Browsing by Author "Gur, Raquel E."

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    Brain-age prediction: Systematic evaluation of site effects, and sample age range and size
    (Wiley, 2024) 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.; Hulshoff Pol, Hilleke E.; 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.; Van't Ent, Dennis; Vecchio, Daniela; Walter, Henrik; Wang, Yang; Weber, Bernd; Wen, Wei; Wierenga, Lara M.; Williams, Steven C. R.; Wu, Mon-Ju; Zunta-Soares, Giovana B.; Bernhardt, Boris; Thompson, Paul; Frangou, Sophia; Ge, Ruiyang; ENIGMA-Lifespan Working Group; Psychiatry, School of Medicine
    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.
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    Genomic loci influence patterns of structural covariance in the human brain
    (National Academy of Science, 2023) Wen, Junhao; Nasrallah, Ilya M.; Abdulkadir, Ahmed; Satterthwaite, Theodore D.; Yang, Zhijian; Erus, Guray; Robert-Fitzgerald, Timothy; Singh, Ashish; Sotiras, Aristeidis; Boquet-Pujadas, Aleix; Mamourian, Elizabeth; Doshi, Jimit; Cui, Yuhan; Srinivasan, Dhivya; Skampardoni, Ioanna; Chen, Jiong; Hwang, Gyujoon; Bergman, Mark; Bao, Jingxuan; Veturi, Yogasudha; Zhou, Zhen; Yang, Shu; Dazzan, Paola; Kahn, Rene S.; Schnack, Hugo G.; Zanetti, Marcus V.; Meisenzahl, Eva; Busatto, Geraldo F.; Crespo-Facorro, Benedicto; Pantelis, Christos; Wood, Stephen J.; Zhuo, Chuanjun; Shinohara, Russell T.; Gur, Ruben C.; Gur, Raquel E.; Koutsouleris, Nikolaos; Wolf, Daniel H.; Saykin, Andrew J.; Ritchie, Marylyn D.; Shen, Li; Thompson, Paul M.; Colliot, Olivier; Wittfeld, Katharina; Grabe, Hans J.; Tosun, Duygu; Bilgel, Murat; An, Yang; Marcus, Daniel S.; LaMontagne, Pamela; Heckbert, Susan R.; Austin, Thomas R.; Launer, Lenore J.; Espeland, Mark; Masters, Colin L.; Maruff, Paul; Fripp, Jurgen; Johnson, Sterling C.; Morris, John C.; Albert, Marilyn S.; Bryan, R. Nick; Resnick, Susan M.; Fan, Yong; Habes, Mohamad; Wolk, David; Shou, Haochang; Davatzikos, Christos; Radiology and Imaging Sciences, School of Medicine
    Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.
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    Greater male than female variability in regional brain structure across the lifespan
    (Wiley, 2021) Wierenga, Lara M.; Doucet, Gaelle E.; Dima, Danai; Agartz, Ingrid; Aghajani, Moji; Akudjedu, Theophilus N.; Albajes‐Eizagirre, Anton; Alnæs, Dag; Alpert, Kathryn I.; Andreassen, Ole A.; Anticevic, Alan; Asherson, Philip; Banaschewski, Tobias; Bargallo, Nuria; Baumeister, Sarah; Baur‐Streubel, Ramona; Bertolino, Alessandro; Bonvino, Aurora; Boomsma, Dorret I.; Borgwardt, Stefan; Bourque, Josiane; Braber, Anouk; Brandeis, Daniel; Breier, Alan; Brodaty, Henry; Brouwer, Rachel M.; Buitelaar, Jan K.; Busatto, Geraldo F.; Calhoun, Vince D.; Canales‐Rodríguez, Erick J.; Cannon, Dara M.; Caseras, Xavier; Castellanos, Francisco X.; Chaim‐Avancini, Tiffany M.; Ching, Christopher R. K.; Clark, Vincent P.; Conrod, Patricia J.; Conzelmann, Annette; Crivello, Fabrice; Davey, Christopher G.; Dickie, Erin W.; Ehrlich, Stefan; Ent, Dennis; Fisher, Simon E.; Fouche, Jean‐Paul; Franke, Barbara; Fuentes‐Claramonte, Paola; Geus, Eco J. C.; Di Giorgio, Annabella; Glahn, David C.; Gotlib, Ian H.; Grabe, Hans J.; Gruber, Oliver; Gruner, Patricia; Gur, Raquel E.; Gur, Ruben C.; Gurholt, Tiril P.; Haan, Lieuwe; Haatveit, Beathe; Harrison, Ben J.; Hartman, Catharina A.; Hatton, Sean N.; Heslenfeld, Dirk J.; Heuvel, Odile A.; Hickie, Ian B.; Hoekstra, Pieter J.; Hohmann, Sarah; Holmes, Avram J.; Hoogman, Martine; Hosten, Norbert; Howells, Fleur M.; Hulshoff Pol, Hilleke E.; Huyser, Chaim; Jahanshad, Neda; James, Anthony C.; Jiang, Jiyang; Jönsson, Erik G.; Joska, John A.; Kalnin, Andrew J.; Karolinska Schizophrenia Project (KaSP) Consortium; Klein, Marieke; Koenders, Laura; Kolskår, Knut K.; Krämer, Bernd; Kuntsi, Jonna; Lagopoulos, Jim; Lazaro, Luisa; Lebedeva, Irina S.; Lee, Phil H.; Lochner, Christine; Machielsen, Marise W. J.; Maingault, Sophie; Martin, Nicholas G.; Martínez‐Zalacaín, Ignacio; Mataix‐Cols, David; Mazoyer, Bernard; McDonald, Brenna C.; McDonald, Colm; McIntosh, Andrew M.; McMahon, Katie L.; McPhilemy, Genevieve; Meer, Dennis; Menchón, José M.; Naaijen, Jilly; Nyberg, Lars; Oosterlaan, Jaap; Paloyelis, Yannis; Pauli, Paul; Pergola, Giulio; Pomarol‐Clotet, Edith; Portella, Maria J.; Radua, Joaquim; Reif, Andreas; Richard, Geneviève; Roffman, Joshua L.; Rosa, Pedro G. P.; Sacchet, Matthew D.; Sachdev, Perminder S.; Salvador, Raymond; Sarró, Salvador; Satterthwaite, Theodore D.; Saykin, Andrew J.; Serpa, Mauricio H.; Sim, Kang; Simmons, Andrew; Smoller, Jordan W.; Sommer, Iris E.; Soriano‐Mas, Carles; Stein, Dan J.; Strike, Lachlan T.; Szeszko, Philip R.; Temmingh, Henk S.; Thomopoulos, Sophia I.; Tomyshev, Alexander S.; Trollor, Julian N.; Uhlmann, Anne; Veer, Ilya M.; Veltman, Dick J.; Voineskos, Aristotle; Völzke, Henry; Walter, Henrik; Wang, Lei; Wang, Yang; Weber, Bernd; Wen, Wei; West, John D.; Westlye, Lars T.; Whalley, Heather C.; Williams, Steven C. R.; Wittfeld, Katharina; Wolf, Daniel H.; Wright, Margaret J.; Yoncheva, Yuliya N.; Zanetti, Marcus V.; Ziegler, Georg C.; Zubicaray, Greig I.; Thompson, Paul M.; Crone, Eveline A.; Frangou, Sophia; Tamnes, Christian K.; Psychiatry, School of Medicine
    For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.
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    Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization
    (bioRxiv, 2023-12-02) Ge, Ruiyang; Yu, Yuetong; Qi, Yi Xuan; Fan, Yunan Vera; Chen, Shiyu; Gao, Chuntong; Haas, Shalaila S.; Modabbernia, Amirhossein; New, Faye; Agartz, Ingrid; Asherson, Philip; Ayesa-Arriola, Rosa; Banaj, Nerisa; Banaschewski, Tobias; Baumeister, Sarah; Bertolino, Alessandro; Boomsma, Dorret I.; Borgwardt, Stefan; Bourque, Josiane; Brandeis, Daniel; Breier, Alan; Brodaty, Henry; Brouwer, Rachel M.; Buckner, Randy; Buitelaar, Jan K.; Cannon, Dara M.; Caseras, Xavier; Cervenka, Simon; Conrod, Patricia J.; Crespo-Facorro, Benedicto; Crivello, Fabrice; Crone, Eveline A.; de Haan, Liewe; de Zubicaray, Greig I.; Di Giorgio, Annabella; Erk, Susanne; Fisher, Simon E.; Franke, Barbara; Frodl, Thomas; Glahn, David C.; Grotegerd, Dominik; Gruber, Oliver; Gruner, Patricia; Gur, Raquel E.; Gur, Ruben C.; Harrison, Ben J.; Hatton, Sean N.; Hickie, Ian; Howells, Fleur M.; Hulshoff Pol, Hilleke E.; Huyser, Chaim; Jernigan, Terry L.; Jiang, Jiyang; Joska, John A.; Kahn, René S.; Kalnin, Andrew J.; Kochan, Nicole A.; Koops, Sanne; Kuntsi, Jonna; Lagopoulos, Jim; Lazaro, Luisa; Lebedeva, Irina S.; Lochner, Christine; Martin, Nicholas G.; Mazoyer, Bernard; McDonald, Brenna C.; McDonald, Colm; McMahon, Katie L.; Nakao, Tomohiro; Nyberg, Lars; Piras, Fabrizio; Portella, Maria J.; Qiu, Jiang; Roffman, Joshua L.; Sachdev, Perminder S.; Sanford, Nicole; Satterthwaite, Theodore D.; Saykin, Andrew J.; Schumann, Gunter; Sellgren, Carl M.; Sim, Kang; Smoller, Jordan W.; Soares, Jair; Sommer, Iris E.; Spalletta, Gianfranco; Stein, Dan J.; Tamnes, Christian K.; Thomopolous, Sophia I.; Tomyshev, Alexander S.; Tordesillas-Gutiérrez, Diana; Trollor, Julian N.; van 't Ent, Dennis; van den Heuvel, Odile A.; van Erp, Theo Gm.; van Haren, Neeltje Em.; Vecchio, Daniela; Veltman, Dick J.; Walter, Henrik; Wang, Yang; Weber, Bernd; Wei, Dongtao; Wen, Wei; Westlye, Lars T.; Wierenga, Lara M.; Williams, Steven Cr.; Wright, Margaret J.; Medland, Sarah; Wu, Mon-Ju; Yu, Kevin; Jahanshad, Neda; Thompson, Paul M.; Frangou, Sophia; Psychiatry, School of Medicine
    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/).