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1. 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

2. 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

3. White matter microstructure in obesity and bipolar disorders: an ENIGMA bipolar disorder working group study in 2186 individuals

Author

Dietze, Lorielle M. F., McWhinney, Sean R., Favre, Pauline, Abé, Christoph, Alexander, Nina, Barkhau, Carlotta, Benedetti, Francesco, Berk, Michael, Bøen, Erlend, Boye, Birgitte, Brosch, Katharina, Canales-Rodríguez, Erick J., Cannon, Dara M., Carruthers, Sean P., Corkum, Emily L. V., Dannlowski, Udo, Díaz-Zuluaga, Ana M., Dohm, Katharina, Elvsåshagen, Torbjørn, Flinkenflügel, Kira, Fortea, Lydia, Furlong, Lisa S., Goldstein, Benjamin I., Grotegerd, Dominik, Gruber, Marius, Haarman, Bartholomeus C. M., Howells, Fleur M., Jahanshad, Neda, Jamalabadi, Hamidreza, Jansen, Andreas, Karantonis, James A., Kennedy, Kody G., Kircher, Tilo T. J., Klahn, Anna Luisa, Kochunov, Peter, Kraus, Anna, Landén, Mikael, López-Jaramillo, Carlos, MacIntosh, Bradley J., Mazza, Elena, McDonald, Colm, McIntosh, Andrew M., Meinert, Hannah, Meinert, Susanne, Melloni, Elisa M. T., Mitchell, Philip B., Nenadić, Igor, Opel, Nils, Phillips, Mary, Piguet, Camille, Polosan, Mircea, Pomarol-Clotet, Edith, Pouchon, Arnaud, Radua, Joaquim, Roberts, Gloria, Ross, Alex J., Rossell, Susan L., Salvador, Raymond, Sim, Kang, Soares, Jair C., Zunta-Soares, Giovana B., Stein, Frederike, Straube, Benjamin, Suo, Chao, Teutenberg, Lea, Thomas-Odenthal, Florian, Thomopoulos, Sophia I., Usemann, Paula, Van Rheenen, Tamsyn E., Versace, Amelia, Vieta, Eduard, Vilajosana, Enric, Mwangi, Benson, Wen, Wei, Whalley, Heather C., Wu, Mon-Ju, Andreassen, Ole A., Ching, Christopher R. K., Thompson, Paul M., Houenou, Josselin, and Hajek, Tomas

Published
2024
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5. Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Author

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

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

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

Published
2024

6. Cerebellar network alterations in adult attention-deficit/hyperactivity disorder

Author

Parkkinen, Salla, Radua, Joaquim, Andrews, Derek S., Murphy, Declan, Dell'Acqua, Flavio, and Parlatini, Valeria

Subjects
Neural circuitry -- Physiological aspects -- Health aspects, Cerebellum -- Physiological aspects -- Health aspects, Attention-deficit hyperactivity disorder -- Development and progression, Health, Psychology and mental health
Abstract

Background: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition that often persists into adulthood. Underlying alterations in brain connectivity have been identified but some relevant connections, such as the middle, superior, and inferior cerebellar peduncles (MCP, SCP, and ICP, respectively), have remained largely unexplored; thus, we sought to investigate whether the cerebellar peduncles contribute to ADHD pathophysiology among adults. Methods: We applied diffusion-weighted spherical deconvolution tractography to dissect the cerebellar peduncles of male adults with ADHD (including those who did or did not respond to methylphenidate, based on at least 30% symptom improvement at 2 months) and controls. We investigated differences in tract metrics between controls and the whole ADHD sample and between controls and treatment-response groups using sensitivity analyses. Finally, we analyzed the association between the tract metrics and cliniconeuropsychological profiles. Results: We included 60 participants with ADHD (including 42 treatment responders and 18 nonresponders) and 20 control participants. In the whole ADHD sample, MCP fractional anisotropy (FA; [t.sub.78] = 3.24, p = 0.002) and hindrance modulated orientational anisotropy (HMOA; [t.sub.78] = 3.01, p = 0.004) were reduced, and radial diffusivity (RD) in the right ICP was increased ([t.sub.78] = -2.84, p = 0.006), compared with controls. Although case-control differences in MCP FA and HMOA, which reflect white-matter microstructural organization, were driven by both treatment response groups, only responders significantly differed from controls in right ICP RD, which relates to myelination ([t6.sub.0] = 3.14, p = 0.003). Hindrance modulated orientational anisotropy of the MCP was significantly positively associated with hyperactivity measures. Limitations: This study included only male adults with ADHD. Further research needs to investigate potential sex- and development-related differences. Conclusion: These results support the role of the cerebellar networks, especially of the MCP, in adult ADHD pathophysiology and should encourage further investigation. Clinical trial registration: NCT 03709940, Introduction Attention-deficit/hyperactivity disorder (ADHD) is a neuro-developmental condition characterized by inattentive or hyperactive-impulsive symptoms, (1) and is estimated to affect about 4% of adults. (2) Adult ADHD has been associated [...]

Published
2024
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7. 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

8. 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

10. The magnitude and variability of neurocognitive performance in first-episode psychosis: a systematic review and meta-analysis of longitudinal studies

Author

Catalan, Ana, McCutcheon, Robert A., Aymerich, Claudia, Pedruzo, Borja, Radua, Joaquim, Rodríguez, Victoria, Salazar de Pablo, Gonzalo, Pacho, Malein, Pérez, Jose Luis, Solmi, Marco, McGuire, Philip, Giuliano, Anthony J., Stone, William S., Murray, Robin M., Gonzalez-Torres, Miguel Angel, and Fusar-Poli, Paolo

Published
2024
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11. Validation of the Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) questionnaire for adults

Author

Solmi, Marco, Thompson, Trevor, Estradé, Andrés, Agorastos, Agorastos, Radua, Joaquim, Cortese, Samuele, Dragioti, Elena, Leisch, Friedrich, Vancampfort, Davy, Thygesen, Lau Caspar, Aschauer, Harald, Schlögelhofer, Monika, Aschauer, Elena, Schneeberger, Andres, Huber, Christian G, Hasler, Gregor, Conus, Philippe, Do Cuénod, Kim Q, von Känel, Roland, Arrondo, Gonzalo, Fusar-Poli, Paolo, Gorwood, Philip, Llorca, Pierre-Michel, Krebs, Marie-Odile, Scanferla, Elisabetta, Kishimoto, Taishiro, Rabbani, Golam, Skonieczna-Żydecka, Karolina, Brambilla, Paolo, Favaro, Angela, Takamiya, Akihiro, Zoccante, Leonardo, Colizzi, Marco, Bourgin, Julie, Kamiński, Karol, Moghadasin, Maryam, Seedat, Soraya, Matthews, Evan, Wells, John, Vassilopoulou, Emilia, Gadelha, Ary, Su, Kuan-Pin, Kwon, Jun Soo, Kim, Minah, Lee, Tae Young, Papsuev, Oleg, Manková, Denisa, Boscutti, Andrea, Gerunda, Cristiano, Saccon, Diego, Righi, Elena, Monaco, Francesco, Croatto, Giovanni, Cereda, Guido, Demurtas, Jacopo, Brondino, Natascia, Veronese, Nicola, Enrico, Paolo, Politi, Pierluigi, Ciappolino, Valentina, Pfennig, Andrea, Bechdolf, Andreas, Meyer-Lindenberg, Andreas, Kahl, Kai G, Domschke, Katharina, Bauer, Michael, Koutsouleris, Nikolaos, Winter, Sibylle, Borgwardt, Stefan, Bitter, Istvan, Balazs, Judit, Czobor, Pál, Unoka, Zsolt, Mavridis, Dimitris, Tsamakis, Konstantinos, Bozikas, Vasilios P, Tunvirachaisakul, Chavit, Maes, Michael, Rungnirundorn, Teerayuth, Supasitthumrong, Thitiporn, Haque, Ariful, Brunoni, Andre R, Costardi, Carlos Gustavo, Schuch, Felipe Barreto, Polanczyk, Guilherme, Luiz, Jhoanne Merlyn, Fonseca, Lais, Aparicio, Luana V, Valvassori, Samira S, Nordentoft, Merete, Vendsborg, Per, Hoffmann, Sofie Have, Sehli, Jihed, Sartorius, Norman, Heuss, Sabina, Guinart, Daniel, Hamilton, Jane, Kane, John, Rubio, Jose, and Sand, Michael

Subjects
Clinical and Health Psychology, Health Sciences, Psychology, Mental Health, Behavioral and Social Science, Brain Disorders, Depression, Mental health, Good Health and Well Being, Humans, Adult, Reproducibility of Results, Pandemics, COVID-19, Surveys and Questionnaires, Outcome Assessment, Health Care, Factor Analysis, Statistical, Psychometrics, Covid-19, Pandemic, COH-FIT, Survey: P-factor: well-being: mental health: psychiatry: psychometric, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundThe Collaborative Outcome study on Health and Functioning during Infection Times (COH-FIT; www.coh-fit.com) is an anonymous and global online survey measuring health and functioning during the COVID-19 pandemic. The aim of this study was to test concurrently the validity of COH-FIT items and the internal validity of the co-primary outcome, a composite psychopathology "P-score".MethodsThe COH-FIT survey has been translated into 30 languages (two blind forward-translations, consensus, one independent English back-translation, final harmonization). To measure mental health, 1-4 items ("COH-FIT items") were extracted from validated questionnaires (e.g. Patient Health Questionnaire 9). COH-FIT items measured anxiety, depressive, post-traumatic, obsessive-compulsive, bipolar and psychotic symptoms, as well as stress, sleep and concentration. COH-FIT Items which correlated r ≥ 0.5 with validated companion questionnaires, were initially retained. A P-score factor structure was then identified from these items using exploratory factor analysis (EFA) and confirmatory factor analyses (CFA) on data split into training and validation sets. Consistency of results across languages, gender and age was assessed.ResultsFrom >150,000 adult responses by May 6th, 2022, a subset of 22,456 completed both COH-FIT items and validated questionnaires. Concurrent validity was consistently demonstrated across different languages for COH-FIT items. CFA confirmed EFA results of five first-order factors (anxiety, depression, post-traumatic, psychotic, psychophysiologic symptoms) and revealed a single second-order factor P-score, with high internal reliability (ω = 0.95). Factor structure was consistent across age and sex.ConclusionsCOH-FIT is a valid instrument to globally measure mental health during infection times. The P-score is a valid measure of multidimensional mental health.

Published
2023

12. Mapping gray and white matter volume abnormalities in early-onset psychosis: an ENIGMA multicenter voxel-based morphometry study

Author

Si, Shuqing, Bi, Anbreen, Yu, Zhaoying, See, Cheryl, Kelly, Sinead, Ambrogi, Sonia, Arango, Celso, Baeza, Inmaculada, Banaj, Nerisa, Berk, Michael, Castro-Fornieles, Josefina, Crespo-Facorro, Benedicto, Crouse, Jacob J., Díaz-Caneja, Covadonga M., Fett, Anne-Kathrin, Fortea, Adriana, Frangou, Sophia, Goldstein, Benjamin I., Hickie, Ian B., Janssen, Joost, Kennedy, Kody G., Krabbendam, Lydia, Kyriakopoulos, Marinos, MacIntosh, Bradley J., Morgado, Pedro, Nerland, Stener, Pascual-Diaz, Saül, Picó-Pérez, Maria, Piras, Fabrizio, Rund, Bjørn Rishovd, de la Serna, Elena, Spalletta, Gianfranco, Sugranyes, Gisela, Suo, Chao, Tordesillas-Gutiérrez, Diana, Vecchio, Daniela, Radua, Joaquim, McGuire, Philip, Thomopoulos, Sophia I., Jahanshad, Neda, Thompson, Paul M., Barth, Claudia, Agartz, Ingrid, James, Anthony, and Kempton, Matthew J.

Published
2024
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13. EMDR therapy vs. supportive therapy as adjunctive treatment in trauma-exposed bipolar patients: A randomised controlled trial

Author

Hogg, Bridget, Radua, Joaquim, Gardoki-Souto, Itxaso, Fontana-McNally, Marta, Lupo, Walter, Reinares, María, Jiménez, Esther, Madre, Mercè, Blanco-Presas, Laura, Cortizo, Romina, Massó-Rodriguez, Anna, Castaño, Juan, Argila, Isabel, Castro-Rodriguez, José Ignacio, Comes, Mercè, Macias, Cristina, Sánchez-González, Roberto, Mur-Mila, Estanislao, Novo, Patricia, Rosa, Adriane R., Vieta, Eduard, Padberg, Frank, Pérez-Solà, Victor, Valiente-Gómez, Alicia, Moreno-Alcázar, Ana, and Amann, Benedikt L.

Published
2024
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15. Physical comorbidities of older age bipolar disorder (OABD) patients: A global replication analysis of prevalence and sex differences

Author

Teixeira, Antonio L., Almeida, Osvaldo P., Lavin, Paola, Barbosa, Izabela G., Alda, Martin, Altinbas, Kursat, Balanzá-Martínez, Vicent, Briggs, Farren B.S., Calkin, Cynthia, Chen, Peijun, Dols, Annemieke, Eyler, Lisa T., Forester, Brent P., Forlenza, Orestes V., Gildengers, Ariel G., Hajek, Tomas, Haarman, Benno, Korten, Nicole, Jimenez, Esther, Lafer, Beny, Levin, Jennifer B., Montejo, Laura, Nunes, Paula V., Olagunju, Andrew T., Oluwaniyi, Stephen, Oudega, Mardien L., Patrick, Regan E., Radua, Joaquim, Rej, Soham, Schouws, Sigfried, Soares, Jair C., Sutherland, Ashley N., Vieta, Eduard, Yala, Joy, and Sajatovic, Martha

Published
2024
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16. Should perception of emotions be classified according to threat detection rather than emotional valence? An updated meta-analysis for a whole-brain atlas of emotional faces processing

Author

Lukito, Steve, Fortea, Lydia, Groppi, Federica, Wykret, Ksenia Zuzanna, Tosi, Eleonora, Oliva, Vincenzo, Damiani, Stefano, Radua, Joaquim, and Fusar-Poli, Paolo

Subjects
Brain -- Social aspects, Brain research -- Social aspects, Health, Psychology and mental health
Abstract

Background: Human navigation of social interactions relies on the processing of emotion on faces. This meta-analysis aimed to produce an updated brain atlas of emotional face processing from whole-brain studies based on a single emotional face-viewing paradigm (PROSPERO CRD42022251548). Methods: We conducted a systematic literature search of Embase, MEDLINE and PsycINFO from May 2008 to October 2021. We used seed-based d mapping with permutation of subject images to conduct a quantitative meta-analysis of functional neuroimaging contrasts between emotional (e.g., angry, happy) and neutral faces. We conducted agglomerative hierarchical clustering of meta- analytic map contrasts of emotional faces relative to neutral faces. We investigated lateralization of emotional face processing. Results: From 5549 studies identified, 55 data sets (1489 healthy participants) met our inclusion criteria. Relative to neutral faces, we found extensive activation clusters by fearful faces in the right inferior temporal gyrus, right fusiform area, left putamen and amygdala, right parahippocampalgyrus and cerebellum; we found smaller activation clusters by angry faces in the right cerebellum and right middle temporal gyrus (MTG) and by disgusted faces in the left MTG. Happy and sad faces did not reach statistical significance. Clustering analyses showed similar activation patterns of fearful and angry faces; activation patterns of happy and sad faces showed the least correlation with other emotional faces. Emotional face processing was predominantly left-lateralized in the amygdala and anterior insula, and right-lateralized in the ventromedial prefrontal cortex. Limitations: Reliance on discretized effect sizes based on peak coordinate location instead of statistical brain maps, and the varying level of statistical threshold reporting from original studies, could lead to underdetection of smaller clusters of activation. Conclusion: Processing of emotional faces appeared to be oriented toward identifying threats on faces, from highest (i.e., angry or fearful faces) to lowest level (i.e., happy or sad faces), with a more complex lateralization pattern than previously theorized. Emotional faces may be processed in latent grouping but organized by threat content rather than emotional valence., Introduction Human beings are a socially complex species. (1) Our ability to navigate social interactions is dependent, in part, on the effective processing of emotions on faces. Such processing allows [...]

Published
2023
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17. Collaborative outcomes study on health and functioning during infection times (COH-FIT): Insights on modifiable and non-modifiable risk and protective factors for wellbeing and mental health during the COVID-19 pandemic from multivariable and network analyses

Author

Solmi, Marco, Thompson, Trevor, Cortese, Samuele, Estradé, Andrés, Agorastos, Agorastos, Radua, Joaquim, Dragioti, Elena, Vancampfort, Davy, Thygesen, Lau Caspar, Aschauer, Harald, Schlögelhofer, Monika, Aschauer, Elena, Schneeberger, Andres, Huber, Christian G., Hasler, Gregor, Conus, Philippe, Cuénod, Kim Q. Do, von Känel, Roland, Arrondo, Gonzalo, Fusar-Poli, Paolo, Gorwood, Philip, Llorca, Pierre-Michel, Krebs, Marie-Odile, Scanferla, Elisabetta, Kishimoto, Taishiro, Rabbani, Golam, Skonieczna-Żydecka, Karolina, Brambilla, Paolo, Favaro, Angela, Takamiya, Akihiro, Zoccante, Leonardo, Colizzi, Marco, Bourgin, Julie, Kamiński, Karol, Moghadasin, Maryam, Seedat, Soraya, Matthews, Evan, Wells, John, Vassilopoulou, Emilia, Gadelha, Ary, Su, Kuan-Pin, Kwon, Jun Soo, Kim, Minah, Lee, Tae Young, Papsuev, Oleg, Manková, Denisa, Boscutti, Andrea, Gerunda, Cristiano, Saccon, Diego, Righi, Elena, Monaco, Francesco, Croatto, Giovanni, Cereda, Guido, Demurtas, Jacopo, Brondino, Natascia, Veronese, Nicola, Enrico, Paolo, Politi, Pierluigi, Ciappolino, Valentina, Pfennig, Andrea, Bechdolf, Andreas, Meyer-Lindenberg, Andreas, Kahl, Kai G., Domschke, Katharina, Bauer, Michael, Koutsouleris, Nikolaos, Winter, Sibylle, Borgwardt, Stefan, Bitter, Istvan, Balazs, Judit, Czobor, Pál, Unoka, Zsolt, Mavridis, Dimitris, Tsamakis, Konstantinos, Bozikas, Vasilios P., Tunvirachaisakul, Chavit, Maes, Michael, Rungnirundorn, Teerayuth, Supasitthumrong, Thitiporn, Haque, Ariful, Brunoni, Andre R., Costardi, Carlos Gustavo, Schuch, Felipe Barreto, Polanczyk, Guilherme, Luiz, Jhoanne Merlyn, Fonseca, Lais, Aparicio, Luana V., Valvassori, Samira S., Nordentoft, Merete, Vendsborg, Per, Hoffmann, Sofie Have, Sehli, Jihed, Sartorius, Norman, Heuss, Sabina, Guinart, Daniel, Hamilton, Jane, Kane, John, Rubio, Jose, Sand, Michael, Koyanagi, Ai, Solanes, Aleix, Andreu-Bernabeu, Alvaro, Cáceres, Antonia San José, Arango, Celso, Díaz-Caneja, Covadonga M., Hidalgo-Mazzei, Diego, Vieta, Eduard, Gonzalez-Peñas, Javier, Fortea, Lydia, Parellada, Mara, Fullana, Miquel A., Verdolini, Norma, Andrlíková, Eva, Janků, Karolina, Millan, Mark J., Honciuc, Mihaela, Moniuszko-Malinowska, Anna, Łoniewski, Igor, Samochowiec, Jerzy, Kiszkiel, Łukasz, Marlicz, Maria, Sowa, Paweł, Marlicz, Wojciech, Spies, Georgina, Stubbs, Brendon, Firth, Joseph, Sullivan, Sarah, Darcin, Asli Enez, Aksu, Hatice, Dilbaz, Nesrin, Noyan, Onur, Kitazawa, Momoko, Kurokawa, Shunya, Tazawa, Yuki, Anselmi, Alejandro, Cracco, Cecilia, Machado, Ana Inés, Estrade, Natalia, De Leo, Diego, Curtis, Jackie, Berk, Michael, Carvalho, Andre F., Ward, Philip, Teasdale, Scott, Rosenbaum, Simon, Marx, Wolfgang, Horodnic, Adrian Vasile, Oprea, Liviu, Alexinschi, Ovidiu, Ifteni, Petru, Turliuc, Serban, Ciuhodaru, Tudor, Bolos, Alexandra, Matei, Valentin, Nieman, Dorien H., Sommer, Iris, van Os, Jim, van Amelsvoort, Therese, Sun, Ching-Fang, Guu, Ta-wei, Jiao, Can, Zhang, Jieting, Fan, Jialin, Zou, Liye, Yu, Xin, Chi, Xinli, de Timary, Philippe, van Winkel, Ruud, Ng, Bernardo, Peña de León, Edilberto, Arellano, Ramon, Roman, Raquel, Sanchez, Thelma, Movina, Larisa, Morgado, Pedro, Brissos, Sofia, Aizberg, Oleg, Mosina, Anna, Krinitski, Damir, Mugisha, James, Sadeghi-Bahmani, Dena, Sheybani, Farshad, Sadeghi, Masoud, Hadi, Samira, Brand, Serge, Errazuriz, Antonia, Crossley, Nicolas, Ristic, Dragana Ignjatovic, López-Jaramillo, Carlos, Efthymiou, Dimitris, Kuttichira, Praveenlal, Kallivayalil, Roy Abraham, Javed, Afzal, Afridi, Muhammad Iqbal, James, Bawo, Seb-Akahomen, Omonefe Joy, Fiedorowicz, Jess, Daskalakis, Jeff, Yatham, Lakshmi N., Yang, Lin, Okasha, Tarek, Dahdouh, Aïcha, Tiihonen, Jari, Shin, Jae Il, Lee, Jinhee, Mhalla, Ahmed, Gaha, Lotfi, Brahim, Takoua, Altynbekov, Kuanysh, Negay, Nikolay, Nurmagambetova, Saltanat, Jamei, Yasser Abu, Weiser, Mark, and Correll, Christoph U

Published
2025
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18. Incidence, prevalence, and global burden of ADHD from 1990 to 2019 across 204 countries: data, with critical re-analysis, from the Global Burden of Disease study

Author

Cortese, Samuele, Song, Minjin, Farhat, Luis C., Yon, Dong Keon, Lee, Seung Won, Kim, Min Seo, Park, Seoyeon, Oh, Jae Won, Lee, San, Cheon, Keun-Ah, Smith, Lee, Gosling, Corentin J., Polanczyk, Guilherme V., Larsson, Henrik, Rohde, Luis A., Faraone, Stephen V., Koyanagi, Ai, Dragioti, Elena, Radua, Joaquim, Carvalho, Andre F., Il Shin, Jae, and Solmi, Marco

Published
2023
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19. An umbrella review of candidate predictors of response, remission, recovery, and relapse across mental disorders

Author

Solmi, Marco, Cortese, Samuele, Vita, Giovanni, De Prisco, Michele, Radua, Joaquim, Dragioti, Elena, Köhler-Forsberg, Ole, Madsen, Nanna M., Rohde, Christopher, Eudave, Luis, Aymerich, Claudia, Pedruzo, Borja, Rodriguez, Victoria, Rosson, Stella, Sabé, Michel, Hojlund, Mikkel, Catalan, Ana, de Luca, Beatrice, Fornaro, Michele, Ostuzzi, Giovanni, Barbui, Corrado, Salazar-de-Pablo, Gonzalo, Fusar-Poli, Paolo, and Correll, Christoph U.

Published
2023
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20. Global and risk-group stratified well-being and mental health during the COVID-19 pandemic in adults: Results from the international COH-FIT Study

Author

Solmi, Marco, Thompson, Trevor, Estradé, Andrés, Agorastos, Agorastos, Radua, Joaquim, Cortese, Samuele, Dragioti, Elena, Vancampfort, Davy, Thygesen, Lau Caspar, Aschauer, Harald, Schlögelhofer, Monika, Aschauer, Elena, Schneeberger, Andres, Huber, Christian G., Hasler, Gregor, Conus, Philippe, Cuénod, Kim Q. Do, von Känel, Roland, Arrondo, Gonzalo, Fusar-Poli, Paolo, Gorwood, Philip, Llorca, Pierre-Michel, Krebs, Marie-Odile, Scanferla, Elisabetta, Kishimoto, Taishiro, Rabbani, Golam, Skonieczna-Żydecka, Karolina, Brambilla, Paolo, Favaro, Angela, Takamiya, Akihiro, Zoccante, Leonardo, Colizzi, Marco, Bourgin, Julie, Kamiński, Karol, Moghadasin, Maryam, Seedat, Soraya, Matthews, Evan, Wells, John, Vassilopoulou, Emilia, Gadelha, Ary, Su, Kuan-Pin, Kwon, Jun Soo, Kim, Minah, Lee, Tae Young, Papsuev, Oleg, Manková, Denisa, Boscutti, Andrea, Gerunda, Cristiano, Saccon, Diego, Righi, Elena, Monaco, Francesco, Croatto, Giovanni, Cereda, Guido, Demurtas, Jacopo, Brondino, Natascia, Veronese, Nicola, Enrico, Paolo, Politi, Pierluigi, Ciappolino, Valentina, Pfennig, Andrea, Bechdolf, Andreas, Meyer-Lindenberg, Andreas, Kahl, Kai G., Domschke, Katharina, Bauer, Michael, Koutsouleris, Nikolaos, Winter, Sibylle, Borgwardt, Stefan, Bitter, Istvan, Balazs, Judit, Czobor, Pál, Unoka, Zsolt, Mavridis, Dimitris, Tsamakis, Konstantinos, Bozikas, Vasilios P., Tunvirachaisakul, Chavit, Maes, Michael, Rungnirundorn, Teerayuth, Supasitthumrong, Thitiporn, Haque, Ariful, Brunoni, Andre R., Costardi, Carlos Gustavo, Schuch, Felipe Barreto, Polanczyk, Guilherme, Luiz, Jhoanne Merlyn, Fonseca, Lais, Aparicio, Luana V., Valvassori, Samira S., Nordentoft, Merete, Vendsborg, Per, Hoffmann, Sofie Have, Sehli, Jihed, Sartorius, Norman, Heuss, Sabina, Guinart, Daniel, Hamilton, Jane, Kane, John, Rubio, Jose, Sand, Michael, Koyanagi, Ai, Solanes, Aleix, Andreu-Bernabeu, Alvaro, Cáceres, Antonia San José, Arango, Celso, Díaz-Caneja, Covadonga M., Hidalgo-Mazzei, Diego, Vieta, Eduard, Gonzalez-Peñas, Javier, Fortea, Lydia, Parellada, Mara, Fullana, Miquel A., Verdolini, Norma, Andrlíková, Eva, Janků, Karolina, Millan, Mark J., Honciuc, Mihaela, Moniuszko-Malinowska, Anna, Łoniewski, Igor, Samochowiec, Jerzy, Kiszkiel, Łukasz, Marlicz, Maria, Sowa, Paweł, Marlicz, Wojciech, Spies, Georgina, Stubbs, Brendon, Firth, Joseph, Sullivan, Sarah, Darcin, Asli Enez, Aksu, Hatice, Dilbaz, Nesrin, Noyan, Onur, Kitazawa, Momoko, Kurokawa, Shunya, Tazawa, Yuki, Anselmi, Alejandro, Cracco, Cecilia, Machado, Ana Inés, Estrade, Natalia, De Leo, Diego, Curtis, Jackie, Berk, Michael, Carvalho, Andre F., Ward, Philip, Teasdale, Scott, Rosenbaum, Simon, Marx, Wolfgang, Horodnic, Adrian Vasile, Oprea, Liviu, Alexinschi, Ovidiu, Ifteni, Petru, Turliuc, Serban, Ciuhodaru, Tudor, Bolos, Alexandra, Matei, Valentin, Nieman, Dorien H., Sommer, Iris, van Os, Jim, van Amelsvoort, Therese, Sun, Ching-Fang, Guu, Ta-wei, Jiao, Can, Zhang, Jieting, Fan, Jialin, Zou, Liye, Yu, Xin, Chi, Xinli, de Timary, Philippe, van Winkel, Ruud, Ng, Bernardo, Peña de León, Edilberto, Arellano, Ramon, Roman, Raquel, Sanchez, Thelma, Movina, Larisa, Morgado, Pedro, Brissos, Sofia, Aizberg, Oleg, Mosina, Anna, Krinitski, Damir, Mugisha, James, Sadeghi-Bahmani, Dena, Sheybani, Farshad, Sadeghi, Masoud, Hadi, Samira, Brand, Serge, Errazuriz, Antonia, Crossley, Nicolas, Ristic, Dragana Ignjatovic, López-Jaramillo, Carlos, Efthymiou, Dimitris, Kuttichira, Praveenlal, Kallivayalil, Roy Abraham, Javed, Afzal, Afridi, Muhammad Iqbal, James, Bawo, Seb-Akahomen, Omonefe Joy, Fiedorowicz, Jess, Daskalakis, Jeff, Yatham, Lakshmi N., Yang, Lin, Okasha, Tarek, Dahdouh, Aïcha, Tiihonen, Jari, Shin, Jae Il, Lee, Jinhee, Mhalla, Ahmed, Gaha, Lotfi, Brahim, Takoua, Altynbekov, Kuanysh, Negay, Nikolay, Nurmagambetova, Saltanat, Jamei, Yasser Abu, Weiser, Mark, and Correll, Christoph U.

Published
2024
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24. Why we need to pursue both universal and targeted prevention to reduce the incidence of affective and psychotic disorders: Systematic review and meta-analysis

Author

Brodeur, Sebastien, Oliver, Dominic, Ahmed, Muhammad S., Radua, Joaquim, Venables, Jemma, Gao, Yueming, Gigante, Vincenzo, Veneziano, Giulia, Vinci, Giulia, Chesney, Edward, Nandha, Sunil, De Micheli, Andrea, Basadonne, Ilaria, Floris, Valentina, Salazar de Pablo, Gonzalo, and Fusar-Poli, Paolo

Published
2024
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28. PRimary carE digital Support ToOl in mental health (PRESTO): Design, development and study protocols

Author

Anmella, Gerard, Primé-Tous, Mireia, Segú, Xavier, Solanes, Aleix, Ruíz, Victoria, Martín-Villalba, Inés, Morilla, Ivette, Also-Fontanet, Antonieta, Sant, Elisenda, Murgui, Sandra, Sans-Corrales, Mireia, Murru, Andrea, Zahn, Roland, Young, Allan H., Vicens, Victor, Viñas-Bardolet, Clara, Martínez-Cerdá, Juan Francisco, Blanch, Jordi, Radua, Joaquim, Fullana, Miquel-Àngel, Cavero, Myriam, Vieta, Eduard, and Hidalgo-Mazzei, Diego

Published
2024
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30. 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

31. 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

32. 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

33. Neuroanatomical heterogeneity and homogeneity in individuals at clinical high risk for psychosis

Author

Baldwin, Helen, Radua, Joaquim, Antoniades, Mathilde, Haas, Shalaila S, Frangou, Sophia, Agartz, Ingrid, Allen, Paul, Andreassen, Ole A, Atkinson, Kimberley, Bachman, Peter, Baeza, Inmaculada, Bartholomeusz, Cali F, Chee, Michael WL, Colibazzi, Tiziano, Cooper, Rebecca E, Corcoran, Cheryl M, Cropley, Vanessa L, Ebdrup, Bjørn H, Fortea, Adriana, Glenthøj, Louise Birkedal, Hamilton, Holly K, Haut, Kristen M, Hayes, Rebecca A, He, Ying, Heekeren, Karsten, Kaess, Michael, Kasai, Kiyoto, Katagiri, Naoyuki, Kim, Minah, Kindler, Jochen, Klaunig, Mallory J, Koike, Shinsuke, Koppel, Alex, Kristensen, Tina D, Bin Kwak, Yoo, Kwon, Jun Soo, Lawrie, Stephen M, Lebedeva, Irina, Lee, Jimmy, Lin, Ashleigh, Loewy, Rachel L, Mathalon, Daniel H, Michel, Chantal, Mizrahi, Romina, Møller, Paul, Nelson, Barnaby, Nemoto, Takahiro, Nordholm, Dorte, Omelchenko, Maria A, Pantelis, Christos, Raghava, Jayachandra M, Røssberg, Jan I, Rössler, Wulf, Salisbury, Dean F, Sasabayashi, Daiki, Schall, Ulrich, Smigielski, Lukasz, Sugranyes, Gisela, Suzuki, Michio, Takahashi, Tsutomu, Tamnes, Christian K, Tang, Jinsong, Theodoridou, Anastasia, Thomopoulos, Sophia I, Tomyshev, Alexander S, Uhlhaas, Peter J, Værnes, Tor G, van Amelsvoort, Therese AMJ, Van Erp, Theo GM, Waltz, James A, Westlye, Lars T, Wood, Stephen J, Zhou, Juan H, McGuire, Philip, Thompson, Paul M, Jalbrzikowski, Maria, Hernaus, Dennis, and Fusar-Poli, Paolo

Subjects
Serious Mental Illness, Prevention, Biomedical Imaging, Mental Health, Clinical Research, Neurosciences, Brain, Humans, Magnetic Resonance Imaging, Psychotic Disorders, ENIGMA Clinical High Risk for Psychosis Working Group, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the 'normativeness' of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.

Published
2022

34. 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

35. Neuroanatomical and cognitive correlates of visual hallucinations in Parkinson’s disease and dementia with Lewy bodies: Voxel-based morphometry and neuropsychological meta-analysis

Author

Pezzoli, Stefania, Sánchez-Valle, Raquel, Solanes, Aleix, Kempton, Matthew J, Bandmann, Oliver, Shin, Jae Il, Cagnin, Annachiara, Goldman, Jennifer G, Merkitch, Doug, Firbank, Michael J, Taylor, John-Paul, Pagonabarraga, Javier, Kulisevsky, Jaime, Blanc, Frederic, Verdolini, Norma, Venneri, Annalena, and Radua, Joaquim

Subjects
Biomedical and Clinical Sciences, Health Sciences, Mental Health, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Acquired Cognitive Impairment, Brain Disorders, Parkinson's Disease, Neurosciences, Aging, 2.1 Biological and endogenous factors, Aetiology, Neurological, Cognition, Hallucinations, Humans, Lewy Body Disease, Magnetic Resonance Imaging, Neuropsychological Tests, Parkinson Disease, Lewy body disease, Visual hallucinations, Dementia with Lewy bodies, Meta-analysis, VBM, MRI, Grey matter volume, Neuropsychology, Memory, Parkinson's disease, Parkinson’s disease, Medical and Health Sciences, Psychology and Cognitive Sciences, Behavioral Science & Comparative Psychology, Biomedical and clinical sciences, Health sciences
Abstract

Visual hallucinations (VH) are common in Parkinson's disease and dementia with Lewy bodies, two forms of Lewy body disease (LBD), but the neural substrates and mechanisms involved are still unclear. We conducted meta-analyses of voxel-based morphometry (VBM) and neuropsychological studies investigating the neuroanatomical and cognitive correlates of VH in LBD. For VBM (12 studies), we used Seed-based d Mapping with Permutation of Subject Images (SDM-PSI), including statistical parametric maps for 50% of the studies. For neuropsychology (35 studies), we used MetaNSUE to consider non-statistically significant unreported effects. VH were associated with smaller grey matter volume in occipital, frontal, occipitotemporal, and parietal areas (peak Hedges' g -0.34 to -0.49). In patients with Parkinson's disease without dementia, VH were associated with lower verbal immediate memory performance (Hedges' g -0.52). Both results survived correction for multiple comparisons. Abnormalities in these brain regions might reflect dysfunctions in brain networks sustaining visuoperceptive, attention, and executive abilities, with the latter also being at the basis of poor immediate memory performance.

Published
2021

36. Efficacy and effectiveness of antipsychotics in schizophrenia: network meta-analyses combining evidence from randomised controlled trials and real-world data

Author

Efthimiou, Orestis, Taipale, Heidi, Radua, Joaquim, Schneider-Thoma, Johannes, Pinzón-Espinosa, Justo, Ortuño, Maria, Vinkers, Christiaan H, Mittendorfer-Rutz, Ellenor, Cardoner, Narcís, Tanskanen, Antti, Fusar-Poli, Paolo, Cipriani, Andrea, Vieta, Eduard, Leucht, Stefan, Tiihonen, Jari, and Luykx, Jurjen J

Published
2024
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37. Coping behaviors to reduce anxiety and depressive symptoms: A prospective repeated assessment study

Author

Fortea, Lydia, Solanes, Aleix, Pomarol-Clotet, Edith, Garcia-Leon, Maria Angeles, Fortea, Adriana, Torrent, Carla, Varo, Cristina, Bonnin, Caterina del Mar, Montejo, Laura, Alonso, Jordi, Carmona, Susana, Soldevila-Matías, Pau, Alustiza, Irene, Arbós, Daniel, Hidalgo-Mazzei, Diego, Grande, Iria, Vieta, Eduard, Fullana, Miquel Àngel, and Radua, Joaquim

Published
2024
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42. Anxiety and depression played a central role in the COVID-19 mental distress: A network analysis

Author

Fico, Giovanna, Oliva, Vincenzo, De Prisco, Michele, Fortea, Lydia, Fortea, Adriana, Giménez-Palomo, Anna, Anmella, Gerard, Hidalgo-Mazzei, Diego, Vazquez, Mireia, Gomez-Ramiro, Marta, Carreras, Bernat, Murru, Andrea, Radua, Joaquim, Mortier, Philippe, Vilagut, Gemma, Amigo, Franco, Ferrer, Montse, García-Mieres, Helena, Vieta, Eduard, and Alonso, Jordi

Published
2023
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44. Improved estimation of the risk of manic relapse by combining clinical and brain scan data

Author

Palau, Pol, Solanes, Aleix, Madre, Merce, Saez-Francas, Naia, Sarró, Salvador, Moro, Noemí, Verdolini, Norma, Sanchez, Manel, Alonso-Lana, Sílvia, Amann, Benedikt L., Romaguera, Anna, Martin-Subero, Marta, Fortea, Lydia, Fuentes-Claramonte, Paola, García-León, Maria A., Munuera, Josep, Canales-Rodríguez, Erick Jorge, Fernández-Corcuera, Paloma, Brambilla, Paolo, Vieta, Eduard, Pomarol-Clotet, Edith, and Radua, Joaquim

Published
2023
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46. Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

Author

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, van Erp, Theo, collaborators, ENIGMA Consortium, 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, and Guo, Hua

Subjects
Brain Disorders, Biomedical Imaging, Mental Health, Schizophrenia, Neurosciences, Mental health, Adult, Algorithms, Cerebral Cortex, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Meta-Analysis as Topic, Middle Aged, Neuroimaging, Young Adult, Brain, Cortical thickness, Gray matter, Mega-analysis, Volume, ENIGMA Consortium collaborators, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

Published
2020

47. Incidence, prevalence, and global burden of autism spectrum disorder from 1990 to 2019 across 204 countries

Author

Solmi, Marco, Song, Minjin, Yon, Dong Keon, Lee, Seung Won, Fombonne, Eric, Kim, Min Seo, Park, Seoyeon, Lee, Min Ho, Hwang, Jimin, Keller, Roberto, Koyanagi, Ai, Jacob, Louis, Dragioti, Elena, Smith, Lee, Correll, Christoph U., Fusar-Poli, Paolo, Croatto, Giovanni, Carvalho, Andre F., Oh, Jae Won, Lee, San, Gosling, Corentin J., Cheon, Keun-Ah, Mavridis, Dimitris, Chu, Che-Sheng, Liang, Chih-Sung, Radua, Joaquim, Boyer, Laurent, Fond, Guillaume, Shin, Jae Il, and Cortese, Samuele

Published
2022
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50. Upward trends in eating disorders, self-harm, and suicide attempt emergency admissions in female adolescents after COVID-19 lockdown

Author

Fortea, Adriana, Fortea, Lydia, Gómez-Ramiro, Marta, Fico, Giovanna, Giménez-Palomo, Anna, Sagué-Vilavella, Maria, Pons, Maria Teresa, Vázquez, Mireia, Baldaquí, Nuria, Colomer, Lluc, Fernández, Tábatha Maria, Gutiérrez-Arango, Felipe, Llobet, Maria, Pujal, Ester, Lázaro, Luisa, Vieta, Eduard, Radua, Joaquim, and Baeza, Inmaculada

Published
2023
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