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Browsing by Author "Kochan, Nicole A."
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Item 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 MedicineStructural 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.Item Correlates of Gait Speed Among Older Adults From 6 Countries: Findings From the COSMIC Collaboration(Oxford University Press, 2023) Sprague, Briana N.; Zhu, Xiaonan; Rosso, Andrea L.; Verghese, Joe; Delbaere, Kim; Lipnicki, Darren M.; Sachdev, Perminder S.; Ng, Tze Pin; Gwee, Xinyi; Yap, Keng Bee; Kim, Ki-Woong; Han, Ji Won; Oh, Dae Jong; Narazaki, Kenji; Chen, Tao; Chen, Sanmei; Brodaty, Henry; Numbers, Katya; Kochan, Nicole A.; Walker, Richard W.; Paddick, Stella-Maria; Gureje, Oye; Ojagbemi, Akin; Bello, Toyin; Rosano, Caterina; COSMIC Consortium; Medicine, School of MedicineBackground: Few studies have compared gait speed and its correlates among different ethnogeographic regions. The goals of this study were to describe usual and rapid gait speed, and identify their correlates across Australian, Asian, and African countries. Methods: We used data from 6 population-based cohorts of adults aged 65+ from 6 countries and 3 continents (N = 6 472), with samples ranging from 231 to 1 913. All cohorts are members of the Cohort Studies of Memory in an International Consortium collaboration. We investigated whether clinical (body mass index [BMI], hypertension, stroke, apolipoprotein status), psychological (cognition, mood, general health), and behavioral factors (smoking, drinking, physical activity) correlated with usual (N = 4 cohorts) and rapid gait speed (N = 3 cohorts) similarly across cohorts. Regression models were controlled for age, sex, and education, and were sex-stratified. Results: Age- and sex-standardized usual gait speed means ranged from 0.61 to 1.06 m/s and rapid gait speed means ranged from 1.16 to 1.64 m/s. Lower BMI and better cognitive function consistently correlated with faster gait speed in all cohorts. Less consistently, not having hypertension and greater physical activity engagement were associated with faster gait speed. Associations with mood, smoking, and drinking were largely nonsignificant. These patterns were not attenuated by demographics. There was limited evidence that the associations differed by sex, except physical activity, where the greater intensity was associated with usual gait among men but not women. Conclusions: This study is among the first to describe the usual and rapid gait speeds across older adults in Africa, Asia, and Australia.Item Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning(Springer Nature, 2022) Lahti, Jari; Tuominen, Samuli; Yang, Qiong; Pergola, Giulio; Ahmad, Shahzad; Amin, Najaf; Armstrong, Nicola J.; Beiser, Alexa; Bey, Katharina; Bis, Joshua C.; Boerwinkle, Eric; Bressler, Jan; Campbell, Archie; Campbell, Harry; Chen, Qiang; Corley, Janie; Cox, Simon R.; Davies, Gail; De Jager, Philip L.; Derks, Eske M.; Faul, Jessica D.; Fitzpatrick, Annette L.; Fohner, Alison E.; Ford, Ian; Fornage, Myriam; Gerring, Zachary; Grabe, Hans J.; Grodstein, Francine; Gudnason, Vilmundur; Simonsick, Eleanor; Holliday, Elizabeth G.; Joshi, Peter K.; Kajantie, Eero; Kaprio, Jaakko; Karell, Pauliina; Kleineidam, Luca; Knol, Maria J.; Kochan, Nicole A.; Kwok, John B.; Leber, Markus; Lam, Max; Lee, Teresa; Li, Shuo; Loukola, Anu; Luck, Tobias; Marioni, Riccardo E.; Mather, Karen A.; Medland, Sarah; Mirza, Saira S.; Nalls, Mike A.; Nho, Kwangsik; O'Donnell, Adrienne; Oldmeadow, Christopher; Painter, Jodie; Pattie, Alison; Reppermund, Simone; Risacher, Shannon L.; Rose, Richard J.; Sadashivaiah, Vijay; Scholz, Markus; Satizabal, Claudia L.; Schofield, Peter W.; Schraut, Katharina E.; Scott, Rodney J.; Simino, Jeannette; Smith, Albert V.; Smith, Jennifer A.; Stott, David J.; Surakka, Ida; Teumer, Alexander; Thalamuthu, Anbupalam; Trompet, Stella; Turner, Stephen T.; van der Lee, Sven J.; Villringer, Arno; Völker, Uwe; Wilson, Robert S.; Wittfeld, Katharina; Vuoksimaa, Eero; Xia, Rui; Yaffe, Kristine; Yu, Lei; Zare, Habil; Zhao, Wei; Ames, David; Attia, John; Bennett, David A.; Brodaty, Henry; Chasman, Daniel I.; Goldman, Aaron L.; Hayward, Caroline; Ikram, M. Arfan; Jukema, J. Wouter; Kardia, Sharon L.R.; Lencz, Todd; Loeffler, Markus; Mattay, Venkata S.; Palotie, Aarno; Psaty, Bruce M.; Ramirez, Alfredo; Ridker, Paul M.; Riedel-Heller, Steffi G.; Sachdev, Perminder S.; Saykin, Andrew J.; Scherer, Martin; Schofield, Peter R.; Sidney, Stephen; Starr, John M.; Trollor, Julian; Ulrich, William; Wagner, Michael; Weir, David R.; Wilson, James F.; Wright, Margaret J.; Weinberger, Daniel R.; Debette, Stephanie; Eriksson, Johan G.; Mosley, Thomas H., Jr.; Launer, Lenore J.; van Duijn, Cornelia M.; Deary, Ian J.; Seshadri, Sudha; Räikkönen, Katri; Radiology and Imaging Sciences, School of MedicineUnderstanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.Item 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 MedicineWe 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/).Item Subjective cognitive decline and rates of incident Alzheimer's disease and non-Alzheimer's disease dementia(Elsevier, 2019-03) Slot, Rosalinde E. R.; Sikkes, Sietske A. M.; Berkhof, Johannes; Brodaty, Henry; Buckley, Rachel; Cavedo, Enrica; Dardiotis, Efthimios; Guillo-Benarous, Francoise; Hampel, Harald; Kochan, Nicole A.; Lista, Simone; Luck, Tobias; Maruff, Paul; Molinuevo, José Luis; Kornhuber, Johannes; Reisberg, Barry; Riedel-Heller, Steffi G.; Risacher, Shannon L.; Roehr, Susanne; Sachdev, Perminder S.; Scarmeas, Nikolaos; Scheltens, Philip; Shulman, Melanie B.; Saykin, Andrew J.; Verfaillie, Sander C. J.; Visser, Pieter Jelle; Vos, Stephanie J. B.; Wagner, Michael; Wolfsgruber, Steffen; Jessen, Frank; Radiology and Imaging Sciences, School of MedicineINTRODUCTION: In this multicenter study on subjective cognitive decline (SCD) in community-based and memory clinic settings, we assessed the (1) incidence of Alzheimer's disease (AD) and non-AD dementia and (2) determinants of progression to dementia. METHODS: Eleven cohorts provided 2978 participants with SCD and 1391 controls. We estimated dementia incidence and identified risk factors using Cox proportional hazards models. RESULTS: In SCD, incidence of dementia was 17.7 (95% Poisson confidence interval 15.2-20.3)/1000 person-years (AD: 11.5 [9.6-13.7], non-AD: 6.1 [4.7-7.7]), compared with 14.2 (11.3-17.6) in controls (AD: 10.1 [7.7-13.0], non-AD: 4.1 [2.6-6.0]). The risk of dementia was strongly increased in SCD in a memory clinic setting but less so in a community-based setting. In addition, higher age (hazard ratio 1.1 [95% confidence interval 1.1-1.1]), lower Mini-Mental State Examination (0.7 [0.66-0.8]), and apolipoprotein E ε4 (1.8 [1.3-2.5]) increased the risk of dementia. DISCUSSION: SCD can precede both AD and non-AD dementia. Despite their younger age, individuals with SCD in a memory clinic setting have a higher risk of dementia than those in community-based cohorts.