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

Author: Jiang, Yuchao, Luo, Cheng, Wang, Jijun, Palaniyappan, Lena, Chang, Xiao, Xiang, Shitong, Zhang, Jie, Duan, Mingjun, Huang, Huan, Gaser, Christian, Nemoto, Kiyotaka, Miura, Kenichiro, Hashimoto, Ryota, Westlye, Lars, Richard, Genevieve, Fernandez-Cabello, Sara, Parker, Nadine, Andreassen, Ole, Kircher, Tilo, Nenadić, Igor, Stein, Frederike, Thomas-Odenthal, Florian, Teutenberg, Lea, Usemann, Paula, Dannlowski, Udo, Hahn, Tim, Grotegerd, Dominik, Meinert, Susanne, Lencer, Rebekka, Tang, Yingying, Zhang, Tianhong, Li, Chunbo, Yue, Weihua, Zhang, Yuyanan, Yu, Xin, Zhou, Enpeng, Lin, Ching-Po, Tsai, Shih-Jen, Rodrigue, Amanda, Glahn, David, Pearlson, Godfrey, Blangero, John, Karuk, Andriana, Pomarol-Clotet, Edith, Salvador, Raymond, Fuentes-Claramonte, Paola, Garcia-León, María, Spalletta, Gianfranco, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Cheng, Jingliang, Liu, Zhening, Yang, Jie, Gonul, Ali, Uslu, Ozgul, Burhanoglu, Birce, Uyar Demir, Aslihan, Rootes-Murdy, Kelly, Calhoun, Vince, Sim, Kang, Green, Melissa, Quidé, Yann, Chung, Young, Kim, Woo-Sung, Sponheim, Scott, Demro, Caroline, Ramsay, Ian, Iasevoli, Felice, de Bartolomeis, Andrea, Barone, Annarita, Ciccarelli, Mariateresa, Brunetti, Arturo, Cocozza, Sirio, Pontillo, Giuseppe, Tranfa, Mario, Park, Min, Kirschner, Matthias, Georgiadis, Foivos, Kaiser, Stefan, Van Rheenen, Tamsyn, Rossell, Susan, Hughes, Matthew, Woods, William, Carruthers, Sean, Sumner, Philip, Ringin, Elysha, Spaniel, Filip, Skoch, Antonin, Tomecek, David, Homan, Philipp, Homan, Stephanie, Omlor, Wolfgang, Cecere, Giacomo, Nguyen, Dana, Preda, Adrian, Thomopoulos, Sophia, Jahanshad, Neda, Cui, Long-Biao, and Yao, Dezhong
Subjects: Humans, Schizophrenia, Male, Female, Adult, Algorithms, Magnetic Resonance Imaging, Gray Matter, Machine Learning, Middle Aged, Brain, Cross-Sectional Studies, Europe, Neuroimaging, Reproducibility of Results, North America, Hippocampus
Abstract: Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.
Published: 2024

2. 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

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. 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

5. Cortical morphology in patients with the deficit and non-deficit syndrome of schizophrenia: a worldwide meta- and mega-analyses.

Author: Banaj, Nerisa, Vecchio, Daniela, Piras, Fabrizio, De Rossi, Pietro, Bustillo, Juan, Ciufolini, Simone, Dazzan, Paola, Di Forti, Marta, Dickie, Erin, Ford, Judith, Fuentes-Claramonte, Paola, Gruber, Oliver, Guerrero-Pedraza, Amalia, Hamilton, Holly, Howells, Fleur, Kraemer, Bernd, Lawrie, Stephen, Radua, Joaquim, Richter, Anja, Salvador, Raymond, Sawa, Akira, Scheffler, Freda, Sim, Kang, Spaniel, Filip, Stein, Dan, Temmingh, Henk, Thomopoulos, Sophia, Tomecek, David, Uhlmann, Anne, Voineskos, Aristotle, Yang, Kun, Jahanshad, Neda, Thompson, Paul, Murray, Robin, Pomarol-Clotet, Edith, Turner, Jessica, Spalletta, Gianfranco, Piras, Federica, Mathalon, Daniel, Potkin, Steven, Van Erp, Theodorus, and Preda, Adrian
Subjects: Humans, Schizophrenia, Magnetic Resonance Imaging, Neuroimaging, Parietal Lobe, Syndrome, Cerebral Cortex
Abstract: Converging evidence suggests that schizophrenia (SZ) with primary, enduring negative symptoms (i.e., Deficit SZ (DSZ)) represents a distinct entity within the SZ spectrum while the neurobiological underpinnings remain undetermined. In the largest dataset of DSZ and Non-Deficit (NDSZ), we conducted a meta-analysis of data from 1560 individuals (168 DSZ, 373 NDSZ, 1019 Healthy Controls (HC)) and a mega-analysis of a subsampled data from 944 individuals (115 DSZ, 254 NDSZ, 575 HC) collected across 9 worldwide research centers of the ENIGMA SZ Working Group (8 in the mega-analysis), to clarify whether they differ in terms of cortical morphology. In the meta-analysis, sites computed effect sizes for differences in cortical thickness and surface area between SZ and control groups using a harmonized pipeline. In the mega-analysis, cortical values of individuals with schizophrenia and control participants were analyzed across sites using mixed-model ANCOVAs. The meta-analysis of cortical thickness showed a converging pattern of widespread thinner cortex in fronto-parietal regions of the left hemisphere in both DSZ and NDSZ, when compared to HC. However, DSZ have more pronounced thickness abnormalities than NDSZ, mostly involving the right fronto-parietal cortices. As for surface area, NDSZ showed differences in fronto-parietal-temporo-occipital cortices as compared to HC, and in temporo-occipital cortices as compared to DSZ. Although DSZ and NDSZ show widespread overlapping regions of thinner cortex as compared to HC, cortical thinning seems to better typify DSZ, being more extensive and bilateral, while surface area alterations are more evident in NDSZ. Our findings demonstrate for the first time that DSZ and NDSZ are characterized by different neuroimaging phenotypes, supporting a nosological distinction between DSZ and NDSZ and point toward the separate disease hypothesis.
Published: 2023

6. 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

7. Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium.

Author: Constantinides, Constantinos, Han, Laura KM, Alloza, Clara, Antonucci, Linda Antonella, Arango, Celso, Ayesa-Arriola, Rosa, Banaj, Nerisa, Bertolino, Alessandro, Borgwardt, Stefan, Bruggemann, Jason, Bustillo, Juan, Bykhovski, Oleg, Calhoun, Vince, Carr, Vaughan, Catts, Stanley, Chung, Young-Chul, Crespo-Facorro, Benedicto, Díaz-Caneja, Covadonga M, Donohoe, Gary, Plessis, Stefan Du, Edmond, Jesse, Ehrlich, Stefan, Emsley, Robin, Eyler, Lisa T, Fuentes-Claramonte, Paola, Georgiadis, Foivos, Green, Melissa, Guerrero-Pedraza, Amalia, Ha, Minji, Hahn, Tim, Henskens, Frans A, Holleran, Laurena, Homan, Stephanie, Homan, Philipp, Jahanshad, Neda, Janssen, Joost, Ji, Ellen, Kaiser, Stefan, Kaleda, Vasily, Kim, Minah, Kim, Woo-Sung, Kirschner, Matthias, Kochunov, Peter, Kwak, Yoo Bin, Kwon, Jun Soo, Lebedeva, Irina, Liu, Jingyu, Mitchie, Patricia, Michielse, Stijn, Mothersill, David, Mowry, Bryan, de la Foz, Víctor Ortiz-García, Pantelis, Christos, Pergola, Giulio, Piras, Fabrizio, Pomarol-Clotet, Edith, Preda, Adrian, Quidé, Yann, Rasser, Paul E, Rootes-Murdy, Kelly, Salvador, Raymond, Sangiuliano, Marina, Sarró, Salvador, Schall, Ulrich, Schmidt, André, Scott, Rodney J, Selvaggi, Pierluigi, Sim, Kang, Skoch, Antonin, Spalletta, Gianfranco, Spaniel, Filip, Thomopoulos, Sophia I, Tomecek, David, Tomyshev, Alexander S, Tordesillas-Gutiérrez, Diana, van Amelsvoort, Therese, Vázquez-Bourgon, Javier, Vecchio, Daniela, Voineskos, Aristotle, Weickert, Cynthia S, Weickert, Thomas, Thompson, Paul M, Schmaal, Lianne, van Erp, Theo GM, Turner, Jessica, Cole, James H, ENIGMA Schizophrenia Consortium, Dima, Danai, and Walton, Esther
Subjects: ENIGMA Schizophrenia Consortium, Brain, Humans, Magnetic Resonance Imaging, Prospective Studies, Schizophrenia, Aging, Adolescent, Adult, Aged, Middle Aged, Female, Male, Young Adult, Mental Health, Serious Mental Illness, Brain Disorders, Neurosciences, Biomedical Imaging, Clinical Research, Behavioral and Social Science, Neurological, Mental health, Good Health and Well Being, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.
Published: 2023

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

Author: Fullerton, Janice, Goikolea, Jose, Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus, Hahn, Tim, Howells, Fleur, Ingvar, Martin, Jahanshad, Neda, Kircher, Tilo, Krug, Axel, Kuplicki, Rayus, Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik, Martyn, Fiona, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa, Mitchell, Philip, Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Ophoff, Roel, Overs, Bronwyn, Pfarr, Julia-Katharina, Pineda-Zapata, Julian, Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai, Roberts, Gloria, Ross, Alex, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter, Sim, Kang, Stein, Dan, Stein, Frederike, Temmingh, Henk, Thiel, Katharina, Thomopoulos, Sophia, van Haren, Neeltje, Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi, Ching, Christopher, Andreassen, Ole, Thompson, Paul, Hajek, Tomas, McWhinney, Sean, Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, Del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick, Cannon, Dara, Dannlowski, Udo, Diaz-Zuluaga, Ana, Dietze, Lorielle, Elvsåshagen, Torbjørn, and Eyler, Lisa
Subjects: Body mass index, antipsychotics, bipolar disorders, cortical thickness, heterogeneity, lithium, obesity, surface area
Abstract: BACKGROUND: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact. METHODS: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations. RESULTS: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI. CONCLUSIONS: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.
Published: 2023

9. 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

10. Face-brain correlates as potential sex-specific biomarkers for schizophrenia and bipolar disorder

Author: Hostalet, Noemí, González, Alejandro, Salgado-Pineda, Pilar, Gonzàlez-Colom, Rubèn, Canales-Rodríguez, Erick J, Aguirre, Candibel, Guerrero-Pedraza, Amalia, Llanos-Torres, María, Salvador, Raymond, Pomarol-Clotet, Edith, Sevillano, Xavier, Martínez-Abadías, Neus, and Fatjó-Vilas, Mar
Published: 2024
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11. Cortical volume abnormalities in schizophrenia: Correlations with symptoms and cognitive impairment

Author: García-León, María Ángeles, Fuentes-Claramonte, Paola, Soler-Vidal, Joan, Ramiro-Sousa, Nuria, Salgado-Pineda, Pilar, Salavert, Josep, Torres, Llanos, Guerrero-Pedraza, Amalia, Tristany, Josep, Karuk, Andriana, Barbosa, Lucila, Del Olmo-Encabo, Pablo, Canut-Altemir, Pedro, Munuera, Josep, Sarró, Salvador, Salvador, Raymond, McKenna, Peter J., and Pomarol-Clotet, Edith
Published: 2024
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12. Human-specific evolutionary markers linked to foetal neurodevelopment modulate brain surface area in schizophrenia

Author: Guardiola-Ripoll, Maria, Almodóvar-Payá, Carmen, Arias-Magnasco, Angelo, Latorre-Guardia, Mariona, Papiol, Sergi, Canales-Rodríguez, Erick J., García-León, María Ángeles, Fuentes-Claramonte, Paola, Salavert, Josep, Tristany, Josep, Torres, Llanos, Rodríguez-Cano, Elena, Salvador, Raymond, Pomarol-Clotet, Edith, and Fatjó-Vilas, Mar
Published: 2023
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13. Intelligence, educational attainment, and brain structure in those at familial high‐risk for schizophrenia or bipolar disorder

Author: Zwarte, Sonja MC, Brouwer, Rachel M, Agartz, Ingrid, Alda, Martin, Alonso‐Lana, Silvia, Bearden, Carrie E, Bertolino, Alessandro, Bonvino, Aurora, Bramon, Elvira, Buimer, Elizabeth EL, Cahn, Wiepke, Canales‐Rodríguez, Erick J, Cannon, Dara M, Cannon, Tyrone D, Caseras, Xavier, Castro‐Fornieles, Josefina, Chen, Qiang, Chung, Yoonho, De la Serna, Elena, Bonnin, Caterina Mar, Demro, Caroline, Di Giorgio, Annabella, Doucet, Gaelle E, Eker, Mehmet Cagdas, Erk, Susanne, Fatjó‐Vilas, Mar, Fears, Scott C, Foley, Sonya F, Frangou, Sophia, Fullerton, Janice M, Glahn, David C, Goghari, Vina M, Goikolea, Jose M, Goldman, Aaron L, Gonul, Ali Saffet, Gruber, Oliver, Hajek, Tomas, Hawkins, Emma L, Heinz, Andreas, Ongun, Ceren Hidiroglu, Hillegers, Manon HJ, Houenou, Josselin, Pol, Hilleke E Hulshoff, Hultman, Christina M, Ingvar, Martin, Johansson, Viktoria, Jönsson, Erik G, Kane, Fergus, Kempton, Matthew J, Koenis, Marinka MG, Kopecek, Miloslav, Krämer, Bernd, Lawrie, Stephen M, Lenroot, Rhoshel K, Marcelis, Machteld, Mattay, Venkata S, McDonald, Colm, Meyer‐Lindenberg, Andreas, Michielse, Stijn, Mitchell, Philip B, Moreno, Dolores, Murray, Robin M, Mwangi, Benson, Nabulsi, Leila, Newport, Jason, Olman, Cheryl A, Os, Jim, Overs, Bronwyn J, Ozerdem, Aysegul, Pergola, Giulio, Picchioni, Marco M, Piguet, Camille, Pomarol‐Clotet, Edith, Radua, Joaquim, Ramsay, Ian S, Richter, Anja, Roberts, Gloria, Salvador, Raymond, Aydogan, Aybala Saricicek, Sarró, Salvador, Schofield, Peter R, Simsek, Esma M, Simsek, Fatma, Soares, Jair C, Sponheim, Scott R, Sugranyes, Gisela, Toulopoulou, Timothea, Tronchin, Giulia, Vieta, Eduard, Walter, Henrik, Weinberger, Daniel R, Whalley, Heather C, Wu, Mon‐Ju, Yalin, Nefize, Andreassen, Ole A, Ching, Christopher RK, Thomopoulos, Sophia I, Erp, Theo GM, Jahanshad, Neda, and Thompson, Paul M
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Bipolar Disorder, Clinical Research, Serious Mental Illness, Neurosciences, Schizophrenia, Mental Health, Aetiology, 2.3 Psychological, social and economic factors, 2.1 Biological and endogenous factors, Mental health, Cognitive Dysfunction, Educational Status, Family, Genetic Predisposition to Disease, Humans, Intelligence, Magnetic Resonance Imaging, Neuroimaging, bipolar disorder, education, intelligence, neuroimaging, relatives, schizophrenia, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract: First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10-5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.
Published: 2022

14. In vivo hippocampal subfield volumes in bipolar disorder—A mega‐analysis from The Enhancing Neuro Imaging Genetics through Meta‐Analysis Bipolar Disorder Working Group

Author: Haukvik, Unn K, Gurholt, Tiril P, Nerland, Stener, Elvsåshagen, Torbjørn, Akudjedu, Theophilus N, Alda, Martin, Alnæs, Dag, Alonso‐Lana, Silvia, Bauer, Jochen, Baune, Bernhard T, Benedetti, Francesco, Berk, Michael, Bettella, Francesco, Bøen, Erlend, Bonnín, Caterina M, Brambilla, Paolo, Canales‐Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Dandash, Orwa, Dannlowski, Udo, Delvecchio, Giuseppe, Díaz‐Zuluaga, Ana M, Erp, Theo GM, Fatjó‐Vilas, Mar, Foley, Sonya F, Förster, Katharina, Fullerton, Janice M, Goikolea, José M, Grotegerd, Dominik, Gruber, Oliver, Haarman, Bartholomeus CM, Haatveit, Beathe, Hajek, Tomas, Hallahan, Brian, Harris, Mathew, Hawkins, Emma L, Howells, Fleur M, Hülsmann, Carina, Jahanshad, Neda, Jørgensen, Kjetil N, Kircher, Tilo, Krämer, Bernd, Krug, Axel, Kuplicki, Rayus, Lagerberg, Trine V, Lancaster, Thomas M, Lenroot, Rhoshel K, Lonning, Vera, López‐Jaramillo, Carlos, Malt, Ulrik F, McDonald, Colm, McIntosh, Andrew M, McPhilemy, Genevieve, Meer, Dennis, Melle, Ingrid, Melloni, Elisa MT, Mitchell, Philip B, Nabulsi, Leila, Nenadić, Igor, Oertel, Viola, Oldani, Lucio, Opel, Nils, Otaduy, Maria CG, Overs, Bronwyn J, Pineda‐Zapata, Julian A, Pomarol‐Clotet, Edith, Radua, Joaquim, Rauer, Lisa, Redlich, Ronny, Repple, Jonathan, Rive, Maria M, Roberts, Gloria, Ruhe, Henricus G, Salminen, Lauren E, Salvador, Raymond, Sarró, Salvador, Savitz, Jonathan, Schene, Aart H, Sim, Kang, Soeiro‐de‐Souza, Marcio G, Stäblein, Michael, Stein, Dan J, Stein, Frederike, Tamnes, Christian K, Temmingh, Henk S, Thomopoulos, Sophia I, Veltman, Dick J, Vieta, Eduard, Waltemate, Lena, Westlye, Lars T, Whalley, Heather C, Sämann, Philipp G, Thompson, Paul M, Ching, Christopher RK, Andreassen, Ole A, Agartz, Ingrid, and Group, ENIGMA Bipolar Disorder Working
Subjects: Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Mental Health, Serious Mental Illness, Neurosciences, Biomedical Imaging, Bipolar Disorder, Mental health, Genetics, Hippocampus, Humans, Magnetic Resonance Imaging, Neuroimaging, ENIGMA Bipolar Disorder Working Group, bipolar disorder subtype, hippocampus, large-scale, lithium, psychosis, structural brain MRI, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract: The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.
Published: 2022

15. A meta‐analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium

Author: Gutman, Boris A, Erp, Theo GM, Alpert, Kathryn, Ching, Christopher RK, Isaev, Dmitry, Ragothaman, Anjani, Jahanshad, Neda, Saremi, Arvin, Zavaliangos‐Petropulu, Artemis, Glahn, David C, Shen, Li, Cong, Shan, Alnæs, Dag, Andreassen, Ole Andreas, Doan, Nhat Trung, Westlye, Lars T, Kochunov, Peter, Satterthwaite, Theodore D, Wolf, Daniel H, Huang, Alexander J, Kessler, Charles, Weideman, Andrea, Nguyen, Dana, Mueller, Bryon A, Faziola, Lawrence, Potkin, Steven G, Preda, Adrian, Mathalon, Daniel H, Bustillo, Juan, Calhoun, Vince, Ford, Judith M, Walton, Esther, Ehrlich, Stefan, Ducci, Giuseppe, Banaj, Nerisa, Piras, Fabrizio, Piras, Federica, Spalletta, Gianfranco, Canales‐Rodríguez, Erick J, Fuentes‐Claramonte, Paola, Pomarol‐Clotet, Edith, Radua, Joaquim, Salvador, Raymond, Sarró, Salvador, Dickie, Erin W, Voineskos, Aristotle, Tordesillas‐Gutiérrez, Diana, Crespo‐Facorro, Benedicto, Setién‐Suero, Esther, Son, Jacqueline Mayoral, Borgwardt, Stefan, Schönborn‐Harrisberger, Fabienne, Morris, Derek, Donohoe, Gary, Holleran, Laurena, Cannon, Dara, McDonald, Colm, Corvin, Aiden, Gill, Michael, Filho, Geraldo Busatto, Rosa, Pedro GP, Serpa, Mauricio H, Zanetti, Marcus V, Lebedeva, Irina, Kaleda, Vasily, Tomyshev, Alexander, Crow, Tim, James, Anthony, Cervenka, Simon, Sellgren, Carl M, Fatouros‐Bergman, Helena, Agartz, Ingrid, Howells, Fleur, Stein, Dan J, Temmingh, Henk, Uhlmann, Anne, Zubicaray, Greig I, McMahon, Katie L, Wright, Margie, Cobia, Derin, Csernansky, John G, Thompson, Paul M, Turner, Jessica A, and Wang, Lei
Subjects: Brain Disorders, Mental Health, Neurosciences, Schizophrenia, Mental health, Good Health and Well Being, Amygdala, Corpus Striatum, Hippocampus, Humans, Multicenter Studies as Topic, Neuroimaging, Thalamus, schizophrenia, structure, subcortical shape, Cognitive Sciences, Experimental Psychology
Abstract: Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.
Published: 2022

16. 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

17. Automated Orientation Detection of 3D Head Reconstructions from sMRI Using Multiview Orthographic Projections: An Image Classification-Based Approach

Author: Heredia-Lidón, Álvaro, González, Alejandro, Guerrero-Mosquera, Carlos, Gonzàlez-Colom, Rubèn, Echeverry, Luis M., Hostalet, Noemí, Salvador, Raymond, Pomarol-Clotet, Edith, Fortea, Juan, Martínez-Abadías, Neus, Fatjó-Vilas, Mar, Sevillano, Xavier, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pertusa, Antonio, editor, Gallego, Antonio Javier, editor, Sánchez, Joan Andreu, editor, and Domingues, Inês, editor
Published: 2023
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18. Association between body mass index and subcortical brain volumes in bipolar disorders–ENIGMA study in 2735 individuals

Author: McWhinney, Sean R, Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J, Cannon, Dara M, Dannlowski, Udo, Díaz-Zuluaga, Ana M, Elvsåshagen, Torbjørn, Eyler, Lisa T, Fullerton, Janice M, Goikolea, Jose M, Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus CM, Hahn, Tim, Howells, Fleur M, Ingvar, Martin, Kircher, Tilo TJ, Krug, Axel, Kuplicki, Rayus T, Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlos, Malt, Ulrik F, Martyn, Fiona M, Mazza, Elena, McDonald, Colm, McPhilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa MT, Mitchell, Philip B, Nabulsi, Leila, Nenadic, Igor, Opel, Nils, Ophoff, Roel A, Overs, Bronwyn J, Pfarr, Julia-Katharina, Pineda-Zapata, Julian A, Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai G, Roberts, Gloria, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter R, Sim, Kang, Stein, Dan J, Stein, Frederike, Temmingh, Henk S, Thiel, Katharina, van Haren, Neeltje EM, Gestel, Holly Van, Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi N, Ching, Christopher RK, Andreassen, Ole, Thompson, Paul M, and Hajek, Tomas
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Psychology, Mental Health, Biomedical Imaging, Nutrition, Brain Disorders, Obesity, Neurosciences, Clinical Research, Prevention, Mental health, Amygdala, Bipolar Disorder, Body Mass Index, Brain, Humans, Magnetic Resonance Imaging, ENIGMA Bipolar Disorders Working Group, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract: Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.
Published: 2021

19. A functional neuroimaging association study on the interplay between two schizophrenia genome-wide associated genes (CACNA1C and ZNF804A)

Author: Guardiola-Ripoll, Maria, Almodóvar-Payá, Carmen, Lubeiro, Alba, Sotero, Alejandro, Salvador, Raymond, Fuentes-Claramonte, Paola, Salgado-Pineda, Pilar, Papiol, Sergi, Ortiz-Gil, Jordi, Gomar, Jesús J., Guerrero-Pedraza, Amalia, Sarró, Salvador, Maristany, Teresa, Molina, Vicente, Pomarol-Clotet, Edith, and Fatjó-Vilas, Mar
Published: 2022
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20. Brain functional abnormality in drug treated and drug naïve adolescents with borderline personality disorder: Evidence for default mode network dysfunction

Author: Salgado-Pineda, Pilar, Ferrer, Marc, Calvo, Natàlia, Costa, Xavier, Ribas, Núria, Lara, Benjamín, Tarragona, Brenda, Fuentes-Claramonte, Paola, Salvador, Raymond, and Pomarol-Clotet, Edith
Published: 2023
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21. Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Author: Nunes, Abraham, Schnack, Hugo G, Ching, Christopher RK, Agartz, Ingrid, Akudjedu, Theophilus N, Alda, Martin, Alnæs, Dag, Alonso-Lana, Silvia, Bauer, Jochen, Baune, Bernhard T, Bøen, Erlend, Bonnin, Caterina del Mar, Busatto, Geraldo F, Canales-Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Chaim-Avancini, Tiffany M, Dannlowski, Udo, Díaz-Zuluaga, Ana M, Dietsche, Bruno, Doan, Nhat Trung, Duchesnay, Edouard, Elvsåshagen, Torbjørn, Emden, Daniel, Eyler, Lisa T, Fatjó-Vilas, Mar, Favre, Pauline, Foley, Sonya F, Fullerton, Janice M, Glahn, David C, Goikolea, Jose M, Grotegerd, Dominik, Hahn, Tim, Henry, Chantal, Hibar, Derrek P, Houenou, Josselin, Howells, Fleur M, Jahanshad, Neda, Kaufmann, Tobias, Kenney, Joanne, Kircher, Tilo TJ, Krug, Axel, Lagerberg, Trine V, Lenroot, Rhoshel K, López-Jaramillo, Carlos, Machado-Vieira, Rodrigo, Malt, Ulrik F, McDonald, Colm, Mitchell, Philip B, Mwangi, Benson, Nabulsi, Leila, Opel, Nils, Overs, Bronwyn J, Pineda-Zapata, Julian A, Pomarol-Clotet, Edith, Redlich, Ronny, Roberts, Gloria, Rosa, Pedro G, Salvador, Raymond, Satterthwaite, Theodore D, Soares, Jair C, Stein, Dan J, Temmingh, Henk S, Trappenberg, Thomas, Uhlmann, Anne, van Haren, Neeltje EM, Vieta, Eduard, Westlye, Lars T, Wolf, Daniel H, Yüksel, Dilara, Zanetti, Marcus V, Andreassen, Ole A, Thompson, Paul M, and Hajek, Tomas
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Brain Disorders, Biomedical Imaging, Neurosciences, Mental Health, Clinical Research, Mental health, Bipolar Disorder, Brain, Humans, Machine Learning, Magnetic Resonance Imaging, Neuroimaging, ENIGMA Bipolar Disorders Working Group, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract: Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.
Published: 2020

22. Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals

Author: Favre, Pauline, Pauling, Melissa, Stout, Jacques, Hozer, Franz, Sarrazin, Samuel, Abé, Christoph, Alda, Martin, Alloza, Clara, Alonso-Lana, Silvia, Andreassen, Ole A, Baune, Bernhard T, Benedetti, Francesco, Busatto, Geraldo F, Canales-Rodríguez, Erick J, Caseras, Xavier, Chaim-Avancini, Tiffany Moukbel, Ching, Christopher RK, Dannlowski, Udo, Deppe, Michael, Eyler, Lisa T, Fatjo-Vilas, Mar, Foley, Sonya F, Grotegerd, Dominik, Hajek, Tomas, Haukvik, Unn K, Howells, Fleur M, Jahanshad, Neda, Kugel, Harald, Lagerberg, Trine V, Lawrie, Stephen M, Linke, Julia O, McIntosh, Andrew, Melloni, Elisa MT, Mitchell, Philip B, Polosan, Mircea, Pomarol-Clotet, Edith, Repple, Jonathan, Roberts, Gloria, Roos, Annerine, Rosa, Pedro GP, Salvador, Raymond, Sarró, Salvador, Schofield, Peter R, Serpa, Mauricio H, Sim, Kang, Stein, Dan J, Sussmann, Jess E, Temmingh, Henk S, Thompson, Paul M, Verdolini, Norma, Vieta, Eduard, Wessa, Michele, Whalley, Heather C, Zanetti, Marcus V, Leboyer, Marion, Mangin, Jean-François, Henry, Chantal, Duchesnay, Edouard, and Houenou, Josselin
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Serious Mental Illness, Neurosciences, Bipolar Disorder, Biomedical Imaging, Mental Health, Clinical Research, Mental health, Adult, Brain, Corpus Callosum, Diffusion Tensor Imaging, Female, Humans, Male, Neural Pathways, White Matter, ENIGMA Bipolar Disorder Working Group, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological psychology
Abstract: Fronto-limbic white matter (WM) abnormalities are assumed to lie at the heart of the pathophysiology of bipolar disorder (BD); however, diffusion tensor imaging (DTI) studies have reported heterogeneous results and it is not clear how the clinical heterogeneity is related to the observed differences. This study aimed to identify WM abnormalities that differentiate patients with BD from healthy controls (HC) in the largest DTI dataset of patients with BD to date, collected via the ENIGMA network. We gathered individual tensor-derived regional metrics from 26 cohorts leading to a sample size of N = 3033 (1482 BD and 1551 HC). Mean fractional anisotropy (FA) from 43 regions of interest (ROI) and average whole-brain FA were entered into univariate mega- and meta-analyses to differentiate patients with BD from HC. Mega-analysis revealed significantly lower FA in patients with BD compared with HC in 29 regions, with the highest effect sizes observed within the corpus callosum (R2 = 0.041, Pcorr
Published: 2019

23. Correction: Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals

Author: Favre, Pauline, Pauling, Melissa, Stout, Jacques, Hozer, Franz, Sarrazin, Samuel, Abé, Christoph, Alda, Martin, Alloza, Clara, Alonso-Lana, Silvia, Andreassen, Ole A, Baune, Bernhard T, Benedetti, Francesco, Busatto, Geraldo F, Canales-Rodríguez, Erick J, Caseras, Xavier, Chaim-Avancini, Tiffany Moukbel, Ching, Christopher RK, Dannlowski, Udo, Deppe, Michael, Eyler, Lisa T, Fatjo-Vilas, Mar, Foley, Sonya F, Grotegerd, Dominik, Hajek, Tomas, Haukvik, Unn K, Howells, Fleur M, Jahanshad, Neda, Kugel, Harald, Lagerberg, Trine V, Lawrie, Stephen M, Linke, Julia O, McIntosh, Andrew, Melloni, Elisa MT, Mitchell, Philip B, Polosan, Mircea, Pomarol-Clotet, Edith, Repple, Jonathan, Roberts, Gloria, Roos, Annerine, Rosa, Pedro GP, Salvador, Raymond, Sarró, Salvador, Schofield, Peter R, Serpa, Mauricio H, Sim, Kang, Stein, Dan J, Sussmann, Jess E, Temmingh, Henk S, Thompson, Paul M, Verdolini, Norma, Vieta, Eduard, Wessa, Michele, Whalley, Heather C, Zanetti, Marcus V, Leboyer, Marion, Mangin, Jean-François, Henry, Chantal, Duchesnay, Edouard, and Houenou, Josselin
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Neurosciences, Psychology, ENIGMA Bipolar Disorder Working Group, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological psychology
Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Published: 2019

24. Structural covariance predictors of clinical improvement at 2-year follow-up in first-episode psychosis

Author: García-Rizo, Clemente, González-Díaz, Jairo M., Pina-Camacho, Laura, Rodríguez-Toscano, Elisa, Zorrilla, Iñaki, Lopez-Pena, Purificación, De-la-Cámara, Concepción, Modrego-Pardo, Pedro, Escartí, Mª. Jose, Nacher, Juan, Vázquez, Guillermo, Cristeto, Sílvia, Valli, Isabel, Torrent, Carla, Baeza, Imma, de la Serna, Elena, Menchón, J.M., Martínez-Zalacaín, Ignacio, Sáiz, Pilar A., González-Blanco, Leticia, Rodriguez-Jimenez, Roberto, Sanchez-Pastor, Luis, Usall, Judith, Butjosa, Anna, Pomarol-Clotet, Edith, Salvador, Raymond, Saiz-Masvidal, Cristina, Contreras, Fernando, Soriano-Mas, Carles, Mezquida, Gisela, Díaz-Caneja, Covadonga M., Vieta, Eduard, Amoretti, Silvia, Lobo, Antonio, González-Pinto, Ana, Janssen, Joost, Sagué-Vilavella, Maria, Castro-Fornieles, Josefina, Bergé, Daniel, Bioque, Miquel, Lois, Noemi G., Parellada, Mara, and Bernardo, Miguel
Published: 2023
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25. Longitudinal analysis of neuropsychiatric symptoms in a large cohort of early-moderate manifest Huntington's disease patients

Author: Ruiz-Idiago, Jesús, Pomarol-Clotet, Edith, and Salvador, Raymond
Published: 2023
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26. The relationship between cognition and functioning in Bipolar Disorder: An investigation using functional imaging during working memory performance

Author: Verdolini, Norma, Alonso-Lana, Silvia, Salgado-Pineda, Pilar, Sarró, Salvador, Salvador, Raymond, Maristany, Teresa, Goikolea, José M., Bonnin, Caterina M., Martín, Inés, Saló, Laura, Romaguera, Ana, Rodriguez-Cano, Elena, Rosa, Adriane R., Vieta, Eduard, and Pomarol-Clotet, Edith
Published: 2023
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27. Multiscale characterization of cortical signatures in positive and negative schizotypy: A worldwide ENIGMA study

Author: Kirschner, Matthias, primary, Hodzic-Santor, Benazir, additional, Kennedy, Leda, additional, Hansen, Justine Y, additional, Antoniades, Mathilde, additional, Nenadic, Igor, additional, Kircher, Tilo, additional, Krug, Alex, additional, Meller, Tina, additional, Dannlowski, Udo, additional, Grotegerd, Dominik, additional, Flinkenfluegel, Kira, additional, Meinert, Susanne, additional, Borgers, Tiana, additional, Goltermann, Janik, additional, Hahn, Tim, additional, Boehnlein, Joscha, additional, Leehr, Elisabeth Johanna, additional, Barkhau, Carlotta, additional, Fornito, Alex, additional, Arnatkeviciute, Aurina, additional, Bellgrove, Mark, additional, Tiego, Jeggan, additional, DeRosse, Pamela, additional, Green, Melissa, additional, Quide, Yann, additional, Pantelis, Christos, additional, Chan, Raymond C K, additional, Wang, Yi, additional, Ettinger, Ulrich, additional, Debbane, Martin, additional, Derome, Melodie, additional, Gaser, Christian, additional, Besteher, Bianca, additional, Diederen, Kelly, additional, Spencer, Thomas J, additional, Houenou, Josselin, additional, Pomarol-Clotet, Edith, additional, Salvador, Raymond, additional, Roessler, Wulf, additional, Smigielski, Lukasz, additional, Kumari, Veena, additional, Premkumar, Preethi, additional, Park, Haeme, additional, Wiebels, Kristina, additional, Lemmers-Jansen, Imke, additional, Gilleen, James, additional, Allen, Paul H, additional, Marsman, Jan-Bernard, additional, Lebedeva, Irina, additional, Tomyshev, Alexander, additional, Fett, Anne-Kathrin, additional, Sommer, Iris, additional, Koops, Sanne, additional, Grant, Phillip, additional, Begue, Indrit, additional, Hernaus, Dennis, additional, Jalbrzikowski, Maria, additional, Paquola, Casey, additional, Lariviere, Sara, additional, Bernhardt, Boris, additional, Valk, Sofie L, additional, Misic, Bratislav, additional, van Erp, Theo GM, additional, Turner, Jessica A, additional, Thompson, Paul M, additional, Aleman, Andre, additional, Dagher, Alain, additional, Kaiser, Stefan, additional, and Modinos, Gemma, additional
Published: 2024
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28. Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

Author: Petrov, Dmitry, Kuznetsov, Boris A. Gutman Egor, van Erp, Theo G. M., Turner, Jessica A., Schmaal, Lianne, Veltman, Dick, Wang, Lei, Alpert, Kathryn, Isaev, Dmitry, Zavaliangos-Petropulu, Artemis, Ching, Christopher R. K., Calhoun, Vince, Glahn, David, Satterthwaite, Theodore D., Andreassen, Ole Andreas, Borgwardt, Stefan, Howells, Fleur, Groenewold, Nynke, Voineskos, Aristotle, Radua, Joaquim, Potkin, Steven G., Crespo-Facorro, Benedicto, Tordesillas-Gutierrez, Diana, Shen, Li, Lebedeva, Irina, Spalletta, Gianfranco, Donohoe, Gary, Kochunov, Peter, Rosa, Pedro G. P., James, Anthony, Dannlowski, Udo, Baune, Bernhard T., Aleman, Andre, Gotlib, Ian H., Walter, Henrik, Walter, Martin, Soares, Jair C., Ehrlich, Stefan, Gur, Ruben C., Doan, N. Trung, Agartz, Ingrid, Westlye, Lars T., Harrisberger, Fabienne, Riecher-Rossler, Anita, Uhlmann, Anne, Stein, Dan J., Dickie, Erin W., Pomarol-Clotet, Edith, Fuentes-Claramonte, Paola, Canales-Rodriguez, Erick Jorge, Salvador, Raymond, Huang, Alexander J., Roiz-Santianez, Roberto, Cong, Shan, Tomyshev, Alexander, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Ciullo, Valentina, Hong, Elliot, Busatto, Geraldo, Zanetti, Marcus V., Serpa, Mauricio H., Cervenka, Simon, Kelly, Sinead, Grotegerd, Dominik, Sacchet, Matthew D., Veer, Ilya M., Li, Meng, Wu, Mon-Ju, Irungu, Benson, Walton, Esther, and Thompson, Paul M.
Subjects: Quantitative Biology - Neurons and Cognition
Abstract: We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on a parametric spherical mapping. Finally, we present an approach for model decision visualization, allowing human raters to see the areas of subcortical shapes most likely to be deemed of failing quality by the machine. Our training data is comprised of 5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our final models reduce human rater time by 46-70%. ResNet outperforms VGGNet and Inception for all of our predictive tasks., Comment: Accepted to Shape in Medical Imaging (ShapeMI) workshop at MICCAI 2018. arXiv admin note: substantial text overlap with arXiv:1707.06353
Published: 2018

29. New insights of the role of the KCNH2 gene in schizophrenia: An fMRI case-control study

Author: Guardiola-Ripoll, Maria, Almodóvar-Payá, Carmen, Lubeiro, Alba, Salvador, Raymond, Salgado-Pineda, Pilar, Gomar, Jesús J, Guerrero-Pedraza, Amalia, Sarró, Salvador, Maristany, Teresa, Fernández-Linsenbarth, Inés, Hernández-García, Marta, Papiol, Sergi, Molina, Vicente, Pomarol-Clotet, Edith, and Fatjó-Vilas, Mar
Published: 2022
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30. Validación de la versión española de la Escala de funcionamiento breve (FAST) en pacientes diagnosticados de esquizofrenia

Author: Lujan-Lujan, Eva Maria, Garcia-Leon, Maria Angeles, Rodriguez-Cano, Elena, Huertas-Martinez, Sergi, Roldan-Merino, Juan, Puig-Llobet, Montserrat, Miguel-Ruiz, Maria Dolores, Salvador, Raymond, Vieta, Eduard, and Pomarol-Clotet, Edith
Published: 2022
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31. Neural correlates of disturbance in the sense of agency in schizophrenia: An fMRI study using the ‘enfacement’ paradigm

Author: Salgado-Pineda, Pilar, Fuentes-Claramonte, P., Spanlang, Bernhard, Pomes, Ausias, Landin-Romero, Ramón, Portillo, Francisco, Bosque, Clara, Franquelo, Jose Carlos, Teixido, Cristina, Sarró, Salvador, Salvador, Raymond, and Pomarol-Clotet, Edith
Published: 2022
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32. Combining fMRI and DISC1 gene haplotypes to understand working memory-related brain activity in schizophrenia

Author: Guardiola-Ripoll, Maria, Sotero-Moreno, Alejandro, Almodóvar-Payá, Carmen, Hostalet, Noemí, Guerrero-Pedraza, Amalia, Ramiro, Núria, Ortiz-Gil, Jordi, Arias, Bárbara, Madre, Mercè, Soler-Vidal, Joan, Salvador, Raymond, McKenna, Peter J., Pomarol-Clotet, Edith, and Fatjó-Vilas, Mar
Published: 2022
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33. Longer illness duration is associated with greater individual variability in functional brain activity in Schizophrenia, but not bipolar disorder

Author: Gallucci, Julia, Pomarol-Clotet, Edith, Voineskos, Aristotle N., Guerrero-Pedraza, Amalia, Alonso-Lana, Silvia, Vieta, Eduard, Salvador, Raymond, and Hawco, Colin
Published: 2022
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34. Negative schizophrenic symptoms as prefrontal cortex dysfunction: Examination using a task measuring goal neglect

Author: Fuentes-Claramonte, Paola, Ramiro, Núria, Torres, Llanos, Argila-Plaza, Isabel, Salgado-Pineda, Pilar, Soler-Vidal, Joan, García-León, María Ángeles, Albacete, Auria, Bosque, Clara, Panicalli, Francesco, Boix, Ester, Munuera, Josep, Tristany, Josep, Sarró, Salvador, Bernardo, Miquel, Salvador, Raymond, McKenna, Peter J., and Pomarol-Clotet, Edith
Published: 2022
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35. Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging

Author: Petrov, Dmitry, Gutman, Boris A., Shih-Hua, Yu, van Erp, Theo G. M., Turner, Jessica A., Schmaal, Lianne, Veltman, Dick, Wang, Lei, Alpert, Kathryn, Isaev, Dmitry, Zavaliangos-Petropulu, Artemis, Ching, Christopher R. K., Calhoun, Vince, Glahn, David, Satterthwaite, Theodore D., Andreasen, Ole Andreas, Borgwardt, Stefan, Howells, Fleur, Groenewold, Nynke, Voineskos, Aristotle, Radua, Joaquim, Potkin, Steven G., Crespo-Facorro, Benedicto, Tordesillas-Gutierrez, Diana, Shen, Li, Lebedeva, Irina, Spalletta, Gianfranco, Donohoe, Gary, Kochunov, Peter, Rosa, Pedro G. P., James, Anthony, Dannlowski, Udo, Baune, Bernhard T., Aleman, Andre, Gotlib, Ian H., Walter, Henrik, Walter, Martin, Soares, Jair C., Ehrlich, Stefan, Gur, Ruben C., Doan, N. Trung, Agartz, Ingrid, Westlye, Lars T., Harrisberger, Fabienne, Riecher-Rossler, Anita, Uhlmann, Anne, Stein, Dan J., Dickie, Erin W., Pomarol-Clotet, Edith, Fuentes-Claramonte, Paola, Canales-Rodriguez, Erick Jorge, Salvador, Raymond, Huang, Alexander J., Roiz-Santianez, Roberto, Cong, Shan, Tomyshev, Alexander, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Ciullo, Valentina, Hong, Elliot, Busatto, Geraldo, Zanetti, Marcus V., Serpa, Mauricio H., Cervenka, Simon, Kelly, Sinead, Grotegerd, Dominik, Sacchet, Matthew D., Veer, Ilya M., Li, Meng, Wu, Mon-Ju, Irungu, Benson, Walton, Esther, and Thompson, Paul M.
Subjects: Quantitative Biology - Quantitative Methods
Abstract: As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70\%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability., Comment: Arxiv version of the MICCAI 2017 Machine Learning in Medical Imaging workshop paper
Published: 2017

36. Structural brain abnormalities in borderline personality disorder correlate with clinical severity and predict psychotherapy response

Author: Sampedro, Frederic, Farrés, Cristina Carmona i, Soler, Joaquim, Elices, Matilde, Schmidt, Carlos, Corripio, Iluminada, Domínguez-Clavé, Elisabet, Pomarol-Clotet, Edith, Salvador, Raymond, and Pascual, Juan C.
Published: 2021
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37. Brain imaging of executive function with the computerised multiple elements test

Author: Fuentes-Claramonte, Paola, Santo-Angles, Aniol, Argila-Plaza, Isabel, Lechón, Miguel, Guardiola-Ripoll, Maria, Almodóvar-Payá, Carmen, Cullen, Breda, Evans, Jonathan J., Manly, Tom, Gee, Abigail, Maristany, Teresa, Sarró, Salvador, Pomarol-Clotet, Edith, McKenna, Peter J., and Salvador, Raymond
Published: 2021
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38. Activation and deactivation patterns in schizophrenia during performance of an fMRI adapted version of the stroop task

Author: Salgado-Pineda, Pilar, Rodriguez-Jimenez, Roberto, Moreno-Ortega, Marta, Dompablo, Mónica, Martínez de Aragón, Ana, Salvador, Raymond, McKenna, Peter J., Pomarol-Clotet, Edith, and Palomo, Tomás
Published: 2021
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39. 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, primary, Raine, Adrian, additional, Kia, Seyed M., additional, Cahn, Wiepke, additional, Arold, Dominic, additional, Banaj, Nerisa, additional, Barone, Annarita, additional, Brosch, Katharina, additional, Brouwer, Rachel, additional, Brunetti, Arturo, additional, Calhoun, Vince D., additional, Chew, Qian H., additional, Choi, Sunah, additional, Chung, Young-Chul, additional, Ciccarelli, Mariateresa, additional, Cobia, Derin, additional, Cocozza, Sirio, additional, Dannlowski, Udo, additional, Dazzan, Paola, additional, de Bartolomeis, Andrea, additional, Forti, Marta Di, additional, Dumais, Alexandre, additional, Edmond, Jesse T., additional, Ehrlich, Stefan, additional, Evermann, Ulrika, additional, Flinkenflügel, Kira, additional, Georgiadis, Foivos, additional, Glahn, David C., additional, Goltermann, Janik, additional, Green, Melissa J., additional, Grotegerd, Dominik, additional, Guerrero-Pedraza, Amalia, additional, Ha, Minji, additional, Hong, Elliot L., additional, Pol, Hilleke Hulshoff, additional, Iasevoli, Felice, additional, Kaiser, Stefan, additional, Kaleda, Vasily, additional, Karuk, Andriana, additional, Kim, Minah, additional, Kircher, Tilo, additional, Kirschner, Matthias, additional, Kochunov, Peter, additional, Kwon, Jun Soo, additional, Lebedeva, Irina, additional, Lencer, Rebekka, additional, Marques, Tiago R., additional, Meinert, Susanne, additional, Murray, Robin, additional, Nenadić, Igor, additional, Nguyen, Dana, additional, Pearlson, Godfrey, additional, Piras, Fabrizio, additional, Pomarol-Clotet, Edith, additional, Pontillo, Giuseppe, additional, Potvin, Stéphane, additional, Preda, Adrian, additional, Quidé, Yann, additional, Rodrigue, Amanda, additional, Rootes-Murdy, Kelly, additional, Salvador, Raymond, additional, Skoch, Antonin, additional, Sim, Kang, additional, Spalletta, Gianfranco, additional, Spaniel, Filip, additional, Stein, Frederike, additional, Thomas-Odenthal, Florian, additional, Tikàsz, Andràs, additional, Tomecek, David, additional, Tomyshev, Alexander, additional, Tranfa, Mario, additional, Tsogt, Uyanga, additional, Turner, Jessica A., additional, van Erp, Theo G. M., additional, van Haren, Neeltje E. M., additional, van Os, Jim, additional, Vecchio, Daniela, additional, Wang, Lei, additional, Wroblewski, Adrian, additional, and Nickl-Jockschat, Thomas, additional
Published: 2024
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40. Face-brain correlates as potential sex-specific biomarkers for schizophrenia and bipolar disorder.

Author: Hostalet, Noemí, primary, Fatjó-Vilas, Mar, additional, González, Alejandro, additional, Gonzàlez-Colom, Rubèn, additional, Salgado-Pineda, Pilar, additional, Canales-Rodriguez, Erick J, additional, Aguirre, Candibel, additional, Guerrero, Amalia, additional, Llanos-Torres, Maria, additional, Salvador, Raymond, additional, Pomarol-Clotet, Edith, additional, Sevillano, Xavier, additional, and Martinez-Abadias, Neus, additional
Published: 2024
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41. Brain Functional Correlates of Recall of Life Events in Medication-Naïve Adolescents with Borderline Personality Disorder

Author: Salgado-Pineda, Pilar, primary, Ferrer, Marc, additional, Calvo, Natàlia, additional, Costa, Xavier, additional, Pozuelo-López, María Ángeles, additional, Ramos-Quiroga, Josep Antoni, additional, Tarragona, Brenda, additional, Fuentes-Claramonte, Paola, additional, Salvador, Raymond, additional, and Pomarol-Clotet, Edith, additional
Published: 2024
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42. Age- and gender-related differences in brain tissue microstructure revealed by multi-component T2 relaxometry

Author: Canales-Rodríguez, Erick Jorge, Alonso-Lana, Silvia, Verdolini, Norma, Sarró, Salvador, Feria, Isabel, Montoro, Irene, Garcia-Ruiz, Beatriz, Jimenez, Esther, Varo, Cristina, Albacete, Auria, Argila-Plaza, Isabel, Lluch, Anna, Bonnin, C. Mar, Vilella, Elisabet, Vieta, Eduard, Pomarol-Clotet, Edith, and Salvador, Raymond
Published: 2021
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43. Interindividual variability of functional connectome in schizophrenia

Author: Santo-Angles, Aniol, Salvador, Raymond, Gomar, Jesús J., Guerrero-Pedraza, Amalia, Ramiro, Núria, Tristany, Josep, Teixidó, Cristina, Ortiz-Gil, Jordi, Aguirre, Candibel, Bosque, Clara, López-Araquistain, Laura, Maristany, Teresa, Salgado-Pineda, Pilar, Sarró, Salvador, McKenna, Peter J., Bernardo, Miquel, Pomarol-Clotet, Edith, and Schwarzbach, Jens
Published: 2021
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44. Biased accuracy in multisite machine-learning studies due to incomplete removal of the effects of the site

Author: Solanes, Aleix, Palau, Pol, Fortea, Lydia, Salvador, Raymond, González-Navarro, Laura, Llach, Cristian Daniel, Valentí, Marc, Vieta, Eduard, and Radua, Joaquim
Published: 2021
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45. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Author: Han, Laura K. M., Dinga, Richard, Hahn, Tim, Ching, Christopher R. K., 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 L. S., Enneking, Verena, Filimonova, Elena, Frenzel, Stefan, Frodl, Thomas, Fu, Cynthia H. Y., 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 G. P., 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 R. J. M., Walter, Henrik, van der Wee, Nic J. A., van der Werff, Steven J. A., 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., Fullerton, Janice M., Goikolea, Jose M., Haarman, Bartholomeus C. M., Hajek, Tomas, Henry, Chantal, Houenou, Josselin, Howells, Fleur M., Ingvar, Martin, Kuplicki, Rayus, Lafer, Beny, Landén, Mikael, Machado-Vieira, Rodrigo, Malt, Ulrik F., McDonald, Colm, Mitchell, Philip B., Nabulsi, Leila, Otaduy, Maria Concepcion Garcia, Overs, Bronwyn J., Polosan, Mircea, Pomarol-Clotet, Edith, Radua, Joaquim, Rive, Maria M., Roberts, Gloria, Ruhe, Henricus G., Salvador, Raymond, Sarró, Salvador, Satterthwaite, Theodore D., Savitz, Jonathan, Schene, Aart H., Schofield, Peter R., Serpa, Mauricio H., Sim, Kang, Soeiro-de-Souza, Marcio Gerhardt, Sutherland, Ashley N., Temmingh, Henk S., Timmons, Garrett M., Uhlmann, Anne, Vieta, Eduard, Wolf, Daniel H., Zanetti, Marcus V., Jahanshad, Neda, Thompson, Paul M., Veltman, Dick J., Penninx, Brenda W. J. H., Marquand, Andre F., Cole, James H., and Schmaal, Lianne
Published: 2021
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46. Psychometric properties of the Spanish version of the Health of Nation Outcome Scales for schizophrenia patients.

Author: Feria‐Raposo, Isabel, García‐León, María Ángeles, Rodríguez‐Cano, Elena, Blanco‐Blanco, Joan, Moreno‐Casbas, María Teresa, Gonzalez‐Castro, Ana Isabel, Gómez‐Lozano, María José, Guerrero‐Pedraza, Amalia, Sarri, Carmen, Portillo, Francisco, Sarró, Salvador, McKenna, Peter J., Salvador, Raymond, and Pomarol‐Clotet, Edith
Subjects: COGNITION disorders diagnosis, HEALTH status indicators, RESEARCH funding, CRONBACH'S alpha, DATA analysis, RESEARCH methodology evaluation, INTERVIEWING, RESEARCH evaluation, FUNCTIONAL assessment, KRUSKAL-Wallis Test, SCHIZOPHRENIA, SEVERITY of illness index, MANN Whitney U Test, PSYCHOMETRICS, STATISTICAL reliability, INTRACLASS correlation, STATISTICS, BARTHEL Index, INTER-observer reliability
Abstract: Accessible SummaryWhat is known on the subject?Functioning is one of the most affected areas in schizophrenia. Social, occupational and personal domains are affected, and these deficits are responsible for a major part of the disability associated with the disorder.There are several instruments to measure functioning, but the HoNOS provides a wide assessment of impairment in 12 areas of functioning.What does the paper add to existing knowledge?The Spanish version of the HoNOS shows good properties in terms of reliability and validity for use in schizophrenia patients.Although some authors divide the scale according to proposed underlying dimensions, in schizophrenia this division may not be appropriate.What are the implications for practice?A reliable and easy‐to‐use measure of impairment in different areas of functioning is useful for optimizing the treatment and rehabilitation of patients with schizophrenia. Introduction: The HoNOS scale was designed for the assessment of psychosocial impairment in various domains. While it is widely used in psychiatric settings, it has not been validated in Spanish for use in patients with schizophrenia. Aim: To examine the psychometric properties of the Spanish version of the HoNOS scale in a sample of schizophrenia patients. Method: A total of 194 individuals aged 18 to 65 with schizophrenia spectrum diagnoses were evaluated using the HoNOS. Illness severity and level of functioning were also assessed. Results: The HoNOS showed moderate internal consistency, good inter‐observer reliability and good test‐retest reliability. Factor analysis revealed an internal structure consisting of four factors, with item distribution differing from the theoretical dimensions proposed for the original scale. Discussion: The Spanish version of the HoNOS scale is a reliable and valid instrument for assessing psychosocial impairment in individuals diagnosed with schizophrenia spectrum disorders. However, further research is needed to determine its internal structure more accurately. Implications for Practice: The HoNOS scale provides researchers and clinicians with a valid measure of impairment in twelve different domains, which can facilitate and guide the treatment of schizophrenia patients. [ABSTRACT FROM AUTHOR]
Published: 2024
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47. Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization

Author: Canales-Rodríguez, Erick J., Daducci, Alessandro, Sotiropoulos, Stamatios N., Caruyer, Emmanuel, Aja-Fernández, Santiago, Radua, Joaquim, Mendizabal, Jesús María Yurramendi, Iturria-Medina, Yasser, Melie-García, Lester, Alemán-Gómez, Yasser, Thiran, Jean-Philippe, Sarró, Salvador, Pomarol-Clotet, Edith, and Salvador, Raymond
Subjects: Physics - Medical Physics
Abstract: Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on the methodology used to combine multichannel signals. Indeed, the two prevailing methods for multichannel signal combination lead to Rician and noncentral Chi noise distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in brain data.
Published: 2014
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48. Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

Author: Glahn, David, Pearlson, Godfrey, Hong, Elliot, Krug, Axel, Carr, Vaughan, Tooney, Paul, Cooper, Gavin, Rasser, Paul, Michie, Patricia, Catts, Stanley, Gur, Raquel, Gur, Ruben, Yang, Fude, Fan, Fengmei, Chen, Jingxu, Guo, Hua, Tan, Shuping, Wang, Zhiren, Xiang, Hong, Piras, Federica, Assogna, Francesca, Salvador, Raymond, McKenna, Peter, Bonvino, Aurora, King, Margaret, Kaiser, Stefan, Nguyen, Dana, Pineda-Zapata, Julian, Radua, Joaquim, Vieta, Eduard, Shinohara, Russell, Kochunov, Peter, Quidé, Yann, Green, Melissa J., Weickert, Cynthia S., Weickert, Thomas, Bruggemann, Jason, Kircher, Tilo, Nenadić, Igor, Cairns, Murray J., Seal, Marc, Schall, Ulrich, Henskens, Frans, Fullerton, Janice M., Mowry, Bryan, Pantelis, Christos, Lenroot, Rhoshel, Cropley, Vanessa, Loughland, Carmel, Scott, Rodney, Wolf, Daniel, Satterthwaite, Theodore D., Tan, Yunlong, Sim, Kang, Piras, Fabrizio, Spalletta, Gianfranco, Banaj, Nerisa, Pomarol-Clotet, Edith, Solanes, Aleix, Albajes-Eizagirre, Anton, Canales-Rodríguez, Erick J., Sarro, Salvador, Di Giorgio, Annabella, Bertolino, Alessandro, Stäblein, Michael, Oertel, Viola, Knöchel, Christian, Borgwardt, Stefan, du Plessis, Stefan, Yun, Je-Yeon, Kwon, Jun Soo, Dannlowski, Udo, Hahn, Tim, Grotegerd, Dominik, Alloza, Clara, Arango, Celso, Janssen, Joost, Díaz-Caneja, Covadonga, Jiang, Wenhao, Calhoun, Vince, Ehrlich, Stefan, Yang, Kun, Cascella, Nicola G., Takayanagi, Yoichiro, Sawa, Akira, Tomyshev, Alexander, Lebedeva, Irina, Kaleda, Vasily, Kirschner, Matthias, Hoschl, Cyril, Tomecek, David, Skoch, Antonin, van Amelsvoort, Therese, Bakker, Geor, James, Anthony, Preda, Adrian, Weideman, Andrea, Stein, Dan J., Howells, Fleur, Uhlmann, Anne, Temmingh, Henk, López-Jaramillo, Carlos, Díaz-Zuluaga, Ana, Fortea, Lydia, Martinez-Heras, Eloy, Solana, Elisabeth, Llufriu, Sara, Jahanshad, Neda, Thompson, Paul, Turner, Jessica, and van Erp, Theo
Published: 2020
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49. Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia

Author: Jiang, Yuchao, primary, Luo, Cheng, additional, Wang, Jijun, additional, Palaniyappan, Lena, additional, Chang, Xiao, additional, Xiang, Shitong, additional, Zhang, Jie, additional, Duan, Mingjun, additional, Huang, Huan, additional, Gaser, Christian, additional, Nemoto, Kiyotaka, additional, Miura, Kenichiro, additional, Hashimoto, Ryota, additional, Westlye, Lars T., additional, Richard, Genevieve, additional, Fernandez-Cabello, Sara, additional, Parker, Nadine, additional, Andreassen, Ole A., additional, Kircher, Tilo, additional, Nenadi?, Igor, additional, Stein, Frederike, additional, Thomas-Odenthal, Florian, additional, Teutenberg, Lea, additional, Usemann, Paula, additional, Dannlowski, Udo, additional, Hahn, Tim, additional, Grotegerd, Dominik, additional, Meinert, Susanne, additional, Lencer, Rebekka, additional, Tang, Yingying, additional, Zhang, Tianhong, additional, Li, Chunbo, additional, Yue, Weihua, additional, Zhang, Yuyanan, additional, Yu, Xin, additional, Zhou, Enpeng, additional, Lin, Ching-Po, additional, Tsai, Shih-Jen, additional, Rodrigue, Amanda L., additional, Glahn, David, additional, Pearlson, Godfrey, additional, Blangero, John, additional, Karuk, Andriana, additional, pomarol-Clotet, Edith, additional, Salvador, Raymond, additional, Fuentes-Claramonte, Paola, additional, angeles, Maria, additional, Spalletta, Gianfranco, additional, Piras, Fabrizio, additional, Vecchio, Daniela, additional, Banaj, Nerisa, additional, Cheng, Jingliang, additional, Liu, Zhening, additional, Yang, Jie, additional, Gonul, Ali Saffet, additional, Uslu, Ozgul, additional, Burhanoglu, Birce Begum, additional, Demir, Aslihan Uyar, additional, Rootes-Murdy, Kelly, additional, Calhoun, Vince D., additional, Sim, Kang, additional, Green, Melissa, additional, Quide, Yann, additional, Chung, Young Chul, additional, Kim, Woo-Sung, additional, Sponheim, Scott R., additional, Demro, Caroline, additional, Ramsay, Ian S., additional, Iasevoli, Felice, additional, Bartolomeis, Andrea de, additional, Barone, Annarita, additional, Ciccarelli, Mariateresa, additional, Brunetti, Arturo, additional, Cocozza, Sirio, additional, Pontillo, Giuseppe, additional, Tranfa, Mario, additional, Park, Min Tae M., additional, Kirschner, Matthias, additional, Georgiadis, Foivos, additional, Kaiser, Stefan, additional, Rheenen, Tamsyn Van, additional, Rossell, Susan, additional, Hughes, Matthew, additional, Woods, William, additional, Carruthers, Sean, additional, Sumner, Philip, additional, Ringin, Elysha, additional, Spaniel, Filip, additional, Skoch, Antonin, additional, Tomecek, David, additional, Homan, Philipp, additional, Homan, Stephanie, additional, Omlor, Wolfgang, additional, Cecere, Giacomo, additional, Nguyen, Dana D, additional, Preda, Adrian, additional, Thomopoulos, Sophia, additional, Jahanshad, Neda, additional, Cui, Long-Biao, additional, Yao, Dezhong, additional, Thompson, Paul M., additional, Turner, Jessica A., additional, van Erp, Theo G.M., additional, Cheng, Wei, additional, and Feng, Jianfeng, additional
Published: 2023
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50. Auditory hallucinations activate language and verbal short-term memory, but not auditory, brain regions

Author: Fuentes-Claramonte, Paola, Soler-Vidal, Joan, Salgado-Pineda, Pilar, García-León, María Ángeles, Ramiro, Nuria, Santo-Angles, Aniol, Llanos Torres, María, Tristany, Josep, Guerrero-Pedraza, Amalia, Munuera, Josep, Sarró, Salvador, Salvador, Raymond, Hinzen, Wolfram, McKenna, Peter J., and Pomarol-Clotet, Edith
Published: 2021
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