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This is an empirical study to investigate the impact of scanner effects when using machine learning on multi-site neuroimaging data.
Quantifying confounding bias in neuroimaging datasets with causal inference
Wachinger, C., Becker, B. G., Rieckmann, A., and Pölsterl, S. (2019) · 1907
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New variants of a method of MRI scale standardization
Nyúl, L. G., Udupa, J. K., and Zhang, X. (2000) · 2000
Earlier work this paper cites.
Zhang, Y., Brady, M., and Smith, S. (2001) · 2001
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Statistical Parametric Mapping: The Analysis of Functional Brain Images
Friston, K., Ashburner, J., Kiebel, S., Nichols, T., and Penny, W., editors (2007) · 2007
Earlier work this paper cites.
N4ITK: improved N3 bias correction
Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., and Gee, J. C. (2010) · 2010
Earlier work this paper cites.
Robust brain extraction across datasets and comparison with publicly available methods
Iglesias, J. E., Liu, C.-Y., Thompson, P. M., and Tu, Z. (2011) · 2011
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SPM: a history
Ashburner, J. (2012) · 2012
Cited alongside, same era.
The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing
Shafto, M. A., Tyler, L. K., Dixon, M., Taylor, J. R., Rowe, J. B., Cusack, R., Calder, A. J., Marslen-Wilson, W. D., Duncan, J., Dalgleish, T., et al. (2014) · 2014
Cited alongside, same era.
UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., and Collins, R. (2015) · 2015
Cited alongside, same era.
Multimodal population brain imaging in the UK Biobank prospective epidemiological study
Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L. R., Griffanti, L., Douaud, G., Okell, T. W., Weale, P., Dragonu, I., Garratt, S., Hudson, S., Collins, R., Jenkinson, M., Matthews, P. M., and Smith, S. M. (2016) · 2016
Cited alongside, same era.
The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample
Taylor, J. R., Williams, N., Cusack, R., Auer, T., Shafto, M. A., Dixon, M., Tyler, L. K., Henson, R. N., et al. (2017) · 2017
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Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank
Alfaro-Almagro, F., Jenkinson, M., Bangerter, N. K., Andersson, J. L., Griffanti, L., Douaud, G., Sotiropoulos, S. N., Jbabdi, S., Hernandez-Fernandez, M., Vallee, E., Vidaurre, D., Webster, M., McCarthy, P., Rorden, C., Daducci, A., Alexander, D. C., Zhang, H., Dragonu, I., Matthews, P. M., Miller, K. L., and Smith, S. M. (2018) · 2018
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Common pitfalls in machine learning applications to multi-center data: tests on the ABIDE i and ABIDE ii collections
Ferrari, E., Bosco, P., Spera, G., Fantacci, M. E., and Retico, A. (2018) · 2018
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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) · 2018
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Fortin, J.-P., Parker, D., Tunc, B., Watanabe, T., Elliott, M. A., Ruparel, K., Roalf, D. R., Satterthwaite, T. D., Gur, R. C., Gur, R. E., et al. (2017) · 2017
Cited alongside, same era.
Statistical challenges in “big data” human neuroimaging
Smith, S. M. and Nichols, T. E. (2018) · 2018
Later among the works it cites.