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

Author

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

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

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

Published
2024

3. 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

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

Author

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

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

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

Published
2024

5. 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

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

Author

Sharkey, Rachel J., Bacon, Chelsea, Peterson, Zeru, Rootes-Murdy, Kelly, Salvador, Raymond, Pomarol-Clotet, Edith, Karuk, Andriana, Homan, Philipp, Ji, Ellen, Omlor, Wolfgang, Homan, Stephanie, Georgiadis, Foivos, Kaiser, Stefan, Kirschner, Matthias, Ehrlich, Stefan, Dannlowski, Udo, Grotegerd, Dominik, Goltermann, Janik, Meinert, Susanne, Kircher, Tilo, Stein, Frederike, Brosch, Katharina, Krug, Axel, Nenadic, Igor, Sim, Kang, Spalletta, Gianfranco, Banaj, Nerisa, Sponheim, Scott R., Demro, Caroline, Ramsay, Ian S., King, Margaret, Quidé, Yann, Green, Melissa Jane, Nguyen, Dana, Preda, Adrian, Calhoun, Vince, Turner, Jessica, van Erp, Theo, and Nickl-Jockschat, Thomas

Published
2024
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7. The interplay between polygenic score for tumor necrosis factor-α, brain structural connectivity, and processing speed in major depression

Author

Flinkenflügel, Kira, Gruber, Marius, Meinert, Susanne, Thiel, Katharina, Winter, Alexandra, Goltermann, Janik, Usemann, Paula, Brosch, Katharina, Stein, Frederike, Thomas-Odenthal, Florian, Wroblewski, Adrian, Pfarr, Julia-Katharina, David, Friederike S., Beins, Eva C., Grotegerd, Dominik, Hahn, Tim, Leehr, Elisabeth J., Dohm, Katharina, Bauer, Jochen, Forstner, Andreas J., Nöthen, Markus M., Jamalabadi, Hamidreza, Straube, Benjamin, Alexander, Nina, Jansen, Andreas, Witt, Stephanie H., Rietschel, Marcella, Nenadić, Igor, van den Heuvel, Martijn P., Kircher, Tilo, Repple, Jonathan, and Dannlowski, Udo

Published
2024
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8. Neural foundation of the diathesis-stress model: longitudinal gray matter volume changes in response to stressful life events in major depressive disorder and healthy controls

Author

Thomas-Odenthal, Florian, Ringwald, Kai, Teutenberg, Lea, Stein, Frederike, Alexander, Nina, Bonnekoh, Linda M., Brosch, Katharina, Dohm, Katharina, Flinkenflügel, Kira, Grotegerd, Dominik, Hahn, Tim, Jansen, Andreas, Leehr, Elisabeth J., Meinert, Susanne, Pfarr, Julia-Katharina, Renz, Harald, Schürmeyer, Navid, Stief, Thomas, Straube, Benjamin, Thiel, Katharina, Usemann, Paula, Winter, Alexandra, Krug, Axel, Nenadić, Igor, Dannlowski, Udo, and Kircher, Tilo

Published
2024
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9. Structural brain abnormalities and aggressive behaviour in schizophrenia: Mega-analysis of data from 2095 patients and 2861 healthy controls via the ENIGMA consortium

Author

Lamsma, Jelle, Raine, Adrian, Kia, Seyed M, Cahn, Wiepke, Arold, Dominic, Banaj, Nerisa, Barone, Annarita, Brosch, Katharina, Brouwer, Rachel, Brunetti, Arturo, Calhoun, Vince D, Chew, Qian H, Choi, Sunah, Chung, Young-Chul, Ciccarelli, Mariateresa, Cobia, Derin, Cocozza, Sirio, Dannlowski, Udo, Dazzan, Paola, de Bartolomeis, Andrea, Di Forti, Marta, Dumais, Alexandre, Edmond, Jesse T, Ehrlich, Stefan, Evermann, Ulrika, Flinkenflügel, Kira, Georgiadis, Foivos, Glahn, David C, Goltermann, Janik, Green, Melissa J, Grotegerd, Dominik, Guerrero-Pedraza, Amalia, Ha, Minji, Hong, Elliot L, Pol, Hilleke Hulshoff, Iasevoli, Felice, Kaiser, Stefan, Kaleda, Vasily, Karuk, Andriana, Kim, Minah, Kircher, Tilo, Kirschner, Matthias, Kochunov, Peter, Kwon, Jun Soo, Lebedeva, Irina, Lencer, Rebekka, Marques, Tiago R, Meinert, Susanne, Murray, Robin, Nenadić, Igor, Nguyen, Dana, Pearlson, Godfrey, Piras, Fabrizio, Pomarol-Clotet, Edith, Pontillo, Giuseppe, Potvin, Stéphane, Preda, Adrian, Quidé, Yann, Rodrigue, Amanda, Rootes-Murdy, Kelly, Salvador, Raymond, Skoch, Antonin, Sim, Kang, Spalletta, Gianfranco, Spaniel, Filip, Stein, Frederike, Thomas-Odenthal, Florian, Tikàsz, Andràs, Tomecek, David, Tomyshev, Alexander, Tranfa, Mario, Tsogt, Uyanga, Turner, Jessica A, van Erp, Theo GM, van Haren, Neeltje EM, van Os, Jim, Vecchio, Daniela, Wang, Lei, Wroblewski, Adrian, and Nickl-Jockschat, Thomas

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Schizophrenia, Brain Disorders, Neurosciences, Biomedical Imaging, Serious Mental Illness, Mental Health, Mental Illness, Mental health
Abstract

BACKGROUND: Schizophrenia is associated with an increased risk of aggressive behaviour, which may partly be explained by illness-related changes in brain structure. However, previous studies have been limited by group-level analyses, small and selective samples of inpatients and long time lags between exposure and outcome. METHODS: This cross-sectional study pooled data from 20 sites participating in the international ENIGMA-Schizophrenia Working Group. Sites acquired T1-weighted and diffusion-weighted magnetic resonance imaging scans in a total of 2095 patients with schizophrenia and 2861 healthy controls. Measures of grey matter volume and white matter microstructural integrity were extracted from the scans using harmonised protocols. For each measure, normative modelling was used to calculate how much patients deviated (in z-scores) from healthy controls at the individual level. Ordinal regression models were used to estimate the associations of these deviations with concurrent aggressive behaviour (as odds ratios [ORs] with 99% confidence intervals [CIs]). Mediation analyses were performed for positive symptoms (i.e., delusions, hallucinations and disorganised thinking), impulse control and illness insight. Aggression and potential mediators were assessed with the Positive and Negative Syndrome Scale, Scale for the Assessment of Positive Symptoms or Brief Psychiatric Rating Scale. RESULTS: Aggressive behaviour was significantly associated with reductions in total cortical volume (OR [99% CI] = 0.88 [0.78, 0.98], p = .003) and global white matter integrity (OR [99% CI] = 0.72 [0.59, 0.88], p = 3.50 × 10-5) and additional reductions in dorsolateral prefrontal cortex volume (OR [99% CI] = 0.85 [0.74, 0.97], p =.002), inferior parietal lobule volume (OR [99% CI] = 0.76 [0.66, 0.87], p = 2.20 × 10-7) and internal capsule integrity (OR [99% CI] = 0.76 [0.63, 0.92], p = 2.90 × 10-4). Except for inferior parietal lobule volume, these associations were largely mediated by increased severity of positive symptoms and reduced impulse control. CONCLUSIONS: This study provides evidence that the co-occurrence of positive symptoms, poor impulse control and aggressive behaviour in schizophrenia has a neurobiological basis, which may inform the development of therapeutic interventions.

Published
2024

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

Author

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

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

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

Published
2023

11. Handedness in schizophrenia and affective disorders: a large-scale cross-disorder study

Author

Mundorf, Annakarina, Lischke, Alexander, Peterburs, Jutta, Alexander, Nina, Bonnekoh, Linda M., Brosch, Katharina, Flinkenflügel, Kira, Goltermann, Janik, Hahn, Tim, Jansen, Andreas, Meinert, Susanne, Nenadić, Igor, Schürmeyer, Navid Nico, Stein, Frederike, Straube, Benjamin, Thiel, Katharina, Teutenberg, Lea, Thomas-Odenthal, Florian, Usemann, Paula, Winter, Alexandra, Dannlowski, Udo, Kircher, Tilo, and Ocklenburg, Sebastian

Published
2024
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12. Beyond the Global Brain Differences: Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers.

Author

Boen, Rune, Kaufmann, Tobias, van der Meer, Dennis, Frei, Oleksandr, Agartz, Ingrid, Ames, David, Andersson, Micael, Armstrong, Nicola, Artiges, Eric, Atkins, Joshua, Bauer, Jochen, Benedetti, Francesco, Boomsma, Dorret, Brodaty, Henry, Brosch, Katharina, Buckner, Randy, Cairns, Murray, Calhoun, Vince, Caspers, Svenja, Cichon, Sven, Corvin, Aiden, Crespo-Facorro, Benedicto, Dannlowski, Udo, David, Friederike, de Geus, Eco, de Zubicaray, Greig, Desrivières, Sylvane, Doherty, Joanne, Donohoe, Gary, Ehrlich, Stefan, Eising, Else, Espeseth, Thomas, Fisher, Simon, Forstner, Andreas, Fortaner-Uyà, Lidia, Frouin, Vincent, Fukunaga, Masaki, Ge, Tian, Glahn, David, Goltermann, Janik, Grabe, Hans, Green, Melissa, Groenewold, Nynke, Grotegerd, Dominik, Grøntvedt, Gøril, Hahn, Tim, Hashimoto, Ryota, Hehir-Kwa, Jayne, Henskens, Frans, Holmes, Avram, Håberg, Asta, Haavik, Jan, Jacquemont, Sebastien, Jansen, Andreas, Jockwitz, Christiane, Jönsson, Erik, Kikuchi, Masataka, Kircher, Tilo, Kumar, Kuldeep, Le Hellard, Stephanie, Leu, Costin, Linden, David, Liu, Jingyu, Loughnan, Robert, Mather, Karen, McMahon, Katie, McRae, Allan, Medland, Sarah, Meinert, Susanne, Moreau, Clara, Morris, Derek, Mowry, Bryan, Mühleisen, Thomas, Nenadić, Igor, Nöthen, Markus, Nyberg, Lars, Ophoff, Roel, Owen, Michael, Pantelis, Christos, Paolini, Marco, Paus, Tomas, Pausova, Zdenka, Persson, Karin, Quidé, Yann, Marques, Tiago, Sachdev, Perminder, Sando, Sigrid, Schall, Ulrich, Scott, Rodney, Selbæk, Geir, Shumskaya, Elena, Silva, Ana, Sisodiya, Sanjay, Stein, Frederike, Stein, Dan, Straube, Benjamin, Streit, Fabian, Strike, Lachlan, Teumer, Alexander, and Teutenberg, Lea

Subjects
15q11.2 BP1-BP2, 1q21.1 distal, Brain structure, Copy number variants, Intraindividual variability, Magnetic resonance imaging, Humans, Chromosome Deletion, Brain, Magnetic Resonance Imaging, Abnormalities, Multiple, Chromosomes, Human, Pair 15, DNA Copy Number Variations
Abstract

BACKGROUND: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure. METHODS: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individuals regional difference and global difference, were used to test for regional differences that diverge from the global difference. RESULTS: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness. CONCLUSIONS: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment.

Published
2024

13. Association of polygenic score and the involvement of cholinergic and glutamatergic pathways with lithium treatment response in patients with bipolar disorder.

Author

Amare, Azmeraw, Thalamuthu, Anbupalam, Schubert, Klaus, Fullerton, Janice, Ahmed, Muktar, Hartmann, Simon, Papiol, Sergi, Heilbronner, Urs, Degenhardt, Franziska, Tekola-Ayele, Fasil, Hou, Liping, Hsu, Yi-Hsiang, Shekhtman, Tatyana, Adli, Mazda, Akula, Nirmala, Akiyama, Kazufumi, Ardau, Raffaella, Arias, Bárbara, Aubry, Jean-Michel, Hasler, Roland, Richard-Lepouriel, Hélène, Perroud, Nader, Backlund, Lena, Bhattacharjee, Abesh, Bellivier, Frank, Benabarre, Antonio, Bengesser, Susanne, Biernacka, Joanna, Birner, Armin, Marie-Claire, Cynthia, Cervantes, Pablo, Chen, Hsi-Chung, Chillotti, Caterina, Cichon, Sven, Cruceanu, Cristiana, Czerski, Piotr, Dalkner, Nina, Del Zompo, Maria, DePaulo, J, Étain, Bruno, Jamain, Stephane, Falkai, Peter, Forstner, Andreas, Frisen, Louise, Frye, Mark, Gard, Sébastien, Garnham, Julie, Goes, Fernando, Grigoroiu-Serbanescu, Maria, Fallgatter, Andreas, Stegmaier, Sophia, Ethofer, Thomas, Biere, Silvia, Petrova, Kristiyana, Schuster, Ceylan, Adorjan, Kristina, Budde, Monika, Heilbronner, Maria, Kalman, Janos, Kohshour, Mojtaba, Reich-Erkelenz, Daniela, Schaupp, Sabrina, Schulte, Eva, Senner, Fanny, Vogl, Thomas, Anghelescu, Ion-George, Arolt, Volker, Dannlowski, Udo, Dietrich, Detlef, Figge, Christian, Jäger, Markus, Lang, Fabian, Juckel, Georg, Konrad, Carsten, Reimer, Jens, Schmauß, Max, Schmitt, Andrea, Spitzer, Carsten, von Hagen, Martin, Wiltfang, Jens, Zimmermann, Jörg, Andlauer, Till, Fischer, Andre, Bermpohl, Felix, Ritter, Philipp, Matura, Silke, Gryaznova, Anna, Falkenberg, Irina, Yildiz, Cüneyt, Kircher, Tilo, Schmidt, Julia, Koch, Marius, Gade, Kathrin, Trost, Sarah, Haussleiter, Ida, Lambert, Martin, Rohenkohl, Anja, Kraft, Vivien, Grof, Paul, and Hashimoto, Ryota

Subjects
Bipolar Disorder, Humans, Female, Male, Multifactorial Inheritance, Adult, Middle Aged, Lithium, Treatment Outcome, Bayes Theorem, Genome-Wide Association Study, Glutamic Acid, Cohort Studies, Lithium Compounds, Acetylcholine, Polymorphism, Single Nucleotide, Antimanic Agents
Abstract

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental health disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithiums possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N = 2367) and replicated in the combined PsyCourse (N = 89) and BipoLife (N = 102) studies. The associations of Li+PGS and lithium treatment response - defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P

Published
2023

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

Author

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

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Machine Learning and Artificial Intelligence, Mental Illness, Clinical Research, Brain Disorders, Serious Mental Illness, Neurosciences, Schizophrenia, Mental Health, Behavioral and Social Science, Biomedical Imaging, Mental health, Good Health and Well Being, ENIGMA, artificial intelligence, brain gray matter, schizophrenia, structural MRI, subtype
Abstract

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Published
2023

15. Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN

Author

Slot, Margot I. E., Urquijo Castro, Maria F., Winter - van Rossum, Inge, van Hell, Hendrika H., Dwyer, Dominic, Dazzan, Paola, Maat, Arija, De Haan, Lieuwe, Crespo-Facorro, Benedicto, Glenthøj, Birte Y., Lawrie, Stephen M., McDonald, Colm, Gruber, Oliver, van Amelsvoort, Thérèse, Arango, Celso, Kircher, Tilo, Nelson, Barnaby, Galderisi, Silvana, Weiser, Mark, Sachs, Gabriele, Kirschner, Matthias, Fleischhacker, W. Wolfgang, McGuire, Philip, Koutsouleris, Nikolaos, and Kahn, René S.

Published
2024
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16. Study protocol TransTAM: Transdiagnostic research into emotional disorders and cognitive-behavioral therapy of the adaptive mind

Author

Hermann, Andrea, Benke, Christoph, Blecker, Carlo R., de Haas, Benjamin, He, Yifei, Hofmann, Stefan G., Iffland, Jona R., Jengert-Stahl, Johanna, Kircher, Tilo, Leinweber, Katrin, Linka, Marcel, Mulert, Christoph, Neudert, Marie K., Noll, Ann-Kathrin, Melzig, Christiane A., Rief, Winfried, Rothkopf, Constantin, Schäfer, Axel, Schmitter, Christina V., Schuster, Verena, Stark, Rudolf, Straube, Benjamin, Zimmer, Raphaela I., and Kirchner, Lukas

Published
2024
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18. 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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19. Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis

Author

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

Published
2024
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20. Factor analysis of lifetime psychopathology and its brain morphometric and genetic correlates in a transdiagnostic sample

Author

Krug, Axel, Stein, Frederike, David, Friederike S., Schmitt, Simon, Brosch, Katharina, Pfarr, Julia-Katharina, Ringwald, Kai G., Meller, Tina, Thomas-Odenthal, Florian, Meinert, Susanne, Thiel, Katharina, Winter, Alexandra, Waltemate, Lena, Lemke, Hannah, Grotegerd, Dominik, Opel, Nils, Repple, Jonathan, Hahn, Tim, Streit, Fabian, Witt, Stephanie H., Rietschel, Marcella, Andlauer, Till F. M., Nöthen, Markus M., Philipsen, Alexandra, Nenadić, Igor, Dannlowski, Udo, Kircher, Tilo, and Forstner, Andreas J.

Published
2024
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22. White and gray matter alterations in bipolar I and bipolar II disorder subtypes compared with healthy controls – exploring associations with disease course and polygenic risk

Author

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

Published
2024
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23. The neural signature of psychomotor disturbance in depression

Author

Wüthrich, Florian, Lefebvre, Stephanie, Mittal, Vijay A., Shankman, Stewart A., Alexander, Nina, Brosch, Katharina, Flinkenflügel, Kira, Goltermann, Janik, Grotegerd, Dominik, Hahn, Tim, Jamalabadi, Hamidreza, Jansen, Andreas, Leehr, Elisabeth J., Meinert, Susanne, Nenadić, Igor, Nitsch, Robert, Stein, Frederike, Straube, Benjamin, Teutenberg, Lea, Thiel, Katharina, Thomas-Odenthal, Florian, Usemann, Paula, Winter, Alexandra, Dannlowski, Udo, Kircher, Tilo, and Walther, Sebastian

Published
2024
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25. Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium.

Author

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

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

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

Published
2023

26. 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

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

Author

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

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

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

Published
2023

28. Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

Author

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

Published
2024
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29. Shared and distinct structural brain networks related to childhood maltreatment and social support: connectome-based predictive modeling

Author

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

Published
2023
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30. Obesity and brain structure in schizophrenia – ENIGMA study in 3021 individuals

Author

McWhinney, Sean R, Brosch, Katharina, Calhoun, Vince D, Crespo-Facorro, Benedicto, Crossley, Nicolas A, Dannlowski, Udo, Dickie, Erin, Dietze, Lorielle MF, Donohoe, Gary, Du Plessis, Stefan, Ehrlich, Stefan, Emsley, Robin, Furstova, Petra, Glahn, David C, Gonzalez- Valderrama, Alfonso, Grotegerd, Dominik, Holleran, Laurena, Kircher, Tilo TJ, Knytl, Pavel, Kolenic, Marian, Lencer, Rebekka, Nenadić, Igor, Opel, Nils, Pfarr, Julia-Katharina, Rodrigue, Amanda L, Rootes-Murdy, Kelly, Ross, Alex J, Sim, Kang, Škoch, Antonín, Spaniel, Filip, Stein, Frederike, Švancer, Patrik, Tordesillas-Gutiérrez, Diana, Undurraga, Juan, Vázquez-Bourgon, Javier, Voineskos, Aristotle, Walton, Esther, Weickert, Thomas W, Weickert, Cynthia Shannon, Thompson, Paul M, van Erp, Theo GM, Turner, Jessica A, and Hajek, Tomas

Subjects
Biomedical Imaging, Obesity, Neurosciences, Serious Mental Illness, Mental Health, Schizophrenia, Nutrition, Clinical Research, Brain Disorders, 2.1 Biological and endogenous factors, Aetiology, Mental health, Humans, Depressive Disorder, Major, Brain, Magnetic Resonance Imaging, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Schizophrenia is frequently associated with obesity, which is linked with neurostructural alterations. Yet, we do not understand how the brain correlates of obesity map onto the brain changes in schizophrenia. We obtained MRI-derived brain cortical and subcortical measures and body mass index (BMI) from 1260 individuals with schizophrenia and 1761 controls from 12 independent research sites within the ENIGMA-Schizophrenia Working Group. We jointly modeled the statistical effects of schizophrenia and BMI using mixed effects. BMI was additively associated with structure of many of the same brain regions as schizophrenia, but the cortical and subcortical alterations in schizophrenia were more widespread and pronounced. Both BMI and schizophrenia were primarily associated with changes in cortical thickness, with fewer correlates in surface area. While, BMI was negatively associated with cortical thickness, the significant associations between BMI and surface area or subcortical volumes were positive. Lastly, the brain correlates of obesity were replicated among large studies and closely resembled neurostructural changes in major depressive disorders. We confirmed widespread associations between BMI and brain structure in individuals with schizophrenia. People with both obesity and schizophrenia showed more pronounced brain alterations than people with only one of these conditions. Obesity appears to be a relevant factor which could account for heterogeneity of brain imaging findings and for differences in brain imaging outcomes among people with schizophrenia.

Published
2022

31. 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

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

Author

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

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

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

Published
2022

33. PsyCog: A computerised mini battery for assessing cognition in psychosis

Author

Gifford, George, Cullen, Alexis E., Vieira, Sandra, Searle, Anja, McCutcheon, Robert A., Modinos, Gemma, Stone, William S., Hird, Emily, Barnett, Jennifer, van Hell, Hendrika H., Catalan, Ana, Millgate, Edward, Taptiklis, Nick, Cormack, Francesca, Slot, Margot E., Dazzan, Paola, Maat, Arija, de Haan, Lieuwe, Facorro, Benedicto Crespo, Glenthøj, Birte, Lawrie, Stephen M., McDonald, Colm, Gruber, Oliver, van Amelsvoort, Thérèse, Arango, Celso, Kircher, Tilo, Nelson, Barnaby, Galderisi, Silvana, Bressan, Rodrigo A., Kwon, Jun Soo, Weiser, Mark, Mizrahi, Romina, Sachs, Gabriele, Kirschner, Matthias, Reichenberg, Abraham, Kahn, René, and McGuire, Philip

Published
2024
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34. 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

35. Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders

Author

Hilbert, Kevin, Böhnlein, Joscha, Meinke, Charlotte, Chavanne, Alice V., Langhammer, Till, Stumpe, Lara, Winter, Nils, Leenings, Ramona, Adolph, Dirk, Arolt, Volker, Bischoff, Sophie, Cwik, Jan C., Deckert, Jürgen, Domschke, Katharina, Fydrich, Thomas, Gathmann, Bettina, Hamm, Alfons O., Heinig, Ingmar, Herrmann, Martin J., Hollandt, Maike, Hoyer, Jürgen, Junghöfer, Markus, Kircher, Tilo, Koelkebeck, Katja, Lotze, Martin, Margraf, Jürgen, Mumm, Jennifer L.M., Neudeck, Peter, Pauli, Paul, Pittig, Andre, Plag, Jens, Richter, Jan, Ridderbusch, Isabelle C., Rief, Winfried, Schneider, Silvia, Schwarzmeier, Hanna, Seeger, Fabian R., Siminski, Niklas, Straube, Benjamin, Straube, Thomas, Ströhle, Andreas, Wittchen, Hans-Ulrich, Wroblewski, Adrian, Yang, Yunbo, Roesmann, Kati, Leehr, Elisabeth J., Dannlowski, Udo, and Lueken, Ulrike

Published
2024
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37. Exploring the complex interrelation between depressive symptoms, risk, and protective factors: A comprehensive network approach

Author

Iovoli, Flavio, Hall, Mila, Nenadic, Igor, Straube, Benjamin, Alexander, Nina, Jamalabadi, Hamidreza, Jansen, Andreas, Stein, Frederike, Brosch, Katharina, Thomas-Odenthal, Florian, Usemann, Paula, Teutenberg, Lea, Wroblewski, Adrian, Pfarr, Julia, Thiel, Katharina, Flinkenflügel, Kira, Meinert, Susanne, Grotegerd, Dominik, Hahn, Tim, Goltermann, Janik, Gruber, Marius, Repple, Jonathan, Enneking, Verena, Winter, Alexandra, Dannlowski, Udo, Kircher, Tilo, and Rubel, Julian A.

Published
2024
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41. 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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44. Depression, cardiometabolic disease, and their co-occurrence after childhood maltreatment: an individual participant data meta-analysis including over 200,000 participants

Author

Souama, Camille, Lamers, Femke, Milaneschi, Yuri, Vinkers, Christiaan H., Defina, Serena, Garvert, Linda, Stein, Frederike, Woofenden, Tom, Brosch, Katharina, Dannlowski, Udo, Galenkamp, Henrike, de Graaf, Ron, Jaddoe, Vincent W. V., Lok, Anja, van Rijn, Bas B., Völzke, Henry, Cecil, Charlotte A. M., Felix, Janine F., Grabe, Hans J., Kircher, Tilo, Lekadir, Karim, Have, Margreet ten, Walton, Esther, and Penninx, Brenda W. J. H.

Published
2023
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45. Cortical and subcortical neuroanatomical signatures of schizotypy in 3004 individuals assessed in a worldwide ENIGMA study

Author

Kirschner, Matthias, Hodzic-Santor, Benazir, Antoniades, Mathilde, Nenadic, Igor, Kircher, Tilo, Krug, Axel, Meller, Tina, Grotegerd, Dominik, Fornito, Alex, Arnatkeviciute, Aurina, Bellgrove, Mark A, Tiego, Jeggan, Dannlowski, Udo, Koch, Katharina, Hülsmann, Carina, Kugel, Harald, Enneking, Verena, Klug, Melissa, Leehr, Elisabeth J, Böhnlein, Joscha, Gruber, Marius, Mehler, David, DeRosse, Pamela, Moyett, Ashley, Baune, Bernhard T, Green, Melissa, Quidé, Yann, Pantelis, Christos, Chan, Raymond, Wang, Yi, Ettinger, Ulrich, Debbané, Martin, Derome, Melodie, Gaser, Christian, Besteher, Bianca, Diederen, Kelly, Spencer, Tom J, Fletcher, Paul, Rössler, Wulf, Smigielski, Lukasz, Kumari, Veena, Premkumar, Preethi, Park, Haeme RP, Wiebels, Kristina, Lemmers-Jansen, Imke, Gilleen, James, Allen, Paul, Kozhuharova, Petya, Marsman, Jan-Bernard, Lebedeva, Irina, Tomyshev, Alexander, Mukhorina, Anna, Kaiser, Stefan, Fett, Anne-Kathrin, Sommer, Iris, Schuite-Koops, Sanne, Paquola, Casey, Larivière, Sara, Bernhardt, Boris, Dagher, Alain, Grant, Phillip, van Erp, Theo GM, Turner, Jessica A, Thompson, Paul M, Aleman, André, and Modinos, Gemma

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Neurosciences, Serious Mental Illness, Schizophrenia, Clinical Research, Mental Health, Mental health, Good Health and Well Being, Bipolar Disorder, Female, Humans, Magnetic Resonance Imaging, Male, Psychotic Disorders, Schizotypal Personality Disorder, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Neuroanatomical abnormalities have been reported along a continuum from at-risk stages, including high schizotypy, to early and chronic psychosis. However, a comprehensive neuroanatomical mapping of schizotypy remains to be established. The authors conducted the first large-scale meta-analyses of cortical and subcortical morphometric patterns of schizotypy in healthy individuals, and compared these patterns with neuroanatomical abnormalities observed in major psychiatric disorders. The sample comprised 3004 unmedicated healthy individuals (12-68 years, 46.5% male) from 29 cohorts of the worldwide ENIGMA Schizotypy working group. Cortical and subcortical effect size maps with schizotypy scores were generated using standardized methods. Pattern similarities were assessed between the schizotypy-related cortical and subcortical maps and effect size maps from comparisons of schizophrenia (SZ), bipolar disorder (BD) and major depression (MDD) patients with controls. Thicker right medial orbitofrontal/ventromedial prefrontal cortex (mOFC/vmPFC) was associated with higher schizotypy scores (r = 0.067, pFDR = 0.02). The cortical thickness profile in schizotypy was positively correlated with cortical abnormalities in SZ (r = 0.285, pspin = 0.024), but not BD (r = 0.166, pspin = 0.205) or MDD (r = -0.274, pspin = 0.073). The schizotypy-related subcortical volume pattern was negatively correlated with subcortical abnormalities in SZ (rho = -0.690, pspin = 0.006), BD (rho = -0.672, pspin = 0.009), and MDD (rho = -0.692, pspin = 0.004). Comprehensive mapping of schizotypy-related brain morphometry in the general population revealed a significant relationship between higher schizotypy and thicker mOFC/vmPFC, in the absence of confounding effects due to antipsychotic medication or disease chronicity. The cortical pattern similarity between schizotypy and schizophrenia yields new insights into a dimensional neurobiological continuity across the extended psychosis phenotype.

Published
2022

46. A Network Control Theory Approach to Longitudinal Symptom Dynamics in Major Depressive Disorder

Author

Hahn, Tim, Jamalabadi, Hamidreza, Emden, Daniel, Goltermann, Janik, Ernsting, Jan, Winter, Nils R., Fisch, Lukas, Leenings, Ramona, Sarink, Kelvin, Holstein, Vincent, Gruber, Marius, Grotegerd, Dominik, Meinert, Susanne, Dohm, Katharina, Leehr, Elisabeth J., Richter, Maike, Sindermann, Lisa, Enneking, Verena, Lemke, Hannah, Witt, Stephanie, Rietschel, Marcella, Brosch, Katharina, Pfarr, Julia-Katharina, Meller, Tina, Ringwald, Kai Gustav, Schmitt, Simon, Stein, Frederike, Nenadic, Igor, Kircher, Tilo, Müller-Myhsok, Bertram, Andlauer, Till F. M., Repple, Jonathan, Dannlowski, Udo, and Opel, Nils

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

Background: The evolution of symptoms over time is at the heart of understanding and treating mental disorders. However, a principled, quantitative framework explaining symptom dynamics remains elusive. Here, we propose a Network Control Theory of Psychopathology allowing us to formally derive a theoretical control energy which we hypothesize quantifies resistance to future symptom improvement in Major Depressive Disorder (MDD). We test this hypothesis and investigate the relation to genetic and environmental risk as well as resilience. Methods: We modelled longitudinal symptom-network dynamics derived from N=2,059 Beck Depression Inventory measurements acquired over a median of 134 days in a sample of N=109 patients suffering from MDD. We quantified the theoretical energy required for each patient and time-point to reach a symptom-free state given individual symptom-network topology (E 0 ) and 1) tested if E 0 predicts future symptom improvement and 2) whether this relationship is moderated by Polygenic Risk Scores (PRS) of mental disorders, childhood maltreatment experience, and self-reported resilience. Outcomes: We show that E 0 indeed predicts symptom reduction at the next measurement and reveal that this coupling between E 0 and future symptom change increases with higher genetic risk and childhood maltreatment while it decreases with resilience. Interpretation: Our study provides a mechanistic framework capable of predicting future symptom improvement based on individual symptom-network topology and clarifies the role of genetic and environmental risk as well as resilience. Our control-theoretic framework makes testable, quantitative predictions for individual therapeutic response and provides a starting-point for the theory-driven design of personalized interventions. Funding: German Research Foundation and Interdisciplinary Centre for Clinical Research, M\"unster, Comment: 21 pages, 2 figures

Published
2021

47. Genetic, Individual, and Familial Risk Correlates of Brain Network Controllability in Major Depressive Disorder

Author

Hahn, Tim, Winter, Nils R., Ernsting, Jan, Gruber, Marius, Mauritz, Marco J., Fisch, Lukas, Leenings, Ramona, Sarink, Kelvin, Blanke, Julian, Holstein, Vincent, Emden, Daniel, Beisemann, Marie, Opel, Nils, Grotegerd, Dominik, Meinert, Susanne, Heindel, Walter, Witt, Stephanie, Rietschel, Marcella, Nöthen, Markus M., Forstner, Andreas J., Kircher, Tilo, Nenadic, Igor, Jansen, Andreas, Müller-Myhsok, Bertram, Andlauer, Till F. M., Walter, Martin, 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

48. An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling

Author

Hahn, Tim, Ernsting, Jan, Winter, Nils R., Holstein, Vincent, Leenings, Ramona, Beisemann, Marie, Fisch, Lukas, Sarink, Kelvin, Emden, Daniel, Opel, Nils, Redlich, Ronny, Repple, Jonathan, Grotegerd, Dominik, Meinert, Susanne, Hirsch, Jochen G., Niendorf, Thoralf, Endemann, Beate, Bamberg, Fabian, Kröncke, Thomas, Bülow, Robin, Völzke, Henry, von Stackelberg, Oyunbileg, Sowade, Ramona Felizitas, Umutlu, Lale, Schmidt, Börge, Caspers, Svenja, Consortium, German National Cohort Study Center, Kugel, Harald, Kircher, Tilo, Risse, Benjamin, Gaser, Christian, Cole, James H., Dannlowski, Udo, and Berger, Klaus

Subjects
Computer Science - Machine Learning, Quantitative Biology - Populations and Evolution
Abstract

The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a cornerstone of biological age-research. However, Machine Learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared due to data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte-Carlo Dropout Composite-Quantile-Regression (MCCQR) Neural Network trained on N=10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared to existing models across ten recruitment centers and in three independent validation samples (N=4,004). In two examples, we demonstrate that it prevents spurious associations and increases power to detect accelerated brain-aging. We make the pre-trained model publicly available.

Published
2021

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

Author

Gallo, Selene, El-Gazzar, Ahmed, Zhutovsky, Paul, Thomas, Rajat M., Javaheripour, Nooshin, Li, Meng, Bartova, Lucie, Bathula, Deepti, Dannlowski, Udo, Davey, Christopher, Frodl, Thomas, Gotlib, Ian, Grimm, Simone, Grotegerd, Dominik, Hahn, Tim, Hamilton, Paul J., Harrison, Ben J., Jansen, Andreas, Kircher, Tilo, Meyer, Bernhard, Nenadić, Igor, Olbrich, Sebastian, Paul, Elisabeth, Pezawas, Lukas, Sacchet, Matthew D., Sämann, Philipp, Wagner, Gerd, Walter, Henrik, Walter, Martin, and van Wingen, Guido

Published
2023
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50. A naturalistic cohort study of first-episode schizophrenia spectrum disorder: A description of the early phase of illness in the PSYSCAN cohort

Author

Slot, Margot I.E., van Hell, Hendrika H., Rossum, Inge Winter-van, Dazzan, Paola, Maat, Arija, de Haan, Lieuwe, Crespo-Facorro, Benedicto, Glenthøj, Birte, Lawrie, Stephen M., McDonald, Colm, Gruber, Oliver, van Amelsvoort, Thérèse, Arango, Celso, Kircher, Tilo, Nelson, Barnaby, Galderisi, Silvana, Weiser, Mark, Sachs, Gabriele, Maatz, Anke, Bressan, Rodrigo A., Kwon, Jun Soo, Mizrahi, Romina, McGuire, Philip, and Kahn, René S.

Published
2024
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