7,365 results on '"Stein, Dan"'
Search Results
2. Maternal age is related to offspring DNA methylation: A meta‐analysis of results from the PACE consortium
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Yeung, Edwina, Biedrzycki, Richard J, Herrera, Laura C Gómez, Issarapu, Prachand, Dou, John, Marques, Irene Fontes, Mansuri, Sohail Rafik, Page, Christian Magnus, Harbs, Justin, Khodasevich, Dennis, Poisel, Eric, Niu, Zhongzheng, Allard, Catherine, Casey, Emma, Berstein, Fernanda Morales, Mancano, Giulia, Elliott, Hannah R, Richmond, Rebecca, He, Yiyan, Ronkainen, Justiina, Sebert, Sylvain, Bell, Erin M, Sharp, Gemma, Mumford, Sunni L, Schisterman, Enrique F, Chandak, Giriraj R, Fall, Caroline HD, Sahariah, Sirazul A, Silver, Matt J, Prentice, Andrew M, Bouchard, Luigi, Domellof, Magnus, West, Christina, Holland, Nina, Cardenas, Andres, Eskenazi, Brenda, Zillich, Lea, Witt, Stephanie H, Send, Tabea, Breton, Carrie, Bakulski, Kelly M, Fallin, M Daniele, Schmidt, Rebecca J, Stein, Dan J, Zar, Heather J, Jaddoe, Vincent WV, Wright, John, Grazuleviciene, Regina, Gutzkow, Kristine Bjerve, Sunyer, Jordi, Huels, Anke, Vrijheid, Martine, Harlid, Sophia, London, Stephanie, Hivert, Marie‐France, Felix, Janine, Bustamante, Mariona, and Guan, Weihua
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Biological Sciences ,Biomedical and Clinical Sciences ,Genetics ,Prevention ,Human Genome ,Clinical Research ,Women's Health ,Aging ,Pediatric ,Good Health and Well Being ,DNA Methylation ,Humans ,Female ,Maternal Age ,Infant ,Newborn ,Child ,Adult ,Male ,Child ,Preschool ,CpG Islands ,Pregnancy ,aging ,child ,DNA methylation ,melatonin ,receptor ,Medical and Health Sciences ,Developmental Biology ,Biological sciences ,Biomedical and clinical sciences - Abstract
Worldwide trends to delay childbearing have increased parental ages at birth. Older parental age may harm offspring health, but mechanisms remain unclear. Alterations in offspring DNA methylation (DNAm) patterns could play a role as aging has been associated with methylation changes in gametes of older individuals. We meta-analyzed epigenome-wide associations of parental age with offspring blood DNAm of over 9500 newborns and 2000 children (5-10 years old) from the Pregnancy and Childhood Epigenetics consortium. In newborns, we identified 33 CpG sites in 13 loci with DNAm associated with maternal age (PFDR
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- 2024
3. Cognitive Embodiment and Anxiety Disorders
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Stein, Dan J.
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- 2020
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4. A Web-Based Group Cognitive Behavioral Therapy Intervention for Symptoms of Anxiety and Depression Among University Students: Open-Label, Pragmatic Trial
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Bantjes, Jason, Kazdin, Alan E, Cuijpers, Pim, Breet, Elsie, Dunn-Coetzee, Munita, Davids, Charl, Stein, Dan J, and Kessler, Ronald C
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Psychology ,BF1-990 - Abstract
BackgroundAnxiety and depression are common among university students, and university counseling centers are under pressure to develop effective, novel, and sustainable interventions that engage and retain students. Group interventions delivered via the internet could be a novel and effective way to promote student mental health. ObjectiveWe conducted a pragmatic open trial to investigate the uptake, retention, treatment response, and level of satisfaction with a remote group cognitive behavioral therapy intervention designed to reduce symptoms of anxiety and depression delivered on the web to university students during the COVID-19 pandemic. MethodsPreintervention and postintervention self-reported data on anxiety and depression were collected using the Generalized Anxiety Disorder-7 and Patient Health Questionnaire-9. Satisfaction was assessed postintervention using the Client Satisfaction with Treatment Questionnaire. ResultsA total of 175 students were enrolled, 158 (90.3%) of whom initiated treatment. Among those initiating treatment, 86.1% (135/158) identified as female, and the mean age was 22.4 (SD 4.9) years. The mean number of sessions attended was 6.4 (SD 2.8) out of 10. Among participants with clinically significant symptoms at baseline, mean symptom scores decreased significantly for anxiety (t56=11.6; P
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- 2021
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5. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study.
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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
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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.
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- 2024
6. Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.
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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
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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.
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- 2024
7. The Association between Childhood Adversity and the Conserved Transcriptional Response to Adversity (CTRA) in Sexual Minority Men
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Dalvie, Shareefa, Li, Michael J., Kalmin, Mariah M., Cole, Steven W., Stein, Dan J., and Shoptaw, Steven J.
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- 2024
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8. Exposure to Violence and Mental Health Outcomes Among Pre-schoolers in a South African Birth Cohort
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Tsunga, Lucinda, Heron, Jon, Lake, Marilyn T., Halligan, Sarah L., Malcolm-Smith, Susan, Hoffman, Nadia, Zar, Heather J., Fraser, Abigail, Stein, Dan J., and Donald, Kirsten A.
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- 2024
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9. Patients’ and Clinicians’ Experiences with In-person, Video, and Phone Modalities for Opioid Use Disorder Treatment: A Qualitative Study
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Wyte-Lake, Tamar, Cohen, Deborah J., Williams, Shannon, Casey, David, Chan, Matt, Frank, Brian, Levander, Ximena A., Stein, Dan, White, Katie Kirkman, and Bailey, Steffani R.
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- 2024
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10. Clinical Characteristics of Youth with Trichotillomania (Hair-Pulling Disorder) and Excoriation (Skin-Picking) Disorder
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Ricketts, Emily J., Peris, Tara S., Grant, Jon E., Valle, Stephanie, Cavic, Elizabeth, Lerner, Juliette E., Lochner, Christine, Stein, Dan J., Dougherty, Darin D., O’Neill, Joseph, Woods, Douglas W., Keuthen, Nancy J., and Piacentini, John
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- 2024
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11. Associations between community-level patterns of prenatal alcohol and tobacco exposure on brain structure in a non-clinical sample of 6-year-old children: a South African pilot study.
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Jonker, Deborah, Donald, Kirsten, Bodison, Stefanie, Brooks, Samantha, Kan, Eric, Steigelmann, Babette, Roos, Annerine, Marshall, Andrew, Adise, Shana, Butler-Kruger, Letitia, Melly, Brigitte, Narr, Katherine, Joshi, Shantanu, Odendaal, Hein, Sowell, Elizabeth, Stein, Dan, and Uban, Kristina
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brain structure ,children ,low-middle income country (LMIC) ,prenatal alcohol exposure (PAE) ,prenatal tobacco exposure (PTE) ,teratogen ,Child ,Pregnancy ,Female ,Humans ,Child ,Preschool ,Pilot Projects ,South Africa ,Prenatal Exposure Delayed Effects ,Brain ,Magnetic Resonance Imaging - Abstract
The current small study utilised prospective data collection of patterns of prenatal alcohol and tobacco exposure (PAE and PTE) to examine associations with structural brain outcomes in 6-year-olds and served as a pilot to determine the value of prospective data describing community-level patterns of PAE and PTE in a non-clinical sample of children. Participants from the Safe Passage Study in pregnancy were approached when their child was ∼6 years old and completed structural brain magnetic resonance imaging to examine with archived PAE and PTE data (n = 51 children-mother dyads). Linear regression was used to conduct whole-brain structural analyses, with false-discovery rate (FDR) correction, to examine: (a) main effects of PAE, PTE and their interaction; and (b) predictive potential of data that reflect patterns of PAE and PTE (e.g. quantity, frequency and timing (QFT)). Associations between PAE, PTE and their interaction with brain structural measures demonstrated unique profiles of cortical and subcortical alterations that were distinct between PAE only, PTE only and their interactive effects. Analyses examining associations between patterns of PAE and PTE (e.g. QFT) were able to significantly detect brain alterations (that survived FDR correction) in this small non-clinical sample of children. These findings support the hypothesis that considering QFT and co-exposures is important for identifying brain alterations following PAE and/or PTE in a small group of young children. Current results demonstrate that teratogenic outcomes on brain structure differ as a function PAE, PTE or their co-exposures, as well as the pattern (QFT) or exposure.
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- 2024
12. Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup
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Huggins, Ashley A, Baird, C Lexi, Briggs, Melvin, Laskowitz, Sarah, Hussain, Ahmed, Fouda, Samar, Haswell, Courtney, Sun, Delin, Salminen, Lauren E, Jahanshad, Neda, Thomopoulos, Sophia I, Veltman, Dick J, Frijling, Jessie L, Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia BJ, Nawjin, Laura, Wang, Li, Zhu, Ye, Li, Gen, Stein, Dan J, Ipser, Jonathan, Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L, Suarez-Jimenez, Benjamin, Zhu, Xi, Kim, Yoojean, He, Xiaofu, Zilcha-Mano, Sigal, Lazarov, Amit, Neria, Yuval, Stevens, Jennifer S, Ressler, Kerry J, Jovanovic, Tanja, van Rooij, Sanne JH, Fani, Negar, Hudson, Anna R, Mueller, Sven C, Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K, Schmahl, Christian, Herzog, Julia I, Říha, Pavel, Rektor, Ivan, Lebois, Lauren AM, Kaufman, Milissa L, Olson, Elizabeth A, Baker, Justin T, Rosso, Isabelle M, King, Anthony P, Liberzon, Isreal, Angstadt, Mike, Davenport, Nicholas D, Sponheim, Scott R, Disner, Seth G, Straube, Thomas, Hofmann, David, Qi, Rongfeng, Lu, Guang Ming, Baugh, Lee A, Forster, Gina L, Simons, Raluca M, Simons, Jeffrey S, Magnotta, Vincent A, Fercho, Kelene A, Maron-Katz, Adi, Etkin, Amit, Cotton, Andrew S, O’Leary, Erin N, Xie, Hong, Wang, Xin, Quidé, Yann, El-Hage, Wissam, Lissek, Shmuel, Berg, Hannah, Bruce, Steven, Cisler, Josh, Ross, Marisa, Herringa, Ryan J, Grupe, Daniel W, Nitschke, Jack B, Davidson, Richard J, Larson, Christine L, deRoon-Cassini, Terri A, Tomas, Carissa W, Fitzgerald, Jacklynn M, Blackford, Jennifer Urbano, Olatunji, Bunmi O, Kremen, William S, Lyons, Michael J, Franz, Carol E, Gordon, Evan M, May, Geoffrey, Nelson, Steven M, Abdallah, Chadi G, Levy, Ifat, and Harpaz-Rotem, Ilan
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Clinical and Health Psychology ,Psychology ,Post-Traumatic Stress Disorder (PTSD) ,Mental Illness ,Behavioral and Social Science ,Anxiety Disorders ,Mental Health ,Brain Disorders ,Mind and Body ,Neurosciences ,Mental health ,Humans ,Stress Disorders ,Post-Traumatic ,Cerebellum ,Female ,Male ,Adult ,Magnetic Resonance Imaging ,Middle Aged ,White Matter ,Gray Matter ,Organ Size ,Deep Learning ,Biological Sciences ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Psychiatry ,Clinical sciences ,Biological psychology ,Clinical and health psychology - Abstract
Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR
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- 2024
13. DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features
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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
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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.
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- 2023
14. Cognitive Science and Psychiatry: An overview
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Stein, Dan J.
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Psychology: Clinical Psychology ,Psychology: Cognitive Psychology ,Clinical Psychology ,Cognitive Psychology - Abstract
Cognitive science is a multidisciplinary field, comprising cognitive psychology, artificial intelligence, linguistics, neuroscience, and anthropology. In recent years, cognitive science has become a predominant paradigm in studies of the mind. This paper reviews work at the emerging interface between cognitive science and psychiatry. It is argued that cognitive science has significant potential as an integrative framework for theorizing and researching psychiatric disorders and their treatment.
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- 1992
15. Schemas in the Cognitive and Clinical Sciences: An Integrative Construct
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Stein, Dan J.
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Psychology: Clinical Psychology ,Psychology: Cognitive Psychology ,Clinical Psychology ,Cognitive Psychology - Abstract
This paper is concerned in general with the intersection of cognitive and clinical science and in particular with schema theory. The use of schema theory in the various subdisciplines of cognitive science, as well as by cognitive-behavioral clinicians and psychoanalytically oriented clinicians is reviewed. It is argued that schema theory, in both cognitive and clinical sciences, allows a focus on mental structures their biological basis, their development and change, and on the way in which they direct psychological events. Schema theory not only enables important advances in different clinical schools, but it allows central clinical themes to be tackled in convergent ways. It is concluded that the schema construct allows integration within cognitive science, within the clinic, and between the two.
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- 1992
16. Disability benefits protect against lost income for South Africans living with Schizophrenia
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Wootton, Olivia, King, Aisha, Moy, Kayley, Stein, Dan J., and Susser, Ezra S.
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- 2024
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17. Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder
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Nievergelt, Caroline M., Maihofer, Adam X., Atkinson, Elizabeth G., Chen, Chia-Yen, Choi, Karmel W., Coleman, Jonathan R. I., Daskalakis, Nikolaos P., Duncan, Laramie E., Polimanti, Renato, Aaronson, Cindy, Amstadter, Ananda B., Andersen, Soren B., Andreassen, Ole A., Arbisi, Paul A., Ashley-Koch, Allison E., Austin, S. Bryn, Avdibegoviç, Esmina, Babić, Dragan, Bacanu, Silviu-Alin, Baker, Dewleen G., Batzler, Anthony, Beckham, Jean C., Belangero, Sintia, Benjet, Corina, Bergner, Carisa, Bierer, Linda M., Biernacka, Joanna M., Bierut, Laura J., Bisson, Jonathan I., Boks, Marco P., Bolger, Elizabeth A., Brandolino, Amber, Breen, Gerome, Bressan, Rodrigo Affonseca, Bryant, Richard A., Bustamante, Angela C., Bybjerg-Grauholm, Jonas, Bækvad-Hansen, Marie, Børglum, Anders D., Børte, Sigrid, Cahn, Leah, Calabrese, Joseph R., Caldas-de-Almeida, Jose Miguel, Chatzinakos, Chris, Cheema, Sheraz, Clouston, Sean A. P., Colodro-Conde, Lucía, Coombes, Brandon J., Cruz-Fuentes, Carlos S., Dale, Anders M., Dalvie, Shareefa, Davis, Lea K., Deckert, Jürgen, Delahanty, Douglas L., Dennis, Michelle F., Desarnaud, Frank, DiPietro, Christopher P., Disner, Seth G., Docherty, Anna R., Domschke, Katharina, Dyb, Grete, Kulenović, Alma Džubur, Edenberg, Howard J., Evans, Alexandra, Fabbri, Chiara, Fani, Negar, Farrer, Lindsay A., Feder, Adriana, Feeny, Norah C., Flory, Janine D., Forbes, David, Franz, Carol E., Galea, Sandro, Garrett, Melanie E., Gelaye, Bizu, Gelernter, Joel, Geuze, Elbert, Gillespie, Charles F., Goleva, Slavina B., Gordon, Scott D., Goçi, Aferdita, Grasser, Lana Ruvolo, Guindalini, Camila, Haas, Magali, Hagenaars, Saskia, Hauser, Michael A., Heath, Andrew C., Hemmings, Sian M. J., Hesselbrock, Victor, Hickie, Ian B., Hogan, Kelleigh, Hougaard, David Michael, Huang, Hailiang, Huckins, Laura M., Hveem, Kristian, Jakovljević, Miro, Javanbakht, Arash, Jenkins, Gregory D., Johnson, Jessica, Jones, Ian, Jovanovic, Tanja, Karstoft, Karen-Inge, Kaufman, Milissa L., Kennedy, James L., Kessler, Ronald C., Khan, Alaptagin, Kimbrel, Nathan A., King, Anthony P., Koen, Nastassja, Kotov, Roman, Kranzler, Henry R., Krebs, Kristi, Kremen, William S., Kuan, Pei-Fen, Lawford, Bruce R., Lebois, Lauren A. M., Lehto, Kelli, Levey, Daniel F., Lewis, Catrin, Liberzon, Israel, Linnstaedt, Sarah D., Logue, Mark W., Lori, Adriana, Lu, Yi, Luft, Benjamin J., Lupton, Michelle K., Luykx, Jurjen J., Makotkine, Iouri, Maples-Keller, Jessica L., Marchese, Shelby, Marmar, Charles, Martin, Nicholas G., Martínez-Levy, Gabriela A., McAloney, Kerrie, McFarlane, Alexander, McLaughlin, Katie A., McLean, Samuel A., Medland, Sarah E., Mehta, Divya, Meyers, Jacquelyn, Michopoulos, Vasiliki, Mikita, Elizabeth A., Milani, Lili, Milberg, William, Miller, Mark W., Morey, Rajendra A., Morris, Charles Phillip, Mors, Ole, Mortensen, Preben Bo, Mufford, Mary S., Nelson, Elliot C., Nordentoft, Merete, Norman, Sonya B., Nugent, Nicole R., O’Donnell, Meaghan, Orcutt, Holly K., Pan, Pedro M., Panizzon, Matthew S., Pathak, Gita A., Peters, Edward S., Peterson, Alan L., Peverill, Matthew, Pietrzak, Robert H., Polusny, Melissa A., Porjesz, Bernice, Powers, Abigail, Qin, Xue-Jun, Ratanatharathorn, Andrew, Risbrough, Victoria B., Roberts, Andrea L., Rothbaum, Alex O., Rothbaum, Barbara O., Roy-Byrne, Peter, Ruggiero, Kenneth J., Rung, Ariane, Runz, Heiko, Rutten, Bart P. F., de Viteri, Stacey Saenz, Salum, Giovanni Abrahão, Sampson, Laura, Sanchez, Sixto E., Santoro, Marcos, Seah, Carina, Seedat, Soraya, Seng, Julia S., Shabalin, Andrey, Sheerin, Christina M., Silove, Derrick, Smith, Alicia K., Smoller, Jordan W., Sponheim, Scott R., Stein, Dan J., Stensland, Synne, Stevens, Jennifer S., Sumner, Jennifer A., Teicher, Martin H., Thompson, Wesley K., Tiwari, Arun K., Trapido, Edward, Uddin, Monica, Ursano, Robert J., Valdimarsdóttir, Unnur, Van Hooff, Miranda, Vermetten, Eric, Vinkers, Christiaan H., Voisey, Joanne, Wang, Yunpeng, Wang, Zhewu, Waszczuk, Monika, Weber, Heike, Wendt, Frank R., Werge, Thomas, Williams, Michelle A., Williamson, Douglas E., Winsvold, Bendik S., Winternitz, Sherry, Wolf, Christiane, Wolf, Erika J., Xia, Yan, Xiong, Ying, Yehuda, Rachel, Young, Keith A., Young, Ross McD, Zai, Clement C., Zai, Gwyneth C., Zervas, Mark, Zhao, Hongyu, Zoellner, Lori A., Zwart, John-Anker, deRoon-Cassini, Terri, van Rooij, Sanne J. H., van den Heuvel, Leigh L., Stein, Murray B., Ressler, Kerry J., and Koenen, Karestan C.
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- 2024
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18. Disorder and Deviance: Where to Draw the Boundaries?
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Stein, Dan J.
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- 2015
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19. The Philosophy of Psychopathy
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Stein, Dan J.
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- 2015
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20. Philosophy of Psychopharmacology
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Stein, Dan J.
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- 2015
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21. Beyond the Global Brain Differences: Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers.
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Boen, Rune, Kaufmann, Tobias, van der Meer, Dennis, Frei, Oleksandr, Agartz, Ingrid, Ames, David, Andersson, Micael, Armstrong, Nicola, Artiges, Eric, Atkins, Joshua, Bauer, Jochen, Benedetti, Francesco, Boomsma, Dorret, Brodaty, Henry, Brosch, Katharina, Buckner, Randy, Cairns, Murray, Calhoun, Vince, Caspers, Svenja, Cichon, Sven, Corvin, Aiden, Crespo-Facorro, Benedicto, Dannlowski, Udo, David, Friederike, de Geus, Eco, de Zubicaray, Greig, Desrivières, Sylvane, Doherty, Joanne, Donohoe, Gary, Ehrlich, Stefan, Eising, Else, Espeseth, Thomas, Fisher, Simon, Forstner, Andreas, Fortaner-Uyà, Lidia, Frouin, Vincent, Fukunaga, Masaki, Ge, Tian, Glahn, David, Goltermann, Janik, Grabe, Hans, Green, Melissa, Groenewold, Nynke, Grotegerd, Dominik, Grøntvedt, Gøril, Hahn, Tim, Hashimoto, Ryota, Hehir-Kwa, Jayne, Henskens, Frans, Holmes, Avram, Håberg, Asta, Haavik, Jan, Jacquemont, Sebastien, Jansen, Andreas, Jockwitz, Christiane, Jönsson, Erik, Kikuchi, Masataka, Kircher, Tilo, Kumar, Kuldeep, Le Hellard, Stephanie, Leu, Costin, Linden, David, Liu, Jingyu, Loughnan, Robert, Mather, Karen, McMahon, Katie, McRae, Allan, Medland, Sarah, Meinert, Susanne, Moreau, Clara, Morris, Derek, Mowry, Bryan, Mühleisen, Thomas, Nenadić, Igor, Nöthen, Markus, Nyberg, Lars, Ophoff, Roel, Owen, Michael, Pantelis, Christos, Paolini, Marco, Paus, Tomas, Pausova, Zdenka, Persson, Karin, Quidé, Yann, Marques, Tiago, Sachdev, Perminder, Sando, Sigrid, Schall, Ulrich, Scott, Rodney, Selbæk, Geir, Shumskaya, Elena, Silva, Ana, Sisodiya, Sanjay, Stein, Frederike, Stein, Dan, Straube, Benjamin, Streit, Fabian, Strike, Lachlan, Teumer, Alexander, and Teutenberg, Lea
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15q11.2 BP1-BP2 ,1q21.1 distal ,Brain structure ,Copy number variants ,Intraindividual variability ,Magnetic resonance imaging ,Humans ,Chromosome Deletion ,Brain ,Magnetic Resonance Imaging ,Abnormalities ,Multiple ,Chromosomes ,Human ,Pair 15 ,DNA Copy Number Variations - Abstract
BACKGROUND: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure. METHODS: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individuals regional difference and global difference, were used to test for regional differences that diverge from the global difference. RESULTS: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness. CONCLUSIONS: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment.
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- 2024
22. The short version of the Sexual Distress Scale (SDS-3): Measurement invariance across countries, gender identities, and sexual orientations.
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Grubbs, Joshua, Hashim, Hashim, Islam, Md, Ismail, Mustafa, Jiménez-Martínez, Martha, Jurin, Tanja, Kalina, Ondrej, Klein, Verena, Költő, András, Lee, Sang-Kyu, Lewczuk, Karol, Lochner, Christine, López-Alvarado, Silvia, Lukavská, Kateřina, Mayta-Tristán, Percy, Milea, Ion, Miller, Dan, Orosová, Oľga, Orosz, Gábor, Ponce, Fernando, Quintana, Gonzalo, Garzola, Gabriel, Ramos-Diaz, Jano, Rigaud, Kévin, Rousseau, Ann, Scanavino, Marco, Schulmeyer, Marion, Sharan, Pratap, Shibata, Mami, Shoib, Sheikh, Sigre-Leirós, Vera, Sniewski, Luke, Spasovski, Ognen, Steibliene, Vesta, Stein, Dan, Strizek, Julian, Štulhofer, Aleksandar, Ünsal, Berk, Vaillancourt-Morel, Marie-Pier, Van Hout, Marie, Bőthe, Beáta, Lin, Chung-Ying, Tsai, Meng-Che, Koós, Mónika, Nagy, Léna, Kraus, Shane, Demetrovics, Zsolt, Potenza, Marc, Ballester-Arnal, Rafael, Batthyány, Dominik, Bergeron, Sophie, Billieux, Joël, Briken, Peer, Cárdenas-López, Georgina, Carvalho, Joana, Castro-Calvo, Jesús, Chen, Lijun, Ciocca, Giacomo, Corazza, Ornella, Csako, Rita, Fernandez, David, Fernandez, Elaine, Fujiwara, Hironobu, Fuss, Johannes, Gabrhelík, Roman, Gewirtz-Meydan, Ateret, Gjoneska, Biljana, and Gola, Mateusz
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Confirmatory factor analysis (CFA) ,International sex survey ,Psychometric analysis ,Sexual distress - Abstract
BACKGROUND: The three-item Sexual Distress Scale (SDS-3) has been frequently used to assess distress related to sexuality in public health surveys and research on sexual wellbeing. However, its psychometric properties and measurement invariance across cultural, gender and sexual subgroups have not yet been examined. This multinational study aimed to validate the SDS-3 and test its psychometric properties, including measurement invariance across language, country, gender identity, and sexual orientation groups. METHODS: We used global survey data from 82,243 individuals (Mean age=32.39 years; 40.3 % men, 57.0 % women, 2.8 % non-binary, and 0.6 % other genders) participating in the International Sexual Survey (ISS; https://internationalsexsurvey.org/) across 42 countries and 26 languages. Participants completed the SDS-3, as well as questions regarding sociodemographic characteristics, including gender identity and sexual orientation. RESULTS: Confirmatory factor analysis (CFA) supported a unidimensional factor structure for the SDS-3, and multi-group CFA (MGCFA) suggested that this factor structure was invariant across countries, languages, gender identities, and sexual orientations. Cronbachs α for the unidimensional score was 0.83 (range between 0.76 and 0.89), and McDonalds ω was 0.84 (range between 0.76 and 0.90). Participants who did not experience sexual problems had significantly lower SDS-3 total scores (M = 2.99; SD=2.54) compared to those who reported sexual problems (M = 5.60; SD=3.00), with a large effect size (Cohens d = 1.01 [95 % CI=-1.03, -0.98]; p < 0.001). CONCLUSION: The SDS-3 has a unidimensional factor structure and appears to be valid and reliable for measuring sexual distress among individuals from different countries, gender identities, and sexual orientations.
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- 2024
23. A global multicohort study to map subcortical brain development and cognition in infancy and early childhood.
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Alex, Ann, Aguate, Fernando, Botteron, Kelly, Buss, Claudia, Chong, Yap-Seng, Dager, Stephen, Donald, Kirsten, Entringer, Sonja, Fair, Damien, Fortier, Marielle, Gaab, Nadine, Gilmore, John, Girault, Jessica, Graham, Alice, Groenewold, Nynke, Hazlett, Heather, Lin, Weili, Meaney, Michael, Piven, Joseph, Qiu, Anqi, Rasmussen, Jerod, Roos, Annerine, Schultz, Robert, Skeide, Michael, Stein, Dan, Styner, Martin, Thompson, Paul, Turesky, Ted, Wadhwa, Pathik, Zar, Heather, Zöllei, Lilla, de Los Campos, Gustavo, and Knickmeyer, Rebecca
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Male ,Female ,Humans ,Infant ,Newborn ,Child ,Preschool ,Child ,Premature Birth ,Cognition ,Brain ,Neuroimaging ,Magnetic Resonance Imaging - Abstract
The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition. The amygdala was the first subcortical volume to mature, whereas the thalamus exhibited protracted development. Males had larger brain volumes than females, and children born preterm or with low birthweight showed catch-up growth with age. Socioeconomic factors exerted region- and time-specific effects. Regarding cognition, males scored lower than females; preterm birth affected all developmental areas tested, and socioeconomic factors affected visual reception and receptive language. Brain-cognition correlations revealed region-specific associations.
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- 2024
24. A Pregnancy and Childhood Epigenetics Consortium (PACE) meta-analysis highlights potential relationships between birth order and neonatal blood DNA methylation
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Li, Shaobo, Spitz, Natalia, Ghantous, Akram, Abrishamcar, Sarina, Reimann, Brigitte, Marques, Irene, Silver, Matt J, Aguilar-Lacasaña, Sofía, Kitaba, Negusse, Rezwan, Faisal I, Röder, Stefan, Sirignano, Lea, Tuhkanen, Johanna, Mancano, Giulia, Sharp, Gemma C, Metayer, Catherine, Morimoto, Libby, Stein, Dan J, Zar, Heather J, Alfano, Rossella, Nawrot, Tim, Wang, Congrong, Kajantie, Eero, Keikkala, Elina, Mustaniemi, Sanna, Ronkainen, Justiina, Sebert, Sylvain, Silva, Wnurinham, Vääräsmäki, Marja, Jaddoe, Vincent WV, Bernstein, Robin M, Prentice, Andrew M, Cosin-Tomas, Marta, Dwyer, Terence, Håberg, Siri Eldevik, Herceg, Zdenko, Magnus, Maria C, Munthe-Kaas, Monica Cheng, Page, Christian M, Völker, Maja, Gilles, Maria, Send, Tabea, Witt, Stephanie, Zillich, Lea, Gagliardi, Luigi, Richiardi, Lorenzo, Czamara, Darina, Räikkönen, Katri, Chatzi, Lida, Vafeiadi, Marina, Arshad, S Hasan, Ewart, Susan, Plusquin, Michelle, Felix, Janine F, Moore, Sophie E, Vrijheid, Martine, Holloway, John W, Karmaus, Wilfried, Herberth, Gunda, Zenclussen, Ana, Streit, Fabian, Lahti, Jari, Hüls, Anke, Hoang, Thanh T, London, Stephanie J, and Wiemels, Joseph L
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Reproductive Medicine ,Biomedical and Clinical Sciences ,Genetics ,Biological Sciences ,Human Genome ,Contraception/Reproduction ,Pediatric ,Clinical Research ,Perinatal Period - Conditions Originating in Perinatal Period ,2.1 Biological and endogenous factors ,Aetiology ,Reproductive health and childbirth ,Good Health and Well Being ,Child ,Female ,Humans ,Infant ,Newborn ,Pregnancy ,Birth Order ,DNA Methylation ,Epigenesis ,Genetic ,Epigenomics ,Biological sciences ,Biomedical and clinical sciences - Abstract
Higher birth order is associated with altered risk of many disease states. Changes in placentation and exposures to in utero growth factors with successive pregnancies may impact later life disease risk via persistent DNA methylation alterations. We investigated birth order with Illumina DNA methylation array data in each of 16 birth cohorts (8164 newborns) with European, African, and Latino ancestries from the Pregnancy and Childhood Epigenetics Consortium. Meta-analyzed data demonstrated systematic DNA methylation variation in 341 CpGs (FDR adjusted P
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- 2024
25. Philosophy and Obsessive–Compulsive Disorder
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Stein, Dan J.
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- 2013
26. Cortical morphology in patients with the deficit and non-deficit syndrome of schizophrenia: a worldwide meta- and mega-analyses.
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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
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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.
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- 2023
27. The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.
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Bruin, Willem, Abe, Yoshinari, Alonso, Pino, Anticevic, Alan, Backhausen, Lea, Balachander, Srinivas, Bargallo, Nuria, Batistuzzo, Marcelo, Benedetti, Francesco, Bertolin Triquell, Sara, Brem, Silvia, Calesella, Federico, Couto, Beatriz, Denys, Damiaan, Echevarria, Marco, Eng, Goi, Ferreira, Sónia, Feusner, Jamie, Grazioplene, Rachael, Gruner, Patricia, Guo, Joyce, Hagen, Kristen, Hansen, Bjarne, Hirano, Yoshiyuki, Hoexter, Marcelo, Jahanshad, Neda, Jaspers-Fayer, Fern, Kasprzak, Selina, Kim, Minah, Koch, Kathrin, Bin Kwak, Yoo, Kwon, Jun, Lazaro, Luisa, Li, Chiang-Shan, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Menchon, Jose, Moreira, Pedro, Morgado, Pedro, Nakagawa, Akiko, Nakao, Tomohiro, Narayanaswamy, Janardhanan, Nurmi, Erika, Zorrilla, Jose, Picó-Pérez, Maria, Piras, Fabrizio, Piras, Federica, Pittenger, Christopher, Reddy, Janardhan, Rodriguez-Manrique, Daniela, Sakai, Yuki, Shimizu, Eiji, Shivakumar, Venkataram, Simpson, Blair, Soriano-Mas, Carles, Sousa, Nuno, Spalletta, Gianfranco, Stern, Emily, Evelyn Stewart, S, Szeszko, Philip, Tang, Jinsong, Thomopoulos, Sophia, Thorsen, Anders, Yoshida, Tokiko, Tomiyama, Hirofumi, Vai, Benedetta, Veer, Ilya, Venkatasubramanian, Ganesan, Vetter, Nora, Vriend, Chris, Walitza, Susanne, Waller, Lea, Wang, Zhen, Watanabe, Anri, Wolff, Nicole, Yun, Je-Yeon, Zhao, Qing, van Leeuwen, Wieke, van Marle, Hein, van de Mortel, Laurens, van der Straten, Anouk, van der Werf, Ysbrand, Thompson, Paul, Stein, Dan, van den Heuvel, Odile, van Wingen, Guido, and Piacentini, John
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Humans ,Connectome ,Brain Mapping ,Magnetic Resonance Imaging ,Brain ,Obsessive-Compulsive Disorder ,Biomarkers ,Neural Pathways - Abstract
Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohens d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohens d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.
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- 2023
28. White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group
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Kim, Bo-Gyeom, Kim, Gakyung, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie, Anticevic, Alan, Arnold, Paul D., Balachander, Srinivas, Banaj, Nerisa, Bargalló, Nuria, Batistuzzo, Marcelo C., Benedetti, Francesco, Bertolín, Sara, Beucke, Jan Carl, Bollettini, Irene, Brem, Silvia, Brennan, Brian P., Buitelaar, Jan K., Calvo, Rosa, Castelo-Branco, Miguel, Cheng, Yuqi, Chhatkuli, Ritu Bhusal, Ciullo, Valentina, Coelho, Ana, Couto, Beatriz, Dallaspezia, Sara, Ely, Benjamin A., Ferreira, Sónia, Fontaine, Martine, Fouche, Jean-Paul, Grazioplene, Rachael, Gruner, Patricia, Hagen, Kristen, Hansen, Bjarne, Hanna, Gregory L., Hirano, Yoshiyuki, Höxter, Marcelo Q., Hough, Morgan, Hu, Hao, Huyser, Chaim, Ikuta, Toshikazu, Jahanshad, Neda, James, Anthony, Jaspers-Fayer, Fern, Kasprzak, Selina, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Kvale, Gerd, Kwon, Jun Soo, Lazaro, Luisa, Lee, Junhee, Lochner, Christine, Lu, Jin, Manrique, Daniela Rodriguez, Martínez-Zalacaín, Ignacio, Masuda, Yoshitada, Matsumoto, Koji, Maziero, Maria Paula, Menchón, Jose M., Minuzzi, Luciano, Moreira, Pedro Silva, Morgado, Pedro, Narayanaswamy, Janardhanan C., Narumoto, Jin, Ortiz, Ana E., Ota, Junko, Pariente, Jose C., Perriello, Chris, Picó-Pérez, Maria, Pittenger, Christopher, Poletti, Sara, Real, Eva, Reddy, Y. C. Janardhan, van Rooij, Daan, Sakai, Yuki, Sato, João Ricardo, Segalas, Cinto, Shavitt, Roseli G., Shen, Zonglin, Shimizu, Eiji, Shivakumar, Venkataram, Soreni, Noam, Soriano-Mas, Carles, Sousa, Nuno, Sousa, Mafalda Machado, Spalletta, Gianfranco, Stern, Emily R., Stewart, S. Evelyn, Szeszko, Philip R., Thomas, Rajat, Thomopoulos, Sophia I., Vecchio, Daniela, Venkatasubramanian, Ganesan, Vriend, Chris, Walitza, Susanne, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij, Xu, Jian, Yamada, Kei, Yun, Je-Yeon, Zarei, Mojtaba, Zhao, Qing, Zhu, Xi, Thompson, Paul M., Bruin, Willem B., van Wingen, Guido A., Piras, Federica, Piras, Fabrizio, Stein, Dan J., van den Heuvel, Odile A., Simpson, Helen Blair, Marsh, Rachel, and Cha, Jiook
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- 2024
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29. Age of onset of obsessive-compulsive disorder differentially affects white matter microstructure
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Vriend, Chris, de Joode, Niels T., Pouwels, Petra J. W., Liu, Feng, Otaduy, Maria C. G., Pastorello, Bruno, Robertson, Frances C., Ipser, Jonathan, Lee, Seonjoo, Hezel, Dianne M., van Meter, Page E., Batistuzzo, Marcelo C., Hoexter, Marcelo Q., Sheshachala, Karthik, Narayanaswamy, Janardhanan C., Venkatasubramanian, Ganesan, Lochner, Christine, Miguel, Euripedes C., Reddy, Y. C. Janardhan, Shavitt, Roseli G., Stein, Dan J., Wall, Melanie, Simpson, Helen Blair, and van den Heuvel, Odile A.
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- 2024
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30. Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference
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Meng, Xiangrui, Navoly, Georgina, Giannakopoulou, Olga, Levey, Daniel F., Koller, Dora, Pathak, Gita A., Koen, Nastassja, Lin, Kuang, Adams, Mark J., Rentería, Miguel E., Feng, Yanzhe, Gaziano, J. Michael, Stein, Dan J., Zar, Heather J., Campbell, Megan L., van Heel, David A., Trivedi, Bhavi, Finer, Sarah, McQuillin, Andrew, Bass, Nick, Chundru, V. Kartik, Martin, Hilary C., Huang, Qin Qin, Valkovskaya, Maria, Chu, Chia-Yi, Kanjira, Susan, Kuo, Po-Hsiu, Chen, Hsi-Chung, Tsai, Shih-Jen, Liu, Yu-Li, Kendler, Kenneth S., Peterson, Roseann E., Cai, Na, Fang, Yu, Sen, Srijan, Scott, Laura J., Burmeister, Margit, Loos, Ruth J. F., Preuss, Michael H., Actkins, Ky’Era V., Davis, Lea K., Uddin, Monica, Wani, Agaz H., Wildman, Derek E., Aiello, Allison E., Ursano, Robert J., Kessler, Ronald C., Kanai, Masahiro, Okada, Yukinori, Sakaue, Saori, Rabinowitz, Jill A., Maher, Brion S., Uhl, George, Eaton, William, Cruz-Fuentes, Carlos S., Martinez-Levy, Gabriela A., Campos, Adrian I., Millwood, Iona Y., Chen, Zhengming, Li, Liming, Wassertheil-Smoller, Sylvia, Jiang, Yunxuan, Tian, Chao, Martin, Nicholas G., Mitchell, Brittany L., Byrne, Enda M., Awasthi, Swapnil, Coleman, Jonathan R. I., Ripke, Stephan, Sofer, Tamar, Walters, Robin G., McIntosh, Andrew M., Polimanti, Renato, Dunn, Erin C., Stein, Murray B., Gelernter, Joel, Lewis, Cathryn M., and Kuchenbaecker, Karoline
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- 2024
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31. Examining the association between posttraumatic stress disorder and disruptions in cortical networks identified using data-driven methods
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Yang, Jin, Huggins, Ashley A., Sun, Delin, Baird, C. Lexi, Haswell, Courtney C., Frijling, Jessie L., Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia B. J., Nawijn, Laura, Veltman, Dick J., Suarez-Jimenez, Benjamin, Zhu, Xi, Neria, Yuval, Hudson, Anna R., Mueller, Sven C., Baker, Justin T., Lebois, Lauren A. M., Kaufman, Milissa L., Qi, Rongfeng, Lu, Guang Ming, Říha, Pavel, Rektor, Ivan, Dennis, Emily L., Ching, Christopher R. K., Thomopoulos, Sophia I., Salminen, Lauren E., Jahanshad, Neda, Thompson, Paul M., Stein, Dan J., Koopowitz, Sheri M., Ipser, Jonathan C., Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L., Wang, Li, Zhu, Ye, Li, Gen, Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K., Schmahl, Christian, Herzog, Julia I., Liberzon, Israel, King, Anthony, Angstadt, Mike, Davenport, Nicholas D., Sponheim, Scott R., Disner, Seth G., Straube, Thomas, Hofmann, David, Grupe, Daniel W., Nitschke, Jack B., Davidson, Richard J., Larson, Christine L., deRoon-Cassini, Terri A., Blackford, Jennifer U., Olatunji, Bunmi O., Gordon, Evan M., May, Geoffrey, Nelson, Steven M., Abdallah, Chadi G., Levy, Ifat, Harpaz-Rotem, Ilan, Krystal, John H., Morey, Rajendra A., and Sotiras, Aristeidis
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- 2024
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32. Self-Report Measures of Sensory Phenomena in Body-Focused Repetitive Behaviors: A Comparison to Healthy Controls
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Clark, Sydney Biscarri, Mouton-Odum, Suzanne, Flessner, Christopher A., Ricketts, Emily J., Peris, Tara S., Dougherty, Darin D., Woods, Douglas W., Stein, Dan J., Lochner, Christine, Grant, Jon E., Keuthen, Nancy J., and Piacentini, John
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- 2024
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33. Compulsive sexual behavior disorder in 42 countries: Insights from the International Sex Survey and introduction of standardized assessment tools.
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Bőthe, Beáta, Koós, Mónika, Nagy, Léna, Kraus, Shane, Demetrovics, Zsolt, Potenza, Marc, Michaud, Aurélie, Ballester-Arnal, Rafael, Batthyány, Dominik, Bergeron, Sophie, Billieux, Joël, Briken, Peer, Burkauskas, Julius, Cárdenas-López, Georgina, Carvalho, Joana, Castro-Calvo, Jesús, Chen, Lijun, Ciocca, Giacomo, Corazza, Ornella, Csako, Rita, Fernandez, David, Fernandez, Elaine, Fournier, Loïs, Fujiwara, Hironobu, Fuss, Johannes, Gabrhelík, Roman, Gewirtz-Meydan, Ateret, Gjoneska, Biljana, Gola, Mateusz, Grubbs, Joshua, Hashim, Hashim, Islam, Md, Ismail, Mustafa, Jiménez-Martínez, Martha, Jurin, Tanja, Kalina, Ondrej, Klein, Verena, Költő, András, Lee, Chih-Ting, Lee, Sang-Kyu, Lewczuk, Karol, Lin, Chung-Ying, Lochner, Christine, López-Alvarado, Silvia, Lukavská, Kateřina, Mayta-Tristán, Percy, Milea, Ionut, Miller, Dan, Orosová, Oľga, Orosz, Gábor, Ponce, Fernando, Quintana, Gonzalo, Quintero Garzola, Gabriel, Ramos-Diaz, Jano, Rigaud, Kévin, Rousseau, Ann, De Tubino Scanavino, Marco, Schulmeyer, Marion, Sharan, Pratap, Shibata, Mami, Shoib, Sheikh, Sigre Leirós, Vera, Sniewski, Luke, Spasovski, Ognen, Steibliene, Vesta, Stein, Dan, Strizek, Julian, Štulhofer, Aleksandar, Ünsal, Berk, and Vaillancourt-Morel, Marie-Pier
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International Sex Survey (ISS) ,addictive behavior ,assessment ,compulsive sexual behavior ,cross-cultural ,validation ,Humans ,Female ,Male ,Reproducibility of Results ,Sexual Behavior ,Sexual Dysfunctions ,Psychological ,Paraphilic Disorders ,Compulsive Behavior - Abstract
BACKGROUND AND AIMS: Despite its inclusion in the 11th revision of the International Classification of Diseases, there is a virtual paucity of high-quality scientific evidence about compulsive sexual behavior disorder (CSBD), especially in underrepresented and underserved populations. Therefore, we comprehensively examined CSBD across 42 countries, genders, and sexual orientations, and validated the original (CSBD-19) and short (CSBD-7) versions of the Compulsive Sexual Behavior Disorder Scale to provide standardized, state-of-the-art screening tools for research and clinical practice. METHOD: Using data from the International Sex Survey (N = 82,243; Mage = 32.39 years, SD = 12.52), we evaluated the psychometric properties of the CSBD-19 and CSBD-7 and compared CSBD across 42 countries, three genders, eight sexual orientations, and individuals with low vs. high risk of experiencing CSBD. RESULTS: A total of 4.8% of the participants were at high risk of experiencing CSBD. Country- and gender-based differences were observed, while no sexual-orientation-based differences were present in CSBD levels. Only 14% of individuals with CSBD have ever sought treatment for this disorder, with an additional 33% not having sought treatment because of various reasons. Both versions of the scale demonstrated excellent validity and reliability. DISCUSSION AND CONCLUSIONS: This study contributes to a better understanding of CSBD in underrepresented and underserved populations and facilitates its identification in diverse populations by providing freely accessible ICD-11-based screening tools in 26 languages. The findings may also serve as a crucial building block to stimulate research into evidence-based, culturally sensitive prevention and intervention strategies for CSBD that are currently missing from the literature.
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- 2023
34. Persistent accelerated epigenetic ageing in a longitudinal cohort of vertically infected HIV-positive adolescents.
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Heany, Sarah, Levine, Andrew, Lesosky, Maia, Phillips, Nicole, Fouche, Jean-Paul, Myer, Landon, Zar, Heather, Stein, Dan, Horvath, Steve, and Hoare, Jacqueline
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Accelerated ageing ,DNA methylation ,Epigenetic clock ,Perinatal HIV ,Humans ,Adolescent ,Child ,Diffusion Tensor Imaging ,Cohort Studies ,HIV Infections ,South Africa ,Aging ,Epigenesis ,Genetic - Abstract
We have previously shown accelerated ageing in adolescents perinatally infected with HIV (PHIV +), based on discrepancies between epigenetic and chronological age. The current study examines follow-up longitudinal patterns of epigenetic ageing and the association of epigenetic ageing with cognition as well as whole brain structure changes in PHIV + and healthy controls enrolled in the Cape Town Adolescent Antiretroviral Cohort Study (CTAAC). The Illumina EPIC array was used to generate blood DNA methylation data from 60 PHIV + adolescents and 36 age-matched controls aged 9-12 years old at baseline and again at a 36-month follow-up. Epigenetic clock software estimated two measures of epigenetic age acceleration: extrinsic epigenetic accelerated ageing (EEAA) and age acceleration difference (AAD) at both time points. At follow-up, each participant completed neuropsychological testing, structural magnetic resonance imaging, and diffusion tensor imaging. At follow-up, PHIV infection remains associated with increased EEAA and AAD. Accelerated epigenetic ageing remained positively associated with viral load and negatively associated with CD4 ratio. EEAA was positively associated with whole brain grey matter volume and alterations in whole brain white matter integrity. AAD and EEAA were not associated with cognitive function within the PHIV + group. Measures of epigenetic ageing, as detected in DNA methylation patterns, remain increased in PHIV + adolescents across a 36-month period. Associations between epigenetic ageing measures, viral biomarkers, and alterations in brain micro- and macrostructure also persist at 36-month follow-up. Further study should determine if epigenetic age acceleration is associated with cognitive functional changes due to brain alterations in later life.
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- 2023
35. Genetic Influences on the Developing Young Brain and Risk for Neuropsychiatric Disorders
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Alex, Ann M, Buss, Claudia, Davis, Elysia Poggi, de los Campos, Gustavo, Donald, Kirsten A, Fair, Damien A, Gaab, Nadine, Gao, Wei, Gilmore, John H, Girault, Jessica B, Grewen, Karen, Groenewold, Nynke A, Hankin, Benjamin L, Ipser, Jonathan, Kapoor, Shreya, Kim, Pilyoung, Lin, Weili, Luo, Shan, Norton, Elizabeth S, O’Connor, Thomas G, Piven, Joseph, Qiu, Anqi, Rasmussen, Jerod M, Skeide, Michael A, Stein, Dan J, Styner, Martin A, Thompson, Paul M, Wakschlag, Laurie, Knickmeyer, Rebecca, and group, for the ENIGMA ORIGINs
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Biological Sciences ,Biomedical and Clinical Sciences ,Genetics ,Biological Psychology ,Psychology ,Pediatric Research Initiative ,Genetic Testing ,Brain Disorders ,Clinical Research ,Mental Health ,Neurosciences ,Intellectual and Developmental Disabilities (IDD) ,Pediatric ,Prevention ,2.1 Biological and endogenous factors ,Aetiology ,Mental health ,Neurological ,Female ,Pregnancy ,Child ,Preschool ,Humans ,Brain ,Mental Disorders ,Neuroimaging ,Phenotype ,ENIGMA ORIGINs group ,Childhood ,Imaging ,Infant ,Magnetic resonance imaging ,Medical and Health Sciences ,Psychology and Cognitive Sciences ,Psychiatry ,Biological sciences ,Biomedical and clinical sciences - Abstract
Imaging genetics provides an opportunity to discern associations between genetic variants and brain imaging phenotypes. Historically, the field has focused on adults and adolescents; very few imaging genetics studies have focused on brain development in infancy and early childhood (from birth to age 6 years). This is an important knowledge gap because developmental changes in the brain during the prenatal and early postnatal period are regulated by dynamic gene expression patterns that likely play an important role in establishing an individual's risk for later psychiatric illness and neurodevelopmental disabilities. In this review, we summarize findings from imaging genetics studies spanning from early infancy to early childhood, with a focus on studies examining genetic risk for neuropsychiatric disorders. We also introduce the Organization for Imaging Genomics in Infancy (ORIGINs), a working group of the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium, which was established to facilitate large-scale imaging genetics studies in infancy and early childhood.
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- 2023
36. Obsessive-Compulsive Disorder Related Disorders: Hypochondriasis, Hoarding Disorder, Olfactory Reference Disorder, Body Dysmorphic Disorder, Trichotillomania, Excoriation Disorder
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Phillips, Katharine A., Rodriguez, Carolyn I., Harding, Kelli J., Fallon, Brian A., Stein, Dan J., Ng, Chee H., Section editor, Lecic-Tosevski, Dusica, Section editor, Alfonso, César A., Section editor, Salloum, Ihsan M., Section editor, Tasman, Allan, editor, Riba, Michelle B., editor, Alarcón, Renato D., editor, Alfonso, César A., editor, Kanba, Shigenobu, editor, Lecic-Tosevski, Dusica, editor, Ndetei, David M., editor, Ng, Chee H., editor, and Schulze, Thomas G., editor
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- 2024
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37. Evolutionary Psychiatry
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Nesse, Randolph M., Stein, Dan J., Schulze, Thomas G., Section editor, Laje, Gonzalo, Section editor, Tasman, Allan, editor, Riba, Michelle B., editor, Alarcón, Renato D., editor, Alfonso, César A., editor, Kanba, Shigenobu, editor, Lecic-Tosevski, Dusica, editor, Ndetei, David M., editor, Ng, Chee H., editor, and Schulze, Thomas G., editor
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- 2024
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38. Obsessive-Compulsive Disorder and Certainty
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Denys, Damiaan, Prosée, Reinier, Stein, Dan J., Mishara, Aaron L., editor, Moskalewicz, Marcin, editor, Schwartz, Michael A., editor, and Kranjec, Alexander, editor
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- 2024
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39. Phenomenological and Neuroscientific Perspectives on Anxiety Disorders
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Stein, Dan J., Denys, Damiaan, Mishara, Aaron L., editor, Moskalewicz, Marcin, editor, Schwartz, Michael A., editor, and Kranjec, Alexander, editor
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- 2024
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40. Right Prefrontal Cortical Thickness Is Associated With Response to Cognitive-Behavioral Therapy in Children With Obsessive-Compulsive Disorder.
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Bertolín, Sara, Alonso, Pino, Martínez-Zalacaín, Ignacio, Menchón, Jose, Jimenez-Murcia, Susana, Baker, Justin, Bargalló, Nuria, Batistuzzo, Marcelo, Boedhoe, Premika, Brennan, Brian, Feusner, Jamie, Fitzgerald, Kate, Fontaine, Martine, Hansen, Bjarne, Hirano, Yoshiyuki, Hoexter, Marcelo, Huyser, Chaim, Jahanshad, Neda, Jaspers-Fayer, Fern, Kuno, Masaru, Kvale, Gerd, Lazaro, Luisa, Machado-Sousa, Mafalda, Marsh, Rachel, Morgado, Pedro, Nakagawa, Akiko, Norman, Luke, Nurmi, Erika, ONeill, Joseph, Ortiz, Ana, Perriello, Chris, Picó-Pérez, Maria, Shavitt, Roseli, Shimizu, Eiji, Simpson, Helen, Stewart, S, Thomopoulos, Sophia, Thorsen, Anders, Walitza, Susanne, Wolters, Lidewij, Thompson, Paul, van den Heuvel, Odile, Stein, Dan, Soriano-Mas, Carles, and Piacentini, John
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anxiety disorders ,cognitive-behavioral therapy ,magnetic resonance imaging ,neuroimaging ,obsessive-compulsive disorder ,Adult ,Adolescent ,Humans ,Child ,Child ,Preschool ,Prefrontal Cortex ,Obsessive-Compulsive Disorder ,Magnetic Resonance Imaging ,Frontal Lobe ,Cognitive Behavioral Therapy - Abstract
OBJECTIVE: Cognitive-behavioral therapy (CBT) is considered a first-line treatment for obsessive-compulsive disorder (OCD) in pediatric and adult populations. Nevertheless, some patients show partial or null response. The identification of predictors of CBT response may improve clinical management of patients with OCD. Here, we aimed to identify structural magnetic resonance imaging (MRI) predictors of CBT response in 2 large series of children and adults with OCD from the worldwide ENIGMA-OCD consortium. METHOD: Data from 16 datasets from 13 international sites were included in the study. We assessed which variations in baseline cortical thickness, cortical surface area, and subcortical volume predicted response to CBT (percentage of baseline to post-treatment symptom reduction) in 2 samples totaling 168 children and adolescents (age range 5-17.5 years) and 318 adult patients (age range 18-63 years) with OCD. Mixed linear models with random intercept were used to account for potential cross-site differences in imaging values. RESULTS: Significant results were observed exclusively in the pediatric sample. Right prefrontal cortex thickness was positively associated with the percentage of CBT response. In a post hoc analysis, we observed that the specific changes accounting for this relationship were a higher thickness of the frontal pole and the rostral middle frontal gyrus. We observed no significant effects of age, sex, or medication on our findings. CONCLUSION: Higher cortical thickness in specific right prefrontal cortex regions may be important for CBT response in children with OCD. Our findings suggest that the right prefrontal cortex plays a relevant role in the mechanisms of action of CBT in children.
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- 2023
41. Decision-Making Tendencies and Voucher Spending Independently Support Abstinence Within Contingency Management for Methamphetamine Use Disorder
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Lake, Marilyn T, Krishnamurti, Tamar, Murtaugh, Kimberly Ling, van Nunen, Lara J, Stein, Dan J, and Shoptaw, Steven
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Biological Psychology ,Psychology ,Clinical Research ,Methamphetamine ,Behavioral and Social Science ,Drug Abuse (NIDA only) ,Substance Misuse ,Clinical Trials and Supportive Activities ,Brain Disorders ,Good Health and Well Being ,Adult ,Humans ,Male ,Behavior Therapy ,Gambling ,Reinforcement ,Psychology ,Reward ,decision-making ,spending ,contingency management ,methamphetamine use disorder ,behavioral economics ,Pharmacology and Pharmaceutical Sciences ,Substance Abuse ,Pharmacology and pharmaceutical sciences ,Biological psychology ,Clinical and health psychology - Abstract
Decision-making tendencies and spending within cash voucher-based interventions have individually been shown to be related to future abstinence among participants with methamphetamine use disorder (MUD), but less is known of their independent contributions. This study of participants in a contingency management (CM) trial investigated whether decision-making and spending were each associated with future abstinence. Thirty-two outpatients with MUD, predominately male (68%) and mixed ancestry (94%) with a median age of 34 years, participated in an 8-week cash voucher-based CM pilot trial. Prior to commencing the trial, participants completed a computerized Iowa Gambling Task (IGT) to measure decision-making preferences for more frequent rewards and longer term gains of greater magnitude. Spending and abstinence of participants were tracked over the duration of the trial. In a secondary analysis, time-lagged counting process Cox Proportional Hazard models were conducted. Baseline decision-making, characterized by a preference for frequent rewards, was associated with a greater likelihood of future spending, Hazard Ratio; HR = 1.13 [1.06: 1.21]. Avoidance of short-term rewards to realize longer term, higher magnitude rewards, and spending at the prior visit were each associated with abstinence on the trial, HR = 1.12 [1.03: 1.22] and HR = 1.32 [1.08: 1.61], respectively. Controlling for decision-making, spending, and cumulative abstinence, prior abstinence remained the largest predictor of future abstinence, HR = 3.85 [2.88: 5.16]. Decision-making tendencies and spending are correlated yet independently associated with abstinence reinforcement in CM. Findings highlight the opportunity for behavioral treatment programs to tailor program structures to individual-specific characteristics. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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- 2023
42. Volume of subcortical brain regions in social anxiety disorder: mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group.
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Groenewold, Nynke, Bas-Hoogendam, Janna, Amod, Alyssa, Laansma, Max, Van Velzen, Laura, Aghajani, Moji, Hilbert, Kevin, Oh, Hyuntaek, Salas, Ramiro, Jackowski, Andrea, Pan, Pedro, Salum, Giovanni, Blair, James, Blair, Karina, Hirsch, Joy, Pantazatos, Spiro, Schneier, Franklin, Talati, Ardesheer, Roelofs, Karin, Volman, Inge, Blanco-Hinojo, Laura, Cardoner, Narcís, Pujol, Jesus, Beesdo-Baum, Katja, Ching, Christopher, Thomopoulos, Sophia, Jansen, Andreas, Kircher, Tilo, Krug, Axel, Nenadić, Igor, Stein, Frederike, Dannlowski, Udo, Grotegerd, Dominik, Lemke, Hannah, Meinert, Susanne, Winter, Alexandra, Erb, Michael, Kreifelts, Benjamin, Gong, Qiyong, Lui, Su, Zhu, Fei, Mwangi, Benson, Soares, Jair, Wu, Mon-Ju, Bayram, Ali, Canli, Mesut, Tükel, Raşit, Westenberg, P, Heeren, Alexandre, Cremers, Henk, Hofmann, David, Straube, Thomas, Doruyter, Alexander, Lochner, Christine, Peterburs, Jutta, Van Tol, Marie-José, Gur, Raquel, Kaczkurkin, Antonia, Larsen, Bart, Satterthwaite, Theodore, Filippi, Courtney, Gold, Andrea, Harrewijn, Anita, Zugman, André, Bülow, Robin, Grabe, Hans, Völzke, Henry, Wittfeld, Katharina, Böhnlein, Joscha, Dohm, Katharina, Kugel, Harald, Schrammen, Elisabeth, Zwanzger, Peter, Leehr, Elisabeth, Sindermann, Lisa, Ball, Tali, Fonzo, Gregory, Paulus, Martin, Stein, Murray, Klumpp, Heide, Phan, K, Furmark, Tomas, Månsson, Kristoffer, Manzouri, Amirhossein, Avery, Suzanne, Blackford, Jennifer, Clauss, Jacqueline, Feola, Brandee, Harper, Jennifer, Sylvester, Chad, Lueken, Ulrike, Veltman, Dick, Winkler, Anderson, Jahanshad, Neda, Pine, Daniel, Thompson, Paul, Stein, Dan, Van der Wee, Nic, and Simmons, Alan
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Adult ,Adolescent ,Humans ,Phobia ,Social ,Magnetic Resonance Imaging ,Brain ,Anxiety ,Neuroimaging - Abstract
There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE
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- 2023
43. Mega-analysis of association between obesity and cortical morphology in bipolar disorders: ENIGMA study in 2832 participants.
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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
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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.
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- 2023
44. Neurobiology of subtypes of trichotillomania and skin picking disorder
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Grant, Jon E, Bethlehem, Richard AI, Chamberlain, Samuel R, Peris, Tara S, Ricketts, Emily J, O’Neill, Joseph, Dougherty, Darin D, Stein, Dan, Lochner, Christine, Woods, Douglas W, Piacentini, John, and Keuthen, Nancy J
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Biological Psychology ,Biomedical and Clinical Sciences ,Clinical Sciences ,Psychology ,Prevention ,Neurosciences ,Mental Health ,Clinical Research ,Good Health and Well Being ,Adult ,Humans ,Female ,Trichotillomania ,Brain ,Impulsive Behavior ,Comorbidity ,skin picking disorder ,imaging ,subtypes ,neurobiology ,Psychiatry ,Clinical sciences ,Biological psychology - Abstract
BackgroundTrichotillomania (TTM) and skin picking disorder (SPD) are common and often debilitating mental health conditions, grouped under the umbrella term of body-focused repetitive behaviors (BFRBs). Recent clinical subtyping found that there were three distinct subtypes of TTM and two of SPD. Whether these clinical subtypes map on to any unique neurobiological underpinnings, however, remains unknown.MethodsTwo hundred and fifty one adults [193 with a BFRB (85.5% [n = 165] female) and 58 healthy controls (77.6% [n = 45] female)] were recruited from the community for a multicenter between-group comparison using structural neuroimaging. Differences in whole brain structure were compared across the subtypes of BFRBs, controlling for age, sex, scanning site, and intracranial volume.ResultsWhen the subtypes of TTM were compared, low awareness hair pullers demonstrated increased cortical volume in the lateral occipital lobe relative to controls and sensory sensitive pullers. In addition, impulsive/perfectionist hair pullers showed relative decreased volume near the lingual gyrus of the inferior occipital-parietal lobe compared with controls.ConclusionsThese data indicate that the anatomical substrates of particular forms of BFRBs are dissociable, which may have implications for understanding clinical presentations and treatment response.
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- 2023
45. Philosophy and Cognitive Neuropsychiatry
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Stein, Dan J
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- 1999
46. Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning
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Bruin, Willem B., Zhutovsky, Paul, van Wingen, Guido A., Bas-Hoogendam, Janna Marie, Groenewold, Nynke A., Hilbert, Kevin, Winkler, Anderson M., Zugman, Andre, Agosta, Federica, Åhs, Fredrik, Andreescu, Carmen, Antonacci, Chase, Asami, Takeshi, Assaf, Michal, Barber, Jacques P., Bauer, Jochen, Bavdekar, Shreya Y., Beesdo-Baum, Katja, Benedetti, Francesco, Bernstein, Rachel, Björkstrand, Johannes, Blair, Robert J., Blair, Karina S., Blanco-Hinojo, Laura, Böhnlein, Joscha, Brambilla, Paolo, Bressan, Rodrigo A., Breuer, Fabian, Cano, Marta, Canu, Elisa, Cardinale, Elise M., Cardoner, Narcís, Cividini, Camilla, Cremers, Henk, Dannlowski, Udo, Diefenbach, Gretchen J., Domschke, Katharina, Doruyter, Alexander G. G., Dresler, Thomas, Erhardt, Angelika, Filippi, Massimo, Fonzo, Gregory A., Freitag, Gabrielle F., Furmark, Tomas, Ge, Tian, Gerber, Andrew J., Gosnell, Savannah N., Grabe, Hans J., Grotegerd, Dominik, Gur, Ruben C., Gur, Raquel E., Hamm, Alfons O., Han, Laura K. M., Harper, Jennifer C., Harrewijn, Anita, Heeren, Alexandre, Hofmann, David, Jackowski, Andrea P., Jahanshad, Neda, Jett, Laura, Kaczkurkin, Antonia N., Khosravi, Parmis, Kingsley, Ellen N., Kircher, Tilo, Kostic, Milutin, Larsen, Bart, Lee, Sang-Hyuk, Leehr, Elisabeth J., Leibenluft, Ellen, Lochner, Christine, Lui, Su, Maggioni, Eleonora, Manfro, Gisele G., Månsson, Kristoffer N. T., Marino, Claire E., Meeten, Frances, Milrod, Barbara, Jovanovic, Ana Munjiza, Mwangi, Benson, Myers, Michael J., Neufang, Susanne, Nielsen, Jared A., Ohrmann, Patricia A., Ottaviani, Cristina, Paulus, Martin P., Perino, Michael T., Phan, K. Luan, Poletti, Sara, Porta-Casteràs, Daniel, Pujol, Jesus, Reinecke, Andrea, Ringlein, Grace V., Rjabtsenkov, Pavel, Roelofs, Karin, Salas, Ramiro, Salum, Giovanni A., Satterthwaite, Theodore D., Schrammen, Elisabeth, Sindermann, Lisa, Smoller, Jordan W., Soares, Jair C., Stark, Rudolf, Stein, Frederike, Straube, Thomas, Straube, Benjamin, Strawn, Jeffrey R., Suarez-Jimenez, Benjamin, Sylvester, Chad M., Talati, Ardesheer, Thomopoulos, Sophia I., Tükel, Raşit, van Nieuwenhuizen, Helena, Werwath, Kathryn, Wittfeld, Katharina, Wright, Barry, Wu, Mon-Ju, Yang, Yunbo, Zilverstand, Anna, Zwanzger, Peter, Blackford, Jennifer U., Avery, Suzanne N., Clauss, Jacqueline A., Lueken, Ulrike, Thompson, Paul M., Pine, Daniel S., Stein, Dan J., van der Wee, Nic J. A., Veltman, Dick J., and Aghajani, Moji
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- 2024
- Full Text
- View/download PDF
47. Epigenome-wide meta-analysis of prenatal maternal stressful life events and newborn DNA methylation
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Kotsakis Ruehlmann, Anna, Sammallahti, Sara, Cortés Hidalgo, Andrea P., Bakulski, Kelly M., Binder, Elisabeth B., Campbell, Megan Loraine, Caramaschi, Doretta, Cecil, Charlotte A. M., Colicino, Elena, Cruceanu, Cristiana, Czamara, Darina, Dieckmann, Linda, Dou, John, Felix, Janine F., Frank, Josef, Håberg, Siri E., Herberth, Gunda, Hoang, Thanh T., Houtepen, Lotte C., Hüls, Anke, Koen, Nastassja, London, Stephanie J., Magnus, Maria C., Mancano, Giulia, Mulder, Rosa H., Page, Christian M., Räikkönen, Katri, Röder, Stefan, Schmidt, Rebecca J., Send, Tabea S., Sharp, Gemma, Stein, Dan J., Streit, Fabian, Tuhkanen, Johanna, Witt, Stephanie H., Zar, Heather J., Zenclussen, Ana C., Zhang, Yining, Zillich, Lea, Wright, Rosalind, Lahti, Jari, and Brunst, Kelly J.
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- 2023
- Full Text
- View/download PDF
48. The Philosophy of Evil
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Stein, Dan J
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- 2006
- Full Text
- View/download PDF
49. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
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Belov, Vladimir, Erwin-Grabner, Tracy, Gonul, Ali Saffet, Amod, Alyssa R., Ojha, Amar, Aleman, Andre, Dols, Annemiek, Scharntee, Anouk, Uyar-Demir, Aslihan, Harrison, Ben J, Irungu, Benson M., Besteher, Bianca, Klimes-Dougan, Bonnie, Penninx, Brenda W. J. H., Mueller, Bryon A., Zarate, Carlos, Davey, Christopher G., Ching, Christopher R. K., Connolly, Colm G., Fu, Cynthia H. Y., Stein, Dan J., Dima, Danai, Linden, David E. J., Mehler, David M. A., Pomarol-Clotet, Edith, Pozzi, Elena, Melloni, Elisa, Benedetti, Francesco, MacMaster, Frank P., Grabe, Hans J., Völzke, Henry, Gotlib, Ian H., Soares, Jair C., Evans, Jennifer W., Sim, Kang, Wittfeld, Katharina, Cullen, Kathryn, Reneman, Liesbeth, Oudega, Mardien L., Wright, Margaret J., Portella, Maria J., Sacchet, Matthew D., Li, Meng, Aghajani, Moji, Wu, Mon-Ju, Jaworska, Natalia, Jahanshad, Neda, van der Wee, Nic J. A., Groenewold, Nynke, Hamilton, Paul J., Saemann, Philipp, Bülow, Robin, Poletti, Sara, Whittle, Sarah, Thomopoulos, Sophia I., van, Steven J. A., Werff, der, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Ho, Tiffany C., Yang, Tony T., Basgoze, Zeynep, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto
- Subjects
Quantitative Biology - Quantitative Methods - Abstract
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (n=5,356) to provide a generalizable ML classification benchmark of major depressive disorder (MDD). Using brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD vs healthy controls (HC) with around 62% balanced accuracy, but when harmonizing the data using ComBat balanced accuracy dropped to approximately 52%. Similar results were observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may achieve more encouraging prospects., Comment: main document 37 pages; supplementary material 24 pages
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- 2022
50. Recalibrating single-study effect sizes using hierarchical Bayesian models.
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Morales, Angelica, London, Edythe, Lorenzetti, Valentina, Luijten, Maartje, Martin-Santos, Rocio, Momenan, Reza, Paulus, Martin, Schmaal, Lianne, Sinha, Rajita, Solowij, Nadia, Stein, Dan, Stein, Elliot, Uhlmann, Anne, van Holst, Ruth, Veltman, Dick, Wiers, Reinout, Yücel, Murat, Zhang, Sheng, Conrod, Patricia, Mackey, Scott, Garavan, Hugh, Cao, Zhipeng, McCabe, Matthew, Callas, Peter, Cupertino, Renata, Ottino-González, Jonatan, Murphy, Alistair, Pancholi, Devarshi, Schwab, Nathan, Catherine, Orr, Hutchison, Kent, Cousijn, Janna, Dagher, Alain, Foxe, John, Goudriaan, Anna, Hester, Robert, Li, Chiang-Shan, and Thompson, Wesley
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case-control differences ,effect size recalibration ,hierarchical Bayesian model ,inflated effect size ,small sample size ,substance dependence - Abstract
INTRODUCTION: There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance. METHODS: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method. RESULTS: The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohens d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. DISCUSSION: Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.
- Published
- 2023
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