P, Dannlowski, Udo, Repple, Jonathan, König, Amelie, de Lange, Siemon C, Opel, Nils, Redlich, Ronny, Meinert, Susanne, Grotegerd, Dominik, Mauritz, Marco, Hahn, Tim, Borgers, Tiana, Leehr, Elisabeth J, Winter, Nils, Goltermann, Janik, Enneking, Verena, Fingas, Stella M, Lemke, Hannah, Waltemate, Lena, Dohm, Katharina, Richter, Maike, Mehler, David M A, Holstein, Vincent, Gruber, Marius, Nenadic, Igor, Krug, Axel, Brosch, Katharina, Schmitt, Simon, Stein, Frederike, Meller, Tina, Jansen, Andreas, Steinsträter, Olaf, Amare, Azmeraw T, Kircher, Tilo, Baune, Bernhard T, van den Heuvel, Martijn P, and Dannlowski, Udo

Abstract

BACKGROUND: Major depressive disorder (MDD) and type 2 diabetes (T2D) are known to share clinical comorbidity and to have genetic overlap. Besides their shared genetics, both diseases seem to be associated with alterations in brain structural connectivity and impaired cognitive performance, but little is known about the mechanisms by which genetic risk of T2D might affect brain structure and function and if so, how these effects could contribute to the disease course of MDD.METHODS: This study explores the association of polygenic risk for T2D with structural brain connectome topology and cognitive performance in 434 nondiabetic MDD patients and 539 healthy controls.RESULTS: Polygenic risk score for T2D across MDD patients and healthy controls was found to be associated with reduced global fractional anisotropy, a marker of white matter microstructure, an effect found to be predominantly present in MDD-related fronto-temporo-parietal connections. A mediation analysis further suggests that this FA variation may mediate the association between PGS and cognitive performance.CONCLUSIONS: Our findings provide preliminary evidence of a polygenic risk for T2D to be linked to brain structural connectivity and cognition in MDD patients and healthy controls, even in the absence of a direct T2D diagnosis. This suggests an effect of T2D genetic risk on white matter integrity, which may mediate an association of genetic risk for diabetes and cognitive impairments.

Published
2022
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50. Shared and Specific Patterns of Structural Brain Connectivity Across Affective and Psychotic Disorders

Author

Repple, Jonathan, Gruber, Marius, Mauritz, Marco, de Lange, Siemon C, Winter, Nils Ralf, Opel, Nils, Goltermann, Janik, Meinert, Susanne, Grotegerd, Dominik, Leehr, Elisabeth J, Enneking, Verena, Borgers, Tiana, Klug, Melissa, Lemke, Hannah, Waltemate, Lena, Thiel, Katharina, Winter, Alexandra, Breuer, Fabian, Grumbach, Pascal, Hofmann, Hannes, Stein, Frederike, Brosch, Katharina, Ringwald, Kai G, Pfarr, Julia, Thomas-Odenthal, Florian, Meller, Tina, Jansen, Andreas, Nenadic, Igor, Redlich, Ronny, Bauer, Jochen, Kircher, Tilo, Hahn, Tim, van den Heuvel, Martijn, Dannlowski, Udo, Repple, Jonathan, Gruber, Marius, Mauritz, Marco, de Lange, Siemon C, Winter, Nils Ralf, Opel, Nils, Goltermann, Janik, Meinert, Susanne, Grotegerd, Dominik, Leehr, Elisabeth J, Enneking, Verena, Borgers, Tiana, Klug, Melissa, Lemke, Hannah, Waltemate, Lena, Thiel, Katharina, Winter, Alexandra, Breuer, Fabian, Grumbach, Pascal, Hofmann, Hannes, Stein, Frederike, Brosch, Katharina, Ringwald, Kai G, Pfarr, Julia, Thomas-Odenthal, Florian, Meller, Tina, Jansen, Andreas, Nenadic, Igor, Redlich, Ronny, Bauer, Jochen, Kircher, Tilo, Hahn, Tim, van den Heuvel, Martijn, and Dannlowski, Udo

Abstract

BACKGROUND: Altered brain structural connectivity has been implicated in the pathophysiology of psychiatric disorders including schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). However, it is unknown which part of these connectivity abnormalities are disorder specific and which are shared across the spectrum of psychotic and affective disorders. We investigated common and distinct brain connectivity alterations in a large sample (N = 1743) of patients with SZ, BD, or MDD and healthy control (HC) subjects.METHODS: This study examined diffusion-weighted imaging-based structural connectome topology in 720 patients with MDD, 112 patients with BD, 69 patients with SZ, and 842 HC subjects (mean age of all subjects: 35.7 years). Graph theory-based network analysis was used to investigate connectome organization. Machine learning algorithms were trained to classify groups based on their structural connectivity matrices.RESULTS: Groups differed significantly in the network metrics global efficiency, clustering, present edges, and global connectivity strength with a converging pattern of alterations between diagnoses (e.g., efficiency: HC > MDD > BD > SZ, false discovery rate-corrected p = .028). Subnetwork analysis revealed a common core of edges that were affected across all 3 disorders, but also revealed differences between disorders. Machine learning algorithms could not discriminate between disorders but could discriminate each diagnosis from HC. Furthermore, dysconnectivity patterns were found most pronounced in patients with an early disease onset irrespective of diagnosis.CONCLUSIONS: We found shared and specific signatures of structural white matter dysconnectivity in SZ, BD, and MDD, leading to commonly reduced network efficiency. These results showed a compromised brain communication across a spectrum of major psychiatric disorders.

Published
2022

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