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13 results on '"Wu, Mon-Ju"'

1. Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group.

Author

Zugman A, Harrewijn A, Cardinale EM, Zwiebel H, Freitag GF, Werwath KE, Bas-Hoogendam JM, Groenewold NA, Aghajani M, Hilbert K, Cardoner N, Porta-Casteràs D, Gosnell S, Salas R, Blair KS, Blair JR, Hammoud MZ, Milad M, Burkhouse K, Phan KL, Schroeder HK, Strawn JR, Beesdo-Baum K, Thomopoulos SI, Grabe HJ, Van der Auwera S, Wittfeld K, Nielsen JA, Buckner R, Smoller JW, Mwangi B, Soares JC, Wu MJ, Zunta-Soares GB, Jackowski AP, Pan PM, Salum GA, Assaf M, Diefenbach GJ, Brambilla P, Maggioni E, Hofmann D, Straube T, Andreescu C, Berta R, Tamburo E, Price R, Manfro GG, Critchley HD, Makovac E, Mancini M, Meeten F, Ottaviani C, Agosta F, Canu E, Cividini C, Filippi M, Kostić M, Munjiza A, Filippi CA, Leibenluft E, Alberton BAV, Balderston NL, Ernst M, Grillon C, Mujica-Parodi LR, van Nieuwenhuizen H, Fonzo GA, Paulus MP, Stein MB, Gur RE, Gur RC, Kaczkurkin AN, Larsen B, Satterthwaite TD, Harper J, Myers M, Perino MT, Yu Q, Sylvester CM, Veltman DJ, Lueken U, Van der Wee NJA, Stein DJ, Jahanshad N, Thompson PM, Pine DS, and Winkler AM

Subjects
Humans, Anxiety Disorders diagnostic imaging, Cerebral Cortex diagnostic imaging, Data Interpretation, Statistical, Meta-Analysis as Topic, Multicenter Studies as Topic methods, Multicenter Studies as Topic standards, Neuroimaging methods, Neuroimaging standards
Abstract

The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
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2. What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group.

Author

Ching CRK, Hibar DP, Gurholt TP, Nunes A, Thomopoulos SI, Abé C, Agartz I, Brouwer RM, Cannon DM, de Zwarte SMC, Eyler LT, Favre P, Hajek T, Haukvik UK, Houenou J, Landén M, Lett TA, McDonald C, Nabulsi L, Patel Y, Pauling ME, Paus T, Radua J, Soeiro-de-Souza MG, Tronchin G, van Haren NEM, Vieta E, Walter H, Zeng LL, Alda M, Almeida J, Alnaes D, Alonso-Lana S, Altimus C, Bauer M, Baune BT, Bearden CE, Bellani M, Benedetti F, Berk M, Bilderbeck AC, Blumberg HP, Bøen E, Bollettini I, Del Mar Bonnin C, Brambilla P, Canales-Rodríguez EJ, Caseras X, Dandash O, Dannlowski U, Delvecchio G, Díaz-Zuluaga AM, Dima D, Duchesnay É, Elvsåshagen T, Fears SC, Frangou S, Fullerton JM, Glahn DC, Goikolea JM, Green MJ, Grotegerd D, Gruber O, Haarman BCM, Henry C, Howells FM, Ives-Deliperi V, Jansen A, Kircher TTJ, Knöchel C, Kramer B, Lafer B, López-Jaramillo C, Machado-Vieira R, MacIntosh BJ, Melloni EMT, Mitchell PB, Nenadic I, Nery F, Nugent AC, Oertel V, Ophoff RA, Ota M, Overs BJ, Pham DL, Phillips ML, Pineda-Zapata JA, Poletti S, Polosan M, Pomarol-Clotet E, Pouchon A, Quidé Y, Rive MM, Roberts G, Ruhe HG, Salvador R, Sarró S, Satterthwaite TD, Schene AH, Sim K, Soares JC, Stäblein M, Stein DJ, Tamnes CK, Thomaidis GV, Upegui CV, Veltman DJ, Wessa M, Westlye LT, Whalley HC, Wolf DH, Wu MJ, Yatham LN, Zarate CA, Thompson PM, and Andreassen OA

Subjects
Humans, Meta-Analysis as Topic, Multicenter Studies as Topic, Bipolar Disorder diagnostic imaging, Bipolar Disorder pathology, Cerebral Cortex diagnostic imaging, Cerebral Cortex pathology, Magnetic Resonance Imaging, Neuroimaging
Abstract

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
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3. Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder.

Author

de Zwarte SMC, Brouwer RM, Agartz I, Alda M, Alonso-Lana S, Bearden CE, Bertolino A, Bonvino A, Bramon E, Buimer EEL, Cahn W, Canales-Rodríguez EJ, Cannon DM, Cannon TD, Caseras X, Castro-Fornieles J, Chen Q, Chung Y, De la Serna E, Del Mar Bonnin C, Demro C, Di Giorgio A, Doucet GE, Eker MC, Erk S, Fatjó-Vilas M, Fears SC, Foley SF, Frangou S, Fullerton JM, Glahn DC, Goghari VM, Goikolea JM, Goldman AL, Gonul AS, Gruber O, Hajek T, Hawkins EL, Heinz A, Hidiroglu Ongun C, Hillegers MHJ, Houenou J, Hulshoff Pol HE, Hultman CM, Ingvar M, Johansson V, Jönsson EG, Kane F, Kempton MJ, Koenis MMG, Kopecek M, Krämer B, Lawrie SM, Lenroot RK, Marcelis M, Mattay VS, McDonald C, Meyer-Lindenberg A, Michielse S, Mitchell PB, Moreno D, Murray RM, Mwangi B, Nabulsi L, Newport J, Olman CA, van Os J, Overs BJ, Ozerdem A, Pergola G, Picchioni MM, Piguet C, Pomarol-Clotet E, Radua J, Ramsay IS, Richter A, Roberts G, Salvador R, Saricicek Aydogan A, Sarró S, Schofield PR, Simsek EM, Simsek F, Soares JC, Sponheim SR, Sugranyes G, Toulopoulou T, Tronchin G, Vieta E, Walter H, Weinberger DR, Whalley HC, Wu MJ, Yalin N, Andreassen OA, Ching CRK, Thomopoulos SI, van Erp TGM, Jahanshad N, Thompson PM, Kahn RS, and van Haren NEM

Subjects
Bipolar Disorder complications, Bipolar Disorder diagnostic imaging, Cognitive Dysfunction diagnostic imaging, Family, Humans, Magnetic Resonance Imaging, Schizophrenia complications, Schizophrenia diagnostic imaging, Schizophrenia etiology, Bipolar Disorder pathology, Cognitive Dysfunction pathology, Educational Status, Genetic Predisposition to Disease, Intelligence physiology, Neuroimaging, Schizophrenia pathology
Abstract

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10 -5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
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4. Hippocampal subfield volumes in children and adolescents with mood disorders.

Author

Tannous J, Amaral-Silva H, Cao B, Wu MJ, Zunta-Soares GB, Kazimi I, Zeni C, Mwangi B, and Soares JC

Subjects
Adolescent, Bipolar Disorder diagnostic imaging, Child, Depressive Disorder, Major diagnostic imaging, Female, Hippocampus diagnostic imaging, Humans, Magnetic Resonance Imaging, Male, Bipolar Disorder pathology, Depressive Disorder, Major pathology, Hippocampus pathology, Neuroimaging methods
Abstract

The hippocampus has been implicated in various mood disorders, with global volume deficits consistently found in patient populations. The hippocampus, however, consists of anatomically distinct subfields, and examination of specific subfield differences may elucidate the possible molecular mechanisms behind psychiatric pathologies. Indeed, adult studies have reported smaller hippocampal subfield volumes in regions within the cornu ammonis (CA1 and CA4), dentate gyrus (DG), and hippocampal tails in both patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD) compared to healthy controls. Subfield differences in pediatric patients with mood disorders, on the other hand, have not been extensively investigated. In the current study, magnetic resonance imaging scans were acquired for 141 children and adolescents between the ages of eight and eighteen (57 with BD, 30 with MDD, and 54 healthy controls). An automated segmentation method was then used to assess differences in hippocampal subfield volumes. Children and adolescents with BD were found to have significantly smaller volumes in the right CA1, CA4, and right subiculum, as well as the bilateral granule cell layer (GCL), molecular layer (ML), and hippocampal tails. The volume of the right subiculum in BD patients was also found to be negatively correlated with illness duration. Overall, the findings from this cross-sectional study provide evidence for specific hippocampal subfield volume differences in children and adolescents with BD compared to healthy controls and suggest progressive reductions with increased illness duration., (Copyright © 2018. Published by Elsevier Ltd.)

Published
2018
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5. Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning.

Author

Wu MJ, Mwangi B, Bauer IE, Passos IC, Sanches M, Zunta-Soares GB, Meyer TD, Hasan KM, and Soares JC

Subjects
Adult, Bipolar Disorder diagnostic imaging, Bipolar Disorder physiopathology, Female, Humans, Male, Middle Aged, Phenotype, Sensitivity and Specificity, Bipolar Disorder diagnosis, Diffusion Magnetic Resonance Imaging methods, Machine Learning, Neuroimaging methods, White Matter diagnostic imaging
Abstract

Diagnosis, clinical management and research of psychiatric disorders remain subjective - largely guided by historically developed categories which may not effectively capture underlying pathophysiological mechanisms of dysfunction. Here, we report a novel approach of identifying and validating distinct and biologically meaningful clinical phenotypes of bipolar disorders using both unsupervised and supervised machine learning techniques. First, neurocognitive data were analyzed using an unsupervised machine learning approach and two distinct clinical phenotypes identified namely; phenotype I and phenotype II. Second, diffusion weighted imaging scans were pre-processed using the tract-based spatial statistics (TBSS) method and 'skeletonized' white matter fractional anisotropy (FA) and mean diffusivity (MD) maps extracted. The 'skeletonized' white matter FA and MD maps were entered into the Elastic Net machine learning algorithm to distinguish individual subjects' phenotypic labels (e.g. phenotype I vs. phenotype II). This calculation was performed to ascertain whether the identified clinical phenotypes were biologically distinct. Original neurocognitive measurements distinguished individual subjects' phenotypic labels with 94% accuracy (sensitivity=92%, specificity=97%). TBSS derived FA and MD measurements predicted individual subjects' phenotypic labels with 76% and 65% accuracy respectively. In addition, individual subjects belonging to phenotypes I and II were distinguished from healthy controls with 57% and 92% accuracy respectively. Neurocognitive task variables identified as most relevant in distinguishing phenotypic labels included; Affective Go/No-Go (AGN), Cambridge Gambling Task (CGT) coupled with inferior fronto-occipital fasciculus and callosal white matter pathways. These results suggest that there may exist two biologically distinct clinical phenotypes in bipolar disorders which can be identified from healthy controls with high accuracy and at an individual subject level. We suggest a strong clinical utility of the proposed approach in defining and validating biologically meaningful and less heterogeneous clinical sub-phenotypes of major psychiatric disorders., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Published
2017
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6. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Author

Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S, New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I, Breier, Alan, Brodaty, Henry, Buckner, Randy L, Buitelaar, Jan K, Cannon, Dara M, Caseras, Xavier, Clark, Vincent P, Conrod, Patricia J, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E, Franke, Barbara, Glahn, David C, Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hatton, Sean, Hickie, Ian B, Pol, Hilleke E Hulshoff, Jamieson, Alec J, Jernigan, Terry L, Jiang, Jiyang, Kalnin, Andrew J, Kang, Sim, Kochan, Nicole A, Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S, Satterthwaite, Theodore D, Saykin, Andrew J, Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W, Soares, Jair C, Spalletta, Gianfranco, Tamnes, Christian K, Trollor, Julian N, Ent, Dennis Van't, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M, Williams, Steven CR, Wu, Mon‐Ju, Zunta‐Soares, Giovana B, Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang, and Group, ENIGMA‐Lifespan Working

Subjects
Biological Psychology, Psychology, Aging, Clinical Research, Neurosciences, Neurological, Mental health, Humans, Adolescent, Female, Aged, Adult, Child, Young Adult, Male, Brain, Aged, 80 and over, Child, Preschool, Middle Aged, Magnetic Resonance Imaging, Neuroimaging, Sample Size, benchmarking, brain aging, brainAGE, ENIGMA‐Lifespan Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Published
2024

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

Author

Groenewold, Nynke, Bas-Hoogendam, Janna, Amod, Alyssa, Laansma, Max, Van Velzen, Laura, Aghajani, Moji, Hilbert, Kevin, Oh, Hyuntaek, Salas, Ramiro, Jackowski, Andrea, Pan, Pedro, Salum, Giovanni, Blair, James, Blair, Karina, Hirsch, Joy, Pantazatos, Spiro, Schneier, Franklin, Talati, Ardesheer, Roelofs, Karin, Volman, Inge, Blanco-Hinojo, Laura, Cardoner, Narcís, Pujol, Jesus, Beesdo-Baum, Katja, Ching, Christopher, Thomopoulos, Sophia, Jansen, Andreas, Kircher, Tilo, Krug, Axel, Nenadić, Igor, Stein, Frederike, Dannlowski, Udo, Grotegerd, Dominik, Lemke, Hannah, Meinert, Susanne, Winter, Alexandra, Erb, Michael, Kreifelts, Benjamin, Gong, Qiyong, Lui, Su, Zhu, Fei, Mwangi, Benson, Soares, Jair, Wu, Mon-Ju, Bayram, Ali, Canli, Mesut, Tükel, Raşit, Westenberg, P, Heeren, Alexandre, Cremers, Henk, Hofmann, David, Straube, Thomas, Doruyter, Alexander, Lochner, Christine, Peterburs, Jutta, Van Tol, Marie-José, Gur, Raquel, Kaczkurkin, Antonia, Larsen, Bart, Satterthwaite, Theodore, Filippi, Courtney, Gold, Andrea, Harrewijn, Anita, Zugman, André, Bülow, Robin, Grabe, Hans, Völzke, Henry, Wittfeld, Katharina, Böhnlein, Joscha, Dohm, Katharina, Kugel, Harald, Schrammen, Elisabeth, Zwanzger, Peter, Leehr, Elisabeth, Sindermann, Lisa, Ball, Tali, Fonzo, Gregory, Paulus, Martin, Stein, Murray, Klumpp, Heide, Phan, K, Furmark, Tomas, Månsson, Kristoffer, Manzouri, Amirhossein, Avery, Suzanne, Blackford, Jennifer, Clauss, Jacqueline, Feola, Brandee, Harper, Jennifer, Sylvester, Chad, Lueken, Ulrike, Veltman, Dick, Winkler, Anderson, Jahanshad, Neda, Pine, Daniel, Thompson, Paul, Stein, Dan, Van der Wee, Nic, and Simmons, Alan

Subjects
Adult, Adolescent, Humans, Phobia, Social, Magnetic Resonance Imaging, Brain, Anxiety, Neuroimaging
Abstract

There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE

Published
2023

8. Intelligence, educational attainment, and brain structure in those at familial high‐risk for schizophrenia or bipolar disorder

Author

Zwarte, Sonja MC, Brouwer, Rachel M, Agartz, Ingrid, Alda, Martin, Alonso‐Lana, Silvia, Bearden, Carrie E, Bertolino, Alessandro, Bonvino, Aurora, Bramon, Elvira, Buimer, Elizabeth EL, Cahn, Wiepke, Canales‐Rodríguez, Erick J, Cannon, Dara M, Cannon, Tyrone D, Caseras, Xavier, Castro‐Fornieles, Josefina, Chen, Qiang, Chung, Yoonho, De la Serna, Elena, Bonnin, Caterina Mar, Demro, Caroline, Di Giorgio, Annabella, Doucet, Gaelle E, Eker, Mehmet Cagdas, Erk, Susanne, Fatjó‐Vilas, Mar, Fears, Scott C, Foley, Sonya F, Frangou, Sophia, Fullerton, Janice M, Glahn, David C, Goghari, Vina M, Goikolea, Jose M, Goldman, Aaron L, Gonul, Ali Saffet, Gruber, Oliver, Hajek, Tomas, Hawkins, Emma L, Heinz, Andreas, Ongun, Ceren Hidiroglu, Hillegers, Manon HJ, Houenou, Josselin, Pol, Hilleke E Hulshoff, Hultman, Christina M, Ingvar, Martin, Johansson, Viktoria, Jönsson, Erik G, Kane, Fergus, Kempton, Matthew J, Koenis, Marinka MG, Kopecek, Miloslav, Krämer, Bernd, Lawrie, Stephen M, Lenroot, Rhoshel K, Marcelis, Machteld, Mattay, Venkata S, McDonald, Colm, Meyer‐Lindenberg, Andreas, Michielse, Stijn, Mitchell, Philip B, Moreno, Dolores, Murray, Robin M, Mwangi, Benson, Nabulsi, Leila, Newport, Jason, Olman, Cheryl A, Os, Jim, Overs, Bronwyn J, Ozerdem, Aysegul, Pergola, Giulio, Picchioni, Marco M, Piguet, Camille, Pomarol‐Clotet, Edith, Radua, Joaquim, Ramsay, Ian S, Richter, Anja, Roberts, Gloria, Salvador, Raymond, Aydogan, Aybala Saricicek, Sarró, Salvador, Schofield, Peter R, Simsek, Esma M, Simsek, Fatma, Soares, Jair C, Sponheim, Scott R, Sugranyes, Gisela, Toulopoulou, Timothea, Tronchin, Giulia, Vieta, Eduard, Walter, Henrik, Weinberger, Daniel R, Whalley, Heather C, Wu, Mon‐Ju, Yalin, Nefize, Andreassen, Ole A, Ching, Christopher RK, Thomopoulos, Sophia I, Erp, Theo GM, Jahanshad, Neda, and Thompson, Paul M

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Bipolar Disorder, Clinical Research, Serious Mental Illness, Neurosciences, Schizophrenia, Mental Health, Aetiology, 2.3 Psychological, social and economic factors, 2.1 Biological and endogenous factors, Mental health, Cognitive Dysfunction, Educational Status, Family, Genetic Predisposition to Disease, Humans, Intelligence, Magnetic Resonance Imaging, Neuroimaging, bipolar disorder, education, intelligence, neuroimaging, relatives, schizophrenia, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10-5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.

Published
2022

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

Author

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

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

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

Published
2022

10. Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders

Author

Committee, Writing, Disorder, Autism Spectrum, French, Leon, Grevet, Eugenio H, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Gruner, Patricia, Guerrero-Pedraza, Amalia, Gur, Raquel E, Gur, Ruben C, Haar, Shlomi, Haarman, Bartholomeus C M, Thomopoulos, Sophia I, Haavik, Jan, Hahn, Tim, Hajek, Tomas, Harrison, Benjamin J, Harrison, Neil A, Hartman, Catharina A, Whalley, Heather C, Heslenfeld, Dirk J, Hibar, Derrek P, Hilland, Eva, Pozzi, Elena, Hirano, Yoshiyuki, Ho, Tiffany C, Hoekstra, Pieter J, Hoekstra, Liesbeth, Hohmann, Sarah, Hong, L. E., Höschl, Cyril, Høvik, Marie F, Howells, Fleur M, Nenadic, Igor, Abe, Yoshinari, Jalbrzikowski, Maria, James, Anthony C, Janssen, Joost, Jaspers-Fayer, Fern, Xu, Jian, Jonassen, Rune, Karkashadze, Georgii, King, Joseph A, Kircher, Tilo, Kirschner, Matthias, Abé, Christoph, Koch, Kathrin, Kochunov, Peter, Kohls, Gregor, Konrad, Kerstin, Krämer, Bernd, Krug, Axel, Kuntsi, Jonna, Kwon, Jun Soo, Landén, Mikael, Landrø, Nils I, Anticevic, Alan, Lazaro, Luisa, Lebedeva, Irina S, Leehr, Elisabeth J, Lera-Miguel, Sara, Lesch, Klaus-Peter, Lochner, Christine, Louza, Mario R, Luna, Beatriz, Lundervold, Astri J, MacMaster, Frank P, Alda, Martin, Maglanoc, Luigi A, Malpas, Charles B, Portella, Maria J, Marsh, Rachel, Martyn, Fiona M, Mataix-Cols, David, Mathalon, Daniel H, McCarthy, Hazel, McDonald, Colm, McPhilemy, Genevieve, Aleman, Andre, Meinert, Susanne, Menchón, José M, Minuzzi, Luciano, Mitchell, Philip B, Moreno, Carmen, Morgado, Pedro, Muratori, Filippo, Murphy, Clodagh M, Murphy, Declan, Mwangi, Benson, Alloza, Clara, Nabulsi, Leila, Nakagawa, Akiko, Nakamae, Takashi, Namazova, Leyla, Narayanaswamy, Janardhanan, Jahanshad, Neda, Nguyen, Danai D, Nicolau, Rosa, O'Gorman Tuura, Ruth L, O'Hearn, Kirsten, Alonso-Lana, Silvia, Oosterlaan, Jaap, Opel, Nils, Ophoff, Roel A, Oranje, Bob, García de la Foz, Victor Ortiz, Overs, Bronwyn J, Paloyelis, Yannis, Pantelis, Christos, Parellada, Mara, Pauli, Paul, Disorder, Bipolar, Ameis, Stephanie H, Picó-Pérez, Maria, Picon, Felipe A, Piras, Fabrizio, Piras, Federica, Plessen, Kerstin J, Pomarol-Clotet, Edith, Preda, Adrian, Puig, Olga, Quidé, Yann, Radua, Joaquim, Anagnostou, Evdokia, Ramos-Quiroga, J Antoni, Rasser, Paul E, Rauer, Lisa, Reddy, Janardhan, Redlich, Ronny, Reif, Andreas, Reneman, Liesbeth, Repple, Jonathan, Retico, Alessandra, Richarte, Vanesa, McIntosh, Andrew A, Richter, Anja, Rosa, Pedro G P, Rubia, Katya K, Hashimoto, Ryota, Sacchet, Matthew D, Salvador, Raymond, Santonja, Javier, Sarink, Kelvin, Sarró, Salvador, Satterthwaite, Theodore D, Arango, Celso, Sawa, Akira, Schall, Ulrich, Schofield, Peter R, Schrantee, Anouk, Seitz, Jochen, Serpa, Mauricio H, Setién-Suero, Esther, Shaw, Philip, Shook, Devon, Silk, Tim J, Arnold, Paul D, Sim, Kang, Simon, Schmitt, Simpson, Helen Blair, Singh, Aditya, Skoch, Antonin, Skokauskas, Norbert, Soares, Jair C, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Asherson, Philip, Spaniel, Filip, Lawrie, Stephen M, Stern, Emily R, Stewart, S Evelyn, Takayanagi, Yoichiro, Temmingh, Henk S, Tolin, David F, Tomecek, David, Tordesillas-Gutiérrez, Diana, Tosetti, Michela, Assogna, Francesca, Uhlmann, Anne, van Amelsvoort, Therese, van der Wee, Nic J A, van der Werff, Steven J A, van Haren, Neeltje E M, van Wingen, Guido A, Vance, Alasdair, Vázquez-Bourgon, Javier, Vecchio, Daniela, Venkatasubramanian, Ganesan, Auzias, Guillaume, Vieta, Eduard, Vilarroya, Oscar, Vives-Gilabert, Yolanda, Voineskos, Aristotle N, Völzke, Henry, von Polier, Georg G, Walton, Esther, Weickert, Thomas W, Weickert, Cynthia Shannon, Weideman, Andrea S, Ayesa-Arriola, Rosa, Wittfeld, Katharina, Wolf, Daniel H, Wu, Mon-Ju, Yang, T. T., Yang, Sikun, Yoncheva, Yuliya, Yun, Je-Yeon, Cheng, Yuqi, Zanetti, Marcus V, Ziegler, Georg C, Bakker, Geor, Franke, Barbara, Hoogman, Martine, Buitelaar, Jan K, van Rooij, Daan, Andreassen, Ole A, Ching, Christopher R K, Veltman, Dick J, Schmaal, Lianne, Stein, Dan J, van den Heuvel, Odile A, Disorder, Major Depressive, Banaj, Nerisa, Turner, Jessica A, van Erp, Theo G M, Pausova, Zdenka, Thompson, Paul M, Paus, Tomáš, Attention-Deficit/Hyperactivity Disorder, Banaschewski, Tobias, Bandeira, Cibele E, Baranov, Alexandr, Bargalló, Núria, Bau, Claiton H D, Baumeister, Sarah, Baune, Bernhard T, Bellgrove, Mark A, Benedetti, Francesco, Disorder, Obsessive-Compulsive, Bertolino, Alessandro, Boedhoe, Premika S W, Boks, Marco, Bollettini, Irene, Del Mar Bonnin, Caterina, Borgers, Tiana, Borgwardt, Stefan, Brandeis, Daniel, Brennan, Brian P, Bruggemann, Jason M, Groups, Schizophrenia ENIGMA Working, Bülow, Robin, Busatto, Geraldo F, Calderoni, Sara, Calhoun, Vince D, Calvo, Rosa, Canales-Rodríguez, Erick J, Cannon, Dara M, Carr, Vaughan J, Cascella, Nicola, Cercignani, Mara, Patel, Yash, Chaim-Avancini, Tiffany M, Christakou, Anastasia, Coghill, David, Conzelmann, Annette, Crespo-Facorro, Benedicto, Cubillo, Ana I, Cullen, Kathryn R, Cupertino, Renata B, Daly, Eileen, Dannlowski, Udo, Parker, Nadine, Davey, Christopher G, Denys, Damiaan, Deruelle, Christine, Di Giorgio, Annabella, Dickie, Erin W, Dima, Danai, Dohm, Katharina, Ehrlich, Stefan, Ely, Benjamin A, Erwin-Grabner, Tracy, Shin, Jean, Ethofer, Thomas, Fair, Damien A, Fallgatter, Andreas, Faraone, Stephen V, Fatjó-Vilas, Mar, Fedor, Jennifer M, Fitzgerald, Kate D, Ford, Judith M, Frodl, Thomas, Fu, Cynthia H Y, Howard, Derek, Fullerton, Janice M, Gabel, Matt C, Glahn, David C, Roberts, Gloria, Gogberashvili, Tinatin, Goikolea, Jose M, Gotlib, Ian H, Goya-Maldonado, Roberto, Grabe, Hans, Green, Melissa J, Patel, Y., Parker, N., Shin, J., Howard, D., French, L., Thomopoulos, S. I., Pozzi, E., Abe, Y., Abe, C., Anticevic, A., Alda, M., Aleman, A., Alloza, C., Alonso-Lana, S., Ameis, S. H., Anagnostou, E., Mcintosh, A. A., Arango, C., Arnold, P. D., Asherson, P., Assogna, F., Auzias, G., Ayesa-Arriola, R., Bakker, G., Banaj, N., Banaschewski, T., Bandeira, C. E., Baranov, A., Bargallo, N., Bau, C. H. D., Baumeister, S., Baune, B. T., Bellgrove, M. A., Benedetti, F., Bertolino, A., Boedhoe, P. S. W., Boks, M., Bollettini, I., Del Mar Bonnin, C., Borgers, T., Borgwardt, S., Brandeis, D., Brennan, B. P., Bruggemann, J. M., Bulow, R., Busatto, G. F., Calderoni, S., Calhoun, V. D., Calvo, R., Canales-Rodriguez, E. J., Cannon, D. M., Carr, V. J., Cascella, N., Cercignani, M., Chaim-Avancini, T. M., Christakou, A., Coghill, D., Conzelmann, A., Crespo-Facorro, B., Cubillo, A. I., Cullen, K. R., Cupertino, R. B., Daly, E., Dannlowski, U., Davey, C. G., Denys, D., Deruelle, C., Di Giorgio, A., Dickie, E. W., Dima, D., Dohm, K., Ehrlich, S., Ely, B. A., Erwin-Grabner, T., Ethofer, T., Fair, D. A., Fallgatter, A. J., Faraone, S. V., Fatjo-Vilas, M., Fedor, J. M., Fitzgerald, K. D., Ford, J. M., Frodl, T., Fu, C. H. Y., Fullerton, J. M., Gabel, M. C., Glahn, D. C., Roberts, G., Gogberashvili, T., Goikolea, J. M., Gotlib, I. H., Goya-Maldonado, R., Grabe, H. J., Green, M. J., Grevet, E. H., Groenewold, N. A., Grotegerd, D., Gruber, O., Gruner, P., Guerrero-Pedraza, A., Gur, R. E., Gur, R. C., Haar, S., Haarman, B. C. M., Haavik, J., Hahn, T., Hajek, T., Harrison, B. J., Harrison, N. A., Hartman, C. A., Whalley, H. C., Heslenfeld, D. J., Hibar, D. P., Hilland, E., Hirano, Y., Ho, T. C., Hoekstra, P. J., Hoekstra, L., Hohmann, S., Hong, L. E., Hoschl, C., Hovik, M. F., Howells, F. M., Nenadic, I., Jalbrzikowski, M., James, A. C., Janssen, J., Jaspers-Fayer, F., Xu, J., Jonassen, R., Karkashadze, G., King, J. A., Kircher, T., Kirschner, M., Koch, K., Kochunov, P., Kohls, G., Konrad, K., Kramer, B., Krug, A., Kuntsi, J., Kwon, J. S., Landen, M., Landro, N. I., Lazaro, L., Lebedeva, I. S., Leehr, E. J., Lera-Miguel, S., Lesch, K. -P., Lochner, C., Louza, M. R., Luna, B., Lundervold, A. J., Macmaster, F. P., Maglanoc, L. A., Malpas, C. B., Portella, M. J., Marsh, R., Martyn, F. M., Mataix-Cols, D., Mathalon, D. H., Mccarthy, H., Mcdonald, C., Mcphilemy, G., Meinert, S., Menchon, J. M., Minuzzi, L., Mitchell, P. B., Moreno, C., Morgado, P., Muratori, F., Murphy, C. M., Murphy, D., Mwangi, B., Nabulsi, L., Nakagawa, A., Nakamae, T., Namazova, L., Narayanaswamy, J., Jahanshad, N., Nguyen, D. D., Nicolau, R., O'Gorman Tuura, R. L., O'Hearn, K., Oosterlaan, J., Opel, N., Ophoff, R. A., Oranje, B., Garcia De La Foz, V. O., Overs, B. J., Paloyelis, Y., Pantelis, C., Parellada, M., Pauli, P., Pico-Perez, M., Picon, F. A., Piras, F., Plessen, K. J., Pomarol-Clotet, E., Preda, A., Puig, O., Quide, Y., Radua, J., Ramos-Quiroga, J. A., Rasser, P. E., Rauer, L., Reddy, J., Redlich, R., Reif, A., Reneman, L., Repple, J., Retico, A., Richarte, V., Richter, A., Rosa, P. G. P., Rubia, K. K., Hashimoto, R., Sacchet, M. D., Salvador, R., Santonja, J., Sarink, K., Sarro, S., Satterthwaite, T. D., Sawa, A., Schall, U., Schofield, P. R., Schrantee, A., Seitz, J., Serpa, M. H., Setien-Suero, E., Shaw, P., Shook, D., Silk, T. J., Sim, K., Simon, S., Simpson, H. B., Singh, A., Skoch, A., Skokauskas, N., Soares, J. C., Soreni, N., Soriano-Mas, C., Spalletta, G., Spaniel, F., Lawrie, S. M., Stern, E. R., Stewart, S. E., Takayanagi, Y., Temmingh, H. S., Tolin, D. F., Tomecek, D., Tordesillas-Gutierrez, D., Tosetti, M., Uhlmann, A., Van Amelsvoort, T., Van Der Wee, N. J. A., Van Der Werff, S. J. A., Van Haren, N. E. M., Van Wingen, G. A., Vance, A., Vazquez-Bourgon, J., Vecchio, D., Venkatasubramanian, G., Vieta, E., Vilarroya, O., Vives-Gilabert, Y., Voineskos, A. N., Volzke, H., Von Polier, G. G., Walton, E., Weickert, T. W., Weickert, C. S., Weideman, A. S., Wittfeld, K., Wolf, D. H., Wu, M. -J., Yang, T. T., Yang, K., Yoncheva, Y., Yun, J. -Y., Cheng, Y., Zanetti, M. V., Ziegler, G. C., Franke, B., Hoogman, M., Buitelaar, J. K., Van Rooij, D., Andreassen, O. A., Ching, C. R. K., Veltman, D. J., Schmaal, L., Stein, D. J., Van Den Heuvel, O. A., Turner, J. A., Van Erp, T. G. M., Pausova, Z., Thompson, P. M., Paus, T., Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Pediatric surgery, Radiology and nuclear medicine, Anatomy and neurosciences, Psychiatry, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Amsterdam Neuroscience - Neurodegeneration, Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, MUMC+: MA Med Staf Spec Psychiatrie (9), Adult Psychiatry, ANS - Compulsivity, Impulsivity & Attention, General Paediatrics, ARD - Amsterdam Reproduction and Development, Radiology and Nuclear Medicine, APH - Personalized Medicine, ANS - Brain Imaging, ANS - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, University of Zurich, Clinical Cognitive Neuropsychiatry Research Program (CCNP), Clinical Neuropsychology, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Child and Adolescent Psychiatry / Psychology, IBBA, and Cognitive Psychology

Subjects
Male, Obsessive-Compulsive Disorder, Bipolar Disorder, Autism Spectrum Disorder, Autism, [SDV]Life Sciences [q-bio], Gene Expression, cytology [Cerebral Cortex], Cohort Studies, Fetal Development, physiology [Gene Expression], 2738 Psychiatry and Mental Health, 0302 clinical medicine, diagnostic imaging [Cerebral Cortex], SCHIZOPHRENIA, BRAIN, Child, Obsessive-compulsive disorder (OCD), Original Investigation, Aged, 80 and over, Cerebral Cortex, 0303 health sciences, pathology [Depressive Disorder, Major], Principal Component Analysis, Adolescent psychiatry, 10058 Department of Child and Adolescent Psychiatry, Middle Aged, diagnostic imaging [Obsessive-Compulsive Disorder], REGIONS, Magnetic Resonance Imaging, 3. Good health, FALSE DISCOVERY RATE, Psychiatry and Mental health, Autism spectrum disorder, Schizophrenia, growth & development [Cerebral Cortex], Child, Preschool, Major depressive disorder, diagnostic imaging [Schizophrenia], Esquizofrènia, Female, Psiquiatria infantil, Psiquiatria de l'adolescència, diagnostic imaging [Autism Spectrum Disorder], Adult, medicine.medical_specialty, Adolescent, Human Development, 610 Medicine & health, diagnostic imaging [Bipolar Disorder], pathology [Autism Spectrum Disorder], diagnostic imaging [Depressive Disorder, Major], 03 medical and health sciences, Young Adult, All institutes and research themes of the Radboud University Medical Center, Neuroimaging, SDG 3 - Good Health and Well-being, CEREBRAL-CORTEX, Child psychiatry, medicine, Attention deficit hyperactivity disorder, Humans, Bipolar disorder, ddc:610, Psychiatry, pathology [Schizophrenia], 030304 developmental biology, Aged, Depressive Disorder, Major, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], business.industry, DENDRITE, Computational Biology, Correction, pathology [Attention Deficit Disorder with Hyperactivity], physiology [Fetal Development], medicine.disease, PATHOLOGY, pathology [Bipolar Disorder], pathology [Obsessive-Compulsive Disorder], 10036 Medical Clinic, Attention Deficit Disorder with Hyperactivity, 10054 Clinic for Psychiatry, Psychotherapy, and Psychosomatics, Case-Control Studies, DENSITY, ORIGINS, HIPPOCAMPUS, diagnostic imaging [Attention Deficit Disorder with Hyperactivity], pathology [Cerebral Cortex], Autisme, business, Neuroscience, 030217 neurology & neurosurgery, physiology [Human Development]
Abstract

[Importance] Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood., [Objective] To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia., [Design, Setting, and Participants] Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244., [Main Outcomes and Measures] Interregional profiles of group difference in cortical thickness between cases and controls., [Results] A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders., [Conclusions and Relevance] In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.

Published
2021

11. methods in : The experience of the generalized anxiety disorder working group

Author

Zugman, André, Harrewijn, Anita, Cardinale, Elise M., Zwiebel, Hannah, Freitag, Gabrielle F., Werwath, Katy E., Bas-Hoogendam, Janna M., Groenewold, Nynke A., Aghajani, Moji, Hilbert, Kevin, Cardoner, N. (Narcís), Porta-Casteràs, Daniel, Gosnell, Savannah, Salas, Ramiro, Blair, Karina S., Blair, James R., Hammoud, Mira Z., Milad, Mohammed, Burkhouse, Katie, Phan, K. Luan, Schroeder, Heidi K., Strawn, Jeffrey R., Beesdo-Baum, Katja, Thomopoulos, Sophia I., Grabe, Hans J., Van der Auwera, Sandra, Wittfeld, Katharina, Nielsen, Jared A., Buckner, Randy, Smoller, Jordan W., Mwangi, Benson, Soares, Jair C., Wu, Mon-Ju, Zunta-Soares, Giovana B., Jackowski, Andrea P., Pan, Pedro M., Salum, Giovanni A., Assaf, Michal, Diefenbach, Gretchen J., Brambilla, Paolo, Maggioni, Eleonora, Hofmann, David, Straube, Thomas, Andreescu, Carmen, Berta, Rachel, Tamburo, Erica, Price, Rebecca, Manfro, Gisele G., Critchley, Hugo D., Makovac, Elena, Mancini, Matteo, Meeten, Frances, Ottaviani, Cristina, Agosta, Federica, Canu, Elisa, Cividini, Camilla, Filippi, Massimo, Kostić, Milutin, Munjiza, Ana, Filippi, Courtney A., Leibenluft, Ellen, Alberton, Bianca A. V., Balderston, Nicholas L., Ernst, Monique, Grillon, Christian, Mujica-Parodi, Lilianne R., van Nieuwenhuizen, Helena, Fonzo, Gregory A., Paulus, Martin P., Stein, Murray B., Gur, Raquel E., Gur, Ruben C., Kaczkurkin, Antonia N., Larsen, Bart, Satterthwaite, Theodore D., Harper, Jennifer, Myers, Michael, Perino, Michael T., Yu, Qiongru, Sylvester, Chad M., Veltman, Dick J., Lueken, Ulrike, Van der Wee, Nic J. A., Stein, Dan J., Jahanshad, Neda, Thompson, Paul M., Pine, Daniel S., Winkler, Anderson M., and Universitat Autònoma de Barcelona

Subjects
Generalized anxiety disorder, Meta-analyses, Data sharing, Neuroimaging, Mega-analyses
Abstract

The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses. This report summarizes the background information and rationale for the various methodological decisions made by the ENIGMA-GAD group. The aim of this work is to help guide other research groups working with large brain imaging data sets

Published
2020

12. Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging

Author

Petrov, Dmitry, Gutman, Boris A, Yu, Shih-Hua Julie, van Erp, Theo G M, Turner, Jessica A, Schmaal, Lianne, Veltman, Dick, Wang, Lei, Alpert, Kathryn, Isaev, Dmitry, Zavaliangos-Petropulu, Artemis, Ching, Christopher R K, Calhoun, Vince, Glahn, David, Satterthwaite, Theodore D, Andreasen, Ole Andreas, Borgwardt, Stefan, Howells, Fleur, Groenewold, Nynke, Voineskos, Aristotle, Radua, Joaquim, Potkin, Steven G, Crespo-Facorro, Benedicto, Tordesillas-Gutiérrez, Diana, Shen, Li, Lebedeva, Irina, Spalletta, Gianfranco, Donohoe, Gary, Kochunov, Peter, Rosa, Pedro G P, James, Anthony, Dannlowski, Udo, Baune, Bernhard T, Aleman, André, Gotlib, Ian H, Walter, Henrik, Walter, Martin, Soares, Jair C, Ehrlich, Stefan, Gur, Ruben C, Doan, N Trung, Agartz, Ingrid, Westlye, Lars T, Harrisberger, Fabienne, Riecher-Rössler, Anita, Uhlmann, Anne, Stein, Dan J, Dickie, Erin W, Pomarol-Clotet, Edith, Fuentes-Claramonte, Paola, Canales-Rodríguez, Erick Jorge, Salvador, Raymond, Huang, Alexander J, Roiz-Santiañez, Roberto, Cong, Shan, Tomyshev, Alexander, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Ciullo, Valentina, Hong, Elliot, Busatto, Geraldo, Zanetti, Marcus V, Serpa, Mauricio H, Cervenka, Simon, Kelly, Sinead, Grotegerd, Dominik, Sacchet, Matthew D, Veer, Ilya M, Li, Meng, Wu, Mon-Ju, Irungu, Benson, Walton, Esther, Thompson, Paul M, Anatomy and neurosciences, Psychiatry, and Amsterdam Neuroscience - Brain Imaging

Subjects
0301 basic medicine, Recall, Computer science, business.industry, shape analysis, Machine learning, computer.software_genre, Article, Support vector machine, 03 medical and health sciences, machine learning, 030104 developmental biology, 0302 clinical medicine, Neuroimaging, Medical imaging, Alternating decision tree, Radiologi och bildbehandling, Artificial intelligence, quality control, business, computer, 030217 neurology & neurosurgery, Radiology, Nuclear Medicine and Medical Imaging, Shape analysis (digital geometry)
Abstract

As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.

Published
2017

13. White matter microstructure associated with anhedonia among individuals with bipolar disorders and high-risk for bipolar disorders.

Author

Diaz, Alexandre Paim, Fernandes, Brisa S., Teixeira, Antonio Lucio, Mwangi, Benson, Hasan, Khader M., Wu, Mon-Ju, Selvaraj, Sudhakar, Suen, Paulo, Zanao, Tamires Araujo, Brunoni, Andre R., Sanches, Marsal, and Soares, Jair C.

Subjects
*BIPOLAR disorder, *ANHEDONIA, *WHITE matter (Nerve tissue), *DIFFUSION tensor imaging, *MENTAL depression, *HYPOMANIA, *BRAIN, *RESEARCH, *RESEARCH methodology, *MAGNETIC resonance imaging, *EVALUATION research, *COMPARATIVE studies
Abstract

Background: Anhedonia - a key symptom of depression - is highly associated with poorer outcomes and suicidal behavior. Alterations in the circuitry of reward-related brain regions have been robustly associated with anhedonia in unipolar depression, but not bipolar disorder (BD). We investigated white matter microstructures associated with anhedonia in participants with BD types I and II and first-degree relatives of patients with BD (BD-siblings).Methods: Eighty participants (BD types I and II: 56 [70%], and BD-siblings: 24 [30%]) underwent diffusion tensor imaging (DTI); Fractional anisotropy (FA) of different tracts were computed. Anhedonia was assessed using item 8, ("inability to feel'') of the MADRS scale. General linear models were used to compare the FA of different tracts in participants with and without anhedonia controlling for several clinical and demographic variables.Results: The mean age of the sample was 37 (± 11) years old, and 68.8% were female. Participants with anhedonia (32.5%) presented lower mean FA in the left uncinate fasciculus (UF) (p = 0.005), right temporal endings of the superior longitudinal fasciculus (SLFT) (p = 0.04), and in the left and right parietal endings of the superior longitudinal fasciculus (SLFP) (p = 0.003, and p = 0.04, respectively). Similar comparisons between participants with or without current depressive episodes and between participants with or without inner tension according to the MADRS did not show significant differences, specificity of the findings for anhedonia.Conclusions: Lower FA in the left UF and SLF are potential neuroimaging markers of anhedonia in individuals with BD and high-risk for BD. [ABSTRACT FROM AUTHOR]

Published
2022
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