73 results on '"Wu, Mon-Ju"'
Search Results
52. T123. Decreased Fractional Anisotropy as a Marker of Aberrant White Matter Integrity in Unaffected Offspring of Patients With Bipolar Disorder
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Melicher, Tomas, primary, Mwangi, Benson, additional, Wu, Mon-Ju, additional, Cao, Bo, additional, Zeni, Cristian, additional, Younes, Kyan, additional, Zunta-Soares, Giovana, additional, Hasan, Khader, additional, and Soares, Jair, additional
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- 2018
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53. F27. Subcortical Volumes in Social Anxiety Disorder: Preliminary Results From Enigma-Anxiety
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Groenewold, Nynke, primary, Bas-Hoogendam, Janna Marie, additional, Amod, Alyssa R., additional, van Velzen, Laura, additional, Aghajani, Moji, additional, Filippi, Courtney, additional, Gold, Andrea, additional, Ching, Christopher R.K., additional, Roelofs, Karin, additional, Furmark, Tomas, additional, Månsson, Kristoffer, additional, Straube, Thomas, additional, Peterburs, Jutta, additional, Klumpp, Heide, additional, Phan, K. Luan, additional, Lochner, Christine, additional, Doruyter, Alexander, additional, Pujol, Jesus, additional, Cardoner, Narcis, additional, Blanco-Hinojo, Laura, additional, Beesdo-Baum, Katja, additional, Hilbert, Kevin, additional, Kreifelts, Benjamin, additional, Erb, Michael, additional, Gong, Qiyong, additional, Lui, Su, additional, Soares, Jair, additional, Wu, Mon-Ju, additional, Westenberg, P. Michiel, additional, Grotegerd, Dominik, additional, Leehr, Elisabeth J., additional, Dannlowski, Udo, additional, Zwanzger, Peter, additional, Veltman, Dick J., additional, Pine, Daniel S., additional, Jahanshad, Neda, additional, Thompson, Paul M., additional, Stein, Dan J., additional, and van der Wee, Nic. J.A., additional
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- 2018
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54. O40. Attention and Reward-Related Decision-Making Deficits Differentiate Youth With Bipolar Disorder From Healthy Individuals: A Machine Learning Study
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Bauer, Isabelle, primary, Suchting, Robert, additional, Mwangi, Benson, additional, Wu, Mon-Ju, additional, Meyer, Thomas, additional, Zunta-Soares, Giovana, additional, and Soares, Jair, additional
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- 2018
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55. 610. Hippocampal Subfield Volumes in Children and Adolescents with Mood Disorders
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Tannous, Jonika, primary, Amaral-Silva, Henrique, additional, Cao, Bo, additional, Wu, Mon-Ju, additional, Zunta-Soares, Giovana, additional, Mwangi, Benson, additional, and Soares, Jair, additional
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- 2017
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56. 612. Hippocampal Subfield Volumes in Mood Disorders
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Soares, Jair, primary, Cao, Bo, additional, Passos, Ives, additional, Mwangi, Benson, additional, Amaral-Silva, Henrique, additional, Tannous, Jonika, additional, Wu, Mon-Ju, additional, and Zunta-Soares, Giovana, additional
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- 2017
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57. 910. Accelerated Epigenetic Aging in Patients with Bipolar Disorder and Their Siblings
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Fries, Gabriel, primary, Bauer, Isabelle E., additional, Wu, Mon-Ju, additional, Spiker, Danielle, additional, Zunta-Soares, Giovana, additional, Walss-Bass, Consuelo, additional, Soares, Jair, additional, and Quevedo, Joao, additional
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- 2017
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58. 613. Obesity-Related Thinning in the Frontal Cortex in Patients with Bipolar I Disorder: Correlations with Functioning
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Soares, Jair, primary, Lavagnino, Luca, additional, Mwangi, Benson, additional, Cao, Bo, additional, Wu, Mon-Ju, additional, Sanches, Marsal, additional, and Zunta-Soares, Giovana, additional
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- 2017
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59. 577. Lifespan Gyrification Trajectories of Human Brain in Healthy Individuals and Patients with Major Psychiatric Disorders
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Soares, Jair, primary, Cao, Bo, additional, Mwangi, Benson, additional, Passos, Ives, additional, Wu, Mon-Ju, additional, Keser, Zafer, additional, Zunta-Soares, Giovana, additional, Xu, Dianping, additional, and Hasan, Khader, additional
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- 2017
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60. 316. Brain Search: A Web-Based Visual and Analytical Platform for Human Brain Development Trajectories
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Soares, Jair, primary, Candano, Carlos A.G., additional, Wu, Mon-Ju, additional, Cao, Bo, additional, Hasan, Khader, additional, and Mwangi, Benson, additional
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- 2017
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61. 611. Individualized Prediction of Euthymic Bipolar Disorder and Euthymic Major Depressive Disorder Patients Using Neurocognitive scores, Neuroimaging Data and Machine Learning
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Soares, Jair, primary, Wu, Mon-Ju, additional, Bauer, Isabelle E., additional, Passos, Ives, additional, Zunta-Soares, Giovana, additional, Glahn, David, additional, and Mwangi, Benson, additional
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- 2017
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62. Lifespan Gyrification Trajectories of Human Brain in Healthy Individuals and Patients with Major Psychiatric Disorders
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Cao, Bo, primary, Mwangi, Benson, additional, Passos, Ives Cavalcante, additional, Wu, Mon-Ju, additional, Keser, Zafer, additional, Zunta-Soares, Giovana B., additional, Xu, Dianping, additional, Hasan, Khader M., additional, and Soares, Jair C., additional
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- 2017
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63. Spatial and frequency-based super-resolution of ultrasound images
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Wu, Mon-Ju, primary, Karls, Joseph, additional, Duenwald-Kuehl, Sarah, additional, Vanderby, Ray, additional, and Sethares, William, additional
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- 2014
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64. Tendon Strain Measurements With Dynamic Ultrasound Images: Evaluation of Digital Image Correlation
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Okotie, Gregory, primary, Duenwald-Kuehl, Sarah, additional, Kobayashi, Hirohito, additional, Wu, Mon-Ju, additional, and Vanderby, Ray, additional
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- 2012
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65. The role of educational attainment and brain morphology in major depressive disorder: Findings from the ENIGMA major depressive disorder consortium
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Sarah Whittle, Divyangana Rakesh, Lianne Schmaal, Dick J. Veltman, Paul M. Thompson, Aditya Singh, Ali Saffet Gonul, Andre Aleman, Aslıhan Uyar Demir, Axel Krug, Benson Mwangi, Bernd Krämer, Bernhard T. Baune, Dan J. Stein, Dominik Grotegerd, Edith Pomarol-Clotet, Elena Rodríguez-Cano, Elisa Melloni, Francesco Benedetti, Frederike Stein, Hans J. Grabe, Henry Völzke, Ian H. Gotlib, Igor Nenadić, Jair C. Soares, Jonathan Repple, Kang Sim, Katharina Brosch, Katharina Wittfeld, Klaus Berger, Marco Hermesdorf, Maria J. Portella, Matthew D. Sacchet, Mon-Ju Wu, Nils Opel, Nynke A. Groenewold, Oliver Gruber, Paola Fuentes-Claramonte, Raymond Salvador, Roberto Goya-Maldonado, Salvador Sarró, Sara Poletti, Susanne L. Meinert, Tilo Kircher, Udo Dannlowski, Elena Pozzi, Whittle, Sarah, Rakesh, Divyangana, Schmaal, Lianne, Veltman, Dick J., Thompson, Paul M., Singh, Aditya, Gonul, Ali Saffet, Aleman, Andre, Uyar Demir, Aslıhan, Krug, Axel, Mwangi, Benson, Krämer, Bernd, Baune, Bernhard T., Stein, Dan J., Grotegerd, Dominik, Pomarol-Clotet, Edith, Rodríguez-Cano, Elena, Melloni, Elisa, Benedetti, Francesco, Stein, Frederike, Grabe, Hans J., Völzke, Henry, Gotlib, Ian H., Nenadić, Igor, Soares, Jair C., Repple, Jonathan, Sim, Kang, Brosch, Katharina, Wittfeld, Katharina, Berger, Klau, Hermesdorf, Marco, Portella, Maria J., Sacchet, Matthew D., Wu, Mon-Ju, Opel, Nil, Groenewold, Nynke A., Gruber, Oliver, Fuentes-Claramonte, Paola, Salvador, Raymond, Goya-Maldonado, Roberto, Sarró, Salvador, Poletti, Sara, Meinert, Susanne L., Kircher, Tilo, Dannlowski, Udo, and Pozzi, Elena
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Adult ,Depressive Disorder, Major ,Brain ,Educational Status ,Humans ,Magnetic Resonance Imaging ,Frontal Lobe - Abstract
Brain structural abnormalities and low educational attainment are consistently associated with major depressive disorder (MDD), yet there has been little research investigating the complex interaction of these factors. Brain structural alterations may represent a vulnerability or differential susceptibility marker, and in the context of low educational attainment, predict MDD. We tested this moderation model in a large multisite sample of 1958 adults with MDD and 2921 controls (aged 18 to 86) from the ENIGMA MDD working group. Using generalized linear mixed models and within-sample split-half replication, we tested whether brain structure interacted with educational attainment to predict MDD status. Analyses revealed that cortical thickness in a number of occipital, parietal, and frontal regions significantly interacted with education to predict MDD. For the majority of regions, models suggested a differential susceptibility effect, whereby thicker cortex was more likely to predict MDD in individuals with low educational attainment, but less likely to predict MDD in individuals with high educational attainment. Findings suggest that greater thickness of brain regions subserving visuomotor and social-cognitive functions confers susceptibility to MDD, dependent on level of educational attainment. Longitudinal work, however, is ultimately needed to establish whether cortical thickness represents a preexisting susceptibility marker. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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- 2022
66. Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis.
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Kennedy E, Liebel SW, Lindsey HM, Vadlamani S, Lei PW, Adamson MM, Alda M, Alonso-Lana S, Anderson TJ, Arango C, Asarnow RF, Avram M, Ayesa-Arriola R, Babikian T, Banaj N, Bird LJ, Borgwardt S, Brodtmann A, Brosch K, Caeyenberghs K, Calhoun VD, Chiaravalloti ND, Cifu DX, Crespo-Facorro B, Dalrymple-Alford JC, Dams-O'Connor K, Dannlowski U, Darby D, Davenport N, DeLuca J, Diaz-Caneja CM, Disner SG, Dobryakova E, Ehrlich S, Esopenko C, Ferrarelli F, Frank LE, Franz CE, Fuentes-Claramonte P, Genova H, Giza CC, Goltermann J, Grotegerd D, Gruber M, Gutierrez-Zotes A, Ha M, Haavik J, Hinkin C, Hoskinson KR, Hubl D, Irimia A, Jansen A, Kaess M, Kang X, Kenney K, Keřková B, Khlif MS, Kim M, Kindler J, Kircher T, Knížková K, Kolskår KK, Krch D, Kremen WS, Kuhn T, Kumari V, Kwon J, Langella R, Laskowitz S, Lee J, Lengenfelder J, Liou-Johnson V, Lippa SM, Løvstad M, Lundervold AJ, Marotta C, Marquardt CA, Mattos P, Mayeli A, McDonald CR, Meinert S, Melzer TR, Merchán-Naranjo J, Michel C, Morey RA, Mwangi B, Myall DJ, Nenadić I, Newsome MR, Nunes A, O'Brien T, Oertel V, Ollinger J, Olsen A, Ortiz García de la Foz V, Ozmen M, Pardoe H, Parent M, Piras F, Piras F, Pomarol-Clotet E, Repple J, Richard G, Rodriguez J, Rodriguez M, Rootes-Murdy K, Rowland J, Ryan NP, Salvador R, Sanders AM, Schmidt A, Soares JC, Spalleta G, Španiel F, Sponheim SR, Stasenko A, Stein F, Straube B, Thames A, Thomas-Odenthal F, Thomopoulos SI, Tone EB, Torres I, Troyanskaya M, Turner JA, Ulrichsen KM, Umpierrez G, Vecchio D, Vilella E, Vivash L, Walker WC, Werden E, Westlye LT, Wild K, Wroblewski A, Wu MJ, Wylie GR, Yatham LN, Zunta-Soares GB, Thompson PM, Pugh MJ, Tate DF, Hillary FG, Wilde EA, and Dennis EL
- Abstract
Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinson's disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia ( p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores ( p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.
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- 2024
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67. Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization.
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Ge R, Yu Y, Qi YX, Fan YV, Chen S, Gao C, Haas SS, Modabbernia A, New F, Agartz I, Asherson P, Ayesa-Arriola R, Banaj N, Banaschewski T, Baumeister S, Bertolino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buckner R, Buitelaar JK, Cannon DM, Caseras X, Cervenka S, Conrod PJ, Crespo-Facorro B, Crivello F, Crone EA, de Haan L, de Zubicaray GI, Di Giorgio A, Erk S, Fisher SE, Franke B, Frodl T, Glahn DC, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Harrison BJ, Hatton SN, Hickie I, Howells FM, Pol HEH, Huyser C, Jernigan TL, Jiang J, Joska JA, Kahn RS, Kalnin AJ, Kochan NA, Koops S, Kuntsi J, Lagopoulos J, Lazaro L, Lebedeva IS, Lochner C, Martin NG, Mazoyer B, McDonald BC, McDonald C, McMahon KL, Nakao T, Nyberg L, Piras F, Portella MJ, Qiu J, Roffman JL, Sachdev PS, Sanford N, Satterthwaite TD, Saykin AJ, Schumann G, Sellgren CM, Sim K, Smoller JW, Soares J, Sommer IE, Spalletta G, Stein DJ, Tamnes CK, Thomopolous SI, Tomyshev AS, Tordesillas-Gutiérrez D, Trollor JN, van 't Ent D, van den Heuvel OA, van Erp TG, van Haren NE, Vecchio D, Veltman DJ, Walter H, Wang Y, Weber B, Wei D, Wen W, Westlye LT, Wierenga LM, Williams SC, Wright MJ, Medland S, Wu MJ, Yu K, Jahanshad N, Thompson PM, and Frangou S
- Abstract
We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/)., Competing Interests: Declaration of interests SSH is supported by NIH National Institute of Mental Health (T32MH122394), and received a travel award from the Society of Biological Psychiatry to attend the annual meeting in 2023. HB declares an institutional grant from the National Health and Medical Research Council; has received compensation for being on an advisory board or a consultant to Biogen, Eisai, Eli Lilly, Roche, and Skin2Neuron; payment for being on the Cranbrook Care Medical Advisory Board, and honoraria for being on the Montefiore Homes Clinical Advisory Board. RMB and HEHP declare partial funding through the Geestkracht programme of the Dutch Health Research Council (Zon-Mw, grant No 10-000-1001), and matching funds from participating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly, Janssen Cilag) and universities and mental health care organizations (Amsterdam: Academic Psychiatric Centre of the Academic Medical Center and the mental health institutions: GGZ Ingeest, Arkin, Dijk en Duin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Holland Noord. Groningen: University Medical Center Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassia psycho-medical center The Hague. Maastricht: Maastricht University Medical Centre and the mental health institutions: GGzE, GGZ Breburg, GGZ Oost-Brabant, Vincent van Gogh voor Geestelijke Gezondheid, Mondriaan, Virenze riagg, Zuyderland GGZ, MET ggz, Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZ Overpelt, OPZ Rekem. Utrecht: University Medical Center Utrecht and the mental health institutions Altrecht, GGZ Centraal and Delta), Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO 51.02.061 to H.H., NWO 51.02.062 to D. B., NWO–NIHC Programs of excellence 433-09-220 to H.H., NWO-MagW 480-04-004 to D. B., and NWO/SPI 56-464-14192 to D.B.); FP7 Ideas: European Research Council (ERC-230374 to D. B.); and Universiteit Utrecht (High Potential Grant to H. H.). RB declares funding by NIH National Institute on Aging (R01AG067420); compensation for being on the scientific advisory board from Alkermes and Cognito Therapeutics with no conflict to the present work; honoraria from academic institutions for talks all under $1000 and $1000 for speaking at MGH/HMS course; travel fees for services to attend the annual meeting from the Simons Foundation; serves as a Director on the Simons Foundation collaborative initiative on aging (SCPAB); is a paid scientific advisory board member for philanthropic grants for The Foundation for OCD Research and the Klarman Family Foundation. BF has received educational speaking fees from Medice. DG reports funding from the NIH. UD is funded through the German Research Foundation (DFG; DA 1151/9- 1, DA 1151/10- 1, DA 1151/11- 1). GS declares funding from the European Commission, DFG, and NSFC. CKT has received grants from the Research Council of Norway and the Norwegian Regional Health Authority, unrelated to the current work. HW reports funding from the German Research Foundation (WA 1539/11-1). NJ reports funding from the NIH and compensation from the International Neuropsychological Society. PT declares a grant from the NIH and travel funded by NIH grants. All other authors declare no competing interests.
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- 2023
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68. Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium.
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Kennedy E, Vadlamani S, Lindsey HM, Lei PW, Jo-Pugh M, Adamson M, Alda M, Alonso-Lana S, Ambrogi S, Anderson TJ, Arango C, Asarnow RF, Avram M, Ayesa-Arriola R, Babikian T, Banaj N, Bird LJ, Borgwardt S, Brodtmann A, Brosch K, Caeyenberghs K, Calhoun VD, Chiaravalloti ND, Cifu DX, Crespo-Facorro B, Dalrymple-Alford JC, Dams-O'Connor K, Dannlowski U, Darby D, Davenport N, DeLuca J, Diaz-Caneja CM, Disner SG, Dobryakova E, Ehrlich S, Esopenko C, Ferrarelli F, Frank LE, Franz C, Fuentes-Claramonte P, Genova H, Giza CC, Goltermann J, Grotegerd D, Gruber M, Gutierrez-Zotes A, Ha M, Haavik J, Hinkin C, Hoskinson KR, Hubl D, Irimia A, Jansen A, Kaess M, Kang X, Kenney K, Keřková B, Khlif MS, Kim M, Kindler J, Kircher T, Knížková K, Kolskår KK, Krch D, Kremen WS, Kuhn T, Kumari V, Kwon JS, Langella R, Laskowitz S, Lee J, Lengenfelder J, Liebel SW, Liou-Johnson V, Lippa SM, Løvstad M, Lundervold A, Marotta C, Marquardt CA, Mattos P, Mayeli A, McDonald CR, Meinert S, Melzer TR, Merchán-Naranjo J, Michel C, Morey RA, Mwangi B, Myall DJ, Nenadić I, Newsome MR, Nunes A, O'Brien T, Oertel V, Ollinger J, Olsen A, de la Foz VOG, Ozmen M, Pardoe H, Parent M, Piras F, Piras F, Pomarol-Clotet E, Repple J, Richard G, Rodriguez J, Rodriguez M, Rootes-Murdy K, Rowland J, Ryan NP, Salvador R, Sanders AM, Schmidt A, Soares JC, Spalleta G, Španiel F, Stasenko A, Stein F, Straube B, Thames A, Thomas-Odenthal F, Thomopoulos SI, Tone E, Torres I, Troyanskaya M, Turner JA, Ulrichsen KM, Umpierrez G, Vilella E, Vivash L, Walker WC, Werden E, Westlye LT, Wild K, Wroblewski A, Wu MJ, Wylie GR, Yatham LN, Zunta-Soares GB, Thompson PM, Tate DF, Hillary FG, Dennis EL, and Wilde EA
- Abstract
Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. These efforts unveil new questions about integrating data arising from distinct sources and instruments. We focus on the most frequently assessed cognitive domain - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated global raw data from 53 studies totaling N = 10,505 individuals. A mega-analysis was conducted using empirical bayes harmonization to remove site effects, followed by linear models adjusting for common covariates. A continuous item response theory (IRT) model estimated each individual's latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance while preserving covariate effects, and our conversion tool is freely available online. This demonstrates that large-scale data sharing and harmonization initiatives can address reproducibility and integration challenges across the behavioral sciences., Competing Interests: Competing Interest Statement: Dr. Arango has been a consultant to or has received honoraria or grants from Acadia, Angelini, Biogen, Boehringer, Gedeon Richter, Janssen Cilag, Lundbeck, Medscape, Menarini, Minerva, Otsuka, Pfizer, Roche, Sage, Servier, Shire, Schering Plough, Sumitomo Dainippon Pharma, Sunovion and Takeda. Dr. Brodtmann serves on the editorial boards of Neurology and International Journal of Stroke. Dr. Diaz-Caneja has received honoraria from Exeltis and Angelinii. Dr. Giza: consultant for NBA, NFL, NHLPA, Los Angeles Lakers; Advisory Board: Highmark Interactive, Novartis, MLS, NBA, USSF; Medicolegal 1–2 cases annually. Dr. Soares: ALKERMES (Research Grant), ALLERGAN (Research Grant), ASOFARMA (Consultant), ATAI (Stock), BOEHRINGER Ingelheim (Consultant), COMPASS (Research Grant), JOHNSON & JOHNSON (Consultant), LIVANOVA (Consultant), PFIZER (Consultant), PULVINAR NEURO LLC (Consultant), RELMADA (Consultant), SANOFI (Consultant), SUNOVIAN (Consultant). Dr. Thompson received partial research support from Biogen, Inc., for research unrelated to this manuscript. Dr. Yatham has been on speaker or advisory boards for, or has received research grants from, Alkermes, Abbvie, Canadian Institutes of Health Research, Sumitomo Dainippon Pharma, GlaxoSmithKline, Intracellular Therapies, Merck, Sanofi, Sequiris, Servier, and Sunovion, over the past 3 years, all outside this work. The collection of this cohort was partially supported by an investigator-initiated research grant from Biogen (US). Biogen had no role in the analysis or writing of this manuscript. Eisai (JP) and Life Molecular Imaging for research unrelated to this manuscript. Dr. Wylie has received research support from the NJ Commission for brain injury research, from the Dept of Veterans’ Affairs, from Biogen, from Bristol, Myers, Squibb, from Genetech, and has served on advisory boards for the CDMRP and the VA. All of these activities are unrelated to this research. The views expressed in this article are those of the author(s) and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or U.S. Government.
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- 2023
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69. Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group.
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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
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- 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.)
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- 2022
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70. What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group.
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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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71. Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder.
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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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72. Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders.
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Patel Y, Parker N, Shin J, Howard D, French L, Thomopoulos SI, Pozzi E, Abe Y, Abé C, Anticevic A, Alda M, Aleman A, Alloza C, Alonso-Lana S, Ameis SH, Anagnostou E, McIntosh AA, Arango C, Arnold PD, Asherson P, Assogna F, Auzias G, Ayesa-Arriola R, Bakker G, Banaj N, Banaschewski T, Bandeira CE, Baranov A, Bargalló N, Bau CHD, Baumeister S, Baune BT, Bellgrove MA, Benedetti F, Bertolino A, Boedhoe PSW, Boks M, Bollettini I, Del Mar Bonnin C, Borgers T, Borgwardt S, Brandeis D, Brennan BP, Bruggemann JM, Bülow R, Busatto GF, Calderoni S, Calhoun VD, Calvo R, Canales-Rodríguez EJ, Cannon DM, Carr VJ, Cascella N, Cercignani M, Chaim-Avancini TM, Christakou A, Coghill D, Conzelmann A, Crespo-Facorro B, Cubillo AI, Cullen KR, Cupertino RB, Daly E, Dannlowski U, Davey CG, Denys D, Deruelle C, Di Giorgio A, Dickie EW, Dima D, Dohm K, Ehrlich S, Ely BA, Erwin-Grabner T, Ethofer T, Fair DA, Fallgatter AJ, Faraone SV, Fatjó-Vilas M, Fedor JM, Fitzgerald KD, Ford JM, Frodl T, Fu CHY, Fullerton JM, Gabel MC, Glahn DC, Roberts G, Gogberashvili T, Goikolea JM, Gotlib IH, Goya-Maldonado R, Grabe HJ, Green MJ, Grevet EH, Groenewold NA, Grotegerd D, Gruber O, Gruner P, Guerrero-Pedraza A, Gur RE, Gur RC, Haar S, Haarman BCM, Haavik J, Hahn T, Hajek T, Harrison BJ, Harrison NA, Hartman CA, Whalley HC, Heslenfeld DJ, Hibar DP, Hilland E, Hirano Y, Ho TC, Hoekstra PJ, Hoekstra L, Hohmann S, Hong LE, Höschl C, Høvik MF, Howells FM, Nenadic I, Jalbrzikowski M, James AC, Janssen J, Jaspers-Fayer F, Xu J, Jonassen R, Karkashadze G, King JA, Kircher T, Kirschner M, Koch K, Kochunov P, Kohls G, Konrad K, Krämer B, Krug A, Kuntsi J, Kwon JS, Landén M, Landrø NI, Lazaro L, Lebedeva IS, Leehr EJ, Lera-Miguel S, Lesch KP, Lochner C, Louza MR, Luna B, Lundervold AJ, MacMaster FP, Maglanoc LA, Malpas CB, Portella MJ, Marsh R, Martyn FM, Mataix-Cols D, Mathalon DH, McCarthy H, McDonald C, McPhilemy G, Meinert S, Menchón JM, Minuzzi L, Mitchell PB, Moreno C, Morgado P, Muratori F, Murphy CM, Murphy D, Mwangi B, Nabulsi L, Nakagawa A, Nakamae T, Namazova L, Narayanaswamy J, Jahanshad N, Nguyen DD, Nicolau R, O'Gorman Tuura RL, O'Hearn K, Oosterlaan J, Opel N, Ophoff RA, Oranje B, García de la Foz VO, Overs BJ, Paloyelis Y, Pantelis C, Parellada M, Pauli P, Picó-Pérez M, Picon FA, Piras F, Piras F, Plessen KJ, Pomarol-Clotet E, Preda A, Puig O, Quidé Y, Radua J, Ramos-Quiroga JA, Rasser PE, Rauer L, Reddy J, Redlich R, Reif A, Reneman L, Repple J, Retico A, Richarte V, Richter A, Rosa PGP, Rubia KK, Hashimoto R, Sacchet MD, Salvador R, Santonja J, Sarink K, Sarró S, Satterthwaite TD, Sawa A, Schall U, Schofield PR, Schrantee A, Seitz J, Serpa MH, Setién-Suero E, Shaw P, Shook D, Silk TJ, Sim K, Simon S, Simpson HB, Singh A, Skoch A, Skokauskas N, Soares JC, Soreni N, Soriano-Mas C, Spalletta G, Spaniel F, Lawrie SM, Stern ER, Stewart SE, Takayanagi Y, Temmingh HS, Tolin DF, Tomecek D, Tordesillas-Gutiérrez D, Tosetti M, Uhlmann A, van Amelsvoort T, van der Wee NJA, van der Werff SJA, van Haren NEM, van Wingen GA, Vance A, Vázquez-Bourgon J, Vecchio D, Venkatasubramanian G, Vieta E, Vilarroya O, Vives-Gilabert Y, Voineskos AN, Völzke H, von Polier GG, Walton E, Weickert TW, Weickert CS, Weideman AS, Wittfeld K, Wolf DH, Wu MJ, Yang TT, Yang K, Yoncheva Y, Yun JY, Cheng Y, Zanetti MV, Ziegler GC, Franke B, Hoogman M, Buitelaar JK, van Rooij D, Andreassen OA, Ching CRK, Veltman DJ, Schmaal L, Stein DJ, van den Heuvel OA, Turner JA, van Erp TGM, Pausova Z, Thompson PM, and Paus T
- Subjects
- Adolescent, Adult, Aged, Aged, 80 and over, Attention Deficit Disorder with Hyperactivity diagnostic imaging, Autism Spectrum Disorder diagnostic imaging, Bipolar Disorder diagnostic imaging, Case-Control Studies, Cerebral Cortex cytology, Cerebral Cortex diagnostic imaging, Cerebral Cortex growth & development, Child, Child, Preschool, Cohort Studies, Computational Biology, Depressive Disorder, Major diagnostic imaging, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Obsessive-Compulsive Disorder diagnostic imaging, Principal Component Analysis, Schizophrenia diagnostic imaging, Young Adult, Attention Deficit Disorder with Hyperactivity pathology, Autism Spectrum Disorder pathology, Bipolar Disorder pathology, Cerebral Cortex pathology, Depressive Disorder, Major pathology, Fetal Development physiology, Gene Expression physiology, Human Development physiology, Obsessive-Compulsive Disorder pathology, Schizophrenia pathology
- 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
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73. Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging.
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Petrov D, Gutman BA, Yu SJ, van Erp TGM, Turner JA, Schmaal L, Veltman D, Wang L, Alpert K, Isaev D, Zavaliangos-Petropulu A, Ching CRK, Calhoun V, Glahn D, Satterthwaite TD, Andreasen OA, Borgwardt S, Howells F, Groenewold N, Voineskos A, Radua J, Potkin SG, Crespo-Facorro B, Tordesillas-Gutiérrez D, Shen L, Lebedeva I, Spalletta G, Donohoe G, Kochunov P, Rosa PGP, James A, Dannlowski U, Baune BT, Aleman A, Gotlib IH, Walter H, Walter M, Soares JC, Ehrlich S, Gur RC, Doan NT, Agartz I, Westlye LT, Harrisberger F, Riecher-Rössler A, Uhlmann A, Stein DJ, Dickie EW, Pomarol-Clotet E, Fuentes-Claramonte P, Canales-Rodríguez EJ, Salvador R, Huang AJ, Roiz-Santiañez R, Cong S, Tomyshev A, Piras F, Vecchio D, Banaj N, Ciullo V, Hong E, Busatto G, Zanetti MV, Serpa MH, Cervenka S, Kelly S, Grotegerd D, Sacchet MD, Veer IM, Li M, Wu MJ, Irungu B, Walton E, and Thompson PM
- 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
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