Browsing by Author "Satterthwaite, Theodore D."

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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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    Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years
    (Wiley, 2022-01) Frangou, Sophia; Modabbernia, Amirhossein; Williams, Steven C.R.; Papachristou, Efstathios; Doucet, Gaelle E.; Agartz, Ingrid; Aghajani, Moji; Akudjedu, Theophilus N.; Albajes-Eizagirre, Anton; Alnæs, Dag; Alpert, Kathryn I.; Andersson, Micael; Andreasen, Nancy C.; Andreassen, Ole A.; Asherson, Philip; Banaschewski, Tobias; Bargallo, Nuria; Baumeister, Sarah; Baur-Streubel, Ramona; Bertolino, Alessandro; Bonvino, Aurora; Boomsma, Dorret I.; Borgwardt, Stefan; Bourque, Josiane; Brandeis, Daniel; Breier, Alan; Brodaty, Henry; Brouwer, Rachel M.; Buitelaar, Jan K.; Busatto, Geraldo F.; Buckner, Randy L.; Calhoun, Vincent; Canales-Rodríguez, Erick J.; Cannon, Dara M.; Caseras, Xavier; Castellanos, Francisco X.; Cervenka, Simon; Chaim-Avancini, Tiffany M.; Ching, Christopher R.K.; Chubar, Victoria; Clark, Vincent P.; Conrod, Patricia; Conzelmann, Annette; Crespo-Facorro, Benedicto; Crivello, Fabrice; Crone, Eveline A.; Dale, Anders M.; Dannlowski, Udo; Davey, Christopher; de Geus, Eco J.C.; de Haan, Lieuwe; de Zubicaray, Greig I.; den Braber, Anouk; Dickie, Erin W.; Di Giorgio, Annabella; Doan, Nhat Trung; Dørum, Erlend S.; Ehrlich, Stefan; Erk, Susanne; Espeseth, Thomas; Fatouros-Bergman, Helena; Fisher, Simon E.; Fouche, Jean-Paul; Franke, Barbara; Frodl, Thomas; Fuentes-Claramonte, Paola; Glahn, David C.; Gotlib, Ian H.; Grabe, Hans-Jörgen; Grimm, Oliver; Groenewold, Nynke A.; Grotegerd, Dominik; Gruber, Oliver; Gruner, Patricia; Gur, Rachel E.; Gur, Ruben C.; Hahn, Tim; Harrison, Ben J.; Hartman, Catharine A.; Hatton, Sean N.; Heinz, Andreas; Heslenfeld, Dirk J.; Hibar, Derrek P.; Hickie, Ian B.; Ho, Beng-Choon; 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; Jernigan, Terry L.; Jiang, Jiyang; Jönsson, Erik G.; Joska, John A.; Kahn, Rene; Kalnin, Andrew; Kanai, Ryota; Klein, Marieke; Klyushnik, Tatyana P.; Koenders, Laura; Koops, Sanne; Krämer, Bernd; Kuntsi, Jonna; Lagopoulos, Jim; Lázaro, Luisa; Lebedeva, Irina; Lee, Won Hee; Lesch, Klaus-Peter; Lochner, Christine; Machielsen, Marise W.J.; Maingault, Sophie; Martin, Nicholas G.; Martínez-Zalacaín, Ignacio; Mataix-Cols, David; Mazoyer, Bernard; McDonald, Colm; McDonald, Brenna C.; McIntosh, Andrew M.; McMahon, Katie L.; McPhilemy, Genevieve; Meinert, Susanne; Menchón, José M.; Medland, Sarah E.; Meyer-Lindenberg, Andreas; Naaijen, Jilly; Najt, Pablo; Nakao, Tomohiro; Nordvik, Jan E.; Nyberg, Lars; Oosterlaan, Jaap; Ortiz-García de la Foz, Víctor; Paloyelis, Yannis; Pauli, Paul; Pergola, Giulio; Pomarol-Clotet, Edith; Portella, Maria J.; Potkin, Steven G.; Radua, Joaquim; Reif, Andreas; Rinker, Daniel A.; Roffman, Joshua L.; Rosa, Pedro G.P.; Sacchet, Matthew D.; Sachdev, Perminder S.; Salvador, Raymond; Sánchez-Juan, Pascual; Sarró, Salvador; Satterthwaite, Theodore D.; Saykin, Andrew J.; Serpa, Mauricio H.; Schmaal, Lianne; Schnell, Knut; Schumann, Gunter; Sim, Kang; Smoller, Jordan W.; Sommer, Iris; Soriano-Mas, Carles; Stein, Dan J.; Strike, Lachlan T.; Swagerman, Suzanne C.; Tamnes, Christian K.; Temmingh, Henk S.; Thomopoulos, Sophia I.; Tomyshev, Alexander S.; Tordesillas-Gutiérrez, Diana; Trollor, Julian N.; Turner, Jessica A.; Uhlmann, Anne; van den Heuvel, Odile A.; van den Meer, Dennis; van der Wee, Nic J.A.; van Haren, Neeltje E.M.; van't Ent, Dennis; van Erp, Theo G.M.; Veer, Ilya M.; Veltman, Dick J.; Voineskos, Aristotle; Völzke, Henry; Walter, Henrik; Walton, Esther; Wang, Lei; Wang, Yang; Wassink, Thomas H.; Weber, Bernd; Wen, Wei; West, John D.; Westlye, Lars T.; Whalley, Heather; Wierenga, Lara M.; Wittfeld, Katharina; Wolf, Daniel H.; Worker, Amanda; Wright, Margaret J.; Yang, Kun; Yoncheva, Yulyia; Zanetti, Marcus V.; Ziegler, Georg C.; Karolinska Schizophrenia Project (KaSP); Thompson, Paul M.; Dima, Danai; Radiology and Imaging Sciences, School of Medicine
    Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
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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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    Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization
    (Elsevier, 2023) Hu, Fengling; Chen, Andrew A.; Horng, Hannah; Bashyam, Vishnu; Davatzikos, Christos; Alexander-Bloch, Aaron; Li, Mingyao; Shou, Haochang; Satterthwaite, Theodore D.; Yu, Meichen; Shinohara, Russell T.; Radiology and Imaging Sciences, School of Medicine
    Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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    Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
    (Springer Nature, 2017-09) 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-Gutirrez, Diana; Shen, Li; Lebedeva, Irina; Spalletta, Gianfranco; Donohoe, Gary; Kochunov, Peter; Rosa, Pedro G.P.; James, Anthony; Dannlowski, Udo; Baune, Berhard T.; Aleman, Andre; 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; Richer-Rossler, Anita; Uhlmann, Anne; Stein, Dan J.; Dickie, Erin W.; Pomarol-Clotet, Edith; Fuentes-Claramonte, Paola; Canales-Rodriguez, Erick Jorge; Salvador, Raymond; Huang, Alexander J.; Roiz-Santianez, 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, Illya M.; Li, Meng; Wu, Mon-Ju; Irungu, Benson; Walton, Esther; Thompson, Paul M.; Medicine, School of Medicine
    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.
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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/).
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    Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3–90 years
    (Wiley, 2022) Dima, Danai; Modabbernia, Amirhossein; Papachristou, Efstathios; Doucet, Gaelle E.; Agartz, Ingrid; Aghajani, Moji; Akudjedu, Theophilus N.; Albajes-Eizagirre, Anton; Alnæs, Dag; Alpert, Kathryn I.; Andersson, Micael; Andreasen, Nancy C.; Andreassen, Ole A.; Asherson, Philip; Banaschewski, Tobias; Bargallo, Nuria; Baumeister, Sarah; Baur-Streubel, Ramona; Bertolino, Alessandro; Bonvino, Aurora; Boomsma, Dorret I.; Borgwardt, Stefan; Bourque, Josiane; Brandeis, Daniel; Breier, Alan; Brodaty, Henry; Brouwer, Rachel M.; Buitelaar, Jan K.; Busatto, Geraldo F.; Buckner, Randy L.; Calhoun, Vincent; Canales-Rodríguez, Erick J.; Cannon, Dara M.; Caseras, Xavier; Castellanos, Francisco X.; Cervenka, Simon; Chaim-Avancini, Tiffany M.; Ching, Christopher R.K.; Chubar, Victoria; Clark, Vincent P.; Conrod, Patricia; Conzelmann, Annette; Crespo-Facorro, Benedicto; Crivello, Fabrice; Crone, Eveline A.; Dannlowski, Udo; Dale, Anders M.; Davey, Christopher; de Geus, Eco J.C.; de Haan, Lieuwe; de Zubicaray, Greig I.; den Braber, Anouk; Dickie, Erin W.; Di Giorgio, Annabella; Doan, Nhat Trung; Dørum, Erlend S.; Ehrlich, Stefan; Erk, Susanne; Espeseth, Thomas; Fatouros-Bergman, Helena; Fisher, Simon E.; Fouche, Jean-Paul; Franke, Barbara; Frodl, Thomas; Fuentes-Claramonte, Paola; Glahn, David C.; Gotlib, Ian H.; Grabe, Hans-Jörgen; Grimm, Oliver; Groenewold, Nynke A.; Grotegerd, Dominik; Gruber, Oliver; Gruner, Patricia; Gur, Rachel E.; Gur, Ruben C.; Hahn, Tim; Harrison, Ben J.; Hartman, Catharine A.; Hatton, Sean N.; Heinz, Andreas; Heslenfeld, Dirk J.; Hibar, Derrek P.; Hickie, Ian B.; Ho, Beng-Choon; 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; Jernigan, Terry L.; Jiang, Jiyang; Jönsson, Erik G.; Joska, John A.; Kahn, Rene; Kalnin, Andrew; Kanai, Ryota; Klein, Marieke; Klyushnik, Tatyana P.; Koenders, Laura; Koops, Sanne; Krämer, Bernd; Kuntsi, Jonna; Lagopoulos, Jim; Lázaro, Luisa; Lebedeva, Irina; Lee, Won Hee; Lesch, Klaus-Peter; Lochner, Christine; Machielsen, Marise W.J.; Maingault, Sophie; Martin, Nicholas G.; Martínez-Zalacaín, Ignacio; Mataix-Cols, David; Mazoyer, Bernard; McDonald, Colm; McDonald, Brenna C.; McIntosh, Andrew M.; McMahon, Katie L.; McPhilemy, Genevieve; Menchón, José M.; Medland, Sarah E.; Meyer-Lindenberg, Andreas; Naaijen, Jilly; Najt, Pablo; Nakao, Tomohiro; Meinert, Susanne; Nordvik, Jan E.; Nyberg, Lars; Oosterlaan, Jaap; Ortiz-García de la Foz, Víctor; Paloyelis, Yannis; Pauli, Paul; Pergola, Giulio; Pomarol-Clotet, Edith; Portella, Maria J.; Potkin, Steven G.; Radua, Joaquim; Reif, Andreas; Rinker, Daniel A.; Roffman, Joshua L.; Rosa, Pedro G.P.; Sacchet, Matthew D.; Sachdev, Perminder S.; Salvador, Raymond; Sánchez-Juan, Pascual; Sarró, Salvador; Satterthwaite, Theodore D.; Saykin, Andrew J.; Serpa, Mauricio H.; Schmaal, Lianne; Schnell, Knut; Schumann, Gunter; Sim, Kang; Smoller, Jordan W.; Sommer, Iris; Soriano-Mas, Carles; Stein, Dan J.; Strike, Lachlan T.; Swagerman, Suzanne C.; Tamnes, Christian K.; Temmingh, Henk S.; Thomopoulos, Sophia I.; Tomyshev, Alexander S.; Tordesillas-Gutiérrez, Diana; Trollor, Julian N.; Turner, Jessica A.; Uhlmann, Anne; van den Heuvel, Odile A.; van den Meer, Dennis; van der Wee, Nic J.A.; van Haren, Neeltje E.M.; van't Ent, Dennis; van Erp, Theo G.M.; Veer, Ilya M.; Veltman, Dick J.; Voineskos, Aristotle; Völzke, Henry; Walter, Henrik; Walton, Esther; Wang, Lei; Wang, Yang; Wassink, Thomas H.; Weber, Bernd; Wen, Wei; West, John D.; Westlye, Lars T.; Whalley, Heather; Wierenga, Lara M.; Williams, Steven C.R.; Wittfeld, Katharina; Wolf, Daniel H.; Worker, Amanda; Wright, Margaret J.; Yang, Kun; Yoncheva, Yulyia; Zanetti, Marcus V.; Ziegler, Georg C.; Thompson, Paul M.; Frangou, Sophia; Karolinska Schizophrenia Project (KaSP); Radiology and Imaging Sciences, School of Medicine
    Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.