The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan
Open accessAbstract
As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.
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Cites
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
Harmonization of cortical thickness measurements across scanners and sites
Fortin, J.-P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., Adams, P., Cooper, C., Fava, M., McGrath, P. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.024 · Cited by 1,580
Self-citationLongitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain
Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., & Davatzikos, C. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-08-03295.2003 · Cited by 1,425
Self-citationfor the ENIGMA-Major Depressive Disorder Working Group, Schmaal, L., Veltman, D. J., van Erp, T. G. M., Sämann, P. G., Frodl, T., Jahanshad, N., Loehrer, E., Tiemeier, H., Hofman, A., et al. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.69 · Cited by 1,172
Self-citationSowell, E. R., Thompson, P. M., Tessner, K. D., & Toga, A. W. (2001) · The Journal of Neuroscience · doi:10.1523/jneurosci.21-22-08819.2001 · Cited by 983
The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data
the Alzheimer’s Disease Neuroimaging Initiative, E. C., Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M. E., Toro, R., Jahanshad, N., Schumann, G., et al. (2014) · Brain Imaging and Behavior · doi:10.1007/s11682-013-9269-5 · Cited by 838
Self-citationTamnes, C. K., Østby, Y., Fjell, A. M., Westlye, L. T., Due-Tønnessen, P., & Walhovd, K. B. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhp118 · Cited by 779
Whitwell, J. L., Przybelski, S. A., Weigand, S. D., Knopman, D. S., Boeve, B. F., Petersen, R. C., & Jack, C. R. (2007) · Brain · doi:10.1093/brain/awm112 · Cited by 607
Mills, K. L., Goddings, A.-L., Herting, M. M., Meuwese, R., Blakemore, S.-J., Crone, E. A., Dahl, R. E., Güroğlu, B., Raznahan, A., Sowell, E. R., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.044 · Cited by 600
Consistent neuroanatomical age-related volume differences across multiple samples
Walhovd, K. B., Westlye, L. T., Amlien, I., Espeseth, T., Reinvang, I., Raz, N., Agartz, I., Salat, D. H., Greve, D. N., Fischl, B., et al. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2009.05.013 · Cited by 506
Satterthwaite, T. D., Connolly, J. J., Ruparel, K., Calkins, M. E., Jackson, C., Elliott, M. A., Roalf, D. R., Hopson, R., Prabhakaran, K., Behr, M., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.056 · Cited by 335
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Zuo, X.-N., Xu, T., & Milham, M. P. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0655-x · Cited by 310
Doshi, J., Erus, G., Ou, Y., Resnick, S. M., Gur, R. C., Gur, R. E., Satterthwaite, T. D., Furth, S., & Davatzikos, C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.073 · Cited by 309
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Erus, G., Battapady, H., Satterthwaite, T. D., Hakonarson, H., Gur, R. E., Davatzikos, C., & Gur, R. C. (2015) · Cerebral Cortex · doi:10.1093/cercor/bht425 · Cited by 226
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Cui, H., Dai, W., Zhu, Y., Kan, X., Gu, A. A. C., Lukemire, J., Zhan, L., He, L., Guo, Y., & Yang, C. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3218745 · Cited by 218
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Metadata
- Authors
- Pomponio, R., Erus, G., Habes, M., Doshi, J., Srinivasan, D., Mamourian, E., Bashyam, V., Nasrallah, I. M., Satterthwaite, T. D., Fan, Y., Launer, L. J., Masters, C. L., Maruff, P., Zhuo, C., Völzke, H., Johnson, S. C., Fripp, J., Koutsouleris, N., Wolf, D. H., Gur, R., Gur, R., Morris, J., Albert, M. S., Grabe, H. J., Resnick, S. M., Bryan, R. N., Wolk, D. A., Shinohara, R. T., Shou, H., & Davatzikos, C.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 208
- Pages
- 116450
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2993515580
- Cited by
- 524
- References
- 15
- FWCI
- 16.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
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