Browsing by Author "Davey, Christopher G."

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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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    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.